SPATIAL AND TEMPORAL VARIATIONS OF AMBIENT AIR QUALITY IN BURDWAN...

SPATIAL AND TEMPORAL VARIATIONS OF AMBIENT AIR QUALITY IN

BURDWAN TOWN, WEST BENGAL, INDIA

Submitted by

SUBRATA CHATTOPADHYAY, M.Sc.

Supervised By

Dr SRIMANTA GUPTA

And

Dr RAJNARAYAN SAHA

THESIS SUBMITTED FOR THE PARTIAL FULFILMENT OF THE

REQUIREMENT OF THE DEGREE OF DOCTOR OF PHILOSOPHY IN

SCIENCE (ENVIRONMENTAL SCIENCE) OF

THE UNIVERSITY OF BURDWAN

DEPARTMENT OF ENVIRONMENTAL SCIENCE

THE UNIVERSITY OF BURDWAN

BURDWAN – 713104

WEST BENGAL

2012

CONTENTS

Pages

ACKNOWLEDGEMENTS

ABSTRACT iii-vi

LIST OF TABLES AND FIGURES vii-xi

LIST OF ABBREVIATIONS xii-xiii

1 INTRODUCTION 1-17

1.1 Air pollution 1

1.2 Urban areas- the focus of air pollution 2

1.3 Meteorological influence in urban air pollution 3

1.4 Air quality problem in South Asia 4

1.5 Effects of air pollution 6

1.6 Major air pollutants 6

1.6.1 Particulate matter pollution 6

1.6.2 Heavy metal pollution 7

1.6.3 Inorganic ion pollution 8

1.6.4 Nitrogen dioxide (NO2) pollution 8

1.6.5 Sulphur dioxide (SO2) pollution 9

1.6.6 Carbon monoxide (CO) pollution 9

1.6.7 Ozone (O3) pollution 10

1.7 GIS- an effective tool in air pollution monitoring 11

1.8 Motivation behind the research work 12

1.9 Objectives of the research work 13

2 LITERATURE REVIEW 18-61

2.1 Literature review on particulate matter [respiratory suspended

particulate matter (RSPM or PM10) and total suspended

particulate matter (TSPM)] and its composition

18

2.1.1 RSPM and TSPM 19

2.1.2 Heavy metals 24

2.1.3 Inorganic ions 28

2.2 Literature review on gaseous pollutant 29

2.2.1 SO2 and NO2 30

2.2.2 Literature review on other gaseous pollutants (CO

and O3)

34

2.3 Literature review on application of principal components

analysis (PCA) and factor analysis for predicting the sources

of air pollution

40

2.4 Literature review on application of air quality index (AQI) in

monitoring of air pollution

41

2.5 Literature review on application of GIS in mapping of air

pollution

45

2.6 Literature review on meteorological effect on air pollution and

modelling of air pollution data

48

3 EXPERIMENTAL METHODOLOGY 62-97

3.1 Description of the study area 62

3.1.1 Geography 62

3.2 History the study area 62

3.2.1 Culture 62

3.3 Burdwan town- at present 63

3.4 Land use / land cover pattern of the study area 63

3.5 The New Burdwan- future scope 63

3.6 Reason behind selection this town as study area 64

3.7 Site selection in the study area 64

3.8 Sampling (6:00 am to next day 6:00 am) and analysis of

gaseous pollutants and particulate matter

66

3.9 National ambient air quality standard (NAAQS) 67

3.10 Principle of operation of APM 460 BL Respirable Dust

Sampler

68

3.11 Brief description of the procedures 68

3.11.1 High volume sampler (HVS) method for RSPM and

TSPM

68

3.11.2 Atomic absorption spectrometric (AAS) method (IS

5182) [Part 22:2004] for heavy metals

70

3.11.3 Methods for water soluble inorganic ions 71

3.11.3.1 Ion selective method for Fluoride 71

3.11.3.2 Estimation of Sodium (Flame

photometric method)

72

3.11.3.3 Estimation of Potassium (Flame

photometric method)

73

3.11.3.4 Estimation of Chloride (Titrimetric

method)

74

3.11.3.5 Estimation of Sulphate (Turbidimetric

method)

74

3.12 Method of sampling for gaseous pollutants 75

3.12.1 Determination of Nitrogen dioxide (NO2) in ambient

air (IS method)

75

3.12.2 Determination of Sulphur dioxide content of the

atmosphere (Tetrachloromercurate absorber or

pararosaniline method or IS method)

76

3.12.3 Ozone monitoring 78

3.12.3.1 GSS technology background 78

3.12.4 CO monitoring 79

3.13 Meteorology and construction of windrose diagram 80

3.14 Statistics 80

3.14.1 Air Quality Index (AQI) 80

3.14.2 Pearson correlation analysis and correlation

coefficient

81

3.14.3 Factor analysis 81

3.15 RS and GIS methodology 83

3.15.1 Supervised classification of study area 83

3.15.2 Digital Elevation Model (DEM) 83

3.15.3 Inverse Distance Interpolation (IDINT) 84

4 RESULTS AND DISCUSSION 98-170

4.1 Spatio-temporal distribution of particulate matter, heavy metal

fraction and inorganic ion in study area

98

4.1.1 Particulate matter status in ambient air in study area 98

4.1.1.1 Respiratory suspended particulate matter

(RSPM or PM10)

98

4.1.1.2 Total suspended particulate matter

(TSPM)

100

4.1.2 Heavy metal concentration in PM10 102

4.1.2.1 Lead (Pb) 102

4.1.2.2 Cadmium (Cd) 104

4.1.2.3 Manganese (Mn) 105

4.1.2.4 Chromium (Cr) 106

4.1.3 Status of inorganic ions in ambient air in study area 107

4.1.3.1 Potassium (K+) 107

4.1.3.2 Sodium (Na+) 108

4.1.3.3 Fluoride (F-) 108

4.1.3.4 Chloride (Cl-) 109

4.1.3.5 Sulphate (SO4

2-) 110

4.2 Spatio-temporal variation of gaseous pollutant in the study

area

111

4.2.1 Sulphur dioxide (SO2) 111

4.2.2 Nitrogen dioxide (NO2) 112

4.2.3 Other gaseous pollutants: Ozone (O3) and Carbon

monoxide (CO)

114

4.2.3.1 Diurnal variation of O3 and CO in March

and April

114

4.2.3.2 Meteorological influence on diurnal

variation of O3 and CO in the study area

116

4.3 Statistical interpretation of analytical parameters 119

4.3.1 Pearson correlations among variables 119

4.3.1.1 Premonsoon 119

4.3.1.2 Postmonsoon 119

4.3.1.3 Winter 120

4.3.2 Factor analysis 121



4.3.2.3 Winter 123

4.3.3 Air Quality Index (AQI) 124



4.3.3.3 Winter 125

4.3.3.4 Percent (%) wise distribution in each

category of AQI

125

4.4 Overall interpretation of spatio-temporal variation of

particulate matter, gaseous pollutants by means of GIS

126

4.4.1 Overall scenario of spatio-temporal variation of

RSPM

126

4.4.2 Overall scenario of spatio-temporal variation of SO2 128

4.4.3 Overall scenario of spatio-temporal variation of NO2 128

4.4.4 Overall scenario of spatio-temporal variation of Pb 129

4.4.5 Overall scenario of spatio-temporal variation of AQI 130

5 SUMMARY AND CONCLUSION 171-180

5.1 Summary of results 171

5.2 Conclusion 174

5.3 Management strategy 178

5.3.1 Pollution management strategies 178

5.3.2 Zoning strategies 179

5.3.3 Command and control approach 179

5.4 Future scope of the work 179

REFERENCES 181-203

ANNEXURES xiv-xxx

ABSTRACT

This research work is intended to serve as an in-depth analysis of spatial and temporal

variation ambient air pollutants patterns in the Burdwan municipality area with the

integration of Geographical Information System (GIS). An average of consecutive

two years of real time database of various criteria pollutants viz. Respiratory

suspended particulate matter (RSPM or PM10), Total suspended particulate matter

(TSPM), heavy metals in PM10 viz. Lead (Pb), Cadmium (Cd), Manganese (Mn),

Chromium (Cr); inorganic ions in PM10 like Potassium (K+), Sodium (Na

+), Fluoride

(F-), Chloride (Cl

-), Sulphate (SO4

2-); gaseous pollutant like Sulphur dioxide (SO2),

Nitrogen dioxide (NO2) and diurnal variation of surface Ozone (O3) and Carbon

monoxide (CO) are taken into consideration for execution of present research work.

Entire monitoring/sampling was done at twenty five monitoring sites covering the

residential, industrial and sensitive zone across the Burdwan municipality area in

three seasons i.e. premonsoon (March to May), postmonsoon (June, July, August,

September) and winter (January and February) in consecutively two years. Sampling

duration was twenty-four hours at each monitoring site and at three seasons except O3

and CO where diurnal variation was taken into account. Respiratory suspended

particulate matter which was also known as PM10 and TSPM were monitored by High

volume sampler (HVS) method using glass fibre filter paper. From these collected

filter papers the water soluble inorganic ions and heavy metals are extracted.

Subsequently, heavy metals are analysed through Atomic absorption spectrometric

(AAS) method. Gaseous pollutants like SO2 and NO2 were collected by bubbling the

sample in specific absorbing reagents put in two impingers and analysed by standard

method. Concentration of O3 and CO is recorded by the portable analyser (model:

aeroQUAL Series200 and PPSMPL gaZguard Tx respectively). Micrometeorology of

each site during monitoring is also taken into consideration in order to find out its role

in dispersion and dilution of pollutants. In this research work micrometeorology such

as wind speed and direction, humidity, temperature and rainfall were also recorded at

each monitoring site during each sampling time. Analytical results show RSPM

concentration ranges from 40.200-323.100 µg/m3, 4.480-277.720 µg/m

3 and 34.260-

363.690 µg/m3 in premonsoon, postmonsoon and winter season respectively with an

average value of 126.824 µg/m3, 86.055 µg/m

3 and

157.823 µg/m

3 respectively. In

general it is observed that the average concentration of PM10 show distinct seasonal

iv

variations with high winter and premonsoon value rather than its postmonsoon value.

The average concentration of TSPM is found as 278.716±191.912 µg/m3,

195.090±149.934 µg/m3 and 334.929±177.431 µg/m

3 in premonsoon, postmonsoon

and winter season respectively. Unlike RSPM, TSPM also shows higher average

concentration in winter followed by premonsoon and postmonsoon. Regarding heavy

metal the average value for Pb, Cd, Mn and Cr in the study area is found 0.160±0.117

µg/m3, 0.173±0.126 µg/m

3, 0.219±0.139 µg/m

3; 0.014±0.019 µg/m

3, 0.011±0.015

µg/m3 and 0.013±0.015 µg/m

3; 0.087±0.062 µg/m

3, 0.208±0.292 µg/m

3, 0.214±0.210

µg/m3; 0.012±0.047 µg/m

3, 0.044±0.209 µg/m

3 and 0.014±0.059 µg/m

3 during

premonsoon, postmonsoon and winter season respectively. Analytical results of water

soluble cations show that average concentrations of K+ and Na

+ are 2.064±1.615

µg/m3, 3.192±7.722 µg/m

3, and 3.210±3.431 µg/m

3; 5.023±3.673 µg/m

3, 4.484±3.617

µg/m3 and 5.754±5.300 µg/m

3 respectively during premonsoon, postmonsoon and

winter season. Whereas concentrations of anions like F-, Cl

- and SO4

2- are

0.232±0.232 µg/m3, 0.340±0.401 µg/m

3 and 0.431±0.633 µg/m

3; 1.663±1.651µg/m

3

,

1.797±1.320 µg/m3 and 1.916±1.935 µg/m

3; 10.734±6.921 µg/m

3, 14.928±20.115

µg/m3 and 14.709±21.131 µg/m

3 in premonsoon, postmonsoon and winter season

respectively. The average concentrations of gaseous pollutants viz. SO2 and NO2 are

10.156±7.411 µg/m3, 7.589±5.340 µg/m

3 and 11.845±7.951 µg/m

3; 97.645±79.034

µg/m3, 95.126±52.355 µg/m

3 and 126.557±83.245 µg/m

3 respectively in premonsoon,

postmonsoon and winter respectively. Overall micro meteorological data for

humidity, temperature, wind speed and rainfall are 29-93%, 18-38.5°C, calm-12

Km/hr and 0-3.8 mm during premonsoon, 35-92%, 11.4-36°C, calm-8.8 Km/hr, 0-8.8

mm in postmonsoon, 25-93%, 8.6-30°C, calm-5.3 Km/hr and no rainfall in winter.

Diurnal variation of O3 and CO along with micrometeorology reflects that in the

month of March the concentration of O3, CO, temperature, humidity and wind speed

ranges from 9.70-33.50 ppb, 0.33-10.25 ppm, 30.7-42.7°C, 27-80.1% and calm-14.1

Km/hr respectively; while in the month of April the variation of O3, CO, temperature,

humidity and wind speed are 3-33 ppb, 0.35-11.95 ppm, 30.6-50.3°C, 26-84.6% and

calm-16.3 Km/hr respectively. From the analytical results it is revealed that in general

some sites always remain above the prescribed standard of RSPM in all the season but

in winter season maximum violence of prescribed standard are occurred in

comparison to the other two seasons. The maximum violence of prescribed standard

v

of TSPM is occurred during premonsoon and winter season. The scenario of Pb is

quite better in the study area as the atmospheric Pb does not exceed the NAAQS

standard anywhere in all selected monitoring sites. The tendency of remaining the Pb

concentration within the permissible level may be due to restricted use of leaded fuel.

Few sites are seen to remain above the standard of Cd. In case of Mn, violence of

prescribed standard is found to occur at many sites but exceptionally high

concentration of Mn is found in traffically congested area. Manganese tricarbinyl

compound which is being used now a day as additive in unleaded petrol to enhance

automobile performance may be possible reason for it. The maximum concentration

of K+ is found in industrial region, mainly dominated by rice mill in all season where

almost a cluster of rice mills are present in the study area. Burning of biomass in this

rice mill region may be the possible source of K+ in the study area. It is also noticed

that in Burdwan Municipality area the average SO4

2- is very high in postmonsoon

season. A possible mechanism of formation of such high concentration of SO4

2- in

postmonsoon is probably due to aqueous phase oxidation of SO2 in cloud droplets.

The concentration of SO2 was comparatively lower in all the seasons than the

prescribed standard of NAAQS in all the monitoring sites. The concentration of NO2

is comparatively high in all the seasons than the prescribed standard of NAAQS in

some of the monitoring sites. Vehicular emission is considered as the principal source

of NO2 in the study area. From the diurnal variation it is observed that the

concentrations of O3 are increased with the decreasing concentration of its precursors

and vice versa. A time lag of 5-7 hour is required for most of these precursor gases to

photochemically produce O3 to its maximum potential. It is also revealed that the

concentration of O3 is also a function of prevailing meteorological conditions.

Multivariate statistical analysis reveals that in all the three seasons both geogenic and

anthropogenic sources viz. soil resuspension, vehicle emission, coal combustion or

biomass burning is identified as major contributor of air pollution in the study area.

Calculated Air quality index (AQI) with surface interpolation technique and its

integration with GIS environment shows that a major portion of the study area

(13.326 sq km to 22.262 sq km) comes under fairly clean category in all the three

seasons whereas clean area covers 0.007 to 2.957 sq km. The moderately polluted

zone covers 0.019 to 11.260 sq km in the study area whereas polluted zones covers 0

to 0.426 sq km.

vi

In view of the alarming level of concentration particulate matter along with heavy

metals such as Cd and Mn and gaseous pollutants such as NO2 proper management

strategies should be taken in collaboration with the local authority.

.

List of Tables

Table no. Title Pages

Table 1.1 Sources and effects of common air pollutants 15

Table 2.1 Summary of major review work in the said research field 52

Table 3.1 Land use/land cover of Burdwan Municipality area 86

Table 3.2 Medical record (2008) of respiratory disease in Burdwan

municipality

87

Table 3.3 Locational details of sampling sites at a glance 88

Table 3.4 Details characteristics about the sampling sites 89

Table 3.5 National Ambient Air Quality Standards 89

Table 3.6 Standards of heavy metals 90

Table 3.7 Primary standards (2008) of CO and O3 90

Table 3.8 National Ambient Air Quality Standards (2009) for CO and

O3

90

Table 3.9 Air quality index Table (Mudri, 1990) 91

Table 3.10 Summary of methodology 92

Table 4.1 Spatial and temporal variation of respiratory suspended

particulate matter (RSPM) in the study area (average of data

obtained in 2008 & 2009)

131

Table 4.2 Spatial and temporal variation of total suspended particulate

matter (TSPM) in the study area (average of data obtained in

2008 & 2009)

132

Table 4.3 Spatial and temporal variation of Pb in the study area

(average of data obtained in 2008 & 2009)

133

Table 4.4 Spatial and temporal variation of Cd in the study area


134

Table 4.5 Spatial and temporal variation of Mn in the study area


135

Table 4.6 Spatial and temporal variation of Cr in the study area


136

Table 4.7 Spatial and temporal variation of K+ in the study area


137

viii

Table 4.8 Spatial and temporal variation of Na+ in the study area


138

Table 4.9 Spatial and temporal variation of F- in the study area


139

Table 4.10 Spatial and temporal variation of Cl- in the study area


140

Table 4.11 Spatial and temporal variation of SO4

2- in the study area


141

Table 4.12 Spatial and temporal variation of SO2 in the study area


142

Table 4.13 Spatial and temporal variation of NO2 in the study area


143

Table 4.14 Zone wise spatial and diurnal variation of O3 and CO in the

study area (a) in the month of March (b) in the month of

April

144

Table 4.15 Spatial and diurnal variation of O3 and CO (site wise) in the

study area

146

Table 4.16 Premonsoon correlation analysis between particulate matter,

gaseous pollutants and meteorological parameter

148

Table 4.17 Postmonsoon correlation analysis between particulate matter,


149

Table 4.18 Winter correlation analysis between particulate matter,


150

Table 4.19 Factor loadings along with its contribution (%) after varimax

rotation in premonsoon

151

Table 4.20 Eigenvalues of factors in premonsoon 152


rotation in postmonsoon

153

Table 4.22 Eigenvalues of factors in postmonsoon 154


rotation in winter

155

Table 4.24 Eigenvalues of factors in winter 156

Table 4.25 Premonsoon AQI status of study area 157

ix

Table 4.26 Postmonsoon AQI status of study area 158

Table 4.27 Winter AQI status of study area 159

Table 4.28 Projected areal coverage of the study area with respect to the

standard of respiratory suspended particulate matter (RSPM)

160


standard of SO2

160


standard of NO2

160


standard of Pb

161

Table 4.32 Projected areal coverage of the study area on basis Air

quality index (AQI)

161

x

List of Figures

Figure no. Title Pages

Figure 1.1 Five years trend of air quality in four mega (metro) cities of

India

17

Figure 3.1 Study area location 94

Figure 3.2 Land use/land cover map of Burdwan Municipality showing

various sampling locations

95

Figure 3.3 Calibration graph for SO4 96

Figure 3.4 Calibration graph for NO2 96

Figure 3.5 Calibration graph for SO2 97

Figure 3.6 Diagram of typical sensor formulation 97

Figure 4.1

(a & b)

Windrose diagram (Premonsoon) [a) 2008 b) 2009] 162

Figure 4.2

(a & b)

Windrose diagram (Postmonsoon) [a) 2008 b) 2009] 162

Figure 4.3

(a & b)

Windrose diagram (Winter) [a) 2008 b) 2009] 163

Figure 4.4 Spatial interpolation of respiratory suspended particulate

matter (RSPM) (Premonsoon)

163


matter (RSPM) (Postmonsoon)

164


matter (RSPM) (Winter)

164

Figure 4.7 Spatial interpolation of SO2 (Premonsoon) 165

Figure 4.8 Spatial interpolation of SO2 (Postmonsoon) 165

Figure 4.9 Spatial interpolation of SO2 (Winter) 166

Figure 4.10 Spatial interpolation of NO2 (Premonsoon) 166

Figure 4.11 Spatial interpolation of NO2 (Postmonsoon) 167

Figure 4.12 Spatial interpolation of NO2 (Winter) 167

xi

Figure 4.13 Spatial interpolation of Pb (Premonsoon) 168

Figure 4.14 Spatial interpolation of Pb (Postmonsoon) 168

Figure 4.15 Spatial interpolation of Pb (Winter) 169

Figure 4.16 Zonation of Air Quality Index (AQI) with the help of inverse

distance interpolation technique (IDINT) ( Premonsoon)

169


distance interpolation technique (IDINT) ( Postmonsoon)

170


distance interpolation technique (IDINT) (Winter)

170

List of abbreviations

AAS Atomic absorption spectrophotometer

APHA American Public Health Association

AQI Air quality index

BDL Below detection limit

Bl Blank

Cd Cadmium

CO Carbon monoxide

Cl Chloride

Conc Concentration

CPCB Central pollution control board

Cr Chromium

Cu Copper

DEM Digital Elevation Model

E East

ESE East- South- East

EU European Union

F Fluoride

GIS Geographical information systems

GT road Grand trunk road

h Hour

hυ Photon (h=constant and υ=frequency)

HVS High volume sampler

IDINT Inverse Distance Interpolation

ISE Ion Selective Electrode

K Potassium

Km/hr Kilometre/hour

km Kilometre

lpm Litre/minute

M Air

Mn Manganese

µg/m3 Micrograms per cubic metre

mg/m3

Milligram per cubic metre

N North

NAAQS National Ambient Air Quality Standards

n.a Not available

NE North- East

NH-2 National highway 2

NNW North -North- West

NW North- West

Na Sodium

Ni Nickel

NO2 Nitrogen dioxide

NRP National research programme

OD Optical density

O3 Ozone

Pb Lead

PM10 Particulate Matter with aerodynamic diameter of 10 micron

PM2.5 Particulate Matter with aerodynamic diameter of 2.5 micron

xiii

PM Particulate matter

ppm Parts per million

RSPM Respiratory suspended particulate matter

RS Remote sensing

S South

SE South- East

SSW South- South-West

SW South- West

SL.No. Serial number

Sm Sample

sq km Square kilometre

sq m Square metre

SO2 Sulphur dioxide

SO4 Sulphate

TSPM Total suspended particulate matter

UN United Nations

UNEP United Nations Environment Programme

UNCED United Nations conference on environment and development

UNCHS United Nations conference on human settlements

W West

WHO World Health Organisation

Zn Zinc

[1]

INTRODUCTION

1.1 Air pollution

lean air has so far been treated as unlimited and free natural resource. But now a

days clean air can no longer be taken for granted. Pollution of the atmosphere

has been an undesirable spin off of human activities presumably since the cavemen

first lit fires. Air pollution is defined as any atmospheric condition in which certain

substances are present in such concentrations and duration that they may produce

harmful effects on man and his environment. The amount of pollutant in the air is

expressed in terms of its mass/volume concentration, usually as micrograms of

pollutant per cubic metre of air (µg/m3). And these concentrations vary widely

depending on the sources of pollution and their distribution, meteorological

conditions and the topographical features in the vicinity.

Common air pollutants include carbon monoxide (CO), nitrogen dioxide

(NO2), sulphur dioxide (SO2), lead (Pb), respiratory suspended particulate matter

(RSPM) and total suspended particulates (TSP) which include dust, smoke, pollen and

other solid particles. Most of these substances occur naturally in low (background)

concentrations, when they are largely harmless; they become pollutants only when

their concentrations are relatively high compared to the background value and begin

to cause adverse effects (Rao,1991).

Some air pollutants may be introduced through natural occurrences such as

volcanic eruptions, wind soil erosion, forest fires, sand storms, dispersion of plant

pollen, etc; however, pollutants are mainly introduced through man-made activities,

C

This section briefly gives the background information regarding the

spatial and temporal variation of ambient air pollution in Burdwan

town, West Bengal, India. Some of the effects of air pollutants on

human health are also mentioned along with their source. The

motivation, as well as specific objectives of this study is also

highlighted.

[2]

INTRODUCTION

particularly industrial manufacturing and motor vehicle operation. These activities are

mainly concentrated in cities and other urban areas, which today are expected to be

holding nearly half the World’s population (UNCHS, 1996).

1.2 Urban areas- the focus of air pollution

This problem of air pollution is accelerated in magnitude with increasing

urbanization. Thus polluted atmosphere has become as a part of urban life.

Urbanization, industrialization and economic growth resulted in a profound

deterioration of urban air quality (Wahid, 2006). As modernization and enhanced

industrial activities led to the increased use of fossil fuels and their derivatives,

particularly in developing countries leading to the emission of particulate as well as

gaseous pollutants into the atmosphere. In urban areas the transportation sector causes

the most pollution, producing CO, Pb and NO2. Industry, power plants and the

burning of solid waste also add to the pollution load.

Cities and urban areas therefore contain the bulk of people that are most

vulnerable to the immediate effects of air pollution. This fact received international

recognition when in 1992, the United Nations Conference on Environment and

Development (UNCED) made specific recommendations in its Agenda 21 (UN, 1992)

with regards to addressing air pollution in cities. One key recommendation was, “…

the establishment of appropriate air quality management capabilities in large cities

and the establishment of adequate environmental monitoring capabilities or

surveillance of environmental quality and the health status of populations”.

Rapid economic growth through urbanisation is causing serious air pollution

related problems in many areas worldwide. The WHO has estimated that 1.4 billion

urban residents in developing countries breathe air in which pollutant concentrations

exceed WHO air quality guidelines (UNEP/WHO, 1992). Urban air pollution

episodes are associated with sudden incidences of high concentrations of pollutants,

which are generally governed by local meteorology, emissions and dispersion

conditions (Mayer, 1999). The major source groups responsible for urban air pollution

are primarily motor traffic and industries. Over a number of years, legislation and

control have led to marked decrease in the air pollution impacts of industries in many

countries. However, there has also been a substantial increase in urban air pollution

[3]

INTRODUCTION

from motor vehicles due to their increased demand to meet transportation needs

(Nagendra and Khare, 2002).

1.3 Meteorological influence in urban air pollution

Meteorology plays a crucial role in air pollution studies. In fact, there is a

strong seasonality in the meteorological factors, which modulates the air quality

levels. The important meteorological variables having influence on the levels of the

pollutants over urban are wind speed and direction, rain amount and duration, air

temperature, and relative humidity. Local meteorology which is often called

micrometeorology has a major role in air pollution. The relation between

micrometeorology and dispersion of air pollutants essentially involves wind in

broader sense. Wind fluctuations over time and space play a crucial role in dispersion

of air pollutants. Wind flow in horizontal direction is a key parameter in transport of

pollutants. A high wind speed always has high dilution capacity. On the contrary low

or mild wind may favours in accumulation of pollutants. Wind direction also largely

influences the pollutants dispersion. Wind direction and wind speed for a given time

period in particular space is known as windrose which helps to understand the

prevailing wind speed to predict the dispersion of pollutant from a point to area

source. Turbulence and atmospheric stability are also very important parameter in

dispersion of air pollutants. The urban surface pattern and temperature or solar

isolation is the key parameter to determine these two. Thermal turbulence and

unstable atmospheric condition always favour the dilution of the emitted air pollutants

whereas mechanical turbulence and stable atmospheric condition do the reverse.

Relative humidity often entertains the formation procedure of some secondary

pollutant whereas wash out effect of rain scavenges the air pollutants from

atmosphere. Thus the analysis of the meteorological observations in the tropical

countries like India showed the maximum solar radiation and the highest atmospheric

temperature was recorded during the summer season (March–May). It was observed

that the velocity of wind was maximum during the months of July followed by August

and September. The predominant wind velocity was observed in the northeast

monsoon season. In the summer months, the observed wind speed was relatively

lower than those in the winter season. The variation of relative humidity showed a

[4]

INTRODUCTION

steep decrease from winter to summer and then a slow increase in the rainy season

and remained steady in winter. So, accurate monitoring and measurement of the

meteorological parameters has always been one of the chief requirements for

understanding the quality of the atmosphere (Seaman, 2003). The chemical reactions

of the pollutants depend on ambient weather conditions and are influenced by short-

wave radiation, air temperature, wind speed, wind direction and relative humidity

(Elminir, 2005). Along with other chemical reactions, dispersion and dilution

processes result in ambient air pollution that shows spatial and temporal variations in

concentrations of different substances. The air quality in cities has been found

correlated with combinations of various meteorological factors. Air quality is affected

not only by emission of pollutants but also by meteorological parameters. The

identification of air pollution sources is an important step in the development of air

quality control strategies. Abatement strategies may significantly improve the air

quality once the sources are identified (Gupta et al., 2004; Wang and Shooter, 2004).

So, temporal variation of air pollution might be taken in account along with the spatial

variations.

1.4 Air quality problem in South Asia

Today, the ambient air in most large Indian cities is severely polluted and this

pollution has a tremendous impact not only on the health of the population but also in

the ecosystem. Industrialisation, the growth in the number of vehicles in urban areas

has lead to a rapid deteoriation of ambient air quality by emitting various kinds of air

pollutants. Urban air pollution has grown in cities like Delhi, Mumbai, and Kolkata,

across the Indian subcontinent in the last decade in an alarming condition (Agarwal et

al., 1999). The World Health Organization ranked Delhi as the fourth-most polluted

mega city of the world (UNEP/WHO, 1992). However, in Indian subcontinent, it is

not just Delhi, but even small and medium towns are deteriorating air quality rapidly

(CPCB, 1995). Out of the 23 mega cities, Delhi is the most polluted followed by

Mumbai, Calcutta, Bangalore, Chennai, Kanpur, Ahmedabad and Nagpur. They have

severe air pollution problems mainly with the average levels of suspended particulate

matter levels much higher than the prescribed standards. Dehradun, located in the

Himalayan foothills in western Uttar Pradesh, now often tops the list of one of the

[5]

INTRODUCTION

most polluted places in urban India (CPCB, 1995). So does Gajroula, a relatively

unknown little town of western Uttar Pradesh, India (CPCB, 1995). Historically, the

level of total suspended particulate (TSP) levels in a number of South Asian cities

have been high (CPCB, 1991; CPCB, 1992; CPCB, 1993; CPCB, 1995). In

comparison, the ambient concentrations of NO2, SO2, CO, and ozone (O3) have been

relatively low, typically not exceeding the WHO health-based guidelines (Aggarwal

et al., 1999). Carbon monoxide (CO), nitrogen dioxide (NO2), and sulphur dioxide

(SO2) can be elevated in megacities but the exceedances above internationally

recognized air quality standards are not of the magnitude observed for particulate

matter (Aggarwal et al., 1999). Large cities in India and Pakistan appear to have very

high concentrations of fine particles (World Bank, 2004). However apart from these

countries, Dhaka in Bangladesh and Kathmandu in Nepal suffer from serious

particulate air pollution, the latter in part because of its topography (being located in a

valley which traps polluted air) (Begum et al., 2004; Carrico et al., 2003). In response

to the emerging scientific evidence that small particles are especially damaging to

health (Dockery et al., 1993; Pope et al., 2002), environmental agencies in advanced

countries like American and European countries began requiring monitoring of

smaller particles, first with a cut-point of 10 µm aerodynamic diameter (PM10) and

more recently with a 2.5 µm aerodynamic diameter (PM2.5). Recently, several large

cities in South Asia have begun monitoring PM10 and in some cases also PM2.5.

Consistent with high ambient TSP levels, the recorded levels of PM10 and PM2.5 have

been found to be elevated (Begum et al., 2004; Carrico et al., 2003; NEERI, 2000;

Khaliquzzaman et al., 1997). In India Central Pollution Control Board (CPCB)

monitors the ambient air quality in various cities on a regular basis. According to

CPCB report in 2008 Chennai had shown all three criteria pollutants viz. RSPM, SO2

and NO2 within the national standards. Other three mega cities such as Mumbai,

Kolkata and Delhi had shown that the annual concentration of SO2 and NO2 are well

within the National Ambient Air Quality Standards (NAAQS) while RSPM indicated

increasing trend in all five consecutive years (Figure 1.1).

[6]

INTRODUCTION

1.5 Effects of air pollution

Air pollution may cause several health problems. It impacts economic

productivity, reduces agricultural productivity, damages property and causes

ecological changes that increase the risk of environmental disaster.

On man, air pollution is now associated with respiratory and eye diseases such

as asthma, lung cancer and conjunctivitis, especially in the young and elderly

(UNEP/WHO, 1992; Patel, 1994). Pb as a pollutant is particularly serious for

children, since relatively low concentrations of lead in the blood may have a

damaging and permanent effect on their mental development (Needleman et al, 1991).

On the environment, air pollution is a major contributor to effects such as acid rain,

which has been responsible for much damage to soil, fish resources and vegetation,

often very far away from the source of the pollutant (Acid Rain 2000, 2001). Air

pollution is also responsible for the effect of smog, which is a reduction in visibility

due to scattering of light by airborne particles. It may also cause offensive odours in

addition to soiling buildings and monuments. However, by far, the most serious long-

term soon threaten the very existence of human life, especially in the coastal and

highland regions. Concern about global warming led to the famous Kyoto Protocol of

1997, through which over 100 countries undertook to reduce their emissions of certain

pollutant gases significantly (NRP, 2001; Brasseur and Pszenny, 2001). The effects

of these pollutants are summarized in Table 1.1. Considering its effects and potential

effects on man and his environment, air pollution is clearly one of the greatest threats

to sustainable development today.

1.6 Major air pollutants

1.6.1 Particulate matter pollution: Particulate pollution, one of the major

environmental health problems, causes approximately three million deaths per year in

the world (WHO, 2001; Borrego et al., 2006). Particulate matter (PM) is a complex

mixture of substances in solid or liquid states in the atmospheric environment (Viana

et al., 2006) and there are quite a few studies on the levels of ionic constituents and

major heavy metals in PM10 that is present in Indian urban areas. A great deal of

attention has focused on particulate matter (PM) pollution due to their severe health

effects, especially fine particles. Several epidemiological studies have indicated a

[7]

INTRODUCTION

strong association between elevated concentrations of inhalable particles (PM10 and

PM2.5) and increased mortality (Peerz and Reyes, 2002; Lin and Lee, 2004; Namdeo

and Bell, 2005). Particulate deposited on skin and in the nasal passage could cause

allergy, skin disease and respiratory diseases. But the most serious health risks are

posed by fine particles which reach the lungs and may accumulate there. This

accumulation of particulate matter in lungs may cause fibrogenesis of lung tissues.

Particulate matter pollution in the atmosphere primarily consists of micron and sub

micron particles from anthropogenic (motor vehicles, biomass and fossil fuel burning)

and natural source (wind blown soils and sea spray) (Cohen, 1998). The relation

between ambient particulate matter and heart disease were recognised in the

nineteenth century when evidence showed that the number of hospital admissions

increased on days that the ambient air concentrations of particulate matter was higher

than the other days (Peters, 2005). Many atmospheric processes including cloud

formation, visibility variation, and solar radiation transfer can be influenced by

atmospheric aeorosols. The gaseous and particulate components of atmospheric

aeorosol have a noticeably role in the deteoriation of air quality (Wu et al., 2002).

In India due to increasing traffic, unplanned urban and industrial development,

growing energy consumption, and the high influx of population to urban areas,

alarming levels of particulate matter are reported in urban atmospheres by different

researchers. The number of urban agglomerations/cities in India with populations over

a million increased from 5 in 1951 to 9 in 1971 and 23 in 1991. The vehicle

population grew from 0.3 million in 1951 to almost 40 million in 1997–1998, more

than a 100-fold increase. Despite the increasing levels of particulate matter,

information on PM10-associated metals in Indian atmosphere is meagre (Mohanraj et

al., 2004).

1.6.2 Heavy metal pollution: There are many investigations on lead (Pb),

cadmium (Cd), manganese (Mn), chromium (Cr ) and other heavy metals in air and

their toxic effects (Onder and Durson, 2006). This may result in a wide variability in

the intake of some metals through food (e.g. seafood), drinking water or air. High

levels of air borne heavy metals as such as Pb, Cd and certain persistent organic

pollutants may also cause neurodevelopment and behavioural defects in children.

Lead (Pb) is a community air pollutant. It is associated with increased blood lead in

[8]

INTRODUCTION

adults. Important symptoms of lead toxicity are anaemia, colic, premature loss of

teeth, changes in bone marrow etc. Heavy metals associated with PM have a definite

influence on the target organ in biological functions affecting the normal

development, growth of body tissues, enzyme activities and their proper functioning

(Fergusson, 1990; Dasilva and Williams, 1991). PM of size less than 10 µm [PM10 or

respirable suspended particulate matter (RSPM)] contains high concentrations of

heavy metals of toxicological interest (Rizzio et al., 1999). About 75 – 90 % of

metals such as copper (Cu), Cd, nickel (Ni), zinc (Zn) and Pb are found in the PM 10

fractions.

1.6.3 Inorganic ion pollution: Aerosols are natural constituents of the earth

atmosphere with stable suspensions representing two or three phase system consisting

of liquid and /or solid particles and a gaseous medium in which the particles are

suspended. Aerosols provide reaction sites for pollutant gases, influence and play a

fundamental role in cloud formation and modify precipitation by functioning

condensation nuclei besides acting as carriers for pollutant transport. Also they have

great influence on meteorology and atmospheric chemistry as well as atmospheric

radiation budget. The characteristics and distribution of atmospheric aerosols are

highly variable, changing spatially, temporally and with altitude and source (Mouli et

al., 2003). Atmospheric aerosols influence many atmospheric processes including

cloud formation, visibility variation and solar radiation transfer (Bodeanie, 1983;

Pusescel et al., 1986) and play a major role in acidification of clouds, rain and fog and

the transport of pollutants from industrial regions to remote and pristine areas

(Swietlicki et al., 1996).

1.6.4 Nitrogen dioxide (NO2) pollution: Atmospheric pollution due to nitrogen

dioxide (NO2) is a subject of grave concern. Generally, oxides of nitrogen (NOx) play

an important role in atmospheric processes on local, regional and global scales.

Unlike many other pollutants, NOx undergoes various complex reactions to generate

several secondary pollutants which are known to be even more harmful than their

precursor. NOx also has several adverse effects on human health and causes diseases

such as pulmonary edema and damages central nervous system, tissue, etc.

Consequently, there is considerable interest in estimating sources of NOx emissions

and its fate in the atmosphere. It is generally accepted that out of total emission of

[9]

INTRODUCTION

NOx in the atmosphere, 70 – 75 % is from vehicular sources in urban cities (Lal and

Patil, 2001). Particulate matters (PM) and nitrogen oxides (NOx) are mostly caused by

diesel vehicles which consist of taxis, minibuses, public buses and goods vehicles

whereas petrol vehicles emit other pollutants harmful to the environment. The

concentration levels of NOx are steadily increasing in urban areas in India. In the city

of Mumbai, annual average ambient concentration in 1978 was reported as 15 µg/m3,

but recent studies have shown levels of 60 µg/ m3. The total amount of NOx emitted

was 28529 metric tons in 1992 of which 19520 metric tons (i.e. 68.4 %) (Larssen et

al., 1994) was from vehicular sources. There has been an alarming increase in the

vehicular growth rate at 7.3 % per annum leading to an increase in ambient NOx

levels.

1.6.5 Sulphur dioxide (SO2) pollution: Sulphur dioxide is one of the toxic gases

emitted during burning of fossil fuel. Goyal and Sidhartha (2002) observed that

monthly mean SO2 concentrations had regular seasonal variations with highest

concentration in winter and lowest in monsoon in Delhi, India. In a study by

Carmichael et al. (2003) involving the measurement of gaseous pollutants at 50

locations in Asia, Africa, South America and Europe, maximum concentration of SO2

was observed in Agra (India), indicative of its major contributions from

anthropogenic emissions like power plants, industrial boilers, heating and cooking. A

long-term study of the impact of SO2 concentration released from the Mathura

refinery on the Taj Mahal (the monument which is adjudged as one of the wonders of

the world) in Agra, India was studied by Goyal and Singh (1990). This study has been

performed because of the suspected toxicity of SO2 that it may react with oxygen in

the presence of water vapour forming sulphuric acid. It has pungent odour and may

cause coughing, suffocation and other respiratory disorder. It also reduces the

capacity to do work. Thus it affects the children, elderly, asthmatics and other

vulnerable populations.

1.6.6 Carbon monoxide (CO) pollution: Carbon monoxide, an odourless and

colourless gas has its major origin in the incomplete combustion of carbonaceous

materials. It is a highly poisonous gas and is generally classified as an asphyxiant. The

chief source of carbon monoxide in the atmosphere is combustion, especially due to

auto mobile exhausts. Carbon monoxide (CO) is formed as a product of incomplete

[10]

INTRODUCTION

combustion of carbonaceous fuels in stationary and mobile sources. High CO

concentrations can cause acute CO intoxication competing with oxygen in binding to

blood haemoglobin. In urban ambient air CO concentrations have been (even under

most adverse conditions) below the concentrations causing acute poisoning, but inside

motor vehicles and indoor environments with indoor sources, such concentrations

have occurred. Epidemiological time-series studies have reported increased relative

risks of daily mortality and morbidity by 0.90 – 4.70 % in association with prevailing

urban air CO concentrations (Touloumi et al., 1994; Burnett et al., 1998).

1.6.7 Ozone (O3) pollution: Ozone (O3) is such type of gas that is found in both

troposphere and stratosphere. When it is present in stratosphere it is acting as

umbrella to UV radiation. But when it is found in troposphere i.e. near to surface it is

considered as pollutant. The existence of O3 is very important since it is involved in

oxidation reactions of troposphere and its tropospheric abundance determines the

oxidizing capacity of the atmosphere. It also plays a key role in biogeochemical

cycles and global climate being a green house gas as it traps radiation at 9.6 µm

emitted by Earth [World Health Organization (WHO), 2005].

Tropospheric O3 is a secondary pollutant produced through a series of

photochemical processes. Its precursors are oxides of nitrogen, CO and Volatile

organic compounds (VOCs) like nonmethane hydrocarbons (NMHCs). Oxides of

nitrogen and CO mainly arise from automobile emissions while non methane

hydrocarbons (NMHCs) are combustion generated. Depending on the Nitrous oxide

(NO) concentrations, O3 may be produced or destroyed during oxidation of CO or no

change occurs. The following reactions occur to give birth to surface ozone (Satsangi

et al., 2004).

i. CO + OH → CO2 + H

H + O2 + M → HO2 + M

HO2 +NO → NO2 + OH

ii. NO2 + hυ NO + O (3

P)

O (3

P) + O2 +M → O3 + M

420 nm

[11]

INTRODUCTION

iii. OH + CH3CH=CH2 + O2 +NO →CH3CHO + HCHO + NO2 +OH → O3

Thus in urban setting O3 is formed from the catalytic reactions of NOx and VOCs

(plus CO) all of which are emitted from vehicles and other combustion sources. O3 is

also the photochemical precursor of OH radicals, which control tropospheric

chemistry.

1.7 GIS- an effective tool in air pollution monitoring

A geographical information system (GIS) is a computer-based tool related to

mapping and analysing spatially distributed phenomena related to the earth. GIS

would be of immense value of for a number of research areas in air field including air

pollution modelling etc. GIS should be viewed as one entity that will be concerned

with dealing and analysing spatial air data. At regional and local scales, studies also

reveal strong linkages among the density and characteristics of human activities, air

quality and various indicators of public health. Since urban areas have higher

population density and more intensive air-polluting activities (such as vehicle traffic,

industries, commercial and domestic activities), they attract more attention than other

places. Finding these relations between air quality and urban activities (so called

urban respiration) has become both a scientific subject and public issue. Improving

our understanding of this complicated phenomenon relies mainly on three aspects: (a)

improved collection technology for measuring spatial and temporal fluctuations of

trace gases at finer grains; (b) sophisticated models which can better explain and

predict air quality based on physical principles and available data; and (c) an

information infrastructure which streamlines the management, interpretation, and

presentation of the data and analyses in order to allow better and broader input,

collaboration, and debate of the models and their policy implications. A series of

extensions can be built by the GIS to adapt its functionality. As examples, the spatial

models of a flat urban area and a street canyon with extensive traffic polluted with

NOx may be constructed. The sources of air pollution are mapped into a few

categories according to the volume of pollution. Their locations and shapes (in case of

the line and area sources) together with the attributes are stored in separate themes.

