DEVELOPMENT OF AIR QUALITY MODEL FOR PREDICTING AIR...
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i DEVELOPMENT OF AIR QUALITY MODEL FOR PREDICTING AIR POLLUTION IN MAJOR CITIES Kishan Chand Mukwana PEE-001 A thesis submitted in fulfillment of the requirements for the award of the degree of Doctor of Philosophy In Energy & Environment Engineering Department of Energy & Environment Engineering Faculty of Engineering Quaid-e-Awam University of Engineering, Science & Technology, Nawabshah 2016
Transcript of DEVELOPMENT OF AIR QUALITY MODEL FOR PREDICTING AIR...
i
DEVELOPMENT OF AIR QUALITY MODEL FOR PREDICTING
AIR POLLUTION IN MAJOR CITIES
Kishan Chand Mukwana
PEE-001
A thesis submitted in fulfillment of the requirements for the award of the
degree of
Doctor of Philosophy
In
Energy & Environment Engineering
Department of Energy & Environment Engineering
Faculty of Engineering
Quaid-e-Awam University of Engineering, Science & Technology,
Nawabshah
2016
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DECLARATION
I declare that this thesis entitled “Development of Air Quality Model for Predicting
Air Pollution in Major Cities” is the result of my own research except as cited in the
references. The thesis has not been accepted for any degree and is not concurrently
submitted in candidature of any other degree.
Signature: _________________________
Name: Kishan Chand Mukwana
Dated: _________________________
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DEDICATION
This humble piece of work is dedicated to my
Parents & Teachers
My beloved father and mother did their best effort to uplift me to the heights of an
ideal life. Their prayers made me to reach at this glory.
My respected teachers imparted profound knowledge to gain this achievement.
I also would like to express my deepest appreciation to my wife and children for their
love, understanding, encouragement and endless patience
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ACKNOWLEDGEMENT
I earnestly pay my homage and gratitude to Almighty ALLAH, the Most Beneficent
and Merciful, Compassionate and Gracious Whose help enabled me to complete this
work.
I would like to express my sincere gratitude and greatest appreciation to Prof. Dr.
Saleem Raza Samo who whole heartedly supervised this research work. He extended
his full support and guidance during this phase of work. It is all due to Dr. Saleem
Raza Samo’s moral support and encouragement that this work was completed. I am
thankful to Prof. Dr. Mukhtiar Ali Unar for his supervision and cooperation.
I am extremely thankful to Honourable Vice Chancellor QUEST, Dean Faculty of
Engineering, Chairman, Energy and Environment Engineering Department,
Administration and Accounts Section and Prof. Dr. Abdullah Saand, Director,
Postgraduate Studies & Research for his cooperation.
I also acknowledge the remarkable support and cooperation provided by the Higher
Education Commission (HEC) of Pakistan for awarding the International Research
Support Initiative Project (IRSIP) under which advanced research work was carried
out in the Environmental Engineering Department of Middle East Technical
University (METU) Ankara Turkey under the valuable guidance of Prof. Dr. Gurdal
Tuncel.
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QUAID –E- AWAM UNIVERSITY OF ENGINEERING,
SCIENCES AND TECHNOLOGY, NAWABSHAH
This thesis, written by Mr. Muhammad Mureed Tunio under the directions of his
supervisor, and approved by all the members of the thesis committee, has been
presented to and accepted by the Dean Faculty of Engineering, in fulfillment of the
requirements of the degree of Doctor of Philosophy (Ph.D) in Energy & Environment
Engineering.
Prof. Dr. Saleem Raza Samo
Supervisor
Internal Examiner
External Examiner
Prof. Dr. Abdullah Saand
Director
Post Graduate Studies & Research
Prof. Dr. Bashir Ahmed Memon
Dean, Faculty of Engineering
Dated: ____________________
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TABLES OF CONTENTS
Description
DECLARATION ii
DEDICATION iii
ACKNOWLEDGMENT iv
CERTIFICATE v
TABLE OF CONTENTS vi
LIST OF TABLES ix
LIST OF FIGURES x
LIST OF ABBREIVATIONS xiv
LIST OF APPENDICES xvi
ABSTRACT xvii
Chapter No. 1 Introduction Page No.
1.1 Introduction 01
1.2 Study area 03
1.3 Meteorology of the study area 05
1.4 Problem statement 06
1.5 Objectives of the study 06
1.6 Importance of study 06
1.7 Methodology of work 06
1.8 Major findings of study 07
1.9 Thesis structure 08
Chapter No. 2 Literature Review
2.1 Introduction 09
Chapter No. 3 Air Pollution
3.1 Introduction 46
3.2 History of air pollution 46
3.3 Major air pollutants and their health effects 47
3.3.1 Particulate matter 49
3.3.2 Carbon monoxide 50
3.3.3 Oxides of sulfur 51
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3.3.4 Oxides of nitrogen 52
3.3.5 Ozone 53
3.3.6 Methane 54
3.3.7 Lead 54
3.3.8 Greenhouse gases 54
3.3.9 Toxic air pollutants 55
3.4 Legislation for air pollution 56
3.5 International legislation 57
3.5.1 Convention on long range trans-boundary air
pollution 57
3.5.2 Framework convention on climate change
57
3.5.3 Kyoto protocol 58
3.5.4 Montreal protocol 58
3.5.5 U.S – Canada bilateral agreement on acid rain 58
3.6 Ambient air quality 59
3.7 Ambient air quality standards 59
3.8 Monitoring of ambient air quality 60
3.9 Status of air pollution in major cities of the world 60
3.10 Status of air pollution in the cities of Pakistan 62
Chapter No. 4 Materials & Methods
4.1 Introduction 64
4.2 Research work area 64
4.3 Data collection locations 67
4.4 Quality assurance in air quality monitoring 68
4.4.1 Life span of equipment 68
4.4.2 Primary set-up and acceptance testing 68
4.4.3 Equipment calibrations 69
4.5 Equipment used for data collection and method
69
4.51 Particulate matter meter 69
4.5.2 Carbon dioxide and carbon monoxide analyzer
70
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4.5.3 Oxides of nitrogen analyzer 71
4.5.4 Oxides of sulfur analyzer 71
Chapter No. 5 Results and Discussions
5.1 Introduction 72
5.2 Results and discussions of Karachi city 72
5.3 Results and discussions of Hyderabad city 86
5.4 Results and discussions of Nawabshah city 98
5.5 Results and discussions of Sukkur city 106
Chapter No. 6 Model Development and Prediction of Air Quality
6.1 Introduction 114
6.2 Air quality models 114
6.2. Air force dispersion assessment model 114
6.2.2 AERMOD modeling system 115
6.2.3 Hybrid ROADway model 115
6.2.4 System for air modeling and analysis 115
6.2.5 Receptor modeling 115
6.2.6 Artificial Neural Network 116
6.3 Air pollutants growth rates 117
6.4 Validation of the developed model 124
Chapter No. 7 Conclusions and Suggestions for Future Work
7.1 Introduction 125
7.2 Major findings and conclusion 125
7.3 Suggestions for future work 127
References 128
Appendix-A 143
Appendix-B 155
Appendix-C 156
Appendix-D 163
Appendix-E 164
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LIST OF TABLES
Table No. Title Page No.
