Vehicular Emission and Economic Activities: A Study in Kolkata

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Vehicular Emission and Economic Activities: A Study in Kolkata ASISH KUMAR PAL A Thesis Submitted to the University of Burdwan, For Partial Fulfillment of the Requirements for the Award of the Degree of Doctor of Philosophy Under the supervision Of Dr. Atanu Sengupta The University of Burdwan, Burdwan - 713104 West Bengal, India

Transcript of Vehicular Emission and Economic Activities: A Study in Kolkata

Vehicular Emission and Economic Activities:

A Study in Kolkata

ASISH KUMAR PAL

A Thesis Submitted to the University of Burdwan,

For Partial Fulfillment of the Requirements for the Award of the

Degree of

Doctor of Philosophy

Under the supervision

Of

Dr. Atanu Sengupta

The University of Burdwan,

Burdwan - 713104

West Bengal, India

Dedicated

To

My Parents

Dr. Atanu Sengupta

Associate Professor,

Dept of Economics

The University of Burdwan

Golapbag, Burdwan-713104

West Bengal (INDIA)

Tel 91-342-2558554 Ext 438

(Office)

919832174249 (Mobile)

Date: ___________________

Certificate

This is to certify that Mr. Asish Kumar Pal has prepared his thesis paper entitled

“Vehicular Emission and Economic Activities: A Study in Kolkata” under my

supervision and guidance. He has fulfilled the requirements relating to the nature,

period of research and presentation of seminar talks etc.

It is also being certified that the research work brings to light the results of an original

investigation made by Asish Kumar Pal. The thesis being submitted now is in partial

fulfillment of the requirements of the Ph.D degree in Arts (Economics) of the University

of Burdwan. It is further to certify that it has not been presented anywhere for any

degree whatsoever either by him or anyone else.

Dr. Atanu Sengupta,

Preface

In the modern world pollution is the buzz word. Any modern city suffers from vehicular

emission that seeks to pollute its skyline. Air pollution is a result of vehicular emission

causes serious health hazard problem having wide socio-economic implication. The

present study wishes to unravel vehicular emission in Kolkata ― An Indian metropolis.

We have considered both macro and micro aspects of vehicular emission. Vehicular

emission has its own dynamics. It is related to the pace and spread of urbanization. It is

related to the vehicular population and road congestion. It also has seasonal fluctuation

pattern. Our macro study wishes to cover all these three aspects. For this we require data

from secondary sources (Census Report, WBPCB Report, Ministry of Petroleum and

Natural Gas, Ministry of Road Transport and Highways etc.). However, it is the

individual who is tormented by vehicular emission. We have selected a particular group

― the traffic police whose duties link them to the day to day pollution in the city. This is

our macro endeavor. Primary data has been collected from the regarding their

assessment, awareness and health conditions. In all we humbly aim to some of the

niceties of vehicular emission that is omnipotent in the city of joy.

Acknowledgements

This thesis has been submitted for partial fulfillment of requirements for the Ph.D degree

in Economics from the University of Burdwan.

I am grateful to various persons at the time of preparation of this thesis for their

valuable guidance and assistance.

I take the opportunity to sincerely acknowledge Dr. Atanu Sengupta, Associate

Professor, Department of Economics, Burdwan University, Burdwan, for his valuable

guidance and encouragement. Unless his kind cooperation and guidance it is not possible

to prepare this thesis.

I am also grateful to other teachers of our department for their spontaneous

cooperation and valuable suggestions.

I wish to thank my friends and fellow research scholars for their help and

cooperation. Special thanks are due to Dr. Krishanu Nath for his cooperation to prepare

the thesis.

I express my gratitude to the traffic police personnel of Kolkata for giving

information and kind cooperation to complete my research work.

I also express my thanks to all of my family members.

However the usual disclaimer applies.

Date:

Place:

----------------------------

Asish Kumar Pal

Department of Economics,

Burdwan University, Burdwan,

West Bengal, India

Contents

Chapter Page No.

1. Introduction..................................................................................................... 1― 7

1.1 Introduction .................................................................................................. 1 - 2

1.2 Vehicular emission in Kolkata ..................................................................... 3 - 4

1.3 Plan of the work ........................................................................................... 4 - 7

2. Literature Review ........................................................................................ 9 ― 22

2.1 Vehicular Emission in General .................................................................. 9 - 13

2.2 Vehicular Emission in Developing countries ........................................... 13 - 18

2.3 Vehicular Emission in Kolkata ................................................................ 18 - 22

3. Data and Methodology of the Study.......................................................... 23― 28

3.1 Introduction ..................................................................................................... 23

3.2 Data description ...................................................................................... 24 – 26

3.3 Methodology ........................................................................................... 26 – 28

4. Urbanisation and Vehicular Population .................................................. 29 ― 49

4.1 Introduction ............................................................................................. 29 – 30

4.2 Data and Methodology ............................................................................ 30 – 31

4.3 Urbanisation - its dynamics with special emphasis on West Bengal ...... 32 – 37

4.4 Urbanisation and Vehicular Ownership .................................................. 37 – 44

4.5 Towards a non – parametric Analysis ..................................................... 45 – 48

4.6 Conclusion ....................................................................................................... 49

5. Urban Vehicular Problems: Some Issues ................................................ 50 ― 69

5.1 Introduction ............................................................................................. 50 – 51

5.2 Urban Conditions in India and West Bengal ........................................... 52 – 53

5.3 Vehicular Population ............................................................................... 54 – 58

5.4 Roadway Congestion in India and West Bengal ..................................... 58 – 59

5.5 Vehicular Pollution ................................................................................. 59 – 62

5.6 Vehicular Ownership Pattern .................................................................. 62 – 67

5.7 Public Transport: Some Issues ................................................................ 67 – 68

5.8 Conclusion ....................................................................................................... 69

6. Analysis of Vehicular Emission in Kolkata ............................................... 70 – 92

6.1 Preliminary View ............................................................................................ 70

6.1.1 Introduction .................................................................................... 70 – 71

6.1.2 Trend in Vehicular Emission ......................................................... 71 – 72

6.1.3 Seasonal Fluctuation in Vehicular Emission .................................. 73 – 77

6.1.4 Status of Other Vehicular Pollutants ...................................................... 78

6.1.5 Seasonal Fluctuation of Air Pollution Level ................................... 78 - 80

6.1.6 Conclusion .............................................................................................. 80

6.2 Spectral Analysis ...................................................................................... 81 - 92

6.2.1 Introduction ............................................................................................ 81

6.2.2 An Overview on Spectral Analysis ................................................ 82 – 83

6.2.3 Data Used ....................................................................................... 83 – 84

6.2.4 Result of the Study ......................................................................... 84 – 91

6.2.5 Conclusion .............................................................................................. 92

7. Effects of Vehicular Emission – A Study of traffic police in Kolkata ... 93 – 117

7.1 Introduction ............................................................................................. 93 – 95

7.2 Rational of the Study ............................................................................... 95 – 97

7.3 Theoretical Framework ......................................................................... 97 – 100

7.4 Data Description ................................................................................... 100 - 110

7.5 Empirical Findings .............................................................................. 109 – 119

7.6 Conclusion ..................................................................................................... 120

8. Conclusion ................................................................................................ 121 – 125

9. Reference .................................................................................................. 126 – 140

Appendix ........................................................................................................ 141 - 167

1

Chapter – 1

Introduction

1.1 Introduction:

“Anna chai, pran chai, alo chai, chai mukto baiu,

chai bal, chai swasthya, ananda ujjwal paramaiu”

―Rabindranath Tagore

“We want food, we want vitality, we want light and the free air

We want strength, health and a meaningful life of love and enjoyment"

Pollution is a staple problem of today’s world. Rich or poor, planed or market

oriented, democratic or autocratic, modern or traditional ― nation all over are steeped in

pollution. While the facets of pollution changes from country to country or region to region,

it is undeniable that we can not live without it. In a way, it is the price of the riches that the

science and technology bestowed upon us. Like a thrown of a rose or the catered pillar from

which the butterfly is brown, pollution is embedded if we are to enjoy the magnificence of

human triumph in science and technology.

Air pollution is one of the very important sources of pollution. It is more prevalent in

the cities and urban conglomerates of the developed and developing world. A major source

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of air pollution in the city proper is the spurt of vehicles. Development is almost

synonymous with a movement from mechanical vehicles (such as bicycles or rickshaws) to

the fuel consuming two wheelers and four wheelers. Vehicles are necessary for transit of

both man and goods across the length and breadth of a city.

However, the vehicular population could have been checked if there would have

been an emphasis on efficient public transit system (such as light-railways, metro-railways

etc.). However, the problem is accentuated both by and increasing inefficiency of public

transport (use of backdated fuel consumption technology) and also the rise in the privately

own two wheelers and four wheelers. Added to this inadequate road space, dilapidated and

ill- maintained road ways, the increase in number of vehicles per kilometer of roads and the

slow average movement of vehicles flare up the problem.

In Delhi, the data shows that of the total 3,000 metric tones of pollutants belched out

everyday, close to two third (66%) is from vehicles. Similarly the contribution of vehicles to

urban air pollution is 52% in Bombay and close to one-third in Kolkata.

The ill effects of vehicular emission are well noted by the scientists (Cochrane et.al,

1978; Wag staff, 1986; Dardanai & Wag staff, 1987; Leu & Gerfin, 1992). Respiratory

problem, incident of air born disease and other sorts of ailment become widely prevalent. It

is thus an urgent task of the policy makers to frame out an efficient way of combating these

demons without hampering the development proper. A study in vehicular emission is very

crucial from the point of view.

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1.2 Vehicular Emission in Kolkata:

The main focus of our study is vehicular emission of Kolkata. Kolkata is a centre of

commerce and trade of West Bengal. It is an important state of business activities in India.

Huge people of rural areas come to Kolkata daily and complete their necessary works. To

complete their activities they require vehicles to move from one area to another area

throughout Kolkata. The people use different types of vehicles like two-wheelers and four

wheelers. These lead to huge amount of vehicle emission. Vehicular emission contributes

30% share of total atmosphere pollution there. The extreme use of vehicles creates pressure

on transport system in our study area. The number of motor vehicles in Kolkata has risen

dramatically over the years. The CPCB report, 1988-89, attributes the pollution problem in

Kolkata to the rise in the number of vehicles. Due to inadequate and narrow road space,

traffic congestion also happens in peak hours. Traffic problem is very serious there. “Due to

this traffic problem vehicles move slowly. These slow moving cars emit a large amount of

smoking” (‘S.M Ghosh’ quoted in Banerjee et. al. 2002). The total road length has not

increased significantly in Kolkata during the last decade and has remained as low as 6% of

the total city area (Banerjee et. al. 2002). Also many vehicles in Kolkata are backdated,

older and made by outmoded technology. This is also another cause of vehicular emission.

Vehicular emission includes various types of pollutants. These pollutants are

suspected particulate matters, respiratory particulate matters, NO2, SO2, CO and lead etc.

Again the level of these pollutants varies according to seasons, it is well known that the

pollution level increases in the winter season and is the lowest in the rainy season. In

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Kolkata however, ‘a festive season’ occurring September, October and early November also

have significant impact. Thus pollution levels are molded not only by natural factors but also

by social factors. The scenario and fluctuation of the pollutants level of the important traffic

points of our study area are shown in the chapter five.

As it is well known, vehicular emission has a long run negative health consequences

for those who are continuously exposed to it. This is a rather broad group including almost

all the citizens of metropolis. However, the impact varies and it depends on the direct

contact with vehicular emission. The traffic police personells of the city are a very

vulnerable group in this regard. Standing long time in the city junctions facing the vehicles

directly has a direct negative effect on them. However, the standard economic theory

prescribes that agents take risk according to the returns. This prescription becomes

problematic when the risks themselves are uncertain. Moreover, the environment conscious

is very low in developing countries such as India. Pollution hazard rarely come into the

‘rational calculi of the traffic men. Moreover the policemen’s duty is divided between –“on

the road” and “off the road”. Obviously the former involves a far greater health hazard with

direct exposure to the vehicular emissions. These duty allocations are determined by the

higher authorities without little choice of the policemen themselves. In such a scenario the

traffic men can only mitigate their health losses if they take some actions to prevent the

detrimental effect of environmental pollution. Our work deals with all the social and

economic calculation that an agent facing pollution hazard has to undergo.

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The objectives of our study are three.

• To understand the nature and causes of vehicular emission in various urban

centers

• To unravel the dynamics of vehicular emission

• To understand the impact of vehicular emission on a particular group of people

― traffic police who are directly exposed to it

1.3 Plan of Chapter:

Our plan of work is arranged by the various chapters in thesis. We wish to carry out

this work from both micro and macro perspective.

In the first chapter we wish to give a brief introduction to our problem. We will also

spell out the relevance of such a study in an underdeveloped economy like India. The

rational of the work will emanate from this elementary discussion.

The second chapter is on literature review. Various studies concerned with

vehicular pollution are discussed in this section. We also critically analyses the earlier

studies and find out the research gap.

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In the third chapter, there are various types of data available on the pollution

parameters. Various national and international agencies publish regular data on pollution

aspects that might be relevant for our study. However, a primary survey is essential when we

deal with some specific issues. Our work tries to use both the secondary and primary data

for the purpose of analysis.

The fourth chapter gives a brief panoramic view of the city of Kolkata with

emphasize on urban development and pollution problems. The problem in Kolkata in

automobile emission is then discussed. A comparative discussion with the other rural areas

of West Bengal is also introduced in this chapter.

In chapter five, we considered the issues related to urban vehicular problems of all

India level as well as West Bengal. This chapter also deals with the macro dynamics of

vehicular pollution in the city of Kolkata. The various aspects of this problem are discussed

in this chapter. Issues such as the alarming increase in vehicular pollution, seasonality and

periodicity of the pollution parameters etc. are dealt in this chapter and the underlying

pattern is sought to be identify.

In the sixth chapter, spectral analysis is the analysis of time series in the frequency

domain. Air pollutant level shows considerable amount of seasonality. Spectral analysis aids

us to confront this seasonality in a systematic manner. Unlike the seasonal auto regressive

schemes (SARIMA), the length of season is not fixed a priori. This is determined by the data

in the spectral analysis.

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In order to analyse the health hazard effect on the traffic police on Kolkata, we have

collected primary data on a set of traffic personnel at the various junctions of the city. We

then use the subjective evaluation to study the impact of health hazard. The health

production function approach is used for this purpose in chapter seven.

In chapter eight, we give a synoptic view of the entire analysis. The links between

the chapters are clarified. We are drawn towards the conclusion through a linkage of the

summaries of several chapters.

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Chapter -2

LITERATURE REVIEW:

2.1 Introduction:

There is a vast literature on vehicular emission and its impact on environment. A

large number of these studies also relate themselves to the questions of socio-economic

causation. It is not our intention to give an encyclopedic view of the entire literature in this

chapter. Our aim is rather modest. We wish to concentrate only on a few studies that are

relevant for the exercise we have undertaken in this dissertation. We divide our review into

three subsections ― a) a synoptic view of the literature of vehicular emission in general b)

vehicular emission in developing countries and c) vehicular emission in Kolkata.

2.2 Vehicular Emission in General:

Faucet and Sevingny (1998) have argued at the same conclusion that inefficient

transportation is the major culprit of air pollution accounting for over 80% of total air

pollutants. This is a clear indication that vehicle emissions are a major source of ambient air

pollution. The form of urban growth in most developing countries has tended to increase the

use of motorized transport, particularly road transport, which leads to increase

environmental impacts.

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The composition of traffic on city roads also affects emission.” The future trend in

vehicle growth can have serious implications and fuel consumption patterns,” warns VSN

Shrinivasan, an energy expert from TERI (Tata Energy Research Institute). A large number

of private vehicles for example, are two-wheelers, which are cheap and reliable but also high

on emissions.

Different studies have been done in the field of motor vehicular emissions in the

different regions of the world, especially to establish the level of air pollution from the

operation of motor vehicles and the general urban air quality as a whole. Three of such

studies which have relevance to this study are: the vehicle activity study in Nairobi, Kenya,

conducted in March 2001 by the U.S. EPA, CE-CERT5, and GSSR6 the evaluation of

evaporative emissions from gasoline powered motor vehicles under South African

conditions, conducted in 2003 by Van des Westhuisena et al. (2004); and the impact of

automobile emissions on the level of platinum and lead in Accra, Ghana conducted in 2001

by Kylander et al. (2003). All of these find strong correlates between air pollution and

vehicular efficiency (in the fuel use).

Ambient temperature and local meteorology influences the concentration and

location of vehicle-emitted pollutants. For example, elevated sulphur dioxide levels are

typically reported in the winter and elevated ground-ozone levels in the summer (Goldberg

et al. 2001; Rainham et al. 2005).

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Cold weather can result in higher levels of pollutants in ambient air due to reduced

atmospheric dispersion and degradation reactions. The genotoxic effects of PM2.5 and

PM10 have also been found to be greater in the winter months (Abou Chakra et al. 2007).

Dispersion of pollutants is also affected by other meteorological factors like humidity, wind

speed and direction and general atmospheric turbulence.

Many studies have found that chronic exposure to high levels of traffic noise

significantly increases the risk for cardiovascular diseases and death by myocardial

infarction (Babisch 2000, Fogari 1994 and Davies 2005). A study in Denmark of 28,744

men with lung cancer found an increased risk among taxi drivers and truck drivers when

compared with other employees, after adjustment for socioeconomic factors (Hansen et al.

1998). Other studies have found similar effects for lung cancer in taxi, truck, and bus drivers

(Borgia et al. 1994, Guberan et al. 1992, Jakobsson et al. 1997, Steenland et al.1990).

A similar study confirms that there is a prevalence of chronic bronchitis and asthma

in street cleaners exposed to vehicle pollutants in concentrations higher than WHO

recommended guidelines, thus leading to significant increase in respiratory problems

Rachou (1995). Other studies in Ethiopia, Mozambique, and Kenya found significantly

higher prevalence of asthma in urban school children exposed to traffic pollution compared

to rural child (Bekele 1997, Mavale-Manuel 2004 and Ng'ang'a 1998).

In 2004, Toronto Public Health released a study that calculated the burden of illness

associated with ambient (outdoor) levels of air pollution in Toronto. The study estimated

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that smog-related pollutants from all sources contributed to about 1,700 premature deaths

and 6,000 hospitalizations each year in Toronto. The study indicated that these deaths would

not have occurred when they did without chronic exposure to air pollution at the levels

experienced in Toronto. TPH stuff used the Air Quality Benefits to determine the burden of

illness and economic impact from traffic related air pollution.

In 2008, Erica Moen conducted a survey on “Vehicle Emissions and Health Impacts

in Abuja, Nigeria” and put a question on the seasonal variation of pollution and fond that the

greatest percentage of respondents (42%) reported that their symptoms are more severe in

the dry season. This implies that documented concentrations may actually be lower than

what is observed in the dry season, which is supported by similar monitoring studies.

Several studies reported significant health risks and increased morbidity and

mortality rates and hospital admissions because of cardio respiratory diseases, oxidative

stress, and an increase in the incidence of cancer among the urban population (Maynard

1999).

Aside from exposures while traveling inside a vehicle, a significant proportion of the

population are exposed through occupations that lead to extended periods of time on or near

roads and highways or close to traffic like asphalt workers (Randem et al. 2004), traffic

officers (de Paula et al. 2005; Dragonieri et al. 2006; Tamura et al. 2003, Tomao et al. 2002,

Tomei et al. 2001), street cleaners (Raachou-Nielsen et al. 1995), street vendors, and

tollbooth workers. Health impacts are greater for these groups who work close to traffic than

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for those that are not occupationally exposed. The ill-effects of this pollution are mainly by

the people who came close contract with it (such as constables, street venders, shop-keepers

etc.).

A study in Copenhagen found that street cleaners had a greater risk for chronic

bronchitis and asthma when compared with cemetery workers (Raaschou-Nielsen et al.

1995). It has been reported that traffic policemen present with airway inflammation and

chronic respiratory symptoms at higher rates than in non-exposed groups (Dragonieri et al.

2006 and Tamura et al. 2003). Asphalt workers have also been reported to have an increased

risk of respiratory symptoms including lung function decline, and chronic obstructive

pulmonary disease (COPD) as compared with other construction workers (Randem et al.

2004).

Individuals living close to major roads are at increased risk of exposure to traffic

related pollution and related health effects. In fact, residential proximity to a major road has

been associated with a mortality rate advancement period of 2.5 years (Finkelstein et al.

2004). Of particular concern are communities close to border crossings, where traffic levels

are high and include a large proportion of transport trucks. For example, individuals living

close to the Peace Bridge, one of the busiest US-Canada crossing points, show a clustering

of increased respiratory symptoms, particularly asthma (Lwebuga-Mukasa et al. 2005;

Oyana et al. 2004 and Oyana et al. 2005).

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Moreover, street vendors frequent high traffic intersections, working both on the

sidewalk and walking through the intersections during slow traffic. Street vendors, therefore,

may be at high risk of developing health effects, which has been documented by a study in

India (Chantanakul 2006).

Traffic wardens, part of the Federal Capital Territory Area Command (FCTAC),

were included in the study to link measured pollutant concentrations with health impacts.

Wardens are the highest exposure group because they stand in intersections and direct

traffic, so they are directly and frequently exposed to vehicle emissions. Thus, it is

reasonable to expect their health status to directly reflect that level of exposure. There are

roughly 300 active traffic wardens in Abuja who work on average 8 hours per day between

the hours of 7am and 8pm, 5-7 days per week, with 2 weeks of vacation per year (Akoni

2008).

2.3 Vehicular Emission in Developing Countries:

A Case Study on Beijing is done by Hao and Wang (2005) urban air pollution is one

of the major environmental issues. Air pollution problems are induced by high-speed

urbanization, rapid economic growth, and explosive motorization. Chinese cities pose a

direct threat to long-term economic sustainability and social benefit. Air pollution problems

in Chinese cities are serious, especially in large cities.

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From the CPCB report of 2010, air pollution is one of the serious environmental concerns

of the urban Asian cities including India where majority of the population is exposed to poor

air quality. Most of the Indian Cities are also experiencing rapid urbanization and the

majority of the country’s population is expected to be living in cities within a span of next

two decades. Since poor ambient air quality is largely an urban problem this will directly

affect millions of the dwellers in the cities. The rapid urbanization in India has also resulted

in a tremendous increase the number of motor vehicles. The vehicle fleets have even

doubled in some cities in the last one decade. This increased mobility, however, come with a

high price. As the number of vehicles continues to grow and the consequent congestion

increases, vehicles are now becoming the main source of air pollution in urban India.

The developing countries suffer more than the developed countries from air pollution

which happens from vehicle emissions. High levels of lead, primarily from vehicle

emissions, have been identified as the greatest environmental danger in a number of large

cities in the developing world. For them the problem is becoming more acute as the numbers

of motor vehicles are growing rapidly. Delhi’s inhabitants inhale the most polluted air in the

country and vehicular pollution is responsible for 64 percent of the pollutants which make it

so (Bhattacharyya et. al. 2002).

