ASSIMILATIVE CAPACITY AND AIR QUALITY STUDIES IN INDUSTRIAL AREA OF MYSORE Mtech Thesis

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JSS MAHAVIDYAPEETHA
Sri Jayachamarajendra College of Engineering
Mysore – 570 006
An Autonomous Institute affiliated to
Visvesvaraya Technological University, Belgaum
ASSIMILATIVE CAPACITY AND AIR QUALITY STUDIES
IN INDUSTRIAL AREA OF MYSORE
Dissertation submitted in partial fulfillment of curriculum prescribed for the
award of the Degree of
MASTER OF TECHNOLOGY
IN
ENVIRONMENTAL ENGINEERING
by
Shahul Hameed K P
(4JC09CEE11)
Under the Guidance of
Dr. M Mahadeva Swamy
Professor
Department of Environmental Engineering,
SJCE, Mysore.
DEPARTMENT OF ENVIRONMENTAL ENGINEERING
September, 2011

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JSS MAHAVIDYAPEETHA
Sri Jayachamarajendra College of Engineering (SJCE),
Mysore - 570 006
CERTIFICATE
This is to certify that the work entitled: ASSIMILATIVE CAPACITY AND AIR
QUALITY STUDIES IN INDUSTRIAL AREA OF MYSORE is a bonafide work carried out by
Shahul Hameed K P bearing USN: 4JC09CEE11 in partial fulfillment of the award of the Degree of
Master of Technology in Environmental Engineering under autonomous scheme of Visvesvaraya
Technological University, Belgaum during the year 2010-11.
Guide Head of the Department
(Dr. M Mahadeva Swamy) (Dr. H.S. Ramesh)
Professor Professor & Head
Dept. of Environmental Engg, Dept. of Environmental Engg
S.J.C.E, Mysore. S.J.C.E, Mysore.
(Dr. B. G Sangameshwara)
Principal, SJCE, Mysore.
Examiners: 1..………………………
Place: Mysore 2..………………………
Date: 3..………………………

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JSS MAHAVIDYAPEETHA
Sri Jayachamarajendra College of Engineering (SJCE),
Mysore - 570 006
UNDERTAKING
This is to certify that the Dissertation entitled ASSIMILATIVE CAPACITY AND AIR QUALITY
STUDIES IN INDUSTRIAL AREA OF MYSORE has been carried out independently by the
undersigned and it has not been submitted elsewhere for the award of Degree.
Place: Mysore Shahul Hameed K P
Date: (USN 4JC09CEE11)

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ABSTRACT
The deteriorating air quality in urban areas is primarily attributed to increase in motor
vehicle population and industrialisation and resulting exhaust emissions. Urban air pollution
is a major environmental problem both in developed and developing countries of the world.
Urbanisation in India is more rapid in and around National Capital as well as the State
Capitals and, over the years, these cities have become major centres for commerce, industry
and education. Air pollution levels in urban areas such as Delhi, Agra, Ahmedabad,
Bengaluru, Chennai, Faridabad, Hyderabad, Jharia, Jodhpur, Kanpur, Kolkata, Lucknow,
Mumbai, Patna, Pune, Solapur and Varanasi, are alarming due to their potential to cause
adverse health effects. Rapid industrialization in Bengaluru has led to increased demand for
resources such as land, electricity and water, and as a result the air pollution levels have
increased. This has led to the migration of industries towards periphery of urban boundaries
of Bengaluru as well as peri-urban areas such as Mysore, Mangalore, Bhadrawathi, etc. The
assimilative or carrying capacity of atmosphere over major urban centres are reduced due to
increased emissions from manufacturing industries coupled with change in atmospheric
conditions such as mixing height, wind motion, ambient air temperature and humidity. Once
the pollutants are released into the atmosphere, they are subjected to various physical,
chemical or photochemical processes that determine their ultimate environmental fate. Their
spatial distribution and concentrations are function of meteorological conditions and
topographic configuration of given surroundings. From the government policy perspective,
there is a compulsory need to understand the potential environmental impact of the new and
emerging technologies, with air quality impact being one of the most important issues to be
addressed. Implementation of air quality management and public warning strategies for
accurate forecasts of the atmospheric concentration of pollutants as function of space and
time are necessary. This can be done by ambient air monitoring, modelling and forecasting
techniques. In view of the above in the present study, Mysore city has been selected to
determine the assimilative capacity of atmosphere due various industrial emission sources for
all three seasons over a period of five years i.e. from 2006 to 2010.
Industrial area located on the northern part of Mysore has been identified as study
area. Industrial area is covering about 20 Sq km with flat terrain is located on out skirts of
Mysore city. Present study which has been carried out to determine assimilative capacity of

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the atmosphere in industrial area, based on Ventilation Coefficient (VC) and isopleths plot.
Preliminary phase of the study include: collection and analysis of meteorological data;
surface data and emission details of industries over a period of 5 years i.e., from 2006 to
2010. The dispersion of pollutants such as SO2 and NO2 from all sources such as industrial
processes, D.G sets and industrial boilers were determined and plotted for unfavourable
meteorological conditions for dispersion of air pollutants for all the seasons such as winter,
pre-monsoon and post-monsoon over a period of five years using ISCST3 which is based on
Gaussian plume dispersion model and GIS tools. Air Quality Indexing of Mysore industrial
area was done based on AQI standards and isopleths plots for SO2 and NO2. In order to
determine the ambient air quality in industrial area of Mysore, eleven monitoring stations
were established, which is inclusive of two newly established monitoring stations as a part of
the present study. The ambient air quality in study area as per air quality monitoring data,
concentrations of SO2 and NO2 was found to be within the standards as per National Ambient
Air Quality Standards (NAAQS).
The results of assimilative capacity over industrial area of Mysore showed that at
lower mean mixing depth of 60 m during winter (7:00 Hrs) the VC was observed to be
500 m2
/sec. Similarly, VC of 2750 m2
/sec was observed during post-monsoon season (14:00
Hrs) at an elevated MMD of 1625 m. The observed values of VC were < 3000 m2
/sec, which
indicate, atmosphere over Mysore industrial area is poor in terms of assimilative capacity as
per VC standards. The analysis of wind data of Mysore city based on windrose plots over a
period of five years i.e., 2006 to 2010. The results of wind data analysis showed that
movement of wind is predominant towards North-East during pre-monsoon and post
monsoon seasons. However, during winter, wind direction was towards South-West. Based
on the wind data and other meteorological characteristics, two new ambient air quality
monitoring stations were established along with other nine monitoring stations. The emission
inventory for industrial area of Mysore showed that major air polluting industries in Mysore
are: J.K Tyres (Plant-1 and Plant-2), Falcon Tyres and Venlon Enterprises. The major
contributor as per emission source was found to be process based when compared with
emissions from D.G sets and industrial boilers. The windrose and isopleths plots showed that
during winter, dispersion of pollutants was near the boundary of North-Western part of
Mysore city and Hootagally residential layout. This may be due to the wind which blows
towards South West during winter season. Based on isopleths plot, the highest concentrations
of SO2 and NO2 in Mysore industrial area was observed to be 240 µg/m3
for all the three

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seasons over a period of five years. However, as per Indian AQI standards, the general air
quality in Mysore industrial area is categorized as good to moderate. Since the isopleths plots
show higher concentrations of SO2 and NO2 i.e. > 368 µg/m3
and > 181 µg/m3
respectively,
within a radius of 0.2 km around the major sources of air polluting industries, such areas are
considered to be poor as per Indian air quality standards.

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ACKNOWLEDGEMENT
I would take this opportunity to express my sincere thanks to my guide
Dr. M Mahadeva Swamy, Professor, Department of Environmental Engineering, for his
support and guidance in successful completion of my project.
Dr. H.S.Ramesh, Professor & HOD, Department of Environmental Engineering
whose constant guidance and encouragement crowned our efforts with success.
It is our privilege to express heartfelt gratitude and respect to
Dr. B.G.Sangameshwara, Principal, Sri Jayachamarajendra College of Engineering,
Mysore for constant encouragement and support.
It would like to express my sincere thanks to Dr. Manojkumar B, Professor,
Department of Environmental Engineering, Dr. K.S Lokesh, Professor, Department of
Environmental Engineering, Dr. S.M Shiva Nagendra, Asst. Professor, IIT Madras,
Dr. P Niranjan, KSPCB – Mysore, Mr. Prakash, KSPCB – Mandya and Mr. Devaraj,
Department of Agriculture and Research, Naganahalli.
I would like to express my heartfelt gratitude to my beloved parents for their love,
affection and support in making this project a success.
I take this opportunity to express my deepest gratitude to my friends who have been a
source of inspiration and support in completing my project.
I would like to thank all my lecturers and non teaching staff of Sri Jayachamarajendra
College of Engineering, for their support and encouragement.
Shahul Hameed K.P.

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CONTENTS
Abstract i
Acknowledgement ii
Contents iii
List of Tables viii
List of Figures ix
CHAPTER 1: INTRODUCTION
1.1 General 1
1.2 Sources of Air Pollutants 2
1.3 Effects of Air Pollutants
1.3.1 Effects of air pollutants on human health 2
1.3.2 Effects of air pollutants on vegetation 4
1.3.3 Effects of air pollutants on materials 4
1.4 Air Quality Standards 5
1.5 Regulatory Acts and Authorities for Developing Air Quality in India 8
1.6 Air Quality Status of Major Cities of India 9
1.7 Objectives 12
CHAPTER 2: LITERATURE REVIEW
2.1 General 13
2.2 Role of Meteorology in Air Pollutant Dispersion 14
2.2.1 Atmospheric stability 15
2.2.2 Mixing height 15
2.2.3 Ventilation co - efficient 22
2.3 Air Quality Prediction Models 29
2.3.1 Basic mathematical models 29
2.3.1.1 Box model 29
2.3.1.2 Gaussian model 30
2.3.1.3 Eulerian model 31
2.3.1.4 Lagrangian model 31

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2.3.2 Air pollutant dispersion models 32
2.3.2.1 Industrial Source Complex Short – Term 3 (ISCST 3) 32
2.3.2.2 Aermic Dispersion Model (AERMOD) 34
2.3.2.3 Complex Terrain Dispersion Model Plus (CTDMPLUS) 39
2.3.2.4 Fugitive Dispersion Model (FDM) 39
2.4 Global Information Systems (GIS) for Air Quality Prediction 40
2.5 Air Quality Indices 46
2.6 Sensitivity Analysis 49
CHAPTER 3 EXPERIMENTAL PROGRAMME AND DATA COLLECTION
3.1 General 50
3.1.1 Meteorological data collection 50
3.1.1.1 Wind data 50
3.1.1.2 Mean Mixing Depth (MMD) 52
3.1.1.3 Study area 52
3.2 Ambient Air Quality Monitoring 52
3.2.1 Selection of ambient air monitoring stations 53
3.2.1.1 Primary factors 53
3.2.1.2 Secondary factors 53
3.2.2 Ambient air quality analysis 53
3.3 Data Analysis 54
3.3.1 Emission inventory 54
3.3.2 Contour map 55
3.3.3 Windrose diagrams 55
3.3.4 Ventilation co-efficient / Assimilative capacity 56
3.3.5 Industrial Source Short - Term 3 (ISCST 3) 56
3.3.5.1 Data input requirements of ISCST 3 57
3.3.5.2 RUN ISCST 3 58
3.3.6 Geographic Information Systems (GIS) 59
3.3.7 Isopleths 60
3.3.8 Air Quality Indices (AQI) 60
3.3.9 Sensitivity analysis 65