1.1 Number of automobiles in the major cities of Sindh 04
4.1 List of names of data collection locations 67
5.1 Minimum, maximum and average value of air pollutants at Al Asif
Square, North Nazimabad, Nursary and Star Gate locations
143
5.2 Minimum, maximum and average value of air pollutants at Tower,
Maripur Road, Nursary and Civil Hospital locations
144
5.3 Minimum, maximum and average value of air pollutants at
Numaish Chorangi, Do Talwar, Clifton and Sea View locations
145
5.4 Minimum, maximum and average value of air pollutants at
Korangi Crossing, Brooks Chorangi, National Refinery Chorangi
and Dawood Chorangi locations
146
5.5 Minimum, maximum and average value of air pollutants at
Millennium Mall, Johar Complex, Karachi University and Gulshan
e Hadeed locations
147
5.6 Minimum, maximum and average value of air pollutants at Hala
Naka, Hyderabad By-Pass, Nasim Nagar, City Gate and Market
Tower locations
148
5.7 Minimum, maximum and average value of air pollutants at Tilk
Incline, Station Road, Gari Khata Chowk, Badin Stop and SITE
Area locations
149
5.8 Minimum, maximum and average value of air pollutants at Press
Club,Gul Center, Hussainabad, Latifabad No. 07 and Latifabad
No. 12 locations
150
5.9 Minimum, maximum and average value of air pollutants at New
Naka, PMU, Shalimar Bus Stand, Mohni Bazarand Sabzi Mandi
locations
151
5.10 Minimum, maximum and average value of air pollutants at
Railway Station, Bucheri Road, Habib Sugar Mills, Society Chowk
and QUEST locations
152
5.11 Minimum, maximum and average value of air pollutants at Old
Sukkur, Lab e Mehran, High Court Road, Eid Gah Road and
Station Road locations
153
5.12 Minimum, maximum and average value of air pollutants at SITE
Area, Canal Road, Hamdard Society, Airport Road and Shikarpur
Road locations
154
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LIST OF FIGURES
Fig. No. Title Page No
1.1 Map showing four cities of the study area 04
1.2 Annual monthly mean rainfall intensity in Karachi city 05
1.3 Annual monthly mean rainfall intensity in Hyderabad city 05
1.4 Annual monthly mean rainfall intensity in Nawabshah city 05
1.5 Annual monthly mean rainfall intensity in Sukkur city 05
3.1 Status of Air Pollution in major cities of the world 61
4.1 Schematic air quality measurement sampling location map of Karachi 65
4.2 Schematic air quality measurement sampling location map of
Hyderabad
65
4.3 Schematic air quality measurement sampling location map of
Nawabshah
66
4.4 Schematic air quality measurement sampling location map of Sukkur 66
4.5 Model Aerocet 5315 Particulate Matter (P.M) Meter 70
4.6 Model IAQ 7545 Carbon dioxide and Carbon Monoxide Analyzer 70
4.7 Model T-200 Oxides of Nitrogen Analyzer 71
4.8 Model T-101 Oxides of Sulfur Analyzer 71
5.1 Minimum, maximum and average value of air pollutants at Al Asif
Square
73
5.2 Minimum, maximum and average value of air pollutants at North
Nazimabad
73
5.3 Minimum, maximum and average value of air pollutants at Nursary 74
5.4 Minimum, maximum and average value of air pollutants at Nursary 75
5.5 Minimum, maximum and average value of air pollutants at Tower 75
5.6 Minimum, maximum and average value of air pollutants at Maripur
Road
76
5.7 Minimum, maximum and average value of air pollutants at Shershah 77
5.8 Minimum, maximum and average value of air pollutants at Civil
Hospital
77
5.9 Minimum, maximum and average value of air pollutants at Numaish
Chorangi
78
5.10 Minimum, maximum and average value of air pollutants at Do Talwar 79
5.11 Minimum, maximum and average value of air pollutants at Clifton 80
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5.12 Minimum, maximum and average value of air pollutants at Sea View 80
5.13 Minimum, maximum and average value of air pollutants at Korangi
Crossing
81
5.14 Minimum, maximum and average value of air pollutants at Brooks
Chorangi
82
5.15 Minimum, maximum and average value of air pollutants at National
Refinery Chorangi
82
5.16 Minimum, maximum and average value of air pollutants at Dawood
Chorangi
83
5.17 Minimum, maximum and average value of air pollutants at Millennium
Mall
84
5.18 Minimum, maximum and average value of air pollutants at Johar
Complex
85
5.19 Minimum, maximum and average value of air pollutants at Karachi
University
85
5.20 Minimum, maximum and average value of air pollutants at Gulshan e
Hadeed
86
5.21 Minimum, maximum and average value of air pollutants at Hala
Naka
87
5.22 Minimum, maximum and average value of air pollutants at
Hyderabad By-Pass
88
5.23 Minimum, maximum and average value of air pollutants at Nasim
Nagar
88
5.24 Minimum, maximum and average value of air pollutants at City Gate 89
5.25 Minimum, maximum and average value of air pollutants at Market
Tower
90
5.26 Minimum, maximum and average value of air pollutants at Tilk Incline 91
5.27 Minimum, maximum and average value of air pollutants at Station Road 91
5.28 Minimum, maximum and average value of air pollutants at Gari Khata
Chowk
92
5.29 Minimum, maximum and average value of air pollutants at Badin Stop 93
5.30 Minimum, maximum and average value of air pollutants at SITE
Hyderabad
94
5.31 Minimum, maximum and average value of air pollutants at Press Club
Hyderabad
95
5.32 Minimum, maximum and average value of air pollutants at Gul Center 96
5.33 Minimum, maximum and average value of air pollutants at Hussainabad 96
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5.34 Minimum, max. and average value of air pollutants at Latifabad No. 07 97
5.35 Minimum, max. and average value of air pollutants at Latifabad No. 12 98
5.36 Minimum, maximum and average value of air pollutants at New Naka 99
5.37 Minimum, maximum and average value of air pollutants at PMU 100
5.38 Minimum, maximum and average value of air pollutants at Shalimar
Bus Stand
100
5.39 Minimum, maximum and average value of air pollutants at Mohni
Bazar
101
5.40 Minimum, maximum and average value of air pollutants at Sabzi Mandi 102
5.41 Minimum, maximum and average value of air pollutants at Railway
Station
103
5.42 Minimum, maximum and average value of air pollutants at Bucheri
Road
103
5.43 Minimum, maximum and average value of air pollutants at Habib Sugar
Mills
104
5.44 Minimum, maximum and average value of air pollutants at Society
Chowk
105
5.45 Minimum, maximum and average value of air pollutants at QUEST 106
5.46 Minimum, maximum and average value of air pollutants at Old Sukkur 107
5.47 Minimum, maximum and average value of air pollutants at Lab e
Mehran
107
5.48 Minimum, maximum and average value of air pollutants at High Court
Road
108
5.49 Minimum, maximum and average value of air pollutants at Eid Gah
Road
109
5.50 Minimum, maximum and average value of air pollutants at Station Road 109
5.51 Minimum, maximum and average value of air pollutants at SITE Area 110
5.52 Minimum, maximum and average value of air pollutants at Canal Road 111
5.53 Minimum, maximum and average value of air pollutants at Hamdard