Geographic information systems (GIS) have been used to make valuable contributions

hυ

[12]

INTRODUCTION

to the understanding and solution of key socioeconomic and environmental problems

such as facility management, public policy, natural resource management, and land

cadastre (Maguire et al., 1991). During the past decade, GIS have also been applied to

the assessment of environmental risk generally and environmental equity specifically.

According to Rejeski (1993) GIS help the risk analysis process moving from its

traditional focus on site-specific problems to a true macro-scale planning and policy

tool so that GIS have the potential in regionalizing the risk analysis process. By

allowing for data integration, spatial analysis and modelling and visualization, GIS

also provide great opportunity for environmental equity analysis (McMaster et al.,

1997). The recent development of remote sensing technology has provided invaluable

biophysical data to be analysed with GIS based socioeconomic data for environmental

applications (Martin and Bracken, 1993; Wilkinson, 1996; Mesev, 2003). There exists

much need for the integrated use of remotely sensed data and GIS data for

environmental equity analysis. This opens up the potential for new forms of analysis.

1.8 Motivation behind the research work

It is a fact that urban air pollution is growing due to increasing vehicle use,

poor and indiscriminate consumption of fossil fuels, industrialisation. However, only

now as the health costs of polluted air are mounting, people are beginning to realise

that clean air is valuable. The health impact of pollution is considerable. Premature

deaths due to respiratory and cardio- vascular diseases and illness like asthma and

bronchitis have increased.

Though several laws are in place to control industrial and vehicular pollution,

compliance has been inadequate. The number of vehicles in India has been steadily

increasing, leading to a concurrent increase in pollution. In the last few years the

number of registered vehicles increased frequently and all vehicles burn petrol or

diesel. Often this fuel is of poor quality due to illegal contamination and engines of

Indian vehicles are not very efficient, leading to increased pollution.

The existing law enforcement and other controls have been unsatisfactory. So,

new policies and strategies that provide polluters economic incentives to clean up are

necessary.

[13]

INTRODUCTION

Burdwan town is a virgin place so far with respect to the air quality

monitoring. This town is developing day by day. The number of registered motor

vehicle in Burdwan is 397550 (Source: District Statistical Officer, Burdwan). So

many projects are proposed to be set up here. So, a systematic monitoring programme

on air quality is solicited for the benefit of the town. Air quality management policies

are usually developed through a series of processes, which include air quality

monitoring, emission inventory preparation and control strategies delineation, and

long-term compliance monitoring (Molina and Molina, 2004). In order to delineate

appropriate air quality management plans, quantification of emissions from different

air pollution sources and their impact on ambient air quality becomes essential. So,

before stepping into any management strategy for reducing air pollution synoptic and

systematic monitoring of ambient air quality is a crying need. So, the following

objectives are aimed.

1.9 Objectives of the research work

Taking the afore-mentioned motivation into consideration, the objectives of this

study are mentioned below.

• To monitor ambient air quality status on the basis of criteria air pollutants viz.

PM10 (RSPM), SPM or TSPM, SO2, NO2, in residential and others, industrial

zone and sensitive zone of Burdwan town.

• To evaluate the heavy metal concentration viz. Pb, Cd, Mn, Cr and water-

soluble inorganic ions (K+, Na

+, F

-, Cl

-, SO4

2-) present in particulate matter

(PM10).

• To monitor secondary pollutant like ground level ozone (O3) and its precursor

like CO along with meteorological parameters having diurnal variation.

• To monitor the micrometeorology and to investigate its influence in dispersion

of pollutants.

• To evaluate air quality status in all the three zones by comparing with National

Ambient Air Quality standards ( 1994) and NAAQS (2009)

• To carry out multivariate statistical analysis of experimental data

[14]

INTRODUCTION

• To calculate the Air Quality Index (AQI) of study area

• To represent the spatio-temporal distribution of pollutants by thematic map

• To make out zonation of high, medium and low air polluted areas of the

Burdwan town by spatial interpolation of AQI

[15]

INTRODUCTION

Table 1.1: Sources and effects of common air pollutants

Pollutant Anthropogenic

Sources

Health Effects Environmental

Effects

Particulate

Matter

Emitted as particles or

formed through

chemical reactions;

burning of wood, diesel

and other fuels;

industrial processes;

agriculture (ploughing,

field burning); unpaved

roads.

Eye, nose and throat

irritation; lung damage;

bronchitis; cancer;

early death.

Source of haze which

reduces visibility.

Ashes, smoke, soot,

and dust can dirty and

discolour structures and

property, including

clothes and furniture.

Lead Combustion of fossil

fuels and leaded

gasoline; paint;

smelters (metal

refineries); battery

manufacturing.

Brain and nervous

system damage

(especially, children),

digestive and other

problems. Some lead-

containing chemicals

cause cancer in

animals.

Harm to wildlife and

livestock.

Mercury Fossil fuel combustion,

waste disposal,

industrial processes

(incineration, smelting,

plants), mining.

Liver, kidney and brain

damage; neurological

and developmental

damage.

Accumulates in food

chain.

Manganese Ferrous and non

ferrous metal casting,

chemical industries,

additive in petrol

Respiratory disease Accumulates in food

chain

Chromium Iron works, rubber

works

Can alter genetic

materials and cause

cancer

Accumulates in food

chain.

Cadmium Tobacco smoke,

combustion of fossil

fuels, fertilisers and

fungicide

Bone damage, affects

liver and kidney

Accumulates in food

chain

[16]

INTRODUCTION

Pollutant Anthropogenic

Sources

Health Effects Environmental

Effects

Nitrogen

dioxides (NO2)

Burning of gasoline,

natural gas, coal, oil.

(Cars are a major

source of NO2.)

Lung damage,

respiratory illnesses,

ozone (smog) effects.

Ozone (smog) effects;

precursor of acid rain

which damages trees,

lakes, and soil; aerosols

can reduce visibility.

Acid rain also causes

buildings, statues, and

monuments to

deteriorate.

Sulphur dioxide

(SO2)

Burning of coal and oil,

especially high-sulphur

coal; industrial

processes (paper

manufacturing, metal

smelting).

Respiratory illness,

breathing problems,

may cause permanent

damage to lungs.

Precursor of acid rain,

which can damage

trees, lakes and soil;

aerosols can reduce

visibility.

Acid rain also causes

buildings, statues, and

monuments to

deteriorate.

Carbon

Monoxide (CO)

Burning of gasoline,

natural gas, coal, oil.

Reduces ability of

blood to bring oxygen

to body cells and

tissues.

Precursor of ground

level ozone

Ozone

(O3)

Secondary pollutant

formed by chemical

reaction of VOCs and

NOx in the presence of

sunlight.

Breathing problems,

reduced lung function,

asthma, irritates eyes,

stuffy nose, reduces

resistance to colds and

infections, premature

aging of lung tissue.

Damages crops, forests,

and other vegetation;

damages rubber, fabric

and other materials;

smog reduces visibility.

Volatile Organic

Compounds

(VOCs)

Fuel combustion,

solvents, paint.

(Cars are a major

source of VOCs.)

Ozone (smog) effects,

cancer and other

serious health

problems.

Ozone (smog) effects,

vegetation damage.

[17]

INTRODUCTION

Figure 1.1: Five years trend of air quality in four mega (metro) cities of India

[18]

LITERATURE REVIEW

2.1 Literature review on particulate matter [respiratory suspended

particulate matter (RSPM or PM10) and total suspended particulate

matter (TSPM)] and its composition

articulate pollution in urban atmosphere has been becoming a matter of

concentration now a day. Different kind of natural as well as anthropogenic

activities are contributing a large amount of particulate matter into ambient air. There

are a number of research works are performed on particulate matter in several cities

like Delhi, Kolkata, Raniganj- Asansol, Dhanbad- Jharia, Lucknow, Mumbai,

Chennai, Visakhapatnam etc. in our nation as well as in abroad like in Taiyuan,

Kathmandu , Guiyang, Dhaka, Pakistan and Italy etc. All these research works reflects

that these particulate matters are comprised of trace amount of heavy metals like Pb,

Cd, Cr, Mn and other inorganic ions like Na+, K

+, F

-, SO4

2-

and Cl-. These secondary

air pollutants are also found to be dependent on the prevailing meteorological

condition. It is noticed that the atmospheric Pb is in decreasing trend now a day due to

phase out of leaded gasoline in cities. Cd and Mn both are originated from mainly

from industrial origin and transportation activities etc. However, a few mentionable

works are as follows.

P

This section takes a closer look at relevant literature of interest for this

particular study. It starts with a description of particulate matter,

followed by trace metals and inorganic ions and gaseous pollutants

like SO2 and NO2. Some relevant studies on other gases like surface

Ozone and CO are also discussed. A brief overview is also given of

these relevant studies that have been conducted at regional as well as

global scale.

[19]

LITERATURE REVIEW

2.1.1 RSPM and TSPM

National level: Balachandran et al. (2000) had examined the concentration of

composition of PM10, the thoracic fraction of the atmospheric particulate matter in

Delhi. PM10 particulates were subdivided into two fractions, coarse (2.1–10 mm) and

fine (2.1 mm). The mean value of PM10 obtained at Daryaganj for four months was

658.45±231.2 µg/m3 which was 1.45 times higher as compared to JNU (454.77±106.2

µg/m3) and 1.1 times higher as compared to Moti Nagar (552.8±225.7 µg/m

3) in

Delhi. All these values were found to be exceeded the national standards (150 µg/m3

for industrial area and 100 µg/m3

for residential area for 24 h) for PM10 for both

industrial and residential area as specified by Central Pollution Control Board of

India.

The monthly mean suspended particulate matter (SPM) concentrations reached

the highest (465.68 µg/m3) in November and the lowest (150.07 µg/m

3) in August.

The high concentration of SPM was obvious in winter due to the presence of low–

level ground-based inversions. However, the high winds and convective atmospheric

conditions in postmonsoon and premonsoon seasons encouraged dispersion of

particulates; on the other hand, the major emission sources of SPM in Delhi were

power stations and industries (Goyal and Sidhartha, 2002).

Road ambient air pollution status along Dhanbad- Jharia road was studied by

Jain and Saxena (2002). The variation in the SPM concentration were found as 351.12

to 411.41 µg/m3 in monsoon and 536.88 to 602.02 µg /m

3 in winter season. According

to Verma et al. (2003) the air was contaminated maximum with both gaseous and

particulate matter in Lucknow. It was observed that RSPM level was higher than the

permissible limit of 140 µg/m3 at all sites and ranged between 150 -945 µg/m

3. The

highest level of RSPM was found at the site with maximum traffic density (6723

vehicles/h) while the least RSPM was found at the site with minimum traffic density

(52 vehicles/h).

Respirable suspended particulate matter (RSPM) and total suspended

particulate matter (TSPM) in Chennai was studied by Pulikesi et al. (2006). It was

found that the TSPM values were exceeded the National Ambient Air Quality

Standards (NAAQS) at Koyambedu, Mandaveli, Taramani and Vallalar Nagar study

[20]

LITERATURE REVIEW

area. Suspended particulate matter (SPM) was also measured by Reddy and Ruj

(2003) in the Raniganj- Asansol area in West Bengal, India. Results of the

investigation indicated that the 95th percentile value of SPM levels exceeded the

limits (200 µg/m3) at different sites and was within the limit of 500 µg/m

3 at one

region. It was also observed that monsoon experienced the lowest SPM levels at the

four monitoring sites, which was because of the wash-out of dust by intermittent

precipitation. It was also observed that in general the SPM levels tended to decrease

with increasing relative humidity. Another important fact was revealed by them that

SPM concentration was higher during the day than during the night. This was mainly

attributed to the hectic industrial, mining and other community activities, as also to

increased vehicular traffic during the day period. SPM values were found to exceed

the prescribed standards in winter at most of the sites and in summer at few sites in

Visakhapatnam (Reddy et al., 2004).

Measurements of the aerosol number concentration and the PM10 mass

concentrations of urban background aerosols in different seasons were performed by

Monkkonen et al. (2004) in New Delhi 2002, including the simultaneous

measurements of NO2, SO2 and CO concentrations. The results indicated an

interesting relationship between the aerosol number and the PM10 mass

concentrations. The number concentration increased with the mass concentration up

to 300 µg/m3. Diurnal mean NO2 concentrations varied from 50 µg/m

3 (2- 6 am) to 79

µg/m3 (6-10 pm) with a maximum value observed in November (170 µg/m

3) and a

minimum in March (6 µg/m3). NO2 concentrations were usually highest in the

evenings (6-10 pm), which could be explained by the traffic peak hour after 6 pm, but

also by the use of natural gas as fuel in cooking ranges.

Kumar and Joseph (2006) had worked on PM2.5 and PM10 to understand the

fine particle pollution in compliance with ambient air quality standards in Mumbai.

The average PM2.5 concentration at ambient and at Kerbsite was 43 and 69 µg/m3.

The correlation coefficients between PM2.5 and PM10 at ambient and at Kerbsite were

0.83 and 0.85 respectively thus indicating that most of the PM2.5 and PM10 were from

similar sources. PM10 levels exceeded the central pollution control board standard

during winter season.

[21]

LITERATURE REVIEW

Gupta et al. (2008) had dealt with air quality monitoring in an urban region of

Kolkata, consisting of residential, commercial and industrial sites having high

population density and pollution. Daily average PM10 concentrations exceeded quite a

number of times the NAAQ standards. Approximately 85 % of the monitored PM10

data at residential area and 70 % at industrial area exceeded NAAQS. The observed

daily average PM10 concentrations were 140.1 µg/m3 and 196.6 µg/m

3, respectively at

residential and industrial sites, while 8 h average concentrations of PM10 at

commercial site were 131.3 µg/m3.

The respirable particulate matter (RPM; PM10) and total suspended particulate

matter (TSP) concentrations in ambient air in Tuticorin, India, were preliminarily

estimated by Sivaramasundaram and Muthusubramanian (2010). Both the RPM and

TSP levels were well below the permissible limits set by the US Environmental

Protection Agency. The RPM concentrations ranged between 20.9 and 198.2 µg/m3,

while the TSP concentrations varied from 51.5 to 333.3 µg/m3 during their study

period.

International level: Clarke et al. (1999) had found that in urban conditions, small

aerosol particles were mostly emitted from combustion processes, i.e. car engines and

industry. Urban aerosols had a higher proportion of vehicular (and possibly industrial)

emissions, which were in very fine size range. The larger particles correspond to the

effects of human activities including road dust raised by vehicular motion, building

activities and industrial emissions. According to Vakeva et al. (1999), Mazzera et al.

(2001), Querol et al. (2001), Viana et al. (2006), Adachi and Tainosho (2004) the

particulates were directly emitted into the atmosphere through natural and manmade

(anthropogenic) processes including transportation, fuel combustion in stationary

sources, industrial processes, land cleaning, wild fires and solid waste disposal. It was

also found that from the particle formation studies, it could be assumed that the

majority of the submicron particles were due to primary emissions from traffic, or at

least particles were formed very close to the sources (car engines) of precursor gases

(Vakeva et al., 1999).

Giri et al. (2006) had measured the ambient particulate matter concentrations

(PM10) at a network of six air monitoring stations in Kathmandu valley during the

[22]

LITERATURE REVIEW

years, 2003 to 2005. The study revealed that particulate concentrations (PM10)

measured were persistently higher at air sampling sites representing roadside areas

compared to the background sites. The highest daily average PM10 mass concentration

(633 µg/m3) for the study period was recorded at Putalisadak air monitoring station in

the year 2005. Within the Kathmandu valley daily 24-h average PM10 ranged from 7

µg/m3

(Matsyagaon in the year 2004 and 2005) to 633 µg/m3 (Putalisadak in the year

2005). The lowest and highest average annual concentration during the study period

was found 47.78 µg/m3 and 199.80 µg/m

3 respectively at Matsyagaon and Putalisadak

air-monitoring sites. It could be assumed that the difference in the observed

concentrations can mostly be attributed to the traffic. Due to the rapid growth of

industrial activities, population and traffic density, people in Kathmandu were facing

serious air pollution problems.

Mulaku and Kariuki (2001) outlined the air quality management capabilities of

developed and developing nations and found that in developing nations, especially

those in Africa, such capabilities were either absent or only rudimentary; the situation

in Kenya was given as an example. They studied to determine the spatial distribution

of TSP in Nairobi, Kenya’s capital city. A map showing the distribution had been

produced, probably the first of its kind for the city, which showed that the levels of

TSP in most of Nairobi were much above the average recommended by the World

Health Organization.

A field study was carried out in central Italy on characterising atmospheric

particulate matter (PM10 and PM2.5) from the point of view of the chemical

composition by Perrino et al. (2007). An evaluation of the sources of PM and an

identification of possible reliable tracers were obtained using a chemical fractionation

procedure in their study. Total concentrations and speciation of metals had been

studied in TSP of Guiyang by Wu et al. (2008) from April 2006 and January 2007 in

PR China. The total average concentrations of five sites were found as 263 and 75.5

µg /m3 for SPM and PM10 by Salam et al. (2008) in greater Dhaka.

Ali and Athar (2008) had investigated the air and noise pollution at selected

sites along three sections of National high way in Pakistan to assess the enormous

impact of transportation system on ambient air quality. Particulate matter (PM10) was

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LITERATURE REVIEW

found very high in all the locations of three sections. By comparing the data with

specified limits of USEPA ambient air quality standards, the concentrations were

found high at all the locations and were in range of 123- 434 µg/m3 against USEPA

ambient air quality standards of limit 150 µg/m3. In all the location particles of soil

were very fine and proportion of clay was higher that could also be the reason of high

value of PM10.

Wang et al. (2008) had worked to define the air quality during the Olympic

Games from August 7 to September 30 in 2007 in Beijing. The results showed that the

average daily concentration of PM10 during observation was 0.19 mg/m3. The

concentration of PM10 was much higher than the values of the standard. Perrino et al.

(2008) had worked on monitoring the inorganic constituents of urban air pollution in

the Lazio region in Central Italy. The results showed a major impact of primary

anthropogenic pollutants on traffic stations and a homogeneous distribution of

secondary pollutants over the regional area. An evaluation of the sources of PM and

an identification of possible reliable tracers were obtained using a chemical

fractionation procedure.

Xie et al. (2009) characterized the ambient particulate pollution, samples of

particulates with aerodynamic diameter less than 10 µm (PM10). Iron-rich particles,

gypsum, cement particles, silicon sulphide particles, ammonium chloride, potassium

sulphate etc were analysed in this research. The majority of the particles were seemed

to originate from coal combustion, which conformed to Taiyuan’s industrial structure.

Coal combustion was the main source of particles in Taiyuan air, as evidenced by the

abundance of the characteristic coal-burning-related particles.

Traffic-related air pollutants were monitored near major roads at 10 sites in

Japan by Naser et al. (2009). Suspended particulate matter (100 % cut-off

aerodynamic diameter at 10 mm), PM2.5 (50 % cut-off aerodynamic diameter at 2.5

mm), and black carbon, from which elemental carbon (EC) content was calculated,

were instantaneously and continuously monitored at four stations at various distances

(about 5, 35, 70 and 150 m) from each of the target roads. They compared the

observed concentrations with concentrations calculated by means of the conventional

Gaussian plume model.

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LITERATURE REVIEW

2.1.2 Heavy metals

National level: Many studies had addressed the problem of pollution by heavy metals

including Cu, Cr, Fe, Ni, Pb, Zn, V, As, Be, Cd, Mn etc. Most of these were short-

term studies of coarse suspended particulates or total suspended particulates (TSP)

(Negi et al., 1996; Khillare et al., 2004; Tripathi et al., 2004). Recently, some studies

had been done on metal composition of PM10 (Balchandran et al., 2000; Karar et al.,

2006; Nair et al., 2006). Sharma and Maloo (2005) studied air quality in Kanpur in

terms of PM10 and PM2.5 and chemical composition in terms of heavy metals in PM10

only. Moreover only in some studies inventory or statistical analysis was carried out

to apportion sources and determined full year seasonal variations (Khillare et al.,

2004; Karar and Gupta, 2006).

The concentrations of major heavy metals such as Pb, Zn, Cd, Ni and Fe

present in the particulate matter were also determined by atomic absorption

spectrophotometer by Balachandran et al. (2000). Chelani et al. (2001) studied the

trend of toxic metals in Mumbai during the period 1993–1998 in ambient air of

Mumbai. They used the classical additive model. Toxic levels of Cd and Cr were

observed to be stationary during this period. Fe, Zn and Ni were found to exhibit

decreasing trend with magnitude of 54.4, 30.0 and 4.6 %/year whereas Pb was

observed to increase 50 %/year. Cd and Mn in the SPM at all the stations were found

below the detection limit of 0.01 µg/m3 along Dhanbad- Jharia road (Jain and Saxena,

2002).

Feng et al. (2003) had explained partly the differences of metals among the

seasons. The levels of suspended particulate matter (SPM) and heavy metals viz. Pb,

Cd, Cr, Ni and Fe were measured by Khillare et al. (2004). The annual average

concentration of SPM in Delhi was found to be 416.34±223 µg/m3. The vertical

profile in polluted areas like city centres and street canyons in Mumbai was monitored

by Tripathi et al. (2004). Thirteen trace metals, namely Ca, Cd, Cr, Cu, Fe, K, Li, Mg,

Mn, Na, Ni, Pb and Zn were estimated in the SPM. The study indicated insignificant

differences in the concentration levels of SPM and trace metals at different heights.

Mohanraj et al. (2004) estimated respirable (RSPM) and non respirable

(NRSPM) fractions of suspended particulate matter along with heavy metals present

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LITERATURE REVIEW

in RSPM in Coimbatore. The mean quantity of heavy metals in RSPM was in the

order; Zn >Cu > Pb >Ni >Cr > Cd. Concentrations of these heavy metals were in the

range of BDL (below detectable level) to 2147 ng/m3 in RSPM. This fact was also

revealed by them that significant positive correlation among metals excepting lead

and copper suggested that they were originated mostly from a common source.

Ambient PM10 was monitored by Karar et al. (2006) in Kolkata during

November 2003 to November 2004. Chromium (Cr), zinc (Zn), lead (Pb), cadmium

(Cd), nickel (Ni), manganese (Mn) and iron (Fe) were the seven toxic trace metals

quantified from the measured PM10 concentrations. The measured toxic trace metals

generally showed inverse relationship with wind speed, relative humidity and

temperature.

Gupta and Kumar (2006) studied the trend of TSP and PM10 in Delhi,

Mumbai, Kolkata and Chennai using t-test adjusted for seasonality, seasonal Kendall

test and intervention analysis for the period 1991–2003. These tests had indicated that

overall PM10 levels in all four metro cities had been decreasing or stationary. There

were limited attempts to study the linear trend of PM10 and toxic metals in India. The

possible reasons for lack of studies in India were one time large investment,

operational cost and the required quality control.

Haritash and Kaushik (2007) had worked on heavy metals in respirable

suspended particulate matter (RSPM) in Hisar. Role of meteorology in concentration

of heavy metals in RSPM were studied by them. Their important findings were that

the concentrations of all the heavy metals were higher in premonsoon (extended

summer) and monsoon followed by postmonsoon, autumn and winter.

Samples of airborne aerosols (PM10 and PM2.5) were collected at an urban and

a rural site of the North central, semi-arid part of India during May 2006 to March

2008 by Kulshrestha et al. (2009) for determining seven trace metals (Pb, Zn, Ni, Fe,

Mn, Cr and Cu) for both sizes. Significant seasonal variations of particulate pollutants

were obtained using the daily average concentration of PM10 and PM2.5 in their study.

Airborne particulate matter (PM10) was collected for a period of 1 year at six

locations in Madurai city by Bhaskar et al. (2010) to analyse PM10 samples for the

estimation of heavy metals and ions in it. The average PM10 concentrations varied

[26]

LITERATURE REVIEW

from 97.2 to 152.5 µg/m3

and gaseous pollutants served as precursors of ionic

particles in the atmospheric environment. Apart from that it was also found that

industrial areas had the highest concentrations of heavy metals such as Fe, Zn and Cr

and also the SO4

2-ions, traffic areas had endured highest concentrations of Cd and the

NO3

− ion.

Ambient concentrations of PM10 were measured in Delhi, Mumbai, Kolkata

and Chennai during 1998–2007 by Gupta et al. (2010). Trend analyses of PM10 and

heavy metals of four mega cities indicated that PM10 and heavy metals showed

decreasing trend. The cause of decreasing trend was explained in this manner that

during the last 10 years, many interventions had been undertaken such as, changes in

fuel quality, better vehicle technologies, improved industrial fuel mix, shifting of

industries outside the city limits, resulting in improvement of urban air quality.

International level: Lum et al. (1982) reported that Cd in urban aerosol was almost

completely in exchangeable form. According to Hlavay et al. (1998) Cd, liberated

during combustion processes, had been shown to occur in elemental and oxide forms

whereas emissions from incineration were predominantly as CdCl2. They also found

that Cr was mostly emitted to the atmosphere from coal combustion, the metal

industry and waste incineration. Hlavay et al. (1998) again revealed that Pb in urban

and industrial areas was emitted by different sources and each of these industries

emitted lead with different form. Emissions from vehicle exhausts had dominated the

contribution of lead to the atmosphere, although smelting operations also contributed

to the atmospheric lead load.

Weiwei et al. (2006) worked on the geochemical characteristics of trace metals

(As, Cr, Co, Cd, Cu, Mn, Ni, Pb, V and Zn) in PM10 as well as their sources and

contributions in Wuhan, the biggest metropolitan in central China. Based on the

results, PM10 in Wuhan was characterized by relatively high levels of As, Cd, Mn, Pb

and Zn compared with other Asian cities. The time series of these elements indicated

that As, Cu and Zn at both sites have similar trends, whereas Pb levels showed

different patterns due to different emission sources. The levels of Cd were similar at

all the sites (9 to 12 ng/m3). The levels of Pb measured at Wuhan (409 and 615 ng/m

3

for the urban and industrial sites) were similar to those reported for Shanghai (515

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LITERATURE REVIEW

ng/m3). The levels of Mn (116 and 227 ng/m

3 at the urban and industrial stations,

respectively) were similar to those reported for Beijing and Shanghai (110–195

ng/m3) higher than those reported for Hong Kong, Tokyo and Taiwan (23–70 ng/m

3).

The levels of Cr (11and 23 ng/m3 at the urban and industrial stations, respectively)

were similar to those reported for Beijing, Shanghai and Taiwan (20–40 ng/m3), but

higher than those reported for Tokyo and Hong Kong (6 ng/m3).

Wu et al. (2008) measured the concentration of heavy metals in China. The

average concentrations were from 14.48 ng/m3 for Cd and 1,161.45 ng/m

3 for Zn. The

concentrations of Cd, Cr, Pb and Zn were significantly higher during winter than

those at other seasons. The concentrations of most elements (Cd, Cr, Pb and Zn) were

significantly higher during winter than those at other seasons. The metal

concentrations in TSP were relatively low during spring and summer seasons. This

kind of seasonal pattern, with lower concentrations of metals in summer, was

supported by the findings of Wong et al. (2003).

Aerosol particulate matters and heavy metal concentrations were measured in

Dhaka (Mahmud et al., 2008). The overall average concentrations of TSP, PM10 and

PM2.5 were 68, 43 and 35 µg/m3, respectively. It was also reported that about 82 %

particles were from fine fraction (PM2.5) and 18 % were from coarse fraction (PM10-

2.5), which indicated mechanical processes were one of the main sources for the

particulate matters in Dhaka. The heavy metal (lead, copper, zinc, and iron)

concentrations were also determined. The average concentration for lead (96 ng/m3)

was lower than the WHO guideline value and also lower than the previous

measurements in Dhaka.

Salam et al. (2008) had found the total average concentrations of As, Cd, Cu,

Fe, Pb, and Zn in PM2.5 was 6.3, 13, 94, 433, 204 and 381 ng/m3 respectively. The Pb

concentration in Dhaka showed a decreasing tendency, presumably due to the ban on

the use of leaded fuel. The overall trace metal concentrations in Dhaka were higher

than those in European (e.g. Spain, Norway) and East Asian (e.g. Taiwan) locations,

but lower than those measured in South east Asian (Kanpur, Delhi, Mumbai, India;

Lahore, Pakistan) cities. Average Cd concentrations ranged from 0.2 to 34.9 ng/m3.

[28]

LITERATURE REVIEW

Kuvarega and Taru (2008) had determined the levels of TSP, PM10 and PM2.5

as well as levels of Pb, Co, Ni and Cd in TSP, PM10 and PM2.5 in Harare. Their major

findings were that the levels of Pb and Ni were generally higher than those of Co and

Cd and this could had been due to high traffic volumes and various industrial

activities in the Workington Industrial Area.

Leilli et al. (2008) assessed the concentrations of total suspended particles

(TSP), particulate matter (PM) with an aerodynamic diameter ≤10 µm (PM10) and air

transmitted particulate trace metals in Tehran. In their study the average

concentrations of heavy metal in TSP were found as Pb: 183.63±147.81 ng /m3; Cr:

13.72±2.40 ng/ m3; and Cd: 6.80±1.97 ng/ m

3 and for PM10 were: 150.36±157.01

ng/m3; Cr: 9.12±2.14 ng/m

3 and Cd: 6.87±2.22 ng/m

3. In 2010 heavy metals (HMs) in

the airborne particulate matter (PM) in Сentral Asia was also monitored by Kulmatov

and Hojamberdiev (2010).

2.1.3 Inorganic ions: Over the last two decades, the water-soluble elements in

atmospheric aerosols were reported more commonly in India (Kulshrestha et al.,

2001; Parashar et al., 2001; Safai et al., 2005; Mouli et al., 2006; Tiwari et al., 2007;

Sharma et al., 2007; Kumar et al., 2008; Reddy et al., 2008(a) etc) and also in Agra

(Kulshrestha et al., 1998; Parmar et al., 2001; Kumar et al., 2003; Gupta et al., 2003;

Kumar et al., 2006, 2007), probably because of the ease of analysis.

Heterogeneous production of sulphate in an urban fog had been investigated

using data collected during the SCAQS program in the Los Angeles area, for the

period of 10-11 December 1987 by Pandis et al. (1992). Significant increase in

sulphate concentration was observed during the afternoon of 11 December. Trajectory

analysis suggested that these high sulphate concentrations were associated with the

arrival at the receptor sites of air parcels that passed through the fog layer the previous

night.

Mouli et al. (2003) collected atmospheric aerosol samples to analyse major

inorganic ions — F-, Cl

-, NO3

-, SO4

2-, Na

+, K

+, NH4

+ and Mg, Ca present in it at

Tirupati. According to them the aerosol ionic composition was found to be dependant

on the aerosol mass, meteorological conditions of the area. They also found that

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LITERATURE REVIEW

composition of the aerosols showed higher concentration of SO4

2- followed by NO3

-,

NH4+

and found to be influenced by local terrestrial sources.

The possibility of chemical transformation of SO2 to SO4 was studied by

Sharma et al. (2003) in the city of Kanpur. It was also concluded that the important

migration pathway in study area for the transformation of SO2 to SO4 appears to be

oxidation of SO2 on the surfaces (of particulate) available in the ambient atmosphere.

It was found that the SO4 levels were considerably high (2.8- 43.6 µg/m3) compared

to levels in cities in the US [(1.9-3.6 µg/m3) Sandberg et al., 1976; (15.7-18.4 µg/m

3)

Altshuller, 1976; (4.0-14.0 µg/m3) Dockery et al., 1993].

The size distributions of different anions and cations viz. SO4

2-, NO3

-, Cl

- and

F-, NH4

+ in ambient air were monitored by Xiu et al. (2004). Han et al. (2005) had

revealed that the concentration of most secondary aerosol components showed a

summer minimum and a winter maximum with higher correlation between them at

Gosan. They also revealed that OC and EC had the highest concentration and good

correlation with ion components, such as K+, Ca

2+ in fall. It means that biomass

burning could significantly influence the ambient fine carbonaceous particulate in fall,

which was primarily long-range transported.

The major water-soluble ions- F-, Cl

-, NO3

-, SO4

2-, Na

+, NH4

+, K

+, Ca and Mg

were analysed by Mouli et al. (2006). They suggested that the ionic composition was

influenced by local terrestrial sources. The presence of SO4 and NO3 may be due to

re-suspension of soil particles (formation by heterogeneous oxidation). Ca, Mg and

Na were mainly soil derived ones.

2.2 Literature review on gaseous pollutant

The gaseous pollutants vary from city to city. As local people’s life style

influence a lot in their emission. It is found that in most of the city SO2 is under

permissible level. Whereas NO2 is somewhere seen to have high concentration as well

as somewhere low concentration. Vehicle emission is the main source of NO2.

Similarly, CO pollution is also dependent vehicle emission. The secondary pollutant

surface O3 is found in different parts of India and abroad. It is found that in most of

the places it is not exceeded its standard but its formation is not only dependent on its

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LITERATURE REVIEW

precursors but also on the prevailing meteorological condition. A few mentionable

works monitoring the gaseous pollutant in ambient air are as follows.

2.2.1 SO2 and NO2

National level: SO2 concentration released from the Mathura refinery on the Taj

Mahal (the monument which is adjudged as one of the wonders of the world) in Agra,

India was studied by Goyal and Singh (1990).

A study on NOx at 19 traffic intersection points within the city of Kolkata was

performed by Mondal et al. (2000). They observed significant seasonal variations

with maximum concentration during winter and minimum during peak monsoon. The

behaviour of oxides of nitrogen (NOx: NO, NO2) was observed by Lal and Patil

(2001) in Mumbai. The monitoring results showed that at larger distance from the

road the level of NO decreased but the concentration of NO2 remained the same

which was very harmful.

Sub-regional and sector level distribution of SO2 and NOx emissions

inventories for India had been estimated for all the 466 Indian districts using base data

for years 1990 and 1995 by Garg et al. (2001). Total SO2 and NOx emissions from

India were 3542 and 2636 Gg, respectively (1990) and 4638 and 3462 Gg (1995)

growing at annual rate of around 5.5 %. The sectoral composition of SO2 emissions

indicated a predominance of electric power generation sector (46 %). Power and

transport sector emissions equally dominated NOx emissions contributing nearly 30 %

each.

Goyal and Sidhartha (2002) had studied to know the concentrations of sulphur

dioxide (SO2) in Delhi. The monthly mean SO2 concentrations were in the range of

16.15- 34.44 µg/m3 and showed regular seasonal variations with the highest

concentrations in winter and lowest in monsoon season. It was observed that high SO2

concentrations were generally associated with the wind blowing from WNW–NW

directions and the high SPM concentrations were usually related to the wind blowing

from W–NW directions. It was noticeable that wind pattern in northern part of India

including Delhi revealed that the wind blew from the western part of India to Bay of

Bengal during most of the time in a year except in monsoon months (June-September)

when the direction of the wind was reverse.

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LITERATURE REVIEW

Reddy and Ruj (2003) measured the concentration of gaseous pollutant in the

Raniganj- Asansol. The 95th percentile values of SO2 levels did not exceed the

reference level at any of the monitoring stations. The 95th percentile value of NOx

was found to be exceeded the limit (80 µg/m3) at some sites but is within the

prescribed limit of 120 µg/m3 at a site. It was observed that the annual average of SO2

values were minimum and almost equal at all the monitoring sites and did not exceed

the reference levels of 80 µg/m3 at any site. The average SO2 levels were relatively

high during winter in comparison with both the summer and the monsoon. The annual

average NOx concentration levels did not cross the reference levels of 80/120 µg/m3 at

any of the four sampling sites. Vehicles were the dominant transportation source of

NOx in this region. The relatively high concentration of NOx at all these sites (annual

average 56.85–60.42 µg/m3) was mainly due to the fact that the two sites (RGC and

BBC) were near to the Grand Trunk Road, one of the busiest National Highways.

In summer and postmonsoon seasons, the concentrations of SO2 and NOx were

within the prescribed limits except at few sites in summer. Winter data showed the

levels of SO2 were exceeding the limits at one residential site in Visakhapatnam

(Reddy et al., 2004). Contribution of pollution from different types of sources in

Jamshedpur, the steel city of India, had been estimated by Bhanarkar et al. (2005). In-

depth zone-wise analysis of the above indicated that for effective air quality

management, industrial source emissions should be given highest priority, followed

by vehicular and domestic sources in Jamshedpur region. The results of the modelling

exercise showed that in the city area, concentration levels of SO2 and NO2 would be

relatively high.

Kumar and Joseph (2006) had also worked on NO2 in Mumbai. Gupta et al.

(2008) showed that concentrations of gaseous compounds were highly dynamic with

significant seasonal variations characterized by high winter and low monsoon levels.

Daily average concentrations of SO2, NO2 and NH3 in the study area were found to be

within the permissible limit of National Ambient Air Quality Standards (NAAQS) as

specified by Central Pollution Control Board, India. The daily average concentrations

of SO2 and NO2 were observed to be 12.3±9.2, and 32.5±14.2, at the residential site,

with 21.3±15.7 and 49.9±9.8 µg/m3 at the industrial site, respectively. The

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LITERATURE REVIEW

corresponding (8 h average) values at the commercial site were 15.5±11.9 and

39.9±17.3 µg/m3.

International level: Valeroso and Monteverde (1992) found that the major air

pollutants were SO2, CO, NO2, HC, Ox and particulate matter. Their experimental

results showed two types of concentrations exhibited by the pollutants depending

upon whether they were photochemically reactive (NOx, SO2, HC and Ox) or non

photochemically reactive and particulate matter). The variation corresponding to non

photochemically reactive pollutants showed two maxima, one in the morning at 8:00

am and secondary in the evening from 8:00-9:00 pm. However, the photochemically

reactive pollutants exhibited only a single maximum at about noontime or early

afternoon. Kato and Akimoto (1992) had estimated SO2 and NOx inventories for 25

Asian countries for some selected years.

Dennekamp et al. (2001) also found that substantial concentrations of NOx

were generated during cooking on gas. The measured concentration of NO2 was

almost 4000 µg/m3after using a gas cooking range consisting of four rings for 75 min

with full power in a laboratory of volume 70 m3. Diurnal mean SO2 concentrations

varied from 9 µg/m3 (2–6 am) to 12 µg/m

3 (6–10 pm) with a maximum value

observed in May (30 µg/m3) and a minimum in November (1.5 µg/m

3). The diurnal

mean CO concentrations varied from 1.8 ppm (10 am–2 pm) to 2.8 ppm (6–10 pm).

El-dars et al. (2004) investigated the ambient sulphur dioxide levels in four

residential locations within the capital region of Egypt. The results indicated that the

measured cumulative ambient SO2 concentrations were in excess of the national and

the international monthly mean exposure limits, irrespective of the type of local

activity. SO2 and PM10 levels were investigated by Turalioglu (2005) in Erzurum

during the periods of 1990-2000, the heating season to assess air pollution level. It

was found that emission values of SO2 and PM had increased dramatically until

1994–1995 winter season; emission values of both pollutants are maximum in 1992–

1993 winter periods. Air quality concentrations of SO2 and PM10 were also found in

parallel with emission values of these pollutants. The decreases in emission of

pollutants were explained in this manner that after 1995, both emissions and air

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LITERATURE REVIEW

quality values of sulphur dioxide and particulate matter were decreased by using high

quality coal instead of low quality lignite.

Salam et al. (2008) also monitored the status of gaseous pollutant in greater

Dhaka. The overall average value of SO2 at five sampling locations in Dhaka was 48

µg/m3, which was very close to the annual average of the World Health

Organization’s (WHO) guideline values for the European Union (WHO 2000: 50

µg/m3). The highest concentration (76.8 µg/m

3) of SO2 was found in the commercial

and heavy traffic areas (BCIC Bhaban, Motijheel), and the lowest concentration (20.7

µg/m3) was found in the semi-urban area (Jahangirnagar University campus, Savar).

The elevated concentration of SO2 in the city centre was probably due to the high

content of sulphur in fossil fuel. The highest concentration of NO2 (40 µg/m3) was

found at Medinava hospital, Dhanmondi site, with medium traffic, whereas the lowest

concentration (5.0 µg/m3) was found at Jahangirnagar University, Savar, a semiurban

location.

The concentrations of SO2 were strongly affected by weather conditions and

varied distinctively with seasons. The concentrations of SO2 were higher in winter

(heating period) because of the burning of high-sulphur coal and lower in summer

than other seasons. In contrast, as a result of industrial pollution controls, in recent

years, the concentration of SO2 during summer in Beijing had been cut down

obviously. The photochemical reaction activity leaded to an obvious decrease of NOx

(NO and NO2) in the daytime (Wang et al., 2008).

According to Ali and Athar (2008) the concentration of nitrogen dioxide was

found in range of 0.02–0.08 ppm in Pakistan. The reason for high concentration of

NO2 at this location could be the presence of large chemical manufacturing plant

adjacent to the road. The nitrogen dioxide concentration at some sampling locations

was higher than USEPA limit of 0.05 ppm, while at some location it was very well

within limit of USEPA ambient air quality standards. Similarly the concentration of

sulphur dioxide was found in the range of 0.02–0.07 ppm and concentrations at all the

sampling locations were within limits of USEPA ambient air quality standards. The

concentration of sulphur dioxide was found highest at a sampling point where it was

in range of 0.05–0.07 ppm due to presence of industrial activity near the road. Traffic-

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LITERATURE REVIEW

related air pollutants like nitrogen oxides (NOx) were monitored near major roads at

10 sites in Japan by Naser et al. (2009).

2.2.2 Literature review on other gaseous pollutants (CO and O3)

National level: Verma et al. (2003) had also worked on O3 being a secondary

pollutant. They found that O3 did not show any linear correlation with the traffic

density. Measurements of the aerosol number concentration and the PM10 mass

concentrations of urban background aerosols in different seasons were performed by

Monkkonen et al. (2004) in New Delhi 2002, including the simultaneous

measurements of NO2, SO2 and CO concentrations.

Surface ozone had been measured at Tranquebar (11° N, 79.9° E), a tropical

rural coastal site on the East coast (the Bey of Bengal) of Southeast India, during the

period from May 1997 to October 2000 by Debaje et al. (2003). The measurements

showed that there existed a significant diurnal cycle of average O3 with a maximum

concentration (33±4ppbv) in the afternoon and average minimum O3 (11±2 ppbv) at

sunrise. O3 also was found to have higher concentration (23±9 ppbv) in May and

lower concentrations (17±7 ppbv) in October at this site.

Satsangi et al. (2004) estimated the seasonal and diurnal variation of surface

ozone with a preliminary analysis of excellence of its critical levels at a semiarid site

of India. They found that monthly average O3 mixing ratios ranged between 8 to 40

ppb with an annual average of 21 ppb. They also found that O3 exposure in their study

were lower than the critical level of O3 and suggested that the level O3 had not any

impact on reduction in crop yields.

Pulikesi et al. (2006) had investigated the concentrations of surface ozone

(O3), oxides of nitrogen (NOx), relative humidity (RH), wind speed (WS) and wind

direction (WD) during the summer of 2005 at five sites in Chennai. The study had

dealt with the characteristics of hourly and daily mean surface O3 under different

climatic conditions, such as temperature, relative humidity, wind speed and wind

direction and other pollutant concentrations. The maximum hourly O3 concentration

reached 53 ppb on 17th May. The ground-level O3 concentration in Chennai varied

between 2 and 53 ppb. The concentration of NOx and O3 were below the prescribed

limits. The mean O3 concentration in all sites had been observed to be higher in the

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LITERATURE REVIEW

wind flow from SSE and S than that of the SSW and WSW. It had also been observed

that surface O3 concentration increased with the increase in temperature and

decreased with increase in relative humidity.