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CHAPTRER: 4 RESULTS AND DISCUSSIONS
4.1 General 66
4.2 Data Collection 66
4.2.1 Meteorological data 66
4.2.1.1 Wind rose plots and seasonal variations 67
4.2.1.2 Mean mixing depth (MMD) determination 72
4.2.2 Surface data 74
4.2.3 Emission inventory 77
4.3 Ambient Air Quality Monitoring 79
4.4 Assimilative Capacity 90
4.5 Isopleths plot and Air quality indices 92
4.6 Sensitivity Analysis of ISCST3 Model 134
CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS
5.1 General 137
5.2 Conclusions 137
5.3 Limitations 138
5.4 Recommendations 139
REFERENCES 140

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LIST OF TABLES
Table 1.1 Materials and their susceptibility to air pollution. 6
Table 1.2 National Ambient Air Quality Standards (NAAQS). 7
Table 2.1 Maximum Ventilation Coefficient (m2
/sec) and its Standard Deviation
Over India. 23
Table 2.2 Estimated ground level concentration values of SO2 in Mangalore
industrial area on flat and hilly terrain. 33
Table 2.3 Parameters used for modeling the deposition of sulphur and nitrogen
using AERMOD. 36
Table 2.4 AQI range, description and corresponding colour codes suggested
by US EPA. 47
Table 2.5 Sub - index and breakpoint pollutant concentration for Indian AQI 47
Table 3.1 Typical meteorological input file in ASC format. 61
Table 3.2 Typical control input file for ISCST3 in .INP format. 62
Table 3.3 Basic files required to run Arc GIS. 64
Table 3.4 General form of co-ordinates data input format to run ArcGIS. 64
Table 4.1 Seasonal variation of wind velocity and direction over Mysore. 68
Table 4.2 General details of industries in Mysore industrial area 78
Table 4.3 Yearly air pollutant emissions from categorized sources of Mysore
industrial area 80
Table 4.4 Yearly air pollutant emissions from different categories and scales
of industries 82
Table 4.5 Pollutant concentration at ambient air quality monitoring stations. 88
Table 4.6 Air quality monitoring data at two monitoring stations 89
Table 4.7 Worst case scenario meteorological conditions considered for plotting
seasonal isopleths over Mysore industrial area 93

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LIST OF FIGURES
Fig 2.1 Spatial distribution of mixing height during winter in India (7:00 Hrs) 17
Fig 2.2 Spatial distribution of mixing height during winter in India (14:00 Hrs) 17
Fig 2.3 Diurnal variation of mixing height in winter season 18
Fig 2.4 Spatial distribution of mixing height during summer in India (7:00 Hrs) 18
Fig 2.5 Spatial distribution of mixing height during summer in India (14:00 Hrs) 19
Fig 2.6 Diurnal variation of mixing height in summer season 19
Fig 2.7 Spatial distribution of mixing height during post monsoon in India
(7:00 Hrs) 20
Fig 2.8 Spatial distribution of mixing height during post monsoon in India
(14:00 Hrs) 20
Fig 2.9 Diurnal variation of mixing height in post monsoon season 21
Fig 2.10 Spatial distribution of ventilation coefficient during winter in India
(7:00 Hrs) 24
Fig 2.11 Spatial distribution of ventilation coefficient during winter in India
(12:00 Hrs) 24
Fig 2.12 Diurnal variation of ventilation coefficient in winter season 25
Fig 2.13 Spatial distribution of ventilation coefficient during summer in India
(7:00 Hrs) 25
Fig 2.14 Spatial distribution of ventilation coefficient during summer in India
(12:00) 26
Fig 2.15 Diurnal variation of ventilation coefficient in summer season 26
Fig 2.16 Spatial distribution of ventilation coefficient during post monsoon 27
Fig 2.17 Spatial distribution of ventilation coefficient during post monsoon
(12:00 Hrs) 27
Fig 2.18 Diurnal variation of ventilation coefficient in post monsoon season 28
Fig 2.19 Predicted 1Hr SO2 concentration (µg/m3
) contours for Canada (Alberta,
Northwest Saskatchewan, and a southern portion of the Northwest
Territories) using AERMOD. 37
Fig 2.20 Predicted 1Hr NO2 concentration (µg/m3
Northwest Saskatchewan, and a southern portion of the Northwest
Territories) using AERMOD. 38
Fig 2.21 The standalone simulation of air quality models, which is extended by
preprocessing and postprocessing software systems 42

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Fig 2.22 The standalone software application for integrated evaluation of air quality42
Fig 2.23 A typical Digital Elevation Model - Left (x and y coordinates range over
2km). Atmospheric dispersion in a uniform north wind with (middle) and
without (right) the DEM 44
Fig 2.24 Average concentration of suspended particulate matter over Song
Thi Vai,Vietnam 44
Fig 2.25 Average concentration of NO2 over Song Thi Vai, Vietnam 45
Fig 2.26 Average concentration of SO2 over Song Thi Vai, Vietnam 45
Fig 3.1 Geographical locations of the study area and its salient features 51
Fig 4.1 Yearly variations of wind speed and direction for winter season
(a) 2010(b) 2009 (c) 2008 (d) 2007 and (e) 2006 69
Fig 4.2 Yearly variations of wind speed and direction for pre-monsoon (a) 2010
(b) 2009 (c) 2008 (d) 2007 and (e) 2006 70
Fig 4.3 Yearly variations of wind speed and direction for post-monsoon (a) 2010
(b) 2009 (c) 2008 (d) 2007 and (e) 2006 71
Fig 4.4 Average seasonal variations in Mean Mixing Depth over Mysore 73
Fig 4.5 Location of industries in the study area of Mysore city 75
Fig 4.6 Toposheet of Mysore industrial study area 76
Fig 4.7 Digital Elevation Model of Mysore industrial area 76
Fig 4.8 Yearly emissions of air pollutants from categorized sources in
percentage (a) Sulphur dioxide and (b) Nitrogen dioxide. 81
Fig 4.9 Yearly emissions of air pollutants from different categories of industries
in percentage (a) Sulphur dioxide and (b) Nitrogen dioxide 83
Fig 4.10 Yearly emissions of air pollutants from different scales of industries
In percentage (a) Sulphur dioxide and (b) Nitrogen dioxide. 84
Fig 4.11 Emission rates of SO2 and NO2 from D.G sets of varying capacities85
Fig 4.12 Ambient air quality monitoring stations in Mysore industrial area 86
Fig 4.13 Ambient air quality monitoring stations established project for
Mysore industrial area 87
Fig 4.14 Yearly variations in V.C for 12Hrs (a) Winter (b) Pre-monsoon and
(c) Post-monsoon 91
Fig 4.15 Sulphur dioxide emissions from D.G sets for the year 2010 (Winter) 95
Fig 4.16 Nitrogen dioxide emissions from D.G sets for the year 2010 (Winter) 96

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Fig 4.17 Sulphur dioxide emissions from Boiler sets for the year 2010 (Winter) 97
Fig 4.18 Sulphur dioxide emissions from all sources for the year 2010 (Winter) 98
Fig 4.19 Nitrogen dioxide emissions from all sources for the year 2010 (Winter) 99
Fig 4.28 Sulphur dioxide emissions from D.G sets for the year 2010 (Pre-Monsoon)108
Fig 4.29 Nitrogen dioxide emissions from D.G sets for the year 2010
(Pre-Monsoon) 109
Fig 4.30 Sulphur dioxide emissions from Boilers for the year 2010 (Pre-Monsoon) 110
Fig 4.31 Sulphur dioxide emissions from all sources for the year 2010
(Pre-Monsoon) 111
Fig 4.32 Nitrogen dioxide emissions from all sources for the year 2010
(Pre-Monsoon) 112
(Pre-Monsoon) 113
(Pre-Monsoon) 114
(Pre-Monsoon) 115
(Pre-Monsoon) 116
(Pre-Monsoon) 117
(Pre-Monsoon) 118

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(Pre-Monsoon) 119
(Pre-Monsoon) 120
Fig 4.41 Sulphur dioxide emissions from D.G sets for the year 2010
(Post-Monsoon) 121
Fig 4.42 Nitrogen dioxide emissions from D.G sets for the year 2010
(Post-Monsoon) 122
Fig 4.43 Sulphur dioxide emissions from Boilers for the year 2010
(Post-Monsoon) 123
(Post-Monsoon) 124
(Post-Monsoon) 125
(Post-Monsoon) 126
(Post-Monsoon) 127
(Post-Monsoon) 128
(Post-Monsoon) 129
(Post-Monsoon) 130
(Post-Monsoon) 131
(Post-Monsoon) 132
(Post-Monsoon) 133
Fig 4.54 Sensitivity analysis plots of NO2 using ISCST3 for different
(a) Ambient temperature (b) Mean mixing depth and (c) Wind velocity 135
Fig 4.55 Sensitivity analysis plots of SO2 using ISCST3 for different
(a) Ambient temperature (b) Mean mixing depth and (c) Wind velocity 136

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CHAPTER 1
INTRODUCTION
1.1 General
New technologies are being developed to satisfy the material needs of human beings.
From a government policy perspective, there is a compulsory need to understand the potential
environmental impact of the new and emerging technologies, with air quality impact being
one of the most important issues to be addressed. Air pollution levels in some urban areas are
alarming due to their potential to cause adverse health effects. Therefore, continuous efforts
are being made to formulate appropriate control and management strategies to deal with this
problem. Control and management strategies for air pollution involve determination of
concentration levels of the pollutants in the ambient air by monitoring, modelling and
forecasting techniques. Direct measurement of a quantity is more reliable and accurate,
however, due to complex behaviour of air pollutants, monitoring involves a huge cost and
consumes more time and manpower. Therefore, while adhering to sufficient accuracy, it is
worthwhile to design all air quality programmes in a cost effective way and make best
possible use and interpretation of monitored data. Air quality models have been developed to
achieve this and also to study the relationship of emission sources and the air quality. Thus,
application of modelling techniques to manage ambient air quality becomes essential exercise
together with monitoring. More than 65,000 chemicals are used in commerce and
industrialized nations of the world. Many of these substances are polychlorinated biphenyl’s
(PCBs), industrial solvents and combustion related compounds are emitted directly to the
atmosphere because of man’s activities. Once they are released into the atmosphere,
pollutants are subjected to various physical, chemical or photochemical processes that
determine their ultimate environmental fate. Their spatial distribution and concentrations are
a function of meteorological conditions and topographic configuration of given surroundings
(Sharma, 1994). Among meteorological parameters, major influencing factors are wind
velocity and its direction, ambient temperature, humidity and rainfall. Hence, the dispersion
of pollutants in ambient atmosphere varies diurnally as well as from season to season.
Optimum atmospheric conditions results in a maximum dispersion of pollutants, thus
indicating safe work hours for industries from a point of view of industrial emissions.