Transport is a vital component of any vibrant city. With the case study of

Ahmedabad, the primary reasons for pollution and lack of management in respect of

transport are lack of integration between land use and transport planning, concentration of

economic and other activities in the core of the city, lack of scientific design of road

15

networks, high rate of growth of vehicles, mixed traffic on roads, and low quality and

adulterated fuels (Brar 2004).

The pressures on transport systems are increasing in most developing countries, as

part of the process of growth. It is even worst in urban areas where population densities are

higher Motor vehicle ownership and use are growing even faster than population, with

vehicle ownership growth rates of 15 to 20 % per year common in some developing

countries (World Bank 1995).

Shariff (2012) expressed his concern that private vehicles today have become the

main means of travel of urban living in developing countries. Consistent economic growth,

rising incomes, and urbanization have led to rapid growth in vehicle ownership and usage.

Private vehicle ownership is also associated with externalities such as traffic congestions,

accidents, inadequate parking spaces and pollutions. Rising vehicle congestion and slower

travel speeds are the most obvious impact of rapid motorization. With the increase in vehicle

ownership, it has been emphasized that the demand for travel to central city areas would

grow far beyond the capacity of the road network. Hence air pollution and other

environmental hazards are another important concern.

According to WHO report published in 1994 the Indian capital is the fourth most

polluted city in the world. And no wonder, the amount of pollutants the transport sector

pumps into Delhi more than the sum of vehicular pollutants emitted in Mumbai, Bangalore,

and Kolkata. However, the Delhi has taken stringent steps recently that brought down the

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vehicular pollution to a considerable extent. India is moving on a fast track with the increase

in GDP by 2.5 times, industrial pollution by 8 times over the last two decades (Pattanaik and

Pattanaik 2002).

Noise, air and water pollution are all serious problems in Indian cities, and transport

sources contribute all three kinds. Heavy transportation in metropolitan cities is a major

contributor to environmental pollution in addition to industrial and commercial activities.

Indian cities face a transport crisis characterized by levels of congestion, noise pollution,

traffic facilities and injuries, and inequality far exceeding those in most European and North

American cities. India’s transport crisis has been exacerbated by the extremely rapid growth

of India’s largest cities in a context of low incomes, limited and outdated transport

infrastructure, rampant suburban sprawl, sharply rising motor vehicle ownership and use,

deteriorating bus services, a wide range of motorized and non-motorized transport modes

sharing roadways, and inadequate as well as uncoordinated land use and transport planning

(Pucher et. al. 2004).

According to India Development Report of 1997 the reasons for deterioration of

urban air quality throughout the Indian cities are growing industrialization without any

priority for pollution abatement, and rising number of motor vehicles especially of poorly

maintained vehicles that used leaded fuel. The rate of generation of solid waste in urban

centers has outpaced population growth in recent years with the wastes normally disposed in

low-lying areas of the city’s outskirts (India: State of the Environment 2001).

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Vehicles are a major source of pollutants in cities and towns. A three-fold increase in

the number of motor vehicles has been found in India in the last decade. The concentration

of ambient air pollutants in the metro-politan cities of India as well as many of the Indian

cities is high enough to cause increased mortality. The life of the urban dwellers of India

may become more miserable which may be the cause of health hazards and worst

devastation. In all the four metro cities SPM was found highest along with the problem of

solid wastes. The noise pollution was noticed more than the prescribed standard in all the

four metro cities. Five and more person residing in a room was faced by more than one

fourth population of Mumbai followed by a little less than one fifth population of Kolkata

and about 10% population of Delhi and Chennai both. India’s urban future is grave (Maity

and Agarwal 2005).

The total pollution load from transport sector has increased from 0.15 million tones

in 1947 to 10.3 million tones in 1997 (TERI 1997). Like many other parts of the world, air

pollution from motor vehicles is one of the most serious and rapidly growing problems in

urban centers of India (UNEP/WHO, 1992). In India, the number of motor vehicles has

grown from 0.3 million in 1951 to approximately 50 million in 2000, of which, two

wheelers (mainly driven by two stroke engines) accounts for 70% of the total vehicular

population.

According to UNEP-WHO report, 1992 and the World Development Report 1992

vehicles are nowhere the principal cause but its relative importance is growing rapidly over

time. In World Bank’s report of 1992 the Bank expressed the premonition that such

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economic growth will be associated with applying environmental damage. Degraded air

quality would be one of such impending damage that may cripple the developing country.

According to the report of CPCB in 2010, air pollution is a major environmental risk

to health and is estimated to cause approximately 2 million premature deaths worldwide per

year. The high level of pollutants are mainly responsible for respiratory and other air

pollution related ailments including lung cancer, asthma etc., which is significantly higher

than the national average (CSE, 2001 and CPCB, 2002).

2.4 Vehicular Emission in Kolkata:

Now coming to Kolkata, condition is not at all better. Traffic problem in Kolkata

seems to be on its way to accruing the dimension of cities like Bangkok, where commuters

more or less live in their vehicles, bathing and getting dressed for works while inching their

way through nightmarish traffic jams. Kolkata’s citizens already spend hours gulping down

fumes while stuck in traffic jams. During the last decade the road length has not increased

significantly in Kolkata. So Traffic jam occurs. As Traffic moves slowly, slow moving

vehicles emit more carbon monoxide, the problem is more surmounted for the kolkata by the

lack of adequate data availabity. This co-effect is toxic to human’s body. It reacts with the

hemoglobin of blood and affects oxygen supply to the brain. Thus it causes death (Banerjee

and Bhattacharjee 2001). They have also observed that the air of Kolkata is polluted

differently in different seasons. During the monsoon season (July-October) the air is

19

comparatively clean due to heavy rainfall and during the summer (April-June) high winds

blow away the pollutants. But air quality is worse during winter months (Nov.-Feb.)

The population residing in the vicinity of the city, daily commuters, and business

people are always exposed to the traffic air containing particulate matters, inorganic gases,

and volatile and semi volatile organic compounds (Chattopadhyay et. al. 2007).

One of the major causes for road congestion and therefore, vehicular emission is the

massive increase in the vehicular pollution plying in and around the Kolkata city area.

According to the report published in the recent Telegraph 7.12.99, the aggregate registered

vehicular population in Kolkata has increased from 6, 34,835 in 1997 to 9, 50,000 by the

end of 1997, in 2006-07, it has increased massively. Due to this huge vehicular pollution

growth, the energy demand (both diesel and petrol) increased manifold. One of the major

factors that determine vehicular emission is the speed of the vehicles. According to the

eminent scientist there exists a critical speed at which the emission is less of the vehicles. If

the speed is well below or well above that critical one then the emission will rise

significantly. So vehicle pollution is one of the important components in air pollution. Citing

a study by NEERI they found that the main concentration levels for various atmospheric

pollutants have increased in all the major cities. According to the World Resource Report

(1996-97) the motor vehicles are responsible for 90% emission of (CO), 85% of sulfur

dioxide (SO2) and 37% of (SPM) in Delhi. According to the same report, automobiles

caused about 52% of the total (NO2) emissions, 5% (SO2) emissions and 24% of SPM

emission in Mumbai in 1992. In the year 1993-94, the share of automobiles in total pollutant

20

load was as high as 64% in Delhi and 52% in Mumbai. The share was 30% in Kolkata in

1988-89. According to a report of the National Commission on urbanization 1988, the

increase in projected travel demand per day in Kolkata in 2001 will be 63% compared to the

1981 base (Haldar 1997).

The vehicular pollution is severe in the Kolkata Municipality Corporation. Due to the

huge population growth, the estimated energy demand (both diesel and petrol) increases

from 352 thousand tones in 1990-91 to 1603 thousand tones in 2000-01, nearly five times

increase, It is also estimated to be 30 percent of the total pollution, (Agarwal 1996).

According to NEERI study in 1973-34, 176.7millions tones of CO emitted in Kolkata’s

atmosphere every day and transports sector contributes 138.7metric tones. Most of the

emission flows from four wheelers (approx. 78%) and two & three wheelers 22% of the total

emission.

A huge number of motor vehicles in Kolkata have been increased over the recent

years. From the CPCB report we know the pollution in Kolkata raises due to large number

of vehicles, the inadequate and narrow road network. In Kolkata, we see a fair number of

rickshaws and bicycles, which reduce the average traffic speed and as a result emission

increases (Chakraborty 1997). According to a report of the National Commission on

urbanization 1988, the increase in projected travel demand per day in Kolkata in 2001 will

be 63% compared to the 1981 base (Haldar 1997).

21

To investigate the health impact of vehicular pollution of Kolkata (former Calcutta),

a cross-sectional study by Lahiri et. al. (2002) was carried out during 2007-2010 among 932

male non-smoking residents of the city and 812-age- and gender-matched rural subjects as

control. The urban group included 460 men who were occupationally exposed to vehicular

pollution: 56 traffic policemen, 188 street hawkers, 82 auto rickshaw drivers, 78 bus drivers

and 56 motor mechanics. Remaining 472 participants from the city were office employees.

Compared with control, urban subjects had increased prevalence of respiratory symptoms,

asthma, headache and reduced lung function, chronic obstructive pulmonary disease and

hypertension. Lahiri et. al. (2002) also compared prevalence of respiratory symptoms such

as breathing problem of individual residing in Kolkata and individuals residing in rural parts

of West Bengal where air pollution Level is not alarming. The study also reported whereas

45 percent of rural individuals complained about such problems, 75 percent of urban

households in Kolkata exhibited respiratory problems.

Majumdar (2007) studies the health cost of air pollution in Kolkata. He argued that

there is a direct link between health and air quality. Deterioration in health quality leads to

the greater vulnerability towards diseases. In order to understand the impact of air quality in

health, Majumdar (2007) collected data on 600 samples KKM from 10 locations in Kolkata.

He collected a number of quantitative and qualitative information from his samples. He

specially collected data on the socioeconomic variables, preponderance of illness and the

health cost of incurred. It was found that the vulnerable people having chronic element and

smoking habits, as well as the minority communities susceptible towards health risks. It was

22

also found that citizen of Kolkata have to bear a significant health cost due to airborne

diseases.

However, a very important dimension of vehicular pollution is the damaged that is

caused due to constant exposure of deadly gases by the different sections of people whose

occupations are directly or indirectly linked with such exposure (such as traffic police ,

street vendors , peddlers etc). Study (Sinha 1993) pointed out to various health elements that

affects these people.

2.5 Conclusion:

The above studies clearly reveal that pollution have a numerous effect on the life of

man. However, pollution itself is a result of a complex socio-economic process. Our aim in

the dissertation is to understand some of the aspects of vehicular emission in the context of

Kolkata. We start our journey with the brief data description.

23

Chapter – 3

Data and Methodology of the Study

3.1 Introduction:

Air pollution has both macro and micro dimensions. On the macro level it increase

the overall toxicity of the air. At the micro level we consider the individual’s reaction while

facing such toxic environment. Our analysis thus required both macro and micro data. The

macro data is culled from various secondary sources-both published and unpublished. The

micro data however required primary data collection. This chapter discusses briefly the data

sources that are used by us. This chapter is divided into two sections. In section 3.2 we

provide the data description. This section is also divided into two subsections. In section

3.2.1 we describe the secondary data collected from various sources like Census data,

District Statistical Hand Book, West Bengal Pollution Control Board report, Ministry of

Road Transport and Highways, Ministry of Petroleum and Natural gas etc. As for primary

data, the affected traffic police personnel were interrogated for a host of information. This is

discussed in subsection 3.2.2. In the last our basic approach to the study is discussed.

24

3.2 Data Description:

3.2.1 Secondary data:

For the secondary data, we have used both published and unpublished documents.

Since our requirements were multifarious, different sources had to be utilized. Some of the

sources are quite general.

(a) Census Data, 2001:

India census gives us a rich data at various levels of desegregation. From the census

we have gathered three types of data. First, the information on urbanization and its related

features are taken from the census. Secondly, the household amenity data is utilized to

collect information on various types of vehicle owning households is collected. Information

is also collected on the literacy level of the household.

(b) Report of WBPCB: 2001-2008

In our study we examine the fluctuation and trend of the different vehicular

particulates and or emitants on the basis of season of Kolkata throughout the year, which are

collected from the report of WBPCB. The West Bengal pollution control Board has

collected these types of data daily by the regular monitoring from the various traffic points

in Kolkata.

25

(c) Ministry of Road Transport and Highways, 1999, 2000.2003:

To examine the growth rate of different types of vehicles over the years we used data

from the Ministry of Road Transport and Highways, 1999, 2000.2003. Data gives

information on the volume of vehicles. The information is necessary to asses the gamut of

vehicular population.

(d) Ministry of Petroleum and Natural Gas, 2002:

In our study to show the actual picture of Air pollution level the largest Indian cities

like Delhi, Mumbai, Kolkata, Chennai and Bangalore etc.we collected data regarding the air

pollution particulates like SPM, RPM, (SO)x , (NO)x, CO and lead etc. from the Ministry

of Petroleum and Natural Gas.

(e) Others:

However, all the official information is incomplete. Thus miscellaneous other

sources were utilized by us. This includes the research carried on by other scholars, District

Statistical Hand Books and some unpublished official information.

26

3.2.2 Primary data:

For primary data we concentrated on a particularly affected group of people-traffic

police. The information is collected through direct questionnaire method. A detailed

questionnaire regarding their occupation, illness, exposure to vehicular pollution etc. was to

be prepared. We surveyed 98 traffic police personnel who are scattered and continuously

exposed to pollution at the time of their duty throughout Kolkata.

3.3 Methodology:

To analyse these data, we also deal with some statistical operations like OLS, Whit’s

Heteroskedastic Consistent Regression, Multiple Regression, Tobit Regression, Non-

Parametric analysis, Spectral analysis, various charts and diagrams in our study.

3.3.1 OLS, Multiple Regression, and Non-Parametric analysis:

In the fourth chapter we have used the multiple regressions for analyzing the

relationship between literacy rate, urban population, and urban vehicle holding household.

In this chapter we have also taken a non parametric analysis to show the pollution of urban

area is higher than the rural area. The non parametric analysis is based in the concept

of ‘distance’ between two independent distribution. Instead of testing the differences at

a particular point, As the regression considers only “average points” non - parametric

method take into consideration all the feasible points of the distribution.

27

3.3.2 Tobit Regression and White’s Heteroskedastic Consistent Regression

The primary data is analysed using the Tobit egression. Since our data are qualitative

in character, Tobit regression is very helpful tool for analyzing the data. Using the

framework of health capital formation, the appropriate structure is built up. The structure

was then utilized as a basis of our Tobit regression. Also, White’s Heteroskedastic

Consistent regression estimate has been utilized for this exercise.

3.3.3 Spectral analysis:

Under the frequency domain analysis this technique enables us to analyse the

relationship between any meaningful components of a pair of time series. If there are some

cyclical components of two time series, then this analysis is also necessary for

distinguishing between the short-term relationship and the long-term relationship has been

recognized in many fields of economics. The parameters, which this new statistical

technique estimate, in regard to the relationship are the closeness of the relationship, the

regression co-efficient and the lead or lag between each pair of components.

The time series χt is expressed as the sum of independently varying cosine and sine

curves with random amplitudes. Thus χt can be exactly fitted by a finite Fourier series, be

xt = a 0 + Σ[ ak cos (λkt) + bk sin (λkt)] (for k = 1 to q)

28

Where ak’s and bk’s are uncorrelated random variables with zero expectations and λ (lambda)

is the frequency expressed in terms of radians per unit time and σ2 is variances. The

frequencies are equally spaced and separated by a small interval. The purpose of the analysis

is to see how the variance of χt is distributed among oscillations of various frequencies.

Fourier series reveals that there are few, if any persistent sinusoidal components in

the data. Nevertheless, the oscillations in this series may be described in sinusoidal terms by

spectrum analysis. This is a method that describes the tendency for oscillations of a given

frequency to appear in the data, rather than the oscillations themselves.

In order to show the periodic fluctuation and to compare the oscillation of the series

of pollutants at different periods, we use the technique Cross S Spectral analysis.

After dealing with the data description, in now turn to the main study. The next three

chapters deal with the macro aspect of vehicular emission.

29

♦♦♦♦

Chapter – 4

Urbanisation and Vehicular Population

4.1 Introduction:

Protection of the global environment is in the interest of all of us living in the lonely

planet. All over the world planners are expressing increasing concern about the control of

air pollution. Every now and then the environment protection agencies announce policies

that are intended to cut down on the level of air pollution. The World Development Report

(2007) has identified three specific sources of air pollution (a) emission from industry (b)

transport and (c) domestic emission.

All these components of air pollution are important by themselves. However, in this

paper, we wish to concentrate on vehicular pollution. The vehicular pollution is important

from many dimensions. First, we live in the era of globalization that is closely linked with

the industrialization and the concomitant urbanization, urban growth is associated with a

growth of vehicular population. Secondly, globalization has led to the emergence and

expansion of the middle income classes. This has boosted consumption in various

consumers’ durables – vehicle is one of the important components of it. In any modern

Indian city one can see the spurt of privately own vehicles in the recent years. Thirdly,

increased privatization has led to dismantling of the public transport system and its

This chapter draws heavily on a published paper: ‘Vehicular Pollution in West Bengal’ (Sengupta and Pal),

Environment & Ecology 30 (1): 130-132, January – March, 2012.

30

replacement by private operators. In all these there is a surge of automobile demand that is

going to contribute heavily towards vehicular population.

This chapter concentrates on the last problem. Specially, we focus on the ownership

and pattern of vehicular population in the urban areas, its features and determining factors. It

is divided into six sections. In section 2 we provide a brief description of the data used and

the methodology utilized for our study. Section 3 describes the evolution of urbanization

with special reference to West Bengal. Section 4 analyses the relation between possession of

polluting vehicles and some other factors. Section 5 studies the relationship in a non-

parametric way. The paper is concluded in section 6.

4.2 Data and Methodology:

In order to understand the nexus between urbanization and vehicular population we

have considered data from secondary sources. The largest body of data available of

urbanization in India is the census data. Census documents the growth of municipalities,

towns and cities for more than 100 years. In the census we get detailed records of the urban

population, its size and composition and its various socio-economic features. All these are

helpful to understand the multifaceted feature of urban development. More over, the 2001

census give us a detailed recording of the various types of assets of the households. Among

the assets we get a picture of the various types of vehicles held by the households. This data

covers information about the ownership of bi-cycles and all type of two wheelers and four

wheelers. These vehicles can be broadly classified as oil consuming (i.e. polluting and non-

31

oil consuming) vehicles. The pollution created by the oil consuming vehicles is very high as

compared to the non fuel consuming vehicles. The census data gives us an opportunity to

understand the possession of the fuel consuming vehicles and several factors that may be

important in this respect. One of the important factors is undoubtedly urbanization. With the

growth of towns and the rise in middle class, there is created a demand for these vehicles.

However the relationship and its intensity are far from obvious. In the case of planned

urbanization with the rising public transport system, the demand may be mitigated to a

certain extent. However if urbanization is unplanned with a languishing (or non-existing)

public transport system, the spurt in private vehicles is often the only logical solution.

In order to stress the relationship, we first depict the pattern of urbanization in the

next section. These give us an idea of the terrain that we have to traverse. Having

understood some of its features, we next move to the econometric analysis of the

relationship between urbanization and the possession of polluting vehicles. However

econometric analysis assumes certain structures- the result that we get may be driven by

such structure. This is the reason why we turn to the non-parametric techniques in order to

corroborate our findings based on econometric analysis. Comparison between these two

techniques may help us to unravel the relationship between urbanization and the possession

of polluting vehicles.

32

4.3 Urbanization - its dynamics with special emphasis to West Bengal.

Kingsley Davis has explained urbanization as process (Davis 1962) of switch from

spread out pattern of human settlements to one of concentration in urban centres. It is a finite

process --- a cycle through which a nation passes as they evolve from agrarian to industrial

society (Davis and Golden 1954).

The onset of modern and universal process of urbanization is relatively a recent

phenomenon and is closely related with industrial revolution and associated economic

development. As industrial revolution started in Western Europe, United Kingdom was the

initiator of industrial revolution. Historical evidence suggests that urbanization process is

inevitable and universal. Currently developed countries are characterized by high level of

urbanization and some of them are in final stage of urbanization process and experiencing

slowing down of urbanization due to host of factors (Brokerhoff; 1999, and Bremman;

1998).

A majority of the developing countries, on the other hand started experiencing

urbanization only since the middle of 20th century. The pattern of urbanization in India is

peculiar. The lopsided dynamics of sectoral composition where the primary sector is

increasingly replaced by an expanding service sector with the slow growth in secondary

sector had its impact on the process of urbanization. It is characterized by continuous

concentration of population and activities in large cities. Kingsley Davis and Golden

(1954) used the term “over - urbanisation” to characterize this process. They argued

33

“over – urbanization” where urban misery and rural poverty exist side by side with

the result city can hardly be called dynamic”. Another commentator observed the

Indian urbanization as a process whereby inefficient, unproductive informal sector

becomes increasingly apparent (Kundu and Basu, 1998). Yet another scholar (Breese,

1969) depicts urbanization in India as pseudo urbanization where in people arrive in

cities not due to urban pull but due to rural push. Reza and Kundu (1978) expressed

opinion of dysfunctional urbanization as well as urban accretion which results in a

concentration of population in a few large cities without a corresponding increase in

their economic base. In short, India’s urbanization is followed by some basic

problems in the field of inadequate housing, unprecedented growth of slums,

inefficient transport system, problems in water supply and sanitation, water pollution

and insufficient provision for social infrastructure (school, hospital, etc.). It is this

lopsided growth that adds to the problem of vehicular pollution.

The developing countries suffer more than the developed countries from air pollution

(Bhattacharya and Banerjee 2002) that happens from vehicle emissions. High levels of lead,

primarily from vehicle emissions, have been identified as the greatest environmental danger

in a number of large cities in the developing world. For them the problem is becoming more

acute as the numbers of motor vehicles are growing rapidly due to rapid urbanization.

One of the interesting studies is done by Bhattacharya and Banerjee in

chapter - 3 of their book – ‘Air pollution and willingness to pay’. They gave a brief

review of the urban air quality and the intensity and spread of vehicular pollution.

34

Citing a study by NEERI they found that the main concentration level for

various atmospheric pollutants have increased in all the major cities. Bhattacharya

and Banerjee identified various causes of this urban pollution - namely - (a) traffic

composition, (b) lack of public transports and (c) insufficient road space.

From this all – India background, we move to West Bengal. First we observe the

pattern of urbanization in West Bengal. The table below shows the growth of census towns

in West Bengal. From the table below and the figure we see that between 1901 and 1951

there was a relatively slow growth in the number of towns in this state. The real breakpoint

is form 1961 after which there is an explosion in the number of urban centers. In recent

census 2001 the growth rate of town was negative (-1.83).