Society
112
5.54 Minimum, maximum and average value of air pollutants at Airport
Road
112
5.55 Minimum, maximum and average value of air pollutants at Shikarpur
Road
113
6.1 Worksheet of OpenAir software for assessment and prediction of air
quality
116
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6.2 Algorithm for prediction of air pollutants in major cities of Sindh
province
117
6.3 Growth rate of Particulate Matter (P.M2.5) in Karachi 118
6.4 Short and long term prediction & comparison of P.M2.5 in major cities
of Sindh
118
6.5 Growth rate of Particulate Matter (P.M10) in Karachi 119
6.6 Short and long term prediction & comparison of P.M10 in major cities of
Sindh
119
6.7 Growth rate of carbon dioxide (CO2) in Karachi 120
6.8 Short and long term prediction & comparison of CO2 in major cities of
Sindh
120
6.9 Growth rate of carbon monoxide (CO) in Karachi 121
6.10 Short and long term prediction & comparison of CO in major cities of
Sindh
121
6.11 Growth rate of oxides of nitrogen (NOx) in Karachi 122
6.12 Short and long term prediction & comparison of NOx in major cities of
Sindh
122
6.13 Growth rate of oxides of sulfur (SOx) in Karachi 123
6.14 Short and long term prediction & comparison of SOx in major cities of
Sindh
123
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LIST OF ABBREVIATIONS
AAQS = Ambient Air Quality Standards
AMS = Aerosol Mass Spectrometry
BC = Black Carbon
BLIERs = Base Level Industrial Emission Requirements
CAAQS = Canadian Ambient Air Quality Standards
CFCs = Chlorofluorocarbons
CH4 = Methane
CLRTAP = Convention on Long Range Trans-boundary Air Pollution
CMAQ = Community Multi Scale Air Quality
CTM = Chemical Transport Models
EC = Elemental Carbon
FCCC = Framework Convention on Climate Change
GCM = General Circulation Model
GHG = Greenhouse Gases
HC = Hydrocarbons
HEC = Higher Education Commission of Pakistan
HNO3 = Nitric acid
IPCC = Intergovernmental Panel on Climate Change
IRSIP = International Research Support Initiatives Program
METU = Middle East Technical University
NEQS = National Environmental Quality Standards
NOx = Oxides of Nitrogen
O3 = Ozone
OC = Organic Carbon
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PAHs = Polycyclic Aromatic Hydrocarbons
PAN = Peroxy Acetyl Nitrate
ppb = Parts per billion
ppm = Parts per million
RTAQFM = Real Time Air Quality Forecasting Model
SO3 = Sulfur trioxide
SOA = Secondary Organic Aerosol
SOA = Secondary Organic Aerosols
SOR = Sulfur Oxidation Ratio
SOx = Oxides of Sulfur
TKE = Turbulent Kinetic Energy
USEPA = United States Environment Protection Agency
VOC = Volatile Organic Compounds
WSII = Water Soluble Inorganic Ions
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LIST OF APPENDICES
Appendix Title Page No
A Air Quality Data Tables 143
B National Environmental Quality Standards (NEQS) Pakistan 155
C Copy of Published research paper based on this Ph.D research work in
an HEC recognized ISI indexed International Journal
156
D List of research papers from this research work 163
E Brief C.V of Scholar 164
xvii
ABSTRACT
The purpose of this work was to examine the level of major air pollutants and their
future prediction in four major cities namely Karachi, Hyderabad, Nawabshah and
Sukkur of Sindh province. The cause of pollution in the cities is due to population
growth, unplanned urbanization, and congested transportation, commercial and
industrial activities. The main parameters considered in this study were particulate
matter (P.M) P.M2.5, P.M10, carbon dioxide (CO2), carbon monoxide (CO), oxides of
nitrogen (NOx) and oxides of sulfur (SOx). A total of twenty locations were selected
in Karachi, fifteen in Hyderabad, and ten each in Nawabshah and Sukkur. The
selection of locations was made on the basis of traffic congestion, commercial
activities and industrial establishments. The pollutants level was measured at different
time intervals of the day such as morning, noon and evening on the basis of daily life
human activities. The data was collected for a whole year with the help of PM meter,
Ambient Air Quality meter, and Nitrogen and Sulfur Analyzers. OpenAir Model was
used to determine the growth rate and future prediction of air pollutants. The
predictions were made from year 2015 to 2050 with an interval of five years.
Out of twenty selected locations of Karachi, four places namely Al Asif Square, North
Nazimabad, Nursary and Star Gate were found more affected due to higher level of
air pollutants where the average concentration of P.M2.5, P.M10, CO and CO2 were
higher than permissible limits. Similarly, the level of P.M2.5, P.M10, and CO2 at
Numaish Chorangi, Do Talwar and Clifton was higher than the permissible NEQS
levels. The concentration of NOx was higher at North Nazimabad, Nursary and Star
Gate locations and SOx were found lower than NEQS. It was revealed that the Sea
View location was free from the air pollutants, may be due to lower traffic load and
sea breeze which may transport air pollutants towards city. In Hyderabad city, the
concentration of P.M2.5, P.M10, and CO2 at five locations namely Hala Naka,
Hyderabad By-Pass, Nasim Nagar, City Gate and Market Tower were higher,
whereas, the air pollutants level were lower than the permissible NEQS levels at all
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other locations. In Nawabshah city, the concentration of P.M2.5 was higher only at
Mohni Bazar and P.M10 was higher than the permissible levels at all locations of
Nawabshah city. The level of CO, NOx and SOx were found within permissible
NEQS level at all locations of the city. In Sukkur city, the concentration of P.M2.5,
P.M10, and CO2 was found higher at High Court Road, Eid Gah Road and Station
Road than permissible levels at all five locations. The level of CO, NOx and SOx
were within NEQS. OpenAir Model was used for determination of growth rate and
future prediction of air pollutants. It was discovered from predicted model results that
the growth rate of pollutants, such as P.M2.5, P.M10, CO2, CO, NOx and SOx varies
from 1.0% to 4.0%, 0.5% to 1.5%, 1.0% to 6.0%, 1.0% to 4.0%, 1.0% to 4.0% and 1.0
to 4.0% respectively.
It is concluded from the study that the level of P.M2.5, P.M10were found higher and
CO2 and CO was almost within permissible levels in all selected cities, whereas, the
level of NOx and SOx were found higher at most of the places in Karachi only. The
model results predicted that concentration of pollutants will be at alarming level up to
year 2050 if the growth rate of population, industrialization and transportation is
continued. The findings of this work provide a baseline data and future predictions of
air pollution level of four major cities of Sindh province. It will help the regulatory
authorities to make effective policies for reduction of air pollutants and take measures
for replacement of fossil fuels with environmental friendly fuels.