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1.2 Sources of Air Pollutants
Air pollutants may originate either from natural or anthropogenic sources. The natural
sources are disintegration and dispersion of crustal materials such as soil, rocks, biomass and
dispersion of sea salt particles. The other sources of significance are gas to particle
conversion, evaporation of clouds and rain drops, forest fires and volcanoes (Sharma, 1994).
Amongst the man made sources the major contributors are atmospheric aerosols, the
industrial processes, transportation, power generation, refuse incineration etc,. Further, the
emissions can be either primary or secondary air pollutants. Primary pollutants are directly
emitted into the atmosphere from industrial sources such as manufacturing processes and
DG sets. However, secondary pollutants are formed by complex chemical interactions
between primary pollutants and atmospheric components such as moisture and ambient air
temperature.
Sources of air pollution are broadly classified into three categories, namely: Point
sources or large stationary sources such as manufacturing industries, power plants, solid
waste disposal sites, municipal incinerators, etc., Area sources or small stationary sources and
mobile sources with indefinite routes such as residential heating equipments which use
coal/oil/gas as fuel, commercial heating facilities, open onsite incineration of refuse, rail yard
locomotives, port vessels, airports, etc,. And line sources or mobile sources with definite
routes which include highway vehicles, railroad and channel vessels (Rao et al., 2008).
1.3 Effects of Air Pollutants
Urban episodes such as: Meuse valley in Belgium (1930), Donora, Pennsylvania
(1948), London smog (1952) and Bhopal tragedy, India (1985) which has caused damage to
human health and property. In general, the major effects of air pollutants are respiratory
diseases, visibility reduction and interference with photosynthesis, corrosion of metals,
spoiling of surfaces and global warming effects.
1.3.1 Effects of air pollutants on human health
Effects of air pollutants on human health vary depending on the nature, type and
concentration of pollutants and duration of exposure. The nature of ailment is complex since
ambient air consists a wide range of pollutants such as carbon dioxide (CO2), sulphur
dioxide(SO2), nitrogen dioxide (NO2), carbon monoxide (CO), lead (Pb), particulate
matter(PM), etc,. Acute (single exposures) exposure to SO2 produces immediate bronchial

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constriction, narrowing of the airways, increased pulmonary resistance, increased airway
reactivity, and changes in metabolism. Chronic (continuous or multiple) exposure results in
swelling of the mucosal tissues and increased secretions. Ambient exposures may aggravate
existing pulmonary diseases. The adverse effects associated with exposure to SO2 seem
worse with humid conditions (Laura, 2003). The effects of CO2 are a reduction in the
pH value of blood serum leading to acidosis. The minimum effects of acidosis are
restlessness and mild hypertension. As the degree of acidosis increases, somnolence and
confusion follow. One of the effects of these changes is a reduced desire to indulge in
physical activity. Embryonic or foetal abnormalities are also possible as the increase in
atmospheric carbon dioxide affects maternal metabolisms in succeeding generations
(Robertson, 2006).Suspended particulate matter (SPM) generally does not have health
effects immediately. Much of the SPM in air that we breathe, such as dust and pollen,
are large enough to be trapped in the nose and throat. However, fine particles (less than 10
microns) generated by combustion of crude oil and polymers are so small that they can
penetrate deep into the lungs, and with time, gradual accumulation of these particles in the
lungs may damage the respiratory system. This can result in distress such as Acute Lower
Respiratory Infection (ALRI - common in children), chronic obstructive pulmonary disease
(COPD - scarring of the lung tissue common in young women), lung cancer (from coal
burning), cardiopulmonary mortality etc (Nordica et al., 2006).
The adverse effects on human health associated with exposure to ambient and indoor
concentrations of CO are related to concentration of COHb in the blood. In individuals with
cardiovascular diseases, COHb levels of 2-6% may impair the delivery of oxygen to the
myocardium causing hypoxia and increasing coronary blood flow demand by nearly 30%.
Foetuses and young infants are more susceptible to CO exposure as fetal Hb has greater
affinity for CO than maternal Hb. Pregnant women have increased alveolar ventilation,
increasing the rate of CO uptake from inspired air. Also, a pregnant woman produces nearly
twice as much endogenous CO. Individuals with low haemoglobin levels are more sensitive
to low-level CO exposure due to their reduced ability to transfer oxygen (Maria et al., 2001).
Diseases caused due to NO2 are related to a individual’s nature of occupation and his
health. Person(s) who works in industries which manufacture nitric acid, exposures of
farmers to silage that has high nitrate fertilization, electric arc welding, etc, usually
experience eye and nasal irritation after exposure to about 15 ppm of NO2 and pulmonary
discomfort being experienced at exposures of 25 ppm of NO2.

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1.3.2 Effects of air pollutants on vegetation
Plants are susceptible to air borne pollutants even at lower air pollutant
concentrations, since pollutants interfere with plant growth as well as with the photosynthetic
process. The presence of air pollutants in the ambient air such as CO, water vapour, dust, etc.
can reduce the amount of light reaching the earth surface (plants) as well as reduces the
photosynthetic activity. Due to this the plant leaf is damaged and lead to various types of
diseases. Forms of damages caused due to air pollutants are: Necrosis is the killing or
collapse of plant tissue is caused due to prolonged exposure of plant leaves to high SO2
concentrations. The necrosis of leaf tip indicates cumulative increase in fluoride levels in a
plant. Chlorosis disease causes the loss or reduction of green plant pigment, chlorophyll
results in changing the plant leaf colour to pale green or yellow. This may be due to
deficiency of some nutrient(s) in plant. Dropping of leaves at an early age has been observed
and this disease is known as abscission. It is caused due to exposure of plants to mild or high
concentrations of ethylene.
1.3.3 Effect of air pollutants on materials
Effects of air pollution on materials can be generally expressed in terms of
discoloration, material loss, structural failure and soiling.
There are no valuation studies or material inventories from which estimates of the
costs of discoloration can be estimated. However, such costs are probably very small.
Structural failure resulting from pollutant exposure also seems unlikely unless either the
design of a building is fundamentally flawed or maintenance has been grossly neglected. In
either case, it seems unreasonable to attribute costs to air pollution, at least in the context of
affluent societies. Acidic deposition covers both the direct effects of sulphur dioxide and the
effects of acid deposition resulting from both SO2 and NOx emissions. It should be noted that
the effects of these air pollutants are set against a background of substantial natural
weathering forces including rain, bacteria, freeze-thaw cycles and sea salt (in coastal
regions). These natural constituents would lead to damage of materials even in the absence of
air pollutants. For a number of materials, the dry deposition of SO2 exerts the strongest
corrosive effect of atmospheric pollutants. Wet deposition of pollutants, expressed as rain
acidity, also has a corrosive effect on certain materials. Although a strong synergistic effect
with SO2 has been observed in laboratory studies, however the same has not been observed in

5
the field. Ozone is known to damage some polymeric materials such as paints, plastics and
rubbers. Of these, damage to rubber seems the most important. It has also been observed to
act synergistically with SO2 in the field. The materials for which damage has been considered
are calcareous stone, mortar, paint, concrete, aluminium and galvanised steel. Although not
exhaustive, this list includes the most sensitive of the materials commonly used by the
construction industry. All non-galvanised steel is assumed to be painted and is therefore
considered as part of the paint inventory. No consideration has been made of the loss of
transparency for glass because modern glass is considered to be very resistant to attack. The
summary of the materials and their susceptibility to air pollution is presented in Table 1.1
(Rabl et al., 2000).
1.4 Air quality standards
The primary aim of the air quality standards is to provide a basis for protecting public
health from the adverse effects of air pollution and for eliminating, or reducing to a
minimum, those air contaminants that are known or likely to be hazardous to human health
and well-being.
In India ambient air quality standards were first adopted on 11 November 1982 in
exercise of its jurisdiction under Section 16 (2) (h) of the Air (Prevention & Control of
Pollution) Act, 1981. CPCB consulted experts in the field of air quality and health effects of
air pollution to formulate the air quality standards. Subsequent to the deliberations of experts
and the consensus reached, CPCB has formulated the ambient air quality standards for most
of the commonly found pollutants. Different standards have been laid down for industrial,
residential, and sensitive areas to protect human health and natural resources from the effects
of air pollution. The national ambient air quality standard as per central pollution control
board (CPCB) is presented in Table 1.2.
The air quality standards have evolved differently in different countries depending on
the exposure condition, socio-economic situation and importance of other health related
problem. Therefore, the actual concentration chosen for standards are different for various air
pollutants in different countries (CPCB, 2001).

6
Table 1.1 Materials and their susceptibility to air pollution.
(Rabl et al., 2000)
Materials Sensitivity to air pollution
Brick Very low
Mortar Moderate to high
Concrete Low
Natural stone (Sand stone, Lime stone
and Marble)
High (Severely affected by SO2)
Unalloyed steel High (Severely affected by SO2)
Stainless steel Very low
Nickel and nickel plated steel
High (Especially in SO2 polluted
environment)
Zinc and galvanized steel
High (Especially in SO2 polluted
environment)
Aluminium steel Very low
Copper Low
Lead Very low

7
Table 1.2 National Ambient Air Quality Standards (NAAQS)
(CPCB, 2011)
Pollutant
Time weighted
avg.
Conc. in ambient air
Method of
measurement
Indl. area
Resi./ Rural
area
Sensitive
area
Sulphur Dioxide
(SO2)
Annual avg* 50 µg/m3
20 µg/m3
Improved West &
Gaek method
Ultraviolet
fluorescence
24 Hours avg ** 80 µg/m3
80 µg/m3
Nitrogen
Dioxide (NO2)
30 µg/m3
Jacob& Hocchieser
method, Gas-Phase
Chemiluminiscence
80 µg/m3
PM10
(<10 µm)
60 µg/m3
Gravimetric, TOEM
and Beta attenuation
100 µg/m3
PM2.5
(<2.5 µm)
40 µg/m3
Gravimetric, TOEM
and Beta attenuation
60 µg/m3
Ozone (O3)
8 Hours** 100 µg/m3
100 µg/m3
UV photometric
Chemiluminiscence
Chemical method
1 Hour** 180 µg/m3
180 µg/m3
Lead (Pb)
Annual avg* 0.50 µg/m3
0.50 µg/m3
AAS Method after
sampling using EPM
2000/eq. filter paper
24 Hours avg ** 1.00 µg/m3
1.00 µg/m3
Carbon
Monoxide (CO)
8Hours avg** 2.00 mg/m3
2.00 mg/m3
Non-dispersive
infrared (NDIR)
Spectroscopy
1 Hour avg 4.00 mg/m3
4.00 mg/m3
Ammonia (NH3)
Annual avg* 100µg/m3 100µg/m3
Chemiluminiscence
Indophenol blue
method
400 µg/m3
Benzene (C6H6) Annual * 5.00 µg/m3
5.00 µg/m3
Gas chromatography