35

Table 4.3.1: Growth of census towns in West Bengal

Census Years Number of Towns Rate of growth overthe previous Census

1901 78 -----1911 81 3.851921 89 9.881931 94 5.621941 105 11.701951 120 14.281961 184 53.331971 223 21.201981 291 30.491991 382 31.272001 375 -1.83

Source: Census Data: 2001

Census year wise number of towns are shown by the following bar diagram.

Fig-4.1: Number of Towns in West Bengal (1901-2001)

Number of Towns in West Bengal (1901-2001)

050

100150200

250300350400450

1901 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001

Number of Towns

Source: Census Data: 2001

We have discussed the evolution of urbanization by the part which is the trend of

urbanization

36

Table: 4.3.2 Trend of Urbanisation

Source: Census Data: 2001

[Trend of urbanization in any particular period = {(% of urban population of the particular year ― % of urban

population of the previous year) / % of urban population of the previous year}* 10}].

In the pre independence period the trend of urbanization was negative in case of Jalpaigunri

and Malda districts in 1911. In 1921 the negative trend was seen in Malda and Nadia. In

1931 it was also negative in Purulia. In post independence period, there were also negative

trends in Coachbihar in 1961, in Darjeeling, Coachbihar, Murshdabad, Bannkura and

Midnapur in 1971. In 1991 the urbanization rate was negative only in 24 pgs (north &

south).The last census year shows the negative trend of urbanization in Westdsinajpur,

Birbhum, Nadia, and Bankura. As in Kolkata the rate of urban population is always cent

percent, the trend is zero percent.

District/Year 1911 1921 1931 1941 1951 1961 1971 1981 1991 2001Darjiling 0.91 1.11 3.42 1.80 3.73 0.91 -0.04 1.95 1.06 0.61

Jalpaigunri -0.60 1.84 2.19 2.80 12.02 2.62 0.54 4.63 1.64 0.90Coachbihar 0.74 0.94 0.47 3.72 7.86 -0.66 -0.24 0.10 1.31 1.66

Westrdinajpur -------- -------- -------- 0.52 41.55 7.41 2.49 1.97 1.94 -0.66Malda -1.03 -0.05 0.62 1.78 1.65 1.07 0.16 1.01 5.24 0.35

Murshidabad 0.81 0.48 0.30 0.96 0.71 0.85 -0.10 1.08 1.14 1.98Birbhum -------- -------- 13.41 2.07 1.24 0.78 0.08 1.79 0.85 -0.46

Bardhaman -------- -------- 2.80 3.81 2.85 2.01 2.52 2.90 1.94 0.53Nadia 0.02 -1.25 3.17 1.64 3.14 0.12 0.18 1.52 0.48 -0.6024 Pgs 1.94 0.64 0.75 1.32 2.10 1.66 1.05 1.04 -1.10 0.01

Hooghly 0.61 0.36 0.89 1.21 2.28 0.55 0.20 1.16 0.56 0.32Bankura 0.52 1.89 0.13 1.79 0.06 0.24 -1.08 1.65 0.87 -1.11Purulia -------- -------- -0.05 3.67 1.92 0.13 0.54 2.55 0.49 0.67

Midnapur 1.21 0.06 3.64 1.90 2.78 0.23 -0.09 1.13 1.60 0.40Howrah 0.53 0.05 0.60 2.41 1.24 2.49 0.36 0.76 0.99 0.16Kolkata 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

37

The long run growth of urbanization is shown below by the following figure.

Table: 4.3.3 Long run Growth of Urbanization

L ong run growth in urbanisation

00.10.20.30.40.50.60.70.80.9

1

Darjiling

J alpaigunri

Coachbihar

Westr

dinajpurMalda

Murshidabad

Birbhum

Bardhaman

Nadia

24 Pgs

Hooghly

Bankura

Purulia

Midnapur

Howrah

Kolkata

Source: Census Data: 2001

From the above figure we see the long run growth of urbanization was the highest position

(0.94) in the district of Murshidabad and it was the lowest (0.01%) in Howrah. This may be

due to the fact that Murshidabad is relatively ruralised district with enough scope for

urbanization. On the other contrary, Howrah is historically an urbanized district.

38

4.4: Urbanization and Vehicular Ownership:

It is argued that urbanization leads to a rise in the middle classes and the

concomitant rise in the ownership of vehicles. We now consider this argument using

the West Bengal data.

Table 4.4.1: Ownership of vehicles (West Bengal-2001)

West Bengal Total no of

Households

Bicycle

Scoter,

Motorcycle,

Moped

Car, Jeep, Van

Total 1,57,15,915 82,64,357 7,90,322 2,97,634

Rural 1,11,61,870 60,59,054 3,41,227 1,35,005

Urban 45,54,045 22,05,305 4,49,095 1,62,629

(Source: Census data, 2001)

In table : 4.4.1 we have shown the different vehicles holding households in

total west Bengal. Then we have divided state by two sectors as rural and urban and

analysed the several vehicles holdings households. There is a clear dichotomy in the

ownership pattern among the rural and urban households. Though the total number of

vehicle owning households is far higher in the rural sector as compared to the urban

sector, most of the vehicle owners in the rural area owns bicycle. In fact, though

bicycle owners in the rural area are much higher, for the other two categories urban

owners far exceed the rural owners. Since bicycle is non-fuel consuming and hence (at

39

least directly) less polluting than the other two categories, it is clear that urbanization

leads to a higher ownership of polluting vehicles.

Table 4.4.2: Ownership of vehicles (% of total)-West Bengal 2001

(Source: Census data 2001)

The percentage figures in table 4.4.2 merely corroborate our earlier findings. From

the above Table, we realize that the total percentage of polluting vehicles in urban

area is greater than rural area in West Bengal. But in case of non polluting vehicles

it is lower in the urban area. Obviously it is clear that the pollution in the urban area

is higher then the rural area. In percentage term, the ownership of fuel consuming

vehicles is more than three times higher in the urban areas.

To fix the idea we consider the district wise distribution of vehicles both in

rural and urban areas of West Bengal. An analysis of the Census data reveals that

in the rural area of Hooghly (rural) we get the highest percentage of polluting

vehicles (8.35%) and Dakshindinajpur is the lowest percentage of polluting vehicles

(2.05%) district. Undoubtedly rural Hooghly is well linked to the urban sector and

West Bengal % of bi-cycle

holding

% of Scoter, Motor

cycle, Moped %Car, Jeep, Van

Total52.59 5.03 1.89

Rural54.28 3.06 1.21

Urban48.43 9.86 3.57

40

shows a high degree of semi-urban or pre-urban features. As for urban areas, we get the

highest percentage of two wheeler & four wheeler vehicles in the district of Barddhaman

(21.44%)-and the least percentage of two wheeler & four wheeler vehicles holding

district is Nadia (7.78%). Here Kolkata is pulled down to the second position. However

the composition of polluting vehicles show a higher percentage of fuel consuming two-

wheelers in Burdwan (18.44%) vis-à-vis Kolkata (8.70%) while Kolkata (6.87%) outstrips

Burdwan (3%) in the percentage distribution of four wheelers. In fact Darjeeling (4.59%)

and 24-Parganas (North) (3.5%) also overwhelm Barddhaman in this matter.

Table 4.4.3: percentage of Polluted vehicles owners (rural and urban) and

percentage of urban Households

District% of polluted

vehicles% urban

households% urbanpopulation

Darjiling 7.8 31.17 32.34Jalpaigunri 5.35 18.49 17.84Coachbihar 3.25 9.18 9.10

Uttardinajpur 3.01 11.39 12.06Dakshindinajpur 2.96 12.07 13.10

Malda 3.37 7.88 7.32Murshidabad 3.26 12.23 12.49

Birbhum 4.73 8.93 8.57Bardhaman 12.17 36.94 36.94

Nadia 3.96 22.18 21.2724 Pgs (North) 8.1 55.54 54.30

Hooghly 9.62 34.95 33.47Bankura 5.81 7.71 7.37Purulia 5.81 10.29 10.07

Medinipur 6.16 10.68 10.24Howrah 8.42 52.09 50.36

24 Pgs (South) 4.63 17.62 15.73Kolkata 15.57 100.00 100.00

Source: census data of India 2001

41

Next we consider the percentage distribution of the owners of polluting

vehicles (urban and rural) and the rate of urbanization in the following table. It is

seen that largely true that districts with the higher degree of urbanization indicates a

higher degree of ownership of pollting vehicles. Kolkata-the fully urbanized district

shows the highest percentage of ownership of polluting vehicles and lowest in

Dakshin Dinajpur.

We now try to evaluate the relationship between the ownership of polluting

vehicles and the factors associated with urbanization. It is generally conjectured that

if the proportion of urban households increases, the probability of polluting vehicles

holdings also increases. There are 100% urban households in Kolkata and we find

there is a highest percentage (15.57%) of polluting vehicles. Another determining

factor is the literacy rate. Conventional wisdom delineates a negative relation between

literacy rate and pollution. In general, if the literacy rate increases, the consciousness

increases leading to a fall in pollution. We want to see whether this factor is important

in determining the ownership of fuel consuming vehicles.

42

Table 4.4.4: Regression results (Urban Households)

Dependent Variable: % of households having polluting vehicles

Regression

Statistics

Multiple R 0.88

R Square 0.78

Adjusted R

Square 0.75

Standard Error 1.72

Observations 18.00

ANOVA

Df SS MS F

Significance

F

Regression 2.00 158.41 79.20 26.87 0.00

Residual 15.00 44.21 2.95

Total 17.00 202.62

Coefficients

Standard

Error t Stat

P-

value

Intercept -1.65 3.20 -0.52 0.61

% urban

households 0.08 0.03 2.26 0.04

lit rate 0.10 0.07 1.53 0.15

43

The regression results show that vehicular population is highly correlated with the

distribution of urban households. Literacy rate has some positive effect on possession of

polluting households (significant at 15% level). This type of perverse relation between

literacy rate and possession of polluting vehicles is quite indicative. High education is often

associated with high income leading to arise in the possession of polluting vehicles.

Similarly we have tried to find out the percentage of polluted vehicles, urban

population and literacy rate in our next regression. The regression results show that

vehicular population is highly correlated with the distribution of urban population. Literacy

rate has some positive effect on possession of polluting households (significant at 14%

level). Again the perverse relation between literacy rate and possession of polluting

vehicles is a result of strong associated of literacy rate with high income leading to arise in

the possession of polluting vehicles.

44

Table 4.4.5: Regression results (Urban Population)

Dependent Variable: % of households having polluting vehicles

Regression Statistics

Multiple R 0.88

R Square 0.78

Adjusted R

Square 0.75

Standard Error 1.71

Observations 18.00

ANOVA

Df SS MS F

Significance

F

Regression 2.00 158.60 79.30 27.02 0.00

Residual 15.00 44.02 2.93

Total 17.00 202.62

Coefficients

Standard

Error t Stat

P-

value

Intercept -1.65 3.18 -0.52 0.61

lit rate 0.11 0.07 1.54 0.14

% urban

population 0.08 0.03 2.28 0.04

45

4.5 Towards a Non-Parametric Analysis:

The above methodologies are a parametric in nature. They depend on a number of

assumptions. The non parametric analysis is based in the concept of ‘distance’ between two

independent distributions. Instead of testing the differences at a particular point, as the

regression does non-parametric method test into consideration all the feasible points of the

distribution.

For non-parametric evaluation suppose A represents the event of “possession of

polluting vehicles”, R –“the rural residence” and U-“the urban residence”. Our proposition

is that the probability of polluting vehicles by rural residence is less than the probability of

polluting vehicles by urban residence, i.e. P (A/R) < P (A/U).

And assume P (A/R) = P (A∩R)/P (R)

P (A/U) = P (A∩U)/P (U)

We measure the probabilities by their observed proportions.

P (A∩R) = No of households in the rural area having polluted vehicles/ No of

households in the rural area

P (A∩U) = No of households in the urban area having polluted vehicles/ No of

households in the urban area

P(R) = proportion of households living in the rural area/total households

P (U) = proportion of households living in the urban area/total households

And P(R) = 1- P (U).

46

Here we have to test whether the probability of polluted vehicles holding by rural

residence is equal to or less than the probability of polluted vehicles holding by urban

resident.

Our first test is mean test.

Table 4.5.1: Mean Test

T - Test: Paired Two Sample for Means

P(A/R) P(A/U)Mean 0.041117 0.131811111

Variance 0.000501 0.001593407Observations 18 18

Pearson Correlation 0.336864Hypothesized Mean

Difference 0Df 17

t Stat -9.95971P ( T<=t ) one – tail 8.21E-09t Critical one – tail 1.739606P ( T<=t ) two – tail 1.64E-08t Critical two – tail 2.109819

Analyzing the data from the above table we find that the t-coefficient is significant

both for one-tailed and two-tailed tests. Hence the mean test justifies that rural people have

lesser probability of holding polluting vehicles.

This test judges the conditional probability of the events to occur between two

distributions at the mean. We test whether the conditional probabilities for households

owning polluting vehicles is high if it is located in the urban area. In contrast to whether it is

located in the rural area.

47

Our null hypothesis is

Ho: mean (P (A/R)) = mean (P (A/U)) and

H: mean (P (A/ R)) < mean (P (A/ U))

However, mere testing of mean does not reveal much about the underlying

distribution.For this we have to compare the distributions of two related variables. The

appropriate test to use depends on the type of data. We first use the non-parametric

Kolgomorov-Smirnov test for testing the difference between the distributions of the two

variables. Our results are given below. From the results below, we see only P (A/U)> P

(A/R) holds the positive ranks and in other cases holds negative ranks and ties. Therefore we

can state that rural people have lesser probability of holding polluting vehicles than urban

people.

Table 4.5.2: Kolgomorov - Smirnov Test

Ranks

N Mean Rank Sum of RanksP (A/U) > P(A/R) Negative Ranks 0a .00 .00

Positive Ranks 18b 9.50 171.00Ties 0c

Total 18a. P(A/U) < P(A/R )

b. P (A/U) > P(A/R)

C. P (A/U) = P(A/R)

Next we consider the Wilcoxon Signed Rank Test and the Sign Test. This test can

be used if the data are continuous. The sign test computes the differences between the two

variables for all cases and classifies the differences as positive, negative, or tied. If the two

variables are similarlydistributed, the number of positive and negative differences will not

differ significantly. The Wilcoxon signed-rank test considers information about both the

48

sign of the differences and the magnitude of the differences between pairs. Because the

Wilcoxon signed-rank test incorporates more information about the data, it is more powerful

than the sign test. In our case however the results from various non-parametric tests

converge. They all clearly demonstrates that P (A/R) - P (A/U) <0.

Table 4.5.2: Test Statistics

Test Statisticsb

P(A/R) – P(A/U)

Z -3.724a

Asymp . Sig. (2- tailed) .000

a. Based on negative ranks.

b. Wilcoxon Signed Ranks Test

Table 4.5.3: Sign Test

Frequencies

N

P (A/U) > P(A/R) Negative Differencesa 0

Positive Differencesb 18

Tiesc 0

Total 18

a. P (A/U) < P(A/R)

B P (A/U) > P(A/R)

c. P (A/U) = P (A/R).

Table 4.5.4: Test Statistics

Test Statistics

P (A/U) > P(A/R)Exact Sig.(2 – tailed) .000a

a. Binomial distribution used.

b. Sign Test

49

4.6. Conclusion:

In this chapter our attempt is to us to test the relationship between urbanization and

vehicular population using the West Bengal data. For our analysis we concentrated on

various secondary sources particularly the census data. The relationship is an expected;

urbanization raises the possession of fuel consuming vehicles.

This is significantly positive both using econometric as well as non-parametric

techniques. Even the literacy rate is perversely affecting the possession of polluting vehicles,

the reason is that higher literacy implies higher human capital and enhances income. The

picture is not bright. Unless proper steps are taken toward efficient use a public transport

system and planned urbanization, there is a little chance in abating the fleet of polluting

vehicles.

From our above discussion we clearly understand that the probability of polluting

vehicles holding is higher in the urban area than the rural area. It has been proved by the

various statistical tests. Therefore we can obviously state that the pollution in the urban area

is more than the rural area in West Bengal. Urbanization leads to a conglomeration of

polluting vehicles and boosting up pollution.

This chapter clears the relationship between urbanization and vehicular population,

also that of polluting vehicles. However, in the large urban conglomerates, there are some

specific issues of vehicular problems. This is the study we turn to our next chapter.

50

Chapter – 5

Urban Vehicular Problems: Some Issues

5.1 Introduction:

Urbanisation is a fact of life in underdeveloped countries. With urbanization comes

the problem of urban transport and mobility. Urbanisation increases the demand for urban

transport manifold. A part of it is made by public transport system. Private owners also ply

vehicles a significant extent. There is also a rising demand of personally own vehicles due to

the expansion of urban middle class.

There is however some serious gaps in the urban transit system. Firstly, the supply of

urban infrastructure and services often lag behind the demand (Pucher, Korattyswaropam, &

Mittal; 2005). This not only leads to a overcrowding of buses and other public vehicles but

also a congestion of the vehicles. The possibility of pollution increases sharply as a

consequence. A much related problem is the inequity in the urban transit system. Side by

side the overcrowded buses and public vehicles, we see cars and private four-wheelers

carrying only one passenger thereby leading to an underutilization of the transport space.

51

In this chapter, we are concerned with three basic issues that have wide implications.

First, the question of equity is considered. This reflected in the ownership of transport and

subsequently its use. With wide ranging poverty in most of the urban cities of India,

ownership of vehicles is scanty. Furthermore, the conditions of road and the flux of traffic

greatly constraints the use of bicycle- the most common form of private transport. This leads

to overdependence on public transport. Second, the problem of road congestion is

considered. The flow of urban transport often outstrips the availability of urban roads. This

leads to slow mobility often cropping up in pollution. Third, we consider the issue of air

pollution. It is shown that a few cities virtually dominate the secretion of polluting gases. All

these problems are interrelated and they often reinforce upon one another. The analysis was

made both with reference to India as well as West Bengal- a major Indian state.

West Bengal has two cities (Kolkata & Asansol) of more than one million

populations according to the 2001 census. There are however a large number of cities and

towns with huge population. The problem of Kolkata is in the serious one. Suffering from a

largely unplanned growth and the proliferations of slums and an unsustainable population

density, Kolkata needs a special mention. The problem becomes serious because it is

economic, political, cultural and educational capital of West Bengal. The preponderance of

all these utilities with a single city makes its really vulnerable. Though recent attempts have

been made in developing Satellite Township around Kolkata, the pressure have not

delimitated to any significant extent. We have compared West Bengal with India to get a

proper perspective of the problem.

52

5.2 Urban Conditions in India and West Bengal:

Most of the developing countries are suffering from rapid urbanization, huge

population growth and rising vehicle. The urbanization in India as well as West Bengal

increased three decades ago. In 1971 it raised from 109 million in 1981 and 217 million in

1991. In 2001 it also up to 285 million increased (office of the Registrar general of India,

2001a; Padam and Singh, 2001).

The speedy growth of India’s cities and the towns of West Bengal have created a

correspondingly a large growth of travel demand. The rapidly increasing levels of motor

vehicle ownership and use have resulted in deep levels of congestion, air and noise pollution.

We have explained the urban situation specifically by the two economic ways. One is

distribution of slum population in the mega cities of India and the main towns of West Bengal.

And another is poverty.

In the urban centers poor people generally live in overcrowded, unhygienic habitat

often termed as slums. Slums are integral part of any large urban centre in the world. In India,

the conglomeration in slums is not insignificant. Now if we follow the picture of slum

population in the mega cities of India, the Census 2001 reveals that there is near 48.88% slum

population in Greater Mumbai. In Kolkata, the relevant figure is 32.55% Chennai 25.60% is

not far away from Kolkata. The situation of Delhi (18.89%) and Bangalore (8.04%) is

comparatively better (Sivaramakrishnan, Kundu & Singh, 2005). These slums are hart bingers

53

of a lot of pollution. The smoke that arises than cooking in overcrowded, mostly places creates

a lot of air pollution.

Another correlates of depreciation is the poverty. Poor people are deprived descent

mean by urban transport. There are lots of measures of poverty. The problem with the official

figure that considers only to bare subsistence. However there are a lot of people who may be

able to meet their minimum subsistence need and yet vingering massive deprivation. The

distribution of asset less may be to more ideal for this purpose. According to the 2001 census

data 18.36% of the urban people of India have no asset. The figure varies from as high as

32.18% in Bihar to a low 6.64% in Chandigarh. In West Bengal the percentage is 20.39%

which is above the national figure. It ranks rather high in the incidents of asset less people,

being one of the poorest owning states in India.

The district wise profile in urban West Bengal again shows the wide variation. The

figure is high 30.97% in Murshidabad and a low 16.7% in undivided Medinipur. Kolkata the

state capital is the second. Thus the problem of poverty is in respect to asset ownership is quite

pronounced in the state. Since the asset list contents the set of two-wheelers. These people

have to depend on public transport for their movement. The pressure on it is enhanced.

54

5.3 Vehicular Population:

Now let us turn to the total fleet population in the country as well as West Bengal. The

figure of given table -1 is below. Due to rising of income of the people, the socio-economic

status changes year after year. So the private vehicles increase more than the increase of

population.

Table-5.3.1: No of vehicles and population in India

Year Actual vehicle (crores)

1971 0.34

1975 0.40

1981 0.79

1985 1.19

1991 2.53

1992 2.73

1993 2.90

1994 3.08

1995 3.31

1996 3.59

1997 3.92

1998 4.28

1999 4.48

2000 4.81

2001 5.35

(Source: Census data, 2001)

55

During the 30 years of period in India, there is about 15.74 fold increase in the total

vehicular population. This is enormous increase from its initial levels. The government also

increases huge public buses due to rapid urbanization.

Table-5.3.2: No of vehicles and population in West Bengal

Year

Actual vehicle

(crores)

1971 0.51

1975 0.74

1981 0.87

1985 1.35

1991 2.05

1994 2.28

2001 3.26

2006 3.58

(Source: Census data, 2001)

Similarly the above figure states that the actual (per-capita) number of vehicles also

increases over the years for the same reason. In the state of West Bengal private vehicles

increase very rapidly. The various types of fuel consuming vehicles also increase year after

year. This is about 7 fold increase in the vehicular population in West Bengal. Apparently this

is somewhat lower than the all India figure. However given the small land area of West

Bengal that accounts for only 2.71% of land area, the difference is not really significant.

56

Next we consider the increase in the types of vehicles. If we follow the growth of

vehicle type in perspective of India the motorcycle ownership increased 15.74 fold increased

between 1981 and 2002. Private car ownership increased almost 7 fold during the same

period. All fuel consuming vehicles increased from 1991 to 2002.