Beig et al. (2007) observed surface ozone (O3) with its precursors namely,

carbon monoxide (CO) and oxides of nitrogen (NOx) simultaneously on diurnal scale

at Pune. According to their findings the maximum ozone reached as high as 85–90

ppbv during February, where as minimum of 10- 15 ppbv was observed during the

monsoon month of August. They revealed that a time lag of 5-7 hour was required for

most of these precursor gases to photochemically produce ozone to its maximum

potential.

Reddy et al. [2008(b)] made an attempt to examine the governing

photochemical processes of surface ozone (O3) formation in rural site. For this

purpose, measurements of surface ozone and selected meteorological parameters had

been monitored at Anantapur, a semi-arid zone in India from January 2002 to

December 2003. The annual average diurnal variation of O3 showed maximum

concentration 46 ppbv at noon and minimum 25 ppbv in the morning. The average

seasonal variation of ozone mixing ratios were observed to be maximum (about 60

ppbv) during summer and minimum (about 22 ppbv) in the monsoon period. The

monthly daytime and night time average surface ozone concentration showed a

maximum (55 ±7 ppbv; 37±7.3 ppbv) in March and minimum (28±3.4 ppbv; 22±2.3

ppbv) in August during the study period. The monthly average high O3 was observed

at noon in March was due to the possible increase in precursor gas concentration by

anthropogenic activity and the influence of meteorological parameters. Mixing ratio

of ozone started increasing at about 7:30 am and it reached a maximum value at about

4:00 pm. The ozone variation was not only controlled by solar radiation, but also by

dynamics. Favourable conditions for photochemical O3 production were high

temperature, high intensity of solar radiation, and sufficiently high concentrations of

NO (Naja and Lal, 2002). Surface temperature was highest (43- 44°C) during March

and April months leading to higher photochemical production. On the other hand,

relative humidity (RH), which was higher during the rainy season, showed negative

correlation with temperature and ozone mixing ratio.

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LITERATURE REVIEW

The surface level measurements of O3, CO, CH4 and light non methane

hydrocarbons (NMHCs) were made at eight different rural sites in the central part of

India during February, 2004 by Lal et al. [2008(a)]. They reported that the average

mixing ratios of O3, which were in the range of 60–90 ppbv, were significantly higher

compared to the typical values reported for urban sites of India. Emission

characteristics of ozone related stress gases at a semi urban site in the Indo-Gangetic

plain was studied by Lal et al. [2008(b)]. In their study elevated levels of O3 were

observed when CO and NOx were in the range of 200-300 ppbv and 20-30 ppbv

respectively.

Carbon monoxide (CO), ozone (O3) and black carbon (BC) aerosol mass

concentrations were analysed from January–December, 2008 over tropical urban

environment of Hyderabad. Higher concentration of BC, CO and ozone was observed

during premonsoon, postmonsoon and winter and lowest concentrations exhibited

during monsoon season (Badarinath et al., 2009).

International level: It was well known fact that O3 forming capacity due to precursor

emissions was highly dependent on the amount of NO2 and HC (Altshuler et al., 1995;

Berastegi et al., 2001; Jiang et al., 1997) and both of these precursors were closely

related to traffic (Mayer, 1999; Cardelino and Chameides, 1995). Pitts and Pitts

(1997) estimated the tropospheric air pollution. A complex VOC-NOx chemistry for

O3 control was discussed in this study. In addition OH, NO3 and O3 were shown to

play a central role in the formation and fate of air borne toxic chemicals, mutagenic

PAH and fine particles.

Oh et al. (1999) worked on the prediction of ozone formation in air pollution.

They intended to test the performance of the ozone formation prediction schemes. The

prediction results of ozone formation were compared to the real data. From the

comparison it could be seen that the prediction scheme based on the parameter

estimation method gave a reasonable accuracy with limited prediction horizon.

Analyses of seasonal and diurnal variations in CO, NO and NO2

concentrations were presented by Shahgedanova et al. (1999). Meteorological

conditions during an intense pollution episode were analysed in the context of the

characteristics of the main sources of pollution. The occurrence of high levels of CO

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LITERATURE REVIEW

concentrations was associated with high pressure systems, surface based inversions

and low wind speeds. High concentrations of NO2 were caused by fumigation of the

surface with pollutants emitted by power plants.

Diem and Comrie (2001) made an attempt to allocate anthropogenic pollutant

emissions over space toward managing ozone pollutions. The two precursors of

ground level ozone viz. VOC and NOx were estimated in this study. The resulting

inventory revealed that on road motor vehicles account for approximately 50 % of

VOC and NOx emissions annually. An on-road motor vehicles and residential wood

combustion were the largest VOC sources in the summer and winter months

respectively.

Bhugwant and Bremaud (2001) had measured ozone and NOx variability along

with Black Carbon and PM10. NOx and PM10 particles were anti-correlated with

ozone, with noticeable ozone destruction during peak hours (mean ~ 6 and 9 ppbv at 7

am and 8 am respectively) when NOx and PM10 concentrations had exhibited

maximum values.

Rizk et al. (2002) had analysed the pesticide impact on air quality especially

surface ozone. In this study it was found that different pesticide’s had potentiality to

ozone formation in troposphere. As for example, organo-phosphorous insecticides

(Dimithocite) were found that its decomposition and degradation in the environment

may be an effective hydrocarbon precursor for surface ozone.

Langmann and Baur (2002) had worked on ozone modelling. In this study, the

impacts of background concentration of O3 on regional scale model results were

analyzed during the summer smog episode in Europe. It was found that depending on

the weather situation, moderately modified assumptions of background O3

concentrations revealed an uncertainty of near surface O3 concentration of 5-15 %.

Throughout the world, several attempts are made to predict the tropospheric

Ozone. Luis et al. (2003) had worked in Mexico City in the year 2003. They

attempted to find the liquefied petroleum gas effect on ozone formation. In this

experimental study of outdoor smog chamber was carried out to determine effects of

liquefied petroleum gas (LPG) on maximum ozone. 60 % additions of commercial

LPG and 60 % propane / 40 % butane mixture of the initial concentration were

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LITERATURE REVIEW

introduced into eight smog chambers containing morning ambient air of Mexico city

metropolitan area (MCMA). The final results showed that by increasing 60 % of the

associated compounds to LPG in the air of MCMA or diminishing 50 % of them, had

not an appreciable influence. The largest effect on ozone formation is determined by

total no methane hydrocarbon (NMHC) contained in the atmosphere, being the

maximum of ozone formed in the smog chambers, on the average it diminished a 55

%.

Stroud et al. (2004) investigated the ozone budget and its dependence on

nitrogen oxides and the production rate of free radicals. Photochemical Box model

was applied to calculate local ozone production and loss rates for the arctic free

troposphere. This model derived that ozone production rates was increased by a factor

of 28 in the 1 - 3 km layer and a factor of 7 in the 3- 6 km layer between February and

May. Gross ozone production rates were calculated to increase linearly with NOx

mixing ratios up to ~ 300 pptv in February and for NOx mixing ratios up to ~ 500 pptv

in May.

Edwards et al. (2004) in USA had investigated O3 exposures and implications

for vegetation in rural areas of central Appalachian Mountains, USA. Response of

vegetation to ozone in these areas was determined using the combination of W126

values (sigmoidally weighted exposure index), the number of hours that average

concentrations≥0.01ppm (N100), and the presence of moderate or more extreme

droughts. In general, W126 and N100 values suggested that negative vegetation

growth responses over most of the 12 year would have been minimal for most sites,

even for those exceeding ozone standards.

The strong spatial and temporal variability of traffic-related air pollution

roadside was monitored by Vardoulakis et al. (2005). It was found that the highest CO

and NOx concentrations during recent years were seen in the region of Paris.

In greater Dhaka CO values varied between industrial, urban and semi-urban

sites. The highest concentration (334 µg/m3) of CO was observed at the industrial site

(Novarties, Tejgaon), whereas the lowest value (42 µg/m3) was found in the semi-

urban area (Jahangirnagar University, Savar). The highest concentration of CO at the

industrial sites was presumably due to the incomplete conversion of fossil fuel during

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LITERATURE REVIEW

different mechanical and industrial processes. The total average concentrations of CO

and O3 were 166.0 and 28 µg/m3, respectively. The total average O3 concentration

was also much lower than the daily maximum values established by WHO 9average

of 100 µg/m3 for an 8-h sample) (Salam et al., 2008).

According to Wang et al. (2008) the peak value of O3 appeared between 11am

and 6 pm local time (maximum appears at 2 pm local time) when the solar radiation

was extremely high in the daytime in Beijing.

In Pakistan Ali and Athar (2008) had found the results of carbon monoxide

were in range of 1.5- 6.1 ppm that was within permissible limit of USEPA Ambient

air quality standards. The comparison of CO at nine locations showed that the highest

values were seen at a site where traffic congestion and volume of traffic was high.

The daily average concentration of CO was 2.25 mg/m3

with the range of

0.72×3.46 mg/m3, which was lower than the National Ambient Air Quality Standard

(Wang et al., 2008).

In Spain the analysis of tropospheric ozone concentration was done by Castell

et al. (2008). They had studied temporal and spatial variations of ozone at different

scales; daily, weekly, seasonally and annually. In this study the link between elevated

ozone concentrations and high values of the recirculation factors (r = 0.7 - 0.9) had

shown the importance of recirculation flows on the local air pollution episodes.

Shipboard measurements of nitrogen dioxides, nitrous acid, nitric acid and

Ozone in eastern Mediterranean sea was done by Vecera et al. (2008). This study

revealed the importance of nitrous and nitric acids for the transport of nitrogen of

marine biota in busy ship lanes. Castellano et al. (2009) identified NOx and O3

episodes to estimate ozone by statistical analysis. Ozone concentration had been

forecasted by time series modelling.

Hosseinibalam et al. (2010) analysed and assessed the ground-level ozone

measured at two stations in Tehran. Ozone as a pollutant showed typical annual,

weekly and diurnal cycles. This study had shown that the ozone level concentrations

were below the WHO guidelines in Tehran during 2000- 2003. It was also found that

the diurnal cycles of O3 were typical for stations that were strongly influenced by

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LITERATURE REVIEW

motor traffic. The research indicated that the ozone level was also affected (positively

or negatively) by meteorological conditions, e.g. relative humidity, solar radiation,

temperature, wind speed and wind direction.

2.3 Literature review on application of principal components

analysis (PCA) and factor analysis for predicting the sources of air

pollution

Factor analysis and principal component analysis have a wider application in

identifying the major source of air pollution. From the literature it is found that these

sources are varied from place to place. However, mainly different anthropogenic

activities like industrial process, transpiration activities, bio mass burning, coal

combustion etc and some natural source like soil resuspension are major contributor

of gaseous as well as particulate pollution. The literatures are as follows.

Principal components analysis (PCA) was used for the investigation of an air

pollutants data base. The data set were made of nearly 400 measurements of 26

gaseous organic compounds and meteorological data. The measurements were carried

out at four different places in Netherlands. PCA was considered as a simple way to

display visually most of the total variation in a few dimensions. It was also considered

very helpful in the identification and recognition of sources and the investigation of

meteorological effects (Verbek et al., 1984).

Balachandran et al. (2000) had applied principle component analysis (PCA) in

their research work in order to identify the major sources of fine and coarse fraction

of PM10. Three major sources were identified, namely vehicular emissions, industrial

emission and soil resuspension for particulate pollution and heavy metal present in it.

Factor analysis was also carried out using the measured elements and had

identified soil and sea salt spray as the main sources for the SPM at all the floors in

Mumbai (Tripathi et al., 2004). Khillare et al. (2004) had applied principal component

analysis (PCA) and found vehicular traffic and industrial emission as the major

contributors of metals in Delhi. According to Kaplunovsky (2005) properties of factor

analysis were a robust method of investigation of pollution source in environmental

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LITERATURE REVIEW

studies. These properties were considered and discussed on the examples of using

methods of factor analysis in air, water and land ecological systems.

Factor analysis was applied by Weiwei et al. (2006) to the datasets focusing on

the apportionment of the mass of selected trace metals. Results indicated that Pb, Cd

and As had a common source (smelting) at both sites, whereas the sources of Ni

varied from coal combustion and steel in Changqian to mineral and traffic in Hankou.

Gupta et al. (2008) had applied principal component analysis. Three factors

were drawn out of the seven variables, which represent 84 % of the variance. The

results showed that local emissions dominated the concentration of SO2, NO2, NH3

and PM10. The major sources of emissions affecting this urban area included mobile

sources along with contributions from industrial sources, coal-fired power plants and

domestic heating.

According to Kulshrestha et al. (2009) principal component analysis revealed

that source of pollution were resuspension of road dust due to vehicular activities,

solid waste incineration and industrial emission at urban site whereas resuspension of

soil dust due to vehicular emission, construction activities and wind blown dust

carrying industrial emission, were common sources at rural site.

2.4 Literature review on application of air quality index (AQI) in

monitoring of air pollution

A few research works is applied to calculate the air quality index value of air

pollutants. The advantage of calculation of AQI is that it takes account the impact of

several air pollutants instead of individual one. So, the total picture with respect to all

the monitored parameters is indicated by specific value corresponding to them.

However, from the literature it is found that there are several methods in calculation

of index value apart from AQI like VST, ORAQI etc. The qualitative value of AQI is

also varied from place to place as well as zone to zone. The literature review

corresponding to AQI is as follows.

AQI was prepared by Verma et al. (2003). They found only one site as “very

clean” where as “fairly clean” and “moderately polluted” category had covered the

entire monitoring sites in Lucknow. Mouli et al. (2004) collected data on air

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LITERATURE REVIEW

pollutants and meteorological variables for the four cities in India including Delhi for

the period July-August 2001 and proposed an air quality index.

Reddy et al. (2004) worked on indices basis on three main criteria pollutants

i.e. suspended particulate matter (SPM), sulphur dioxide (SO2) and oxides of nitrogen

(NOx) in Visakhapatnam. They used the Oak Ridge Air Quality Index (ORAQI)

which was found to be most useful in depicting ambient air quality into single index

number and also to meet the criteria of uniform air quality index. Calculated indices

revealed that, in winter as well as in summer, most of the locations experienced poor

or bad air quality, which was mainly due to higher concentration of SPM and certain

extent of SO2 values. The overview of Visakhapatnam air quality status at different

locations of the city were in descriptive categories viz. excellent, good, fair, poor, bad

or dangerous for winter, summer and postmonsoon seasons.

Kaushik et al. (2006) assessed the ambient air quality status in the fast

growing urban centres of Haryana state, India. The samples were collected for total

suspended particulate matter (TSPM), respirable suspended particulate matter (PM10),

sulphur dioxide (SO2) and oxides of nitrogen (NO2) during different seasons from

eight districts of Haryana during January, 1999 to September, 2000. Air Quality Index

(AQI) prepared for these cities showed that residential, sensitive and commercial

areas were moderately to severely pollute which was a cause of concern for the

residents of these cities. The high levels of TSPM and SO2 especially in winter were

of major health concern because of their synergistic action. The ambient air

concentration of TSPM was observed more than the stipulated standard values at

almost all the sites. Gaseous pollutants (SO2 and NO2) were found below the

permissible limits at all the centres except a few sites. The levels of SO2 during the

year 2000 sampling period were well below the permissible limit at all the sites. The

SO2 concentrations in the ambient air in cities of developed countries had mostly

decreased in the last two or three decades, due to strict emission control, increased use

of low sulphur fuel and industrial restructuring. The residential, commercial and

industrial areas in all the cities exhibited lower concentration whereas sensitive

locations in few cities exhibited higher levels of NO2 compared to the prescribed

limits. The high NO2 levels and its increasing trend were observed in cities with high

vehicular activity. Seasonal comparison of SO2 and NO2 levels showed a higher value

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LITERATURE REVIEW

in winter than observed in premonsoon and postmonsoon seasons. TSPM, PM10, SO2

and NO2 level in this study had been used to calculate AQI. The higher value of an

index referred to a greater level of air pollution and consequently greater health risks.

The AQI revealed that the residential, sensitive and commercial areas were

moderately to severely polluted (AQI 50–125) whereas the industrial areas, on an

average, lie in the range of fairly clean (AQI 25–50) to moderately polluted (AQI 50–

75) except the industrial area in Yamunanagar, which was moderately to heavily

polluted.

According to results of Sing (2006) the air quality monitoring in their study

area showed that the concentration levels of SPM and PM10 exceeded the NAAQS at

most of the sampling stations while concentration levels of NOx and SO2 were found

to be much below the NAAQS. Air Quality Depreciation Index had been applied to

the observed data. This undoubtedly visualised the deterioration in the air quality in

the coal mining areas of Korba coalfields. Depreciation in air quality from the most

desired value of ‘0’ was clearly apparent as the air quality depreciation index

(AQdep) values at all the locations are less than −3.0. The highest AQdep value was

calculated at Jhagrha Village (−3.439) which was selected as reference station.

Flemming et al. (2005) had tried to demarcate air quality classification scheme

for observed ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2) and

particulate matter (PM10) time series in Germany. Six (O3 and NO2) and five (SO2 and

PM10) different regimes were identified by means of hierarchical clustering. The

stability of the clusters in relation to variable scaling and transformation was ensured

by a cross-validation test based on re-sampling. They presented a classification of AQ

data into different regimes for O3, NO2, SO2 and PM10. The O3 classification

distinguished between ‘mountain/costal’, ‘rural’, ‘suburban’,‘urban’,‘urban-

street’and‘street’ regimes. For NO2, a particular ‘mountain’ regime did not exist, but,

in addition to the ‘street’ regime, a ‘severe-traffic’ regime was introduced to allow for

the wide range of high mean concentrations at sites that are strongly influenced by

traffic.

Giri et al. (2006) had categorized the ambient air quality in Kathmandu

according to the MOPE. In Nepal, Ministry of Population and Environment (MOPE)

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LITERATURE REVIEW

had categorized five different types of air quality categories based on levels of PM10.

The categories prescribed were in the range: 0 - 60 µg/m3 as “Good”, 60 - 120 µg/m

3

as “Moderate”, 121 - 350 µg/m3 as “Unhealthy”, 351 - 425 µg/m

3 as “Very

Unhealthy” and >425 µg/m3 as “Hazardous”. In Matsyagaon most of the observations

were under good category (< 60 µg/m3) and the PM10 level only occasionally

exceeded the national standard. Percentage of observations falling under unhealthy

category (121 -350 µg/m3) were high in Thamel (51.5 %), Patan Hospital (77.1 %)

and Putalisadak (77.7 %). In these monitoring stations a few observations even

exceeded very unhealthy category (351-425 µg/m3). It could be assumed that the

difference in the observed concentrations could mostly be attributed to the traffic. Due

to the rapid growth of industrial activities, population and traffic density, people in

Kathmandu were facing serious air pollution problems.

Mohan and Kandya (2007) had used Air Quality Index (AQI) which is an

index for reporting daily air quality in Delhi. They made an attempt to quantify the

changes in the AQI on annual and seasonal (winter, summer, monsoon and

postmonsoon) basis for 9 years. A shift in worst AQI season from winter to summer

was noted. There was also an increase in NO2 concentration at all sites from 2000

onwards.

Ali and Athar (2008) had found in Pakistan that in terms of AQI the carbon

monoxide varied from good to moderate (B to C) at three sections, and at majority of

the location it was B (Good). In terms AQI rating the air quality for NO2 at some

location was from poor to very poor, while at other location it was in the range of

good. Comparison of data with AQI levels the air quality for SO2 was in range of A–E

and at some locations while it was from moderate to poor and very good to good (A to

B) at another locations. Comparison of PM10 with AQI levels the quality of ambient

air was very poor (E) at all the locations. The extent of the increased particulate

matter was dependent upon traffic volume, length of delays and meteorological

conditions (wind direction and speed). The source of PM10 in additions to traffic

exhausts the thickly populated, loss of vegetation and soil characteristics.

In Delhi Jain and Khare (2008) had worked on the air quality management

(AQM) which included monitoring, modelling and control of air emissions to

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LITERATURE REVIEW

eliminate or limit its impact on surrounding environment. The total vulnerable score

(VST) was obtained from the expression of Ghose et al. (2004). On basis of

vulnerable index seven monitoring sites in Delhi (from 1997 to 2004) revealed that

the situation of air pollution with respect to CO, NO2, SO2, RSPM, SPM, Pb was

medium to low category.

Khamdan et al. (2009) used the non-parametric Kruskal–Wallis (KW) test

which showed significant spatial variations and interactions of spatial-temporal

among five mobile monitoring stations for 11 air pollutants (PM10, PM2.5, NO2, NH3,

CO, SO2, H2S, O3, benzene, toluene and p-xylene) in winter and spring in the

Kingdom of Bahrain. KW mean rank for PM10 showed a clear spatial-temporal

variation among site-season groups (χ2 = 202, df = 8, p <0.000). The MW test further

indicated significant difference between spring and winter (U = 35,537.5, Z = −11.7,

p < 0.000). KW mean rank for PM 2.5 exhibited significant spatial variation (χ2 =

184.7, df = 9, p < 0.000) and the MW test resulted significant seasonal variation

between spring and winter (U= 46,523.5, Z = −11.9, p <0.000). The KW mean rank

for Ozone and NH3 showed a clear spatial variation among sites (χ2 = 317.3, df = 8, p

<0.000) and (χ2 = 487.9, df = 8, p < 0.000) respectively. Predominant sources of NH3

were animal husbandry and fertilizer application. KW mean rank for benzene and

toluene showed a clear spatial variation among sites. KW mean rank for H2S showed

a clear spatial variation among sites (χ2 = 101.5, df = 8, p < 0.000). NO2, O3 and SO2

showed exceedance above the standards and guidelines values.

2.5 Literature review on application of GIS in mapping of air

pollution

GIS has become a useful tool in air pollution study. It helps in comprehensive

manner to understand the scenario of air pollution in city by generating a thematic

map. In this way it is evident from the literature that proper management strategies

can be taken with the help of his data base management system. A few mentionable

research works on application of GIS are as follows.

A series of extensions was built by Matejicek (2005) into the GIS to adapt its

functionality. As examples, the spatial models of a flat urban area and a street canyon

with extensive traffic polluted with NOx were constructed. The sources of air

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LITERATURE REVIEW

pollution were mapped into a few categories according to the volume of pollution.

Their locations and shapes (in case of the line and area sources) together with the

attributes were stored in separate themes. Influential sources of pollution, among

others, were represented by NOx (mostly traffic-related air pollution mapped as the

line sources) and SO2 (mostly stationary air pollution registered as the point sources).

Some other works were previously done by Jensen (1998) in mapping human

exposure to traffic air pollution by using GIS. In case of large scale air quality

modelling, more detailed spatial data were needed to include the impact of buildings

and other manmade barriers on the distribution of air pollutants (Janour, 1999).

Pummakarnchana et al. (2005) aimed to build up an easy monitoring system

using low cost portable gas sensing systems ‘solid state gas sensors’ so as to carry out

air pollution monitoring over an extensive area and to be able to report real time air

quality data through wireless internet GIS. Later all this modelling can be referred to

as an air pollution monitoring system.

Weng and Yang (2006) had investigated the relationship of local air pollution

pattern with urban land use and with urban thermal landscape using a GIS approach.

Ambient air quality measurements for sulphur dioxide, nitrogen dioxide, carbon

monoxide, total suspended particles and dust level were obtained for Guangzhou City

in South China between 1981 and 2000. Results showed that the spatial patterns of air

pollutants probed were positively correlated with urban built-up density and with

satellite derived land surface temperature values, particularly with measurements

taken during the summer. Because of the locations of industrial plants, high

population density, clustering of catering industry and low air flushing rates, two

urban localities (namely, Liwan and Yuanchun) became the pollution hubs of SO2,

dust and other pollutants. In case of SO2 the intra-year variation was characterized a

higher level of concentration in the winter months and a lower level in the summer

months. The level of SO2 concentration was largely related to the industrial use of

coal as a major energy source and the use of coal combustion for cooking and heating

in catering industry and households. The spatial pattern of NOx concentration was

highly correlated with the pattern of the city’s transportation network. TSP

concentration was always higher than 0.25 mg/m3, with some “spots” bearing even

higher levels of concentrations. CO concentration was found relatively stable during

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LITERATURE REVIEW

the observed years, ranging from 2.0 to 3.16 mg/m3. The study demonstrated that GIS

was effective in examining the spatial pattern of air pollution and its association with

urban built-up density. Positive correlation between the concentrations of the

pollutants probed and satellite derived land surface temperature (LST) values,

particularly with the summer LST map, indicated that both ambient air quality and

LST were associated with land use. This linkage was apparently complex, given the

fact that it involved multiple variables and varied with the environmental and socio-

economic settings of cities under investigation.

Afshar and Delavar (2007) did a GIS-based air pollution modelling in Tehran.

In this research a prediction model had been developed for air pollution in 2004 using

the data of 2002 and 2003. Additionally by using the method of local contribution to

concentration in canyon streets, the concentration of both CO and NO2 at each month

and for six highways of Tehran and for each vehicle was calculated. The prediction

model was a GIS-based model that had taken geometry of the streets and vehicle

numbers.

Song (2008) used GIS mainly for the preprocessing and postprocessing of data

to be displayed in digital map layers and visualized in 3D scenes. Moreover, for

preprocessing and post processing, deterministic and geostatistical methods (ordinary

kriging, IDW, i.e. inverse distance weight) were used for spatial interpolation,

geoprocessing and raster algebra in multi-criteria evaluation and risk assessment

methods. Digital elevation model (DEM) data processed using 3D spatial

interpolation can be a valuable tool for assessing pollutant distribution in space. It can

be used as screening methods to more closely target key areas and to develop more

detailed measuring and modelling strategies. A detailed study on variations of major

air pollutants and daily air pollution index (API) in Causeway Bay area during the

period of 1999–2003 was reported in this research work based on the statistical

analyses, the diurnal variations of SO2, NO2, CO and RSP levels. The monthly

varying processes of main pollutants presented different patterns during the study

period generally with lower levels in summer and higher levels in other seasons.

Ganguly and Broderick (2010) described an assessment of CO predictions at

an urban street canyon site in Ireland using two Gaussian based dispersion models,

[48]

LITERATURE REVIEW

namely STREET and Operational Street Pollution Model (OSPM). The monitored CO

profile displayed morning and evening peaks coinciding with periods of maximum

travel demand. The evening peak was higher than the morning peak because traffic

congestion on Pearse Street was worst at this time of day, causing average vehicle

velocities to fall and unit emissions to rise.

2.6 Literature review on meteorological effect on air pollution and

modelling of air pollution data

Air pollution is largely dependent on micrometeorology of a particular place.

As the accumulation and dispersion of air pollutants is dependent on the stability of

the ambient air as well as wind speed and direction. From the literature it is found that

high speed wind in unstable atmosphere dilute the concentration of air pollutants vice

versa. Rainfall is also playing a crucial role. Due to scavenging effect of rain lesser

quantity of air pollutants are found particularly in rainy season. On the other hand

calm and stable atmosphere in winter helps in accumulating air pollutants. A few

mentionable research works are as follows.

Wise and Comrie (2001) analysed PM and O3 data over the time period 1990–

2003 for the South western United States in five metropolitan areas. Filter method

was used by them to determine meteorological effects on PM concentrations and to

separate out those effects in order to examine underlying trends.

Patil (2003) had made a methodology to develop a GIS based air pollution

surface model by using different continuous surface generation techniques. In this

study, different interpolation techniques for mapping the variation of point data over

space had been compared. The methods of spatial prediction used for the study were

some local deterministic interpolation methods such as Thiessen Polygons, Inverse

Distance and Thin Plate Splines.

Ambient air quality data was monitored by Chelani and Devotta (2007) during

2000 to 2003 at 10 sites in Delhi. According to them after introduction of CNG the

temporal variation of air quality data showed the significant effect of shift to CNG

(Compressed Natural Gas) in vehicles.

[49]

LITERATURE REVIEW

Elminir (2005) had investigated the dependence of air pollutants on

meteorology with the aim of understanding the governing processes pollutants phase

interaction. Intensive measurements of particulate matter (PM10) and gaseous

materials (e.g. CO, NO2, SO2 and O3) were monitored regularly in 2002 at 14

measurement sites distributed over the whole territory of Great Cairo by the Egyptian

Environmental Affairs Agency to assess the characteristics of air pollutants. The

correlation coefficient between air pollutant concentrations and relative humidity was

not very high. The lower correlation coefficient for all pollutants illustrated the

competition of two mechanisms: atmospheric dispersion (particles are removed from

contaminated surface air by dry deposition and by wet deposition in precipitation) and

aerosolization from surfaces (emissions of aerosol particles by vehicles travelling on

the city’s narrow roads, industry and resuspended soil dust). The association between

primary pollutants and temperature was found to be weak or insignificant. The

temperature changes did not significantly influence SO2 and NO2 concentrations.

Positive correlation was found between ambient temperature and concentration of

NO2 in July and December. Surface ozone (O3) which is an indicator of photoxidation

processes, showed a clear trend of increasing with temperature. Higher O3

concentrations had been found during spring/summer months and lowest

concentrations were found during winter. Non-availability of sufficient solar radiation

and washout of pollutants resulted in near absence of photochemical ozone production

during this period. Further, rainfall and sky cover, which were higher during winter

showed negative correlation with temperature and surface O3 concentration.

Bahattin et al. (2007) investigated the relation between meteorological factors

and pollutants concentrations in Karabuk City. Due to the heavy industrial

foundations and the usage of low quality coal for heating purposes, air pollution had

been the primary environmental problem in Karabuk. According to the results

obtained from the multiple linear regression analysis, there was not a strong

relationship between the meteorological factors and the SO2 and PM concentrations in

urban Karabuk. The major reason for this condition was that the city was surrounded

by mountains and hills. The wind speed also was not high enough to transport the

pollution away from the city atmosphere.

[50]

LITERATURE REVIEW

Akpinar et al. (2008) studied the relationship between monitored air pollutant

concentrations such as SO2 and the total suspended particles (TSP) data and

meteorological factors such as wind speed, temperature, relative humidity, solar

radiation and atmospheric pressure. They investigated all these parameters in the

months of October, November, December, January, February and March during the

period of 3 years (2003, 2004 and 2005) for Elazıg city. It can be seen from the

findings that both SO2 and TSP concentrations were slightly decreased with

increasing wind speed. This situation showed that when wind speed was high,

pollutants diluted by dispersion. The volume and dilution of the polluted air were

mainly controlled by wind speed and its directions. It was seen that wind speed was

more effective on SO2 concentration than that of TSP concentration. Both SO2 and

TSP concentrations were decreased with increasing temperature. TSP concentrations

were increased with increasing relative humidity. According to the results of linear

and non-linear regression analysis, it was found that there was a moderate and weak

level of relation between the air pollutant concentrations and the meteorological

factors in Elazıg city. The maximum SO2 and TSP values were in January-February

months, which were the coldest months of the year in Elazıg city.

Bhaskar et al. (2008) had monitored the PM10 and Pb in Madurai. The

observed PM10 concentrations varied from 88.1 to 226.9 µg/m3 and lead

concentrations ranged between 0.21 to 1.18 µg/m3. The concentrations of the

pollutants were mostly below the Indian air quality standards and were generally

comparable with those concentrations observed in most other Indian urban areas. And

added to that AERMOD model was validated simultaneously by comparing the

predicted levels with the estimated levels of PM10. A dispersion model was used by

Venkatram et al. (2007) to estimate the impact of traffic emissions on air quality

beside highway located in Raleigh, NC.

Ilten and Selici (2008) had worked on relationship between air pollutants

particularly TSP and SO2 and meteorological parameters viz. wind speed,

temperature, relative humidity and pressure. According to their results obtained

through the analysis, higher TSP and SO2 concentrations were strongly related to

colder temperatures, lower wind speed, higher atmospheric pressure and higher

relative humidity.

[51]

LITERATURE REVIEW

Azmi et al. (2010) had monitored the trend and status of air quality and their

correlation with the meteorological factors at different air quality monitoring stations

in the Klang Valley. Five major air pollutants (PM10, CO, SO2, O3 and NO2) were

investigated. The major outcome of their findings revealed that the concentrations of

PM10 and O3 were predominantly related to regional tropical factors, such as the

influence of biomass burning and of ultra violet radiation from sunlight.

A non-steady point source of pollution at a known location in the atmosphere

was applied by Kathirgamanathan et al. (2004). Their goal was to build up an inverse

model capable of finding the release history of atmospheric pollution by using

measured gas concentration data at just one location on the ground and identify the

factors which affect the accuracy of the model predictions.

There were so many studies dealing with annual trends for couple of years

over Delhi such as the work by Aneja et al. (2001). They studied the criteria

pollutants over Delhi and compared the trends with US standards.

An ambient air quality study was undertaken by Zabalza (2007) in two cities

(Pamplona and Alsasua) of the province of Navarre in northern Spain from July 2001

to June 2004. The ambient levels of PM10, ozone, NOx and SO2 were measured. Mean

annual PM10 concentrations in Pamplona and Alsasua reached 30 and 28 µg/m3,

respectively. The mean O3 concentrations were 45 and 55 µg /m3

in Pamplona and

Alsasua, respectively which were above the values reported for the main Spanish

cities. The mean NO and NO2 levels were very similar in both sites (12 and 26 µg/m3,

respectively). Mean SO2 levels were 8 µg/m3

in Pamplona and 5 µg/m3in Alsasua.

Hourly levels of PM10, NO and NO2 showed similar variations with the typically two

coincident maximums during traffic rush hours demonstrating a major anthropogenic

origin of PM10, in spite of the sporadic dust outbreaks.

Sivaramasundaram and Muthusubramanian (2010) found that wind speed was

the most important meteorological factor affecting the concentrations of the pollutants

of interest. Higher concentrations of RSPM were observed in the summer seasons.

The TSPM concentrations were relatively lower in the postmonsoon seasons. This

was due to the fact that in the postmonsoon season, higher relative humidity prevailed

[52]

LITERATURE REVIEW

in the atmosphere which could make the particulate to coagulate and to become

heavier thereby helping in quick settling.

Criteria pollutants (NO2, SO2, CO, PM2.5 and PM10) were monitored by

Biswas et al. (2011) in Delhi. The major findings were that the annual ratios of

CO/NOx were considerably higher than SO2/NOx confirming that vehicular source

emissions were the primary contributors to air pollution in Delhi.

A brief overview of all these studies is given below in Table 2.1.

Table 2.1: Summary of major review work in the said research field

SL.

No.

Author Year Journal / thesis/ book/

conference/ website

Focus area of the study

1 Altshuller 1976 J Air Pollut Control

Assoc

Transformation of sulphur

dioxide to sulphates

2 Sandberg et al. 1976 J Air Pollut Control

Assoc

Sulphate and nitrate

particulates

3 Lum et al. 1982 Environ Technol Lett Heavy metals in urban

particulate matter

4 Verbek et al. 1984 Atmos Environ PCA for organic air

pollutants

5 Goyal and Singh 1990 Atmos Environ Sulphur dioxide

6 Kato and

Akimoto

1992 Atmos Environ SO2 and NOx

7 Pandis et al. 1992 Atmos Environ Heterogeneous sulphate

production in an urban fog

8 Valeroso and

Monteverde

1992 Atmosfera Variations of gaseous and

particulate matter

9 Dockery et al. 1993 New Eng J Med Air pollution and mortality

10 Altshuler et al. 1995 J Air Waste Manage

Assoc

Ambient ozone

11 Cardelino and

Chameides

1995 J Air Waste Manage

Assoc

Model for analysing ozone

precursor relationships

12 Negi et al. 1996 Environ Monit Assess Heavy metals and

particulate matter

[53]

LITERATURE REVIEW

SL.

No.


conference/ website


13 Jiang et al. 1997 Atmos Environ Sensitivity of ozone to

VOC and NOx emissions

14 Pitts and Pitts 1997 Science Ozone and PAH pollution

15 Hlavay et al. 1998 Analyst Heavy metals in

particulate matter

16 Jensen 1998 J Haz Mat GIS application in air

pollution

17 Kulshrestha et al. 1998 J Atmos Chem Chemical characteristics

of aerosols

18 Clarke et al. 1999 Sci Total Environ Particle size and chemical

composition

19 Janour et al. 1999 Analyst Simulation of air pollution

20 Mayer 1999 Atmos Environ Air pollution in cities

21 Oh et al. 1999 Korean J Chem Eng Ozone formation in air

pollution

22 Shahgedanova et

al.

1999 Water Air Soil Pollut CO, NO and NO2

23 Vakeva et al. 1999 Atmos Environ Aerosol particles and

gaseous pollutants

24 Balachandran et

al.

2000 Environ Int PM10, Pb, Cd, Mn, Cr,

PCA

25 Mondal et al. 2000 Atmos Environ

NOx

26 Aneja et al. 2001 Environ Modell

software

Criteria pollutants

27 Berastegi et al. 2001 Atmos Environ Long-term changes of

ozone and traffic

28 Bhugwant and

Brémaud

2001 Atmos Chem Black carbon, PM10, O3

and NOx

29 Chelani et al. 2001 Bull Environ Contam

Toxicol

Toxic metals

[54]

LITERATURE REVIEW

SL.

No.


conference/ website


30 Dennekamp et al. 2001 Occup Environ Med Ultrafine particles and

NOx

31 Diem and Comrie 2001 Environ Manage Ozone pollution

management

32 Garg et al. 2001 Atmos Environ SO2 and NOx

33 Kulshrestha et al. 2001 Curr Sci Water-soluble elements in

atmospheric aerosols

34 Lal and Patil 2001 Environ Monit Assess The behaviour of oxides of

nitrogen

35 Mazzera et al. 2001 Chemosphere PM10 and sulphate aerosol

36 Mulaku and

Kariuki

2001 International

Conference on Spatial

Information for

Sustainable

Development Nairobi,

Kenya 2–5 October

2001

Spatial distribution of TSP

37 Parashar et al. 2001 Environ Monit Assess Precipitation and aerosol

studies

38 Parmar et al. 2001 Atmos Environ Size distribution of

atmospheric aerosol

39 Querol et al. 2001 Atmos Environ PM10

40 Wise and Comrie 2001 Atmos Environ PM and Ozone

41 Goyal and

Sidhartha

2002 Atmos Environ SPM and gaseous

pollutant

42 Jain and Saxena 2002 Environ Monit Assess SPM and gaseous

pollutant

43 Langmann and

Bauer

2002 Atmos Chem Ozone modelling

44 Naja and Lal 2002 J Geophys Res Surface ozone

45 Rizk et al. 2002 Environ Manage Health Pesticide impact on

surface O3

46 Debaje et al. 2003 Atmos Environ Surface O3

[55]

LITERATURE REVIEW

SL.

No.


conference/ website


47 Feng et al. 2003 Sci Total Environ Metallic elements of TSP,

PM2.5 and PM2.5–10

48 Gupta et al. 2003 Atmos Environ Measurement of gaseous

and particulate matter

49 Kumar et al. 2003 J Geophys Res Atmospheric dry

deposition

50 Luis et al. 2003 Atmos Environ Liquefied petroleum gas

effect on Ozone formation

51 Mouli et al. 2003 J Haz Mat Major inorganic ion

composition of

atmospheric aerosols

52 Patil 2003 http://faculty.mu.edu.sa

/public/uploads/133815

5574.363air-58.pdf

GIS based air pollution

surface modelling

53 Reddy and Ruj 2003 Environ Monit Assess SPM and gaseous

pollutant

54 Sharma et al. 2003 Atmos Environ Atmospheric sulphate

under high PM10

concentration

55 Verma et al. 2003 Bull Environ Contam

Toxicol

RSPM and gaseous

pollutant, AQI

56 Wong et al. 2003 Atmos Environ Heavy metals

57 Adachi and

Tainosho

2004 Environ Int Heavy metals

58 Edwards et al. 2004 Environ Monit Assess O3 exposures and

implications for vegetation

59 El-Dars et al. 2004 Environ Monit Assess Sulphur dioxide

60 Ghose et al. 2004 Environ Sci Policy Impacts of vehicular

emissions on urban air

quality

61 Kathirgamanatha

n et al.

2004 Environ Modell Assess Estimation of atmospheric

pollution from a non-

steady point source

[56]

LITERATURE REVIEW

SL.

No.


conference/ website


62 Khillare et al. 2004 Environ Monit Assess SPM and heavy metals,

factor analysis

63 Mohanraj et al. 2004 Arch Environ Contam

Toxicol

RSPM, NRSPM and

heavy metals

64 Monkkonen et al. 2004 Atmos Environ PM10 and gaseous

pollutant

65 Mouli et al. 2004 Int J Environ Pollut Modelling and quality

indexing

66 Reddy et al. 2004 Environ Monit Assess SPM and gaseous

pollutant, ORAQI

67 Satsangi et al. 2004 J Atmos Chem Ozone and exceedance of

its critical levels

68 Stroud et al. 2004 Atmos Chem Ozone budget and its

dependence on oxides of

nitrogen

69 Tripathi et al. 2004 Atmos Environ Vertical distribution of

atmospheric trace metals,

factor analysis

70 Xiu et al. 2004 Atmos Environ Water soluble inorganic

ions in size fractionated

particulate matters

71 Bhanarkar et al 2005 Atmos Environ SO2 and NO2

72 Flemming et al. 2005 Atmos Environ Air quality classification

scheme

73 Han et al. 2005 Environ Monit Assess Seasonal variation of

chemical composition

74 Elminir 2005 Sci Total Environ Dependence of urban air

pollutants on meteorology

75 Kaplunovsky 2005 HAIT J Sci Eng Factor analysis in

environmental studies

76 Matejicek 2005 Adv Geosci GIS application in air

pollution

77 Pummakarnchana

et al.

2005 Sci Technol Adv Mat GIS modelling

[57]

LITERATURE REVIEW

SL.

No.


conference/ website


78 Safai et al. 2005 Aer Air Qual Res Aerosols in two different

winter seasons

79 Sharma and

Maloo

2005 Atmos Environ Metal composition of

PM2.5

80 Turalioglu 2005 Environ Monit Assess Sulphur dioxide and

particulate matter

81 Vardoulakis et al. 2005 Atmos Environ Traffic-related air

pollution

82 Giri et al. 2006 Int J Environ Sci

Technol

PM10, AQI

83 Gupta and Kumar 2006 Atmos Environ Trend in TSP and PM10

84 Karar et al. 2006 Environ Monit Assess Chromium, zinc, lead,

cadmium, nickel,

manganese, and iron in

PM10

85 Karar and Gupta 2006 Atmos Res Seasonal variations and

chemical characterization

of ambient PM10

86 Kumar and

Joseph

2006 Environ Monit Assess PM2.5, PM10 , TSP and

NO2

87 Kumar et al. 2006 Environ Sci Technol Aerosol particle

88 Kaushik et al. 2006 Environ Monit Assess AQI

89 Mouli et al. 2006 Environ Monit Assess Chemical composition of

atmospheric aerosol

90 Nair et al. 2006 Atmos Environ Metal composition of

PM10

91 Pulikesi et al. 2006 J Haz Mat RSPM, TSPM and

gaseous pollutant

92 Singh 2006 Environ Monit Assess SPM ,PM10 and gaseous

pollutant

93 Viana et al. 2006 Chemoshere Chemical characterization

of particulate matter

[58]

LITERATURE REVIEW

SL.