8
Adsorption - GC
Benzo(a)Pyrene
(BaP)-
Particulate
phase only
Annual* 1.00 ng/m3
1.00 ng/m3
Solvent extraction
followed by HPLC/GC
analysis
Arsenic (As) Annual* 6.00 ng/m3
6.00 ng/m3
AAS/ICP method after
sampling on EMP
Nickel (Ni) Annual* 20 ng/m3
20 ng/m3
AAS/ICP method after
sampling on EPM
* Annual arithmetic mean of minimum 104 measurements in a year twice a week 24 hourly at uniform interval.
** 24-hourly/8-hourly/1-hourly values should be met 98% of the time in a year. However, 2% of the time, it
may exceed but not on two consecutive days of monitoring.
1.5 Regulatory acts and authorities for developing air quality in India
With rapid advancement of science and technology and cropping up of new problems,
provisions of new regulatory measures for abatement of environmental pollution including air
pollution came into existence. Some of the provisions were general laws like the Indian Penal
Code, Criminal Procedure Code, Civil Procedure Code, Specific Relief Act and Police Act,
where provisions to control pollution are of general nature. Some of the specific laws were
the Oriental Gas Company Act 1857, Indian Boiler’s Act 1923, Motor Vehicle Act 1939,
Factory Act 1948, and the Industries (Development and Regulation Act) 1951 which do not
specifically deal with air pollution but some of their provisions do deal with problems of
controlling the same (CPCB, 2001). CPCB, SPCBs, and PCCs were constituted in September
1974 under the provision of the Water (Prevention and Control of Pollution) Act 1974. CPCB
is under the administrative control of the Central Government. SPCBs and PCCs were also
constituted under the same Act. CPCB coordinates the activities of SPCBs and PCCs, and
also advises the Central Government on all matters concerning the prevention and control of
environmental pollution. CPCB, SPCBs, and PCCs are responsible for implementing the
legislation relating to prevention and control of environmental pollution. They also develop
rules and regulations that prescribe the standards for emission and effluent of air and water
pollutants and noise level. CPCB also provides technical services to MoEF for implementing

9
the provisions of the Environment (Protection) Act, 1986. CPCB continued its activities in
the assessment of pollution in different areas, strengthening monitoring mechanisms for
assisting environmental quality, and taking steps for prevention and control of pollution from
different services through coordinated programs with SPCBs and PCCs. In addition, CPCB
also interacts with voluntary and nongovernment organizations for appropriate participation
and wide dissemination of information to the public (CPCB, 2001).
The Air (Prevention and Control) Act 1981 was enacted by the Parliament under
Article 253 of the Constitution to take appropriate steps to prevent and control air pollution
and fulfil the proclamation adopted by the United Nations Conference on the Human
Environment held in Stockholm in June 1972. Further the Environment (Protection) Act 1986
was passed by the Parliament by virtue of the powers vested in it under Article 253 of the
Constitution in the wake of the Bhopal tragedy and to further implement the decisions of the
UN Conference on the Human Environment 1972, insofar as they relate to the appropriate
steps to be taken for the protection and improvement of the human environment. This Act is
an umbrella legislation enacted to provide a framework for the Central Government for
coordinating the activities of various Central and State authorities established under the
Water and Air Acts. According to its preamble, the objective of the Environment Act 1986 is
“to provide the protection and improvement of environment and for matters connected
therewith” (CPCB, 2001).
The EPCA for the National Capital Region was constituted by the Central
Government vide notification no. S.O. 93 (E) dated 29 January 1988 for a period of 2 years
with effect from the date of notification. Subsequently, its tenure was extended for 2 years in
January 2000. In January 2002, its tenure was further extended by another one year. EPCA
shall exercise the following powers and perform functions for protecting and improving the
quality of environment and prevention and control of environmental pollution (CPCB, 2001);
 Exercise the powers under Section 5 of the Environment Protection Act, 1986 for issuing
directions in respect of complaints pertaining to violation of environmental standards,
industrial location, pollution prevention and hazardous waste handling.
 Take all necessary steps to ensure compliance of specified emission standards by
vehicles;
 Issue directions under Section 5 of the said Act, including banning or restricting an
industry, process of operation emitting noise;

10
 Deal with environmental issues pertaining to the National Capital Region;
 Monitor the progress of the action plan for control of pollution drawn up by MoEF as
contained in the White Paper on Pollution in Delhi with Action Plan; and
 Exercise the power of entry, inspection, search and seizure under Section 10.
1.6 Air Quality Status of Major Cities of India
Urbanisation in India is more rapid in and around National Capital as well as the State
Capitals and, over the years, these cities have become major centres for commerce, industry
and education. Enormous increase in number of industries has resulted in increased emission
of air pollutants and, as a result, levels of air pollutants such as respirable suspended
particulate matter, sulphur dioxide, nitrogen dioxide, etc, are found to exceed the prescribed
standards of air quality in all major cities. The Honourable Supreme Court has identified
sixteen cities namely Agra, Ahmedabad, Bengaluru, Chennai, Faridabad, Hyderabad, Jharia,
Jodhpur, Kanpur, Kolkata, Lucknow, Mumbai, Patna, Pune, Solapur and Varanasi in addition
to Delhi for which action plans are being formulated and implemented to control air
pollution.
The concentration of the industries in and around Bengaluru city has considerably
increased in recent years. Also the increase in the number of industrial layouts have risen
from year to year with major additions from the electronic and manufacturing units at various
industrial areas around Bengaluru city. There are about 4015 industries in and around
Bengaluru, these industries naturally catalysed more commercial growth and thereby
increased in concentrations of air pollutants has been observed. Among these industries,
about 215 industries are identified as hazardous. Due to discontinuous power supply, there
are large number of DG sets are being used in commercial establishments as well as in
industries which increase the pollutant concentration in ambient air (CPCB, 2006). The
increasing commercial and industrial activities, the transport system is increasing day by day
in Chennai City resulting in deterioration of air quality. There is no major air polluting
industries within Chennai city other than the power plants. As far as the industrial sector is
concerned the major source of pollution is from the utilities like Boilers and Generator sets.
Apart from this, the major source of air pollution in Chennai city is from the coal and iron ore
handling units located within the Chennai Port Trust area (CPCB, 2006). The majority of
industries in Agra comprise of foundries. Besides a number of petha processing industries are
operating in the city, which mainly uses coal as fuel. Besides these, there are halwaiis,

11
kumhars and bharbhujas who use coal, cow dung and wood as their domestic fuel. However,
Kumhars uses cow dung as their fuel because, the type of firing system adopted by them is
more effective and efficient (CPCB, 2006).
Varanasi is a place of religious, historical and tourist importance. The city produces of
various handicraft products such as silk sarees, carpets, jari jamdani, rags, customary knitting
of jamavars etc. The major portion of the industrial units belongs to the categories of general
engineering, metal processing and products, machinery. During the recent years, more and
more textile related units are coming up in the city. The city is having Lahartara industrial
area which is being located within the city (CPCB, 2006). Ahmadabad is one of the major
industrial cities in India, and it has been called the ‘Manchester of the East’ due to its many
textile industries. As far as industrial pollution is concerned, it may be attributed to air
polluting industrial units, which are about 490 in number within the Municipal Limits on
periphery of the city (CPCB, 2006).The air pollution in Faridabad are due to the vehicular
traffic, industries and natural dust. Pollutant concentration from the local industries such as
brick kilns, thermocol, plastic and cement industries which has affected the human health,
crops and animals. Vehicular emissions constitute a very important component of any
strategy to control air pollution in Delhi. There are three thermal power plants at
Indraprastha, Badarpur and Rajghat which emits sulphur dioxide, oxides of nitrogen and
suspended particulate matter. The industrial pollution load due to thermal power plants and
cement plant has been estimated in Delhi (CPCB, 2006). In Kanpur, emission load from
domestic & commercial sources reveals major particulate pollution is from use of coal
followed by wood and related fuel. There are more than 70 large-scale industries, which are
mainly of leather products, cotton textiles, chemical products, metal industries, etc. While
there are 138 small scale industries which are mainly for leather products, metal products,
food products, rubber & Plastics, cotton textiles etc. Among industrial sources, Panki thermal
Power plant alone contributes maximum pollutant load when compared with other industrial
sources (CPCB, 2006).
There are large numbers of small and medium scale industries located in Hyderabad/
Secunderabad. There are various areas, which are being distributed across Hyderabad and
these industrial areas are Azamabad, Azamabad, Chandulal Baradhari, Sanath Nagar and
scattered units in Amberpet, Bahadurpura, Candrayangutta and Musheerabad. These
industrial areas were heart of the city. As the city grows, these industrial areas were
surrounded by residential areas (CPCB, 2006). There are 252 different product based

12
industries being operated in the Kolkata city metropolitan area. Most important industries
within Kolkata Metropolitan Area other than Tanneries and Thermal Power Plants are
chemicals, metals, acid, paints & varnish, mineral oils, coal, lead smelters, battery, textiles,
drugs, glass and ceramics, soap and detergents, dyes, rubber, plastic, leather, food and
beverages etc. There are more than 12,000 industries in the city causing air and water
pollution (CPCB, 2006).
Rapid industrialization in Bengaluru has led to increased demand for resources such
as land, electricity and water, and also the air pollution levels have increased. This has led to
the migration of industries towards periphery of urban boundaries of Bengaluru and to nearby
cities like Mysore. Mysore city is basically a place historical and tourist importance and is
also one of the industrial hubs of Karnataka. Mysore district comprise of four major industrial
zone out of which one zone is situated at the outskirts of Mysore city. Prominent industrial
estates located at outskirts of the city are Hebbal, Koorghalli, Metagalli, Thandya, Belagola,
Hootagalli, Hinkal, Yadavagiri and Belavadi, which occupies a total area of 3914 acres. The
industrial area consists of 38 green category industries, 19 orange category industries and 59
red category industries. The types of industries include textiles, tyre manufacturing, chemical,
distilleries, bulb manufacturing, etc. Apart from stack emissions from these industries, a large
number of DG sets are being used to have their own power supply power.
The magnitude of impact on the city due to an ever increasing pollution levels caused
by the industries are assessed by air monitoring and a futuristic idea about the pollution levels
are determined using various air pollutant dispersion models. The magnitude of air pollutant
concentrations in surrounding areas is a function of local meteorological conditions, stack
emission rates and topography. The predicted pollutant levels show the spatial distribution of
pollutants and hence indicate air quality and assimilative capacity of the atmosphere over the
given area. This serve as a tool for setting guidelines for safe working hours for industries
and number of industries that can be accommodated in given area.
1.7 Objectives
Main objective of the project work is to determine the assimilative capacity of
atmosphere industrial area, Mysore.
Following are the specific objectives;
 To collect and interpret meteorological data’s such as wind speed, temperature, and
wind direction for past five years and to plot windrose diagrams.