Fig: 5.3.2.1 Growth of India’s motor vehicle fleet by type of vehicles

Grow th of India's motor vehicle f leet by type of vehicle

0

500000000

1000000000

1500000000

2000000000

2500000000

3000000000

3500000000

4000000000

4500000000

1981 1986 1991 1996 2002

Year

No

of v

ehic

le

Goods VehicleMotor car/JeepMotorcycle/scooterother motorised vehicleBuses

(Source: Ministry of Road Transport and Highways, 1999, 2000.2003)

(Note: “others” includes tractors, trailers, motorized three wheelers and passenger vehicles)

57

The low-density development around Indian cities has made cars and motorcycle

ownership increasingly affordable. Rising incomes among the Indian middle and upper classes

have made car and motorcycle ownership increasingly affordable. Fig-1 shows the number of

motorcycle/scooter has increased upward. This upward trend indicates from 1981-2002 the

ownership of motorcycle/scooter has increased in huge amount due to raising income as well

as standard of living of people in India. Other goods vehicles, motorized vehicles and buses

also increased during this period.

Fig: 5.3.2.2 Growth of West Bengal’s motor vehicles fleet by type of vehicles

Grow th of West Bengal's motor vehicles fleet by type of vehicles

0

500000

1000000

1500000

2000000

2500000

1981 1986 1991 1995 2001 2007 2008 2009

Year

No

of v

ehic

le

Goods Vehicle

Motor car/Jeep

Motorcycle/scooter

Taxi/Contact carriage

Mini bus/Stagecarriage

Auto Rickshow

Tractor/Tailors

Otherss

(Source: Ministry of Road Transport and Highways, 1999, 2000.2005, 2010)

(Note: other miscellaneous vehicles that are not separately classified)

58

And then we consider the case of West Bengal. This figure: 2 show the extremely

rapidly growth of motorcycle ownership and other private and public fuel consuming vehicles.

The motorcycle vehicles increase a large proportion from 1981-2008 and after that decreased.

Goods vehicle and motor car/jeep increased rapidly from 2001-2008. At the same time all

other typical polluted vehicles increased.

However the crucial factor in the transport use is not only availability of transport but

also the congestion in the urban transport. For if the vehicles can not move at a significant

space, then the possibility of pollution increases. Also it creates a further disutility to the

travelers. Since their movement is slow and requires a lot of time.

5.4 Roadway Congestion in India and West Bengal:

Traffic congestion is probably the most troubling feature in the cities of developing

countries. It affects all modes of transportation and all socio-economic groups. Average

roadway speeds for motor vehicles in Mumbai fell by half from 1962 to 1963, from 38 km/h

to only 15-20 km/h (Gakenheimer, 2002). In Delhi, the average vehicular speed fell from 20-

27 km/h in 1997 to only 15 km/h in 2002 (Times of India, 2002). Moreover the periods of

congestion in Delhi now last 5h: from 8.30 to 10.30 in the morning and from 4.30 to 7.30 in

the evening in Chennai, 10 to 15 km/h overall but falls to only 7 km/h in the centre (Times of

India, 2003), (World Bank, 2002).

59

The cause of congestion is the rapid increase in travel demand. According to the World

Bank, 2002, the average annual rate of growth of travel demand has been 2.2% in Kolkata,

4.6% in Mumbai, 9.5% in Delhi and 6.9% in Chennai.

Congestion also happens if the total road space increases smaller than the raising of

total vehicles. As a result of this, usable road density decreases. Here we have tried to show

the actual congestion picture of urban West Bengal.

There are some discernable features of congestion. First there is a high degree of road

congestion in the city of Kolkata. The figure is substantially higher than that of the other urban

centers in the state of particular maintained can be made of the district of Howrah. 24 Pgs (N),

Nadia and Burdwan that have congestion rate lower than that of Kolkata but higher than the

most of the other districts. However, in north, Darjeeling shows a surprising high congestion

rate as compared to the other surrounding districts. This may be probably due to the high

tourist concentration in the district. In urban Jalpaigunri and urban Malda also, the congestion

is high.

5. 5 Vehicular Pollution:

Air pollution is the serious problem in Indian cities as well as West Bengal towns. As

shown in the following diagrams. The levels of air pollution concentrations are the

compositions of suspended particulate matters (SPM), respiratory particulates matters (RPM),

NOx and SOx according to the World Health of Organisation (WHO). CO and Lead pollutants

60

have dramatically fallen over the past decade from 1995 to 2000 (Ministry of Petrolium and

Natural Gas, 2002). In 1997 Tata energy Research Institute (TERI) found an important source

of air pollution remains the large and mostly old fleet of two-wheelers (motorcyclecles and

scooters) and three-wheelers (auto-rickshaws) with highly inefficient, poorly maintained, very

polluting 2-stroke engines. The Indian Government has already notified to reduce particulate

pollution by mandating conversion of all buses, auto-rickshaws, and taxies in Delhi to CNG

fuel by January 2001 (Urban Transport Crisis, 2005).

Fig: 5.5.1 Air Pollution Levels in the largest Indian Cities

Air Pollution levels in the largestIndian cities

050

100150200250300350400450

Mumba

i

Kolkata Delh

i

Chenn

ai

Banga

lore

Hydera

bad

City

Con

cent

ratio

n in

pcm

(mic

rora

ms) SPM

RPMSO2NO2

(Source: Ministry of Petroleum and Natural Gas, 2002)

61

The all India scenario clearly shows that the three metros Mumbai, Kolkata and

Delhi are the culprits for emitting SPM and RPM. Among them Kolkata is the largest

emitter of SPM while Delhi of RPM. For other metros the emission rate is not very

significant. However, the emission of SO2 and NO2 are almost similar across the metros.

Fig: 5.5.2 Air Pollution Levels in the Towns of West Bengal

Air Pollution Levels in the largesttowns of West Bengal

050

100150200250300350400

Haldia

Kolkata

Howrah

Durgap

ur

Asans

ol

Towns

Con

cent

ratio

n in

pcm

(mic

rogr

ams) SPM

RPMSO2NO2

(Source: Ambient air quality report of W.B, 2006)

Now coming to the West Bengal figure Kolkata remains the almost sole culprit in

emitting all these positions gasses (SPM, RPM, SO2 and NO2). So Kolkata , the capital city

of West Bengal is in the dangerous situation. Other urban Conglomerates are almost

62

insignificant in this regard. This is in spite of the fact that appears in the million plus city in

2001 census.

5.6 Vehicular Ownership Pattern:

Another important aspect of urban transport system is the possession of vehicles. To

facilitate our analysis we distinguish between two types of vehicle – fuel consuming or

polluting (Scooter, motorcycle, moped, car, jeep and van etc.) and non-polluting vehicles

(bicycle).

We first consider the distribution of polluting and non-polluting vehicles across the

states of India. It reveals a unique region divide. Eastern region lags for behind the others

zone in the distribution of polluting vehicles. The states of the northern region are

dominating. As per the distribution of non-polluting vehicles, the eastern region is well-

endow. The gini-coefficient shows that there is a higher inequality in the interstate

distribution of polluting vehicles as compare to the non-polluting vehicles. Now the state-

wise distribution of percentage of vehicular ownership per household is given in the

following table.

63

Table: 5.6.1 State-wise distribution of percentage of vehicular ownership per household

Region State

%Ownership ofpolluting

vehicles/household

%Ownership ofnon-polluting

vehicles/householdNorth

Chandigarh 58.62 68.34Delhi 40.99 37.58

Haryana 23.24 50.05Himachal 10.03 9.10

J & K 10.93 12.78Punjab 37.40 71.76

Rajasthan 15.62 36.23Uttar Pradesh 12.62 69.48Uttaranchal 14.61 30.88

EastAssam 7.26 46.39Bihar 4.55 40.64

Jharkhand 10.88 50.32Orissa 8.94 51.96

West Bengal 6.92 52.59West

Chhattisgarh 12.17 59.83Goa 2.94 4.38

Gujrat 24.53 37.31Madhya Pradesh 13.86 42.80

Maharastra 16.56 30.07South

Andhra Pradesh 11.30 32.83Karnataka 17.51 30.14

Kerala 13.80 18.69Tamilnadu 18.28 42.44

North East 7.55 20.48Group of Union

territories 23.64 39.78Value of Gini Co-

efficient 0.35 0.25

(Source: Census data, 2001)

Next we consider per household distribution of polluting vehicles across the Indian

state in the table 5.6.2. It is clearly evident that for almost all the states the percentage of

households having polluting vehicles is much higher in the urban areas than the rural areas.

64

In some of the states the difference is almost five times or more. The lack of proper

roads, inadequacy of fuel fill-up stations etc might be a contributory factor to these

distortions.

Table: 5.6.2 State-Wise distribution of ownership per Household polluting vehicles all over India

Region State

Ownership ofpolluting

vehicles/household(rural)

Ownership of urbanpolluting

vehicles/household(urban)

NorthChandigarh 23.33 62.79

Delhi 27.99 41.91Haryana 15.68 41.93Himachal 7.96 25.88

J & K 5.32 27.64Punjab 30.69 49.89

Rajasthan 8.94 37.5Uttar Pradesh 8.17 30.32Uttaranchal 7.41 40.3

EastAssam 4.47 23.7Bihar 3.10 18.43

Jharkhand 4.55 3.49Orissa 5.14 32.64

West Bengal 4.27 13.43West

Chhattisgarh 6.44 36.41Goa 40.03 58.78

Gujrat 13.66 41.57Madhya Pradesh 6.89 34.13

Maharastra 9.61 26.02South

Andhra Pradesh 5.66 28.4Karnataka 8.65 34.14

Kerala 10.25 25.14Tamilnadu 11.81 27.35

North East 8.74 18.74Group of Union

territories 8.36 37.79Value of Gini Co-

efficient0.38 0.22

(Source: Census data, 2001)

65

Coming now to the inter-zonal disparity, the north-zone seems to have a very high

percentage of households in the ownership category. On the other hand the figure is very low

for the eastern-region. The situation is medium in the western states. The picture is more or

less same for the urban households.

The picture for non-polluting ownership is completely reverse. Here, the rural areas

outperform the urban areas for almost all the states. Zone-wise, the eastern region is

dominating, closely followed by southern and western states. In all it is seen that the

percentage of polluting vehicles ownership is high in the urban areas and low in the rural

areas. This points out to the stark reality of the urban centers. The state-Wise distribution of

ownership per household non- polluting vehicles all over India is shown in table 5.6.3 in the

next page.

66

Table: 5.6.3 State-Wise distribution of ownership per household non- polluting vehicles all overIndia

Region State

Ownership of ruralnon-polluting

vehicles/household(rural)

Ownership of urbannon- polluting

vehicles/household(urban)

NorthChandigarh 76.67 37.21

Delhi 72.01 58.09Haryana 84.32 58.07Himachal 92.04 74.12

J & K 94.68 72.36Punjab 69.31 50.11

Rajasthan 91.06 62.5Uttar Pradesh 91.83 69.68Uttaranchal 92.59 59.7

EastAssam 95.53 76.3Bihar 96.9 81.57

Jharkhand 95.45 96.51Orissa 94.86 67.36

West Bengal 95.73 86.57West

Chhattisgarh 93.56 63.59Goa 59.97 41.22

Gujrat 86.34 58.43Madhya Pradesh 93.11 65.87

Maharastra 90.39 73.98South

Andhra Pradesh 94.34 71.6Karnataka 91.35 65.86

Kerala 89.75 74.86Tamilnadu 88.19 72.65

North East 91.26 81.26Group of Union

territories 91.64 62.21Value of Gini Co-

efficient0.05 0.11

(Source: Census data, 2001)

As per the question of equity is concerned, the ownership pattern is highly

skewed as the census data reveals. As for the distribution of polluting (fuel consuming)

67

vehicles the gini coefficient is 0.38 in rural and 0.22 in urban. However, for the non

polluting vehicles the corresponding figures are 0.05 (rural) and 0.11 (urban). Thus we

see that there is a greater equality in the distribution of non – polluting vehicles, at least

in rural India. So far as the polluting vehicle is concerned, its distribution is highly

skewed.

5.7 Public Transport: some issues

The inadequacy of private transport pressurizes the public transport mechanism. Due

to the faulty urban transport policies, “the cost of travel” especially for the poor has been

increasing considerably. The Indian cities vary considerably in terms of the population, area,

urban form, topography and economic activities. Thus the requirement of public transport

mechanism varies from one city to another. In the metropolises, over crowded public buses

hopelessly stuck on congestive road ways with an average speed of 6-10 km/h

(Gakenheimer and Zegras, 2003). However even this meager public transport system are

beyond affordability of most the Indian poors. Due to the recent phenomenon inflation and

rise in the cost of living there is an enormous pressure on the poor man’s budget. Kolkata

had India’s only underground metro system 16.5 km while Delhi is constructing 62.5 km

more extensive metro. In contrast, Chennai has an hybrid system of both surface and

elevated metro extending up to 19.8 km suggested (Puchar, Korattyswaropam, & Mittal,

2005). Also, Kolkata has the India’s only reaming tram system (6.8 km double track

network) though old and seriously deteriorating tax and vehicles.

68

From the cost side, Kolkata imposes least on its passengers – covering 42% of the

revenue through passenger’s fares. This may be one of the reason why public transport

system (Marwah, Sibal and Sawant, 2001) is popular among the urban poor. The use of

public transport varies across the cities. It is highest in Kolkata (80% of the trips) followed

by Mumbai (60%), Chennai and Delhi (42%). It is even lower in all other cities.

Debates emanate on the relative efficiency of public and private transport systems. It

is argued that the private operators carry a much larger intake of passengers per bus per day,

earning more revenue and requiring less staff than the public system. Thus it enjoys a higher

profitability and lower cost. On the contrary, the employees in the private secure face lower

wages, less job securities and less job securities and less fringe benefits viz., pension and

Health Insurance (Pucher, Korattyswaroopam & Ittyerah, 2004).

However, the scarcity of public funds and the inefficiency of the public system lend

its support to the issue of privatization. Equally important is the use of new technologies and

replacement of overuse buses, so as to reduce air pollution to a considerable extent.

69

5.8 Conclusion:

Our urban centers show increasing demand for transport. There is however a number

of problems associated with urban transportation. In this paper we hope to deal with some of

these problems. These problems are multifaceted and often entangled with one another. Here

we propose to cover some of the issues with the respect of both India as well as West

Bengal. The inequalities in private urban transit systems are documented. Though there is

arise in the total number of vehicles, there is a sizable portion of the urban families who

have no vehicles. The problem is complicated by the lack of adequate road space and

congestion that prevents the use of bicycles. Severe problems of air pollution are also noted.

Kolkata has one of the cheapest public transport systems among the Indian metropolis.

However even this ‘cheap’ transit system is beyond the reach of the very poor in this city.

Further more, the system is also inefficient with waste of resources. An urban transport

planner has a tough task. He has to balance between equity, efficiency and sustainability of

transit system. This requires long run planning by the urban planners.

Having delt in details about the relationship between urbanization, vehicular

population and pollution in chapter 4 and 5. Now turn our attention to Kolkata – the site for

our study. The next three chapters deal with these problems. In chapter ― 6 we introduce

some aspects of vehicular emission as revealed from the macro data. Chapter ― 7 considers

the seasonal fluctuation of emission. In chapter ― 8 is a macro study of the effect of

emission on the traffic police personnel in Kolkata.

70

Chapter – 6

Analysis of Vehicular Emission in Kolkata

6.1 Preliminary View

6.1.1 Introduction:

Air pollution has been promoted by developments that typically occur as countries

become industrialized: growing cities, increasing traffic, rapid economic development and

industrialization, and higher levels of energy consumption. The high density of population to

urban areas, increase in consumption patterns and industrial development have led to the

problem of air pollution. Recently, in India, air pollution is widespread in urban areas where

vehicles are the major contributors and in a few other areas with a high concentration of

industries and thermal power plants. Vehicular emissions are of particular concern since

these are ground level sources and thus have the maximum impact on the general

population. Also, vehicles contribute significantly to the total air pollution load in many

urban areas.

The direct impact of a growth in various causal factors/pressures is the increase in

the emission loads of various pollutants, which has led to deterioration in the air quality. In

India, there is no systematic time series data available related to air pollutant emission loads

71

and trends. The availability of emission factors for Indian conditions in another issue that

has not been given due attention so far.

TERI (1998) provides some broad estimates of the increase in pollution load from

various sectors in India. The total estimated pollution load from the transport sector

increased from 0.15 million tones in 1947 to 10.3 million tones in 1997.

6.1.2 Trend in vehicular emission:

The terrific increase in number of vehicles has also resulted in a significant increase

in the emission load of various pollutants. The quantum of vehicular pollutants emitted is

highest in Delhi followed by Mumbai, Bangalore, Calcutta and Ahmedabad. Here we have

wanted to focus the vehicular pollution in Kolkata.

Apart from the concentration of vehicles in urban areas, other reasons for increasing

vehicular pollution are the types of engines used, age of vehicles, congested traffic, poor

road conditions, and outdated automotive technologies and traffic management systems.

Vehicles are a major source of pollutants in metropolitan cities.

Under the National Ambient Air Quality Monitoring (NAAQM) network, three

criteria air pollutants, namely, SPM, SO2, and NO2 have been identified for regular

monitoring at all the 290 stations spread across the country. By the WBPCB report we have

shown the trends of four air pollutants mainly SPM, RPM, SO2 and NO2.

72

The emission level has shown downward trend in recent years that is shown in the following

table.

Table 6.1.2.1: Trend of Vehicular Emission

Year SPM RPM SO2 NO22001 2178.07 2178.07 105.29 764.572002 1954.79 1006.14 64.97 749.502003 2502.36 1186.79 65.07 673.002004 2855.73 1005.76 105.68 678.012005 2706.11 1297.81 102.65 936.542006 2569.85 1253.76 92.36 764.022007 2221.64 1044.36 66.57 716.17

(Source: WBPCB Report)

Next we have shown the trend of major vehicular pollution in Kolkata by the following

diagram.

Figure: 6.1.2.2 Trend of Major Vehicular Pollutants in Kolkata

Trend of Major Vehicular Pollutantsin Kolkata

0.00500.00

1000.001500.002000.002500.003000.00

20012002

20032004

20052006

2007

Year

Po

lluta

nts

(Mic

rog

ram

s)

SPMRPMSO2NO2

(Source: WBPCB Report)

73

From the above figure we can clearly understand level of SPM & RPM are very

high. In recent years level of various kinds of pollutants are downward because the major

intervention came in the form of substituting fuel in vehicles, building of flyovers to check

road congestion, etc. However the level is still high.

6.1.3 Seasonal Fluctuation in Vehicular Emission:

The area of study is mainly in Kolkata, the eastern Gateway of India, the capital city

of West Bengal, and one of the most populous cities in the country, is a centre of commerce,

trade and industry in east and north east region. The extent of the city is longitudinal,

running from north to south. The geographical area of the city of Kolkata had undergone

wide changes in the last three centuries.

The government has taken a number of vehicular emission control measures, pollution

prevention technologies; action plan for problem areas, development of environmental

awareness. Yet despite all these measures, vehicular pollution still remains one of the major

environmental problems. At the same time there have been success stories as well such the

reduction of ambient lead levels (due to introduction of unleaded petrol) and comparatively

lower SO2 level (due to progressive reduction of sulphar content in fuel).

74

6.1.3.1 Variation in SPM:

Suspended particulate matter is one of the most critical air pollutants in most of the

urban areas in the country and permissible standards are frequently violated several

monitored locations.

Figure: 6.1.3.1(a) Season-wise SPM Level in Kolkata

Season wise SPM Level in Kolkata

0200400600800

10001200

2001

2002

2003

2004

2005

2006

2007

Year

SP

M (M

icro

gram

s)

WinterMonsoonSummerFestival

(Source: WBPCB Report)

The above diagram is certain pattern in the SPM level in Kolkata. First on average

there has been a dramatic draw in the SPM level in the recent year (2007). This is a

significant drop, given the near static picture over four years 2003, 2004, 2005 and 2006.

These drops may be a result of the introduction of fuel efficient vehicles with improved

technology. Some stringency in the government effort, given the pressure of international

norms may be omnipotent. Also there is a wide season – wise variation in the SPM level. It

is high in the winter season and low in the monsoon. The festive season shows a significant

75

disposal of SPM into the city’s atmosphere. The social factors play an important role in the

environmental degradation along with the natural factors. Thus environmental problem

causes to be mainly a natural phenomenon, at least so far as the air pollution is concerned.

6.1.3.2 Variation in RPM:

WBPCB report reveals that Respiratory particulate matter is also much higher than

the national standard in residential areas. The situation worsens during winter month that is

shown in next figure.

Figure: 6.1.3.2(b) Season-wise RPM Level in Kolkta

Season wise RPM Level in Kolkata

0100200300400500600700

2001

2002

2003

2004

2005

2006

2007

Year

RPM

(Mic

rogr

ams)

WinterMonsoonSummerFestival

(Source: WBPCB Report)

76

A similar season wise is also observed in the emission of RPM. The social factors

could not be ignored in any meaningful analysis of urban air pollution. RPM does not fall

much as SPM in 2007. But the level of RPM has comparatively fallen in 2007 than the

previous years.

6.1.3.3 Variation in SO2:

In comparison to SPM, RPM, and NO2, SO2 is low. And it is also low enough to

have any significant health effect. In case of SO2 the seasonal variation is also pronounced.

Again the social phenomenon of festivity plays a dominant role.

Figure: 6.1.3.3(c) Season-wise SO2 Level in Kolkta

Season wise SO2 Level in Kolkata

0100200300400500600700

2001

2002

2003

2004

2005

2006

2007

Year

SOx

(Mic

rogr

ams)

WinterMonsoonSummerFestival

(Source: WBPCB Report)

From the above figure we see it is low in 2007 than the previous years.

77

6.1.3.4 Variation in NO2:

This is also another contributor element of vehicular pollution.

Figure: 6.1.3.4(d) Season-wise NO2 Level in Kolkta

Season wise NO2 Level in Kolkata

050

100150200250300350

2001

2002

2003

2004

2005

2006

2007

Year

NO

x (M

icro

gram

s)

WinterMonsoonSummerFestival

(Source: WBPCB Report)

It also reveals a wide seasonal fluctuation and the role of the local festivity. In case

of NO2 we can not see a lower position of NO2 level in 2007. This was more or less over the

years. We realize that levels of pollutants were high in 2001.Then over the years these have

decreased. This is because of the various measures taken by government to mitigate

emissions from transport sector.

78

6.1.4 Status of other Vehicular Pollutants:

There are some other air pollutants viz lead (Pb) and Carbon monoxide (CO). The

salient results of these additional parameters at some stations in the metropolitan city of

Kolkata in the respective years. The lead and carbon monoxide levels at most locations were

much higher than the prescribed permissible limit.This is because of high traffic density and

large number of motor vehicles operating on the roads.