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CHAPTER NO. 1
INTRODUCTION
1.1 INTRODUCTION
Humans as well as all the living beings‘ survival is very much depends on few
commodities and substances like air, water, soil, food, etc. gifted by Almighty Allah to
this world. Without those commodities and substances the chance of survival would
have been impossible. These commodities and substances are part and parcel of natural
environment. Equally important is their existence as it is as the Super Creature created
them. Out of these substances air is unique in the sense that without this, living beings
can die within few minutes. Other important thing for air is that it must be clean and free
from pollutants. Air is available in atmosphere, which surrounds the planet earth‘s
surface. Planet earth is the only planet of the universe in which presence of air is found.
However, as the civilization evolved, urbanization, transportation and industrialization
took place; the natural state of the air started to lose. Now a day‘s air is not that clean
and pure as it was initially available with the planet earth. Many impurities and
pollutants are present in this air which currently is surrounding the planet earth.
When pollutants add up in the air, the phenomenon is referred as air pollution. Air
pollution is explained as ―contamination of the indoor or outdoor environment by any
chemical, physical or biological agent that modifies the natural characteristics of the
atmosphere. Household combustion devices, motor vehicles, industrial facilities and
forest fires are common sources of air pollution. Pollutants of major public health
concern include particulate matter, carbon monoxide, ozone, nitrogen dioxide and sulfur
dioxide. Outdoor and indoor air pollution causes respiratory and other diseases, which
can be fatal‖ [1].
In the beginning, when small amounts of pollutants were released by the household fire
wood or forest fires or eruption of volcanoes, the released pollutants were diluted with
the natural purification system. The circumstances started to change entirely when
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actually the industrialization, mass transportation and urbanization took place. The air
pollution can be classified into two distinct categories, one is natural and the other one is
that of anthropogenic. The pollutants released from forest fires or volcanic activities are
placed into the natural air pollution category. Whereas the pollutants released from
human influenced activities like industries and vehicles are placed into the
anthropogenic air pollution category.
Air pollution is not a simple phenomenon. It is very complicated in variety of ways.
Once the air pollutants are released from their sources into the atmosphere, they do not
remain as it is; rather they involve in various reactions and formulate newer substance
and compounds. The pollutants which are directly released in the earth‘s atmosphere are
called primary pollutants like; carbon monoxide, carbon dioxide, oxides of nitrogen
(NOx), oxides of sulfur (SOx), hydrocarbons (HC) and volatile organic compounds
(VOC). While the pollutants which are formed after reacting with primary pollutants in
the atmosphere are called as secondary pollutants like; sulfur trioxide (SO3), ozone (O3),
nitrate (NO3), nitric acid (HNO3) and sulfuric acid (H2SO4).
Air pollution is causing irreparable damage to ―the natural environment in general and
human beings in particular‖. Human health is heavily affected by the exposures of
existing air pollutants in the atmosphere and consequently with their inhalation. All the
age group people are exposed and experience the respiratory disorders. However the
major effect is found on children and older age population group. In addition to this the
natural environment is effected by the occurrences of greenhouse effect, global warming
and acid rain. Due to this reason, scientists and policy planners are focusing their
attention on studying the phenomenon of air pollution and its control.
The situation in Pakistan regarding air pollution is also turning critical one. Being a
developing country the urbanization, transportation and industrialization is taking place
speedily. These activities are polluting the natural environment and air pollution is
taking place. The two other factors can also be blamed for such deteriorating air
pollution. One is that of burning of municipal and industrial solid waste in open areas
and the other is that of burning of agricultural bio-mass. In the absence of proper
3
disposal of solid waste municipal or industrial; it is openly burned and consequently it
releases numerous pollutants in the atmosphere causing the air pollution. The burning
solid waste contains heterogeneous substances like, paper, plastics, wooden pieces,
kitchen waste, packaging materials etc. Such heterogeneous material emits out
complicated air pollutants and they add up in the atmosphere. The other practice is that
of burning of bio-mass which is actually the waste material left after the harvesting of
the crops. Particularly the burning of sugarcane and banana crops leaves are burned in
the fields. Such burning also adds air pollutants in the atmosphere.
Being a developing country, the country started progress in establishing larger and
smaller industrial and manufacturing plants during the past thirty years. At the time of
independence 34 large and 500 small size industrial units were operating. Presently the
large number of industrial units has crossed the 0.2 million figure. Karachi as a mega as
well as port city of the country experienced good industrial establishments. The statistics
represent about 6600 industries are operating out of which more or less 1200 industries
are considered critical from environmental pollution point of view. These critical
industries comprise of refineries, power plants, steel industries, cement industries,
fertilizer factories, chemicals manufacturing industries, textile, automobile
manufacturing, etc.
1.2 STUDY AREA
In this research study four cities of Sindh province were taken into consideration. The
Sindh province is the second most populated province of the country having population
of 30.44 million. The four cities which are investigated for this study include Karachi,
Hyderabad, Nawabshah and Sukkur Figure 1.1. These four cities of Sindh province were
selected to determine the ―ambient air quality data‖. The Karachi city is considered one
of the major cities of the world with an approximate population of 9.856 million. The
population of Hyderabad is 9,11,000, Nawabshah is 2,83,000 and Sukkur is 4,62,000 [2].
These selected 04 cities of Sindh province are showing trends of brisk growth in
residents, vehicular traffic and increasing number of industries. As the population
4
increase the demand for automobiles increases. Table 1.1 shows the number of different
automobiles plying on the roads of major cities of Sindh province.
Table No. 1.1: Number of automobiles in the major cities of Sindh
S.
No
Type of
vehicle
Sindh Karachi Hyderabad Nawabshah Sukkur
1 Car/Jeep 549127 520628 9914 56 940
2 Motor cycles 755335 568942 20951 3835 41920
3 Taxis 31291 26003 5015 …. 112
4 Rickshaw 43882 27868 9325 47 430
5 Bus/Mini bus 24541 11988 8985 76 937
6 Trucks 32571 19560 9735 72 695
7 Tractors 49844 1770 8655 531 2800
8 Pickups/
vans
59020 50527 2986 06 1249
Total 1545611 1227286 75565 4623 49083
Source: Bureau of Statistics 2011, Government of Pakistan.
Figure 1.1: Map showing the four cities of the study area.
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1.3 METEOROLOGY OF THE STUDY AREA
The province of Sindh is located in the southern part of Pakistan. From climate and
meteorology and climate point of view the province is situated in a subtropical region. It
is hot in summer season and cool in winter. The summer temperature rises up to 50oC
between May and July and minimum average temperature prevails about 2oC during the
months of December and January. The annual average rainfall shown is about seven
inches which falls mainly during the monsoon season during the months of July to
September. The annual average rainfall intensity of Karachi, Hyderabad, Nawabshah and
Sukkur is shown in figures 1.2, 1.3, 1.4 and 1.5 respectively. The Arabian Sea is located
on the southern part of the province. The wind patterns are initiated from the deep sea
region and moves towards the landmass [3].