13
 To identify and select strategic points for air quality monitoring in the study area
based on windrose plots and surface data.
 To determine the ventilation coefficient and carrying capacity of atmosphere over the
study area based on wind speed and mixing height data.
 To predict the spatial distribution of three major pollutants such as SO2 and NO2 in
study area using ISCST3 model.
 To determine the air quality index of Mysore industrial area.
CHAPTER 2
LITERATURE REVIEW
2.1 General
The dispersion of pollutants in ambient atmosphere is a function of variables such as
wind speed, wind direction, ambient air temperature, humidity, stack height and diameter,
stack gas temperature, emission rate and pollutant species present in the flue gas. The
secondary factors that control the rate of dispersion of pollutants in atmosphere are terrain or
profile of ground and land use pattern. Due to the variable nature of meteorological factors
governing the dispersion of pollutants in atmosphere, it is required to consider a wide time
range of meteorological data. This enables us to arrive at a nearly true dispersive nature of
pollutants in atmosphere at different hours of a day and seasons of a year. Modern day
pollutant dispersion studies in ambient air are based on Gaussian plume dispersion model
which is applied for point source(s) serve as a base for a wide range of computer generated
pollutant dispersion models. The Gaussian plume dispersion model was subsequently revised
and modified so as to account for various atmospheric stability class, terrain, down wash and
background pollutant concentrations. In addition to computer generated mathematical

14
models, GIS based tools are also applied for statistical and graphical analysis of movement of
pollutants on a global scale hence giving a much clearer idea about pollution potential of
source(s).
Since meteorology play a vital role in pollutant dispersion, many researchers have
shown its significance in their studies. These studies generally deals with spatial distribution
of pollutants which are mainly based on mathematical model(s) such as Industrial Source
Complex Short Term 3 (ISCST3), American Dispersion Model (AERMOD) and Geographic
Information System (GIS). However, this chapter discusses about the various studies
conducted by different research workers in relation to significance of meteorology in
pollutant dispersion as well as spatial distribution of air borne pollutants using computer
generated mathematical models and GIS.
2.2 Role of Meteorology in Air Pollutant Dispersion
Dispersion or degradation of pollutants in air either at macro or molecular level is due
to meteorological factors such as atmospheric stability, mixing height, wind speed, ambient
temperature and humidity. All the above said meteorological parameters constitute the
carrying capacity or assimilative capacity of atmosphere over a given place. Assimilative
capacity of atmosphere over a given area is the maximum pollutant load that can be
discharged in to the atmosphere without violating the best designed use of air resources, thus
plays a significant role in determining safe working hours for industrial operations. It largely
depends on sources of pollutant emission and the state of atmosphere (dispersion,
transformation and removal) and sink mechanisms (receptor response) of air pollutants
prevailing over the given area at a given time. Hence, meteorology plays a vital role in
environmental management supplementing other effects like optimal resource utilization and
adoption of abatement strategies.
Good meteorological conditions characterized with higher wind speeds and elevated
MMD results in better dispersion of pollutants in atmosphere. In fact the upward movement
of plume may be hindered due to overlaying lid of dense and cold layer of wind in the upper

15
strata of atmosphere. The pollutant dispersion in ambient air is also a function of time of a
day and season of a year since the solar radiation on to earth’s surface varies from hour to
hour in a day and season to season. Usual trend of pollutant dispersion over a day shows that
hour with higher MMD facilitates enhanced dispersion of pollutants in the atmosphere.
Research has revealed that summer season is characterized with better dispersion of
pollutants in atmosphere. As a matter of fact, Noon hours of summer (12 - 15 Hrs) is the ideal
time for maximum dispersion of pollutants. In addition to these factors, the topography and
geographic location also plays a vital role on the dispersion of pollutants in atmosphere. For
an instance, pollutant sources situated in a valley point experience lesser pollutant dispersion
due to strong wind over the hillock which prevents the upward movement of pollutant plume.
Similarly, air pollution sources situated on ridge or flat terrain experience higher dispersion
of pollutants when compared with latter case. Hence, it is evident that meteorology coupled
with topography plays a vital role in dispersion of pollutant plume (s) and resulting ambient
pollutant concentrations in a locality (Hall et al., 1999).
2.2.1 Atmospheric stability
Stability differs from layer to layer in the atmosphere. During daytime, solar heating
destroys the surface inversions or stable layers. However, stable layers may be found aloft
several hundred meters above the ground. The stability of the atmosphere is highly dependent
upon the vertical distribution of temperature with height. The rate at which the temperature of
ambient air decreases with height is referred as environmental lapse rate (ELR). The rate of
change in temperature of parcel of dry air with elevation when displaced adiabatically, is
known as dry adiabatic lapse rate (DALR). Mathematically, it is expressed in eqn. (2.1) as:
−
д�
д�
=
�
��
= �� = . °
Normally, ELR is less than DALR. However, during conditions of intense solar
radiation on flat terrain, environmental lapse rate increases beyond the adiabatic value at
shallow depths (10 - 50 m). When the environmental temperature decreases faster with height
than the adiabatic rate, then the environmental lapse rate is termed as super-adiabatic. Rising
air parcel, cooling at the adiabatic lapse rate becomes warmer and less dense than its
( 2.1 )

16
environment. The parcel is said to be in unstable equilibrium as buoyancy tends to accelerate
it upwards. However, when the environmental lapse rate is less than adiabatic, the rising air
parcel becomes cooler and denser than its environment and tends to return to its starting point
resulting in stable equilibrium. In case, when the temperature increases with altitude, the
lapse rate becomes negative and the atmospheric condition is termed as inversion. It is a
condition of strong stability (Attri et al., 2008).
2.2.2 Mixing height
Mixing Height or Mixing Depth is used by to quantify the vertical height of mixing in
the atmosphere. The concept of mixing height (MH) is based on the principle that when the
atmosphere is heated from below due to the solar radiation, it becomes unstable and gives rise
to vertical motion and mixing. Mixing height is thus, defined as the top of a surface based
layer in which vertical mixing is relatively vigorous and the lapse rate is approximately dry
adiabatic. These conditions are normally seen during cloudless condition during day time. In
the case of a ground based inversion, the mixing height is zero or absent. Forecasting of
mixing height is done with the aid of the vertical temperature profile. A radiosonde is sent
aloft and temperatures at various altitudes are radioed back. The altitude at which the dry
adiabatic line intersects the radiosonde measurements is taken as the maximum mixing depth
(MMD). The MMD is a function of stability. In unstable air, the MMD is higher while it is
lower in stable air. There is a seasonal variation of mixing height. During summer daylight
hours, MMD can be a few thousand meters, where it can be a few hundred meters in winter.
It also varies during the course of a day. It is lowest at night and increases as the day
progresses. With a measure of both MMD and wind speed with respect to height, we get a
good idea of the amount of pollutant dispersion. The mixing height shows increasing trend
during the day with the increase of surface air temperature, achieving maxima around the
time of maximum surface temperature and later in the afternoon over rural / open areas in fair
weather situation. However, it is more likely to remain persistent to heights of roughly a
hundred meters or so over urban areas owing to slower cooling than rural / open areas
(Attri et al., 2008).
The mixing height (MH) values gradually increase after sunrise, reach maximum in
the afternoon and then decrease gradually till night in all the seasons. Spatial distribution of
MH over India during winter season from 0700 hrs to 1900 hrs IST is shown in Fig 2.1 and
Fig 2.2. Maximum mixing height (MMH) was obtained over most stations (45%) at 1600 hrs

17
IST followed by 1400 hrs (36%) and 1500 hrs (19%), respectively during winter season.
Variation of MMH for different standard levels during the day in winter is shown in Fig 2.3.
Average coefficient of variation of MMH was of the order of 18 % (10-28 %) over entire
country during this season(Attri et al., 2008).
In summer, the spatial distribution of MH during 0600 -2000 hrs in India is shown in
Fig 2.4 and Fig 2.5. Maximum values of MH were found at 1600 hrs over highest number of
stations (42 %) followed by 1500 hrs (26 %), 1400 hrs (19 %) and least at 1300 hrs (13 %).
Coefficient of variation of MMH over India was found around 16 % (7-25 %). Variation of
MMH for different standard levels during the day in summer is shown in Fig 2.6. For post
monsoon season, spatial distribution of MH from 0700 hrs to 1900 hrs is shown in Fig 2.7
and Fig 2.8. MMH followed more or less similar pattern as that during winter season i.e.
maximum value at most stations (45 %) at 1600 hrs IST followed by 1400 hrs (42 %),
1600 hrs (10 %) and 1300 hrs (3 %). Variation of MMH for different standard level is shown
in Fig 2.9. Coefficient of variation of MMH was 17 % (9 – 26 %) during summer season at
different stations (Attri et al., 2008).
Fig 2.1 Spatial distribution of mixing height during winter in India (7:00 Hrs)
(Attri et al., 2008)

18
Fig 2.2 Spatial distribution of mixing height during winter in India (14:00 Hrs)
Fig 2.3 Diurnal variation of mixing height in winter season

19
Fig 2.4 Spatial distribution of mixing height during summer in India (7:00 Hrs)

20
Fig 2.5 Spatial distribution of mixing height during summer in India (14:00 Hrs)
Fig 2.6 Diurnal variation of mixing height in summer season
Fig 2.7 Spatial distribution of mixing height during post monsoon in India (7:00 Hrs)

21
Fig 2.8 Spatial distribution of mixing height during post monsoon in India (14:00 Hrs)

22
Fig 2.9 Diurnal variation of mixing height in post monsoon season
2.2.3 Ventilation coefficients
Ventilation coefficient (VC) is a product of mean layer wind (MLW) and mixing
height. The MLW is a measure of average rate of horizontal transport of air in mixing layer.
Hence, VC is a measure of the rate of volume of horizontal transport of air within the mixing
layer and is a useful parameter to identify air sheds carrying capacity. High mixing heights
with light wind may have a similar impact on pollution transport as low mixing heights
associated with strong winds. The Ventilation Coefficients (VC) generally follow similar
pattern as that of mixing height i.e. gradual increases after sunrise, reaching to maximum in

23
the afternoon and thereafter it decreases gradually till night in all the seasons under study.
Maximum Ventilation Coefficients (MVC) values at different stations with SD are presented
in Table 2.1.
Spatial distribution of VC during winter season from 0700 hrs to 1900 hrs IST is
shown in Fig 2.10 and Fig 2.11. Maximum values of Ventilation Coefficient were computed
at 1500 hrs at 45 % stations, followed by 1400 hrs (32 %), 1600 hrs (20 %) and 1300 hrs
(3 %) over the country. Coefficient of variation of MVC over India was found to be 17 %.
Variation of MVC for different pollution potential levels during the day have been presented
in Fig 2.12. Spatial distribution of VC during summer from 0600-2000 hrs IST in India is
shown in Figure 2.13 and 2.14. MVC followed the pattern of maximum MH over the country
viz. highest values were computed at over more stations. (36 %) at 1600 hrs, followed by
1500 hrs (32 %), 1400 hrs (19 %) and 1300 hrs (13 %). Variation of MVC for different
pollution potential levels are as shown in Fig 2.15. Coefficient of variation of MVC was
found of the order of 16 % in the country. Spatial distribution of VC during post monsoon
season from 0700 hrs to 1900 hrs in India is shown in Fig 2.16 and Fig 2.17. MVC followed
similar pattern as that of MH over the country during this season i.e. maximum value at most
stations (45 %) was computed at 1600 hrs IST, followed by 1400 hrs (42 %), 1600 hrs (10 %)
and 1300 hrs (3 %). Variation of MVC for different pollution potential levels during the day
is shown in Figure 2.18. Coefficient of variation of MVC was highest in this season over
India (Attri et al., 2008).
Table 2.1 Maximum Ventilation Coefficient (m2
/sec) and Standard Deviation over
India. (Attri et al,. 2008)
Name of Station
Seasons
Winter Pre-monsoon Post-monsoon
V.C S.D V.C S.D V.C S.D
Ahmedabad 7650.3 1759.5 15708.9 1885.1 7653.7 1224.6