6.1.5 Seasonal Fluctuation of Air Pollution level:

The methodological condition and turbulence in the atmosphere are the primary

factors affecting pollutant distribution and dispersion pattern and producing seasonal

variations. There are wide fluctuations in seasonal conditions within the country as the

seasonal conditions within the country as the seasons are not uniform throughout the country

due to diversity in physical and climatic conditions.

During monsoon (June to August), frequent rains wash down the air born particulates

and other gaseous pollutants. Therefore, the period between June to mid September is the

cleanest period in the year and frequent rain does not allow pollutants to build up to higher

concentration in ambient air though the pollution generating sources remain the same

throughout. The winter months of December to February are relatively much calm

conditions facilitate more stability to atmosphere and consequently slow dispersion of

pollutants generated and help in build up of pollutants generated and help in build up of

79

pollutants in vicinity of pollution sources. The general pollutant levels in terms of

percentage violation of standards increase considerably during winter basically due to lower

ambient temperature, calm conditions, lower mixing depth, pollution inversion and high

traffic density on the roads. Frequent change in wind direction in the atmosphere during

March to May months create turbulent conditions. Local disturbances in environment causes

frequent dust storm and hazy condition. Moreover, the winds from Thar desert area brings

dusty winds from arid and semi arid region, building up high particulate matter levels in

ambient air in these months, mostly contributing soil borne particles. In the festival season

there is huge conglomeration of people in Kolkata. Festive-shoppings, Pandel-hoppings and

increase business activities are some of the factors that increase the flux of urban traffic and

increase trips per vehicle. Since there is no concomitant rise in the road space, the huge flow

of traffic leads to congestion of road space, low vehicle speed and accident proneness-all

adds to the plight of urban people. It is this up search in social activity centering the festivity

that adds to the vehicular pollution.

Thus we find that Kolkata’s air quality due to vehicular emission is conditioned both

by natural and social factors. The festive season by itself is largely responsible for falling air

quality during their time when the natural factors may not be so omnipotent. Thus the

analyzing pollution pattern, Policy makers have to give a due consideration to this point.

At last we have shown the categorization of air quality on the basis of seasonal trend

that is represented by the following table.

80

Table 6.1.5.1: Seasonal Trend Based categorization of air Quality

(Source: WBPCB Report)

6.1.6 Conclusion:

The chapter is tentative. It brings out the important aspect of air pollution. For the

each of study, we have segregated the entire time period into four seasons-winter, summer,

monsoon and festival. These are of them are quite concern in the environmental literature.

The fourth season is tropical of Kolkata – arising mainly due to social causes. The data

reveals that this so called new season ranks very high in the disposal of environmental

waste. Thus an interesting suggestion of the paper is the role of environmental fallen in

increasing pollution and climate hazard. The point is clearly brought out by our analysis.

Category Period Critical Air Pollutants Feature

Moderate

pollution

March to

May

Particulate Matter

Low humidity, high turbulence,

frequent change in wind speed

Low

pollution

June to

August --------

Cleaner period due to high

humidity, rains and monsoon month

High

pollution

December to

February Particulate Matter

Low inversion, calm conditions,

unfavorable meteorological conditions

High-

medium

pollution

September to

November Particulate Matter

High conglomeration of people, high

emission, low vehicle speed

81

6.2 Spectral Analysis:

6.2.1 Introduction:

Rapid economic development of Kolkata has resulted in the rise of vehicles. The

percent growth of vehicles is higher than the growth of population. There is high vehicle

ownership, inefficient public transport, mixed on roads and the widely used adulterated fuel

has lead to the traffic congestion on roads and in turn the vehicular pollution. The West

Bengal pollution control board monitors ambient air quality of major urban centers

regularly. Air quality at respirable height is also monitored in some important traffic in the

city of Kolkata. In case of vehicular emission suspended particulate matter, respiratory

particulate matter, sulphur dioxide and nitrogen dioxide are measured in some major

sections in there. In studied years we see wide fluctuations in across months. It is low in the

monsoon season and high in the winter period.

The standard time domain analysis may fail to catch these seasonal fluctuations.

However cyclical pattern is an essential feature of environment parameters. We have divided

our chapter into 5 sections. First is introduction. Literature review is pointed in second then

methodology in third chapter. We have designed result of study in chapter 4 and it is

concluded in last chapter.

82

6.2.2 An overview on spectral analysis:

For a single time series, spectral analysis decomposes the overall variance of the

observations into components at different frequencies, producing what is called a spectral

density function. The estimated spectral density function of the series has large peak at

frequencies of one cycle and two cycles per year.

There are general approaches to time series analysis first, analysis in the domain

where we have auto and cross relation analysis regression, and the fitting of time domain

model at described by ‘Box and Genkins’, time series analysis, forecasting and control,

(Holder-Day, 1970). Secondly analysis in the frequency domain where we have auto and

cross spectral analysis. In most cases there is no special peak but rather power is

concentrated at these low frequencies and the amplitude of these long term component

decrease smoothly with decreasing period, (Granger; 1967, Farely and Hitch; 1969)

Spectral method proposed by (Hannan) will always be asymptotically efficient; they

are in frequently used because these components demanding at every large sample are

presumably required (Engel and Gardener; 1974).

Now coming to the angle of cross spectral analysis, it is a technique for examining

the relationship between two time series at various frequencies. The technique may be used

for time series while “arise in a similar footing” (Kins and watts; 1968)

83

Cross spectral analysis is recognized as a powerful analytical tool for the analysis of

time varying behaviour in a number of disciplines. (Barksdle, Hilliard and Guffy; 1974)

suggest that to explain ‘cross spectral analysis and illustrate the use of the new technique

studying the interaction between advertising and sales

The theory of spectral analysis is based on notion that a time-series is a sample

record of a stationary, stochastic process and the assumption that these measurements can be

used to estimate the characteristic of the process generating the data. The estimated idea of

spectral analysis is that a time series may be decomposed by component each of which

associate with frequency.

6.2.3 Data Used:

The data are purely collected from West Bengal Pollution Control Board. WBPCB

collected SPM, RPM, SO2 and NO2 from the some important locations in Kolkata at the

peak time when huge vehicles moved throughout the area. The particular time is at 8 a.m. to

11 a.m. and 4 p.m. to 7 p.m. At these times a lot of passenger cars, Buses, two-wheelers

moves extensively. The Board collected data on lead (Pb) and carbon-monxide (CO)

emission also. But these data are scattered, inadequate and a little and not covered for all

other important locations in recent years. These studied locations continuously face traffic

congestion. Actually the transport system in Kolkata is unique and consists of buses, local

trains, metro-rail, trams, taxies, and auto-rickshaws, coupled with slow moving traffic like

rickshaws, pulled by human beings, bi-cycles and walking. It is also interesting that despite

84

this varied mode of transport, the entire system is under severe strain due to congestion. Due

to such congestion the vehicles emit a large quantity of smoke. WBPCB monitors the

ambient air quality of major sections in Kolkata every day in every year. These vehicular

pollution data are undertaken from 14 major locations during the year from 2001 to 2007.

From these data we primarily observe a season wise clear trend in every year.

In this chapter, first we have shown the monthly fluctuation of the different

pollutants and then focused seasonal trend in the monthly level of pollution created by

different pollutants in different areas of Kolkata. This statistical tool analyses the deviation

of the series as a whole into periodic components of different frequencies and periods.

Smooth series have stronger periodic components at low frequencies. In our study we

calculate periodogram value and spectral density for individual frequency component for

univariate data as well as bivariate data. We also calculate cross spectral density, cross

periodogram, coherency and phase spectrum. All these results for univariate and bivariate

data are plotted under each frequency.

6.2.5 Result of the Study:

In order to understand whether there is any fluctuation in the different pollutants we

first plot the monthly distribution of pollutant level for different years (2001-2008). As a

sample, we produce the picture on monthly distribution of SPM in 2001. Since all figures

are similar, we relegate the similar figures for other pollutants and for other years in our

appendices.

85

Our analysis clearly shows that there is a discernable pattern of fluctuations in the

pollutant levels. For example the monthly level of SPM in 2001 reaches a very high level

between December to February with march showing a declining trend. The level

continuously drops up to August. Thence forth it again rises towards the pick level. Thus the

maximum pollution occurs in winter season while it is low in the monsoon season. More or

less similar picture are seen for all the pollutants considered by us. We now see whether

spectral analysis helps us to unravel this cyclical fluctuation.

Figure: 6.2.5.a Monthly Fluctuation of SPM 2001

(Source: WBPCB Report)

In our study the method of spectral analysis is applied to analyse the seasonal trend

of the pollutants in different traffic points of Kolkata. The spectral plots procedure is used to

identify periodic performance in time series. Instead of analyzing the variation from one

time point to the next, it analyses the deviation of the series as a whole into periodic

components of different frequencies. Smooth series have stronger periodic components at

low frequencies.

Monthly Fluctuation of SPM 2001

0100200300400500600

Apr MayJu

ne July

Aug Sep Oct Nov Dec Jan

Feb Mar

Month

Pollu

tion

86

The sine and cosine transforms periodogram value and spectral density estimate for

each frequency or period component. When bivariate analysis is selected then cross-

periodogram, squared coherency and phase spectrum are estimated for each frequency or

period component. For univariate and bivariate analysis we plot periodogram and spectral

density for each frequency and period and we also plot squared coherency and, phase

spectrum and gain for each frequency and period for every paired series for bivariate

analysis.

We start by looking month-wise data of pollutants in Kolkata. The data show the

obvious trend, since the periodogram1 represents a sequence of peaks with the lowest

frequency.

Figure: 6.2.5.b Spectral Periodogram of SPM 2001

Spectral Periodogram of SPM 2001

0.000000

20000.000000

40000.000000

60000.000000

1 2 3 4 5 6

Fr e que nc y

(Source: WBPCB Report)

1 A very few of the spectral diagrams are shown in the text. The others are relegated to the appendix because ofthe paucity of space.

87

Figure: 6.2.5.c Spectral Density of SPM 2001

Spectral Density of SPM 2001

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

1 2 3 4 5 6

Fr e que nc y

(Source: WBPCB Report)

The plot of the periodogram shows a sequence of peaks that stand out from the

background noise (random), with the lowest frequency peak at a frequency 0.0833 and also

at the period 12.

Spectrum analysis will also identify the correlation of sine and cosine functions of

different frequency with the observed data. If a large correlation (sine or cosine coefficient)

is identified, one can conclude that there is a strong periodicity of the respective frequency

(or period) in the data. The periodogram values can be interpreted in terms of variance (sum

of squares) of the data at the respective frequency or period.

The largest values in the periodogram column of all series occur at a frequency of

0.0833 implies the strong periodicities in data, precisely what you expect to find if there is

an annual periodic component. Therefore, this information confirms the identification of the

lowest frequency peak with an annual periodic component.

88

But the other peaks at higher frequencies are best to analyse with the spectral density

function, which is simply a smoothed version of the periodogram. The spectral density

consists a distinct peak that appears not to be equally spaced but the lowest frequency peak

simply represents the smoothed version of the peak at 0.0833. Smoothing provides a means

of eliminating the background noise from a periodogram, allowing the underlying structure

to be more clearly isolated. To understand the significance of the higher frequency peaks,

remember that the periodogram is calculated by modeling the time series as the some of

cosine and sine functions. Periodic components that have the shape of a sine or cosine

function (sinusoidal) show up in the periodogram of each series as single peaks, with the

lowest frequency peak in the series occurring at the frequency of the periodic component.

Hence we have now accounted for all of the discernible structure in the spectral density plot

and conclude that the data contain a single periodic component of 12 months. Using the

spectral plots procedure you have confirmed the existence of an annual periodic component

of a time series and you have verified that no other significant periodicities are present. The

spectral density was seen to be more useful that the periodogram for uncovering the

underlying structure because the spectral density smoothes out the fluctuations that are

caused by the non-periodic component of the data.

Now to uncover the correlations between two series at different frequencies the

cross-spectrum analysis is indispensable. For two covariance-stationary series of equal

length and comparable intervals, it is possible to estimate the cross power spectrum, or

simply the cross-spectrum, and compute certain spectral statistics which provide information

on the relationships between the frequency components of the two series. Just as the auto

89

spectrum of a single series is the Fourier transform of the auto-covariance, the cross

spectrum is the Fourier transform of the cross covariance. The cross-spectrum is a complex

quantity composed of a real (in-phase) element called the co-spectrum. Under this analysis,

cross-spectrum measures the strength of association between the components of two series

at each frequency by revolving the crossed series. Under this bi-variate analysis we obtain

the real parts of cross peridogram, co-spectral, cross amplitude, squared coherency, phase

spectrum, and gain for each frequency.

Figure: 6.2.5.d Cross Periodogram of SPM 2001 Figure: 6.2.5.f Cross Density of SPM 2001

Cross Periodogram of SPM 2001

-10000.000000

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

50000.000000

1 2 3 4 5 6

Fr equency

Cross Density of SPM 2001

0.000000

10000.000000

20000.000000

30000.000000

1 2 3 4 5 6

Frequency

(Source: WBPCB Report) (Source: WBPCB Report)

Figure: 6.2.5.g Cross Amplitude of SPM 2001

Cross Amplitude of SPM 2001

0.000000

10000.000000

20000.000000

30000.000000

1 2 3 4 5 6

Fr e que nc y

(Source: WBPCB Report)

90

However, as shown above the series were created so that they would contain two

strong correlated periodicities. Here the cross-spectrum consists of real numbers. These can

be smoothed to obtain the cross-density estimates. Looking at the results of cross-

periodogram, we see the strong periodicity at frequency 0.0833. Likewise we get the similar

results of cross-density. The square root of the sum of the squared cross-density and quad-

density values is called the cross-amplitude. The cross amplitude can be interpreted as a

measure of covariance between the respective frequency components in the two series.

One can standardize the cross-amplitude values by squaring them and dividing by

the product of the spectrum density estimates for each series. The result is called the squared

coherency, which can be interpreted similar to the squared correlation coefficient that is the

coherency value is the squared correlation between the cyclical components in the series at

the respective frequencies.

Phase describes the angular shift if the crossed series relative to the base series. The

phase estimates may be used to define the lead or lag relationship between the two

processes. In our study the positive value of phase implies the angular shift of the crossed

series relative to the base series illuminates that the base series leads the crossed series.

Conversely the negative value of phase implies that the base series lags the crossed series.

Another statistical value termed as gain may be described as the ratio between the

amplitude of values in the crossed series and the amplitude of values in the base series. The

91

gain value is computed by dividing the cross-amplitude value by the spectrum density

estimates for one of the two series in the analysis.

Figure: 6.2.5.h Spectral Coherency of SPM 2001

Squared Coherncy of SPM 2001

0.000000

0.200000

0.400000

0.600000

0.800000

1.000000

1.200000

1 2 3 4 5 6

Fr e que nc y

(Source: WBPCB Report)

Figure: 6.2.5.i Phase Spectrum of SPM 2001

Phase Spectrum of SPM 2001

-1.000000

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

Fr e que nc y

(Source: WBPCB Report)

Figure: 6.2.5.j

Gain of SPM 2001

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr e que nc y

(Source: WBPCB Report)

92

6.2.6 Conclusion:

The main purpose of this study is to demonstrate the seasonal trends (Periodicity) of

the different pollutants in Kolkata. This new technique is justified since the above discussion

exhibits the significant results of spectral estimates, which really represents the seasonal

periodicities of the pollutants under equal time intervals. We also understand correlation

between two series at different frequencies, using cross-spectral analysis. The strong

correlated periodicities among different series of pollutants under different years can be

analysed by the value of coherence, phase and also gain value etc. These totally indicate that

the model has a great implication of predicting long run trends.

Having briefly survey the macro aspects of vehicular emission, it is now our turn to

peep into the macro features. Since it is the people who have to live and thrive in the

polluted environment with high risks of health hazard, their position is crucial to tackle the

entire problem.

93

Chapter ― 7

Effects of Vehicular Emission ― A Study of traffic police in Kolkata

7.1 Introduction

Environmental economists are concerned about the effects of environmental

degradation on the life of people who may be adversely affected by them (Agarwal; 1996,

and Ghosh; 1998). In the tradition of ecological economics various methods (Contingent

valuation etc.) have been developed to address the economic cost that is levied on the

affected people.

However, these effects may be of two origins - place of residence and place of work.

An individual or a family may be exposed to toxic environment in their very place of living

and similarly an individual may face toxicity in his/her workplace environment (Sinha;

1993, Faiz et.al; 1996, Chakraborty; 1997, WHO-UNDP Report; 1988, and Bhattacharryya

& Banerjee; 2002). Since, workplace occupy a significant amount of the individual daily

schedule, the threats cannot be ignored.

Neo-classical economists argue that rational agents self-select themselves into jobs

according to their risk proneness. Numerous studies have approached the problems of job

hazard pay and worker risk; (Viscusi; 1978, Buhai and Cottoni; 2011,, Rosen; 1974, 1986,

94

Hersch and Viscusi; 1990, 2001). However in many situations, a person’s choice of the

appropriate job (in parlance with the risk and return) is severely restricted2. This is

particularly true in the developing economies with scanty knowledge of environmental

hazard, asymmetry in information and weak enforcement mechanism. In such cases workers

often observe a decline in their working capacity. It is the worker’s interest then to prevent

(or at least reduce) the negative impact on working capacity.

We see whether better consciousness about the environmental hazard of their jobs

can shield a person against the odds or at least mitigate the riskiness. This type of awareness

building would then seem very essential for the worker’s welfare.

In the current chapter we deal with the empirical study of the traffic police personnel

in Kolkata, India. According to the existing rules, any incumbent policemen under the

Kolkata police has to perform the job of traffic police for a certain period of their entire

tenure. However there are significant differences among the traffic polices regarding the

environmental health hazard regarding their jobs. Our aim is to understand whether such

awareness is any way helpful towards maintaining their working capacity. We also try to

find out regarding their assessment of jobs using a number of parameters.

Our chapter is divided into five sections. In section 2 the rationale of our study is

provided. Section 3 provides a theoretical underpinning of our chapter. In section 4 we

2 Long years ago in 1776, Adam Smith opined that hazardous jobs are associated with higher payment. Heconstructed the theory of compensating differentials in wage payments.

95

discuss the data collection procedure with a short description of data. Section 5 provides our

empirical findings. The conclusion is given in the final section.

7.2 Rationale of the study:

In order to assess the impact of continuous exposure to motor vehicle emissions on

human beings, a pioneering word is done by Sinha (1993) on a group of 100 traffic police

constables in Jaipur, India. These constables were posted at the road intersections with

considerable amount of exposure to deadly gasses (CO, HC & SO2) for about six hours or

more daily. He found that a majority of the constables suffer from some short of physical

disorder respiratory difficulties and digestive problems. There was a high incidence of

tubercolerosis among the younger constables (20-30 years of age). Coming to Kolkata, the

condition is not at all better. There are the traffic police personnel who are continuously

monitoring traffic regulations at the busy intersections in city. They are always at greater

risk to the exposure of air pollution. These personnel comprising nearly 130 male traffic

police constables aged from 25 to 55 years serving at busy roads/intersections within KMA

for a period of 3 moths to 16 years. Many of them complained of frequent headache,

irritation of eyes, disturb sleeps, increased cough and respiratory problem and most of them

have indigestion problem (Basu and Brama; 2000).

Huge theoretical structure exists on job hazard and worker’s attitude towards it. The

literature starts from Smith who argued that people self-select them towards particular jobs

according to the risk and hazard attached with them. This work has been extended by

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number of studies (see paragraph 1). However the basic assumption of these approaches is

that the incumbent worker is aware of the risk or hazard i.e. associated with their prospective

jobs.

The same cannot be maintained when such hazards arises from exposure to

environmental risk. People are often not aware of exposure to such risks. Some of its

symptoms (such as lead poising, breathing problem etc.) may take years to develop.

Moreover the environmental factors are multifaceted and complex. Hence the causal effect

cannot be usually asserted. The problem is more pronounced in a developing economy. The

only way to mitigate this situation is the development of genuine environmental awareness.

There may be two ways in which this information incompleteness may develop in

the case of environmental risks. Firstly, a particular job may involve number of duties –

some of which may be (environmental) risk-prone, some less risky while some others with

virtually no risk attached with them. Thus, instead of simply depicting jobs are risky and

riskless, it is very likely that the jobs that the incumbent chooses may have varying degrees

of risk according to the nature of duties attached.

Secondly, there may be a genuine lack of awareness about the extent of danger due

to environmental pollution. The environmental theorists opine that the environment is a

highly non-linear dynamic structure often leading to chaotic fluctuation. As such, it is often

impossible fully stress out the impact of environmental hazards. Moreover there is often a

lack of environmental awareness among the common people. The common people are

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generally myopic. Long run effects are often outside their provided. Hence, job decisions

based on possible environmental hazard are often incomplete and based on shaky

foundations.

7.3 Theoretical Framework:

The economists have viewed health as an important component of human capital

(Grossman 1972 a, b, 2000; Case and Deaton, 2005; Cropper, 1977; Muuerinen and Le

Grand, 1985; Becker, 1964; Mushkin, 1962; Fuchs, 1966. A simple argument in the

literature is that a person’s working capacity depends on his current health. Health is thus an

asset on which the individual’s make purposeful decision of investment and maintenance.

However like all other capital assets health also as depreciation in the form of using up of

health capacities. The health investment production function has developed by ‘Grossman’

is an easy way of capturing the complex process of health generation and maintenance.

There are several theoreticians (Borjas, 2004; Boskin 1974; who are developed the

occupational choice model based on this human capital framework.

There have been a number of controversies regarding the Grossman’s mode on

health as a form of human capital. The most important criticism, as acknowledged by a

Grossman himself, is that of Ehrilch and Chuma, (1990). These main argument is that in the

model Grossman does not specify and optimum health threshold, instead he assumes that

individual instantly adjust themselves to the optimum health investment. This brings a very

serious empirical relation of a positive relationship between health expenditure and health.

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However a number of studies (Cochrane et.al, 1978; Wag staff, 1986; Dardanai & Wag

staff, 1987; Leu & Gerfin, 1992) opined that the relationship between health care and health

level is negative. This limitation is removed by a health threshold model provided by

(Galama & Kepteyn, 2009). In the new model spaces are opened up between health level

and health attainment. Empirically it is then found that blue colored workers allow their

health to fall to a much lower level than the white colored workers. Similarly rooms are

made of temporary medical innless that does not itself relate to the long run health

prospects.

The most important extension for our purpose is the avenues that the new approach

opens up for the analysis of occupational health. (Hurley, Kliebenstein and Orazem (1999),

in their approach present health condition of an individual depend on the human capital that

he is endowed with the behaviors that positively and negatively affect health and also the

occupational variables. Following Hurley, Kliebenstein and Orazem (1999), we can write

the health production process as follows.

hit = ƒ(Hit, Oit, µ it) ---------------------------- (1)

Where hit is the measure of health for an individual i at time t, Hit is the human capital

variable that an individual has accumulated over his life time. Here Hit includes both the

effects of positive investment (e.g. nutrition, good health, habits and healthy exercises etc.).