No.


conference/ website


94 Weiwei et al. 2006 Environ Geol PM10, As, Cr, Co, Cd, Cu,

Mn, Ni, Pb, V and Zn,

factor analysis

95 Weng and Yang 2006 Environ Monit Assess GIS application in air

pollution

96 Afshar and

Delavar

2007 Environ Inform Arch A GIS-based air pollution

modelling

97 Bahattin et al. 2007 GU J Sci The relation between

meteorological factors and

pollutants

98 Beig et al. 2007 J Atmos Chem Simultaneous

measurements of O3 and

its precursors

99 Chelani and

Devotta

2007 Environ Monit Assess Air quality

assessment:before and

after CNG

100 Haritash and

Kaushik

2007 Environ Monit Assess Heavy metals in RSPM

101 Kumar et al. 2007 Aer Air Qual Res Characteristics of aerosols

102 Mohan and

Kandya

2007 Environ Monit Assess AQI

103 Perrino et al. 2007 Environ Monit Assess PM10 and PM2.5

104 Sharma et al. 2007 J Atmos Chem Ammonia and PM

105 Tiwari et al. 2007 Atmos Environ Modelling of monsoon

rain chemistry and source

apportionment

106 Venkatram et al. 2007 Atmos Environ Air quality data near

roadways using a

dispersion model

107 Zabalza et al. 2007 Environ Monit Assess Urban atmospheric

pollution

108 Akpinar et al. 2008 Environ Monit Assess Relationship between

meteorological parameters

and air pollutant

[59]

LITERATURE REVIEW

SL.

No.


conference/ website


109 Ali and Athar 2008 Environ Monit Assess PM10 and gaseous

pollutant, AQI

110 Bhaskar et al. 2008 Air Qual Atmos Health AERMOD model of PM10

111 Castell et al. 2008 Environ Monit Assess Analysis of tropospheric

O3

112 Gupta et al. 2008 Atmos Res PM10 and gaseous

pollutant, PCA

113 Ilten and Selici 2008 Environ Monit Assess Impacts of some

meteorological parameters

on air pollution

114 Jain and Khare 2008 Environ Monit Assess Vulnerability analysis

115 Kumar et al. 2008 J Earth Syst Sci Chemical characteristics

of aerosols

116 Kuvarega and

Taru

2008 Environ Monit Assess Heavy metals in TSP,

PM10 and PM2.5

117 Lal et al. 2008(a) J Atmos Chem O3, CO and NMHCs

118 Lal et al. 2008 (b) Atmos Chem Ozone related trace gases

119 Leilli et al. 2008 Air Quality Atmos

Health

TSP and heavy metal

120 Mahmud et al. 2008 Pak J Anal Environ

Chem

TSP, PM10 and

PM2.5,heavy metals like

lead, copper, zinc and iron

121 Perrino et al. 2008 Environ Monit Assess Particulate matter

122 Reddy et al. 2008(a) J Earth Syst Sci Chemical characteristics

of aerosols

123 Reddy et al. 2008(b) Atmos Chem Surface ozone

124 Salam et al. 2008 Air Qual Atmos Health SPM and PM10, heavy

metal and gaseous

pollutant

125 Song 2008 Remote Sensing and

Spatial Information

Sciences

GIS application in air

pollution

[60]

LITERATURE REVIEW

SL.

No.


conference/ website


126 Vecera et al. 2008 Water Air Soil Pollut Shipboard measurements

of nitrogen dioxide,

nitrous Acid, nitric acid

and O3

127 Wang et al. 2008 Air Qual Atmos Health PM10 and gaseous

pollutant

128 Wu et al. 2008 Bull Environ Contam

Toxicol

TSP, Cd, Cr, Pb and Zn

129 Badarinath et al. 2009 J Atmos Chem Carbon monoxide (CO),

ozone (O3) and black

carbon (BC)

130 Castellano et al. 2009 Water Air Soil Pollut Identification of NOx and

O3

131 Khamdan et al. 2009 Environ Monit Assess Kruskal–Wallis (KW) test

for 11 air pollutants

132 Kulshrestha et al. 2009 Sci Total Environ trace metals in PM10 and

PM2.5, PCA

133 Naser et al. 2009 Atmos Environ NOx, PM2.5 and EC

134 Xie et al. 2009 Air Qual Atmos Health PM10

135 Azmi et al. 2010 Air Qual Atmos Health Trend and status of air

quality

136 Bhaskar et al. 2010 Environ Monit Assess Ionic and heavy metal

composition of PM10

137 Ganguly and

Broderick

2010 Air Qual Atmos Health Estimation of CO

concentrations

138 Gupta et al. 2010 J Haz Mat Trend analyses of PM10

and heavy metals

139 Kulmatov and

Hojamberdiev

2010 Air Qual Atmos Health Heavy metals in PM

140 Hosseinibalam et

al.

2009 Environ Monit Assess Surface O3

[61]

LITERATURE REVIEW

SL.

No.


conference/ website


141 Sivaramasundara

m and

Muthusubramani

an

2010 Air Qual Atmos Health PM10 and TSP

142 Biswas et al. 2011 Atmos Clim Sci Criteria pollutants (NO2,

SO2, CO, PM2.5 and PM10)

[62]

EXPERIMENTAL METHODOLOGY

3.1 Description of the study area

ardhaman the alternative name of Burdwan has been a district capital since the

time of Mughals. Later on it became a district headquarters of British India.

Burdwan is an alternative name for the city, which remains in use since the British

period. The origin of this name dates back to the VI century BCE and is ascribed to

Vardhaman Swami or Mahavira (599-527 BCE), the twenty-fourth Jain Tirthankar.

So, this place is of immense importance for a long era.

3.1.1 Geography: Burdwan is located at 23°.12′ - 23°.15′’N, 87°.48′ - 87°.53′E. It

has an average elevation of 40 metres (131 ft). The city is situated 1100 km from New

Delhi and a little less than 100 km north-west of Kolkata on the Grand Trunk Road

(NH-2) and Eastern Railway. The chief rivers are the Damodar and Banka.

3.2 History the study area

During period of Jahangir this place was named Badh-e-dewan (district

headquarters). The town owes its historical importance to being the headquarters of

the Maharajas of Burdwan. Sadhak Kamalakanta as composer of devotional songs and

Kashiram Das as a poet and translator of the great Mahabharata were possibly the best

products of such an endeavour. The great rebellious poet Kazi Nazrul Islam and Kala-

azar-famed U. N. Brahmachari came of as the relatively recent illustrious sons of this

soil. The town became an important centre of North-Indian classical music as well.

3.2.1 Culture: Burdwan has a multi-cultural heritage. The deuls (temples of rekha

type) found here are reminiscent of Bengali Hindu architecture. The old temples bear

B

In this section, a description of the study area and the sampling sites

are presented. The sampling procedures, followed by a brief

description of the air quality standards and GIS methods are given.

The analytical techniques along with the statistical techniques are also

discussed.

[63]


signs of Hinduism, mostly belonging to the Sakta and Vaishnava followers.

Archeological evidences suggest that this region, forming a major part of radh Bengal,

could be traced even back to 4000 BCE (Source: Wikipedia).

3.3 Burdwan town- at present

Burdwan town is the heart of the Burdwan district. This town is of immense

importance as it is rich in its culture and facilities. The chief educational institutions

are The University of Burdwan, Burdwan Raj College, MUC Womens’ college,

Vivekananda college along with two technical institutes etc. The district hospital i.e.

Burdwan medical college and hospital is also situated in this town. So many

commercial markets and shopping malls are found here. This town is also embedded

with so many modern developments like cinema hall, super markets etc. The largest

health city of Asia is also proposed to be set here. So, a rapid urbanization engulfs this

town with an increasing number of populations opting a better life style/facility day

by day. Now this town is populated by 327937 according to Literacy survey 2008.

3.4 Land use / land cover pattern of the study area

Burdwan Municipality area is divided into 35 no of wards having a total area

of 25.29 square kilometres (sq km). The main land use of the municipality is

dominated by residential area (36.88 %). Though it is a municipality area, but

agriculture is still practiced in few wards. However crop land became dominated land

use in those wards. Rice mill is the major industry of this municipality. Overall 2.51

% and 2.59 % area of Burdwan municipality (Table 3.1) is covered by rice mill

cluster and mixed urban with mill areas. Overall 2.95 % and 2.70 % area of this

municipality is covered by commercial areas and mixed urban with commercial areas

respectively which seems that more commercialization is necessary for future

development related to residential area (Gupta and Roy, 2012).

3.5 The New Burdwan- future scope

Burdwan town (Figure 3.1), the heart of the district is also given much

importance now a day. The Govt. of West Bengal is trying to bring in many new

projects to facilitate the growth of Burdwan Township. Two large developments on a

[64]


Public Private Partnership (ppp) are coming up on the NH-2 connecting Kolkata and

Delhi, on which Burdwan town lies. One of these is a Bus Terminus, with retail and

other hospitality services. The other is a Mini Township at Goda, Burdwan. Also on

the highway, this 250+ acre mini township is being developed by Bengal Shrachi

Housing Development Ltd. It will revolutionise the way people see residential units in

Burdwan. The Burdwan Development Authority is also playing a big role in these

PPP projects (Source: Wikipedia).

3.6 Reason behind selection this town as study area

Not only being a busy town but also being nearest to Durgapur, Burdwan was

given importance keeping in mind that Durgapur is not far away from this town.

Durgapur is one of the most polluted cites in India and air pollutants had the capacity

to travel a long distance. Apart from several residential projects a major public private

project “the largest heath city of Asia” was also proposed here. In the study area i.e. in

Burdwan municipality air quality related work is not reported so far. Not only that no

systematic air quality-monitoring programme with GIS approach was reported from

this town. From this point of view the quality of ambient air of this town deserved a

systematic as well as scientific investigations so that proper strategies could be taken

to mitigate in case of any pollution was found. Not only that but also the medical

report (Table 3.2), collected from the Govt. Hospital of this town, reflected that health

problem due to air pollution is increasing day by day. Hence the quality of ambient air

of this town deserved a systematic as well as scientific investigation so that proper

strategies could be taken to mitigate this problem in the grass root level.

3.7 Site selection in the study area

Altogether 25 locations encompassing all the three areas were selected

randomly for air quality monitoring (Figure 3.2). Details of the sampling locations are

represented in Table 3.3 and 3.4. Mainly the schools, colleges, university and children

parks are enlisted as sensitive zones whereas the places beside road and others

residential areas are considered as residential zones and others as per Central

Pollution Control Board (CPCB). The place where the industries (mainly rice mills in

[65]


the study area) are aggregated is considered here as industrial zone. The locational

details are given below.

(i) Residential and other zone: There are many monitoring sites in residential

and other zone. Among them the monitoring sites of Goda, Tarabag, Bhatchala1,

Bhatchala2, Jagatber, Jilpibagan, and Bermore are surrounded by residential

complexes. But there are rice mills beside the site Bhatchala1 and Batchala 2. The

monitoring sites viz. Tinkonia bus stand, Curzongate and Golapbagmore are beside

the Grand trunk (GT) road. The residential and other zone included the places listed

as follows 1) Goda (R1) 2) Tarabag (R2) 3) Jagatber (R3) 4) Jilpibagan (R4) 5)

Bermore (R5) 6) Bhangakuti (R6) 7) Policeline1(R7) 8) Ichlabad (R8) 9)

Bhatchala1(R9) 10) Bhatchala2 (R10) 11) Tinkonia (District bus stand) (R11) 12)

Curzon gate (R12) 13) Barabazar( R13) 14) Kanchannagar (R14)15) Golapbagmore

(R15)

(ii) Industrial zone: The industrial zone of Burdwan is dominated by many

rice mills and small scale industries. The site Alamganj1 is surrounded by mainly rice

mills. But the site Alamganj2 is quite away from the rice mills. The site Tejganj2 is

situated in industrial zone. It is just beside the NH-2 whereas the site Tejganj1 is

situated in other side of NH-2. The Industrial zone included the places listed as

follows 1) Tejganj1 (I1) 2) Tejganj2 (I2) 3) Alamganj1 (I3) 4) Alamganj2 (I4)

(iii) Sensitive zone: Mainly educational institutes like school, colleges,

children park and places near by hospitals along with areas which are ecologically

sensitive are taken as sensitive zone. The educational institute is given much

importance as these places are related with the welfare of children and students.

Department of Environmental Science in The University of Burdwan (S6) and

Vidyathri School (S2) are situated just beside the Grand trunk or G.T. Road. Other

site like Kalpataru children park (S3) is beside a rice mill and Alamganj primary

school (S4) is surrounded by a group of ricemills i.e. it is situated in the ricemill

region of the town. The other sensitive sites are Raj college (S1) which is just beside

the Burdwan Medical college and hospitals and Sadhanpur Polytechnique college (S5)

which is surrounded by a main road (Kalna road) of the town and agricultural field.

[66]


3.8 Sampling (6:00 am to next day 6:00 am) and analysis of

gaseous pollutants and particulate matter

Sampling was carried out consecutively during two years viz. 2008 and 2009.

In three seasons the sampling was done for twenty-four (24) hours at each site in each

year. The seasonal classification was followed as per specification laid by Indian

meteorological department (Murty, 2004). March, April and May months were

considered as premonsoon season and June, July, August and September were

considered as postmonsoon season and Janbuary and February were considered as

winter. Air quality parameter such as respirable suspended particulate matter (RSPM)

which is also known as PM10 was monitored by using Respirable Dust Sampler

(Envirotech APM 460BL) following the standard procedure by IS: 5182 [Part

4):1999]. Glass fiber filter paper, popularly known as GF/A filter paper was used for

determination of RSPM and the flow rate was kept at 1-1.5 m3/min. Then from this

filter paper collected after sampling, heavy metals and the water soluble inorganic

ions are extracted as per the standard procedure written in the section 3.11.2 and

3.11.3. The model Envirotech APM 460BL had a cyclone separator, which separated

the coarser particulate matter larger than 10 µm from air stream (drawn into the

sampler) before filtering on GF/A filter paper. Air was also allowed to pass through

two impingers having specific 50 ml absorbing reagent for SO2 and NO2. The average

flow rate through the impingers was taken for the calculation of gaseous samples.

After the sampling the impinger samples were kept in iceboxes and transferred to a

freeze until the analysis was done. Potassium tetrachloromercurate and sodium

hydroxide were used as absorbing reagents for SO2 and NO2 respectively to arrest SO2

in the form of dichlorosulfitomercurate complex measured spectrophotometrically at

560 nm and NO2 as sodium nitrite measured at 540 nm. For analysis of SO2 and NO2

by spectrophotometeric method, described in IS: 5182 [(Part 2):2001] and IS: 5182

[(Part 6 or VI):1975] were followed by two popular methods (West and Gaeke, 1956;

Jacobs and Hochcheiser, 1958). The concentration of CO and ground level O3 were

monitored for one year with CO analyzer (PPSMPL gaZguard Tx CO) and ozone

analyzer (aeroQUAL Series200).

[67]


These two parameters were monitored in a different manner from the others

parameter. For these two parameters instead of seasonal variation diurnal variation

was observed. The diurnal analysis of CO and O3 was monitored in four zones i.e.

residential, sensitive, traffic and industrial areas for conducting the research work.

At each station sampling was done with duration of 7:00 – 8:00 am (morning), 12:00

– 1:00 pm (noon) and 4:00 – 5:00 pm (afternoon). Entire monitoring was done

throughout the month of March and April in 2009. The average O3 concentrations

were determined based on the 1- hour average values recorded by the analyser. In

this study, the comparison of surface ozone with meteorological parameters along

with its main precursor CO had been analysed. All parameters were averaged over 1-

hour time period. Thereafter, in the morning and afternoon the concentrations of O3

and CO are measured at twenty five locations (as mentioned earlier) in the study

area.

3.9 National Ambient Air Quality Standard (NAAQS)

The National ambient air quality standards are presumed to generally protect

human health, agricultural productivity, commercial activities, tourism and aesthetic

value of ecosystem which might be affected due to the poor air quality. NAAQS or

other standards like WHO standards are built up in order to determine the critical

threshold of air pollution or to provide a safe dose of pollution. These guide lines

specify pollutants of interest along with their concentrations, averaging time and

assessment procedures etc. These standards are of two types viz. primary and

secondary. Primary standards set limits to protect public health, including the health

of "sensitive" populations such as asthmatics, children and the elderly. Secondary

standards set limits to protect public welfare, including protection against decreased

visibility, damage to animals, crops, vegetation and buildings.These guidelines also

help governments in setting standards suitable to local conditions as well as take risk

management decisions in making framework of air quality management. National

ambient air quality standard (NAAQS) is represented in Table 3.5. Other standard is

given in Table 3.6, 3.7 and 3.8. Collected data of air pollutants in the study area are

compared with these standards in this research work.

[68]


3.10 Principle of operation of APM 460 BL Respirable Dust

Sampler

Ambient air laden with suspended particulates enters the system through the

Inlet pipe. As the air passes through the cyclone, coarse, non respirable dust is

separated from the air stream by centrifugal forces acting on the solid particles. These

separated particulates fall through the cyclone’s conical hooper and is collected in the

sampling bottle placed at its bottom. The fine dust forming the respirable fraction of

the total suspended particulate matter (TSPM) passes through the cyclone and is

carried by the air stream to the filter paper, clamped between the top cover and filter

adaptor assembly. The respirable dust (RSP) is retained by the filter and the carrier air

exhausted from the system through the blower. Along with particulate monitoring

facility a compartment is attached with it for gaseous sampling also.

3.11 Brief description of the procedures

Throughout the experiment the parameters which have been studied are as

follows- respiratory suspended particulate matter, total suspended particulate matter,

Pb, Cd, Mn, Cr, K+, Na

+, Cl

-, F

-, SO4

2-, nitrogen dioxide (NO2), sulphur dioxide

(SO2), ozone (O3), carbon monoxide (CO) and meteorological parameters like

temperature, humidity , wind speed and wind direction.

The principle and measurement about these parameters are described below.

3.11.1 High volume sampler (HVS) method for RSPM and TSPM

Principle: In HVS method the air is first drawn with certain flow rate into a cyclone

type inlet and separator, which passes only the smaller ‘respirable’ particulates with

(Stoke’s equivalent) diameter less than 10 micro metre. These are then collected on

the filter. As before, while the larger ‘non respirable’ particulates are collected on a

collector in the separator unit. RSPM is calculated by measuring the mass collected on

the filter and the volume of air sampled. TSPM is calculated by measuring also and

adding the mass collected on the collector.

Instrument used: Envirotech APM 460 BL Respirable Dust Sampler, Digital

electronic balance machine (model no SARTORIUS AG GOTTINGEN GM 312)

[69]


Analysis of samples

• Before sampling the blank filter paper is conditioned and weighted

• After sampling the filter paper is removed.

• Then it is conditioned and weighed.

• In case of the RSPM option, there is also the non-respirable (larger particles)

fraction stored cup which is handled in the same manner as above steps.

Calculations of mass of particulate matter

The following formula is used to calculate the air volume (m3) sampled

V= [(Qi+Qf)/2] × T ......................................................... Equation (3.1)

where V = STP equivalent (25 ºC, 1 atm) air volume sampled in m3.

Qi= initial air flow rate in m3/min, STP

Qf= final flow rate in m3/ min, STP

T= sampling period in minutes

RSPM (µg/m3) = (Wf -Wi) x10

6/ V ................................. Equation (3.2)

where V= volume of air sampled in m3

Wf = weight of exposed filter in gram

Wi= initial weight of filter in gram

To calculate the NSRPM concentration in above equation in same manner by

substituting Wp+Wf in the place Wf and Wp +Wi in place of Wi where Wp = Weight

of material that was used to collect the NRSPM in gram. The TSPM concentration is

achieved by adding the concentration of RSPM and NRSPM.

TSPM = RSPM + NRSPM ............................................. Equation (3.3)

[70]


3.11.2 Atomic absorption spectrometric (AAS) method (IS 5182) [Part 22:2004]

for heavy metals

The size of the filter paper used for measurement of PM10 is usually adequate

for further analysis for trace elements. So, toxic heavy metals like Pb, Cd, Mn and Cr

are analysed by the following procedure.

Principle: This method is based on acid digestion and atomic absorption

spectrometry for the chemical analysis of lead samples collected on filters from

ambient air. The method is applicable to ambient air samples with particulate lead

contents, such that the amount of deposited particulate lead collected on the filter is

greater than 1 µg if the final determination is made by flame atomic absorption

spectrometry. Final determination by graphite furnace atomic absorption spectrometry

allows measurement of quantities of less than 1 µg, but is only applicable after

experimental validation of detection limits.

Instrument used: Atomic Absorption Spectrophotometer (GBC Abanta), Respirable

Dust sampler, Hot plate

Reagents used

• Distilled water

• Nitric Acid (HNO3),concentrate and dilute both

• Lead Standard Solution (1 000 µg pb/ml)

• Hydrogen peroxide (30%)

Digestion procedure for Filters: The exposed glass filters are cut into pieces by

means of clean stainless steel scissors and transferred into a 250 ml beaker. To the

beaker 6 ml of concentrated nitric acid, 4 ml of hydrogen peroxide (30 %) and 50 ml

of distilled water are added. Covering it with a watch glass is heated on a hot plate

until most of the acid has evaporated. Same procedure is repeated at least twice. This

is continued until the residue is barely dry and a white ash appears. The residue is

dissolved in 5 ml of concentrated nitric acid. The digest is filtered, with repeated

small washings of nitric acid into a 25 ml volumetric flask and made up to mark with

[71]


dilute nitric acid. A blank unexposed filter paper is similarly digested for blank

correction.

Calculations of heavy metals

The mass concentration of lead is expressed in micrograms per cubic meter in

air sample to the nearest 0.1 µg/m3 using equation

Pb (conc) = [Pb (sm) –Pb (Bl)] × V1/V .......................... Equation (3.4)

where Pb (conc) = mass concentration of particulate lead in µg/ml

Pb (Sm) = concentration of particulate lead, in µg/ml in sample solution

Pb (Bl) = concentration of particulate lead, in µg/ml in blank solution

V1= total volume of digested sample in ml and

V = volume of air sampled in m3 [as per equation (3.1)].

Cd, Mn and Cr are analysed and calculated in the same manner and procedure of Pb.

3.11.3 Methods for water soluble inorganic ions

The water soluble fraction of the filter paper collecting PM10 is extracted by

dissolving the cut pieces of the filter paper into distilled water for seven days. Then it

is filtered and the filtered solution is used for the analysis of K+, Na

+, Cl

-, F

-, SO4

2-

following the standard procedure. After determination of the said parameters from the

standard procedure, their concentration is got by applying formula as per equation 3.4.

3.11.3.1 Ion selective method for Fluoride

Principle: The fluoride electrode consists of a single lanthanum fluoride crystal, the

internal portion of which is in contact with a constant concentration of fluoride ion

and an internal reference electrode. Upon contact of the external electrode surface

with the test solution (standard or unknown) a potential difference is set up across the

crystal, which is related to the fluoride ion concentrations in contact with the crystal

surfaces. An external reference electrode in the test solution completes the circuit and

allows measurement of the membrane or crystal potential The relationship between

potential and fluoride ion concentration is described by a form of the Nernst equation

(E = E° - RT ln aF-), it is the log fluoride ion activity that is related to change in

[72]


measured potential. Consequently, variations in ionic strength between samples and

standards and among samples must be prevented. Similarly, since it is the free

fluoride ion activity that yields the electrode response, formation of complex species

(Al, Fe) or undissociated hydrofluoric acid must be prevented. The procedure is

designed to maintain control of these problems.

Instruments used: Ion Selective Electrode (ISE) (Orion 4 Star), Magnetic stirrer.

Reagents

• Fluoride standard solution

• Working standard fluoride of 10 ppm should be prepared just before starting

the analysis.

• Total ionic strength adjustment buffer (TISAB)

Procedure: Electrode operation was done according to the manual. Firstly a

calibration graph was done over the appropriate concentration range, such that the

TISAB constitutes 50 per cent of the solution by volume. Linear millivolt response

versus log of concentration should be obtained from roughly 0.2 to 2,000 mg/l. In the

linear region, three standards should suffice to determine the standard curve. In non-

linear regions, i.e. low levels, more data points are necessary. Sample is mixed to be

analysed with an equal volume of TISAB in a beaker. If high levels of Al (>3 mg/ L)

or Fe (>200 mg/ L) are present, the procedure for distillation should be followed.

Fluoride concentration is determined directly from electrode. The electrodes should

be rinsed and dried between samples. Frequent recalibration should be made with an

intermediate standard. The detection limit is about 0.02 mg/ L fluoride.

3.11.3.2 Estimation of Sodium (Flame photometric method)

Principle: Trace amounts of sodium can be determined by flame emission

photometry at 589 nm. Sample is nebulized into a gas flame under carefully

controlled, reproducible excitation conditions. The sodium resonant spectral line at

589 nm is isolated by interference filter or by light dispersing devices such as prisms

or gratings. Emissions light intensity is measured by a photo tube, photomultiplier, or

photo diode. The light intensity at 589 nm is approximately proportional to the

[73]


sodium concentration. Alignment of the wavelength dispersing device and wavelength

readout may not be precise. The appropriate wavelength setting, which may be

slightly more or less than the 589 nm, can be determined from the maximum emission

intensity when aspirating a sodium standard solution, and then used for emission

measurements. The calibration curve may be linear but has a tendency to level off or

even reverse at higher concentrations; work should be done in the linear and non-

linear range.

Reagents

• Double distilled water

• Standard stock sodium solution (1000 mg/L)

• Intermediate standard sodium solution (100 mg/L)

Procedure: Different standard sodium (Na) solution of following concentrations for

calibration curve was prepared from intermediate standard sodium solution (100

mg/L): 2, 4, 6, 8, 10 mg/L. A blank solution was also prepared. The intensity of the

different standard solutions was measured with a flame photometer (SYSTRONICS-

128) using a Na-filter (APHA, 1998).

The intensity of the sodium in the unknown sample was measured in a similar manner

by taking 5 ml sample water in 50 ml volumetric flasks and then diluted it up to the

mark.

3.11.3.3 Estimation of Potassium (Flame photometric method)

Principle: Same procedure as Sodium determination by Flame Photometer except the

wave length is 766.5 nm.

Reagents

• Double distilled water

• Standard stock potassium solution

• Intermediate standard potassium solution (100 mg/L)

[74]


Procedure: Different standard potassium (K) solutions for calibration curve of

following strength (2, 4, 6, 8, 10 mg/L) were prepared from the intermediate standard

potassium solution. A blank solution was also prepared. Intensity of the different

standard solutions was measured with a flame photometer (SYSTRONICS-128) with

a K- filter (APHA, 1998). The sample was analysed in the same procedure.

3.11.3.4 Estimation of Chloride (Titrimetric method)

Reagents

• 0.0141 (N) AgNO3 (Silver nitrate)

• K2CrO4 (Potassium chromate) Indicator

Procedure: 5 ml sample was taken in a conical flask, then 2-3 drops of K2CrO4

indicator was added to it and solution was titrated against 0.0141 (N) AgNO3. The end

point was marked by a brick red precipitate. The titrant volume was noted and the

chloride content was calculated (APHA, 1998).

Calculation

S

100045.35 N V/LCl mg

- ×××= ................................... Equation (3.5)

where, V = volume of titrant, ml

N = normality of titrant

S = volume of sample, ml

3.11.3.5 Estimation of Sulphate (Turbidimetric method)

Reagents

• Conditioning reagent

• Barium chloride

• Standard sulphate solution (100 ppm)

Procedure: 100 ml of clear sample is taken (not containing more than 40 mg/L of

SO4) or a suitable aliquot is diluted to 100 ml in a 250 ml conical flask. 5.0 ml of

conditioning reagent is added to it. Care should be taken not to add the conditioning

[75]


reagent in all the samples simultaneously. This is to be added to each sample just prior

to the further processing. The sample is stirred on a magnetic stirrer and during

stirring; add a spoonful of BaCl2 crystals. Stirring is done only for 1 minute after

addition of BaCl2 (APHA, 1998).

After the stirring is over, the optical density is read on a spectrophotometer at

420 nm, exactly after 4 minutes. The concentration of sulphate is found out from the

standard curve. Standard curve (Figure 3.3) was prepared employing the same

procedure described above, for the sample from 0.0 to 40.0 mg/L at the interval of 5

mg/L.

3.12 Method of sampling for gaseous pollutants

The absorbing solutions of specific amount (as per standard methods) are

taken in each of the impinger tubes set in HVS. To adjust the individual flow rates

through the impinger tubes the inlet of one of the impinger tubes is connected to the

rota meter and outlet to one of the four input connections of the manifold.

Let this initial flow rate be F1 and final rate be F2.

Calculation of volume of air (m3) sampled through the absorber (impinger tube):

The volume of air sampled, v is given

v (m3) =[(F1+F2) × T × 10

-3]/2 ....................................... Equation (3.6)

where, F1 = initial flow rate at start of sampling, lpm

F2 = final flow rate at end of sampling, lpm

T = sampling time period in minutes

3.12.1 Determination of Nitrogen dioxide (NO2) in ambient air (IS method)

Principle: Nitrogen dioxide (NO2) is collected by bubbling air through a sodium

hydroxide solution to form a stable solution of sodium nitrite (NaNO2). The nitrite ion

produced during sampling is reacted with phosphoric acid, sulphanilamide and N-

1(napthyl) ethylene diamine dihydrochloroide to form an azo dye and then determined

colorimetrically. The method is applicable for the collection of 4 to 24 hours samples

in the fields and subsequent analysis in the laboratory.

[76]


Reagents

• Absorbing reagent (4 gm sodium hydroxide is dissolved in distilled water and

diluted to 1000 ml with distilled water)

• Sulphanilamide solution

• NEDA Solution

• Hydrogen peroxide solution

• Standard Nitrite Solution(1000 µg/ml)

Analysis: 10 ml sample is pipetted into a conical flask. Then 1.0 ml of H2O2 is added.

It is mixed well. Then 10 ml sulphanilamide is added. After well mixing 1.4 ml

NEDA solution is added. Pink colour is developed. The intensity of the colour is

measured by taking OD of the sample at 540 nm in spectrophotometer. Following the

same procedure the colour of the standards of different concentration is developed and

measured at the same operating wavelength. The standard curve for NO2 is presented

in Figure 3.4.

Calculations of NO2

NO2 (µg /m3) = [(µg NO2/ml) × 50] / (v × 0.35) ............ Equation (3.7)

where 50 = volume of absorbing reagent used in sampling in ml

v = volume of air sampled in m3 [calculated as in equation (3.6)]

0.35 = empirical factor for collection efficiency.

3.12.2 Determination of Sulphur dioxide content of the atmosphere

(Tetrachloromercurate absorber or pararosaniline method or IS method)

Principle: Sulphur dioxide in an air sample is absorbed into a solution of potassium

tetrachloromercurate (TCM) by aspirating a measured air sample through an absorber

vessel. This procedure results in the formation of dichlorosulfitomercurate (11)

complex which resists oxidation by the oxygen in the air. Ethylenediamine-tetra acetic

acid disodium salt (EDTA) is added to this solutions to form complex heavy metals

that catalyse the oxidation of the collected sulphur dioxide. Once this

[77]


dichlorosulfitomercurate complex is formed, it is stable to strong oxidants (e.g. ozone

and oxides of nitrogen). After sampling is completed, the solution is treated first with

a solution of sulphamic acid to destroy the nitrite anion. It is then treated with

solutions of formaldehyde and pararosaniline. In this way the complex is made to

react with pararosaline and methylsulphonic acid. The absorbance of the solution is

measured by means of spectrophotometer.

Reagents

• Absorbing reagent (TCM)

• Sulphamic acid (0.6 %)

• Formaldehyde

• Stock Iodine (0.1N)

Analysis: 5 ml sample is taken into a 25ml volumetric flask and volume is brought to

10 ml with absorbing reagent. 10 ml unexposed TCM similarly is taken for reagent

blank in 25 ml volumetric flask. 1ml 0.6 % sulphamic acid is added into each. They

are left for 10 minutes. 2 ml of 0.2 % formaldehyde solution and 5 ml of

pararosaniline solution are added respectively. Violet colour is developed. Just after

30 minutes volume is made up with boiled and cooled distilled water and taken OD in

560 nm. Distilled water is used as optical reference. Same procedure is followed for

the developing colour and measurement of different strength of SO2 for standard

curve. The standard curve for SO2 is presented in Figure 3.5.

Calculations of SO2

SO2 (µg /m3) = [(A-A0) × B /v] × D ............................... Equation (3.8)

where v = air volume as per equation (3.6)

A = the sample absorbance

A0 = the reagent blank absorbance

B = calibration factor in µg / absorbance unit

D = dilution factor (for 24 hour D =10)

[78]


3.12.3 Ozone monitoring

An analyser from aeroQUAL Series200 has been used for the measurement of

ozone through the sites. The measurement unit of this instrument is ppm or mg/m3.

The low concentration ozone head 0.008 to 0.500 ppm and high concentration ozone

head 0.20 to 20.00 ppm.

3.12.3.1 GSS technology background: Aeroqual delivers very high quality products

based on a fundamental understanding of the chemistry, reaction rates and electronic

interactions between materials and gases.

Gas Sensitive Semiconductor (GSS) technology is exclusive to Aeroqual. It is

a combination of smart measurement techniques and mixed metal oxide

semiconductor sensors that exhibit an electrical resistance change in the presence of a

target gas.

This resistance change is caused by a loss or a gain of surface electrons as a

result of adsorbed oxygen reacting with the target gas. If the oxide is an n-type, there

is either a donation (reducing gas) or subtraction (oxidizing gas) of electrons from the

conduction band. The result is that n-type oxides increase their resistance when

oxidizing gases such as NO2 and O3 are present while reducing gases such as CO and

hydrocarbons lead to a reduction in resistance. The converse is true for p-type oxides

where electron exchange due to gas interaction leads either to a rise (oxidizing gas) or

a reduction (reducing gas) in electron holes in the valence band. This then translates

into corresponding changes in electrical resistance. Quantitative response from the

sensor is possible as the magnitude of change in electrical resistance is a direct

measure of the concentration of the target gas present.

Since it is the surface reaction that causes the change in electrical resistance in

the sensing oxide, it is beneficial to maximize the surface area to intensify the

response to gas. To take advantage of this effect, commercial gas sensors consist of

highly porous oxide layers, which are either printed or deposited onto alumina chips.

The electrodes are usually co-planar and located at the oxide/chip interface (Figure

3.6). A heater track is also applied to the chip to ensure the sensor runs “hot” which

[79]


minimizes interference from humidity and increases the speed of response. The

microstructure of the oxide, its thickness and its running temperature are optimised to

improve selectivity. Catalytic additives, protective coatings and activated-carbon

filters are also applied to enhance selectivity.

Aeroqual's GSS technology is the culmination of more than 25 years of

materials research perfecting material formulations and optimizing sensor driver

algorithms. Through the use of proprietary microprocessor driven code, reliable

surface amount production, rigorous calibration procedures and exhaustive testing,

Aeroqual has improved accuracy, T90 response, cross-sensitivities and sensor drift

over competing technologies. The Aeroqual range of GSS sensor-based products is

unique in the global monitoring of surface ozone and has been designed to provide

near scientific accuracy, high reliability and functionality. Aeroqual’s concept of fully

interchangeable sensor heads eliminates the need for field calibration and provides

users with unique application focused solutions.

3.12.4 CO monitoring

Principle: A small electrochemical sensor with three electrodes is used for detection

of gas. Exposure to CO (Carbon monoxide) will produce an electrical current directly

proportion to gas concentration. The signal is then amplified by the instrument to give

the corresponding reading on the display and activates alarms at present levels.

Description: PPSMPL gaZguard Tx CO (Carbon Monoxide) is small lightweight

personal portable indicator and dual level alarm designed to detect Carbon Monoxide

(CO) in the ppm range. This instrument is powered by user replaceable four standard

‘AA’ size disposable / rechargeable batteries. The digital LCD gives easily readable

accurate continuous readings. Adjustable audible and visual alarms are at two

different concentrations levels. Exposure to 50 ppm carbon monoxide gives a

pulsating audible and visual indication alarm. If exposure is more than 100 ppm of

CO gives a continuous visual indication and audible alarm. Battery indication (low

bat on LCD display) ‘BAT’ lets people know when to change the batteries in case of

disposable batteries and when to charge the batteries in case of rechargeable batteries.

Warm up time is virtually instantaneous. In the fresh air condition the instrument is

[80]


switched ON, waited for zero on the display or adjusted zero. The instrument is then

ready to monitor the gas.

3.13 Meteorology and construction of windrose diagram

According to Murty (2004) meteorological measurements of special interest in

air pollution can be classified into two categories, viz. primary meteorological

measurements and secondary meteorological measurements. Temperature, wind speed

and wind direction are example of primary meteorological measurements whereas

humidity, precipitation are representative of secondary meteorological measurements.

However, local meteorology or micrometeorology is quite different from the regional

meteorology. Temperature, wind speed and wind direction are considered as the most

critical micrometeorology parameter in controlling the dispersion of air pollutants. In

each sampling location meteorological parameters such as humidity, temperature,

wind speed, wind direction and rainfall were recorded in premonsoon, postmonsoon

and winter seasons. Humidity and temperature were measured by a portable

hygrometer (Model-HTC-1), rainfall is measured by a digital rain gauge (Model-

RGR126; Make-Oregon) meter whereas wind speed and direction is measured by a

digital anemometer along with wind vane (Model-Lutron-AM-4201). For three

seasons windrose diagrams were prepared by using windrose pro software. The total

rainfall data was also collected from the District seed and agriculture seed

multiplication farm, Burdwan.

3.14 Statistics

The statistical approaches, applied to the collected data are written below.

3.14.1 Air Quality Index (AQI): An AQI could be defined as a scheme that

transforms the (weighted) values of individual air pollution related parameters into

single number. Air quality index (Tiwari and Ali, 1987) was also measured here for

each place in each zone. The concentrations of RSPM, SO2, NO2 and Pb in the study

area are used for AQI. At first air quality rating of each parameter used for monitoring

is calculated in each zone by the formula as:

[81]


i) q =100×V/Vs ; where q =quality rating; V = observed value of parameter ;

Vs = value recommended for that parameter.

If total ‘n’ no of parameters were considered for air monitoring, then

geometric mean of these ‘n’ number of quality ratings was calculated in

the following way:

ii) g =anti log {(log a+log b+…………….logx)/n}; where g = geometric

mean; a, b, c, d, x =different values of air quality rating; and n = number of

values of air quality rating, log = logarithm. Air quality status (Mudri,

1990) on the basis of AQI is represented in Table 3.9.

3.14.2 Pearson correlation analysis and correlation coefficient: The Pearson

correlation is applied to know the degree of association among the variables. The

Pearson correlation among all the monitored parameters is calculated by using the

following formula

( )( )

( )yx

i

x

1i

SS1n

YYiXXr

−

−−∑=

−

where X and Y are two variables, with means X and Y respectively with standard

deviations SX and SY.

3.14.3 Factor analysis: Multivariate analysis technique called principal component

analysis (PCA) has been widely used to identify possible sources of ambient

pollution. Among multivariate techniques, PCA is often used as an exploratory tool to

identify the major sources of air pollutant emissions (Bruno et al., 2001; Guo et al.,

2004). Factor analysis is a statistical technique that can be applied to a set of variables

in order to reduce their dimensionality. In factor analysis (FA), a set of variables is

first normalized as Xit as shown in equation (3.9) so that their variances are unity.

Xit = (Cit –Ci)/di ............................................................. Equation (3.9)

where Cit is the concentration of the variable i in the sample t, Ci and di are the

arithmetic mean and standard deviation of the variable i for all samples included in

the analyses. These common factors are typically characterized as pollutant source

[82]


types in air pollution studies. The factor analysis model applied in the field of air

pollution is given in equation (3.10).

Xit =∑=

N

J 1

LijSjt + Eit .................................................... Equation (3.10)

where Lij is the factor loading of the variable i in the source j with N number of

sources, Sjt is the factor score of the source j for sample t and Eit is the residual of

variable i in the sample t not accounted by the j sources or factors. In this study, FA

was carried out on gaseous pollutants, heavy metals, ions present in PM10 and

meteorological factors. The factors that have been determined are rotated to transform

the initial matrix to easily interpret. The sum of the eigen values is not affected by

rotation but it will alter the eigen values of particular factors and will change the

factor loadings. The varimax rotation of the matrix was selected which attempts to

minimize the number of parameters that have high loadings on a factor. This

enhanced the interpretation of the factors. Factor analysis determines factor F in such

a way so as to explain as much of the total variation in the data as possible with as

few of these factors as possible. In factor analysis, Fi is the i th factor calculated by

equation (3.11).

Fi = ∑=

P

i 1

Wij Xj = W1X1+Wi2Xi2+……+WipXip .......... Equation (3.11)

where the w's are the factor weights (to be estimated from the data) chosen so as to

maximize the quantity and the X's are the original variables in standardized form. The

second factor F2 is such that weighted linear combination of the variables which is

uncorrelated with F1 and which accounts for the maximum amount of the remaining

total variation not already accounted for by F1. The higher the factor weight for a

given variable, the more that variable contributes to the overall factor score and the

higher the factor loading. Higher factor loadings of a particular element can help in

identifying the possible sources (Henry et al., 1984). The factors obtained are rotated

to achieve the meaningful underlying vectors with more interpretability (Gupta et al.,

2008).

[83]


3.15 RS and GIS methodology

The following RS and GIS methodologies were adopted for carrying out the

research work.

3.15.1 Supervised classification of study area: Supervised classification of the

Burdwan town was performed with the help of Resourcesat-1 satellite image and

Geomatica V.10.2 software. Map collected from Burdwan municipality was

considered as base map. Base map was georeferenced at latitude/longitude projection

system with a datum level of India-Nepal (D076) with an output pixel spacing of

0d00'00.1900". For georeferencing ground control points (GCPs) were collected from

study area by using Germin 12 GPS receiver. Burdwan municipality area was clipped

from the satellite imagery and image to image georeferencing was done by using

already georeferenced base map. Then supervised classification was run by using

maximum likelihood classifier with null class. Thereafter both landuse/landcover and

base maps were reprojected to Universal Transverse Marcater Projection (UTM)

system. Twenty five (25) air sampling locations were then downloaded to the

classified image from GPS through Mapsource software. Locational details along

with different air quality parameters and their concentrations were attached to this 25

spatial data as an attribute data.

3.15.2 Digital Elevation Model (DEM): DEM is generated on the basis of sampling

points, stored as a point layer along with attributes such as RSPM, SO2, NO2 and Pb

etc. DEM is generated by using VEDIMINT algorithm in the Geomatica V.10.1

software. The output DEM is represented as a zonation map of the said parameters.

The algorithm consists of three major steps plus an optional step for processing 2D

features. In the first step, input vector points (RSPM, SO2, NO2 and Pb) concentration

with respect to different locations) are reprojected to the raster coordinates and burned

into the raster buffer, with the elevations generated due to different concentration of

the said parameters interpolated linearly between vector nodes. 2D layers are ignored

in this stage. If multiple elevation values are scanned into a single pixel, the maximum

value is assigned the pixel, and the pixel is marked as a cliff. In the second step, the

elevation at each DEM pixel is interpolated from the source elevation data. The

[84]


interpolation process is based on an algorithm called Distance Transform.

Interpolation is made between the source elevations and elevations at equal-distance

points from source locations. If 2D vector layers are present, they are scan converted

into a flag buffer during the optional step. The 2D features are also initialized to

prepare for use in the smoothing stage. In step three, a finite difference method is used

to iteratively smooth the DEM grid. The algorithm uses over relaxation technique to

accelerate the convergence. During the iterations, the source elevation values are

never changed, while the interpolated values are updated based on the neighbourhood

values.

3.15.3 Inverse Distance Interpolation (IDINT): Inverse distance interpolation is

used to read the gray level values for an arbitrary number of pixel locations in order to

generate a raster image based upon interpolation between the specified gray levels.

This method of interpolation combines the idea of Thiessen polygon with the gradual

change of trend surface. It considers weighted moving average. Weights are computed

from a linear function of distance between sets of points and the points to be

predicted. In this method the size of the starting radius is specified, which defines the

starting search area for interpolation points around grid point. For estimation of

ambient air quality of unsampled locations, spatial interpolation is required with a

satisfying level of accuracy. Interpolation is based on the principle of spatial auto-

correlation or spatial interdependence, which measure the degree of relationship

between near and distance points. Spatial auto-correlation determines if values are

interrelated. There are many spatial interpolation algorithms for spatial data sets.