24
Amini Divi 3046.4 335.1 3500.6 560 1458.4 247.928
Auranagabad 6112.1 733.44 19803.01 1782.27 8759.3 1576.62
Bengaluru 6536.8 849.784 11391.5 1025.235 5790.3 752.739
Bhopal 989.2 1203.916 20133.7 4228.077 6583.1 987.465
Bhubneshwar 5737.4 1061.419 7762.1 1474.799 3499.4 489.916
Mumbai 5385.1 1308.57 4946.23 989.2 4797.1 623.61
Kolkata 4948.5 672.92 17513.5 2451.89 6914.5 1244.61
Chikalathane 6106.3 1312.85 19790.7 1781.163 8756.8 1313.52
New delhi 6127.6 1255.4 19269.4 1926.94 9021.2 180424
Panaji 7138.2 1213.46 3745.8 711.702 4874.5 799.92
Gorakhpur 2887.9 548.53 9287.12 1671.66 1772.3 407.56
Guwahati 2963.8 622.23 2180.33 523.2 2710.9 460.7
Gwalior 2779.1 643.77 15997.50 3359.37 6347.9 1586.975
Hyderabad 7443.4 967.642 11979.1 2156.238 6364.7 1463.72
Jagdalpur 5264.3 737.002 13938.3 2230.128 4808.8 865.584
Jodhpur 5328.6 852.48 10860.6 2823.756 9453.2 2174.236
Karaikal 5740.3 861.23 4792.26 814.64 4748.56 669.9
Lucknow 4053.2 972.72 15497.39 3254.37 4685.21 1241.525
machilipatnam 3983.8 677.246 4394.1 615.174 3939.7 630.352
Chennai 5313.3 637.596 5701.7 570.17 5065.3 658.489
Mangaluru 5677.6 1078.74 2797.9 503.622 2295.4 413.172
Minicoy 3469.7 555.152 3675.6 624.852 3786.6 605.856
Mohanbari 1891.1 264.74 1753.22 227.89 2878.22 505.0957
Nagpur 5413.5 1407.51 17964.2 2514.988 5281.2 897.804
Patiala 3854.2 462.48 17078.88 3074.04 4459.9 936.579
Patna 2731.1 355.043 6652.6 1530.098 1999.4 459.862

25
Portblair 2393.8 526.636 1871.8 411.796 1346.1 323.064
Srinagar 1106.3 177.088 6540.8 981.12 4405.4 925.134
Thiruvananthapuram 2899.4 434.91 4451.2 712.192 3306.5 429.845
Visakapatnam 1983.4 436.26 2560.50 268.8 3057.9 672.738
Fig 2.10 Spatial distribution of ventilation coefficient during winter in India (7:00 Hrs)

26
Fig 2.11 Spatial distribution of ventilation coefficient during winter in India (12:00 Hrs)
Fig 2.12 Diurnal variation of ventilation coefficient in winter season

27
Fig 2.13 Spatial distribution of ventilation coefficient during summer in India (7:00 Hrs)

28
Fig 2.14 Spatial distribution of ventilation coefficient during summer in India (12:00)
Fig 2.15 Diurnal variation of ventilation coefficient in summer season

29
Fig 2.16 Spatial distribution of ventilation coefficient during post monsoon (7:00 Hrs)
Fig 2.17 Spatial distribution of ventilation coefficient during post monsoon (12:00 Hrs)

30
Fig 2.18 Diurnal variation of ventilation coefficient in post monsoon season

31
2.3 Air Quality Prediction Models
Atmospheric dispersion/ air quality prediction models are mathematical expressions
to describe physical and chemical processes of air pollutants in atmosphere at various
atmospheric stability conditions. These models have been used to predict the impacts arising
out of the developmental activities involving air pollutant(s) emissions. To analyse the
dispersion of air pollutants in the atmosphere, air quality models have been used, which
requires meteorological data’s such as wind velocity and its direction, temperature, stability,
mixing height, rainfall, stack emission rate, stack dimensions etc,. In addition to emission,
meteorological and stack details, surface / topographical details are also required to have a
nearly realistic simulation of pollutant movement and its relative degradation at a given
atmospheric stability condition(s) over a given area / region.
Air pollution models constitute set of formulas that take into account the sources of
pollution in a given area, the amounts of pollutants emitted by each source, chemical
reactions, meteorological conditions and topographical features. The successful application of
a model relies on a detailed emission inventory of all sources and accurate meteorological
data applicable to the area. The choice of the type of model to be applied for a specific study
depends on a number of factors such as objective of the study, availability of input data size
and topography of the study area. Calculations involved in air pollutant dispersion is based on
initial rise of plume from the source and dispersion due to meteorological factors such as
wind velocity and its directions, stability of atmosphere (Abdul Wahab, 2004). Air pollutant
dispersion model studies will help us in predicting impacts for the future by considering the
climatology and source information, which acts as a tool in decision making at the project
clearance stage and are also used for regulatory purposes in trying to apportion contributions
from various existing sources to a net observed pollution level (Attri et al., 2008).
2.3.1 Basic mathematical models
Modelling of pollutant dispersion is completed using mathematical algorithms. There
are several basic mathematical algorithms are being used and these models are; Box model,
Gaussian model, Eulerian model and Lagrangian model.
2.3.1.1 Box model
The box model is the simplest of the modelling algorithms. It assumes the airshed in the
shape of a box. The box model is represented using following equation :

32
= Q × A + u × C n × W × H – u × C × W × H ( 2.2 )
Where,
Q = Pollutant emission rate per unit area
C = Homogeneous species concentration within the airshed
V = Volume described by box
�� = Species concentration entering airshed
A = Horizontal area of box
u = Wind speed normal to the box
H = Mixing height
Although Box model has been used to predict the dispersion of air pollutant over
urban area, but it has got its own limitations. It assumes that the pollutant is homogeneous
across the airshed, and it is used to estimate average pollutant concentrations over very large
area.
2.3.1.2 Gaussian model
The Gaussian models are the most common mathematical models being used for air
pollutant dispersion. These models are based on the assumption that the pollutant will
disperse according to the normal statistical distribution. Gaussian distribution equation is
given by
C x, y, z =
π σ σ
{exp
− −
σ
+ exp
− +
σ
} {exp (
−
σ
) }
Where,
C ( ,,) = Pollutant concentration as a function of downwind position
(x, y, z)
Q = Mass emission rate
U = Wind speed
� = Standard deviation of pollutant concentration in y (horizontal)
direction
� = Standard deviation of pollutant concentration in z (vertical)
direction
y = Distance in horizontal direction
z = Distance in vertical direction
H = Effective stack height
( 2.3 )

33
2.3.1.3 Eulerian model
Eulerian model solves a conservation of mass equation for a given pollutant.
Eqn. (2.4) follows the form:
∂< i>
∂
= −U × ∆ < C > − ∆ < C U′
> + ∆ < C > + < S >
Where,
U = Ū + U’
U = Wind field vector U(x, y, z)
Ū = Average wind field vector
U' = Fluctuating wind field vector
c = < c > + cI
c = Pollutant concentration
< c > = Average pollutant concentration
cI
= Fluctuating pollutant concentration
D = Molecular diffusivity
�� = Source term
2.3.1.4 Lagrangian model
Lagrangian models predict pollutant dispersion based on a shifting reference grid.
Shifting reference grid is generally based on the prevailing wind direction, or vector, or
general direction of the dust plume movement. The Lagrangian model has the following
form:
c r, t = ∫ ∫ p r, t|r′
, t′ S r′
, t′
−∞
dr′
dt′
Where,
< c (r, t) > = Average pollutant concentration at location ‘r’ at time t
S (r’, t’) = Source emission term
p ( r , t | r’ , t’) = Probability function that an air parcel is moving from location
r’ at time t’ to location r at time t
The above mathematical model has got its own limitations such as effects of variation
local metrological conditions and emission characteristics when theoretical results are
compared with actual field measurements. This is due to the dynamic nature of the model.
Measurements are generally made at stationary points, while the model predicts pollutant
concentration based upon a moving reference grid (Chakraborty et al., 2008).
( 2.4 )
( 2.5 )

34
2.3.2 Air pollutant dispersion models
Various researchers have worked on air pollutant dispersion studies using numerous
computer generated models such as industrial source complex short term (ISCST),
AERMOD, Complexn Terrain Dispersion Plus (CTDMPLUS) and Fugitive Dust Model
(FDM) which are based on modified Gaussian dispersion model equations. These model
equations along with their field applications have been discussed in the following sections.
2.3.2.1 Industrial Source Complex Short Term (ISCST3)
The Industrial Source Complex (ISC) Short Term model provides options to model
emissions from a wide range of sources that might be present at a typical industrial source
complex. It is a short range (~50km) dispersion model designed to support US EPA’s
regulatory options. ISCST3 is an air quality model is based on Gaussian plume diffusion
equation which assumes time independence in the input meteorology and source
concentration. It does not take into account changes due to photo reactions and calculates
concentrations of non-photo reactive pollutants and deposition fluxes from a wide variety
sources. Input options include the use of stack-tip downwash, buoyancy-induced dispersion,
final plume rise, a routine for processing averages when calm winds occur, and default values
for wind profile exponents and for the vertical potential temperature gradients. The Short
Term model also incorporates COMPLEX1 screening model dispersion algorithms for
receptors in complex terrain. The user may select either rural or urban dispersion parameters,
depending on the characteristics of the source location (Venkatram et al,. 2003). The volume
source option and the area source option can also be used to simulate line sources. Many
investigators have used ISCST3 model to predict ground level concentrations of SO2. The
investigations carried out at Mangalore industrial area ascertain the impact of 1000 MW
thermal power plants, operation of existing industries and cumulative impact
due to thermal power plant on ambient air quality in respect of ground level
concentration of SO2. The model was run for hourly meteorological data sets for winter.
(Amitava Bandyopadhyay, 2009). The outcome of the study using ISCST3 is presented in
Table 2.2.