Figure 1.2: Annual monthly mean rain-
fall intensity in Karachi city
Figure 1.3: Annual monthly mean
rainfall intensity in
Hyderabad city
Figure 1.4: Annual monthly mean
rainfall intensity in
Nawabshah city
Figure 1.5: Annual monthly mean rain-
fall intensity in Sukkur city
[Source: Pakistan Climate Statistics]
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1.4 PROBLEM STATEMENT
Our urban areas are turning highly polluted places for the living beings in general &
humans in particular. The air pollutants released from industrial and vehicular sources
are causing irreparable damage to natural environment and are equally responsible for
damaging the humans‘ health.
1.5 OBJECTIVES OF THE STUDY
a) Collection and analysis of data (P.M, CO2, CO, NOx, SOx,) from major cities of
Sindh Province.
b) Study of prediction models previously developed for the applications.
c) Development and training of feed forward neural networks as the prediction model.
d) Validation of the developed model.
1.6 IMPORTANCE OF WORK
This research work has made available a detailed data base of air pollution prevailing in
the four cities of Sindh province for the stakeholders and the regulatory authorities. This
study pinpoints the current pollution level of the air pollutants in the ambient air of these
selected cities as well predicts the future state of ambient air pollution. Awareness will
emerge in the minds of every citizen and will start cooperating with the government
agencies in combating the air pollution. As an important baseline this study will be
helpful in controlling out the ambient air pollution and restoring the natural status of
ambient air quality.
1.7 METHODOLOGY OF WORK
The level of major air pollutants and their future prediction of four major cities namely
Karachi, Hyderabad, Nawabshah and Sukkur of Sindh province were examined. The
main parameters considered in this study were particulate matter (P.M) P.M2.5, P.M10,
carbon dioxide (CO2), carbon monoxide (CO), oxides of nitrogen (NOx) and oxides of
sulfur (SOx). A total of twenty locations were selected in Karachi, fifteen in Hyderabad,
7
and ten each in Nawabshah and Sukkur. The selection of locations was made on the
basis of traffic congestion, commercial activities and industrial establishments. The
pollutants level was measured at different time intervals of the day such as morning,
noon and evening on the basis of daily life human activities. The data was collected for a
whole year with the help of PM meter, Ambient Air Quality meter, and Nitrogen and
Sulfur Analyzers. OpenAir Model was used to determine the growth rate and future
prediction of air pollutants. The predictions were made from the year 2015 to 2050 with
an interval of five years.
1.8 MAJOR FINDINGS OF STUDY
Out of twenty selected locations of Karachi, four places namely Al Asif Square, North
Nazimabad, Nursary and Star Gate were found more affected due to higher level of air
pollutants where the average concentration of P.M2.5, P.M10, CO and CO2 were higher
than permissible limits. The Sea View location was free from the air pollutants, may be
due to lower traffic load and sea breeze which may transport air pollutants towards city.
In Hyderabad city, the concentration of P.M2.5, P.M10, and CO2 at five locations namely
Hala Naka, Hyderabad By-Pass, Nasim Nagar, City Gate and Market Tower were higher
than permissible NEQS levels at all other locations. In Nawabshah city, the
concentration of P.M2.5 was higher only at Mohni Bazar and P.M10 was higher than the
permissible levels at all locations of Nawabshah city. In Sukkur city, the concentration
of P.M2.5, P.M10, and CO2 was found higher at High Court Road, Eid Gah Road and
Station Road than permissible levels at all five locations. The predictions of air
pollutants were made using OpenAir model, and found that the growth rate of pollutants,
such as P.M2.5, P.M10, CO2, CO, NOx and SOx varies from 1.0% to 4.0%, 0.5% to 1.5%,
1.0% to 6.0%, 1.0% to 4.0%, 1.0% to 4.0% and 1.0 to 4.0% respectively. It is concluded
from the study that the level of P.M2.5, P.M10 were found higher and CO2 and CO was
almost within permissible levels in all selected cities, whereas, the level of NOx and
SOx were found higher at most of the places in Karachi only. It is predicted that the
concentration of pollutants will be at alarming level up to year 2050 if the growth rate of
population, industrialization and transportation is continued.
8
1.9 THESIS STRUCTURE
Chapter 1 highlights the background of air pollutants, problems related with air
pollutants, purpose of study and its importance. This chapter also includes the major
findings of present research work and future predictions of air pollutant. In chapter 2,
literature review on particulate matter, carbon dioxide, carbon monoxide, oxides of
nitrogen and oxides of sulfur is discussed. The literature cited was of peer reviewed
papers, international conference proceedings, latest published books, reports and
organizational documents. The general description of major air pollutants and their
health impacts, national and international legislation, and national environmental quality
standards are discussed in Chapter 3. Whereas chapter 4 describes the materials and
methods adopted for examination and prediction of air pollutants. The instruments used,
and method of data collection and analysis is also elaborated in this section. However
chapter 5 illustrates results obtained at various selected locations. The results are
tabulated in tables and represented through figures. In chapter 6 different models used
for prediction of air quality are described. The selected model is justified for the future
prediction of air pollutants and validation of results. Chapter 7 illuminates the major
findings of the study, general conclusion and suggestion for future work.
9
CHAPTER NO. 2
LITERATURE REVIEW
2.1 INTRODUCTION
In this chapter, literature review on particulate matter, carbon dioxide, carbon monoxide,
oxides of nitrogen and oxides of sulfur is discussed. The literature cited were peer
reviewed papers, international conference proceedings, and latest published books,
reports and organizational documents. For that, more than three hundred seventy
scientific research papers, books, web pages, and articles were cited. Some of the
relevant literature is given below.
Marie S. O‘Neill et al (2003) has mentioned about the severity of air pollution and
poverty and urges about proper attention by scientists and policy makers. He has
mentioned that approximately 1.5 billion people presently live in contaminated air and
65 percent of the world‘s population is expected to live in cities by 2025. On world level
more than 40 percent children had to live in polluted cities in the developing countries.
The findings shows that contaminated air severe health related disorders than previously
expected, having lower life expectancy, high mortality and hospital visits, birth related
complications and respiratory disorders including asthma [4].
Wellenius GA et al (2006) determined in his study about the association in between
ambient air pollution and the rate of hospitalization for cardio related disorders in
Allegheny County in Pittsburgh, Pennsylvania from year 1987 to 1999. The study
concentrated at 55,000 patients admitted with a primary diagnosis of congestive cardio
disorders. The particulate matter, carbon monoxide, nitrogen dioxide and sulfur dioxide,
but not ozone, were positively and significantly found associated with the rate of
admission on the same day, with the strongest associations observed with carbon
monoxide, nitrogen dioxide, and particulate matter. The associations with carbon
monoxide and nitrogen dioxide were the most robust in two-pollutant models. The result
9
10
suggests that short-term elevations in air pollution from traffic-related sources may
trigger acute cardiac decomposition in heart failure patients [5].