Shepard (1968) discussed in detail inverse distance weighting; Deutsch and Journel

(1998) discussed kriging. Goodman and O'Rourke (1997) discussed in detail about

splines. There are two categories of interpolation techniques, deterministic and

geostatistical. Deterministic interpolation technique creates surfaces based on the

measured points or mathematical formulas. Methods such as inverse distance

interpolation (IDINT) are based on the extent of the similarity of the cells while

geostatistics interpolation such as kriging are based on statistics and are used for more

advance prediction surface modeling that also include some measure of the accuracy

of the prediction. Kriging is similar to IDW (inverse distance weighting) in the sense

[85]


that it uses a weighting mechanism that assigns more influence to the nearer data

points to interpolate values at unknown locations. However, instead of using inverse

distance weighting approach kriging uses variograms. As a measure of spatial

variability, a variogram replaces the Euclidean distance by a structural distance that is

specific to the attribute and the field under study (Deutsch and Journel, 1998). For

special correlation, a perfect semivariogram is required for which parameters can be

determined. The geochemical data in the study area are non-stationary because many

of the closely located points have values drastically different from each other.

Because of this reason, IDINT method has been used for interpretation of data points

instead of kriging in order to generate maps of continuous maps of geochemical

parameters. IDINT interpolation determines cell values using a linearly-weighted

combination of a set of sample points. The weight is a function of inverse distance.

The farther an input point is from the output cell location, the less importance it has in

the calculation of the output value. Because the IDINT is a weighted distance average,

the average cannot be greater than the highest or less than the lowest input. Therefore,

it cannot create ridges or valleys if these extremes have not already been sampled.

Also, because of the averaging, the output surface will not pass through the sample

points. The best results from IDINT are obtained when sampling is well distributed to

represent the local variation that needs to be simulated. In IDINT the measured values

(known values) closer to prediction location will have more influence on the predicted

value (unknown value) than those farther away. More specifically, IDINT assumes that

each measured point has a local influence that diminishes with increase in distance.

Thus, points in the near neighborhood are given high weights, whereas points at a far

distance are given small weights (Lixin Li, 2004). The general formula of IDINT

interpolation (Johnston et al., 2001) is written below.

( ) ( )∑=

λ=

N

1i

iwiy,xw ........................................................ Equation (3.12)

( )( )∑=

′

=λ

N

1k

p

dk1pdi

1i .................................................. Equation (3.13)

[86]


where w (x,y) is the predicted value at location (x,y), N is the number of nearest known

points surrounding (x,y),are the weights assigned to each known point value wi at

location (xi,yi), di are the Euclidean distances between each (xi,yi) and (x,y), and p is

the exponent, which influences the weighting of wi on w, in the present study p value

of 3 and starting search radius 650 m.

A brief overview of the methodology is given in Table 10.

Table 3.1: Land use/land cover of Burdwan Municipality area

Land Use/Land Cover Classes Area (sq m) % of

Area

Evergreen Vegetation Surrounding the forest area 504737.45 2.00

Spar evergreen Vegetation Along with Residential area 2990965.69 11.83

Crop Land 2111009.03 8.35

Residential area 9327609.73 36.88

Commercial area 746664.20 2.95

Mixed urban with commercial area 682345.40 2.70

Ephemeral Ponds 64287.35 0.25

Barren land 499139.04 1.97

Nonforested Wet land 181319.25 0.72

Mixed Forest 260609.08 1.03

Streams & Canals 296275.35 1.17

Perennial Fishing ponds 1537423.73 6.08

Sandy area other than beaches 260797.79 1.03

Perennial Nonfishing ponds 156724.00 0.62

Rice mill clusters 634821.83 2.51

Mixed urban with mill areas 654919.49 2.59

Herbaceous Range Land 1131224.57 4.47

Play Ground 113981.09 0.45

Institutions 72370.44 0.29

Hospital 10253.27 0.04

Services Area 47240.51 0.19

Nurseries 7202.45 0.03

Dense Evergreen Vegetation Along with residential

area 2997318.94 11.85

Total Area 25289239.67 100.00

(Source: Gupta and Roy, 2012)

[87]


Table 3.2: Medical record (2008) of respiratory disease in Burdwan municipality

Serial No Month Case Death

1 January 17 03

2 February 12 01

3 March 34 00

4 April 09 01

5 May 22 00

6 June 19 05

7 July 18 01

8 August 04 01

9 September 09 00

10 October 16 01

11 November 39 03

12 December 30 00

(Data Obtained from Medical Record Department Burdwan Medical College &

Hospital Burdwan, West Bengal)

[88]


Table 3.3: Locational details of sampling sites at a glance

Local name of the sites site label Lat/Long

Goda R1 23o

15'06.73" N. 87 o

50'22.17" E

Tarabag R2 23o

15'05.38" N. 87 o

50'44.79" E

Jagatber R3 23o

13'16.49" N. 87 o

51'51.58" E

Jilpibagan R4 23o

13'14.60" N. 87 o

51'37.13" E

Bermore R5 23o

13'23.31" N. 87 o

51'35.69" E

Bhangakuti R6 23 o

15'22.78" N. 87 o

51'08.22" E

Policeline1 R7 23 o

13'23.13" N. 87 o

52'39.57" E

Ichlabad R8 23 o

14'02.36 "N. 87 o

52'04.69" E

Bhatchala1 R9 23o

13'45.94" N. 87 o

51'29.94" E

Bhatchala2 R10 23o

14'04.87" N. 87 o

51'16.12" E

Tinkonia R 11 23o

14'58.06" N. 87 o

52'06.26" E

Curzongate R12 23o

14'25.48" N. 87 o

52'03.57" E

Barabazar R13 23 o

14'24.04N ". 87 o

51'23.53" E

Kanchannagar R14 23 o

14'14.39" N.87 o

49'41.95" E

Golapbagmore R15 23o

15'27.42" N. 87 o

50'59.15" E

Tejganj1 I1 23o

13'29.61" N. 87 o

50'48.05" E

Tejganj 2 I2 23o

13'32.38" N. 87 o

50'57.75" E

Alamganj1 I3 23o

13'55.76" N. 87 o

50'53.54" E

Alamganj2 I4 23o

14'06.98" N. 87 o

50’57.72" E

Rajcollege S1 23o

14'43.95" N. 87 o

51'26.42" E

Vidyarthi School S2 23o

14'00.26" N. 87 o

51'55.75" E

Kalpataru children park S3 23o

14'09.51" N. 87 o

50'48.84" E

Alamganj primary school S4 23 o

13'54.41"N. 87 o

50'57.01"E

SadhanpurPolytechnique college S5 23o

15'00.32" N. 87 o

52'53.27" E

Department of Environmental

Science

S6 23o

15'25.28" N. 87 o

50'55.87" E

[89]


Table 3.4: Details characteristics about the sampling sites

Sampling Sites Description

R11, R12, R13, R15, R6,

R7 Residential area with high traffic density

R3, R4, R5, R8, R14 Residential area with moderate traffic density

R1, R2, R10 Residential area with low traffic density

R9 Residential area influenced by industrial emission

I1 and I2 Industrial area with high traffic density

I4 Industrial area with moderate traffic density

I3 Industrial area with low traffic density

S2, S5, S6 Sensitive area with high traffic density

S1 Sensitive area with moderate traffic density

S3

Sensitive area with moderate traffic density and highly

influenced by industrial emission

S4

Sensitive area with high traffic and highly influenced

by industrial emission

Table 3.5: National Ambient Air Quality Standards

All Pollutants

(µµµµg/m3)

Only CO in mg/m3

Time

weighted

Concentration in ambient air in average

Sensitive Industrial Residential

and others

Sulphur dioxide (SO2) 24h 30 120 80

Nitrogen dioxide

(NO2)

24h 30 120 80

Suspended particulate

Matter(SPM)

24h 100 500 200

Respirable Suspended

Particulate Matter

(RSPM)

24h 75 150 100

Lead (Pb) 24h 0.75 1.5 1

Carbon

Monoxide(CO)

1h 2 10 4

(Source: Central pollution control board, Delhi, 1994)

[90]


Table 3.6: Standards of heavy metals

Heavy metals Annual mean Standard (ng/m3)

Pb 500 ( European limit value)

Mn 150 (WHO)

Cd 5 (European target value 2004/ 107/CE)

(Source: Weiwei et al., 2006)

Table 3.7: Primary standards (2008) of CO and O3

Primary Standard by NAAQS

(2008)

Ozone (ppb) CO (ppm)

120 35

Table 3.8: National Ambient Air Quality Standards (2009) for CO and O3

Pollutant Time weighted

average

Industrial, Residential,

Rural and other area

Ecologically

sensitive area

CO (mg/m3) (1 h) 04 04

O3 (µg/m3) (1 h) 180 180

[91]


Table 3.9: Air quality index Table (Mudri, 1990)

Category AQI of ambient air Description of ambient air quality

I Below 10 Very clean

II Between 10-25 Clean

III Between 25-50 Fairly clean

IV Between 50-75 Moderately Polluted

V Between 75-100 Polluted

VI Between 100-125 Heavily polluted

VII Above 125 Severely polluted

[92]


Table 3.10: Summary of methodology

SL.

No.

Parameters Name of

Methods

Method of

Analysis

Apparatus used

1 RSPM and

TSPM

HVS method Gravimetric

methods

High volume

sampler

(Envirotech APM

460BL)

2 Pb, Cd, Mn,

Cr

Atomic absorption

spectrometric

(AAS) method (IS

5182) Part

22:2004

AAS methods Atomic absorption

spectrometer(GBC

Avanta)

3 Potassium

(K+)

Flamephotometric

Method

Flamephotometer

(Systronic-128)

4 Sodium (Na+) Flamephotometric

Method

Flamephotometer

(Systronic-128)

5 Fluoride (F-) Ion selective

Method

ISE Orion 4 star ISE

6 Chloride (Cl-)

Argentometric

Method

Titrimetric

methods

7 Sulphate

(SO4

2-)

Turbidimetric

Method

Spectrophotometric Spectrophotometer

(Systronic-169)

8 SO2 IS method (West-

Gaeke method)


(Systronic-169)

9 NO2 IS method

(Jacobs-

Hochcheiser)


(Systronic-169)

10 Ozone(O3) aeroQUAL

Series200

11 Carbon

monoxide

(CO)

PPSMPL gaZguard

Tx CO

12 i) Humidity

and

temperature

ii) Rainfall

iii) Wind

speed and

direction.

i) Portable

hygrometer

(ModelHTC-1).

ii) Digital rain

gauge (Model-

RGR126; Make-

Oregon).

iii) Digital

anemometer along

with wind vane

(Model-Lutron-

AM-4201).

[93]


SL.

No.

Parameters Name of

Methods

Method of

Analysis

Apparatus used

13 Windrose

diagram

Windrose pro

software

14 GIS

methodology

Digital Elevation

Model (DEM),

Inverse distance

interpolation

(IDINT)

Inverse distance

weightage

Geomatica

15 Multivariate

statistical

analysis

PCA Varimax rotation XLSTAT (2011)

[94]


Figure 3.1: Study area location

[95]


Figure 3.2: Land use/land cover map of Burdwan Municipality showing various

sampling locations

[96]


Figure 3.3: Calibration graph for SO4

Figure 3.4: Calibration graph for NO2

[97]


Figure 3.5: Calibration graph for SO2

Figure 3.6: Diagram of typical sensor formulation

Sensing layer

Gold electrodes Alumina substrate

[98]

RESULTS AND DISCUSSION

he results are obtained by making average of two year’s data (2008 & 2009) and

these average data are discussed. Detail micrometeorology of each monitoring

site is given in annexure XIV. Total amount of rainfall in different season during the

study is also given in annexure XV. Spatio-temporal variation of air pollutants,

statistical analyses and Air quality index (AQI) application are represented first,

followed by GIS mapping along with windrose diagram on these average data.

4.1 Spatio-temporal distribution of particulate matter, heavy metal

fraction and inorganic ion in study area

4.1.1 Particulate matter status in ambient air in study area: In this section the

spatial and temporal variation of the respiratory suspended particulate matter (RSPM)

and total suspended particulate matter (TSPM) in the study area are discussed.

4.1.1.1 Respiratory suspended particulate matter (RSPM or PM10): It is observed

that RSPM concentration ranging from 40.200-323.100 µg/m3, 4.480-277.720 µg/m

3

and 34.260-363.690 µg/m3 in premonsoon, postmonsoon and winter season

respectively with an average value of 126.824 µg/m3, 86.055 µg/m

3 and

157.823

µg/m3 respectively (Table 4.1).

In general it is observed that the average concentration of PM10 show distinct

seasonal variations with high winter and premonsoon value in the study area than the

postmonsoon value. This drop down in the concentration of PM10 in postmonsoon

may be due to the monsoonal washout effect of particles. While during winter low

T

In this section, the results obtained from the analysis are given and

discussed. The meteorological influence and the possible implications

thereof on this study are also discussed followed by some key-

observations and discussion from the GIS mapping. A few general and

special features of the pollutants along with their spatial and seasonal

variations are also taken into account.

[99]


mixing heights leads to an accumulation of pollutants for longer time. Similar kind of

findings is also reported by Gupta et al. (2008). At S4 site it is observed to have a very

high concentration of PM10 (363.690 µg/m3) in winter season. S4 is situated in

industrial region of the town. So, the accumulation of such a high level of RSPM may

be endured at this site due to the emission from the surrounding rice mills,

resuspension of dusts from the paved and unpaved road etc along with the prevailing

meteorological condition. R11 site shows the maximum level (323.100 µg/m3 and

277.720 µg/m3) of PM10 in both premonsoon and postmonsoon seasons. This site is

situated just beside the district bus stand having high traffic density throughout the

day. So, emissions from automobiles may be the major source of particulate pollution

in this location. This observation is also been corroborated with the work of Ali and

Athar (2008) and Giri et al. (2006). Both of them found that high concentration of

PM10 in some selected places of Pakistan and Kathmandu which is attributed due to

traffic volume, length of days and meteorological condition.

Comparison with measurements of other urban areas and cities: In Delhi from

February 1998 to May 1998 it was reported that the average PM10 concentration were

658.450±231.200 µg/m3 at Daryaganj, 454.770±106.200 µg/m

3 in Jawaharlal Nehru

University and 552.800±225.700 µg/m3 at Motinagar ( Balachandran et al., 2000 ).

Giri et al. (2006) had recorded the lowest and highest average annual concentration

during their study period which was found as 47.780 µg/m3 and 199.800 µg/m

3

respectively at Matsyagaon and Putalisadak air-monitoring sites in Kathmandu. Ali

and Athar (2008) had found PM10 in range of 123.000-434.000 µg/m3

in Pakistan.

Gupta et al. (2008) observed daily average PM10 concentrations were 140.100 µg/m3

and 196.600 µg/m3 respectively at residential and industrial sites, while 8 h average

concentrations of PM10 at commercial site were 131.300 µg/m3

in Kolkata.

Comparison with National ambient air quality standard (NAAQS) standard:

During premonsoon season 53.33 % of the residential sites have exceeded the

prescribed standard (NAAQS) of PM10 except site R1, R2, R7, R10, R13, R14 and

R15. Compared to premonsoon in postmonsoon only 46.66 % of the residential site

viz. R5, R9, R10, R11, R12, R14 and R15 have higher level of PM10 than the

standard. But in winter almost 67 % of residential site crossed the prescribed limit.

[100]


So, in winter season maximum violence of prescribed standard is occurred in

comparison to the other two seasons. In case of industrial site only I3 has exceeded

the prescribed standard in premonsoon season. The concentration of PM10 in the entire

industrial sites lies below the NAAQS standard in postmonsoon and lies above the

said standard at the site I1, I2, and I4 in winter. Regarding sensitive site, except S2,

S5 and S6 all sites lie above the standard in premonsoon and winter season where as

all site showed the lower level of PM10 than the standard except S4 in postmonsoon.

Similar type of temporal exceedence of prescribed standard of PM10 is also reported

by Perrino et al. (2007) in Europe, Balachandran et al. (2000) in India and Ali and

Athar (2008) in Pakistan. Balachandran et al. (2000) had also found that all the

estimated concentrations of PM10 were found to exceed the Indian ambient air quality

standards in the capital of India. Not only in India but also in abroad the concentration

of PM10 shows the tendency to cross the prescribed limit. As for example, the

threshold limit set by the European legislation for PM10 is frequently exceeded in

many European urban areas (Perrino et al., 2007).

4.1.1.2 Total suspended particulate matter (TSPM): In the study area the average

concentration of TSPM is found as 278.716±191.912 µg/m3, 195.090±149.934 µg/m

3

and 334.929±177.431 µg/m3 in premonsoon, postmonsoon and winter season

respectively (Table 4.2).

Like RSPM, total suspended particulate matter (TSPM) shows higher average

concentration in winter followed by premonsoon and postmonsoon. There is a general

tendency of decreasing TSPM level after the initial shower of monsoon. This is

supported by the findings of Ravindra et al. (2003) who found that there is a

significant decrease in TSPM level just after the initial shower of monsoon in Delhi.

Reddy and Ruj (2003) also found that the average TSPM concentration was much

higher in winter than summer and monsoon. But Jain and Saxena (2002) has found

that in Dhanbad and Jharia there is not much variation in TSPM concentration in five

monitoring stations in monsoon (351.120-411.410 µg/m3) and winter (536.880-

602.020 µg/m3).

[101]


In both premonsoon and postmonsoon season the maximum concentration of

TSPM (739.899 µg/m3 and 670.500 µg/m

3) is recorded in R11 location whereas in

winter it is recorded in I2 (737.977 µg/m3) location. This high concentration of TSPM

in R11 may be explained in the same manner as explained in case of RSPM in the

previous section. I2 being located in the rice mill region burning of fossil fuels and

biomass may contribute huge amount of particulate matter. In addition to that this

place is just beside the busiest road i.e. National Highway 2 (NH-2). So, particulate

emission from traffic partially added to the particulate matter to rice mill emission.

Comparison with measurements of other urban areas and cities: The ground level

TSPM in ambient air of Mumbai ranged from 200.000 to 300.000 µg/m3 in residential

areas (Suseela et al., 2000; Sadasivan and Negi, 1990), above 500.000 µg/m3 in mixed

zones of industrial and commercial areas (Sharma and Patil, 1991) and about

1000.000 µg/m3 at local hot spots like busy traffic junctions (Vinod kumar et al.,

2001). TSPM varied from 144.500 to 359.400 µg/m3 in the summer season and

ranged between 154.900 to 282.400 µg/m3 in winter season in Madurai (Kulndai,

2003). It was also found that TSPM concentration ranged from 295.800 µg/m3 to

385.700 µg/m3 by Raja Mohan (2000) in Madurai.

Comparison with National ambient air quality standard (NAAQS) standard: In

premonsoon site R3, R4, R5, R6, R8, R9, R11, R12, and R15 i.e. almost 73.33 % of

residential area lie above the prescribed standard while in postmonsoon it is reduced

to 53.33 % with respect to cross the limit. In winter all residential site (73.33 %)

exceeds the standard except R3, R4, R5 and R13. This finding is very much similar to

the observation of Reddy and Ruj (2003) who had reported that the residential area in

their study field i.e. Raniganj-Asansol area is also above the standard. So, residential

wood stove, burning of fossil fuel, transportation activities (as almost all residential

sites are just beside a major or minor roads), soil resuspension may be the major

contributor of high level of TSPM in the study area.

Regarding industrial site only I2 and I4 have TSPM level above the standard

in winter only. Jain and Saxena (2002) had found that similar kind of observation in

Dhanbad and Jharia. They found that five locations in industrial areas were above the

[102]


standard in winter season. In case of sensitive sites all have the SPM level above the

standard in winter. While site S5 in premonsoon along with S1, S2 and S6 in

postmonsoon are seen to have lower level of TSPM below the standard.

4.1.2 Heavy metal concentration in PM10: In this section the spatial and temporal

variation of the heavy metals viz. Pb, Cd, Mn and Cr fractionated from the respiratory

suspended particulate matter (RSPM) in the study area are discussed.

4.1.2.1 Lead (Pb): The average value for lead (Pb) in the study area is found

0.160±0.117 µg/m3, 0.173±0.126 µg/m

3 and 0.219±0.139 µg/m

3 in premonsoon,

postmonsoon and winter respectively in the study area (Table 4.3). It is observed that

the average concentration of Pb shows distinct seasonal variations with high winter

and postmonsoon value in the study area than the premonsoon value. The high

concentration of Pb in winter may be attributed to relatively stable atmospheric

condition with low dispersion rate. Inspite of the wash out effect of rain high relative

humidity and high wind speed may result in high Pb concentration in postmonsoon

than premonsoon. Similar type of observation is reported by Haritash and Kaushik

(2007).

In premonsoon site R4 is seen to have a very high concentration while in

postmonsoon and winter site R15 and I2 have very high concentration respectively.

Site R15 and I2 are located just beside two main roads like Grand trunk road (GT

road) and National highway 2 (NH-2) respectively. A very high concentration of Pb is

also observed in site R1 and R13 in winter. R4 and R1 are behind a minor road while

R13 is behind the one of the busiest road i.e. Barabazar road. So, vehicle exhausts

could be a major source of this much atmospheric lead in the study area. Apart from

vehicle emission, soil resuspension along the road side may also release some short of

Pb. As in the past few decades continuous use of leaded petrol and emission of Pb

into atmosphere has led to a conservable concentration of Pb in soil along road side.

The movement of vehicles renders the dust containing Pb, resuspended in air

(Haritash and Kaushik, 2007).

A very high concentration of atmospheric Pb in comparison to other site in the

study area is also found at R1, R13, and I1 in winter and at I4, I3 site in postmonsoon.

[103]


Thus, mainly residential and industrial sites are observed to have higher concentration

of atmospheric Pb throughout the study area. This observation can be explained in this

manner. Atmospheric Pb is produced from the combustion of leaded fuel, especially

diesel and petrol, combustion of coal, incineration of wastes and fertiliser

manufacture (Noller et al., 1981; Nriagu, 1988; Melgarejo et al., 1986; Agrawal et al.,

1980; Folio et al., 1982; Pirrone et al., 1996; Van Grieken et al., 1982). So, in

residential area coal combustion may be a major source of atmospheric Pb. Apart

from coal combustion burning of garbage may also release some atmospheric lead in

residential area. Haritash and Kaushik (2007) had noticed similar type of observation

in Hisar. They noticed that higher concentration of Pb is found in commercial and

residential area.

The presence of higher concentration of lead in ambient air of industrial region

may be corroborated due to huge transportation activity (mainly from trucks carrying

goods) and combustion process in industrial region. This phenomenon is also

supported by some published works. Weiwei et al. (2006) had revealed that the high

levels of Pb registered in Wuhan, central China may be related to industrial emissions

from coal combustion and metallic based industry. Mohanraj et al. (2004) found the

highest level of Pb (2147.000 ng/m3) in industrial region in Coimbatore suggesting

the importance of industrial operations in determining the ambient concentrations of

lead.

Comparison with measurements of other urban areas and cities: The average

value of atmospheric Pb is found as 96.000 ng/ m3 in Dhaka, Bangladesh by Mahmud

et al. (2008). So, the atmospheric Pb in Dhaka is much more below the value found in

the study area. Mahmud et al. (2008) also reported that the Pb concentration in Dhaka

city is considerably lower than other Southeast Asian cities. This is probably due to

the ban of the leaded gasoline and two strokes engine (locally called baby taxi) by the

Government of Bangladesh. However, not only in Dhaka but also four important

megacities of India viz. Delhi, Mumbai, Kolkata and Chennai are reported to have the

decline trend in atmospheric Pb (Gupta et al., 2010). The lead concentration in the

study area is almost ten times higher than the value of atmospheric Pb (30.000–50.000

ng/m3) in EU cities reported so far (Weiwei et al., 2006). Tripathi (1994) had found

[104]


the average concentration of airborne lead as 0.300 µg/m3 in Varanasi which is mostly

similar with the value of atmospheric Pb found in our study area.

Comparison with National ambient air quality standard (NAAQS) standard: The

atmospheric Pb does not exceed the NAAQS standard anywhere in all selected

monitoring sites. The permissible value according to WHO and EU standard is 0.500

µg/m3 which is marginally exceeded only at site R4 (0.583 µg/m

3) in premonsoon.

This site is located beside the road but surrounded with many residential houses. So,

apart from vehicle emission coal combustion may contribute this much of

atmospheric Pb concentration in this particular site. However, the tendency of

remaining the Pb concentration within the permissible level in this town may be due

to ban on the use of leaded fuel. This findings of remaining the airborne Pb below the

standard is supported by the published work of Salam et al. (2008) (Bangladesh),

Tripathi (1994) (Varanasi) and Jain and Saxena (2002) (Dhanbad and Jharia).

4.1.2.2 Cadmium (Cd): The average value for cadmium (Cd) is 0.014±0.019 µg/m3,

0.011±0.015 µg/m3 and 0.013±0.015 µg/m

3 in premonsoon, postmonsoon and winter

season respectively in the study area (Table 4.4). Maximum Cd concentration is found

in R10 during premonsoon and in I3 site during postmonsoon and winter season. Site

R10 is located just beside a road while site I3 is located absolutely in the rice mill

zone. So, elevated level of Cd may be due to the release of it from the different

anthropogenic activities like combustion of coal and waste material in residential area

as well as by the local people (mainly the labours of rice mill) residing beside the

industrial area. Elevated level of Cd in industrial region is found in several research

works viz. the higher value of Cd in Chinese town is found due to the combustion and

smelting emission in industrial sector (Weiwei et al., 2006). Salam et al. (2008) had

also observed that the highest value of Cd is found in the industrial area in Dhaka.

Mohanraj et al. (2004) had observed the highest value of Cd (9.100 ng/m3) in

industrial region in Coimbatore. Apart from these research works Kuvarega and Taru

(2008) had revealed that burning of papers in residential and industrial areas also

contribute Cd in the atmospheric air of Harare.

[105]



value of Cd in the study area is very much similar to the total average value of Cd

(13.000 ng/m3)

found in PM2.5 in Dhaka (Salam et al., 2008) and to the concentration

of Cd (14.480 ng/m3) found in total suspended particulate matter in Guiyang (Wu et

al., 2008). In Kolkata Cd concentration is varying in the range of 0.950 to 12.000

ng/m3

at residential site (Kasba) and 1.000 to 19.000 ng/m3 at industrial site

(Cossipore) (Karar et al., 2006). So, the Cd concentration Kolkata is almost same with

the Cd concentration found in the study area. The annual average concentration of Cd

for Delhi was found to be 6.760±6.320 ng/m3 (Khillare et al., 2004) and in Tehran Cd

ranged as 6.800±1.970 ng/m3(Leilli et al., 2008). Thus, the value of Cd concentration

in Delhi and Tehran are far below the value of Cd found in the study area.

Comparison with EU standard (According to 2004/107/CE): The EU target value

for Cd is 0.005 µg/m3. Six monitoring site among the fifteen residential monitoring

sites have exceeded this standard of Cd in premonsoon and winter and five stations

showed the same tendency in postmonsoon. All industrial sites exceed the prescribed

standard in winter and in postmonsoon (except I4 site). Among the sensitive sites five

monitoring station exceed the EU limit of Cd in premonsoon. Four monitoring sites

exceed the same in postmonsoon and three sensitive sites stay within the limit in

winter.

4.1.2.3 Manganese (Mn): The average value for manganese (Mn) in the study area is

0.087±0.062 µg/m3, 0.208±0.292 µg/m

3 and 0.214±0.210 µg/m

3 respectively in

premonsoon, postmonsoon and winter season (Table 4.5). The maximum

concentration of Mn is found at site R3 (0.220 µg/m3) and S1 (0.822 µg/m

3) in

premonsoon and winter season respectively. Both of these two sites are situated

beside road. So, vehicle emission may be a major source of Mn at these sites in the

study area. The exceptionally higher level (1.424 µg/m3) is found at site R11, the

district bus stand of the city in postmonsoon. So, automobile exhaust is the main

source of pollution here. Manganese tricarbinyl compound which being an additive is

used in unleaded petrol to enhance automobile performance (Kulshrestha et al., 2009).

This could be the most probable reason to have such a high level of Mn concentration

here. Apart from that the higher concentration of Mn in the study area also may

[106]


happen due to various anthropogenic activities like transportation activities, ferrous

and nonferrous casting, construction activities and resuspension of soil dust. It is also

an established fact that the rain washes the suspended dust but high wind speed,

direction and congested traffic due to rain may help in accumulation of Mn in air

negating the effect of rain. The same phenomenon was also reported by Haritash and

Kaushik (2007). They found that the average concentration Mn in Hisar was high in

monsoon.

Comparison with measurements of other urban areas and cities: comparing the

value of Mn with urban areas reported so far it is found that the lower range of Mn

(0.012 µg/m3, 0.014 µg/m

3 and 0.012 µg/m

3) in the study area is very much similar to

the value of Mn in EU cities (10.000-30.000 ng/m3) and the higher range (0.220

µg/m3,1.424 µg/m

3and 0.822 µg/m

3) remain quite close to the value of Mn (110.000-

195.000 ng/m3) in Chinese town as reported by Weiwei et al. (2006). The Mn

concentration in Agra (0.900 µg/m3) as per the report of Kulshrestha et al. (2009) is

very much similar to value of Mn in the study area.

Comparison with WHO standard: According to WHO guidelines recommended

level of Mn is 0.150 µg/m3. Out of fifteen residential sites almost 26.4 % and 40 % of

the monitoring site lie above the WHO standard in premonsoon and winter whereas

only 20 % has crossed the limit in postmonsoon. Regarding industrial area all sites lie

below the standard in premonsoon whereas lie above the standard in all site in both

postmonsoon and winter season except I1. All the monitoring sites falling in the

sensitive category have lower concentration of Mn than the standard except S3 in

premonsoon. Three sensitive sites viz. S4, S5 and S6 lie above the standard in

postmonsoon. Almost all the sensitive sites (S1, S2, S3, S4 and S6) are above the

standard in winter season except S5. Thus, the maximum violence of the prescribed

standard has occurred in winter season in all the monitoring sites. The stable

atmospheric condition in winter favouring less dispersion of pollutants may help in

more accumulation of Mn in all sites.

4.1.2.4 Chromium (Cr): The average value of Cr in study area is 0.012±0.047 µg/m3,

0.044±0.209 µg/m3 and 0.014±.0.059 µg/m


[107]


season respectively (Table 4.6). Except a few sites all the monitoring sites have no Cr

in ambient air in the study area. The concentration of Cr is found very high in S1

(1.025 µg/m3). Cycle repairing shops beside this site, dealing with rubber works to

engineering works and grill works, electroplating, chrome Ni plating may be a major

contributor of Cr in this location. However, apart from these sources Cr is mostly

emitted to the atmosphere from coal combustion, the metal industry and waste

incineration (Hlavay et al., 1998). Not only that emission and abrasion from auto

mobiles may also be the source of Cr in the study area. The anomalous concentration

of Cr in the study area is validated by a similar work by Vertika and Upreti (2007).

Comparison with measurements of other urban areas and cities: Leilli et al.

(2008) had found that average concentration of Cr as 9.120±2.140 ng/m3 in Tehran.

Bhaskar et al. (2010) had found Cr concentration as 0.110 to 0.800 µg/m3 as a mean

in Madurai. Mohanraj et al. (2004) in Coimbatore found the range of Cr as BDL to

87.000 ng/m3 and mean as 14.200±14.100 ng/m

3. The amount of Cr found in the

above mentioned cities are quite comparable with the ambient Cr present in the study

area. Weiwei et al. (2006) had reported that the range of Cr in European cities as

1.000 to 10.000 ng/m3 in general but 20.000 to 30.000 ng/m

3 in the place near some

steel hot spot. So, in general the European cities are having less Cr in air than the

study area. Kulshestha et al. (2009) had reported Cr concentration as 0.300 µg/m3

in

Agra. So, in Agra comparatively, more Cr concentration is found in ambient air than

the study area.

4.1.3 Status of inorganic ions in ambient air in study area: In this section the

spatial and temporal variation of the inorganic ions viz. K+, Na

+, F

-, Cl

-, SO4

2-

extracted from the respiratory suspended particulate matter (RSPM) in the study area

are discussed.

4.1.3.1 Potassium (K+): The average concentration of K

+ found in the study area is

2.064±1.615 µg/m3, 3.192±7.722 µg/m

3 and 3.210±3.431 µg/m

3 in premonsoon,

postmonsoon and winter season respectively (Table 4.7). It is observed that the K+ has

the highest average value at winter when PM10 also has the same. Normally, soil is

considered to be the main source of K+. The fine particles of K

+ may be released into

[108]


the atmosphere by burning of plant material (Cooper, 1980). The maximum

concentration of K+ is found in industrial region i.e. 5.495 µg/m

3 in I3 site, 39.200

µg/m3and16.533 µg/m

3 in I2 site premonsoon, postmonsoon and winter season

respectively. Industrial area is dominated by rice mills and in the process of boiling

rice dried plant materials are used as fuel. These burnt plant material could be a major

source of K+ in the study area. Cooper (1980) had found that the burning of plant

material may release fine particles congaing K+.


value of K+ found in Tirupati by Mouli et al. (2003) and Mouli et al. (2006) were

214.578 ng/m3

and 0.410 µg/m3

respectively. Tripathi et al. (2004) had reported the

average value as 0.600 µg/m3 in Mumbai. All these reported values are much lower

than the average value of K+ in the study area.

4.1.3.2 Sodium (Na+): The average concentration of Na

+ is found to be 5.023±3.673

µg/m3, 4.484±3.617 µg/m

3and 5.754±5.300 µg/m

3 in premonsoon, postmonsoon and

winter season respectively in the study area (Table 4.8). The maximum concentration

of Na+ is found in industrial region at site I1 (12.885 µg/m

3) and I2 (22.667 µg/m

3)

during premonsoon and winter season. Site R12 (11.000 µg/m3) is showing maximum

level of Na+ in postmonsoon season. Resuspension of soil derived particles from the

road side could be the possible reason of Na+ in the ambient air in the study area.

Comparison with measurements of other urban areas and cities: Like K+ average

value of Na+ in Tirupati (751.497 ng/m

3) [Mouli et al., 2003] is much lower than

study area but it can be comparable to the level of Na+ found in Mumbai (12.540

µg/m3) [Tripathi et al., 2004] and in Tirupati during 2006 (3.370 µg/m

3) [ Mouli et al.,

2006].

4.1.3.3 Fluoride (F-): The average concentration of fluoride is seen to be 0.232±0.232

µg/m3, 0.340±0.401 µg/m

3 and 0.431±0.633 µg/m

3 in premonsoon, postmonsoon and

winter season respectively (Table 4.9). The maximum concentration is found in I2 site

during postmonsoon and winter season and in R12 during premonsoon. Maximum

concentration of F- at site R12 may be contributed from combustion of coal in that

particular place. According to Xiu et al. (2004) burning of coal and decomposition of

[109]


biomass may release F- in air. Apart from burning of coal from the surrounding area

of the rice mill zone, husk which is widely used in boiler operation in rice mill sector,

may also add F- in the air in site I2 both during postmonsoon and winter.

Comparison with measurements of other urban areas and cities: The F-

concentration found in Tirupati were 20.090 ng/m3 in 2003 (Mouli et al., 2003) and

0.080 µg/m3 in 2006 ( Mouli et al., 2006) which were much less than the F

- found in

the study area. Only the fluoride found in size fraction part in Shanghai (0.440 µg/m3

and 0.670 µg/m3 during spring and winter at industrial site; 0.310 µg/m

3 at

miscellaneous site during both spring and winter season) by Xiu et al. (2004) is

comparable with the ambient fluoride concentration in the study area.

4.1.3.4 Chloride (Cl-): The average chloride concentrations found as

1.663±1.651µg/m3

, 1.797±1.320 µg/m3 and 1.916±1.935 µg/m

3 in this town in

premonsoon, postmonsoon and winter season respectively (Table 4.10). Cl- in coarser

particle might have two formation mechanisms. One was sea salt transformation and

the other might come from the photochemical reactions. Not only that waste

incinerator also gives birth to Cl- in air. As study area is far away from sea the other

two reasons may play the major contributors for Cl- concentration. Coal combustion is

also could be a possible source of Cl- in the study area. Xiu et al. (2004) found that

during the combustion chlorine in coal can be emitted in the form of chloride and

condensed into fine particle or through photochemical reactions on the fine particle

surface in Shanghai.

Chloride is also produced by gaseous HCl through reaction

HCl + NH3 (g) → NH4Cl (Spurny, 2000).

Comparison with measurements of other urban areas and cities: This average

value of Cl- is 231.188 ng/ m

3 in Tirupati (Mouli et al., 2003) whereas 2.440 and

3.150 µg/m3 in PM2.5 fraction of particulate matter in Shanghai (Xiu et al., 2004).

Mouli et al. (2006) had found average Cl- value as 1.059 µg/m

3 in the semi urban site

near Tirupati. The average value of Cl- found in the study area found is mostly similar

to the value of Cl- in Tirupati (2006) whereas almost half to the average value of Cl

-

in Shanghai.

[110]


4.1.3.5 Sulphate (SO42-

): The average concentration of SO4

2- in the study area is

10.734±6.921 µg/m3, 14.928±20.115 µg/m

3 and 14.709±21.131 µg/m

3 in

premonsoon, postmonsoon and winter season respectively (Table 4.11). The ionic

composition of the aerosol shows a trend SO4

2- >Cl

- >F

- for anions and Na

+> K

+ for

cations. This finding of array is also supported by Mouli et al. (2003). Chemical

conversion of SO2 to sulphate is the major reason for the elevated level of SO4 in the

study area. This finding is also very much similar to research work of Bhaskar et al.

(2010) in Kochaidai where the overall SO4

2- concentration ranges from 1.010 to

14.220 µg/m3. The average SO4

2- concentration in the study area is very high in

postmonsoon season. A possible mechanism of formation of SO4

2- is aqueous phase

oxidation of SO2 in cloud droplets (Hegg and Hobbs, 1982; Seigneur and Saxena,

1988; Pandis et al., 1992). Not only that, micro meteorology like wind velocity,

temperature, solar radiation also play a key role in gas phase reaction involving OH

radicals which should have more contribution to the formation of SO4

2-. The

maximum SO4

2- concentration is found in site R4 (29.227 µg/m

3) in premonsoon and

I2 (99.736 µg/m3) in postmonsoon and in R13 (88.884 µg/m

3) during winter. Site I2 is

located in industrial region and conventional fuels like coal, furnace oils are used for

industrial activities emitting SO2. Therefore, the emitted SO2 may be converted to

SO4

2- depending upon prevailing meteorological condition. Site R4 and R13 are

beside a major road of the town. There is no industrial activity but emission from

diesel driven auto mobiles may be the major contributor of SO4

2- concentration in

these two sites. It is also noticed that SO2 concentration is very less in these sites. So,

it can be concluded that emitted SO2 may be converted completely to SO4

2- in

favourable conditions.

Comparison with measurements of other urban areas and cities: Mouli et al.

(2003) reported that the average SO4

2- concentration in Tirupati is 2484.715 ng/m

3.

Mouli et al. (2006) also found that the average concentration of SO4

2- is 5.72 µg/m

3.

These values are almost half of the average value of SO4 concentration in the study

area.

[111]


4.2 Spatio-temporal variation of gaseous pollutant in the study

area

In this section the spatial and temporal variation of the gaseous pollutants like

SO2, NO2, other gases viz. CO and O3 in the study area are discussed.

4.2.1 Sulphur dioxide (SO2): The average value of SO2 in the study area is

10.156±7.411 µg/m3, 7.589±5.340 µg/m

3 and 11.845±7.951 µg/m

3 in premonsoon,

postmonsoon and winter season respectively (Table 4.12).

Among industrial, residential and sensitive sites maximum SO2 level is

observed in (site I2) industrial site where SO2 is found as 23.490 µg/m3, 21.300 µg/m

3

and 30.800 µg/m3 during premonsoon, postmonsoon and winter season respectively.

This might be possibly due to emission from industrial boiler, heating and cooking

sources. The finding of the study area is similar to the findings by Gupta et al. (2008).

It is generally found that all the sites have high winter concentration of SO2 than

premonsoon and low value in postmonsoon season. Precipitation driven wash out may

lower down the postmonsoon value of SO2. Reddy and Ruj (2003) also found the

value of SO2 were high in winter followed by summer and monsoon. Sometimes a

few sites viz. R1, R3, R6 and I3 have higher level of SO2 concentration during

postmonsoon like in comparison to other season. Burning of coal by local people may

be responsible factor for the elevated concentration of SO2. This type of observation is

also reported by Valeroso and Monteverde (1992) in Manila where monsoonal value

of SO2 sometimes become higher negating the effect of rain.

Comparison with measurements of other urban areas and cities: The overall

average value of SO2 in Dhaka is 48 µg/m3 (Salam et al., 2008). But the highest

concentration of SO2 is found in commercial and heavy traffic zone (76.8µg/m3) in

Dhaka by Salam et al. (2008). Verma et al. (2003) found the concentration of SO2 as

41.92 µg/m3

at Lucknow. The concentration of sulphur dioxide was found in Pakistan

in the range 0.02-0.007 ppm (Ali and Athar, 2008). In Haryana SO2 ranged from 9.85

µg/m3 to 37.0 µg/m

3 ( Kaushik et al., 2006). The daily average of SO2 in Kolkata is

12.3±9.2 µg/m3 in residential site, 21.3±15.7 µg/m

3 in industrial and 8 h value of SO2

[112]


was 15.5±11.9 µg/m3 (Gupta et al., 2008). Among these findings Kolkata’s scenario

with respect to SO2 is mostly similar to our study area.

Comparison with National ambient air quality standard (NAAQS) standard: The

concentration of SO2 is comparatively lower in all the seasons than the prescribed

standard of NAAQS in all the monitoring sites. Similar kind of SO2 status was also

high lighted by other research workers such as Reddy and Ruj (2003), Ali and Athar

(2008) and Gupta et al. (2008). Reddy and Ruj (2003) had reported that the all the

monitoring sites in their study field i.e. Raniganj-Asansol area are below the standard

of SO2. Jain and Saxena (2002) in Dhanbad and Jharia also observe the SO2

concentration to be below the prescribed standards. Apart from NAAQS standard the

average value of SO2 is much more below the World Health Organization’s (WHO)

guide line (WHO, 2000: 50 µg/m3) and for the European Union.

4.2.2 Nitrogen dioxide (NO2): The average concentration of NO2 for premonsoon,

postmonsoon and winter are 97.645±79.034 µg/m3, 95.126±52.355 µg/m

3 and

126.557±83.245 µg/m3

respectively in premonsoon, postmonsoon and winter

respectively in the study area (Table 4.13). Through out the study area NO2 level is

very high. The maximum concentration is observed in R5 site which is in the tune of

368.170 µg/m3

in winter season and 202.430 µg/m3

in postmonsoon. The maximum

concentration is found during premonsoon at R6 (363.800 µg/m3). Site R6 is just

beside a major road (Grand Trunk road i.e. GT road) of the town. So, this elevated

level might be attributed to the high traffic density of the town. Similar findings are

also reported by a published work of Weng and Yang (2006). Site R5 is not just

beside the major road of the town. This site is beside a narrow road of the town. So,

the possible cause of the maximum concentration of NO2 at R6 may be due to

influence of road geometry on NO2 level. This finding is supported by the published

work of Khamdan et al. (2009) who found the highest NO2 concentration beside the

roads characterized by being narrow and confined, with many traffic lights and

roundabouts.