In this function µit is the original individual specific health endowment.

The occupational variables are captured by Oit. These variables measure the presence

of an intensity of exposure to environmental hazards that decreases the individual’s current

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health attributes. For the estimation procedure, generally a linearised form of the above

production process is taken into account. The Hurley et.al (1999) study uses a number of

dummy and proxy variables to capture the occupational hazards that are associated with the

individual’s occupational choice.

In our model we try to prove deeper into the components of yt. Our simple argument

is that there are many aspects of occupational hazards that can be properly mitigated if

sufficient steps are taken in the right direction. However it is the level of awareness to the

occupational risk that become an important determinant towards adoption appropriate

protective measures.

We may reformulate the Farely et.al health production function as follow.

hit = ƒ(Hit, ψ (Oit), µ it)------------------------------- (2)

Where ψ (Oit) is awareness function of Oit. We assume that ψ'>0 and ψ''>0. It simply

means that awareness increases with the level of pollution and at an increasing rate.

The introduction of the awareness function has a very serious implication for hit. Consider

two identical individuals for whom the only difference is the level of awareness.

Taking a linear form as (Hurley, Kliebenstein and Orazem, 1999) we then get

― ― ― ―hpt = α Hpt + β ψ p(Opt) + γ µpt)------------------------- (3)

― ― ― ―hp’t = α Hpt + β ψ p’ (Opt) + γ µpt) ------------------------ (4)

Now, from equation (3) ― (4)

― ― ― ―hpt ― hp’t = ψ p(Opt) ― ψ p’ (Opt)

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This simply implies that the differential health level of an individual depends upon

on the awareness differential of occupational hazards. This brings out a testable hypothesis

that we can empirically verify. This is our task in the rest of the chapter.

7.4 Data description:

From the above theoretical model it is clear that there are some important socio-

psychological determinants of a person’s occupational health. These data are micro theoretic

in nature and not available from any large scale macro survey. This necessitates a detailed

survey of the micro responses of the traffic polices in our sample city. Our selection of

Kolkata is purposive. The data on vehicular emission3 clearly indicates Kolkata is one of

the most polluting cities in India. Increasing population pressure, inadequate road space,

existence of old fashion polluting vehicles, overcrowding of slow moving vehicles adds to

its woe. The thick air is inhaled by the city’s pedestrians and the traffic polices are the worst

victim.

As noted early in this chapter Kolkata police have no separate traffic police cadres.

All recruitments to the lower cadre of the Kolkata Police are exposed to the environment

risk of the polluted air space in the city. Their job is however divided into two types of

duties – in off-roads and on- roads. The former is relatively less risky with regards to

environmental hazards. However once again there is no choice between the types of duties.

It is prefixed.

3 See: figure in page 60.

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Kolkata has a track record of air pollution and pollution hazard. Numerous studies by

the environmental scientists have documented the deterioration in air quality and the health

conditions of the inhabitants in the city. (Chattopadhya et.al; 2007, Samanta et.al; 1998,

Chatterjee et.al; 1988). These studies reveal that environmental hazard is wide spread near

the main junctions in the city. However there are few studies that relate vehicular pollution

to the perception and utility of those who are directly exposed to it. The theoretical exercise,

given above, sets up a structure whereby these issues could be properly addressed. It is now

time to look at it empirically.

The West Bengal Pollution Control Board (WBPCB) gives us detailed information4

regarding the air pollution level of selected 14 junctions of the city. This data show that

there is a great intensity of air pollution at these junctions. Also it is observed there is a large

amount of seasonal fluctuation in the air pollution level. The seasonality pattern across the

junctions is almost identical. It is highest during the winter season and lowest in monsoon

season. This chapter wishes to observe whether the traffic police personnel5 posted at these

junctions are well aware of the situation and the protection they take to combat this.

4 See WBPCB Report for various years.5 Undoubtedly, they are one of the worst suffer of automobile pollution being constantly exposed to thepolluted air over long stretches over time.

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Fig- 7.4.1.a Monthly fluctuation at 14 junctions (RPM)-20016

Fig- 7.4.1.b Monthly fluctuation at 14 junctions (NO2)-2007

(Source: WBPCB Report)

For this we collected information from 98 traffic police personnel who are having

regular duties in these junctions. These traffic police are recruited from the general police

staff. Hence, their choice is very limited regarding the nature of job. However, they are

provided a number of protective of protective gears to salvage themselves from the

environmental ill-effects among the protective gears that are provided to the traffic police

are helmet, mask, umbrella, sunglass, glucose tablets, regular medical check-up etc.

6 We give year only two of the fluctuation diagrams. The remaining are in appendix.

Monthly Fluctuation of RPM 2001

0

100

200

300

400

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

Mont h

Monthly Fluctuation of NO2 2007

0

50

100

150

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

Month

103

However, the traffic police personnel are not always aware about the usefulness of these

protective gears. Hence, they may fail to use these protective gears properly. As the

consequence they expose themselves to greater environmental risk and health hazards. This

adversely affects their productive capacities and also adds to their medical expenditure.

Hence by proper utilization of these gears such unwanted cost might be reduced. This

chapter wishes to unravel these irrational tendencies that arise due to low information about

environmental risk and low awareness.

In the present study we concentrate on the traffic police at 14 selected junctions of

the city. A detailed questionnaire was prepared to elicit a wide amount of information about

these policemen. The questionnaire may be sub-grouped under several headings-(a) familial

information (b) assessment of work condition (c) health and fitness (d) environment

consciousness in general (e) environment consciousness about their job and (f) the

preventive measures taken.

We culled information about the policemen socio-economic features – their parental

information and their familial history. We also collected information about their years of

service, their income, their duty hours, their fitness, and other such relevant information

regarding health and job.

A number of subjective information is also gathered. We have questions regarding

their job satisfaction, preference for duties, and likeness of the job etc. These questions are

designed in order to elicit various information regarding the nature of the problems faced by

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the policemen, the possible solutions, suggestions for improvement and similar other

parameters. The data on the use of protective mechanism, training, types of protective

mechanism and the frequency of use were also generated in these interviews.

First we consider some basic socio-economic features of our sample policemen.

About 42.86% of our policemen are between 20 – 35 years of age while the 53.06% are in

the higher age group. However, out of them (only 4.08%) are above 50. In case of family

size, 59.18% holds 4 – 8 family members, 29.59% is in small scale family while 11.22% of

the sample policemen are in the larger family group. Considering the job experience of the

traffic police personnel in our study area, we see only 14.29% have above 15 years of job

experience, 51.02% have 1 - 8 years of job experience while some others (11.22%) have 9 –

15 years. The average years of job experience is roughly 10 years (9.96 to be precise). Thus

our sample largely covers the policemen who should have their full working capacity at the

time of sampling.

Table 7.4.2 (a) Age of the sample policemen

AgePercentage oftraffic police

20-35 42.8636-50 53.06

above 50 4.08

(Source: Author’s survey)

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Table 7.4.2 (b) Family size of the traffic police

Average Familysize

Percentage oftraffic police

1-4 29.594-8 59.18

Above 8 11.22

(Source: Author’s survey)

Table 7.4.2 (c) Job experience of the traffic police personnel

Years of jobPercentage oftraffic police

1-8 51.029-15 34.69

above 15 14.29

(Source: Author’s survey)

Now, we come to education. 84.69% of the policemen have 8 – 12 years of

schooling. The higher education is rare (only 15.31%). However, if we compare it with their

father’s schooling. A substantial positive mobility is observed. Though, 34.69% of the

fathers had below 8 years of schooling, this has disappeared in their next generation.

However, in the upper echelon, there is stagnancy. In both the generation only 15.31% have

higher education. Also, in 81.63% of family of the traffic police personnel have a maximum

education of graduation or at least matriculation.

Table 7.4.2(d) Educational status of traffic policemen

Years ofschooling

percentage oftraffic police

8-12 84.6912-15 13.27

Above 15 2.04

(Source: Author’s survey)

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Table 7.4.2(e) Educational status of the father’s of the traffic policemen

Years of father’sschooling

percentage oftraffic police

Below 8 34.698-12 50.00

Above 12 15.31

(Source: Author’s survey)

Table 7.4.2(f) Educational status of the family of the traffic policemen

(Source: Author’s survey)

As to the economic background, most of them come from agricultural family

(41.84%). The next big chunk is the government service (25.51%) while business account

for 15.31%. Thus most of the policemen could have no idea about the hazards that are

involved in the life of an urban traffic policeman.

Table 7.4.2(g) Ancestral family occupation of the traffic police

Father’s OccupationPercentage oftraffic police

Agriculture 41.84Rural-non farm 9.18Urban-non farm 2.04

Government service 25.51Private 6.12

Business 15.31

(Source: Author’s survey)

Maximum schoolingin family

percentage oftraffic police

1-9 4.0810-15 81.63

Above 15 14.29

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In order to test their awareness about the environmental hazards of their job, a series

of question were constructed. We used some well known facts of vehicular pollution and

found out whether the police personnel were aware of them. The intensity of awareness is

measured by the proportion of right answers. For example only 36.73% of the policemen

could identify winter season is ‘most polluted’. On the contrary, 55.09% thinks that the

summer and monsoon season are ‘most polluted’. This is in complete contradiction with the

real data published by WBPCB7. 20% declare their complete ignorance in this matter. Also

detailed information regarding their assessment of the problems of vehicular emissions and

its causes were also asked.

Table 7.4.3 Perception about the most polluted season

Most polluted season Most Polluted Month %Police PersonnelFestive September-November 16.33Winter December-February 36.73

Summer March-May 48.98Monsoon June-August 6.12No idea ----------------------- 2.04

(Source: Author’s survey)

Before proving into the main analysis we first concentrate on some simple features

of the sample police personnel. The sample police personnel complain that they suffer from

number of disease headache, skin and eye problem, lumbago, hernia, hydroceal, asthma,

bronchitis, breathing problem and others etc. Many of these are linked to non pollutants

disease – such as lumbago and hernia may be linked with continuous standing at a particular

7 According to WBPCB reports, average figures for 2001-2007 reveals winter season to be most pollutedfollowed by the festive season. The least polluted is monsoon.

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point over a long stretch of tine. However a majority of disease (headache, skin and eye

problem, asthma, hydroceal, bronchitis, and breathing problem etc) are airborne caused by

continuous exposure to the polluted air. Many of these diseases could have been prevented

had the police personnel taken proper care in using the protective gears that are necessary.

Thus in a direct physical way lack of awareness leads to a erosion of health. This adversely

affects his purse because of the cumulating medical expenditure.

Table 7.4.4 Affected police personnel by the different diseases

Disease % of police personnel affectedSkin 50.00

Headache 41.84Eye 73.47

Knee 55.10Lumbago 7.14

Hernia 8.16Hydroceal 9.18

Asthma 6.12Bronchitis 8.16

Breathing Problem 20.41

(Source: Author’s survey)

We also consider some other related health variables. The first is fitness. Roughly

57% of the police personnel are fit according to their own assessment. However, only

32.65% undertake regular medical check-up.

Our next query is regarding the utilization of the protective gears that are provided to

the police personnel. The percentage of users of the various protective gears is abysmally

low. It is seen that the highest percentage of users are that of helmet (0.80%). This might be

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due to tropical climate on duty on the road. Next the percentages of users are that of

umbrella (0.60%) and sunglass (0.59%) respectively. These might be due to heavy rain and

the scorching heat they have to face in the tropical atmosphere where they operate.

However, one of the most important protective gears (masks) is used by only o.26% of the

police men. Only 0.06% uses all the protective gears. These non-optimal uses of protective

mechanism may have a direct link to the high percentage of morbidity among them.

Table 7.4.5 Percentage users of protective units

Protective units used % of usersHelmet 0.80

Umbrella 0.60Sunglass 0.59Rain coat 0.42

Masks 0.26Anklet 0.08

Glucose 0.18All 0.06

(Source: Author’s survey)

(* Note: The percentage figure exceeds 100 because of multiple uses)

These data reveals the precarious condition in which the police personnel find

themselves. They have very little choice of the nature of their duties. This is couple with

their non-optimal use of the protective gears available to them. As a consequence they are

under fire ― facing serious health problems.

In our survey we collected rather exhaustive data regarding subjective and objective

conditions of the traffic police and their awareness of the pollution hazard. We feel that the

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data set can be usefully exploited for analyzing the problems at our hand. In the next section

we turn to this question.

7.5 Empirical Findings:

The main purpose of the chapter is to relate the extent of the knowledge of the police

personnel about hazard related to the job and the subjective and objective conditions in

which their job places them at the present. Among the objective features, our emphasis on

the productive capacity i.e. directly related to health. This has two consequences. On the one

hand, a deterioration of health increases medical expenditure of the police personnel. This

has negative dent on his earnings. Directly the person’s earnings capacity is also related to

their productiveness thus there are two ways in which health has an impact on the general

well-being of the policemen.

For the analysis we have used a number of composite indices. Each index is a

weighted average of a number of attributes.

In our study we have considered two indicators for this purpose fitness and health.

The question of health is complex involving the suffering from any chronic disease,

morbidity, medical problems and medical history. For fitness our concern is narrow. Here

we concentrated on the present capability in performing the traffic duty. These two concepts

are closely related but not same. Health refers to a wider age appropriate scenario. However

fitness is capability to adjust with the duty. A healthy person who is at the end of his carrier

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may not be fit for this job. Similarly a person at a young age may also become unfit due to

some physical or mental constraints with he is born.

Next we consider subject wise aspect – job likeness and job satisfaction. Again these

concepts though related are not the same. Likeness is related to the initial decision making

about the job with limited a priori information. On the other hand, job satisfaction refers to

the preference towards the job after some experiences have been gathered. It is possible that

initial affinity may turn into disillusion. Also possible that the person is slowly adjusting

himself to the job and becoming satisfied through initially he/she may not like the job.

A number of independent variable is considered for analysis. These variables can be

broken up into three subgroups ─ a) family related b) subjective evaluation of the job c)

awareness of environment hazard and d) some demographic features. Under family features

we have two variables. One of them is an indexisation of the family history containing such

features as past experience of jobs by any family members, occupation, residents, education

etc. The next important feature is the number of children. The job evaluation indices include

job likeness, job satisfaction (except in the regression where it is dependent variable), duty

hours, health and fitness (except in the regression where there are dependent variables), the

pollution awareness has been constructed through a detailed set of questions. We consider

both pollution awareness in general and awareness of vehicle pollution. For measuring

pollution awareness we tested their knowledge regarding their nature and extent of pollution

(viz. most polluted season and most polluted time in the day). They are marked according to

the correct answer. A similar exercise conducted on the awareness of vehicular pollution.

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These are two important determinant of pollution. To our surprise there are many who

scored poorly in terms of this text. This confirms our idea that these people are under fire –

their unaware of the risk they are facing while carrying out their duties.

It is clear that all the explanatory variables are not equally important. Further they are might

be mutual dependence between the so-called independent variables resulting in

multicollinearity. In order to remove such discrepancy, we run a step wise regression using

all the dependent variables for each of our four dependent variables. This step regression

helps us to identify the variables that are more important in each of the independent cases.

First we state in brief the basic statistical properties of our variables in table 1. From

table 1 it is clear that most of the quantitative variables lie within a certain range (roughly 0

― 2.5 most of the cases while 0 ― 1) in a few cases. The mean and the variance of the

variables are at a comparable level. The quantitative variables (age and number of children)

are also within the comparable stratum. This vindicates the quantity of variables and their

comparability for statistical and analytical purposes.

We have used both the ordinary least square and Tobit regression technique in the

analytical part. Since, this is a cross-sectional analysis, the problem of heteroscadisticityis

very much important. To tackle this problem we have used this White’s heteroscadisticity

consistent estimates instead of using the standard OLS structure. Such heteroscadisticity

consistent regression would yield consistent estimate of the regression coefficients. In order

to avoid the problem of multicolinearity the step regression technique was used to select the

appropriate variables.

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In our exercise there is still problem in using OLS. This arises because the dependent

variables are truncated and qualitative in nature. The Tobit estimation may be more useful to

deal with such problems. These Tobit estimates are juxtaposed vis-à-vis the OLS

coefficients in order to bring out their comparability and reliability. It is seen that for all our

Tobit regressions the standard criteria for model selection (Akaiki, Schwarz and Hannan-

Quinn) are quite high. This proves that Tobit may not be an appropriate regression technique

in our case.

We now consider the health. In the table below we give the ordinary least square,

heteroscadisticity consistent regression and the Tobit. Our analysis clearly indicates their

health parameter is negatively related with age. This clearly shows that health decreases

with age – a very natural conclusion. The family parameter, an indicator of initial level of

human capital in health is also significant under all the three types of regressions. Similarly

the fitness parameter is also positively related with health.

. Again, the increase in pollution awareness helps to maintain better health. Generally

traffic police personnel can take several protective measures to mitigate the continuous

exposure to vehicular pollution and all resulting fatigue that strains on muscles and nerve

(Banerjee and Das; 2001). Protective head gears, murex, anklet, umbrella, sunglass, glucose-

powders and several other estimates are available. Most of these are supplied by their

employers. However, the police personnel in Kolkata rarely used all these protective

devices. This is an indication of their toxity in assessing the environmental risk. Proper use

of these protective mechanisms might help a lot in mainating health that is crucial for their

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working performance. Our regression clearly indicates that from the policy point of view,

mere supply of the protective gears is not a pathway to a better heath. Arousing

environmental awareness is a sure way to enhance the production of health.

Second we come to fitness. It is clear that fitness is positively related with job

likeness and health. These are in the expected direction. However there exist a perverse

negative relation between job satisfaction and fitness and pollution awareness and fitness. In

the case of job satisfaction the perverse relationship may be a result of over working with

increase enthusiasm. However the negative correlation pollution awareness is tricky. It may

be the case that the traffic policemen become aware of pollution hazard through their

continuous exposure to polluted environment. Such a type of ‘learning’ process may be a

painful exercise. If the government has taken other appropriate training programme or any

other type of information dissemination process, then this type of ‘harsh’ learning procedure

would have been procedure.

Our third dependent variable is the job-likeness. Surprisingly pollution awareness

has no role in determining these variables. It clearly indicates that when jobseekers search

for job, environment consideration rarely enters into their pictures. This is true even when

the job has a considerable amount of environmental risk. The family features, as usual, are

an important determinant of job-likeness as the fitness. People feel that they can carry a job

of police personnel only if they are fit enough. The result is usual and not surprising.

However the total insensitivity to environment risk while selecting job is an alarming

feature. This reflects a general lack of environmental consciousness in the society.

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Our fourth variable is job satisfaction, unlike job likeness; it determines the

assessment after indulging in the job. Strangely enough even here pollution awareness has

no role to play. Rather the nature of duty and the family burden (as captured by the variable

children) are important. However some effects of pollution awareness may be filtered into

the variable duties itself. These are two types of job for the police personnel – on the street

and off the street. On the street job is required to continuous exposure to environmental

pollutants. Hence, the medical risk is very high. Off the street duty is including some of

office works that are generally far away from the busy streets of the city. Here the pollution

risk is almost zero. Again experience is negatively related with job satisfaction (though not

significant).

By studying the various regressions we find a strange phenomenon. In none of the

cases awareness of vehicular pollution is stepped in. Though there is general pollution

awareness a specific knowledge about vehicular pollution is almost absent. However there is

clear dichotomy between the health variable and normative assessment of the job. It is true

that environmental awareness enters into the determination of health variable. However this

factor does not enter into the subjective assessment of the job. The result reflects a high

insensitivity towards environmental risk and pollution hazard. The policemen’s behavior

appears myopic. Drain in wealth due to the risk imposed to health has a long run

consequence that the policeman could not fathom. Only the direct possibilities of income

profile looms large in our policemen’s perception of the job.