Zanobetti A. et al (2005) states that risk of heart attack in the elderly persons increase as
they exposes to increased particulate pollution. In this research work various cities were
studied and investigation was made for the association between particulate matter
exposures and emergency hospitalization for heart attacks among elderly residents of
twenty one cities of the United States of America. The researchers obtained medical data
on hospital admissions for 300,000 heart attacks over a period of fourteen year. The
developed statistical model was able to control possible perplexing by weather. The
association was nearly linear but risks increased most sharply at daily concentrations less
than 50 µg/m3 [6].
Lewis TC et al (2005) determined that ambient air pollution decreases lung function in
asthmatic children in African-American and Latino children who are on medication for
asthma treatment or those with upper respiratory inflammations that are badly affected
by current levels of air pollution. The findings analyzed the relationship between lung
function and atmospheric air pollution levels of ozone and two measures of particulate
matter i.e. PM10 and PM2.5. The study concentrated on eighty six children in two week
seasonal assessments from winter 2001 to spring 2002. Two parameters of lung function
were determined one at peak flow and the other at forced expiratory volume in one
second (FEV1). The children who were on corticosteroids for their asthma, particulate
matter and eight hour peak ozone were both associated with poorer lung function two
days after exposure. The study found that the children with symptoms of respiratory
inflammation, PM2.5 and PM10 were associated with poorer lung function for three to
five days after exposure, whereas eight hour peak ozone concentrations were associated
with poorer lung function after one to two days time period [7].
Urch B et al (2006) conducted research on twenty three healthy nonsmoking adults. The
investigations points out that pollution from vehicles in city centers may be the leading
cause of adverse impacts on cardiovascular system. Exposure to higher levels of air
pollutants may result in quick hypertensive response, and may promote acute
11
cardiovascular disorders in sensitive individuals. In addition to this if this exposure
continued unabated, there is increased possibility that air pollution could contribute to
long-term impairment an individual‘s blood pressure levels. The prolonged exposure to
the atmospheric air pollution could in the result increase the risk for developing
chronically much increased blood pressure and possibility of hypertension [8].
Mills NL et al (2005) focused on thirty healthy non-smoking men, aged between 20-38
years. The participants infused with injections of a vasodilator and researchers measured
their blood flow rates during and after exposure. The findings indicated that inhalation of
diesel exhaust during physical exercise reduce the blood vessels‘ ability to dilate, or
expand, and also decreases the levels of an enzyme within the human body that helps in
prevention of clots in the blood [9].
Gilmour PS et al (2005) determined that atmospheric pollution in the form of particulate
matter substances condense the blood and heighten inflammation chances. The
researchers observed the inflammation and blood clotting impacts were detected from
the samples collected from lungs cells, umbilical cord cells and immune cells that are
immune system cells. The collected samples were analyzed for six and twenty four hour
exposure interval to particulate matter. The data showed that clotting of blood was
recorded very high in different cell types. The rate of death of macrophage cells also
showed significant increase. The exposure of particulate matter also boosted
inflammatory activity. The particulate matter pollution has shown the ability to change
the immune system cell structure and endothelial cell function [10].
O‘Neill MS et al (2005) focused to compare data from previous clinical trials with
twenty four hour concentrations of particulate matter, sulfates, and black carbon. Air
pollutant levels were evaluated for their relationships with vascular reactivity. The
significant and key finding was the relationship between sulfate particles and decreased
vascular reactivity. The results relate air pollutants exposure and physiological responses
in the form of abnormal cardiovascular outcomes. The remarkable relationship was
observed between vascular reactivity and exposure to particulate matter pollution,
particularly sulfates and higher affinity among persons having diabetes. Enhanced rates
12
of cardio disorder related hospitalization during higher particulate matter pollution days
among people with diabetes may be partially explained by impairments in endothelial
function, vascular smooth muscle function, and subsequent coronary artery vascular
responses [11].
Sagiv SK et al (2005) has determined an increased threat of premature births associated
with exposure to particulate matter pollution and oxides of sulfur during the last six
weeks of pregnancy. This research study focused on population consisted of single baby
born to mothers from year 1997 to 2001 in four Pennsylvania counties namely Beaver,
Philadelphia, Allegheny, and Lackawanna. The authors adopted a time-series analysis
that eliminates potential perplexing by individuals threat factors that do not change over
short periods of time. This increased threat of premature birth was small, but the public
health impact could be significant because of the large populations chronically exposed
to higher levels of atmospheric air pollution [12].
A. Alonso et al (2012) states that linear relationships between vehicle speed and
turbulent kinetic energy (TKE) values are found with coefficients of determination (R2)
of 0.75 and 0.55 for the lorry and van respectively. The vehicle-induced fluctuations in
the wind components (u′, v′ and w′) showed the highest values for the longitudinal
component (v) because of the wake-passing effect. In the analysis of wake produced by
moving vehicles it is indicated how the turbulence dissipates in relation to a distance d
and height h. The TKE values were found to be higher at the measuring points closer to
the surface during the wake analysis [13].
Chak K. Chan & Xiaohong Yao (2008) states that China is one of the main economically
growing countrybeside that it is one of the major environmental menaces country of the
world. Presently, Beijing, Shanghai, and the Pearl River Delta region including Hong
Kong, Guangzhou, and Shenzhen and their neighboring localities are the most
economically vibrant regions in China. They accounted for about 20% of the total GDP
in China in 2005 [14].
13
P.S. Monks et al (2009) states that ambient air quality trespass all scales in the
atmosphere from the local to the global level. Air quality has intensified effects on
humans‘ health, diverse ecosystems, and heritage and climate changes. New insights are
reviewed in order to concentrate on both natural and plant emissions. A number of
options are made available as a way of integrating the process view into the atmospheric
context that include the atmospheric oxidation efficiency, halogen and HOx chemistry,
night time chemistry, tropical chemistry, heat waves, and the biomass burning. New
findings with respect to the travel of air pollutants across the scales are discussed, in
particular the move to quantify the impact of long-range transport on regional air quality.
Particularly the policy challenges for clean and acceptable air quality and climate change
policy are discussed [15].
P.N. Pegas et al (2012) observed air pollutants present in the ambient air inside the
school buildings‖ may affect children's health and influence learning performance and
attendance. This study determined air pollutant levels within and outside school
premises at different places including city centre and suburban in Aveiro, Portugal,
between April 2010 and June 2010. The objective was evaluating sequentially comfort
parameters like temperature, relative humidity, carbon dioxide and carbon monoxide and
additionally indoor and outdoor levels of Volatile Organic Compounds, nitrogen
dioxide, particulate matter and bio-aerosols. Particulate matter samples were analyzed
and characterized, for the first time, for the water soluble inorganic ions (WSII), organic
carbon (OC), elemental carbon (EC) and organic speciation. The carbon dioxide and bio-
aerosol concentrations were observed much greater than the permissible maximum
values to the occupants' comfort. Concentrations of the nitrogen dioxide were recorded
higher in the outdoors. The daily indoor particulate matter concentrations were always
higher than those outdoors, except on weekends, indicating that the physical activity of
students and class works highly contributed to the emission and re-suspension of
particulates. In addition to this almost all identified volatile organic compounds showed
indoor/out-door ratios greater than one. This ratio denotes an important contribution
from indoor sources at both schools [16].