Generally, the winter with stable atmospheric condition and less precipitation

helps in accumulation of higher concentration of NO2 in comparison to the other two

[113]


seasons in the study area. In Dhanbad and Jharia more concentration of NOx is found

in winter by Jain and Saxena (2002). However still some deviations in this

observation are found in the study area. Site R8, R13, I1, I2, I4 have higher

postmonsoon value than the other two seasons. This might be explained by over

crowded condition in the town of Burdwan, which was also the centre of commercial

activities in the district. The building structures were constructed literally wall to wall

with very narrow streets separating one block from the other. Even the vehicular

traffic was at most times bumper to bumper and sometimes at a stand still every time

it rained. Hence, the increased amount of exhaust gases in the air has negated the

effect of the monsoon rains. This type of anomaly is again supported by Valeroso and

Monteverde (1992) in Manila where monsoonal value of NO2 sometimes become

higher negating the effect of rain.

Comparison with measurements of other urban areas and cities: Average

concentration of NO2 in Lucknow is 38.240 µg/m3 (Verma et al., 2003). Whereas in

Kolkata the average value of NO2 is 32.500±14.200 µg/m3in residential areas and

49.900±9.800 µg/m3in industrial areas (Gupta et al., 2008) which is much lower than

the average value found in the study area. The NO2 levels in Haryana were between

10.600 µg/m3 (Hisar) to 83.600 µg/m

3 (Gurgaon) and 17.700 µg/m

3 (Bhiwani) to

117.100 µg/m3

(Yamunanagar) in sensitive site and industrial areas respectively

(Kaushik et al., 2006). This value is very much similar to the observed value in the

study area. The increasing trend of NO2, observed in these cities along with our study

area may be due to high vehicular activity. However, the concentration of NO2 was

found in Pakistan in the range as 0.020-0.080 ppm by Ali and Athar (2008) and as

21.000 µg/m3 which is half of WHO guideline value 2005 [(40.000 µg/m

3) (WHO,

2005)] in Dhaka by Salam et al. (2008).

Comparison with National ambient air quality standard (NAAQS) standard:

Within industrial sites, I1 exceeds the standard of NO2 in postmonsoon season only.

To explain the later it could be said NO2 was not only dependent on rainfall but also

dependent on vehicle density and the distance of the monitoring site from road (Lal

and Patil, 2001). Site I3 has the higher-level of NO2 than the standard in winter

season. All the sensitive sites have exceeded its standard of 30 µg/m3

in all seasons.

[114]


The residential sites also have a tendency to exceed the prescribed limit of NO2

almost in all seasons except a few sites like R1, R3, R12, R14, R9 in postmonsoon

and R1, R4, R5 R7, R10, R12, R13, R14 in premonsoon and R9, R10, R11and R13 in

winter. Lal and Patil (2001) also observed that the concentration of NOx exceeded the

CPCB standard for the residential area ( 80 µg/m3) at all sites in Mumbai. According

to their report that even at the distance of 48 m from the source, the concentration of

NOx was high and more than the specified value. Weng and Yang (2006) had also

observed that the Chinese NAQS for NOx (0.1 mg/m3) for residential areas was

exceeded by the city as a whole from 1986 to 1999. Same observation is reported by

Ali and Athar (2008) in Pakistan where the sampling locations were higher than the

USEPA limit of 0.05 ppm.

4.2.3 Other gaseous pollutants: Ozone (O3) and Carbon monoxide (CO)

4.2.3.1 Diurnal variation of O3 and CO in March and April: Both surface Ozone

(O3) and Carbon monoxide (CO) is monitored during the month of March and April

over three times a day for one hour duration at four sampling sites being

representatives of four zones viz. residential zone, industrial zone, sensitive zone and

traffic zone. Meteorological conditions are also taken into account to know its

influence in these criteria pollutants in the study area. In the month of March the

concentration of O3, CO, temperature, humidity and wind speed ranges from 9.70-

33.50 ppb, 0.33-10.25 ppm, 30.7-42.7°C, 17.4-80.1 % and calm-14.1 Km/hr

respectively; while in the month of April the variation of O3, CO, temperature,

humidity and wind speed are 3-33 ppb, 0.35-11.95 ppm, 30.6-50.3°C, 26-84.6 % and

calm-16.3 Km/hr respectively [Table 4.14 (a) & (b)]. The predominant wind direction

is South West, South East and North East in the March while in April it is in the

direction of South West, South and South East direction. It is found that the

concentration of O3 is not only function of its precursor viz. CO but also a function of

prevailing meteorological conditions. It is observed that the concentrations of O3 are

increased with the decreasing concentration of its precursor and vice versa.

The peak amplitude of O3 is generally in the noon (12:00-1:00 pm) time

whereas CO is observed to have the same in the morning hours between 7:00 am to

[115]


8:00 am. In the study area during both the monitoring months i.e. March and April

minimum concentration of O3 is found in morning whereas CO level has reached to

maximum. However, in the noon or afternoon the same observation becomes reverse

i.e. O3 reached its maximum value and CO reached to its minimum value. This

finding might be explained in this manner. The amounts of precursor in the morning

play a major role on after noon O3 peak levels. This phenomenon has been

investigated using the lag correlation study by Beig et al. (2007). They reveal that a

time lag of 5-7 hour is required for most of these precursor gases to photo chemically

produce O3 to its maximum potential. From the diurnal study it can be interpreted that

the precursor pollutants of O3 is built up in early morning hours along with its

maximum concentration in noon or afternoon.


value of O3 and CO are 22.78 ppb and 2.46 ppm in March; 16.62 ppb and 2.73 ppm in

April. This amount of O3 is quite less than the maximum concentration of ground

level O3 found by Beig et al. (2007)(43.02±16.47 ppbv) at Pune. Debaje et al. (2003)

at Tranquebar had found the maximum concentration of O3 (23.00±9.00 ppbv) in

summer. It is also reported that on the diurnal scale, O3 shows maximum

concentration in afternoon (2:00 pm-4:00 pm) and minimum (7:00 am-8:00 am local

time) at Pune (Tiwari and Peshin, 1995). Pulikeshi et al. (2006) reported that the

concentration of O3 varies between 2.00 to 53.00 ppb in Chennai. Salam et al. (2008)

had measured CO and O3 at five locations in Dhaka. Their sampling time was 8 hour

for O3 and 1 hour for CO. CO was observed to have the maximum concentration as

334.00 µg/m3 in industrial site. But in the study area the maximum concentration is

associated in traffic area (14.69 mg/ m3). Total average concentration of O3 was 28

µg/m3 in Dhaka which is lower than the daily maximum value of 100 µg/m

3 (WHO,

2005). The same scenario is observed in study area. In Pakistan CO is found within

the range of 1.50 to 5.30 ppm, 2.80 to 6.10 ppm and 2.70 to 5.60 ppm (Ali and Athar,

2008) which are very much similar to the concentration of CO in the study area.

Comparison with NAAQS (1994) standard: According to NAAQS (1994) the limit

value of 1 hour average CO are 2.0 mg/m3

(sensitive area), 10.0 mg/m3(for industrial

area ) and 4.0 mg/m

3(residential, rural and other areas). Comparing with this standard

[116]


it is noticed that all sensitive sites have exceeded its standard in both morning and

afternoon session except S4 in morning and S6 at afternoon session. The opposite

scenario is observed in industrial site. It is observed that the entire industrial site does

not exceed its standard. Among the residential sites R3, R8, R10, R11, R12, R13 and

R15 have crossed the said limit of 1 hour CO (Table 4.15).

Comparison with primary standard: The NAAQS primary standard is 35 ppm for

CO. Primary standards are set as threshold limit to protect public health, including the

health of "sensitive" populations such as asthmatics, children, and the elderly. So,

with respect to primary standard CO is quite safe in this town. Average value of 1

hour CO and O3 are 3.44±2.58 ppm (range: 0.80-12.95 ppm) and 23.17±8.70 ppb

(range: 7.00-45.00 ppb) respectively (Table 4.15). The NAAQS primary standard is

0.12 ppm (120.00 ppb) for O3. Therefore, with respect to primary standard O3 shows

no more danger right now to this town.

Comparison with NAAQS (2009) standard: The recent NAAQS standard (2009)

has limited the 1 hour CO value as 4 mg/m3 and for O3 180 µg/m

3 for industrial,

residential, rural and other areas along with ecological sensitive area. The average

value found in this town for CO and O3 are 4.24±3.17 mg/m3 (range: 0.98-15.19

mg/m3) and 48.89±17.53 µg/m

3 (14.77-94.95 µg/m

3) (Table 4.15). So, the average

CO value in the study area has crossed the prescribed limit. On the other hand O3 is

again far below the standard. This phenomenon is supported by the findings of Verma

et al. (2003) as they had found that the concentration of ground level O3 in sensitive

sites of Lucknow was not exceeded its standard.

4.2.3.2 Meteorological influence on diurnal variation of O3 and CO in the study

area

Micrometeorology plays a significant role in distribution of the secondary

pollutant. In the following section micrometeorological influence with respect to

different zones i.e. industrial, residential, sensitive and traffic area has been discussed.

Industrial Area: In the morning wind speed is initially low i.e. 4.5 Km/hr and

thereafter is increased to 7.2 Km/hr in the noon in March. The increased wind speed

may add some precursor from the surrounding to the measurement site which may

[117]


result the higher O3 concentrations (33.40 ppb) at noon [Table 4.14 (a) & (b)]. O3

again is showing a significant positive correlation with wind speed (Satsangi et al.,

2004) at this site. So, wind helps to accumulate the precursors of O3. The maximum

O3 concentration was found at noon (30.40 ppb), when the temperature is also

maximum i.e. 42.7 ºC. In this time relative humidity is minimum (17.4 %). The

humidity reached maximum 80.1 % in the early morning. The maximum CO

concentration (2.46 ppm) was found in the morning in March. Other than vehicle

emission from rice mill also add some of CO in this site. The situation of O3 is quite

different in the month of April. The minimum ozone concentration is reported as

11.50 ppb in morning and the maximum O3 concentration (33.00 ppb) is found at

noon, when the temperature is high (43.9°C) (Bhugwant and Brémaud, 2001). Early

morning, as the vehicular density has increased, the CO concentration is also

increased. So, in both March and April the maximum concentration of CO is found in

the morning in this site. Apart from different anthropogenic emission of CO early

morning inversion could also be a probable reason for it.

Residential Area: In the month of March the hourly O3 concentrations of the three

sessions of the day of monitoring are seen as a function of relative humidity, wind

speed and temperature. In early morning, O3 concentration is lower than noon and

afternoon [Table 4.14 (a) & (b)]. The average value O3 concentration increases

gradually as the temperature increases and relative humidity decreases. O3

concentration has reached its first peak (33.50 ppb) in 12:00 pm -1:00 pm. This type

of behaviour is also recorded by National Research Council (1991). Meteorology for

this particular observation is in this manner: temperature - 35.9 ºC, relative humidity-

27 % and wind speed-0.8 Km/hr. The O3 concentration was decreased slightly in the

afternoon i.e. 31.30 ppb when the wind speed was calm. The maximum O3

concentration of this station is 33.5 ppb at noon. The temporal variation of O3 in the

month of April was little bit different from March. In early morning O3 concentration

is 17.00 ppb when the temperature is very high (50.3 ºC), relative humidity is low

(31.6 %) and wind speed is 2.5 Km /hr on that day. High temperature always favours

the photochemical process of O3 formation. Some researchers had reported the same

observation in Chennai where high concentration of O3 is found with high

[118]


temperature and low humidity (Pulikeshi et al., 2006). Apart from this work Jain et al.

(2005) had also found higher levels of O3 at Delhi due to warm and sunny days along

with low humidity. The average O3 concentration is decreased gradually as the

temperature is also decreased. In the noon the temperature is 45 ºC and the

concentration of O3 becomes 14.00 ppb when wind speed is 3.6 km/hr. O3 has showed

a significant positive correlation with wind speed (Satsangi et al., 2004). So, inspite of

low temperature this much of concentration of O3 is found in the noon. The CO

concentration is also low at this time. In case of afternoon the ozone concentration is

decreased following the fall of the temperature. But in the afternoon the CO

concentration (4.85 ppm) becomes higher. It might be due to combustion from

domestic sources. Sometime it is also observed that inspite of increase in the

precursor of tropospheric O3 the fall in O3 concentration was recorded as due to the

temperature fall (Narumi, et al., 2009).

Sensitive area: In the month of March the highest concentration of O3 (29.50 ppb) is

observed in the afternoon [Table 4.14 (a) & (b)] when temperature was 33.3 ºC and

relative humidity is 57 %. It is clearly noticed that in morning O3 concentration is

18.50 ppb when CO concentration was 1.53 ppm. CO concentration is decreased

when O3 concentration is increased in afternoon. The scenario of these pollutants in

the month of April in the same spot is reflecting that in the morning, the minimum

concentration of O3 is found as 13.50 ppb when the temperature is 30.6 ºC and CO

concentrations is also high. The cause of high CO concentration in the morning may

be due to the combustion of domestic sources (combustion of coke from the domestic

area surrounding the agricultural land). In the contrary O3 concentration has reached

its first peak at afternoon. Beig et al. (2007) had revealed that a time lag of 5-7 hour is

required for most of these precursor gases to produce O3 photo chemically to its

maximum potential. It may be one of the causes for the increasing O3 concentration in

the afternoon with the decreasing value of CO concentration.

Traffic area: This sampling site is the most traffically congested area of the town. In

the month of March it was found that the maximum O3 concentration is recorded as

18.00 ppb at noon [Table 4.14 (a) & (b)]. It is seen that relative humidity reached

maximum in the early morning and gradually it is decreased. On the other hand

[119]


relative humidity decreased its value as the ambient air temperature increased. As this

site is a traffically congested area CO concentration is increased in the morning with

the increasing number of the vehicular density. The same findings were replicated in

the afternoon. In April at this sampling spot it is found that the O3 concentration

reached its maximum (15.00 ppb) at noon when the temperature also has reached its

peak (43.4 ºC) and at this time humidity is 42.8 % and the wind speed is 6.4 Km/hr. It

was seen that CO concentration is found very high in the morning when the traffic

activity / vehicular density is also very high.

4.3 Statistical interpretation of analytical parameters

4.3.1 Pearson correlations among variables

4.3.1.1 Premonsoon: From the correlation analysis it is observed that RSPM has

positive correlation with TSPM (r value is 0.994) at 0.01 levels. Not only that RSPM

has also good positive correlation with Mn (r = 0.567), SO4

2- (r = 0.523) and F (r =

0.635). In the same manner TSPM has again positive correlation with Mn (r = 0.598),

SO4

2- (r = 0.572) and F

- (r = 0.589). Positive correlation also exists between rainfall

and SO4

2- (r = 0.598) at 0.01 significant level (Table 4.16). Mn is originated mainly

from vehicle and industry. Whereas F- may come from the coal combustion and

biomass burning. SO4

2-, the secondary pollutant mainly comes in ambient air from

photochemical oxidation of sulphur containing precursors such as SO2, H2S and CS2

etc. Amongst them SO2 which is originated from burning of biomass, is the largest

contributor of SO4

2-. So, indirectly biomass burning is the possible cause of SO4

2- in

ambient air. Thus, vehicle emission and biomass burning could be the possible

sources for particulate matter, Mn, F- and SO2 in the study area. Aqueous phase

oxidation of SO2 gives birth to atmospheric SO4

2-. Rather this emitted SO2 is

converted to SO4

2- by aqueous phase reaction. So, a positive correlation is found

between SO4

2- and rainfall.

4.3.1.2 Postmonsoon: Like premonsoon in postmonsoon TSPM and RSPM is highly

correlated (r = 0.968) in a positive manner. RSPM and TSPM also have strong

positive correlation with Mn (r = 0.519 and r = 0.587) and Na+ (r = 0.613 and r =

0.536). K+ is noticed to be significantly (0.01 level) correlated with SO2 (r = 0.537),

[120]


SO4

2-

(r = 0.859), F- (r = 0.615) positively whereas with temperature (r =-0.764)

negatively. Mn is having a relation with F- (r = 0.581) in this season. This may happen

due to emission from vehicles used in industrial sector where mainly the high F-

concentration originated from burning of husk is found. Temperature is showing a

negative correlation with both SO4

2-

(r =-0.633) and F- (r =-0.511). Humidity and

rainfall is showing positive correlation (Table 4.17). This finding is supported partly

by the published work of Mouli et al. (2006) who found that a relatively good

correlation between aerosol mass and SO4

2-, K

+ and Na

+ as well as SO4

2- and K

+.

Potassium, F- and SO4

2-

are related to the burning procedure. But only Na is of soil

origin. So, majority of particulate matter either originate from soil dust or from

burning of biomass. Temperature has a negative relation with the SO4

2- or F

-. This

may happen because when the temperature falls the local people may use more

biomass for burning to generate heat. It is an established fact that when humidity

increases rainfall occurs. That’s why humidity and rainfall have a positive relationship

between them. Han et al. (2005) found a very well correlation between K+ and SO4

2-

which were suggested as tracer of bio mass burning.

4.3.1.3 Winter: In winter RSPM and TSPM both have significant positive relation (r

= 0.955). A significant positive relation also exits between RSPM and K+ (r = 0.509).

RSPM is also positively related to Na+ (r = 0.661), Cl

- (r = 0.540) and F

- (r = 0.541).

Just like RSPM, TSPM has also a 0.01 level significant positive relation with K (r =

0.550), Na+ (r = 0.663), Cl

- (r = 0.564) and F

- (r = 0.634). Mn is positively related

with K+ (r = 0.779) and F

- (r = 0.623) whereas Na

+ is correlated with K

+ (r = 0.676),

Cl- (r = 0.748) and F

- (r = 0.641). F

- is associated with SO2 (r =0.574), K

+ (r = 0.825)

and Cl- (r = 0.654) (Table 4.18). In explanation of this positive correlation the

following assumption may be taken into consideration. As discussed earlier

particulate matter containing K+, Cl

- and F

- are mainly of biomass burning origin. Mn

is related to K+ and F

-. So, industrial origin (where biomass burning occurs frequently

and traffic density is also high) is the common source of them. As Na+, Cl

- and F

- may

be of natural origin that is why they again are correlated. F- and SO2 may come from

combustion of coal. Again Na+, K

+ and Cl

- are of natural origin, so they are

correlated.

[121]


4.3.2 Factor analysis

Several factor rotation techniques such as varimax, quartimax, direct oblimin,

equamax etc are available in literature. In this study varimax rotation, which is most

commonly used has been adopted. Varimax rotated factor analysis was carried out

using XLSTAT (2011) with particulate matter, trace elements present in it and

gaseous pollutant s along with meteorological parameters. Thus six/five factors are

selected on the criteria of cumulative percentage variance > 70 % and eigen value >

1.0 (Table 4.19 to 4.24). The following assumptions are considered in this analysis.

The higher the absolute value of the loading, the more the chemical elements are

considered as fingerprints of the emission source, allowing for the identification of

specific emission sources. If two variables in factor are of same algebraic sign, a

direct relationship exists between the variables and the factor vice versa.

4.3.2.1 Premonsoon: In premonsoon season six factors are extracted based on the

said criteria. This factor accounts for 22.28 % of the variance with high load value of

RSPM, TSPM and Mn (factor loadings 0.76-0.94) in the observations. SO4

2- and F

-

are also observed to be moderately loaded (0.65-0.66) in this factor. So, the source of

SO4

2- and F

- may be included. However, TSPM, RSPM are clustered with Mn which

denotes mainly vehicle origin. So, emission from vehicle could be a major source

contributor of pollutant source in the study area.

In second factor which accounts 12.23 % of the variance, potassium is

principal component with high load value of 0.80. It is a major tracer of vegetative

burning (Tripathy et al., 2004). A medium load value of Pb and rainfall (- 0.59 to -

0.64) is also observed in this factor. Pb and rainfall is found to be positively related as

they both are of same sign. So, it can be said that during heavy rain scavenges out the

resuspended soil containing Pb in it.

The third factor represents high load value of temperature (- 0.85) and medium

load value of SO2 (0.66) which accounts for only 9.77 % of the total variance. For

space heating in colder temperature local people burnt more coal. So, SO2 and

temperature is inversely related. This finding is supported by the published work of

[122]


Bridgman et al. (2002) revealed that SO2 concentrations were strongly related to

colder temperature.

The fourth factor (10.21 %) is mainly attributed to Cr (0.80) and NO2 (0.80).

Cr is of industrial origin. But vehicle tier made of rubber etc may release Cr in

atmosphere. So, resuspended dust due to vehicular movements as well as vehicle

emission is found from this factor.

The fifth factor (8.91 %) is contributed by Cd (0.67) and humidity (0.68)

mainly. The high relative humidity may result in higher concentration of trace metals

(Haritash and Kaushik, 2007). So, emitted Cd from coal combustion and waste

incineration may accumulate more in humid atmospheric condition.

The sixth (12.05 %) factor is highly loaded by Na+ (0.86) and Cl

- (0.72). Cl

- is

of mainly originated from coal burning. So, soil resuspension and coal combustion

may cause it.

In premonsoon one natural source of pollution i.e. soil resuspension and two

anthropogenic sources viz. vehicle emission, coal combustion or biomass burning is

identified as major source of air pollution.

4.3.2.2 Postmonsoon: The first factor accounts for 16.77 % of the variance, with a

high load value of RSPM (0.81), TSPM (0.86) and Mn (0.87) just like premonsoon.

So, vehicle emission along with resuspended road dust is the common source of these

pollutants in the study area. A medium load value (0.56) of F-is also observed in this

factor.

The second factor represents 20.15 % of the variance of the samples in the

dataset. It is made up with high load value of K+ (0.95), SO4

2- (0.85) and temperature

(-0.86) whereas medium load value of SO2 (0.63) and F- (0.61). K

+ is usually related

to biomass combustion (Weiwei et al., 2006) presents a high load value along with

SO4

2- and temperature (- 0.86) in this factor. Similarly SO2 and F

- are also originated

from biomass burning. SO4

2- in ambient air is mainly originated from photochemical

oxidation of sulphur containing precursors like SO2. Biomass burning is the principal

source of SO2 in the atmosphere. Therefore, conversion of SO2 to sulphate was the

[123]


major reason in this city. And temperature and SO2 are inversely related (Bridgman et

al., 2002).

The third factor (11.30 %) is attributed by Pb (0.90) and medium load value of

Cd(0.69) and NO2 (- 0.57). Coal combustion and incineration of wastes are the

common source of Pb and Cd. So, combustion of coal and wastes along with vehicle

emission could be the possible source of them in the study area.

The fourth factor is mainly attributed to humidity with 12.99 % variance. A

high load value of humidity (0.85) and medium load value of rainfall (0.69) are also

observed in this factor. Gupta et al. (2008) also found humidity as the representative

of a factor in Kolkata. In this factor humidity is not correlating with other pollutants.

The fifth factor (8.73 %) is accounted by Cr (0.88). Solid waste incineration

and vehicle tier also may release it.

The sixth factor (10.40 %) is corroborated by Na (- 0.69). Na+ is of soil origin.

So, road soil resuspension may be one factor in postmonsoon.

So, in postmonsoon one geogenic source i.e. soil resuspension and other

anthropogenic source like vehicle emission along with combustion of biomass and

waste material are identified as major source of air pollution in the study area.

4.3.2.3 Winter: In winter five factors are extracted. The first factor (30.91 %) is by

RSPM (0.84), TSPM (0.86), Mn (0.73), K+ (0.82), Na

+ (0.82), F

- (0.81) and Cl

-

(0.71). In this factor particulate matter is clustered together with metals and ions

which are of anthropogenic origin as well as geogenic origin. So, this factor

represents contribution from vehicle emission and resuspended road dust due to

vehicle movements along with burning of biomass. SO2 also has a medium (0.55)

load value. Biomass burning could be the possible anthropogenic source.

The second factor (10.52 %) is accounted by Cd (0.75) and temperature (-

0.87). The major sources of Cd in the study area are far found as the combustion of

coal and waste material. So, it can be concluded that in colder temperature

combustion procedure releasing Cd in air is increased by the local people.

[124]


The third factor accounts 10.33 % variance with a high load value of rainfall (-

0.83) which is not related with any other pollutants.

The fourth factor accounts for 10.59 % with high load value of NO2 (- 0.69)

and SO4 (0.72). This factor is also attributed to winds peed in medium load value (-

0.57). So, vehicle and biomass burning could be traced as source of air pollution from

this factor. Wind speed has no direct impact on SO4

2- concentration where as direct

impact on NO2 concentration. This may happen due to location of pollution source.

Perhaps wind from particular direction is helping in accumulation of the emitted NO2.

The fifth factor (9.97 %) is attributed to high load value of humidity (0.80) and

Cr (0.64) and moderate load value of SO2 (- 0.57) with moderate loading. Just like

other heavy metal Cr a tracer of solid waste incineration is also positively influenced

by humidity. Moderate load value of negative sign indicates its different origin.

Besides combustion procedure auto mobiles, especially diesel driven vehicles also

emit SO2 in considerable quantity (Bhaskar et al., 2010).

So, in winter like other two seasons, one geogenic pollution source i.e. soil

resuspension along with anthropogenic sources viz. vehicle emission and combustion

of coal and waste are identified.

4.3.3 Air Quality Index (AQI)

Table 4.25 to 4.27 represent air quality index and air quality category

corresponding to the index calculated on basis of four criteria pollutants viz. PM10,

NO2, SO2 and Pb. The seasonal variation of AQI for each site in each zone are

achieved to reflect the town’s status in premonsoon, postmonsoon and winter season.

4.3.3.1 Premonsoon: In premonsoon the residential sites R1, R2, R3, R14 and

industrial sites I1, I2 are found as “Clean” in premonsoon season. R5, R6, R7, R8,

R10, R12, R13, R15, I3, I4 are found to fall in “Fairly clean” category whereas R4,

R9 and R11 are found as “Moderately Polluted” category. So, mainly three categories

are observed amongst the residential and industrial zone in premonsoon season. In the

same season a little bit more pollution is observed amongst the sensitive sites. The

AQI value reflects that except S5 and S6 (falling under “Fairly clean” category), the

rest is under “Moderately polluted” zone to “Polluted” zone (Table 4.25).

[125]


4.3.3.2 Postmonsoon: In postmonsoon season R1, R2, R13, I1, I2 and I3 are

represented as “Clean” while all other residential site and industrial site are

represented as “Fairly clean” category except R5. So, the air quality status becomes

quite better in the premonsoon season. Regarding sensitive zone S1 falls in “very

clean” category and S2, S6 are in “fairly clean” and the rest sensitive sites are found

as “Moderately polluted”. As the monsoon approaches to the town it is observed that

due to scavenging effect of rain the situation becomes quite clear with respect to AQI

value (Table 4.26).

4.3.3.3 Winter: The Winter AQI reflects that R1, R9, R15, I2 are “Moderately”

polluted and R3. R13, I3 are “Clean” category and the rest is of “Fairly clean”

category. The sensitive sites are found as the most polluted in this season in

comparison to the other two seasons. S4 is severely polluted while S5, S6 are

moderately polluted and S1, S2, S3 are found to lie in “Polluted” category. So, the

pollution level with respect to AQI again increased due to less circulation for stable

and stagnant condition of winter in the study area (Table 4.27).

4.3.3.4 Percent (%) wise distribution in each category of AQI: About 53 %, 73 %

and 66 % of residential area falls under “fairly clean” category in premonsoon,

postmonsoon and winter season respectively while 26 %, 20 % and 13 % of the said

area comes under “clean” category. “Moderately polluted” category is found lowest

(6.66 %) in postmonsoon and found highest (20 %) in premonsoon and winter season.

In postmonsoon 75 % of the industrial area comes under ‘Clean” category

while it becomes 25 % in winter and 50 % in premonsoon. In winter an additional

polluted category apart from “Clean and “Fairly clean” category is observed i.e.

“Moderately polluted” category, covering 25 % of industrial site in winter season.

In case of sensitive zone a “very clean” category (16 %) is observed through

out postmonsoon season, while 33 % of each category i.e. “Clean”, moderately

polluted and “polluted” is observed in premonsoon season. During postmonsoon

season 33 % and 50 % of the monitoring sites comes under “Fairly clean” and

“moderately polluted” category. But the pollution level has increased in winter season

[126]


as the AQI status is found as 33 % of “Moderately polluted” category, 50 % of

“Polluted” category and 16 % of “Severely polluted” category.

4.4 Overall interpretation of spatio-temporal variation of

particulate matter, gaseous pollutants by means of GIS

In this section overall air quality status of the study area is interpreted by

means of thematic distribution of particulate matter and gaseous pollutant aided by

windrose diagram. Areal coverage (sq km) of the study area with respect to different

categories of AQI is also represented by means of inverse distance interpolation

technique (IDINT) in a GIS environment.

Spatio-temporal variation of RSPM, SO2, NO2, Pb are represented by Figure

4.4, 4.5 & 4.6; Figure 4.7, 4.8 & 4.9; Figure 4.10, 4.11 & 4.12; Figure 4.13, 4.14 &

4.15. Windrose diagrams are represented by Figure 4.1 (a) & (b); Figure 4.2 (a) & (b)

and Figure 4.3 (a) & (b). Projected areas under different areas under the different

categories of AQI are represented in Figure 4.16, Figure 4.17 and Figure 4.18.

4.4.1 Overall scenario of spatio-temporal variation of RSPM: In premonsoon

three highly concentric zones of PM10 (i.e. the zone is having RSPM value greater

than 150 µg/m3 and covering 8.148 sq km area (Table 4.28) is found in Burdwan

municipality area. One of these zones is seen in between north to east, other is in

between south to east and the third one is in south west (Figure 4.4). The mechanism

of accumulation and dispersion of pollutants is quite complex in nature. As pollutant’s

fate is also affected by local land use pattern and domestic behaviour of local people

sometime negating the effect of season. Not only those other meteorological factors

viz. wind direction and speed is also a major factor in determining the pollution level

in an urban area. Wind speed is important in flushing out of pollutants as strong winds

flush out the pollutants of the system while low winds allow pollution level to rise

(Gupta et al., 2008).

The premonsoon windrose of the study area is reflecting that the predominant

wind direction in this season was from N, NE, SE, S, SSW, SW and W. However,

high speed wind mainly from SE, SW and S [Figure 4.1(a) & (b)]. This high speed

wind in the particular direction might have carried the RSPM from the industrial area

[127]


and traffic area and helped in their accumulation in the three zones in the north to

east, south to east and south west part of the town. Figure 4.4 also represents that from

this highly concentric zone RSPM seems to be diluted and spread out in different

concentration zone of RSPM like a zone having RSPM value in between 100-150

µg/m3 (6.783 sq km), 75- 100 µg/m

3 (3.921 sq km) and less than 75 µg/m

3 (6.448 sq

km).

In postmonsoon the region between north east to east and southwest part of the

town are seen to have highly concentrated zone (> 150 µg/m3 and covering 4.028 sq

km) of RSPM (Figure 4.5). Predominant wind direction in this season is in the

direction of N, NE, S, SSW and SW. It is observed that a very high velocity wind

blows from the direction of ESE, S, SSW and W [Figure 4.2 (a) & (b)]. Accumulation

of particulate matter and wind speed are inversely related. So, a comparatively a

lower concentration of RSPM is found in the area of the town affected by the high

speed wind blew from these said direction. Concentric zone having RSPM value in

between 100-150 µg/m3

are covering 5.621 sq km, between 75- 100 µg/m3

are of

5.105 sq km ) and less than 75 µg/m3are of 10.546 sq km).

In winter the north western, northern, south eastern, north to north western,

south to south western and western part of the town are dominated by highly

concentrated zone of (> 150 µg/m3 covering 14.413 sq km) RSPM (Figure 4.6). Wind

direction is from N, NW, NNW, NE, SW and S. high speed wind is from N, NW,

NNW and S [Figure 4.3 (a) & (b)]. Apart from wind speed, less rainfall in this season

favoured the accumulation of such a high level of RSPM in the said part of the town.

Another zone having RSPM value in between 100-150 µg/m3 are of 8.063 sq km

whereas in between 75- 100 µg/m3

are of 1.202 sq km and less than 75 µg/m3is

covering 1.622 sq km area.

Apart from these observations of meteorological influence on RSPM in the

three seasons the windrose diagram is also reflecting that the calm condition is

prevailed as whereas only 3 and 11 % in premonsoon; 11 and 12 % in postmonsoon;

12 and 27 % in winter. An elevated pollutant concentration is contributed with the

increase of calm condition favouring stable atmospheric condition and less dispersion

[128]


of pollutants vice versa (Gupta et al., 2008). The total rainfall is 236.4 mm and 393.8

mm in premonsoon; 1296.8 and 900.4 mm in postmonsoon; 29.9 and 474.3 mm in

winter season during two monitoring years. The scrubbing effect of particulate

pollution is also a function of total rainfall in that season. So, a low level of particulate

matter is found in postmonsoon instead the calm condition being very much similar

with winter and premonsoon. Thus, due to the influence of micro as well as

macrometeorology, more pollution is found in winter followed by premonsoon and

postmonsoon.

4.4.2 Overall scenario of spatio-temporal variation of SO2: The study area is

covered under only one concentric zone of less than 30 µg/m3 in three seasons (Table

4.29). So, the scenario is same in premonsoon, postmonsoon and winter (Figure 4.7,

Figure 4.8 and Figure 4.9).

4.4.3 Overall scenario of spatio-temporal variation of NO2: In premonsoon two

highly concentrated zones of NO2 i.e. the zone is having NO2 value greater than 120

µg/m3 (4.836 sq km) (Table 4.30) are found in the study area. One of these zones is

seen in mainly northern part, other is in south to east. Then just like RSPM, NO2 also

seems to be diluted and spread out in different concentric zone of NO2 like a zone

having NO2 value in between 80-120 µg/m3 (6.113 sq km), 30- 80 µg/m

3 (14.003 sq

km) and less than 30 µg/m3 (0.530 sq km) [Figure 4.10].

Premonsoon windrose diagram reflects that N, NE, SE, S, SSW, SW and W

are the predominant wind direction in this season in the study area. However, winds

having high speed come mainly from SE, SW and S. This high speed wind of wind

pattern might have carried the NO2 from traffic emission along the major road in the

southern part of the town and helped in their accumulation in the northern zone and

also in south eastern zone of the town. Whereas wind speed ranging from low to

moderate particularly from N and NE portion helps in building a very high

concentration of NO2 in northern part after the emission of NO2 from the major road

in the northern part of the town. N and NE part is also a dominant agriculture area of

the town. So, NOx emission from this agricultural area may be the major contributor

in this portion.

[129]


In postmonsoon the situation becomes more polluted as it is seen that three

concentric zone of greater than 120 µg/m3 (4.973 sq km) in northern part, south

eastern part and southern part of the town (Figure 4.11). A concentric zone of 80-120

µg/m3 (9.748 sq km) is covering almost half portion of the total area of the town in

the north eastern portion. Rest half of the total town area is covered by the concentric

zone of 30-80 µg/m3

(10.297 sq km) except a smaller portion of the town in the south

having concentric zone of 80-120 µg/m3. An area of 0.398 sq km is covered by less

than 30 µg/m3 in the study area. Predominant wind direction in this season is from N,

NE, S, SSW and SW. A very high velocity wind speed blew from ESE, S, SSW and

W. So, high velocity wind from south may disperse the accumulated NO2 in the south

portion of the study area whereas more accumulation of NO2 has occurred in the same

direction of prevailing wind pattern.

In winter more pollution with respect to NO2 is seen as the whole north

western part and south eastern part of the town are having greater than 120 µg/m3

(12.269 sq km) NO2. Only the middle portion between these zone (north western part

and south eastern part having greater than 120 µg/m3 NO2) are covered by the

concentric zone of 80-120 µg/m3 (8.446 sq km) and by the concentric zone of 30-80

µg/m3

(4.304 sq km) [Figure 4.12]. Again just like postmonsoon only 0.396 sq km

area is covered by less than 30 µg/m3 in the study area. Wind direction was from N,

NW, NNW, NE, SW and S. So, it can be said that more accumulation of NO2 is

occurring with the prevailing wind pattern particularly in north western portion of the

study area. High speed wind blew from the same direction i.e. N, NW and NNW. As

discussed earlier that in winter calm condition (12 and 27 %) prevails more than the

other two seasons. Thus, it may concluded that stable atmosphere and low mixing

height have trapped the pollutant in particularly in those area affected by these

direction. High speed wind blew also from S which might have carried the NO2 and

helped in accumulation in south eastern part.

4.4.4 Overall scenario of spatio-temporal variation of Pb: The scenario of Pb is

just same as SO2. The whole study area is covered under the concentric zone of less

than 0.75 µg/m3 (Table 4.31) (Figure 4.13, Figure 4.14 and Figure 4.15).

[130]


4.4.5 Overall scenario of spatio-temporal variation of AQI: Premonsoon

Thematic map reveals that the western part of the town mainly covered by clean area

(2.957 sq km) (Table 4.32) while the minor part of northern portion, south western

part and south eastern part of the town represent the moderately polluted (3.290 sq

km) region. A very small portion of the town (beside S2) is seen to be polluted (0.031

sq km) while majority of the town is covered by fairly clean area (18.753 sq km)

(Figure 4.16).

The premonsoon windrose of the study area is reflecting that the predominant

wind direction in this season was from N, NE, SE, S, SSW, SW and W. However,

high speed wind mainly comes from SE, SW and S.

After the offset of monsoon same scenario is observed. Fairly clean area

(22.262 sq km) is covering the whole town except a few portions in the middle to

north and north western part. This portion is found as clean (2.744 sq km) while a

little patch of very clean (0.298 sq km) category is seen beside S1. Moderately

polluted (0.019 sq km) patch is seen behind S5 (Figure 4.17). It seems that the clean

area is shifted towards the north western part of the town in compassion to

premonsoon. This phenomenon was also influenced by meteorological phenomenon

which might be supported by wind rose diagram. The predominant wind direction in

this season is from N, NE, S, SSW and SW. A very high velocity wind speed blew

from ESE, S, SSW and W.

In winter fairly clean (13.326 sq km) and moderately polluted region (11.260

sq km) cover the major portion of the town. Severely polluted (0.001 sq km), heavily

polluted (0.012 sq km) are found beside S4. Southern portion S4 and small region

behind S1 is polluted (0.426 sq km) while the rest under clean (0.007 sq km) (Figure

4.18). It looks that the pollutants were seemed to be dispersed more in these direction

from the polluted region which is also supported by the windrose diagram. As it is

observed that in winter the predominant wind direction was from N, NW, NNW, NE,

SW and S. high speed wind is from N, NW, NNW and S.