35
Table 2.2 Estimated ground level concentration values of SO2 in Mangalore industrial
area on flat and hilly terrain. (Amitava Bandyopadhyay, 2009)
Sl.No Case
Predicted SO2 using ISCST3
Distance (km)
Concentration
(µg/m3
)
1. Influence of thermal power plant alone
on vicinity
2 11.6
50 41.2
2. Influence of existing industries alone.
5 233.8
15 181.8
20 225.9
25 160.2
38 260.75
50 91.3
3. Combined effect of above two
5 220.2
15 175.1
20 210.3
25 150.4
40 260.1
50 131.1
4. Combined effect of 1 and 2 with 90%
treatment at source
5 79.12
15 91.02
20 70.21
25 64.89
40 81.21
50 25.2
5. Combined effect of 1 and 2 with 90%
treatment at source
2 51.21
48 89.23

36
50 44.23
2.3.2.2 Aermic Dispersion Model (AERMOD)
AERMOD is a steady-state plume model. For stable boundary layer (SBL), it assumes
the concentration distribution to be Gaussian in both the vertical and horizontal. In the
convective boundary layer (CBL), the horizontal distribution is also assumed to be Gaussian,
however, the vertical distribution is described with a bi-Gaussian probability density function
(pdf) under convective conditions when vertical plume dispersion is non-Gaussian. In the
CBL, AERMOD treats plume lofting, whereby a portion of plume mass, released from a
buoyant source, rises to and remains near the top of the boundary layer before becoming
mixed into the CBL. AERMOD also tracks any plume mass that penetrates into the elevated
stable layer, and then allows it to re-enter the boundary layer when and if appropriate. For
sources in both the CBL and the SBL AERMOD treats the enhancement of lateral dispersion
resulting from plume to meander. Using a relatively simple approach, AERMOD
incorporates current concepts about flow and dispersion in complex terrain. Where
appropriate the plume has been modelled as either impacting and/or following the terrain
(Cimorelli et al., 2004).
The software combines the United States Environmental Protection Agency's (US-
EPA) AERMOD model, the AERMAP terrain processor, the AERMET meteorological
processor, and other user-friendly components including the functionality of a user interface
(Krzyzanowski, 2010). Input data for AERMET includes hourly cloud cover observations,
surface meteorological observations such as wind speed and direction, temperature, dew
point, humidity and sea level pressure and twice a day upper air sounding. Meteorological
data can be input for multiple heights and wind speed, temperature and turbulence are treated
as vertical profiles. AERMOD calculates the convective and mechanical mixing height.
Plume dispersion is determined by turbulence profiles that vary with height. Under unstable
conditions, AERMOD plume displacement is caused by random convective velocities. The
model incorporates the effects of increased surface heating from urban area on pollutant
dispersion under stable atmospheric conditions (Manju Mohan et al., 2009). AERMOD is
suggested for use in multi-source pollutant dispersion studies. Sources can be individually
modelled as rural or urban. A number of additional user-defined parameters are required for
wet and dry deposition estimates in AERMOD. The parameters used are given in Table 2.3

37
and are assumed equal for all sources in the model run, despite the fact that they may vary
with temperature or source (Krzyzanowski, 2010).
AERMOD simulates five different plume types depending on the atmospheric
stability and on the location in and above the boundary layer: direct, indirect, penetrated,
injected, and stable. During stable conditions, plume is modelled with the familiar horizontal
and vertical Gaussian formulations. During convective conditions, the horizontal distribution
is still Gaussian; the vertical concentration distribution results from a combination of three
plume types, they are:
 The direct plume material within the mixed layer that initially does not interact with
the mixer layer lid;
 The indirect plume material within the mixed layer that rises up and tends to initially
loft near the mixed layer top; and
 The plume starts dispersing in the mixing layer but, due to its buoyancy, penetrates
into the elevated stable layer.
During convective conditions, AERMOD also handles injected source where the stack
top (or release height) is greater than the mixing height. Injected sources are modelled as
plumes in stable conditions, however, the influence of the turbulence and the winds within
the mixed layer are considered in the inhomogeneity calculations as the plume material
passes through the mixed layer to reach receptors.
In AERMOD, a skewed vertical velocity pdf, �� is modeled using a bi-Gaussian
distribution as:
pw =

√ πσw
exp [−
w−w
σw
] +

√ πσw
exp [−
w−w
σw
]
Where,
1
 and 2
 are weighting coefficients for the two distributions with 1 2 1
 
  .
The parameters of the pdf ( 1 2 1 2 1 2
, , , , ,
w w
w w     ) are functions of w
 (the total or
overall root mean square vertical turbulent velocity), the vertical velocity skewness
3 3
/ w
S w 
 (where is the third moment of w), and a parameter 1 1 2 2
/ / 2
w w
R w w
 
   
.
( 2.6 )

38
Table 2.3 Parameters used for modelling the deposition of sulphur and nitrogen using
AERMOD. (Krzyzanowski, 2010)
Module Parameter Units SO2 NO2
Gas deposition
Diffusivity in air cm2
/Sec 0.1089 0.1361
Diffusivity in
water
cm2
/Sec 1.541×10-5
1.879×10-5
Cuticular
resistance
Sec/cm 20 0.1
Henry’s law
constant
Pa-m3
/mol 81.70 3,070.4
Particle
deposition
Percent fine
particles
% 0.99 0.99
Mean size fine
particles
µm 1 1

39
Fig 2.19 Predicted 1Hr SO2 concentration (µg/m3
northwest Saskatchewan, and a southern portion of the Northwest
Territories) using AERMOD. (Krzyzanowski, 2010)

40
Fig 2.20 Predicted 1Hr NO2 concentration (µg/m3
northwest Saskatchewan, and a southern portion of the Northwest
Territories) using AERMOD. (Krzyzanowski, 2010)

41
2.3.2.3 Complex Terrain Dispersion Model Plus (CTDMPLUS)
CTDMPLUS is a refined Gaussian plume dispersion model designed to estimate
hourly concentrations of plume material from elevated point sources at receptors on or near
isolated terrain features. This model can assess stable and neutral atmospheric conditions as
well as day time, unstable conditions. Its use of meteorological data and terrain information is
different from other regulatory models in that considerable detail for both types of input data
is required and is supplied by preprocessors specifically designed for CTDMPLUS.
In air pollution dispersion modelling under stable to neutral conditions, a central
feature of CTDMPLUS model has been used to separate the flow in the vicinity of a hill into
two separate layers by adopting critical dividing streamline height. Flow in the upper layer
has sufficient kinetic energy to pass over the top of the hill, while the streamlines in the lower
layer are constrained to flow in a horizontal plane around the hill. In modelling unstable or
convective conditions, the model relies on a probability density function (PDF) description of
the vertical velocities to estimate the vertical distribution of pollutants. Hourly profiles of
wind and temperature measurements are used by CTDMPLUS to compute plume rise, plume
penetration and other convective scaling parameters. In stable/neutral conditions, the profiles
of turbulence data are used to compute dispersion parameter values at plume height. The
model also calculates on an hourly basis how the plume trajectory is deformed by each hill.
The computed concentration at each receptor is then derived from the receptor position on the
hill and the resultant plume position and shape (Venkatram et al., 2003).
2.3.2.4 Fugitive Dust Model (FDM)
The Fugitive Dust Model has been specifically designed for computing concentration
and deposition impacts for fugitive dust sources. The sources may be point, line or area
sources. The model has not been designed to compute the impacts of the buoyant point
sources; thus it contains no plume rise algorithm. The model is generally based on the well-
known Gaussian Plume formulation for computing concentrations, and it has been
specifically adapted to incorporate the improved gradient transfer deposition algorithm.

42
Emissions for each source are apportioned by the user into a series of particle size classes. A
gravitational settling velocity and deposition velocity are calculated by FDM for each class.
Concentrations and depositions are computed at all user selected receptor locations and up to
500 receptors and 200 sources can be processed. The sources can be of three types: point, line
or area sources. The line source and area source algorithms are based on algorithms in the
CALINE3 model. For area sources, the user supplies the coordinates of the centre and the
dimension in the x and y directions. Area sources need not be square, it rather can be
rectangular, up to an aspect ratio of 1 to 5 (ratio of width to length). Area sources with the
length greater than five times the width must be divided in a series of area sources, or
modelled as a line source. The model divides the area source into a series of line sources
perpendicular to the wind direction.
The fugitive dust model was applied to estimate and predict the pollutant
concentration from Codli iron ore mine, Goa, India. The main emission sources for dust in
the open cast mining operations were exposed to pit surfaces, haul roads, loading and
unloading operations for the overburden as well as ore, exposed overburden dumps, stock
yard for ore as well as product, processing plant and workshop area. The predicted average
concentration levels of SPM at the 7 sites during winter season ranged from 243-594 μg/m3
.
During summer season the predicted average concentration level of SPM at these sites ranged
from 229-612 μg/m3
and during post monsoon the predicted average concentration at the sites
ranges from 21-93 μg/m3
(Gurudeep singh et al., 2006).
2.4 Global Information Systems (GIS) for Air Quality Prediction
Global Information Systems (GIS) is a powerful technique for surveying, mapping
and monitoring earth resources and environment and is an excellent platform upon which
different types of spatially referenced data can be united for analysis and display purposes.
This technique has become indispensable and increasingly more meaningful because of its
synoptic coverage of satellites over large areas rendering, its cost and time effectiveness.
Further, in areas that are difficult to access, this technique is the only option for obtaining
required data more effectively. Prior to the advent of GIS technology, many operations that
involved the concerted utilization of data sets derived from different sources and in different
formats used to be carried out using an approach in which hard copy maps were generated
and overlaid upon one another which is costly and time consuming and yields substandard
results.

43
Dispersion models are physical models that use existing data on emissions and
meteorological and topologic conditions to create maps of pollutant concentrations. Such
models are generally based on Gaussian plume dispersion equations (Tage et al., 2001).
Atmospheric dispersion equations are based on parameters which are strongly affected by
spatial and temporal variations. These variations significantly affect the spray drift’s
behaviour and trajectory. The spatial dimension is thus essential in atmospheric dispersion
modelling, but also represents the Geographical Information System’s paradigm. GIS have so
become an adequate tool to analyze and visualize spatial based environmental models. The
coupling of environmental models with Digital Elevation Model (DEM) and the GIS plays a
vital role in enhancement of the environmental models (Nicholas et al., 2010). The recent
development of spatial data management in the frame of GIS has created the new era of
environmental modelling. More powerful computers have made running air quality models at
global and locale spatial scales possible. In order to understand the function of more complex
models, the modelling system should consist of subsystems such as point and area sources of
pollution, spatial description of terrain elevations, meteorological data and air quality
monitoring networks. Obviously, the use of the GIS has become essential in providing
boundary conditions to the air quality models. Certainly, the use of the GIS in air pollution
modelling can be extended moreover to processing the surface data. Many models have been
coupled with the GIS in the past to simulate various environmental processes. Due to the
four-dimensional nature of distribution of atmospheric pollutants, the concept of the GIS is
extended to include temporal variations of three-dimensional spatial data. Considering to a
huge volume of numerical calculations, two-dimensional interpolations into the horizontal
layers are used to interpolate three dimensional atmospheric data onto a model grid system.
The interpolations, integrations of land cover surface data and the GIS analyses focused on
small scale spatial models carried out in the kilometre grid.
A few scenarios are established to integrate air quality models into the GIS. The basic
level is represented by the standalone software application for simulation of air quality
models (ISCST3), it is accompanied by data inputs and outputs. Integration of air quality
models with GIS comprise many steps, and a typical example of steps carried out during the
simulation of air quality models is shown in Fig 2.21. The integrated emission evaluation
systems offer alternative ways of using the emission models together with selected
functionality of the GIS. A number of software applications are focused on design of
relational databases and their interconnection together with standard air quality modelling
systems. The structure of the programs developed with spatial software is shown in Fig 2.22.