14
Janez Zibert & Jure Praznikar, (2009) focused on particulate matter and black carbon
which were analyzed in the Port of Koper. The obtained results showed that 10 clusters
in the black carbon case produced 03 clusters with just one member day and 07 clusters,
whereas in particulate case, 01 cluster has a single-member day and 07 clusters
possessed different member days. The results showed that the clusters with smaller
bimodal patterns and mean daily concentration for both types of measurements [17].
D. Fowler et al (2012) states that the presence of trace gases and aerosols between the
earth's surface and the atmosphere like NO2, NO, N2O, O3, HNO3, SO2, NH3, VOC, and
CH4, and particulate substances of the size ranging from 1 nanometer to 10 micro meter
including organic and inorganic chemical substances. New instrumentation and scientific
techniques have enabled technical advances in quantification of air pollutant levels. For
example mass spectrometric methods help out in Aerosol Mass Spectrometry (AMS)
used for direct measurements in the field. The methodologies adopted for monitoring,
modeling and flux measurement at a local, regional and global scale shows significant
development in the area of research and investigation applications [18].
Jesse H. Kroll & John H. Seinfeld (2008) states that particulate matter and secondary
organic aerosol (SOA) accounts for a substantial proportion lower atmospheric
tropospheric aerosol. The formation of semi-volatile and possibly nonvolatile substances
make up SOA and are found in a complex series of reactions of a large number of
organic substances. The basic focus is chemical processes that can change the volatility
of organic compounds due to oxidation reactions in the gas phase or reactions in the
particle phase or repetition of the reaction over long time period [19].
Elmar Uherek et al (2010) states that air pollutant emissions from vehicular traffic have
considerable effect on the living organisms, ecosystem, climate change and atmosphere.
The control of vehicular emissions has significantly reduced emissions of carbon
monoxide, oxides of nitrogen, particulate matter substances and volatile organic
compounds in particular. Such measures have improved the air quality and consequently
the health impacts in developed countries were reduced. However in developing
15
countries the air emissions are growing rapidly and in the result affecting vast population
in those territories. Other major issue is that of global warming due to change in
radiative balance caused by the particulate matter and ozone. It is expected that in
coming times vehicular emissions have to come down with the strategic measures and
would result in decrease in global air pollution [20].
Daniel J. Jacob & Darrell A. Winner (2009), founds that air quality is closely associated
meteorology of atmosphere and is hence sensitive to changes in climate. The recent
trends have given estimates of change in climate having correlations of air quality with
meteorological parameters by the use of various models like chemical transport models
(CTMs) or general circulation model (GCM) simulations focusing climate change in the
current century. The outcome results of lower atmospheric ozone and temperature in
polluted environments indicate out a detrimental impact on global warming. The general
circulation model and chemical transport model indicate that change in climate change
single handedly will raise summertime lower atmospheric ozone in polluted localities
from 1ppb to 10 ppb in the coming few decades. This will develop significant impact in
urban areas and during heavy air pollution episodes [20].
Roger Atkinson (2000) investigated status reactions of inorganic HOx and NOx
substances with selected substances of volatile organic compounds (VOCs) and their
degradation. Currently there are quite good qualitative as well as quantitative
understanding about the earth‘s lower atmosphere and the ―chemistry of oxides of
nitrogen and volatile organic compounds‖ interact for the photochemical formation of
ozone. Within the last five years much development has recorded. In addition to this
reactions of ozone with alkenes, and the interactions of OH reactions along with
aromatic hydrocarbons is expected. However, still there are some areas of ambiguity
which cause effect on the working ability of model applications [21].
I.S.A. Isaksen et al (2009) determined that chemically active climate compounds are
either primary pollutants like methane or secondary pollutants like ozone, both formed
and removed in the atmosphere. The anthropogenic pollutants interaction in the
16
atmosphere acts in two way process i.e. firstly emissions of pollutants change the
atmospheric chemistry and secondly change in climate, with changes in temperature,
atmospheric stability, the hydrological cycle, and biosphere interactions. The significant
change is associated with oxidation potential with involvement of substances like ozone
and the hydroxyl ions. The authors derive the results about anthropogenic emissions
having wider impacts on climate. Through these findings it is determined that which
areas can be more affected and which areas on lesser scale with the tropospheric
oxidation process [22].
I.S.A. Isaksen et al (2009) determined that chemically active climate compounds are
either primary pollutants like methane or secondary pollutants like ozone, both formed
and removed in the atmosphere. The anthropogenic pollutants interaction in the
atmosphere acts in two way process i.e. firstly emissions of pollutants change the
atmospheric chemistry and secondly change in climate, with changes in temperature,
atmospheric stability, the hydrological cycle, and biosphere interactions. The significant
change is associated with oxidation potential with involvement of substances like ozone
and the hydroxyl ions. The authors derive the results about anthropogenic emissions
having wider impacts on climate. Through these findings it is determined that which
areas can be more affected and which areas on lesser scale with the tropospheric
oxidation process [22].
Yu Zhao et al (2012) estimated that China's air emissions of carbon dioxide with the help
of bottom-up emission inventory framework. The findings show that annual emissions
are estimated to have increased from 7126 to 9370 million tons carbon dioxide from year
2005 to year 2009. The largest share of this contamination is from industrial setups
particularly from power plants. In some sectors the results show very high annual
emission rates as is evident from emission estimates of year 2005 to year 2008 as
compared with other similar studies [23].
Yang Zhang et al (2012) used RT-AQFand global model for prediction of metrological
parameters and inputs were simulated through software‘s. The present statistical,
numerical, and computational techniques are quite important to evaluate the future
17
predictions. The newer version of real time air quality forecasting model systems has got
quite good capabilities to carry out such investigation and compile the information. The
simulated results were found more proficient by using latest computational technologies
[24].
J.N. Cape et al (2012) mentioned in their research that black carbon in the air and its
exposure cause human health. The life span of black carbon particularly the finer
particulates (PM2.5) are capable to travel very long distance. It is therefore essential to
determine the scale of such impacts and the spatial footprint for the control of such
emission. However the relevant models and the estimations indicate their life span as
around one week. The standard deviation of concentration data is linearly associated
with hydrocarbon‘s life spans and the same statistical method can be used for the black
carbon. The yearly average results show black carbon life span in the range of four to
twelve days for an assumed hydroxyl concentration of 7 × 105 cm
−3. This research
finding ascertain the function of wet deposition as an important elimination process for
black carbon as there is no difference in rainfall spells in between summer and winter at
the sample measuring location. The black carbon life span was remarkably higher in
year 2010, during which 23% less rainfall took place than the preceding three years [25].