[131]


Table 4.1: Spatial and temporal variation of respiratory suspended particulate matter

(RSPM) in the study area (average of data obtained in 2008 & 2009)

Site Premonsoon Postmonsoon Winter

RSPM (µg/m3) RSPM (µg/m

3) RSPM (µg/m

3)

R1 42.500 22.800 203.900

R2 40.900 15.020 174.700

R3 230.000 93.300 34.260

R4 303.300 35.900 41.180

R5 267.920 174.400 78.800

R6 111.400 16.300 281.080

R7 99.660 77.500 153.410

R8 135.000 34.500 237.500

R9 100.000 109.980 142.600

R10 71.620 137.970 175.690

R11 323.100 277.720 114.230

R12 268.400 203.330 102.460

R13 69.510 100.000 47.160

R14 60.110 161.900 153.410

R15 98.100 141.000 98.000

I1 134.000 92.980 178.900

I2 54.100 50.100 316.200

I3 175.800 71.110 55.800

I4 60.410 80.950 231.100

S1 123.600 10.950 216.280

S2 44.870 29.910 190.700

S3 131.200 51.580 166.400

S4 88.500 97.610 363.690

S5 40.200 60.080 133.170

S6 96.400 4.480 54.950

Minimum 40.200 4.480 34.260

Maximum 323.100 277.720 363.690

Average 126.824 86.055 157.823

Standard deviation 85.859 67.322 86.578

[132]


Table 4.2: Spatial and temporal variation of total suspended particulate matter

(TSPM) in the study area (average of data obtained in 2008 & 2009)


TSPM (µg/m3) TSPM (µg/m

3) TSPM (µg/m

3)

R1 97.325 52.285 466.828

R2 93.661 35.013 400.640

R3 526.700 213.956 78.568

R4 694.557 82.326 91.515

R5 612.737 399.934 171.659

R6 255.462 40.500 563.050

R7 197.728 177.475 206.384

R8 268.191 134.800 404.330

R9 229.000 264.078 240.210

R10 161.4298 316.134 337.990

R11 739.899 670.500 254.480

R12 529.257 293.020 234.941

R13 138.187 229.000 108.138

R14 119.415 379.781 351.769

R15 224.649 323.341 252.800

I1 266.204 212.924 403.753

I2 123.545 99.529 737.977

I3 349.244 162.842 127.174

I4 138.532 211.040 529.912

S1 283.440 31.950 404.910

S2 103.840 59.419 438.568

S3 299.898 119.430 320.704

S4 201.202 239.810 722.418

S5 92.730 119.276 264.556

S6 221.064 8.900 259.950

Minimum 92.730 8.900 78.568

Maximum 739.899 670.500 737.977

Average 278.716 195.090 334.929


[133]


Table 4.3: Spatial and temporal variation of Pb in the study area (average of data



Pb (µg/m3) Pb (µg/m

3) Pb (µg/m

3)

R1 0.084 0.264 0.423

R2 0.119 0.094 0.281

R3 0.030 0.161 0.101

R4 0.583 0.122 0.286

R5 0.077 0.058 0.050

R6 0.086 0.135 0.349

R7 0.162 0.121 0.026

R8 0.211 0.053 0.058

R9 0.179 0.268 0.226

R10 0.245 0.264 0.099

R11 0.240 0.160 0.279

R12 0.213 0.103 0.121

R13 0.136 0.031 0.449

R14 0.076 0.034 0.120

R15 0.173 0.451 0.163

I1 0.183 0.205 0.461

I2 0.026 0.103 0.481

I3 0.097 0.423 0.156

I4 0.249 0.443 0.318

S1 0.138 0.030 0.192

S2 0.293 0.114 0.157

S3 0.198 0.098 0.200

S4 0.026 0.102 0.314

S5 0.140 0.310 0.100

S6 0.026 0.176 0.053

Minimum 0.026 0.030 0.026

Maximum 0.583 0.451 0.481

Average 0.160 0.173 0.219


[134]


Table 4.4: Spatial and temporal variation of Cd in the study area (average of data



Cd (µg/m3) Cd (µg/m

3) Cd (µg/m

3)

R1 0.005 0.034 0.020

R2 0.001 0.009 0.002

R3 0.001 0.001 0.014

R4 0.013 0.001 0.008

R5 0.005 0.001 0.004

R6 0.001 0.004 0.006

R7 0.005 0.001 0.002

R8 0.018 0.001 0.002

R9 0.024 0.001 0.002

R10 0.082 0.031 0.002

R11 0.005 0.010 0.025

R12 0.005 0.001 0.001

R13 0.005 0.001 0.016

R14 0.016 0.001 0.002

R15 0.049 0.013 0.011

I1 0.001 0.021 0.032

I2 0.001 0.028 0.036

I3 0.002 0.064 0.067

I4 0.002 0.001 0.010

S1 0.013 0.006 0.002

S2 0.007 0.002 0.002

S3 0.014 0.020 0.014

S4 0.001 0.001 0.024

S5 0.018 0.008 0.002

S6 0.045 0.019 0.009

Minimum 0.001 0.001 0.001

Maximum 0.082 0.064 0.067

Average 0.014 0.011 0.013


[135]


Table 4.5: Spatial and temporal variation of Mn in the study area (average of data



Mn (µg/m3) Mn (µg/m

3) Mn (µg/m

3)

R1 0.012 0.141 0.048

R2 0.031 0.071 0.026

R3 0.220 0.073 0.077

R4 0.196 0.200 0.115

R5 0.204 0.119 0.059

R6 0.098 0.042 0.281

R7 0.044 0.092 0.012

R8 0.075 0.014 0.088

R9 0.108 0.076 0.277

R10 0.169 0.019 0.457

R11 0.083 1.424 0.210

R12 0.123 0.085 0.368

R13 0.069 0.143 0.130

R14 0.030 0.241 0.169

R15 0.049 0.125 0.055

I1 0.025 0.058 0.026

I2 0.075 0.278 0.739

I3 0.043 0.176 0.188

I4 0.120 0.567 0.391

S1 0.043 0.019 0.822

S2 0.074 0.014 0.158

S3 0.167 0.076 0.193

S4 0.068 0.383 0.254

S5 0.028 0.470 0.012

S6 0.021 0.287 0.196

Minimum 0.012 0.014 0.012

Maximum 0.220 1.424 0.822

Average 0.087 0.208 0.214


[136]


Table 4.6: Spatial and temporal variation of Cr in the study area (average of data



Cr (µg/m3) Cr (µg/m

3) Cr (µg/m

3)

R1 n.a n.a n.a

R2 n.a n.a n.a

R3 n.a n.a n.a

R4 n.a n.a n.a

R5 n.a n.a n.a

R6 n.a 0.005 0.047

R7 n.a n.a n.a

R8 n.a n.a n.a

R9 0.081 n.a n.a

R10 n.a n.a n.a

R11 n.a n.a n.a

R12 n.a 0.037 0.294

R13 n.a n.a n.a

R14 n.a n.a 0.001

R15 n.a n.a n.a

I1 n.a n.a n.a

I2 n.a n.a n.a

I3 n.a n.a n.a

I4 n.a n.a n.a

S1 n.a 1.025 n.a

S2 n.a n.a n.a

S3 n.a n.a n.a

S4 0.226 n.a 0.005

S5 n.a n.a n.a

S6 n.a n.a n.a

Minimum n.a n.a n.a

Maximum 0.226 1.025 0.294

Average 0.012 0.044 0.014


( n.a stands for not available)

[137]


Table 4.7: Spatial and temporal variation of K+ in the study area (average of data



K+ (µg/m

3) K

+ (µg/m

3) K

+ (µg/m

3)

R1 1.524 1.184 2.917

R2 0.892 0.395 0.333

R3 4.667 5.889 1.480

R4 0.792 0.395 1.645

R5 0.792 0.409 2.467

R6 3.417 1.708 4.917

R7 0.792 0.395 1.292

R8 1.083 1.292 4.541

R9 2.895 0.395 0.111

R10 3.783 2.083 4.702

R11 3.867 5.096 4.474

R12 0.812 0.415 1.313

R13 1.184 0.395 1.250

R14 1.086 2.909 3.590

R15 0.792 0.778 1.583

I1 0.792 2.750 0.461

I2 5.300 39.200 16.533

I3 5.495 6.333 2.533

I4 1.217 4.196 5.333

S1 4.500 0.711 8.864

S2 1.086 0.395 0.111

S3 1.513 1.250 3.750

S4 1.349 0.395 2.250

S5 1.184 0.415 1.875

S6 0.792 0.425 1.938

Minimum 0.792 0.395 0.111

Maximum 5.495 39.200 16.533

Average 2.064 3.192 3.210


[138]


Table 4.8: Spatial and temporal variation of Na+ in the study area (average of data



Na+ (µg/m

3) Na

+ (µg/m

3) Na

+ (µg/m

3)

R1 4.857 0.099 10.833

R2 6.400 0.132 5.917

R3 5.500 0.778 0.164

R4 6.917 2.138 2.763

R5 5.769 8.667 0.625

R6 7.833 4.917 8.917

R7 1.083 5.750 0.125

R8 1.583 1.083 10.459

R9 0.033 5.250 3.889

R10 1.086 2.583 6.845

R11 8.400 8.846 0.658

R12 11.434 11.000 2.219

R13 1.184 4.667 8.500

R14 0.559 10.667 7.885

R15 9.667 1.333 0.167

I1 12.885 3.583 5.592

I2 5.500 5.067 22.667

I3 7.033 7.111 1.020

I4 6.833 0.395 0.230

S1 2.833 0.044 9.773

S2 6.110 2.813 5.833

S3 0.493 6.429 11.583

S4 2.566 9.286 9.583

S5 0.592 9.067 6.971

S6 8.417 0.395 0.625

Minimum 0.033 0.044 0.125

Maximum 12.885 11.000 22.667

Average 5.023 4.484 5.754


[139]


Table 4.9: Spatial and temporal variation of F- in the study area (average of data



F- (µg/m

3) F

- (µg/m

3) F

- (µg/m

3)

R1 0.208 0.127 0.427

R2 0.140 0.016 0.168

R3 0.562 0.110 0.055

R4 0.130 0.186 0.205

R5 0.414 0.201 0.075

R6 0.298 0.020 0.081

R7 0.083 0.088 0.143

R8 0.335 0.174 0.279

R9 0.137 0.325 0.197

R10 0.132 0.202 0.299

R11 0.508 1.324 0.225

R12 1.148 0.386 0.273

R13 0.100 0.046 0.367

R14 0.084 0.350 0.381

R15 0.101 0.314 0.148

I1 0.127 0.578 0.286

I2 0.118 1.371 3.104

I3 0.193 1.029 0.168

I4 0.092 0.042 1.528

S1 0.217 0.097 0.517

S2 0.202 0.050 0.290

S3 0.079 0.132 0.620

S4 0.163 0.244 0.712

S5 0.080 0.979 0.100

S6 0.151 0.118 0.131

Minimum 0.079 0.016 0.055

Maximum 1.148 1.371 3.104

Average 0.232 0.340 0.431


[140]


Table 4.10: Spatial and temporal variation of Cl- in the study area (average of data



Cl- (µg/m

3) Cl

- (µg/m

3) Cl

- (µg/m

3)

R1 2.846 0.164 3.324

R2 3.101 0.118 1.241

R3 1.658 1.655 0.193

R4 0.423 3.321 1.312

R5 2.874 3.321 0.221

R6 3.324 0.829 2.907

R7 2.907 2.491 0.038

R8 3.740 1.662 2.290

R9 0.223 2.071 5.534

R10 0.234 2.491 2.672

R11 4.651 1.677 0.365

R12 0.118 0.164 0.031

R13 0.030 2.370 1.241

R14 0.027 4.229 2.877

R15 2.766 2.766 0.224

I1 4.780 0.900 1.966

I2 1.989 0.333 7.970

I3 2.735 4.987 0.228

I4 0.023 0.437 2.074

S1 0.118 0.141 0.164

S2 0.023 1.334 3.324

S3 0.023 1.779 4.157

S4 0.035 2.222 1.658

S5 0.025 2.652 1.677

S6 2.907 0.819 0.202

Minimum 0.023 0.118 0.031

Maximum 4.780 4.987 7.970

Average 1.663 1.797 1.916


[141]


Table 4.11: Spatial and temporal variation of SO4

2- in the study area (average of data



SO42-

(µg/m3) SO4

2- (µg/m

3) SO4

2- (µg/m

3)

R1 10.564 8.653 2.172

R2 8.886 6.670 0.574

R3 10.043 27.706 8.157

R4 29.227 40.630 10.095

R5 28.323 28.923 11.086

R6 8.559 5.820 0.003

R7 9.586 2.394 11.988

R8 9.472 15.470 18.523

R9 10.996 7.760 15.369

R10 12.528 7.645 7.830

R11 13.511 5.135 14.691

R12 10.231 1.118 0.983

R13 4.733 24.639 88.884

R14 4.733 8.298 4.481

R15 14.456 11.259 7.593

I1 1.858 14.272 3.873

I2 0.270 99.736 20.082

I3 2.340 9.128 13.429

I4 7.436 10.393 70.208

S1 9.472 6.760 1.405

S2 8.428 4.035 33.907

S3 11.266 3.015 3.650

S4 12.393 4.767 2.394

S5 8.157 15.155 5.205

S6 20.893 3.829 11.131

Minimum 0.270 1.118 0.003

Maximum 29.227 99.736 88.884

Average 10.734 14.928 14.709


[142]


Table 4.12: Spatial and temporal variation of SO2 in the study area (average of data



SO2 (µg/m3) SO2 (µg/m

3) SO2 (µg/m

3)

R1 3.670 7.060 5.100

R2 2.280 1.470 2.880

R3 2.130 4.240 2.290

R4 10.340 7.960 10.600

R5 2.210 8.480 16.950

R6 4.470 12.530 3.180

R7 13.200 12.720 19.110

R8 3.940 6.800 12.720

R9 20.340 3.180 20.400

R10 16.950 4.370 12.130

R11 6.930 3.410 5.450

R12 3.210 19.090 5.600

R13 22.850 2.006 1.320

R14 4.550 9.750 6.360

R15 14.300 2.700 16.490

I1 20.340 5.060 22.800

I2 23.490 21.300 30.800

I3 4.000 10.600 10.000

I4 7.510 12.720 18.200

S1 2.680 0.120 20.250

S2 14.320 7.300 3.640

S3 14.940 12.850 12.010

S4 21.960 6.360 22.790

S5 5.800 3.180 9.750

S6 7.500 4.480 5.300

Minimum 2.130 0.120 1.320

Maximum 23.490 21.300 30.800

Average 10.156 7.589 11.845


[143]


Table 4.13: Spatial and temporal variation of NO2 in the study area (average of data



NO2 (µg/m3) NO2 (µg/m

3) NO2 (µg/m

3)

R1 50.410 44.050 238.120

R2 110.490 90.730 161.870

R3 109.740 72.280 230.510

R4 29.190 141.110 163.690

R5 63.150 202.430 368.170

R6 363.800 168.950 87.960

R7 36.760 90.040 129.660

R8 85.200 196.000 85.460

R9 207.120 63.040 78.170

R10 42.280 22.860 70.840

R11 140.430 130.000 79.050

R12 30.790 67.950 166.700

R13 16.100 113.120 30.340

R14 45.970 54.710 80.400

R15 133.410 97.990 158.820

I1 67.980 125.780 25.800

I2 60.890 100.200 84.000

I3 93.940 23.600 120.890

I4 29.910 90.440 54.900

S1 77.500 39.940 54.510

S2 207.130 50.270 158.580

S3 52.300 60.290 62.620

S4 209.280 80.520 101.460

S5 67.000 59.980 80.670

S6 110.350 191.870 290.740

Minimum 16.100 22.860 25.800

Maximum 363.800 202.430 368.170

Average 97.645 95.126 126.557


[144]


Table 4.14: Zone wise spatial and diurnal variation of O3 and CO in the study area (a) in the month of March (b) in the month of April

March

O3

(ppb)

O3

(ppm)

Ozone

(µg/m3)

CO

(ppm)

CO

(mg/m3)

Temperature

(°C)

Humidity

(%)

Wind speed

(Km/hr)

Industrial

area

Morning (7:00-8:00 am) 9.70 0.010 20.47 2.46 3.03 31.1 80.1 4.5

Noon (12:00-1:00 pm) 30.40 0.030 64.14 1.23 1.51 42.7 17.4 7.2

Afternoon (4:00-5:00 pm) 25.60 0.026 54.02 0.70 0.86 40.2 30.4 3.0

Residential

area

Morning (7:00-8:00 am) 25.60 0.026 54.02 1.63 2.01 33.3 34.0 1.0

Noon (12:00-1:00 pm) 33.50 0.034 70.69 0.45 0.55 35.9 27.0 0.8

Afternoon (4:00-5:00 pm) 31.30 0.031 66.04 0.52 0.64 30.7 34.0 calm

Sensitive

area

Morning (7:00-8:00 am) 18.50 0.019 39.04 1.53 1.88 31.4 53.0 9.5

Noon (12:00-1:00 pm) 27.30 0.027 57.60 0.48 0.58 34.3 55.0 14.1

Afternoon (4:00-5:00 pm) 29.50 0.030 62.25 0.33 0.39 33.3 57.0 6.8

Traffic

area

Morning (7:00-8:00 am) 13.50 0.014 28.49 7.18 8.83 32.6 47.5 1.8

Noon (12:00-1:00 pm) 18.00 0.018 37.98 2.85 3.50 33.6 45.27 1.0

Afternoon (4:00-5:00 pm) 10.50 0.011 22.16 10.25 12.60 36.7 40.66 2.3

(a)

[145]


April

O3

(ppb)

O3

(ppm)

Ozone

(µg/m3)

CO

(ppm)

CO

(mg/m3)

Temperature

(°C)

Humidity

(%)

Wind speed

(Km/hr)

Industrial

area

Morning (7:00-8:00 am) 11.50 0.012 24.27 1.41 1.73 37.2 55.5 16.3

Noon (12:00-1:00 pm) 33.00 0.033 69.63 0.91 1.12 43.9 41.2 6.5

Afternoon (4:00-5:00 pm) 25.70 0.026 54.23 0.95 1.17 42.7 43.2 12.2

Residential

area

Morning (7:00-8:00 am) 17.00 0.017 35.87 1.77 2.18 50.3 31.6 2.5

Noon (12:00-1:00 pm) 14.00 0.014 29.54 0.84 1.03 45.0 26.0 3.6

Afternoon (4:00-5:00 pm) 3.00 0.003 6.33 4.85 5.96 44.2 27.0 0.1

Sensitive

area

Morning (7:00-8:00 am) 13.50 0.014 28.49 1.62 1.99 30.6 84.6 calm

Noon (12:00-1:00 pm) 23.00 0.023 48.53 1.18 1.45 35.2 76.8 3.9

Afternoon (4:00-5:00 pm) 26.00 0.026 54.86 0.35 0.43 34.0 82.3 calm

Traffic

area

Morning (7:00-8:00 am) 4.00 0.004 8.44 11.95 14.69 32.9 81.9 calm

Noon (12:00-1:00 pm) 15.00 0.015 31.65 4.50 5.53 43.4 42.8 6.4

Afternoon (4:00-5:00 pm) 13.70 0.014 28.91 2.39 2.94 40.5 48.6 6.4

(b)

[146]


Table 4.15: Spatial and diurnal variation of O3 and CO (site wise) in the study area

Site Time O3

(ppb)

O3

(µg/m3)

CO

(ppm)

CO

(mg/m3)

R1 morning 23.00 48.53 2.05 2.52

afternoon 32.00 67.52 2.75 3.38

R2 morning 22.00 46.42 1.00 1.23

afternoon 32.00 67.52 0.80 0.98

R3 morning 45.00 94.95 3.50 4.31

afternoon 30.00 63.3 1.50 1.85

R4 morning 24.00 50.64 1.90 2.34

afternoon 35.00 73.85 0.80 0.98

R5 morning 22.00 46.42 1.28 1.57

afternoon 11.00 23.21 1.50 1.85

R6 morning 27.00 56.97 2.10 2.58

afternoon 34.00 71.74 1.20 1.48

R7 morning 12.60 26.59 0.89 1.10

afternoon 18.00 37.98 3.03 3.73

R8 morning 15.00 31.65 6.00 7.38

afternoon 28.00 59.08 4.55 5.60

R9 morning 22.00 46.42 2.10 2.58

afternoon 30.00 63.3 2.28 2.81

R10 morning 18.00 37.98 6.40 7.87

afternoon 32.00 67.52 4.48 5.51

R11 morning 11.00 23.21 4.65 5.72

afternoon 16.00 33.76 7.90 9.72

R12 morning 16.00 33.76 5.03 6.19

afternoon 21.00 44.31 6.58 8.09

R13 morning 12.00 25.32 3.67 4.51

afternoon 18.00 37.98 12.35 15.19

R14 morning 21.00 44.31 3.18 3.91

afternoon 34.00 71.74 2.33 2.86

R15 morning 11.00 23.21 5.02 6.17

afternoon 12.00 25.32 10.03 12.33

I1 morning 12.00 25.32 2.10 2.58

afternoon 31.00 65.41 0.96 1.17

I2 morning 22.00 46.42 1.22 1.50

afternoon 31.00 65.41 0.84 1.03

I3 morning 25.00 52.75 5.26 6.47

afternoon 30.00 63.3 3.26 4.01

I4 morning 26.00 54.86 1.58 1.94

afternoon 28.00 59.08 1.48 1.81

S1 morning 12.00 25.32 7.34 9.03

afternoon 16.00 33.76 9.20 11.32

S2 morning 21.00 44.31 3.85 4.74

afternoon 30.00 63.3 3.60 4.43

[147]


Site Time O3

(ppb)

O3

(µg/m3)

CO

(ppm)

CO

(mg/m3)

S3 morning 23.00 48.53 2.46 3.02

afternoon 29.00 61.19 2.52 3.10

S4 morning 19.00 40.09 1.55 1.91

afternoon 30.00 63.3 2.93 3.60

S5 morning 7.00 14.77 4.53 5.57

afternoon 35.00 73.85 4.14 5.09

S6 morning 19.00 40.09 1.80 2.21

afternoon 28.00 59.08 1.00 1.23

Average 23.17 48.89 3.44 4.24

Maximum 45.00 94.95 12.95 15.19

Minimum 7.00 14.77 0.80 0.98

Standard deviation 8.70 17.53 2.58 3.17

[148]


Table 4.16: Premonsoon correlation analysis between particulate matter, gaseous pollutants and meteorological parameter

Correlation matrix (Pearson (n)):

Variables RSPM TSPM Pb Cd Mn Cr SO2 NO2 K+

Na+

SO42-

Cl-

F-

Rainfall Humidity Temp-

erature

Wind

speed

RSPM 1

TSPM 0.994 1

Pb 0.360 0.364 1

Cd -0.185 -0.167 0.137 1

Mn 0.567 0.598 0.346 0.075 1

Cr -0.111 -0.099 -0.215 -0.092 -0.037 1

SO2 -0.314 -0.308 0.069 0.164 -0.040 0.414 1

NO2 -0.083 -0.051 -0.204 -0.090 -0.070 0.379 0.031 1

K+

0.126 0.141 -0.261 -0.020 0.144 -0.051 -0.023 0.202 1

Na+

0.396 0.374 0.105 -0.180 -0.045 -0.230 -0.174 0.129 -0.057 1

SO4

2- 0.523 0.572 0.377 0.271 0.496 0.050 -0.227 -0.025 -0.343 0.090 1

Cl-

0.235 0.227 -0.202 -0.145 -0.267 -0.258 -0.212 0.182 0.056 0.502 0.008 1

F-

0.635 0.589 -0.007 -0.213 0.331 -0.088 -0.428 0.014 0.077 0.424 0.125 0.080 1

Rainfall 0.284 0.307 0.470 0.190 0.144 -0.087 -0.072 -0.164 -0.257 0.141 0.598 -0.091 -0.147 1

Humidity -0.072 -0.050 -0.150 0.126 -0.221 -0.041 -0.191 0.193 0.088 0.311 0.029 0.029 -0.109 0.173 1

Temperature 0.005 -0.024 -0.247 -0.085 -0.254 0.061 -0.352 0.047 -0.258 0.045 0.053 0.257 0.092 -0.078 0.115 1

Wind speed -0.430 -0.428 0.136 -0.065 -0.229 0.212 0.222 0.335 -0.353 0.025 -0.165 -0.109 -0.383 0.084 0.113 0.309 1

Values in bold are different from 0 with a significance level alpha=0.01

[149]


Table 4.17: Postmonsoon correlation analysis between particulate matter, gaseous pollutants and meteorological parameter



Na+

SO42-

Cl-

F-


erature

Wind

speed

RSPM 1

TSPM 0.968 1

Pb 0.050 0.070 1

Cd -0.140 -0.138 0.468 1

Mn 0.519 0.587 0.168 -0.028 1

Cr -0.221 -0.223 -0.240 -0.077 -0.139 1

SO2 0.048 -0.068 -0.066 0.123 -0.060 -0.275 1

NO2 -0.019 0.040 -0.293 -0.344 0.117 -0.222 0.012 1

K+ -0.039 -0.044 -0.023 0.320 0.142 -0.070 0.537 -0.026 1

Na+ 0.613 0.536 -0.189 -0.075 0.314 -0.242 0.368 -0.085 0.039 1

SO4

2- -0.105 -0.103 -0.148 0.107 0.000 -0.090 0.381 0.185 0.859 -0.044 1

Cl- 0.301 0.365 0.165 0.174 0.025 -0.272 -0.095 -0.084 -0.172 0.441 -0.039 1

F- 0.375 0.372 0.222 0.443 0.581 -0.127 0.225 -0.099 0.615 0.459 0.439 0.182 1

Rainfall -0.309 -0.306 0.016 0.172 0.027 -0.061 -0.123 0.353 -0.094 -0.307 -0.133 -0.209 -0.153 1

Humidity -0.484 -0.455 -0.028 0.490 -0.187 0.256 -0.335 0.149 -0.056 -0.377 -0.068 -0.133 -0.008 0.550 1

Temperature -0.130 -0.088 0.221 -0.297 -0.103 -0.100 -0.448 0.111 -0.764 -0.238 -0.633 0.055 -0.511 0.104 0.087 1

Wind speed -0.096 -0.033 -0.221 0.007 0.049 -0.176 -0.165 0.218 -0.233 0.050 -0.195 0.032 -0.014 0.119 0.197 0.399 1


[150]


Table 4.18: Winter correlation analysis between particulate matter, gaseous pollutants and meteorological parameter



Na+

SO42-

Cl-

F-


erature

Wind

speed

RSPM 1

TSPM 0.955 1

Pb 0.380 0.465 1

Cd -0.005 0.068 0.409 1

Mn 0.428 0.448 0.197 0.056 1

Cr -0.077 -0.066 -0.113 -0.170 0.163 1

SO2 0.480 0.427 0.178 0.202 0.424 -0.195 1

NO2 -0.394 -0.294 -0.396 -0.131 -0.331 0.083 -0.252 1

K+

0.509 0.550 0.299 0.223 0.779 -0.099 0.480 -0.253 1

Na+

0.661 0.663 0.494 0.114 0.461 -0.116 0.300 -0.382 0.676 1

SO4

2- -0.117 -0.080 0.292 0.049 0.044 -0.160 -0.079 -0.260 0.020 -0.042 1

Cl-

0.540 0.564 0.453 0.073 0.347 -0.185 0.359 -0.334 0.503 0.748 0.060 1

F-

0.541 0.634 0.493 0.296 0.623 -0.069 0.574 -0.271 0.825 0.641 0.270 0.654 1

Rainfall 0.111 0.155 0.307 0.101 -0.165 -0.049 -0.177 0.279 -0.018 0.200 -0.124 0.152 -0.001 1

Humidity -0.134 -0.144 -0.135 -0.326 -0.116 0.199 -0.501 0.251 -0.257 -0.223 -0.211 -0.209 -0.424 -0.015 1

Temperature 0.314 0.222 -0.246 -0.430 0.116 -0.044 0.106 -0.238 0.032 0.274 0.012 0.155 0.063 0.088 -0.198 1

Wind speed -0.102 -0.122 -0.440 -0.014 -0.152 0.263 0.004 0.371 -0.157 -0.294 -0.060 -0.161 -0.132 -0.268 0.024 -0.367 1


[151]


Table 4.19: Factor loadings along with its contribution (%) after varimax rotation in premonsoon

Factor loadings after Varimax rotation: Contributions of the variables (%) after Varimax rotation:

F1 F2 F3 F4 F5 F6 F1 F2 F3 F4 F5 F6

RSPM 0.93 -0.05 -0.02 -0.07 -0.07 0.26 22.59 0.12 0.02 0.27 0.28 3.38

TSPM 0.94 -0.05 0.00 -0.04 -0.01 0.24 23.15 0.12 0.00 0.09 0.01 2.93

Pb 0.34 -0.64 0.43 -0.22 0.05 0.02 2.98 19.70 11.09 2.74 0.19 0.02

Cd -0.07 -0.04 0.10 -0.15 0.67 -0.32 0.15 0.06 0.64 1.38 29.77 4.98

Mn 0.76 -0.03 0.23 0.01 0.01 -0.30 15.11 0.05 3.28 0.00 0.01 4.36

Cr 0.00 -0.02 0.00 0.80 -0.12 -0.36 0.00 0.03 0.00 37.17 0.99 6.21

SO2 -0.31 -0.13 0.66 0.31 -0.13 -0.23 2.61 0.76 26.20 5.40 1.12 2.54

NO2 -0.03 0.18 0.00 0.80 0.09 0.25 0.03 1.52 0.00 36.72 0.58 3.10

K+

0.14 0.80 0.29 0.06 0.11 0.08 0.51 30.54 4.92 0.22 0.79 0.28

Na+

0.23 -0.11 0.02 0.03 0.02 0.86 1.36 0.63 0.03 0.04 0.02 36.12

SO42-

0.65 -0.46 -0.16 0.04 0.42 -0.14 11.04 10.04 1.48 0.11 11.48 0.91

Cl-

0.01 0.11 -0.22 -0.06 -0.07 0.72 0.01 0.53 2.89 0.23 0.32 25.02

F-

0.66 0.22 -0.26 -0.05 -0.31 0.22 11.35 2.29 4.06 0.16 6.20 2.28

Rainfall 0.28 -0.59 0.12 -0.09 0.51 0.06 2.02 16.98 0.81 0.49 16.94 0.17

Humidity -0.14 0.12 -0.16 0.18 0.68 0.37 0.55 0.65 1.45 1.97 30.79 6.86

Temperature -0.10 -0.19 -0.85 0.14 -0.07 0.08 0.28 1.67 43.04 1.05 0.32 0.35

Wind speed -0.49 -0.55 -0.03 0.46 -0.05 0.10 6.27 14.33 0.06 11.96 0.18 0.48

[152]


Table 4.20: Eigenvalues of factors in premonsoon

Eigenvalues

F1 F2 F3 F4 F5 F6

Eigenvalue 4.10 2.52 2.05 1.60 1.36 1.20

% variance 24.11 14.80 12.08 9.40 8.01 7.07

Cumulative % 24.11 38.91 50.99 60.39 68.39 75.47

% variance (after Varimax rotation)

% variance 22.28 12.23 9.77 10.21 8.91 12.05

Cumulative % 22.28 34.52 44.29 54.50 63.42 75.47

[153]


Table 4.21: Factor loadings along with its contribution (%) after varimax rotation in postmonsoon


F1 F2 F3 F4 F5 F6 F1 F2 F3 F4 F5 F6

RSPM 0.81 -0.04 -0.03 -0.35 -0.05 -0.28 23.18 0.04 0.06 5.60 0.19 4.58

TSPM 0.86 -0.08 -0.03 -0.28 -0.06 -0.27 25.81 0.18 0.05 3.58 0.23 4.03

Pb 0.13 -0.15 0.90 -0.02 -0.24 0.11 0.63 0.62 42.06 0.02 3.92 0.71

Cd -0.10 0.33 0.69 0.46 0.08 -0.29 0.37 3.15 24.77 9.74 0.42 4.91

Mn 0.87 0.09 0.06 0.12 -0.07 0.09 26.84 0.23 0.17 0.65 0.29 0.46

Cr -0.12 -0.04 -0.13 0.03 0.88 0.22 0.48 0.05 0.87 0.04 52.12 2.78

SO2 -0.17 0.63 -0.04 -0.31 -0.38 -0.14 1.01 11.53 0.10 4.39 9.95 1.12

NO2 0.15 0.01 -0.57 0.34 -0.48 0.28 0.84 0.00 16.77 5.34 15.61 4.40

K+

0.05 0.95 0.09 0.01 -0.04 0.10 0.07 26.51 0.45 0.00 0.08 0.60

Na+

0.42 0.17 -0.20 -0.26 -0.08 -0.69 6.21 0.84 2.10 3.02 0.43 27.12

SO42-

-0.05 0.85 -0.10 -0.02 -0.11 0.12 0.10 21.17 0.51 0.01 0.85 0.80

Cl-

0.14 -0.13 0.18 -0.07 -0.14 -0.72 0.65 0.49 1.76 0.19 1.36 29.14

F-

0.56 0.61 0.27 0.19 0.04 -0.29 10.94 10.95 3.67 1.61 0.13 4.64

Rainfall -0.06 -0.09 -0.01 0.69 -0.21 0.34 0.12 0.25 0.01 21.40 2.98 6.53

Humidity -0.25 -0.03 0.10 0.85 0.26 0.05 2.26 0.04 0.50 32.89 4.57 0.17

Temperature -0.11 -0.86 0.01 0.09 -0.24 0.05 0.46 21.84 0.01 0.40 3.88 0.12

Wind speed -0.03 -0.27 -0.34 0.50 -0.21 -0.37 0.03 2.12 6.13 11.11 2.99 7.88

[154]


Table 4.22: Eigenvalues of factors in postmonsoon

Eigenvalues

F1 F2 F3 F4 F5 F6

Eigenvalue 4.11 3.18 2.05 1.77 1.37 1.17

% variance 24.20 18.69 12.04 10.44 8.06 6.91

Cumulative % 24.20 42.89 54.93 65.37 73.43 80.34


% variance 16.77 20.15 11.30 12.99 8.73 10.40

Cumulative % 16.77 36.92 48.22 61.21 69.94 80.34

[155]


Table 4.23: Factor loadings along with its contribution (%) after varimax rotation in winter


F1 F2 F3 F4 F5 F1 F2 F3 F4 F5

RSPM 0.84 -0.22 -0.14 -0.06 -0.07 13.38 2.76 1.14 0.20 0.27

TSPM 0.86 -0.09 -0.19 -0.06 -0.04 14.12 0.49 2.03 0.19 0.09

Pb 0.46 0.45 -0.46 0.49 0.01 4.01 11.15 12.06 13.42 0.01

Cd 0.12 0.75 -0.11 0.09 -0.35 0.26 31.03 0.72 0.44 7.17

Mn 0.73 0.01 0.38 0.18 0.14 10.11 0.01 8.08 1.78 1.11

Cr 0.05 0.02 0.30 -0.16 0.64 0.05 0.03 5.14 1.42 24.25

SO2 0.55 0.03 0.29 -0.11 -0.57 5.84 0.04 4.79 0.64 19.45

NO2 -0.36 0.16 -0.13 -0.69 0.11 2.43 1.48 0.93 26.79 0.71

K+

0.82 0.16 0.16 0.08 -0.10 12.88 1.38 1.55 0.37 0.65

Na+

0.82 -0.08 -0.27 0.15 -0.07 12.71 0.37 4.22 1.20 0.27

SO42-

-0.10 0.15 0.14 0.72 -0.14 0.20 1.33 1.15 28.61 1.08

Cl-

0.71 -0.01 -0.22 0.14 -0.15 9.63 0.00 2.76 1.07 1.32

F-

0.81 0.23 0.11 0.20 -0.25 12.63 3.04 0.70 2.24 3.66

Rainfall 0.07 0.07 -0.83 -0.20 0.00 0.11 0.24 39.02 2.18 0.00

Humidity -0.19 -0.06 -0.13 -0.12 0.80 0.71 0.20 0.92 0.77 37.49

Temperature 0.17 -0.87 -0.09 0.12 -0.20 0.56 42.59 0.42 0.83 2.35

Wind speed -0.14 0.26 0.50 -0.57 0.04 0.36 3.87 14.36 17.83 0.11

[156]


Table 4.24: Eigenvalues of factors in winter

Eigenvalues

F1 F2 F3 F4 F5

Eigenvalue 5.80 1.89 1.78 1.57 1.26

% variance 34.10 11.11 10.46 9.22 7.43

Cumulative % 34.10 45.21 55.68 64.90 72.33


% variance 30.91 10.52 10.33 10.59 9.97

Cumulative % 30.91 41.44 51.77 62.36 72.33

[157]


4.25: Premonsoon AQI status of study area

Place RSPM

(µg/m3)

SO2

( µg/m3)

NO2

( µg/m3)

Pb

(µg/m3)

AQI Status

Residential

R1 42.50 3.67 50.41 0.084 17.78 Clean

R2 40.90 2.28 110.49 0.119 20.89 Clean

R3 230.00 2.13 109.74 0.03 22.39 Clean

R4 303.30 10.34 29.19 0.583 53.70 Moderately

polluted

R5 267.92 2.21 63.15 0.077 25.70 Fairly clean

R6 111.40 4.47 363.8 0.086 39.81 Fairly clean

R7 99.66 13.20 36.76 0.162 33.11 Fairly clean

R8 135.00 3.94 85.20 0.211 34.67 Fairly clean

R9 100.00 20.34 207.12 0.179 58.88 Moderately

polluted

R10 71.62 16.95 42.28 0.245 37.15 Fairly clean

R11 323.10 6.93 140.43 0.24 58.88 Moderately

polluted

R12 268.40 3.21 30.79 0.213 30.90 Fairly clean

R13 69.51 22.85 16.10 0.136 26.92 Fairly clean

R14 60.11 4.55 45.97 0.076 19.50 Clean

R15 98.10 14.30 133.41 0.173 47.86 Fairly clean

Industrial

I1 134.00 20.34 67.98 0.183 16.98 Clean

I2 54.10 23.49 60.89 0.026 13.80 Clean

I3 175.80 4.00 93.94 0.097 35.48 Fairly clean

I4 60.41 7.51 29.91 0.249 27.54 Fairly clean

Sensitive

S1 123.60 2.68 77.50 0.138 51.29 Moderately

polluted

S2 44.87 14.32 207.13 0.293 93.33 Polluted

S3 131.20 14.94 52.30 0.198 79.43 Polluted

S4 88.50 21.96 209.28 0.026 67.61 Moderately

polluted

S5 40.20 5.80 67.00 0.140 45.71 Fairly clean

S6 96.40 7.50 110.35 0.026 44.67 Fairly clean

[158]


Table 4.26: Postmonsoon AQI status of study area

Place RSPM

(µg/m3)

SO2

( µg/m3)

NO2

( µg/m3)

Pb

(µg/m3)

AQI Status

Residential

R1 22.80 7.06 44.05 0.264 23.44 Clean

R2 15.02 1.47 90.73 0.094 13.18 Clean

R3 93.30 4.24 72.28 0.161 29.51 Fairly clean

R4 35.90 7.96 141.11 0.122 29.51 Fairly clean

R5 174.40 8.48 202.43 0.058 40.74 Moderately

polluted

R6 16.30 12.53 168.95 0.135 29.51 Fairly clean

R7 77.50 12.72 90.04 0.121 36.31 Fairly clean

R8 34.50 6.80 196 0.053 25.12 Fairly clean

R9 109.98 3.18 63.04 0.268 30.90 Fairly clean

R10 137.97 4.37 22.86 0.264 27.54 Fairly clean

R11 277.72 3.41 130 0.16 41.69 Fairly clean

R12 203.33 19.09 67.95 0.103 45.71 Fairly clean

R13 100.00 2.006 113.12 0.031 18.20 Clean

R14 161.90 9.75 54.71 0.034 26.30 Fairly clean

R15 141.00 2.70 97.99 0.451 39.81 Fairly clean

Industrial

I1 92.98 5.06 125.78 0.205 24.55 Clean

I2 50.10 21.30 100.2 0.103 23.99 Clean

I3 71.11 10.60 23.6 0.423 21.88 Clean

I4 80.95 12.72 90.44 0.443 33.88 Fairly clean

Sensitive

S1 10.95 0.12 39.94 0.03 7.41 Very clean

S2 29.91 7.30 50.27 0.114 39.81 Fairly clean

S3 51.58 12.85 60.29 0.098 52.48 Moderately

polluted

S4 97.61 6.36 80.52 0.102 56.23 Moderately

polluted

S5 60.08 3.18 59.98 0.31 51.29 Moderately

polluted

S6 4.48 4.48 191.87 0.176 33.88 Fairly clean

[159]


Table 4.27: Winter AQI status of study area

Place RSPM

(µg/m3)

SO2

( µg/m3)

NO2

( µg/m3)

Pb

(µg/m3)

AQI Status

Residential

R1 203.90 5.10 238.12 0.423 63.10 Moderately

polluted

R2 174.70 2.88 161.87 0.281 43.65 Fairly clean

R3 34.26 2.29 230.51 0.101 22.91 Clean

R4 41.18 10.60 163.69 0.286 42.66 Fairly clean

R5 78.80 16.95 368.17 0.05 44.67 Fairly clean

R6 281.08 3.18 87.96 0.349 45.71 Fairly clean

R7 153.41 19.11 129.66 0.026 35.48 Fairly clean

R8 237.50 12.72 85.46 0.058 38.90 Fairly clean

R9 142.60 20.40 78.17 0.226 53.70 Moderately

polluted

R10 175.69 12.13 70.84 0.099 38.90 Fairly clean

R11 114.23 5.45 79.05 0.279 38.02 Fairly clean

R12 102.46 5.60 166.70 0.121 36.31 Fairly clean

R13 47.16 1.32 30.34 0.449 19.05 Clean

R14 153.41 6.36 80.40 0.12 34.67 Fairly clean

R15 98.00 16.49 158.82 0.163 50.12 Moderately

polluted

Industrial

I1 178.90 22.80 25.80 0.461 34.67 Fairly clean

I2 316.20 30.80 84.0 0.481 58.88 Moderately

polluted

I3 55.80 10.00 120.89 0.156 23.99 Clean

I4 231.10 18.20 54.90 0.318 38.90 Fairly clean

Sensitive

S1 216.28 20.25 54.51 0.192 97.72 Polluted

S2 190.70 3.64 158.58 0.157 75.86 Polluted

S3 166.40 12.01 62.62 0.200 83.18 Polluted

S4 363.69 22.79 101.46 0.314 151.36 Severely

polluted

S5 133.17 9.75 80.67 0.100 67.61 Moderately

polluted

S6 54.95 5.30 290.74 0.053 54.95 Moderately

polluted

[160]


Table 4.28: Projected areal coverage of the study area with respect to the standard of

respiratory suspended particulate matter (RSPM)

Range RSPM (area in sq km)

Premonsoon Postmonsoon Winter

<75 6.448 10.546 1.622

75 - 100 3.921 5.105 1.202

100 - 150 6.783 5.621 8.063

> 150 8.148 4.028 14.413


SO2

Range SO2 (area in sq km)


<30 25. 300 25. 300 25. 300


NO2

Range NO2 (area in sq km)


<30 0.530 0.398 0.396

30 - 80 14.003 10.297 4.304

80 -120 6.113 9.748 8.446

>120 4.836 4.973 12.269

[161]



Pb

Range Pb (area in sq km)


< 0.75 25. 300 25. 300 25. 300

Table 4.32: Projected areal coverage of the study area on basis Air quality

index(AQI)

Status AQI (area in sq km)


Very clean Nil 0.298 Nil

Clean 2.957 2.744 0.007

Fairly clean 18.753 22.262 13.326

Moderately Polluted 3.290 0.019 11.260

Polluted 0.031 Nil 0.426

Heavily polluted Nil Nil 0.012

Severely polluted Nil Nil 0.001

[162]


(a) (b)

Figure 4.1(a & b): Windrose diagram (Premonsoon) [a) 2008 b) 2009]

(a) (b)

Figure 4.2(a & b): Windrose diagram (Postmonsoon) [a) 20008 b) 2009]

Windrose diagram of Premonsoon Season Windrose diagram of Premonsoon Season

Windrose diagram of Postmonsoon Season Windrose diagram of Postmonsoon Season

[163]


(a) (b)

Figure 4.3(a & b): Windrose diagram (Winter) [a) 20008 b) 2009]

Figure 4.4: Spatial interpolation of respiratory suspended particulate matter (RSPM)

(Premonsoon)

Windrose diagram of Winter Season

Area Boundary

Windrose diagram of Winter Season

[164]



(Postmonsoon)


(Winter)

[165]


Figure 4.7: Spatial interpolation of SO2 (Premonsoon)

Figure 4.8: Spatial interpolation of SO2 (Postmonsoon)

[166]


Figure 4.9: Spatial interpolation of SO2 (Winter)

Figure 4.10: Spatial interpolation of NO2 (Premonsoon)

[167]


Figure 4.11: Spatial interpolation of NO2 (Postmonsoon)

Figure 4.12: Spatial interpolation of NO2 (Winter)

[168]


Figure 4.13: Spatial interpolation of Pb (Premonsoon)

Figure 4.14: Spatial interpolation of Pb (Postmonsoon)

[169]


Figure 4.15: Spatial interpolation of Pb (Winter)

Figure 4.16: Zonation of Air Quality Index (AQI) with the help of inverse distance

interpolation technique (IDINT) (Premonsoon)

[170]



interpolation technique (IDINT) (Postmonsoon)


interpolation technique (IDINT) (Winter)

[171]

SUMMARY AND CONCLUSION

5.1 Summary of results

espiratory suspended particulate matter (RSPM) in the study area is found to

have higher winter concentration (157.823±86.578 µg/m3) in comparison to

premonsoon (126.824±85.859 µg/m3) and postmonsoon (86.055±67.322 µg/m

3). The

maximum concentration of RSPM is 323.100 µg/m3, 277.720 µg/m

3, 363.690 µg/m

3

during premonsoon, postmonsoon and winter season respectively. The minimum

value of RSPM is found as 40.200 µg/m3, 4.480 µg/m

3 and 34.260 µg/m

3 in

premonsoon, postmonsoon and winter respectively.

The average concentration of TSPM is found as 278.716±191.912 µg/m3,

195.090±149.934 µg/m3 and 334.929±177.431 µg/m

3 in premonsoon, postmonsoon

and winter season respectively. TSPM is found to have 739.899 µg/m3, 670.500

µg/m3 and 737.977 µg/m

3 as maximum concentration in premonsoon, postmonsoon

and winter season respectively whereas 92.730 µg/m3, 8.900 µg/m

3 and 78.568 µg/m

3

as minimum value respectively in the above mentioned seasons.

The average value for Pb in the study area is found 0.160±0.117 µg/m3,

0.173±0.126 µg/m3, 0.219±0.139 µg/m


respectively with its maximum value as 0.583 µg/m3,0.451 µg/m

3,0.481 µg/m

3 and

minimum value as 0.026 µg/m3,0.030 µg/m

3 and 0.026 µg/m

3.

The average value for Cd is 0.014±0.019 µg/m3, 0.011±0.015 µg/m

3 and

0.013±0.015 µg/m3 in premonsoon, postmonsoon and winter season with the range of

0.001 to 0.082 µg/m3,

0.001 to 0.064 µg/m3 and 0.001 to 0.067 µg/m

3 in the study

area.

R

This section briefly highlights the overall summary of major findings

along with the suggestion of probable management strategies

regarding the reduction of pollution load in the study area.

[172]


The average value for Mn is 0.087±0.062 µg/m3, 0.208±0.292 µg/m

3,

0.214±0.210 µg/m3 with the range of 0.012 to 0.220 µg/m

3, 0.014 to 1.424 µg/m

3 and

0.012 to 0.822 µg/m3 in premonsoon, postmonsoon and winter respectively the study

area.

The average value of Cr in study area is found 0.012±0.047 µg/m3,

0.044±0.209 µg/m3 and 0.014 ±.0.059 µg/m

3 with the range of n.a to 0.226 µg/m

3, n.a

to 1.025 µg/m3 and n.a to 0.294 µg/m


season respectively.

Among the water soluble cations and anions in aerosol, the average

concentration of K+ found in study area is 2.064±1.615 µg/m

3, 3.192±7.722 µg/m

3,

and 3.210±3.431 µg/m3 with the range of 0.792 to 5.495 µg/m

3, 0.395 to 39.200

µg/m3

and 0.111 to 16.533 µg/m3 during premonsoon, postmonsoon and winter

season respectively.

The average value of Na+ is found to be 5.023±3.673 µg/m

3, 4.484±3.617

µg/m3and 5.754±5.300 µg/m

3 with the range of 0.033 to 12.885 µg/m

3, 0.044 to

11.000 µg/m3

and 0.125 to 22.667 µg/m3

during premonsoon, postmonsoon and

winter season respectively.

Fluoride has an average concentration of 0.232±0.232 µg/m3, 0.340±0.401

µg/m3 and 0.431±0.633 µg/m

3 with the range of 0.079 to 1.148 µg/m

3, 0.016 to 1.371

µg/m3

and 0.055 to 3.104 µg/m3 in pre, post and winter season respectively.