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Table 7.5.1: Descriptive statistics of the variables under studies

StatisticsVariables Mean variance Max MinFit 1.29 0.543645 2.27551 0Fam 0.40 0.012171 0.673469 0.040816Jobl 1.38 0.14065 1.653061 0.734694Duty 1.29 0.165403 1.836735 0.459184Hlth 0.34 0.037527 0.644898 0Polaw 1.19 0.184906 2.377551 0.331633Awvp 1.46 23.44753 48.7449 0.110204Jobs 1.76 0.43025 2.857143 0.581633Age 37.67 47.10877 54 24Children 1.63 1.925521 8 0

Table 7.5.2.1A: Results of regression analysis (Dependent Variable: hlth)

Ols Hetcov Tobit

Variable Coefficient t-ratio Variable coefficient t-ratio Variable Coefficient z-statAge -0.0076827 2.913 * Age -7.68E-03 -3.142 * Age -0.00835 -2.426 *Fam -0.56304 -3.268 * Fam -0.56304 -3.43 * Fam -0.58253 -3.1068 *Fit 0.073982 2.802 * Fit 7.40E-02 3.049 * Fit 0.082122 2.7179 *

Polaw 0.11806 2.75 * Polaw 0.11806 2.472 * Polaw 0.130444 3.2053 *

(Note: *- 1٪ level of significance and **- 5٪ level of significance)

Table 7.5.2.1B: Regression statistics ((Dependent Variable: hlth)

Ols-hetcov StructureR-SQUARE = 0.3364, R-SQUARE ADJUSTED = 0.3003VARIANCE OF THE ESTIMATE-SIGMA**2 = 0.38181

STANDARD ERROR OF THE ESTIMATE-SIGMA = 0.61791SUM OF SQUARED ERRORS-SSE= 35.127

MEAN OF DEPENDENT VARIABLE = 1.2928LOG OF THE LIKELIHOOD FUNCTION = -88.7817

Table 7.5.2.1C: Tobit Regression (Dependent Variable: hlth)

Tobit structureMean dependent var 0.335881 S.D. dependent var 0.193720Censored obs 10 Sigma 0.185746Log-likelihood 11.17109 Akaike criterion -10.34219Schwarz criterion 5.167618 Hannan-Quinn -4.068791

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Table 7.5.2.2A: Results of regression analysis (Dependent Variable: fit)

Ols Hetcov TobitVariable coefficient t-ratio variable coefficient t-ratio variable coefficient z-stat

Hlth 0.81109 2.376 * * Hlth 0.81109 -2.336 * * hlth 0.906835 2.5189 * *Jobl 0.66626 3.911 * Jobl 0.66626 3.522 * jobl 0.676391 3.6333 *

Jobs -0.21924 -2.213 * * Jobs -0.21924 -2.323 * * jobs -0.23175-2.1716 *

*Age -0.024103 -2.543 * * Age -2.41E-02 -2.319 * * age 0.028122 -2.6516 *

Polaw -0.44319 -2.925 * Polaw -0.44319 -3.392 * polaw -0.48615 -2.481 * *

(Note: *- 1٪ level of significance and **- 5٪ level of significance)

Table 7.5.2.2B: Regression statistics (Dependent Variable: fit)

Ols-hetcov StructureR-SQUARE = 0.2268, R-SQUARE ADJUSTED = 0.1935

VARIANCE OF THE ESTIMATE-SIGMA**2 = 0.29867E-01STANDARD ERROR OF THE ESTIMATE-SIGMA = 0.17282

SUM OF SQUARED ERRORS-SSE= 2.7776MEAN OF DEPENDENT VARIABLE = 0.33643

LOG OF THE LIKELIHOOD FUNCTION = 35.5499

Table 7.5.2.2C: Tobit Regression (Dependent Variable: fit)

Tobit structureMean dependent var 1.293524 S.D. dependent var 0.737323Censored obs 8 Sigma 0.641131Log-likelihood -96.59263 Akaike criterion 207.1853Schwarz criterion 225.2800 Hannan-Quinn 214.5042

118

Table 7.5.2.3A: Results of regression analysis (Dependent Variable: jobl)

Ols Hetcov TobitVariable coefficient t-ratio variable coefficient t-ratio variable coefficient z-stat

Age 0.010159 1.874 age 1.02E-02 1.772 age 0.010145 1.615Fam 0.85376 4.246 * fam 0.85376 3.828 * fam 0.843702 1.6928Fit 0.20358 2.575 * * fit 0.20358 2.926 * fit 0.20517 2.1339 * *

(Note: *- 1٪ level of significance and **- 5٪ level of significance)

Table 7.5.2.3B: Regression statistics (Dependent Variable: jobl)

Table 7.5.2.3C: Tobit Regression (Dependent Variable: jobl)

Tobit structureMean dependent var 1.383174 S.D. dependent var 0.375034Censored obs 0 Sigma 0.329570Log-likelihood -30.27915 Akaike criterion 70.55831Schwarz criterion 83.48314 Hannan-Quinn 75.78614

Ols-hetcov structureR-SQUARE = 0.2189, R-SQUARE ADJUSTED = 0.1939VARIANCE OF THE ESTIMATE-SIGMA**2 = 0.11258

STANDARD ERROR OF THE ESTIMATE-SIGMA = 0.33552SUM OF SQUARED ERRORS-SSE= 10.582

MEAN OF DEPENDENT VARIABLE = 1.3812LOG OF THE LIKELIHOOD FUNCTION = -29.9919

119

Table 7.5.2.4A: Results of regression analysis (Dependent Variable: jobs)

Ols Hetcov Tobitvariable Coefficient t-ratio Variable coefficient t-ratio variable coefficient z-stat

Age -0.014127 -1.477 Age -0.014127 -1.567 age -0.0141639 -1.4389Duty 0.47843 3.088 * Duty 0.47843 3.165 * duty 0.478971 3.0491 *

children 0.094556 1.998 * children 0.094556 2.349 * * children 0.0944038 1.7017

(Note: *- 1٪ level of significance and **- 5٪ level of significance)

Table 7.5.2.4B: Regression statistics (Dependent Variable: jobs)

Ols-hetcov structureR-SQUARE = 0.1320, R-SQUARE ADJUSTED = 0.1043VARIANCE OF THE ESTIMATE-SIGMA**2 = 0.38637STANDARD ERROR OF THE ESTIMATE-SIGMA = 0.62159SUM OF SQUARED ERRORS-SSE = 36.319MEAN OF DEPENDENT VARIABLE = 1.7580LOG OF THE LIKELIHOOD FUNCTION = -90.4170

Table 7.5.2.4C: Tobit Regression (Dependent Variable: jobs)

Tobit structureMean dependent var 1.757028 S.D. dependent var 0.655934Censored obs 0 Sigma 0.607878Log-likelihood -90.27343 Akaike criterion 190.5469Schwarz criterion 203.4717 Hannan-Quinn 195.7747

120

7.6 Conclusion:

It has long been predicted by ‘Adam Smith’ that hazardous job should get risk

premium that equate it with the low hazard (Zero hazard) jobs. However if the workers are

unconscious of the hazard, the smithian risk premium story may go astray. This is the case

with our sample traffic police in Kolkata to whom the environmental risk does not loom

large in their assessment of the job. Thus raising pollution awareness among these who face

environmental hazard in their day to day activities is of utmost necessary. Unless their

awareness levels of workers increase, welfare of them cannot be ushered in the economy.

121

Chapter―8

Conclusion:

“Give us back those forests, take the cities”

(Rabindranath Tagore)

We live in an age of vehicles. Vehicles have raised our mobility, increased our

efficiency and have added to a substantial loss of the cost in transition. However like all the

gifts of modern science increase in vehicular population has its negative effect. Emission

from vehicles has deteriorated our environment. Of course, the faulty road structure and

congestion have fueled the problem.

Our study was initiated by the problems of vehicular emission inflicting heavy losses

in the environment. This proves to be a great burden for the humanity. We have

concentrated on the urban conglomerate of Kolkata where our study was directed.

It has two parts. In the first part, the objective assessment was made regarding the

spate and volume of vehicular emission. The problems of urban transit system and its

fragility discussed. Issues and problems were raised.

In the second part, we consider asset of people who are directly exposed to the

vehicular emission. They are the traffic police personnel working all day and night at the

122

busy junctions of the city. We assess their perception of the environmental pollution. The

steps they have taken to mitigate the problems are raised. An objective evaluation was made

regarding their health and fitness status. They are linked with their awareness and the little

they can do while surrounded by the five that is ready to engulf them.

In this study, we have briefly discussed the main conclusion under each chapter.

Here, we just collect them so that a better view can be approached.

The first chapter is a brief introduction of our study including the plan of the work

and objectives.

The second chapter is a literature survey. Thre types of study are surveyed in this

chapter. First, a general view of the vehicular emission is given. Secondly, we contextualize

it for the third world countries. Thirdly, we concentrate on India and finally, in Kolkata.

In the third chapter, the nature of data was discussed. We used both primary and

secondary data. Secondary data was collected from census and other official publications.

The secondary data was interview based. These two types of data are combined to get a

comprehensive view of the topic.

Fourth chapter attempts to test the relationship between urbanization and

vehicular population using the West Bengal data. For our analysis we concentrated on

123

various secondary sources particularly the census data. The relationship is an expected;

urbanization raises the possession of fuel consuming vehicles.

This is significantly positive both using econometric as well as non-parametric

techniques. Even the literacy rate is perversely affecting the possession of polluting vehicles,

the reason is that higher literacy implies higher human capital and enhances income. The

picture is not bright. Unless proper steps are taken toward efficient use a public transport

system and planned urbanization, there is a little chance in abating the fleet of polluting

vehicles.

From our above discussion we clearly understand that the probability of polluting

vehicles holding is higher in the urban area than the rural area. It has been proved by the

various statistical tests. Therefore we can obviously state that the pollution in the urban area

is more than the rural area in West Bengal. Urbanization leads to a conglomeration of

polluting vehicles and boosting up pollution.

Chapter five is concerned with the relation between urbanization and traffic

problems. Our urban centers show increasing demand for transport. There is however a

number of problems associated with urban transportation. In this paper we hope to deal with

some of these problems. These problems are multifaceted and often entangled with one

another. Here we propose to cover some of the issues with the respect of both India as well

as West Bengal. The inequalities in private urban transit systems are documented. Though

there is a rise in the total number of vehicles, still a sizable portion of the urban families who

124

have no vehicles. The problem is complicated by the lack of adequate road space and

congestion that prevents the use of bicycles. Severe problems of air pollution are also noted.

Kolkata has one of the cheapest public transport systems among the Indian metropolis.

However even this ‘cheap’ transit system is beyond the reach of the very poor in this city.

Further more, the system is also inefficient with waste of resources. An urban transport

planner has a tough task. He has to balance between equity, efficiency and sustainability of

transit system. This requires long run planning by the urban planners.

The chapter six has two parts. First, it brings out the important aspect of air

pollution. For the each of study, we have segregated the entire time period into four seasons-

winter, summer, monsoon and festival. These are of them are quite concern in the

environmental literature. The fourth season is tropical of Kolkata – arising mainly due to

social causes. The data reveals that this so called new season ranks very high in the disposal

of environmental waste. Thus an interesting suggestion of the paper is the role of

environmental fallen in increasing pollution and climate hazard. The point is clearly brought

out by our analysis.

The second part of this chapter demonstrates the seasonal trends (Periodicity) of the

different pollutants in Kolkata. This new technique is justified since the above discussion

exhibits the significant results of spectral estimates, which really represents the seasonal

periodicities of the pollutants under equal time intervals. We also understand correlation

between two series at different frequencies, using cross-spectral analysis. The strong

correlated periodicities among different series of pollutants under different years can be

125

analysed by the value of coherence, phase and also gain value etc. These totally indicate that

the model has a great implication of predicting long run trends.

The seventh chapter takes up the micro issue ― impact of vehicular emission on

traffic police. It has long been predicted by Adam Smith that hazardous job should get risk

premium that equate it with the low hazard (Zero hazard) jobs. However if the workers are

unconscious of the hazard, the Smithian risk premium story may go astray. This is the case

with our sample traffic police in Kolkata to whom the environmental risk does not loom

large in their assessment of the job. Thus raising pollution awareness among these who face

environmental hazard in their day to day activities is of utmost necessary. Unless their

awareness levels of workers increase, welfare of them cannot be ushered in the economy.

At the end of our journey we feel that the problems of vehicular emission are

multifaceted. It has both macro and micro dimension. The dynamics reveals the pattern and

volume of vehicular emission. However, the micro aspect study is the impact at individual

level. It covers the people to face it. Thus vehicular emission is not only environmental

phenomenon. It is both a cause and effect of the air pollution due to vehicles. Appropriate

measures are urgently needed to save our beautiful planet from an untimely death.

126

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141

Appendix (Figures of chapter- 6):

R P M P e r i o d o g r a m o f 2 0 0 1

0 . 0 0 0 0 0 0

2 0 0 0 0 . 0 0 0 0 0 0

4 0 0 0 0 . 0 0 0 0 0 0

6 0 0 0 0 . 0 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

R P M S p e c t r a l D e n s i t y o f 2 0 0 1

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

1 2 3 4 5 6

F r e q u e n c y

C r o ss P e r i o d o g r a m o f R P M 2 0 0 1

-10000.000000

0.00000010000.000000

20000.000000

30000.00000040000.000000

50000.000000

1 2 3 4 5 6

Fr e que nc y

C r o s s D e ns i t y o f R P M 2 0 0 1

0.0000002000.0000004000.0000006000.0000008000.00000010000.00000012000.00000014000.000000

1 2 3 4 5 6

F r e q u e n c y

S qua r e d C ohe r nc y o f R P M 2 0 0 1

0.0000000.2000000.4000000.6000000.8000001.0000001.200000

1 2 3 4 5 6

Fr e que nc y

C r o ss A mp l i t ud e o f R P M 2 0 0 1

0.0000002000.0000004000.0000006000.0000008000.00000010000.00000012000.00000014000.000000

1 2 3 4 5 6

Fr e que nc y

P ha s e S p e c t r um o f R P M 2 0 0 1

- 1.000000

- 0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

F r e q u e n c y

Ga i n of RP M 2 0 0 1

0.000000

0.500000

1.000000

1.500000

2.000000

2.500000

1 2 3 4 5 6

Fr equency

M o nt hly F luct uat io n o f R PM 2 0 0 1

050100150200250300350

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

142

Densi t y of SO2 2001

0.00000050.000000100.000000

150.000000200.000000250.000000

1 2 3 4 5 6

F r e q u e n c y

SO2 Cross Periodogram 2001

-100.000000

0.000000

100.000000

200.000000

300.000000

1 2 3 4 5 6

Fr equency

S O2 C r oss D e nsi t y 2 0 0 1

-50.000000

0.000000

50.000000

100.000000

150.000000

1 2 3 4 5 6

Fr equency

SO2 Squared Coherncy 2001

0.000000

0.2000000.400000

0.6000000.800000

1.0000001.200000

1 2 3 4 5 6

Fr equency

SO2 Cross Amplitude 2001

0.000000

50.000000

100.000000

150.000000

1 2 3 4 5 6

Frequency

SO2 Phase Spect rum 2001

-4.000000

-2.000000

0.000000

2.000000

4.000000

1 2 3 4 5 6

Fr equency

G a i n o f S O 2 2 0 0 1

0. 000000

1. 000000

2. 000000

3. 000000

4. 000000

5. 000000

1 2 3 4 5 6

F r e q u e n c y

M ont hl y Fl uc t ua t i on of S O2

0

10

20

30

40

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

Pe r io d o g r am o f SO2 2001

0 .000000

100 .000000

200 .000000

300 .000000

400 .000000

500 .000000

1 2 3 4 5 6

F r e q u e n c y

143

M o n t h l y F l u c t u a t i o n o f N O 2

02 04 06 08 0

1 0 01 2 01 4 01 6 0

Ap

r

Ma

y

Ju

ne

Ju

ly

Au

gS

ep

Oc t

No

vD

ec

Ja

nF

eb

Ma

r

M o n t h

Po

llu

tan

t

P er i odogr am of NO2 2001

0.000000

1000.000000

2000.000000

3000.000000

4000.000000

5000.000000

1 2 3 4 5 6

F r e q u e n c y

S pe c t r a l De nsi t y of NO2 2 0 0 1

0.000000

1000.000000

2000.000000

3000.000000

4000.000000

1 2 3 4 5 6

F r e q u e n c y

C r o s s P e r i o d o g r a m o f N O 2 2 0 0 1

-2000. 000000

-1000. 000000

0. 000000

1000. 000000

2000. 000000

3000. 000000

4000. 000000

5000. 000000

1 2 3 4 5 6

F r e q u e n c y

Cross Densit y of NO2 2001

-1000.000000

0.000000

1000.000000

2000.000000

3000.000000

4000.000000

1 2 3 4 5 6

Fr equency

NO2 Cross amplitude 2001

0.000000

1000.000000

2000.000000

3000.000000

4000.000000

1 2 3 4 5 6

Fr e que nc y

Squared Coherncy of NO2 2001

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr e que nc y

N O 2 P h a s e s p e c t r u m 2 0 0 1

- 3 .0 0 0 0 0 0

- 2 .0 0 0 0 0 0

- 1.0 0 0 0 0 0

0 .0 0 0 0 0 0

1.0 0 0 0 0 0

2 .0 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

G a in o f NO 2 2 0 0 1

0 .0 0 0 0 0 00 .5 0 0 0 0 01.0 0 0 0 0 0

1.5 0 0 0 0 02 .0 0 0 0 0 02 .5 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

144

S P M S p e c t r a l P e r i o d o g r a m 2 0 0 2

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

50000.000000

60000.000000

1 2 3 4 5 6

F r e q u e n c y

SPM Sp ect ral D ensit y 2 0 0 2

0.0000005000.00000010000.00000015000.00000020000.00000025000.00000030000.00000035000.000000

1 2 3 4 5 6

Fr equency

S P M Cr oss P e r i odogr a m 2 0 0 2

-10000.000000

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

50000.000000

1 2 3 4 5 6Fr equency

S P M c r oss De nsi t y 2 0 0 2

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

1 2 3 4 5 6

Fr equency

Cr oss Ampl i t ude of SPM 2002

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

1 2 3 4 5 6

Fr equency

SPM co herncy 2 0 0 2

0.000000

0.2000000.400000

0.600000

0.8000001.000000

1.200000

1 2 3 4 5 6

Fr equency

G a i n o f S P M 2 0 0 2

0 . 0 0 0 0 0 0

1 . 0 0 0 0 0 0

2 . 0 0 0 0 0 0

3 . 0 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

Monthly Fluctuation of SPM 2002

0100

200300

400500

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

SPM Phase 2002

-1.000000

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

Fr equency

145

S pe c t r a l P e r i odogr a m of RP M 2 0 0 2

0.000000

5000.000000

10000.000000

15000.000000

20000.000000

1 2 3 4 5 6

Fr equency

S pe c t r a l De nsi t y of RP M 2 0 0 2

0.000000

5000.000000

10000.000000

15000.000000

20000.000000

1 2 3 4 5 6

Fr equency

Cr oss P e r i odogr a m of RP M 2 0 0 2

-5000.000000

0.000000

5000.000000

10000.000000

15000.000000

20000.000000

1 2 3 4 5 6

Fr equency

Cross Density of RPM 2002

0.000000

5000.000000

10000.000000

15000.000000

1 2 3 4 5 6

Fr e que nc y

Cross Amplitude of RPM 2002

0.000000

5000.000000

10000.000000

15000.000000

1 2 3 4 5 6

Fr e que nc y

Sq uared C o herncy o f R PM 2 0 0 2

0.000000

0.200000

0.400000

0.600000

0.800000

1.000000

1.200000

1 2 3 4 5 6

Fr equency

P hase S pect r um of RP M 2002

-1.000000

-0.500000

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr equency

Gain of RPM 2002

0.000000

0.500000

1.000000

1.500000

2.000000

1 2 3 4 5 6

F r e q u e n c y

Monthly Fuctuation of RPM 2002

0100

200300

400500

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

146

P e r i odogr a m of S O2 2 0 0 2

0.000000

20.000000

40.000000

60.000000

80.000000

1 2 3 4 5 6

Fr equency

D ensit y o f SO2 2 0 0 2

0.000000

10.000000

20.000000

30.000000

40.000000

50.000000

1 2 3 4 5 6

Fr equency

C r oss P e r i odogr a m of S O2 2 0 0 2

-20.000000-10.0000000.00000010.00000020.00000030.00000040.00000050.000000

1 2 3 4 5 6

Fr equency

C r oss D e nsi t y o f S O2 2 0 0 2

-10.000000

0.000000

10.000000

20.000000

30.000000

1 2 3 4 5 6

Fr e que nc y

Cr oss Ampl i t ude of S O2 2 0 0 2

0.0000005.00000010.00000015.00000020.00000025.00000030.000000

1 2 3 4 5 6

Fr equency

S qua r e d Cohe r nc y os S O2 2 0 0 2

0.000000

0.2000000.400000

0.6000000.800000

1.0000001.200000

1 2 3 4 5 6

Fr equency

Phase Sp ect rum o f SO2 2 0 0 2

-4.000000-3.000000-2.000000-1.0000000.0000001.0000002.0000003.0000004.000000

1 2 3 4 5 6

Fr equency

Gai n of SO2 2002

0.000000

1.000000

2.000000

3.000000

4.000000

1 2 3 4 5 6

Fr e que nc y

Monthly Fluctuation of SO2 2002

0

5

10

15

20

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M o nt h

147

Per i odogr am of NO2 2002

0.000000

2000.000000

4000.000000

6000.000000

1 2 3 4 5 6

Fr equency

De nsi t y of NO2 2 0 0 2

0.000000

1000.000000

2000.000000

3000.000000

4000.000000

1 2 3 4 5 6

Fr equency

C r o ss P e r i o d o g r a m o f N O 2 2 0 0 2

0.000000500.0000001000.0000001500.0000002000.0000002500.0000003000.0000003500.0000004000.000000

1 2 3 4 5 6

F r e q u e n c y

C r o ss D e n si t y o f N O 2 2 0 0 2

0.000000500.0000001000.0000001500.000000

2000.0000002500.0000003000.0000003500.000000

1 2 3 4 5 6

F r e q u e n c y

Cross Amplitude of NO2 2002

0.000000

1000.000000

2000.000000

3000.000000

4000.000000

1 2 3 4 5 6

Fr equency

S q u a r e d C o h e r n c y o f N O 2 2 0 0 2

0.000000

0.200000

0.400000

0.600000

0.800000

1.000000

1.200000

1 2 3 4 5 6

F r e q u e n c y

P ha se S pe c t r um of NO2 2 0 0 2

-1.000000

-0.500000

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr equency

Ga i n of NO2 2 0 0 2

0.000000

1.000000

2.000000

3.000000

4.000000

1 2 3 4 5 6

F r e q u e n c y

Monthly Fluctuation of NO2 2002

0

50

100

150

200

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

148

P er i odogr am of SP M 2003

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

1 2 3 4 5 6

F r e q u e n c y

Densi t y of SP M 2003

0.000000

10000.000000

20000.000000

30000.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss P er i odogr am of SP M 2003

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss De nsi t y of S P M 2 0 0 3