44
The methods available for coupling of air dispersion models with GIS are full integration,
loose coupling and tight coupling. Coupling of physical model which involves unsteadiness
and uncertainties, with GIS that are tending to provide an accurate numerical copy of the
study area’s surface. Thus, GIS can be used to apply the model in a richer geo-referenced
numerical environment.
Fig 2.21 The standalone simulation of air quality models, which is extended by
preprocessing and postprocessing software systems. (Matejicek, 2003)

45
Fig 2.22 The standalone software application for integrated evaluation of air quality
(Matejicek, 2003)
GIS capabilities regarding DEM generation and exploitation are significantly
improving the dispersion of pollutants, as they allow the model to be run on any local
topography. Furthermore, GIS permits the map directly to drift process and to get standard
atmospheric concentrations at given geographical coordinates. It also becomes easy to make
the pollutant cloud interacts with other relevant geodata, and to conduct advanced risk
analysis.
Considering the integration of air quality models, the scope and scale of urban areas
problems make the GIS a powerful tool for management of spatial and temporal data,
complex analyses and visualization. Due to the ability to manage a number of spatial and
temporal data formats, data structures created in the frame of the GISs open the ways to
building air quality information systems that synthesize geospatial and temporal air quality
data to support spatio-temporal analysis and dynamic modelling. Much progress has been
made with the mapping of environmental data and the creation of national, regional and local
data sets. For instance, the air quality models are not being included into the GIS. As
standalone software applications, they use various data formats, which can usually operate
independently with their own GIS database. Similarly, air quality management agencies are
creating GIS data sets to support their operations without any data standards that can support
spatio-temporal analysis and dynamic modelling. The requirements for the integrated spatial

46
modelling of air quality in the frame of the GIS represent a common geospatial coordinate
system, vector themes (points, lines and areas) for description of surface objects (buildings,
bridges, vegetation) supported by DEM and vector themes for representation of air pollution
inputs (local point, line and area sources of pollution, long-distance transport of air pollution).
The DEM is an important tool which enhances the simulation of pollutant dispersion over a
complex terrain. Pollutant dispersion studies over Calais, France shows the difference
between plume dispersion with and without considering DEM data in Fig 2.23.
Application of air dispersion model coupled with GIS in mapping SPM, SOX and
NOX over the city of Song Thi Vai river basin in Vietnam shows the effective and realistic
pollutant dispersion over the city. The study revealed the increase in concentration of SPM,
SOX and NOX twice as on January for the month of February 2007. Use of air quality models
without GIS on other hand predicted about an increase of 1.4 times the pollutant
concentrations for month of February as shown in Fig 2.24 to Fig 2.26 (Xuan et al., 2008).
Fig 2.23 Left: A typical Digital Elevation Model (x and y coordinates range over 2 km).
Atmospheric dispersion in a uniform north wind with (middle) and without
(right) the DEM. (Nicolas et al., 2010)

47
Fig 2.24 Average concentration of suspended particulate matter over Song Thi Vai,
Vietnam. (Xuan et al., 2008)
Fig 2.25 Average concentration of NO2 over Song Thi Vai, Vietnam.

48
(Xuan et al., 2008)
Fig 2.26 Average concentration of SO2 over Song Thi Vai, Vietnam.
(Xuan et al., 2008)
2.5 Air Quality Indices
In 1976 the EPA established a uniform AQI, called the pollutant standard index (PSI),
for the use of state and local agencies to assess urban air quality on a voluntary basis. AQI is
a mathematical combination of the concentrations of air pollutants (weighted in some fashion
to reflect the estimated health impact of the specific pollutant) which gives an approximate
numerical measure of the quality of the air at a given time. These indices have little scientific
basis but have been used to inform the public (in a qualitative fashion) of the degree of
pollution present at a given time. AQI is a tool, introduced by Environmental Protection
agency (EPA) in USA to measure the levels of pollution due to major air pollutants. The AQI
includes sub-indices for ozone (O3), PM, carbon monoxide (CO), sulphur dioxide (SO2), and
nitrogen dioxide (NO2), which relate ambient pollutant concentrations to index values on a
scale from 0 to 500. The index is normalized across pollutants by assigning an index value of
100 to the primary NAAQS for each pollutant and an index value of 500 as the pollutant level
associated with risks of significant harm. The EPA made major changes in 1999 to its
previous AQI. The changes resulted in index values ranging from 0 to 500 being divided into
six groups to characterize the relationship between daily air quality and associated

49
public health effects. The air quality indices have been calculated using the US
Environmental Protection Agency procedure to assess the status of ambient air quality near
busy traffic intersections in Bangalore, India. Table 2.4 presents the AQI ranges,
corresponding to health effects and associated colour codes suggested by the Environmental
Protection Agency (Shiva Nagendra et al., 2007). The values of sub indices of each pollutant
are taken to represent overall AQI at any given location.
The major air pollutant, which could cause potential harm to human health has been
included are SO2, NO2, SPM, RSPM (PM10), CO, and O3. Concentrations of all six pollutants
are not necessary to calculate the index, although desirable. The index is so designed that at
the minimum, three pollutants SPM, SO2, and NO2 are sufficient to calculate the index. The
method involved formation of subindices for each pollutant and aggregation of sub-indices.
Table 2.5 presents the linear segmented relationship for sub-index values and the
corresponding pollutant concentrations that are calibrated to Indian conditions (Prakash et al.,
2010). There are primarily two steps involved in formulating an AQI, first the formation of
sub-indices for each pollutant, and second the aggregation (breakpoints) of sub-indices. The
segmented linear function shown eqn. (2.7) is used for relating the actual air pollution
concentrations (of each pollutant) to a normalized number (i.e. sub-index).
Table 2.4 AQI range, descripton and corresponding colour codes suggested by US EPA.
(Shiva Nagendra et al., 2007)
Index
Values
Category
Colour
Codes
Purpose
0-50 Good Green Convey positive message about air quality
51-100 Moderate Yellow
Convey message that daily air quality is
acceptable from public health perspective, but
every day in this range could result in potential
for chronic health effects; and for O3 convey a
limited health notice for extremely sensitive
individuals
101-150
Unhealthy for
sensitive group
Orange
Health message for members of sensitive
groups.
151-200 Unhealthy Red Health advisory of more serious effects for
sensitive groups and notice of possible effects

50
for general population when appropriate.
201-300
Very
unhealthy
Purple
Health alert of more serious effects for
sensitive groups and the general population.
301-500 Hazardous Maroon Health warnings of emergency conditions.
Table 2.5 Sub - index and breakpoint pollutant concentration for Indian AQI.
(Prakash et al., 2010)
Sub-
Index
Category
Pollutants (µg/m3
)
SO2
24 avg.
NO2
24 avg.
SPM
24 avg.
PM10
24 avg.
CO
8 avg.
O3
8 avg.
0-100 Good 0-80 0-80 0-200 0-100 0-2 0-157
101-200 Moderate 81-367 81-180 201-260 101-150 2.1-12 158-235
201-300 Poor 368-786 181-564 261-400 151-350 12.1-17 236-784
301-400
Very
Poor
787-1572 565-1272 401-800 351-420 17.1-35 785-980
401-500 Severe >1572 >1272 >800 >420 >35 >980
An index for any given pollutant is its concentration expressed as a percentage of the relevant
standard. The maximum value of sub indices of each pollutant was taken to represent overall
AQI of the location. The mathematical equations for calculating sub-indices were developed
by considering heath criteria of the Environmental Protection Agency and Indian NAAQS,
Central Pollution Control Board, 2000 show in eqn. (2.7) (Shiva Nagendra et al., 2007).
IP =
− L
− L
CP − BPLO + ILO (2.7)
Where,
IP = Air quality index for pollutant “P” (Rounded to nearest integer).
CP = Actual ambient concentration of pollutant “P”.
BPHI = Upper end breakpoint concentration ( ≥ CP ).

51
BPLO = Lower breakpoint concentration ( ≤ CP ).
ILO = Sub-index or AQI corresponding to BPLO.
IHI = Sub-index or AQI corresponding to BPHI (From table)
The air quality depreciation index, is a measure of deterioration in air quality on an
arbitrary scale that ranges between 0 and –10. An index value of ‘0’represents most desirable
air quality having no depreciation from the best possible air quality with respect to the
pollutants under consideration while an index value of –10 represents maximum depreciation
or worst air quality. Index values differing from 0 towards –10 represent successive
depreciation in air quality from the most desirable. The air quality depreciation index is
defined as follows:
AQ p = Σ =
n
AQ × CW − Σ =
n
CW (2.8)
Where,
AQ = Air quality index for ith
parameter.
CW = Composite weight of ith
parameter.
n = Total number of pollutants.
The values of the AQi are obtained from the value function curves. In the value
function curves the value of 0 signifies worst air quality and value of 1 represents the best air
quality for corresponding pollutant concentration. The value of ��in eqn. (2.9) is calculated
as follows;
CW =
T i
Σi=
n T i
× (2.9)
TW =AW + BPIW + HW
Where,
TW = Total weight of ith
parameter.
AW = Aesthetic weight of ith
parameter.
BPIW = Bio-physical impact weight of ith
parameter.
2.6 Sensitivity Analysis
Validation and dependency of air quality models on certain essential operating
parameters such as meteorological and stack characteristics are tested against pollutant
concentration along centre line of the plume boundary along X-axis. Sudden change in
pollutant concentrations for same parameter i.e., at different wind velocity or ambient
temperature or mean mixing depth or stack gas temperature, etc., shows the dependency of
the model on that particular parameter in predicting pollutant concentrations. This indicates

52
the governing parameters such as meteorological or emission parameter (s), in predicting air
pollutant concentrations. Hence, the accuracy of the governing parameter will control the
overall prediction studies. In general, dispersion of pollutant in the atmosphere is due to both
diffusion as well as bulk motion. Air pollution studies are based on diffusion equation, the
concept of sensitivity plays a vital role in parameter estimation and sampling depth.
Sensitivity is a partial derivative, which represents the change in air pollutant concentrations
resulting from a change in model parameter. The purpose of this section is to describe the
behaviour of these sensitivities in time and space under varying conditions, to offer physical
explanations of their observed behaviour, and to relate this behaviour to parameter
estimation, sampling and design problems.
CHAPTER 3
EXPERIMENTAL PROGRAMME AND DATA COLLECTION
3.1 General
Air pollutant dispersion studies and its impact on the ambient air quality of the study
area requires collection of the existing air pollution sources, flue gas characteristics,
meteorological characteristics, topographical features of the study area, etc. The ambient air
quality of various pollutants such as oxides of nitrogen (NOX), oxides of sulphur (SOX) and
suspended particulate matter have mentioned at identified locations and used for validating

ASSIMILATIVE CAPACITY AND AIR QUALITY STUDIES IN INDUSTRIAL AREA OF MYSORE Mtech Thesis

ASSIMILATIVE CAPACITY AND AIR QUALITY STUDIES IN INDUSTRIAL AREA OF MYSORE Mtech Thesis

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