Whereas Cl- concentration is found as 1.663±1.651µg/m

3

, 1.797±1.320 µg/m3

and 1.916±1.935 µg/m3 with the range of 0.023 to 4.780 µg/m

3, 0.118 to 4.987 µg/m

3

and 0.031 to 7.970 µg/m3

in premonsoon, postmonsoon and winter season

respectively.

The average concentration of SO4

2- is 10.734±6.921 µg/m

3 (range: 0.270 to

29.227 µg/m3), 14.928±20.115 µg/m

3 (range: 1.118 to 99.736 µg/m

3) and

14.709±21.131 µg/m3 (range: 0.003 to 88.884 µg/m

3) in premonsoon, postmonsoon

and winter season respectively.

[173]


Regarding the gaseous pollutants, the average value of SO2 is 10.156±7.411

µg/m3 (range: 2.130 to 23.490 µg/m

3), 7.589±5.340 µg/m

3 (range: 0.120 to 21.300

µg/m3) and 11.845±7.951 µg/m

3 (range: 1.320 to 30.800 µg/m

3) in premonsoon,

postmonsoon and winter season respectively.

The average concentration of NO2 for premonsoon, postmonsoon and winter

are 97.645±79.034 µg/m3 (range: 16.100 to 363.800 µg/m

3), 95.126±52.355 µg/m

3

(range: 22.860 to 202.430 µg/m3) and 126.557±83.245 µg/m

3 (range: 25.800 to

368.170 µg/m3) respectively in the study area.

The average value for CO and O3 found in this town are 4.24±3.18 mg/m3

(range: 0.98 – 15.19 mg/m3) and 48.89±17.53 µg/m

3 (range: 14.77 - 94.95 µg/m

3).

From the correlation analysis it is observed that RSPM has positive correlation

with TSPM (r value is 0.994), Mn (r = 0.567), SO4

2- (r = 0.523) and F

- (r = 0.635) at

0.01 significant levels. TSPM has again positive correlation with Mn ( r= 0.598),

SO4

2-

(r = 0.572) and F- (r = 0.589). Positive correlation also exists between rainfall

and SO4

2- (r=0.598) at 0.01 significant level.

In postmonsoon TSPM and RSPM is highly correlated (r = 0.968) in a positive

manner. RSPM and TSPM also have strong positive correlation with Mn (r = 0.519

and r = 0.587) and Na+ (r = 0.613 and r = 0.536). K

+ is noticed to be significantly

(0.01 level) correlated with SO2 (r = 0.537), SO4

2-(r = 0.859), F

-(r = 0.615) positively

whereas with temperature (r = - 0.764) negatively. Mn is having a relation with F- (r =

0.581) in this season.

In winter RSPM and TSPM both have significant positive relation (r = 0.955).

RSPM is positively related to K+ (r = 0.509), Na

+ (r = 0.661), Cl

- (r = 0.540), and F

- (r

= 0.541). Just like RSPM, TSPM has also a 0.01 level significant positive relation

with K+ (r = 0.550), Na

+ (r = 0.663), Cl

- (r = 0.564) and F

- (r = 0.634). Mn is

positively related with K+ (r = 0.779) and F

- (r = 0.623) whereas Na

+ is correlated

with K+ (r = 0.676), Cl

- (r = 0.748) and F

- (r = 0.641). F

- is associated with SO2 (r

=0.574), K+ (r = 0.825) and Cl

- (r = 0.654).

From factor analysis few sources of air pollution are identified. In premonsoon

one natural source of pollution i.e. soil resuspension and two anthropogenic sources

[174]


viz. vehicle emission, coal combustion or biomass burning is identified as major

source of air pollution. In postmonsoon one geogenic source i.e. soil resuspension and

other anthropogenic source like vehicle emission along with combustion of biomass

and waste material are identified as major source of air pollution in the study area

whereas in winter like other two seasons, one geogenic pollution source i.e. soil

resuspension along with anthropogenic sources viz. vehicle emission and combustion

of coal and waste are identified.

5.2 Conclusion

The average concentration of PM10 showed distinct seasonal variations with

high winter and premonsoon value in the study area than the postmonsoon value. This

postmonsoon fall down in PM10 may be attributed to the monsoonal washout effect of

particles, whereas during winter low mixing heights leads to an accumulation of

pollutants for long time. The accumulation of such a high level of RSPM in winter

may also be attributed to the emission from the vehicles, local rice mills, resuspension

of dusts from the paved and unpaved road etc. The prevailing micrometeorological

condition also is the inducing factor for accumulation of RSPM. Among the above

mentioned sources, emissions from automobiles are assumed to be the major source

of particulate pollution in the study area. In general some sites always remain above

the prescribed standard of RSPM in all the season but in winter season maximum

violence of prescribed standard are occurred in comparison to the other two seasons.

Like RSPM, TSPM also has the higher average concentration in winter followed by

premonsoon and postmonsoon. The maximum violence of prescribed standard of

TSPM is occurred during premonsoon and winter season both.

The scenario of Pb shows quite better picture in the study area as the

atmospheric Pb is not exceeded the NAAQS standard anywhere in all selected

monitoring sites. The tendency of remaining the Pb concentration within the

permissible level may be due to restriction on the use of leaded fuel. Vehicle exhausts

may be a major source of this much atmospheric lead in the study area. Apart from

vehicle emission, road side soil may also release some Pb. Road side soil is taken into

consideration as a source of Pb because in the past few decades continuous use of

[175]


leaded petrol and emission of Pb into atmosphere has led to a conservable

concentration of Pb in soil along road side and the movement of vehicles renders

these dust containing Pb, resuspended in air.

A considerable amount of Cd concentration is also found in the study area.

Few sites also remain above the standard. The release of Cd may be from the different

anthropogenic activities like combustion of coal and waste material in residential as

well as industrial area and transportation process.

In case of Mn violence of prescribed standard is occurred at many sites but

exceptionally high concentration of Mn is found in a traffically congested area.

Manganese tricarbinyl compound is used as additive in unleaded petrol to enhance

automobile performance. This could be a most probable reason to have such a high

level of Mn concentration at that place. Apart from that, the violence of prescribed

standard of Mn may happen due to various anthropogenic activities like transportation

activities, ferrous and nonferrous casting, construction activities and resuspension of

soil dust etc. Trace amount of Cr is found in the study area in general. An

exceptionally high concentration of Cr is found at a sensitive site. This anomaly is

attributed to small scale cycle repairing shops which are present just behind the site.

The industrial region is mainly dominated by rice mills in the study area. The

maximum concentration of K+ is found in industrial region in all season. In the

process of boiling rice in these rice mills dried plant materials are used as fuel which

may be the major contributor of K+

in the industrial site. Normally, soil is considered

to be the main source of K+. But the fine particles of K

+ may be released into the

atmosphere by burning of plant material. So, soil and burning of plant material are

detected as the source of ambient K+ in the study area. Soil resuspension beside the

road could the possible reason for Na+ in the study area as in general soil derived

particles are mainly considered as the source of Na+. Fluoride (F

-) may be contributed

by emissions from biomass burning in industrial sector and coal burning in Burdwan

Municipality as the maximum concentration is found in an industrial site in

postmonsoon and winter season except a residential site where it is seen to be

maximum in premonsoon. The concentration of soluble fluoride was so low that a

[176]


little research had focused on fluoride in PM10 in India. It is found that biomass

burning and coal burning may contribute to this much of fluoride in the ambient air of

the study area. Coal combustion is also could be a possible source of Cl- in the study

area.

It is noticed that in Burdwan Municipality area the average SO4

2- is very high

in postmonsoon season. A possible mechanism of formation of SO4

2- is aqueous phase

oxidation of SO2 in cloud droplets. Not only that micro meteorology like wind

velocity, temperature, solar radiation also play a key role in gas phase reaction

involving OH radicals which should have more contribution to the formation of SO4

2-.

Therefore, SO2 may be converted to SO4

2- depending upon prevailing meteorological

condition.

All the monitoring sites are observed to have high winter value of SO2 than

premonsoon and low value in postmonsoon season. Precipitation driven wash out may

lower down the postmonsoon value of SO2. The concentration of SO2 was

comparatively lower in all the seasons than the prescribed standard of NAAQS in all

the monitoring sites. So, this scenario is quite safe to this town.

The NO2 level was very high through out the study area. All the sensitive sites

have exceeded its standard of 30 µg/m3

in all season. The residential site is also having

a tendency to cross the set limit of NO2 almost in all season excerpt a few sites. High

vehicle density may cause such elevated level of NO2 in the study area. One industrial

site is seen to exceed the standard of NO2 concentration in postmonsoon season only.

To explain the finding it could be said NO2 was not only dependent on rainfall but

also dependent on vehicle density and the distance of the monitoring site from road.

Ultimately it is concluded that the scenario of NO2 is not good in the study area.

The concentration of O3 is not only function of its precursor viz. CO but also a

function of prevailing meteorological conditions. It is observed that the concentrations

of O3 are increased with the decreasing concentration of its precursors and vice versa.

A time lag of 5-7 hour is required for most of these precursor gases to photo

chemically produce O3 to its maximum potential. This is also found that on diurnal

scale, these precursor pollutants of O3 is built up in early morning hours along with a

[177]


maximum in noon or afternoon. Comparing with its latest standard (NAAQS, 2009)

so far average CO value has crossed the limit. On the other hand O3 is again far below

the standard. CO being a threat to the study area needs proper management strategies

to check down its value and O3 is quite safe so far.

From the above observations it may be said that proper management strategies

should be taken in case of RSPM, Cd, Mn, NO2 and CO. These particular pollutants

are showing tendency to cross their prescribed limit in the study area. So, immediately

their value should be checked down.

From statistical analysis the five major sources of ambient air pollution are

identified throughout the year in the study area. They are soil resuspension, vehicle

emission, coal combustion, biomass burning and waste incineration.

Regarding meteorological influence on ambient air pollution in the study area

it can be said that wind speed and wind direction are the key factors in dispersion of

pollutants. The GIS picture reveals that spread of air pollutants occurs in the same

direction of mild wind speed whereas dilution of pollutant is occurred in the direction

of high wind speed. The predominant wind direction in the study area is N, NE, NW,

NNW SE, S, SSW, SW and W throughout the year. The temporal modelling AQI

reflects that more pollution is found in winter followed by premonsoon and

postmonsoon. Thematic map (Premonsoon) reveals that the western part of the town

mainly covered by clean area (2.957 sq km) while the minor part of northern portion,

south western part and south eastern part of the town represent the moderately

polluted (3.290 sq km) region. A very small portion of the town is seen to be polluted

(0.031 sq km) while majority of the town is covered by fairly clean area (18.753 sq

km). After the offset of monsoon same scenario is observed. Fairly clean area (22.262

sq km) is covering the whole town except a few portions in the middle to north and

north western part in post monsoon. This portion is found as clean (2.744 sq km)while

a little patch of very clean (0.298 sq km) category and moderately polluted (0.019 sq

km) patch is seen. In winter fairly clean (13.326 sq km) and moderately polluted

region (11.260 sq km) covers the major portion of the town. Severely polluted (0.001

[178]


sq km), heavily polluted (0.012 sq km) and polluted region (0.426 sq km) along with

fairly clean region cover the town in this season also.

5.3 Management strategy

A few management strategies can be taken for the study area which is

discussed below for reducing the pollution load in the town in making a cleaner, safer

and sustainable habitat for all.

5.3.1 Pollution management strategies

• Technological measures such as use of clean fuels/environmentally benign

fuels like CNG which may reduce the emission of CO and NO2 in the ambient

air of the town. As CNG powered vehicles emit 85 % less NOX, 70 % less

reactive HC’s, and 74 % less CO than similar gasoline powered vehicles

(http://daq.state.nc.us/motor/cng/).

• Regular monitoring, servicing and replacement of engines of Bus Taxi, Tracks

or any other four wheelers are to be done to maintain the vehicle health as well

as environmental health. As old engine do much pollution.

• Use of catalytic converter may also helps in reducing the pollution load.

• Dilution of pollutants may also be done by increasing chimney height of rice

mills in the study area.

• Control of pollution at source can be done by using Cyclone separator, Bag

filter, Electrostatic precipitator, Spray tower beside the rice mill industry to

scavenge out the emitted particulate matter.

• Collaborative work should be needed with Burdwan municipality in order

particularly in zonation of industry, sensitive and residential places.

[179]


5.3.2 Zoning strategies

• Proper location of projects may help in reducing pollution load.

• Preparation of windrose pattern along with micrometeorological detailing in

the study area to detect the most prevailing high speed and medium speed

wind is a crying need. As accumulation of pollutants and wind speed are

inversely related. So, projects may be done that particular site of the town

having high wins speed whereas opposite site of the medium wind speed

region.

• Location of industries may be also laid out as per environmental guidelines

• Priority should be given to compatible industries so that, wastes from one

could be used as raw materials for the other, thus minimizing the net pollution

• Development of green belts around the town may increase the aesthetics value

as well as filtering the ambient air of the town.

5.3.3 Command and control approach

• Enforcement of pollution control norms in various types of industrial unit

depending on their production processes may help in reducing the pollution

load.

• Introduction of Environmental Audit in the small scale industry may help in

checking and maintaining the surrounding air clean.

• Environmental Impact Assessment from the planning stage and selection of

sites for location of industries may also be effective in reducing pollution load.

• Clearance by MOEF of all projects above a certain size and fragile areas is

also needed in this town.

5.4 Future scope of the work

• Mainly the inorganic pollutants are monitored in this study area. So, the

organic gaseous pollutant like volatile organic compounds (VOCs), Poly

aromatic hydrocarbons (PAHs) etc can be monitored.

[180]


• TSPM and PM10 are monitored. So, in future PM2.5 can be monitored.

• Apart from GIS, AERMOD modeling and other model based on Gaussian

prediction model can be applied in prediction of a particulate pollutant of

major concern as well as proper zonation of the town.

• Black carbon in particulate matter also can be monitored in future.

• The aeromicrobial study also can be done in future.

• A health related survey due to air pollution along with systematic monitoring

can be useful in demarcating the hazard prone area.

[181]

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xiv

Annexure I: Detail scenario of respiratory suspended particulate matter (RSPM) in the study area (during 2008 &2009)

Premonsoon Postmonsoon Winter Premonsoon Postmonsoon Winter

Site RSPM (µg/m3) RSPM (µg/m

3) RSPM (µg/m

3)

RSPM

(µg/m3)

RSPM

(µg/m3)

RSPM

(µg/m3)

R1 30.400 28.900 189.500 54.600 16.700 218.300

R2 34.000 19.400 156.800 47.800 10.640 192.600

R3 189.000 78.450 26.450 271.000 108.150 42.070

R4 248.900 41.200 29.800 357.700 30.600 52.560

R5 204.000 156.800 57.000 331.840 192.000 100.600

R6 112.800 14.800 292.460 110.000 17.800 269.700

R7 85.400 58.900 34.800 113.920 96.100 47.140

R8 160.000 68.700 228.600 110.000 0.300 246.400

R9 72.900 118.200 156.600 127.100 101.760 128.600

R10 56.000 102.800 189.400 87.240 173.140 161.980

R11 174.000 278.500 100.900 472.200 276.940 127.560

R12 245.700 197.600 125.600 291.100 209.060 79.320

R13 51.800 89.900 49.400 87.220 110.100 44.920

R14 56.700 159.000 147.800 63.520 164.800 159.020

R15 49.000 123.000 86.800 147.200 159.000 109.200

I1 121.000 34.890 156.840 147.000 151.070 200.960

I2 46.800 42.700 356.000 61.400 57.500 276.400

I3 195.500 58.900 59.180 156.100 83.320 52.420

I4 68.700 68.900 198.470 52.120 93.000 263.730

S1 128.000 12.400 207.890 119.200 9.500 224.670

S2 44.800 34.500 204.800 44.940 25.320 176.600

S3 85.400 55.400 150.700 177.000 47.760 182.100

S4 89.300 89.500 350.480 87.700 105.720 376.900

S5 25.900 59.700 132.400 54.500 60.460 133.940

S6 93.200 5.290 71.300 99.600 3.670 38.600

xv

Annexure II: Detail scenario of total suspended particulate matter (TSPM) in the study area (during 2008 & 2009)


Site TSPM (µg/m3) TSPM (µg/m

3) TSPM (µg/m

3)

TSPM

(µg/m3)

TSPM

(µg/m3)

TSPM

(µg/m3)

R1 69.421 60.160 491.680 125.229 44.410 441.976

R2 71.820 45.080 422.280 115.502 24.946 378.999

R3 468.640 181.390 75.510 584.760 246.521 81.627

R4 650.158 90.260 78.960 738.956 74.392 104.071

R5 382.095 381.689 159.160 843.379 418.179 184.158

R6 255.030 39.890 581.670 255.895 41.110 544.431

R7 187.740 183.060 82.990 207.716 171.890 104.898

R8 294.240 188.570 365.080 242.142 81.030 443.580

R9 193.360 266.470 265.570 264.640 261.685 214.850

R10 134.720 282.250 349.680 188.140 350.018 326.300

R11 525.240 558.980 225.380 954.558 782.020 283.580

R12 449.930 292.760 254.090 608.584 293.280 215.792

R13 111.780 250.350 80.590 164.594 207.650 135.686

R14 116.450 376.340 272.440 122.379 383.222 431.098

R15 175.085 316.064 243.980 274.213 330.6184 261.620

I1 272.640 155.340 358.18 259.769 270.508 449.325

I2 105.834 95.480 781.180 141.256 103.577 694.773

I3 373.820 148.540 137.370 324.669 177.144 116.977

I4 154.190 203.650 419.660 122.874 218.430 640.165

S1 277.058 42.520 429.230 289.821 21.380 380.590

S2 116.860 66.370 467.980 90.820 52.468 409.156

S3 206.860 133.610 310.180 392.936 105.250 331.229

S4 191.760 213.810 753.660 210.645 265.810 691.176

S5 82.180 125.190 252.750 103.280 113.362 276.361

S6 223.440 10.450 316.360 218.689 7.350 203.540

xvi

Annexure III: Detail scenario of Pb in the study area (during 2008 & 2009)


Site Pb (µg/m3) Pb (µg/m

3) Pb (µg/m

3) Pb (µg/m

3) Pb (µg/m

3) Pb (µg/m

3)

R1 0.078 0.294 0.525 0.089 0.235 0.321

R2 0.099 0.119 0.301 0.139 0.069 0.260

R3 0.010 0.200 0.098 0.050 0.123 0.104

R4 0.725 0.076 0.260 0.440 0.167 0.312

R5 0.053 0.044 0.058 0.101 0.071 0.041

R6 0.060 0.109 0.365 0.112 0.160 0.332

R7 0.173 0.118 0.035 0.152 0.123 0.018

R8 0.201 0.055 0.048 0.221 0.052 0.068

R9 0.192 0.278 0.202 0.166 0.258 0.250

R10 0.213 0.279 0.088 0.277 0.249 0.110

R11 0.264 0.172 0.285 0.217 0.148 0.273

R12 0.196 0.116 0.131 0.231 0.091 0.110

R13 0.114 0.014 0.591 0.159 0.048 0.307

R14 0.122 0.039 0.116 0.030 0.029 0.124

R15 0.152 0.377 0.154 0.194 0.524 0.172

I1 0.199 0.194 0.478 0.167 0.216 0.444

I2 0.025 0.102 0.509 0.028 0.104 0.453

I3 0.085 0.415 0.165 0.110 0.431 0.147

I4 0.255 0.430 0.272 0.243 0.456 0.364

S1 0.154 0.040 0.201 0.122 0.020 0.183

S2 0.320 0.130 0.134 0.266 0.097 0.180

S3 0.213 0.113 0.254 0.183 0.084 0.146

S4 0.011 0.080 0.302 0.042 0.125 0.327

S5 0.143 0.331 0.110 0.137 0.289 0.089

S6 0.019 0.199 0.063 0.034 0.152 0.043

xvii

Annexure IV: Detail scenario of Cd in the study area (during 2008 & 2009)


Site Cd (µg/m3) Cd (µg/m

3) Cd (µg/m

3) Cd (µg/m

3) Cd (µg/m

3) Cd (µg/m

3)

R1 0.005 0.036 0.019 0.005 0.031 0.022

R2 0.002 0.010 0.002 0.000 0.008 0.002

R3 0.001 0.002 0.014 0.001 0.000 0.013

R4 0.014 0.001 0.008 0.011 0.001 0.009

R5 0.005 0.001 0.004 0.004 0.001 0.003

R6 0.001 0.004 0.005 0.002 0.004 0.006

R7 0.005 0.001 0.002 0.005 0.001 0.002

R8 0.018 0.001 0.001 0.017 0.001 0.002

R9 0.024 0.001 0.002 0.023 0.001 0.001

R10 0.079 0.031 0.002 0.085 0.031 0.002

R11 0.005 0.010 0.024 0.004 0.011 0.026

R12 0.007 0.002 0.001 0.002 0.001 0.001

R13 0.005 0.001 0.016 0.004 0.001 0.017

R14 0.018 0.001 0.002 0.015 0.001 0.001

R15 0.052 0.011 0.010 0.046 0.014 0.011

I1 0.001 0.024 0.041 0.001 0.018 0.023

I2 0.001 0.026 0.034 0.001 0.029 0.039

I3 0.002 0.069 0.059 0.001 0.060 0.075

I4 0.002 0.002 0.011 0.003 0.001 0.010

S1 0.015 0.005 0.002 0.011 0.006 0.002

S2 0.006 0.002 0.002 0.008 0.002 0.002

S3 0.016 0.020 0.015 0.012 0.021 0.013

S4 0.002 0.002 0.025 0.000 0.000 0.023

S5 0.020 0.010 0.002 0.017 0.006 0.002

S6 0.044 0.018 0.006 0.047 0.021 0.011

xviii

Annexure V: Detail scenario of Mn in the study area (during 2008 & 2009)


Site Mn (µg/m3) Mn (µg/m

3) Mn (µg/m

3) Mn (µg/m

3) Mn (µg/m

3) Mn (µg/m

3)

R1 0.014 0.156 0.050 0.011 0.126 0.045

R2 0.030 0.097 0.028 0.032 0.044 0.024

R3 0.255 0.082 0.093 0.186 0.065 0.061

R4 0.202 0.194 0.010 0.191 0.206 0.221

R5 0.189 0.128 0.064 0.219 0.110 0.054

R6 0.102 0.045 0.215 0.095 0.040 0.348

R7 0.046 0.101 0.015 0.043 0.083 0.009

R8 0.078 0.012 0.091 0.073 0.016 0.085

R9 0.105 0.083 0.287 0.110 0.069 0.268

R10 0.019 0.017 0.521 0.318 0.021 0.392

R11 0.081 1.590 0.201 0.085 1.257 0.218

R12 0.143 0.094 0.395 0.104 0.076 0.340

R13 0.068 0.132 0.120 0.070 0.155 0.140

R14 0.029 0.201 0.185 0.031 0.281 0.154

R15 0.053 0.142 0.055 0.044 0.108 0.056

I1 0.029 0.062 0.028 0.022 0.054 0.023

I2 0.084 0.289 0.947 0.065 0.267 0.530

I3 0.045 0.197 0.195 0.042 0.155 0.181

I4 0.135 0.572 0.403 0.104 0.561 0.379

S1 0.051 0.020 0.902 0.035 0.019 0.743

S2 0.075 0.015 0.128 0.073 0.012 0.188

S3 0.188 0.082 0.206 0.147 0.071 0.179

S4 0.072 0.402 0.305 0.064 0.364 0.202

S5 0.030 0.500 0.014 0.026 0.440 0.010

S6 0.022 0.246 0.030 0.021 0.328 0.361

xix

Annexure VI: Detail scenario of Cr in the study area (during 2008 & 2009)


Site Cr (µg/m3) Cr (µg/m

3) Cr (µg/m

3) Cr (µg/m

3) Cr (µg/m

3) Cr (µg/m

3)

R1 n.a n.a n.a n.a n.a n.a





R6 n.a 0.003 0.086 n.a 0.006 0.008



R9 0.052 n.a n.a 0.110 n.a n.a



R12 n.a 0.027 0.507 n.a 0.047 0.082


R14 n.a n.a 0.001 n.a n.a 0.001


I1 n.a n.a n.a n.a n.a n.a




S1 n.a 1.025 n.a n.a 1.025 n.a

S2 n.a n.a n.a n.a n.a n.a


S4 0.191 n.a 0.008 0.262 n.a 0.002



( n.a stands for not available)

xx

Annexure VII: Detail scenario of K+ in the study area (during 2008 & 2009)


Site K+ (µg/m

3) K

+ (µg/m

3) K

+ (µg/m

3) K

+ (µg/m

3) K

+ (µg/m

3) K

+ (µg/m

3)

R1 2.021 0.073 1.451 1.027 2.296 4.382

R2 1.098 0.203 0.394 0.687 0.586 0.272

R3 5.064 0.872 0.098 4.269 10.905 2.862

R4 1.215 0.701 1.457 0.370 0.088 1.833

R5 0.542 0.042 0.855 1.043 0.777 4.080

R6 2.021 1.106 8.024 4.812 2.311 1.809

R7 1.025 0.145 0.125 0.560 0.644 2.458

R8 1.506 1.085 3.015 0.660 1.498 6.066

R9 0.025 0.502 0.112 5.764 0.287 0.110

R10 1.023 3.045 8.125 6.543 1.122 1.280

R11 5.250 9.025 0.246 2.483 1.168 8.702

R12 1.087 0.809 0.021 0.538 0.020 2.604

R13 2.036 0.202 1.014 0.332 0.588 1.486

R14 0.876 3.561 2.643 1.295 2.257 4.537

R15 0.846 1.025 0.165 0.739 0.531 3.002

I1 1.502 3.458 0.802 0.083 2.042 0.119

I2 4.087 34.089 28.015 6.513 44.311 5.051

I3 9.025 0.050 1.025 1.964 12.617 4.040

I4 2.055 0.512 0.251 0.380 7.881 10.415

S1 2.955 0.025 10.254 6.045 1.398 7.473

S2 0.515 0.602 0.121 1.656 0.187 0.101

S3 0.502 2.025 4.025 2.524 0.475 3.476

S4 0.055 0.215 2.240 2.643 0.575 2.260

S5 0.459 0.458 2.032 1.910 0.371 1.719

S6 0.815 0.298 0.862 0.770 0.551 3.013

xxi

Annexure VIII: Detail scenario of Na+ in the study area (during 2008 & 2009)


Site Na+ (µg/m

3) Na

+ (µg/m

3) Na

+ (µg/m

3) Na

+ (µg/m

3) Na

+ (µg/m

3) Na

+ (µg/m

3)

R1 4.021 0.073 6.451 5.693 0.125 15.215

R2 4.098 0.203 5.943 8.702 0.060 5.891

R3 5.064 0.872 0.098 5.936 0.683 0.230

R4 7.215 2.015 3.457 6.619 2.262 2.070

R5 3.542 9.042 0.855 7.996 8.291 0.395

R6 8.021 4.106 8.024 7.645 5.728 9.809

R7 1.025 5.145 0.125 1.141 6.355 0.125

R8 1.506 1.085 3.015 1.660 1.082 17.903

R9 0.025 6.023 5.125 0.040 4.477 2.653

R10 1.023 3.045 8.125 1.148 2.122 5.566

R11 10.250 9.025 0.246 6.550 8.668 1.070

R12 11.087 12.890 3.021 11.781 9.110 1.417

R13 2.036 4.202 9.014 0.332 5.132 7.986

R14 0.876 9.561 8.643 0.243 11.772 7.127

R15 9.846 1.025 0.165 9.488 1.642 0.169

I1 16.023 3.458 6.021 9.746 3.709 5.163

I2 4.087 4.089 28.015 6.913 6.044 17.318

I3 9.025 0.050 1.025 5.040 14.172 1.014

I4 8.055 0.512 0.251 5.612 0.277 0.209

S1 2.955 0.025 10.254 2.712 0.064 9.291

S2 6.215 3.025 5.021 6.005 2.600 6.645

S3 0.502 5.025 9.025 0.485 7.832 14.142

S4 3.055 0.215 9.240 2.077 18.357 9.927

S5 0.459 12.458 9.032 0.726 5.675 4.911

S6 9.215 0.298 0.862 7.618 0.491 0.388

xxii

Annexure IX: Detail scenario of F- in the study area (during 2008 & 2009)


Site F- (µg/m

3) F

- (µg/m

3) F

- (µg/m

3) F

- (µg/m

3) F

- (µg/m

3) F

- (µg/m

3)

R1 0.194 0.143 0.398 0.221 0.111 0.455

R2 0.102 0.008 0.165 0.178 0.023 0.172

R3 0.587 0.120 0.055 0.536 0.100 0.055

R4 0.131 0.196 0.214 0.129 0.175 0.197

R5 0.594 0.210 0.085 0.235 0.192 0.066

R6 0.022 0.020 0.015 0.574 0.020 0.146

R7 0.075 0.065 0.125 0.091 0.111 0.161

R8 0.312 0.185 0.235 0.358 0.164 0.323

R9 0.106 0.346 0.201 0.167 0.304 0.192

R10 0.107 0.202 0.301 0.157 0.202 0.296

R11 0.502 1.548 0.235 0.514 1.100 0.215

R12 0.851 0.413 0.289 1.444 0.360 0.257

R13 0.125 0.051 0.502 0.076 0.040 0.231

R14 0.015 0.361 0.403 0.153 0.339 0.359

R15 0.098 0.415 0.158 0.104 0.214 0.138

I1 0.145 0.655 0.154 0.109 0.502 0.417

I2 0.114 2.100 4.025 0.122 0.641 2.183

I3 0.223 1.940 0.152 0.163 0.118 0.185

I4 0.073 0.021 1.025 0.112 0.063 2.032

S1 0.241 0.085 0.521 0.192 0.109 0.513

S2 0.212 0.032 0.182 0.192 0.068 0.398

S3 0.069 0.102 0.671 0.088 0.162 0.569

S4 0.120 0.268 0.615 0.205 0.219 0.808

S5 0.021 0.822 0.199 0.139 1.136 0.001

S6 0.147 0.008 0.152 0.155 0.228 0.110

xxiii

Annexure X: Detail scenario of Cl- in the study area (during 2008 & 2009)


Site Cl- (µg/m

3) Cl

- (µg/m

3) Cl

- (µg/m

3) Cl

- (µg/m

3) Cl

- (µg/m

3) Cl

- (µg/m

3)

R1 3.124 0.155 3.548 2.569 0.174 3.099

R2 3.127 0.214 1.252 3.075 0.023 1.230

R3 1.949 0.810 0.203 1.367 2.500 0.183

R4 0.524 3.564 1.603 0.321 3.078 1.021

R5 2.154 3.854 0.304 3.594 2.788 0.138

R6 4.026 0.902 3.348 2.622 0.755 2.466

R7 3.516 2.140 0.043 2.299 2.841 0.033

R8 3.625 1.545 2.015 3.855 1.779 2.565

R9 0.098 1.586 5.908 0.348 2.557 5.160

R10 0.102 1.649 4.624 0.365 3.333 0.719

R11 3.315 1.605 0.123 5.988 1.750 0.607

R12 0.102 0.095 0.031 0.135 0.233 0.031

R13 0.025 1.847 1.540 0.034 2.894 0.942

R14 0.023 2.849 3.450 0.031 5.608 2.304

R15 2.815 2.146 0.213 2.717 3.385 0.235

I1 5.825 1.205 2.130 3.734 0.595 1.803

I2 0.206 0.614 8.549 3.773 0.053 7.391

I3 3.562 5.231 0.213 1.908 4.743 0.243

I4 0.001 0.500 2.546 0.045 0.375 1.602

S1 0.099 0.184 0.099 0.138 0.098 0.230

S2 0.000 0.987 3.894 0.046 1.682 2.753

S3 0.019 1.864 4.528 0.028 1.694 3.785

S4 0.037 2.630 1.745 0.032 1.815 1.570

S5 0.001 2.514 1.745 0.050 2.791 1.609

S6 3.021 1.205 0.203 2.793 0.433 0.201

xxiv

Annexure XI: Detail scenario of SO4

2- in the study area (during 2008 & 2009)


Site SO42-

(µg/m3) SO4

2- (µg/m

3) SO4

2- (µg/m

3) SO4

2- (µg/m

3) SO4

2- (µg/m

3) SO4

2- (µg/m

3)

R1 10.132 10.245 3.021 10.996 7.061 1.322

R2 8.912 11.246 0.055 8.859 2.095 1.093

R3 11.025 18.255 10.458 9.060 37.157 5.857

R4 24.598 38.200 10.025 33.855 43.060 10.165

R5 18.947 28.457 12.458 37.698 29.390 9.714

R6 8.542 5.085 0.002 8.575 6.556 0.004

R7 8.459 2.041 12.045 10.713 2.748 11.931

R8 8.457 16.255 19.240 10.487 14.684 17.806

R9 12.346 10.245 16.255 9.647 5.274 14.483

R10 12.548 11.245 8.025 12.508 4.046 7.635

R11 16.489 4.980 15.254 10.533 5.291 14.128

R12 10.457 0.985 1.054 10.006 1.250 0.913

R13 8.542 19.320 90.470 0.923 29.418 87.288

R14 3.025 15.185 5.021 6.441 1.411 3.940

R15 15.849 10.250 8.259 13.062 12.269 6.926

I1 0.894 15.248 5.031 2.822 13.297 2.715

I2 0.246 120.000 32.142 0.295 79.471 8.022

I3 3.456 10.254 15.780 1.224 8.003 11.079

I4 7.146 10.254 85.147 7.727 10.532 55.270

S1 9.025 6.485 1.045 9.918 7.034 1.765

S2 0.325 7.210 34.780 16.531 0.859 33.034

S3 14.025 3.250 4.580 8.508 2.780 2.720

S4 18.155 4.252 4.050 6.631 5.282 0.739

S5 12.030 16.025 6.254 4.285 14.285 4.155

S6 31.025 3.485 12.354 10.762 4.173 9.909

xxv

Annexure XII: Detail scenario of SO2 in the study area (during 2008 & 2009)


Site SO2 (µg/m3) SO2 (µg/m

3) SO2 (µg/m

3) SO2 (µg/m

3) SO2 (µg/m

3) SO2 (µg/m

3)

R1 5.020 8.200 3.260 2.320 5.920 6.940

R2 2.860 1.840 1.100 1.700 1.100 4.660

R3 3.420 3.200 0.100 0.840 5.280 0.480

R4 13.480 8.200 10.400 7.200 7.720 10.800

R5 2.950 7.800 12.800 1.470 9.160 21.100

R6 5.700 13.800 1.250 3.240 11.260 5.110

R7 14.940 13.200 20.450 11.460 12.240 17.770

R8 5.200 4.850 12.840 2.680 8.750 12.600

R9 18.920 3.800 26.400 21.760 2.560 14.400

R10 19.400 4.200 19.700 14.500 4.540 4.559

R11 7.200 3.100 7.500 6.660 3.720 3.400

R12 4.500 24.500 8.400 1.920 13.680 2.800

R13 24.500 2.008 1.020 21.200 2.005 1.620

R14 6.200 5.890 6.900 2.900 13.610 5.820

R15 19.000 2.100 14.020 9.600 3.300 18.960

I1 15.840 3.820 20.850 24.840 6.300 24.750

I2 7.290 22.800 34.800 39.690 19.800 26.800

I3 7.800 7.100 9.000 0.200 14.100 11.000

I4 5.400 9.350 21.400 9.620 16.090 15.000

S1 2.420 0.200 16.800 2.940 0.040 23.700

S2 15.700 2.450 2.560 12.940 12.150 4.720

S3 20.480 14.800 12.890 9.420 10.900 11.130

S4 3.200 8.400 22.800 40.720 4.320 22.780

S5 6.400 11.200 3.180 5.200 8.300 3.180

S6 8.400 6.200 3.800 6.600 2.760 6.800

xxvi

Annexure XIII: Detail scenario of NO2 in the study area (during 2008 & 2009)


Site NO2 (µg/m3) NO2 (µg/m

3) NO2 (µg/m

3) NO2 (µg/m

3) NO2 (µg/m

3) NO2 (µg/m

3)

R1 68.200 54.800 203.900 32.620 33.300 272.340

R2 98.400 73.900 203.600 122.580 107.560 120.140

R3 123.400 58.700 198.600 96.080 85.860 262.420

R4 35.600 146.800 154.200 22.780 135.420 173.180

R5 59.600 203.500 324.800 66.700 201.360 411.540

R6 39.000 79.800 169.000 34.520 100.280 90.320

R7 39.000 79.800 169.000 34.520 100.280 90.320

R8 75.600 201.300 64.850 94.800 190.700 106.070

R9 194.600 58.900 74.450 219.640 67.180 81.890

R10 36.890 24.580 85.600 47.670 21.140 56.080

R11 164.520 140.900 61.540 116.340 119.100 96.560

R12 24.600 50.400 174.900 36.980 85.500 158.500

R13 18.900 92.400 45.000 13.300 133.840 15.680

R14 44.870 56.600 91.840 47.070 52.820 68.960

R15 112.400 102.600 168.290 154.420 93.380 149.350

I1 103.800 85.000 31.240 32.160 166.560 20.360

I2 64.500 120.000 103.200 57.280 80.400 64.800

I3 104.750 29.600 128.640 83.130 17.600 113.140

I4 26.900 94.700 72.700 32.920 86.180 37.100

S1 61.800 33.600 61.400 93.200 46.280 47.620

S2 221.360 50.890 145.870 192.900 49.650 171.290

S3 54.500 71.200 64.500 50.100 49.380 60.740

S4 207.800 90.500 82.400 210.760 70.540 120.520

S5 68.400 57.230 63.500 65.600 62.730 97.840

S6 121.400 223.400 303.700 99.300 160.340 277.780

xxvii

Annexure XIV: Detail data of spatio-temporal variation of micrometeorology

a) Premonsoon b) Postmonsoon C) Winter

Premonsoon

Site

Rainfall

(mm) Humidity (%)

Temperature

(°C)

Wind

speed(Km/hr)

Max Min Max Min

R1 0.0 85.0 54.0 34.5 24.5 6.0

R2 0.0 87.0 45.0 34.5 27.0 8.0

R3 0.0 91.0 31.0 33.0 20.5 2.0

R4 3.8 87.0 40.0 32.0 19.0 8.0

R5 0.0 87.0 37.0 36.8 19.6 2.0

R6 0.0 84.0 58.0 33.5 26.0 12.0

R7 0.0 86.0 11.0 35.0 21.5 calm

R8 0.0 47.0 38.0 36.0 23.5 10.0

R9 0.0 80.0 32.0 28.8 19.0 6.0

R10 0.0 87.0 48.0 30.0 21.5 4.0

R 11 0.0 87.0 43.0 33.5 21.0 calm

R12 0.0 70.0 49.0 30.8 25.0 1.4

R13 0.0 85.0 36.0 38.5 26.0 12.0

R14 1.0 85.0 52.0 35.0 21.8 8.0

R15 0.0 91.0 36.0 33.0 26.5 8.0

I1 0.0 90.0 48.0 30.0 20.5 10.0

I2 0.0 70.0 40.0 24.0 18.0 2.0

I3 0.0 89.0 52.0 34.0 26.5 4.0

I4 0.0 81.0 36.0 33.0 21.0 10.0

S1 0.0 71.0 53.0 33.5 20.0 4.8

S2 0.0 83.0 41.0 29.0 18.6 10.0

S3 0.0 87.0 29.0 32.0 19.0 4.0

S4 0.0 90.0 37.0 35.0 24.0 10.0

S5 0.0 93.0 52.0 31.0 19.0 4.0

S6 2.5 88.0 51.0 35.0 21.5 5.0

(a)

xxviii

Postmonsoon

Site

Rainfall

(mm) Humidity (%)

Temperature

(°C)

Wind

speed(Km/hr)

Max Min Max Min

R1 2.6 89.0 81.0 32.2 25.5 4.0

R2 0.0 90.0 67.0 33.0 26.5 2.0

R3 0.0 78.0 52.0 29.7 22.0 1.8

R4 0.0 82.0 48.0 33.4 23.0 3.4

R5 0.0 80.0 40.0 26.9 19.0 0.5

R6 0.0 90.0 72.0 32.0 26.0 4.0

R7 0.0 85.0 35.0 30.5 21.0 1.8

R8 1.0 84.0 62.0 33.0 24.0 7.0

R9 0.0 81.0 44.0 35.0 24.0 0.1

R10 0.0 92.0 51.0 26.0 16.0 2.0

R 11 0.0 70.0 56.0 25.9 19.4 4.6

R12 0.0 74.0 34.0 28.1 18.8 0.5

R13 0.0 84.0 54.0 31.9 24.0 5.6

R14 0.4 71.0 47.0 36.0 23.5 6.0

R15 0.0 71.0 48.0 30.7 24.0 1.2

I1 0.0 89.0 53.0 36.5 26.5 10.0

I2 0.0 89.0 39.0 20.0 11.4 calm

I3 0.0 89.0 75.0 28.8 18.0 1.0

I4 0.0 68.0 21.0 38.3 26.3 1.0

S1 0.0 89.0 75.0 28.8 18.0 1.0

S2 0.0 65.0 34.0 26.9 22.9 2.3

S3 0.0 78.0 46.0 29.0 20.0 8.8

S4 0.0 70.0 40.0 24.0 18.0 2.0

S5 0.0 88.0 51.0 35.5 23.0 5.4

S6 8.8 92.0 95.0 30.0 24.0 4.0

(b)

xxix

Winter

Site

Rainfall

(mm) Humidity (%)

Temperature

(°C)

Wind

speed(Km/hr)

Max Min Max Min

R1 0.0 90.0 33.0 30.0 16.5 calm

R2 0.0 93.0 57.0 24.5 14.0 2.0

R3 0.0 91.0 45.0 26.5 13.5 6.0

R4 0.0 91.0 36.0 26.0 12.0 0.0

R5 0.0 80.0 39.0 23.2 8.6 8.0

R6 0.0 85.0 70.0 20.6 14.0 5.0

R7 0.0 81.0 40.0 35.0 25.0 2.0

R8 0.0 72.0 58.0 28.0 21.0 5.3

R9 0.0 92.0 41.0 26.0 14.5 4.0

R10 0.0 75.0 53.0 30.0 20.4 1.0

R 11 0.0 86.0 34.0 22.5 10.0 calm

R12 0.0 84.0 47.0 28.0 15.0 6.0

R13 0.0 80.0 40.0 28.0 14.0 1.0

R14 0.0 75.0 35.0 30.0 20.0 2.6

R15 0.0 83.0 27.0 23.0 10.0 8.0

I1 0.0 90.0 25.0 28.0 15.0 calm

I2 0.0 78.0 28.0 28.8 14.0 2.0

I3 0.0 87.0 32.0 22.0 9.0 6.0

I4 0.0 81.0 37.0 29.0 13.0 4.0

S1 0.0 71.0 51.0 29.0 23.1 0.5

S2 0.0 87.0 26.0 34.0 22.5 4.0

S3 0.0 92.0 22.0 29.8 14.5 4.0

S4 0.0 88.0 25.0 34.0 11.7 4.0

S5 0.0 78.0 42.0 29.0 22.0 1.9

S6 0.0 89.0 55.0 27.0 17.0 2.0

(c)

xxx

Annexure XV: Total rainfall data in premonsoon, postmonsoon and winter season

during study period

Total rainfall (mm) in

premonsoon


postmonsoon


Winter

2008 2009 2008 2009 2008 2009

236.4 393.8 1296.8 900.4 29.9 474.3

LIST OF PUBILICATIONS

1. Ambient air quality status of Burdwan town, West Bengal. (2009) Poll Res 28 (4):

643-647.

2. Spatial and temporal variation of urban air quality: A GIS approach. (2010) JEP(1):

264-277.

3. Spatial and temporal variation of surface Ozone and its precursors in Burdwan

Municipality area. (2011) Res J Chem Environ15 (2):82-90.

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