0.0000005000.00000010000.00000015000.00000020000.00000025000.000000

1 2 3 4 5 6

Fr equency

C r o ss A m p l i t u d e o f S P M 2 0 0 3

0 . 000000

5000 . 000000

10000 . 000000

15000 . 000000

20000 . 000000

25000 . 000000

1 2 3 4 5 6

F r e q u e n c y

Coherncy of SPM 2003

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr equency

Phase of SPM 2003

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

Fr e que nc y

G a i n o f SP M 2 0 0 3

0. 000000

0. 500000

1. 000000

1. 500000

2. 000000

1 2 3 4 5 6

F r e q u e n c y

Monthly Fluctuation of SPM 2003

0

100

200

300

400

500

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

149

Periodogram of RPM 2003

0.000000

5000.000000

10000.000000

15000.000000

20000.000000

1 2 3 4 5 6

Fr equency

Density of RPM 2003

0.000000

5000.000000

10000.000000

15000.000000

1 2 3 4 5 6

Frequency

Cr oss Per iodogr am of RPM 2003

0.000000

5000.000000

10000.000000

15000.000000

1 2 3 4 5 6

Fr equency

C r o s s D e ns i t y o f R P M 2 0 0 3

0.000000

2000.000000

4000.000000

6000.000000

8000.000000

10000.000000

1 2 3 4 5 6

Fr e que nc y

C r o ss A m p l i t u d e o f R P M 2 0 0 3

0.000000

2000.0000004000.000000

6000.0000008000.000000

10000.000000

1 2 3 4 5 6

Fr e que nc y

Square d Cohe rncy of RPM 2003

0.0000000.2000000.4000000.6000000.8000001.0000001.200000

1 2 3 4 5 6

F r e q u e n c y

Pgase Spect rum of RPM 2003

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

Fr equency

G a i n o f R P M 2 0 0 3

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

F r e q u e n c y

Monthly Fluctuation of RPM 2003

050

100150

200250

300

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

150

P e r i odogr a m of S O2 2 0 0 3

0.000000

20.000000

40.000000

60.000000

80.000000

1 2 3 4 5 6

Fr equency

D ensit y o f SO2 2 0 0 3

0.000000

10.000000

20.000000

30.000000

40.000000

50.000000

1 2 3 4 5 6

Fr equency

Cr oss P e r i odogr a m of S O2 2 0 0 3

-10.000000

0.00000010.000000

20.00000030.000000

40.00000050.000000

60.000000

1 2 3 4 5 6

Fr equency

Cross Density of SO2 2 0 0 3

0.000000

10.000000

20.000000

30.000000

40.000000

1 2 3 4 5 6

Fr equency

Cr oss Ampl i t ude of SO2 2003

0.000000

20.000000

40.000000

60.000000

1 2 3 4 5 6

F r e q u e n c y

S qua r e d Cohe r nc y of S O2 2 0 0 3

0.0000000.2000000.4000000.6000000.8000001.0000001.200000

1 2 3 4 5 6

Fr equency

Phase Spect rum of SO2 2003

-1.500000

-1.000000

-0.500000

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr equency

Ga i n of S O2 2 0 0 3

0.000000

1.000000

2.000000

3.000000

4.000000

1 2 3 4 5 6

Fr equency

Monthly Fluctuation of SO2 2003

0

5

10

15

20

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

Fr e que nc y

151

P e r i odogr a m of NO2 2 0 0 3

0.000000

500.000000

1000.000000

1500.000000

2000.000000

1 2 3 4 5 6

Fr equency

De nsi t y of NO2 2 0 0 3

0.000000

500.000000

1000.000000

1500.000000

1 2 3 4 5 6

Fr equency

Cr oss P e r i odogr a m of NO2 2 0 0 3

-200.000000

0.000000

200.000000

400.000000

600.000000

800.000000

1 2 3 4 5 6

Fr equency

C r o ss D e n si t y o f N O2 2 0 0 3

0.000000

100.000000

200.000000

300.000000

400.000000

1 2 3 4 5 6

Fr e que nc y

Cross Amplitude of NO2 2003

0.000000100.000000200.000000300.000000400.000000500.000000

1 2 3 4 5 6

Fr equency

Squared Coherncy of NO2 2003

0.0000000.200000

0.4000000.6000000.800000

1.0000001.200000

1 2 3 4 5 6

Fr equency

Phase Spectrum of NO2 2003

-1.500000

-1.000000

-0.500000

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr equency

Gai n of No2 2003

0.000000

0.200000

0.400000

0.600000

0.800000

1.000000

1 2 3 4 5 6

F r e q u e n c y

Monthly Fluctuation of NO2 2003

0

50

100

150

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

152

Per i odogr am of SPM 2004

0.000000

20000.000000

40000.000000

60000.000000

1 2 3 4 5 6

Fr e que nc y

De si t y of S P M 2 0 0 4

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

1 2 3 4 5 6

Fr equency

Cr oss P er i odogr am of SP M 2004

-20000.000000

0.000000

20000.000000

40000.000000

60000.000000

1 2 3 4 5 6

F r e q u e n c y

C r o ss D e n si t y o f S P M 2 0 0 4

-10000.000000

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

1 2 3 4 5 6

F r e q u e n c y

C r o s s A m p l i t u d e o f S P M 2 0 0 4

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

1 2 3 4 5 6

F r e q u e n c y

C o h e r n c y o f S P M 2 0 0 4

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr e que nc y

P ha se of S P M 2 0 0 4

-1.000000

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

F r e q u e n c y

Ga i n of SP M 2 0 0 4

0. 000000

0. 500000

1. 000000

1. 500000

2. 000000

1 2 3 4 5 6

F r e q u e n c y

M ont hl y Fl uc t ua t i on of S P M 2 0 0 4

0100200300400500

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

153

P e r i odogr a m of R P M 2 0 0 4

0. 000000

5000. 000000

10000. 000000

15000. 000000

1 2 3 4 5 6

F r e q u e n c y

Densi t y of RP M 2004

0.000000

5000.000000

10000.000000

15000.000000

1 2 3 4 5 6

F r e q u e n c y

Cross Per iodogram of RPM 2004

-5000.000000

0.000000

5000.000000

10000.000000

15000.000000

1 2 3 4 5 6

Fr equency

Cr oss Densi ty of RPM 2004

-5000.000000

0.000000

5000.000000

10000.000000

1 2 3 4 5 6

Fr equency

C r os s A mpl i t ude of R P M 2 0 0 4

0. 000000

5000. 000000

10000. 000000

1 2 3 4 5 6

F r e q u e n c y

C o h e r n c y o f R P M 2 0 0 4

0 . 000000

0 . 500000

1 . 000000

1 . 500000

1 2 3 4 5 6

F r e q u e n c y

P h a se o f R P M 2 0 0 4

-0.500000

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

F r e q u e n c y

Ga i n of R P M 2 0 0 4

0.000000

0.5000001.000000

1.500000

2.000000

1 2 3 4 5 6

F r e q u e n c y

Monthly Fluctuation of RPM 2004

0

100

200

300

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

154

P er i odogr am of SO2 2004

0.000000

50.000000

100.000000

150.000000

200.000000

1 2 3 4 5 6

F r e q u e n c y

D e n si t y o f S O 2 2 0 0 4

0.000000

50.000000

100.000000

150.000000

1 2 3 4 5 6

Fr e que nc y

Cross Periodogram of SO2 2004

-100.000000

0.000000

100.000000

200.000000

1 2 3 4 5 6

Fr equency

Cr oss De nsi t y of S O2 2 0 0 4

0.00000020.00000040.00000060.000000

80.000000100.000000120.000000

1 2 3 4 5 6Fr equency

C r o s s A mp l i t u d e o f S O 2 2 0 0 4

0 . 0 0 0 0 0 0

5 0 . 0 0 0 0 0 0

1 0 0 . 0 0 0 0 0 0

1 5 0 . 0 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

Cohe r nc y of S O2 2 0 0 4

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr equency

Phase Of SO2 2004

-1.500000-1.000000-0.5000000.0000000.5000001.0000001.5000002.000000

1 2 3 4 5 6

Fr equency

G a i n o f SO 2 2 0 0 4

0 . 000000

1 . 000000

2 . 000000

3 . 000000

1 2 3 4 5 6

F r e q u e n c y

M o nt hly F luct uat io n o f SO2 2 0 0 4

05101520253035

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

155

P er i odogr am of NO2 2004

0.0000001000.0000002000.000000

3000.0000004000.000000

1 2 3 4 5 6

F r e q u e n c y

Densi t y of NO2 2004

0.000000

1000.000000

2000.000000

3000.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss Per i odogr am of NO2 2004

-1000.000000

0.000000

1000.000000

2000.000000

3000.000000

4000.000000

1 2 3 4 5 6

Fr e que nc y

Cr oss Densi t y of NO2 2004

-5000.000000

0.000000

5000.000000

10000.000000

1 2 3 4 5 6

F r e q u e n c y

C r o s s A mp l i t u d e o f N O 2 2 0 0 4

0 . 000000

500 . 000000

1000 . 000000

1500 . 000000

2000 . 000000

1 2 3 4 5 6

F r e q u e n c y

C o h e r n c y o f N O 2 2 0 0 4

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr e que nc y

P h a s e o f N O 2 2 0 0 4

-0 . 500000

0 . 000000

0 . 500000

1 . 000000

1 . 500000

1 2 3 4 5 6

F r e q u e n c y

Gai n of R P M 2004

0. 0000001. 000000

2. 000000

1 2 3 4 5 6

Fr equen cy

M ont hl y Fl uc t ua t i on of NO2 2 0 0 4

0

50

100

150

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

156

P er i odogr am of SP M 2005

0.000000

50000.000000

100000.000000

150000.000000

1 2 3 4 5 6

F r e q u e n c y

Densi t y of SP M 2005

0.000000

50000.000000

100000.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss P er i odogr am of SP M 2005

-50000.000000

0.000000

50000.000000

100000.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss Densi t y of SP M 2005

-10000.000000

0.000000

10000.000000

20000.000000

30000.000000

1 2 3 4 5 6

F r e q u e n c y

C r o ss A m p l i t u d e o f S P M 2 0 0 5

0.000000

10000.000000

20000.000000

30000.000000

1 2 3 4 5 6

F r e q u e n c y

Coher ncy of SP M 2005

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

F r e q u e n c y

Phase of SPM 2005

-1.000000

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

Fr equency

Gai n of SP M 2005

0.0000000.5000001.0000001.500000

1 2 3 4 5 6

Fr equency

M ont hl y Fl uc t ua t i on of S P M 2 0 0 5

0.00200.00400.00600.00800.001000.00

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

157

Per i odogr am of RPM 2005

0.000000

5000.000000

10000.000000

15000.000000

1 2 3 4 5 6

Fr e que nc y

Densi t y of RP M 2005

0.000000

2000.000000

4000.000000

6000.000000

8000.000000

10000.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss P er i odogr am of RP M 2005

-5000.000000

0.000000

5000.000000

10000.000000

15000.000000

1 2 3 4 5 6

F r e q u e n c y

C r o ss D e n si t y o f R P M 2 0 0 5

0 . 000000

2000 . 000000

4000 . 000000

6000 . 000000

8000 . 000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss Ampl i t ude of RP M 2 0 0 5

0.000000

2000.000000

4000.000000

6000.000000

8000.000000

1 2 3 4 5 6

Fr equency

Coher ncy of RPM 2005

0.000000

0.5000001.000000

1.500000

1 2 3 4 5 6

Fr equency

Phase of RPM 2005

-0.600000

-0.400000

-0.200000

0.000000

0.200000

0.400000

0.600000

1 2 3 4 5 6

Fr equency

Gain of RPM 2005

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr equency

M ont hl y Fl uc t ua t i on of RP M 2 0 0 5

0

100

200

300

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

158

Per i odogr am of SO2 2005

0.00000

50.00000

100.00000

150.00000

1 2 3 4 5 6

Fr e que nc y

Per iodogr am of SO2 2005

0.000000

50.000000

100.000000

150.000000

1 2 3 4 5 6

Fr equency

Cr oss P er i odogr am of SO2 2005

-50.000000

0.000000

50.000000

100.000000

150.000000

1 2 3 4 5 6

F r e q u e n c y

C r o ss D e n si t y o f S O 2 2 0 0 5

0.00000020.000000

40.00000060.000000

80.000000100.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss Ampl i tude of SO2 2005

0.000000

50.000000

100.000000

1 2 3 4 5 6

Fr equen cy

C o h e r n c y o f S O 2 2 0 0 5

0 . 0 0 0 0 0 0

0 . 2 0 0 0 0 0

0 . 4 0 0 0 0 0

0 . 6 0 0 0 0 0

0 . 8 0 0 0 0 0

1 . 0 0 0 0 0 0

1 . 2 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

P hase of SO2 2005

-1.500000

-1.000000

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

F r e q u e n c y

Ga i n of SO2 2 0 0 5

0. 000000

1. 000000

2. 000000

3. 000000

1 2 3 4 5 6

Fr equen cy

M ont hl y Fl uct uat i on of SO2 2005

0

10

20

30

40

Apr May June Jul y Aug Sep Oct Nov Dec Jan Feb Mar

M o n t h

159

Per i odogr am of NO2 2005

0.000000

500000.000000

1000000.000000

1500000.000000

2000000.000000

1 2 3 4 5 6

Fr e que nc y

De nsi t y of NO2 2 0 0 5

0.000000

500000.000000

1000000.000000

1500000.000000

2000000.000000

1 2 3 4 5 6 7

Fr equency

C r os s P e r i odogr a m of N O2 2 0 0 5

-60000.000000

-40000.000000

-20000.000000

0.000000

20000.000000

40000.000000

1 2 3 4 5 6

F r e q u e n c y

C r o ss D e n si t y o f N O 2 2 0 0 5

-25000.000000

-20000.000000-15000.000000

-10000.000000

-5000.000000

0.0000005000.000000

10000.000000

15000.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss Ampl i t ude of NO2 2 0 0 5

0.0000005000.00000010000.00000015000.00000020000.00000025000.00000030000.000000

1 2 3 4 5 6

Fr equency

Cohe r nc y of NO2 2 0 0 5

0.0000000.2000000.4000000.6000000.8000001.0000001.200000

1 2 3 4 5 6

Fr equency

P ha se of NO2 2 0 0 5

-3.000000-2.000000-1.0000000.0000001.0000002.0000003.0000004.000000

1 2 3 4 5 6

Fr equency

Gai n of NO2 2005

0.000000

1.000000

2.000000

1 2 3 4 5 6

Fr equen cy

M ont hl y Fl uct uat i on of NO2 2005

0

50

100

150

Apr May June Jul y Aug Sep Oct Nov Dec Jan Feb Mar

M o n t h

160

P er i odogr am of SP M 2006

0.00000010000.000000

20000.00000030000.000000

40000.00000050000.000000

60000.000000

1 2 3 4 5 6

F r e q u e n c y

Densi t y of SP M 2006

0.000000

10000.000000

20000.000000

30000.000000

40000.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss P er i odogr am of SP M 2006

-10000.000000

0.000000

10000.000000

20000.000000

30000.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss De nsi t y of S P M 2 0 0 6

-5000.000000

0.000000

5000.000000

10000.000000

15000.000000

20000.000000

1 2 3 4 5 6

F r e q u e n c y

C r o s s A mp l i t u d e o f SP M 2 0 0 6

0 . 0 0 0 0 0 0

10 0 0 0 . 0 0 0 0 0 0

2 0 0 0 0 . 0 0 0 0 0 0

3 0 0 0 0 . 0 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

C o h e r n c y o f S P M 2 0 0 6

0 . 0 0 0 0 0 0

0 . 5 0 0 0 0 0

1. 0 0 0 0 0 0

1. 5 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

P hase of SP M 2006

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

F r e q u e n c y

G a i n o f SP M 2 0 0 6

0.0000000.5000001.0000001.500000

1 2 3 4 5 6

F r e u e n c y

M ont hl y Fl uc t ua t i on of S P M 2 0 0 6

0100200300400500600

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

161

P e r i odogr a m of RP M 2 0 0 6

0.000000

5000.000000

10000.000000

15000.000000

20000.000000

1 2 3 4 5 6

Fr equency

De nsi t y of RP M 2 0 0 6

0.0000002000.000000

4000.0000006000.000000

8000.00000010000.000000

12000.000000

1 2 3 4 5 6

F r e q u e n c y

C r o s s P e r i o d o g r a m o f R P M 2 0 0 6

- 5 0 00 . 0 00000

0 . 0 00000

5000 . 0 00000

10000 . 0 00000

15000 . 0 00000

1 2 3 4 5 6

Fr e que n c y

C r o s s D e n s i t y o f R P M 2 0 0 6

0 . 0 0 0 0 00

2000 . 0 0 0 0 00

4000 . 0 0 0 0 00

6000 . 0 0 0 0 00

8000 . 0 0 0 0 00

10000 . 0 0 0 0 00

1 2 3 4 5 6

F r e q u e n c y

Cr oss Ampl i tude of RPM 2006

0.000000

5000.000000

10000.000000

1 2 3 4 5 6

f r e que nc y

C o h e r n c y o f R P M 2 0 0 6

0.000000

0.200000

0.400000

0.600000

0.800000

1.000000

1.200000

1 2 3 4 5 6

Fr e que nc y

P hase of RP M 2006

-1.000000

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

F r e q u e n c y

Gai n of RP M 2006

0.000000

0.500000

1.000000

1.500000

2.000000

1 2 3 4 5 6

F r e q u e n c y

M ont hl y Fl uct uat i on of RP M 2006

0

100

200

300

Apr May June Jul y Aug Sep Oct Nov Dec Jan Feb Mar

M o n t h

162

P er i odogr am of SO2 2006

0.00000020.00000040.00000060.00000080.000000

1 2 3 4 5 6

F r e q u e n c y

D e n si t y o f S O 2 2 0 0 6

0.000000

20.000000

40.000000

60.000000

80.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss P er i odogr am of SO2 2006

-50.000000

0.000000

50.000000

100.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss Densi t y of SO2 2006

0.000000

50.000000

100.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss Ampl i t ude of SO2 2006

0.000000

20.000000

40.000000

60.000000

80.000000

1 2 3 4 5 6

F r e q u e n c y

C ohe r nc y of SO2 2 0 0 6

0. 000000

0. 500000

1. 000000

1. 500000

1 2 3 4 5 6

F r e q u e n c y

P hase of SO2 2006

-0.200000

0.000000

0.200000

0.400000

0.600000

1 2 3 4 5 6

F r e q u e n c y

Gai n of RP M 2006

0.000000

1.000000

2.000000

3.000000

4.000000

1 2 3 4 5 6

F r e q u e n c y

M ont hl y Fl uc t ua t i on of S O2 2 0 0 6

0

10

20

30

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

163

P er i odogr am of NO2 2006

0.000000

1000.0000002000.000000

3000.000000

1 2 3 4 5 6

F r e q u e n c y

D e n s i t y o f N O 2 2 0 0 6

0 . 000000

500 . 000000

1000 . 000000

1500 . 000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss P er i odogr am of NO2 2006

-500.000000

0.000000

500.000000

1000.000000

1500.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss Densi t y of NO2 2006

-200.000000

0.000000

200.000000

400.000000

600.000000

800.000000

1 2 3 4 5 6

F r e q u e n c e

C r o s s A mp l i t u d e o f N O 2 2 0 0 6

0 . 0 0 0 0 0 0

5 0 0 . 0 0 0 0 0 0

10 0 0 . 0 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

Coher ncy of NO2 2006

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

F r e q u e n c y

P hase of NO2 2006

-2.000000

-1.000000

0.000000

1.000000

1 2 3 4 5 6

F r e q u e n c y

Gai n of NO2 2006

0.000000

1.000000

2.000000

3.000000

1 2 3 4 5 6

F r e q u e n c y

M o n t h l y F l u c t u a t i o n o f N O 2 2 0 0 6

0

2 0

4 0

6 0

8 0

1 0 0

1 2 0

Ap

r

Ma

y

Ju

ne

Ju

l y

Au

g

Se

p

Oc

t

No

v

De

c

Ja

n

Fe

b

Ma

r

M o n t h

Po

llu

tio

n

164

P er i odogr am of SP M 2007

0.00

20000.00

40000.00

60000.00

1 2 3 4 5 6

F r e q u e n c y

Densi t y of SP M 2007

0.000000

20000.000000

40000.000000

1 2 3 4 5 6

Fr equen cy

Cr oss P er i odogr am of SP M 2007

-20000.000000

0.000000

20000.000000

40000.000000

1 2 3 4 5 6

Fr equen cy

Cr oss Densi t y of SP M 2007

0.000000

10000.000000

20000.000000

30000.000000

1 2 3 4 5 6

F r e q u e n c y

C r os s A mpl i t ude of SP M 2 0 0 7

0.00000010000.00000020000.00000030000.000000

1 2 3 4 5 6

Fr e que nc y

Coher ncy of SP M 2007

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

F r e q u e n c y

P hase of SP M 2007

-1.000000

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

F r e q u e n c y

Gai n of SP M 2007

0.000000

0.500000

1.000000

1.500000

2.000000

1 2 3 4 5 6

F r e q u e n c y

M ont hl y Fl uct uat i on of SP M 2007

0

200

400

600

Apr May June Jul y Aug Sep Oct Nov Dec Jan Feb Mar

M o n t h

165

P er i odogr am of RP M 2007

0.000000

5000.000000

10000.000000

15000.000000

20000.000000

1 2 3 4 5 6

F r e q u e n c y

Densi t y of RP M 2007

0.000000

5000.000000

10000.000000

15000.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss Per i odogr am of RPM 2007

-5000.000000

0.000000

5000.000000

10000.000000

15000.000000

1 2 3 4 5 6

Fr equency

Cr oss Densi t y of RP M 2007

0.000000

2000.000000

4000.000000

6000.000000

8000.000000

10000.000000

1 2 3 4 5 6

F r e q u e n c y

C r os s A mpl i t ude of R P M 2 0 0 7

0. 000000

5000. 000000

10000. 000000

1 2 3 4 5 6

Fr equen cy

Coher ncy of RP M 2007

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

F r e q u e n c y

P hase of RP M 2007

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

F r e q u e n c y

Ga i n of R P M 2 0 0 7

0. 000000

1. 000000

2. 000000

1 2 3 4 5 6

Fr equen cy

M ont hl y Fl uc t ua t i on of RP M 2 0 0 7

0

100

200

300

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h

166

P er i odogr am of SO2 2007

0.000000

5.000000

10.000000

15.000000

1 2 3 4 5 6

F r e q u e n c y

Densi t y of SO2 2007

0.000000

5.000000

10.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss P e r i odogr a m of S O2 2 0 0 7

-2.000000

0.000000

2.000000

4.000000

6.000000

8.000000

10.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss De nsi t y of S O2 2 0 0 7

0.000000

2.000000

4.000000

6.000000

8.000000

1 2 3 4 5 6

Fr equency

Coher ncy of SO2 2007

0.000000

0.500000

1.000000

1.500000

1 2 3 4 5 6

Fr equency

C r o s s A mp l i t u d e o f SO 2 2 0 0 7

0 . 0 0 0 0 0 0

5 . 0 0 0 0 0 0

10 . 0 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

P h a s e o f S O 2 2 0 0 7

- 1 . 5 0 0 0 0 0

- 1 . 0 0 0 0 0 0

- 0 . 5 0 0 0 0 0

0 . 0 0 0 0 0 0

0 . 5 0 0 0 0 0

1 . 0 0 0 0 0 0

1 . 5 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

G a i n o f SO 2 2 0 0 7

0 . 0 0 0 0 0 0

2 . 0 0 0 0 0 0

4 . 0 0 0 0 0 0

6 . 0 0 0 0 0 0

1 2 3 4 5 6

F r e q u e c y

M ont hl y Fl uct uat i on of SO2 2007

0

5

10

15

Apr May June Jul y Aug Sep Oct Nov Dec Jan Feb Mar

M o n t h

167

P er i odogr am of NO2 2007

0.000000

500.000000

1000.000000

1500.000000

2000.000000

1 2 3 4 5 6

F r e q u e n c y

Densi t y of NO2 2007

0.000000

500.000000

1000.000000

1500.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss P er i odogr am of NO2 2007

-500.000000

0.000000

500.000000

1000.000000

1 2 3 4 5 6

F r e q u e n c y

Cr oss Densi t y of NO2 2007

-200.000000

0.000000

200.000000

400.000000

600.000000

1 2 3 4 5 6

F r e q u e n c y

C r o s s A mp l i t u d e o f N O 2 2 0 0 7

0 . 0 0 0 0 0 0

5 0 0 . 0 0 0 0 0 0

10 0 0 . 0 0 0 0 0 0

1 2 3 4 5 6

F r e q u e n c y

C o h e r n c y o f N O 2 2 0 0 7

0 . 000000

0 . 200000

0 . 4000000 . 600000

0 . 800000

1 . 000000

1 . 200000

1 2 3 4 5 6

F r e q u e n c y

P hase of NO2 2007

-1.000000

-0.500000

0.000000

0.500000

1.000000

1 2 3 4 5 6

F r e q u e n c y

G a i n o f N O 2 2 0 0 7

0.0000001.0000002.0000003.000000

1 2 3 4 5 6

Fr e que nc y

M ont hl y Fl uc t ua t i on of NO2 2 0 0 7

0

50

100

150

Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar

M ont h