D.S. Lee et al (2010) states that air traffic, changes the atmospheric composition on
global scale and thus results in ozone depletion and change in climate. In the past these
major impacts were presented in the Intergovernmental Panel on Climate Change (IPCC)
in year 1999 in the international conference. The climate change associated impacts of
aviation were blamed due to ―long-term impacts from carbon dioxide emissions and
shorter-term impacts from non- carbon dioxide emissions and their impacts‖. The non
carbon dioxide impacts included emissions in the form of particulate matter, oxides of
nitrogen and water vapour. The findings of year 2005 about radiative forcing from
aviation were recorded as 55 mW m−2. The future forcings may rise by 3–4 of the year
2000 levels, in year 2050. It is believed that due to aviation emissions of carbon dioxide
the average surface temperature stay for many hundreds of years. Whereas, in case of
non carbon dioxide the impacts on temperature they stay for some decades. The
18
emissions of oxides of nitrogen cause production of ozone that is a climate warming gas
and the balance in the natural composition of atmosphere stays [26].
Lingxiao Yang et al (2012) investigated daily particulate matter samples collected
simultaneously at an urban site and a rural site in Jinan, China from March 2006 to
February 2007. The samples were analyzed for major organic and inorganic substances
which included twenty four elemental and carbonaceous species to determine the spatial
and temporal variations of particulate matter to determine their contributions to visibility
impairment disorders‖. The annual mean levels of PM2.5 were 148.71 μg m−3
and
97.59 μg m−3
at urban and rural sites respectively. The predominant component of PM2.5
was (NH4)2SO4 at urban site and organic mass at rural site. ―The higher sulfur oxidation
ratio (SOR) and ratios at rural site indicated that the formation of secondary inorganic
ions and secondary organic aerosols (SOA) could be accelerated in the urban area. Large
size (NH4)2SO4 and large size organic mass were the most important contributors to
visibility impairment at urban and rural sites accounting for 43.80% and 41.02%
respectively [27].
Madsen C. et al (2006) estimated ambient air pollution levels by using the geographical
information system (GIS) Air Quality Information System (AirQUIS) prepared at the
Norwegian Institute of Air Research (NILU). This software package gathers information
on meteorology, air emissions, primary air pollution levels and terrestrial topography.
The software determines the ambient exposure levels based on saved and stored location
wise addresses on a square kilometer grid and larger receptor points near busy roads.
Based on these results the mean nitrogen dioxide exposures were calculated at the
subject home addresses for four weeks time. This modeled nitrogen dioxide values were
quantified for the analyses and were compared with annual mean values of nitrogen
dioxide showing quite similar results to the four weeks variable [28].
Wakefield SE et al (2001) in their study concentrated on many aspects air pollution and
its impact on the environment. The outcome shows that perceptions about air pollution
impacts are socially and culturally prevail in the minds of people. For example, when the
19
analyses were restricted to participants with asthma the measurements of air pollution
coming from vehicular emissions were increasingly overestimated in comparison to that
of the modeled levels. Higher side reporting of air pollution and its exposure within
people with respiratory disorders were found in other studies too. Some current health
related disorders arising from air pollution emissions were found associated with a
disease that most people would be likely to associate with it. The suffering patients too
associate their sufferings a result of obesity, but not from social circumstances, or from
pollution which is more prevalent in the deprived areas of Oslo [29].
M. Aresta (2010) states that air pollutants are significant risk factor for multiple health
conditions including respiratory infections, heart disease, and lung cancer, according to
the WHO. The health effects caused by air pollution may include difficulty in breathing,
wheezing, coughing and aggravation of existing respiratory and cardiac conditions.
These effects can result in increased medication use, increased doctor or emergency
room visits, more hospital admissions and premature death. The human health effects of
poor air quality are far reaching, but principally affect the body's respiratory system and
the cardiovascular system. Individual reactions to air pollutants depend on the type of
pollutant a person is exposed to, the degree of exposure, the individual's health status
and genetics. The most common sources of air pollution include particulate matter,
ozone, nitrogen dioxide, and sulfur dioxide. Both indoor and outdoor air pollution have
caused approximately 3.3 million deaths worldwide. Children aged less than five years
that live in developing countries are the most vulnerable population in terms of total
deaths attributable to indoor and outdoor air pollution [30].
Sufian M. et al (2011) founds that airborne particulate matter (PM10) contains a large
number of geno-toxic and carcinogenic substances. Ambient air is reported to be
mutagenic in many areas worldwide. Particulate matter has been linked to premature
mortality, lung cancer, respiratory and cardiovascular health problems. In the present
study, we investigated the geno-toxicity of the ambient air PM10 extractable organic
matter (EOM) collected at Arafat and Muzdalifa in Makkah, Saudi Arabia. The amounts
of sixteen polycyclic aromatic hydrocarbons (PAHs) in the EOM were quantified. The
20
PM10 averages in Arafat and Muzdalifa were 158 and 444.5μg/m3, respectively which
exceeded the U.S. maximum daily of 150μg/m3. The reference site PM10 was 6.1μg/m
3.
The EOM averages in Arafat, Muzdalifa and the reference site were respectively 46.4,
151.6 and 6.15μg/m3 and the PAH averages in the EOM were 2.8, 5.0 and 0.19ng/m
3.
The EOM samples were mutagenic in the salmonella TA98 test and damaged human
blood cells DNA in the comet assay in a dose related response. The bacterial revertants
and the comet tail moment means were higher in Muzdalifa than in Arafat. Regression
analyses of both showed a positive relation between each and the EOM concentrations
tested (P < 0.001). For routine ambient air geno-toxicity monitoring, the use of
salmonella TA98 and the comet tests are recommendable. This study provides
information for the first time on the PM10 air pollutants and its geno-toxic activity in
Arafat and Muzdalifa [31].
El Assouli SM. et al (2007) founds the big difference in PM10 values between Arafat and
Muzdalifa could be in part due to the fact that in Arafat the vehicles stopped during the
day time and most of the pilgrims stayed in tent camps, meanwhile in Muzdalifa there is
no tents and vehicles move in and out of the area continuously during the night time
because of overcrowding, the pilgrims sometimes do not arrive there until late at night.
The total amounts of PAHs in Arafat during the 2004 and 2006 Hajj seasons were 3.36
and 2.22ng/m3 respectively. In Muzdalifa PAH concentrations were 5.17 and 4.76 ng/m
3
in 2004 and 2006 respectively, while the reference site was only 0.18 and 0.19 ng/m3.
The major PAHs component was benzo perylene in both Arafat and Muzdalifa in both
pilgrimage seasons. In 2004, the concentration of this compound in Arafat was 22% of
the total PAHs and 63% in Muzdalifa. In 2006, the concentrations were 35 and 13% in
Arafat and Muzdalifa respectively. In Arafat and in 2004, the second highest PAH
compound was phenanthrene (15%) and in 2006 it was indeno (1232-cd) pyrene (16%).
In Muzdalifa, second to benzo perylene was indeno (123-cd) pyrene in both 2004 (12%)
and in 2006 (9%). The other PAHs detected in Arafat in 2004 were fluoranthene
(0.39ng/m3), pyrene (0.339ng/m
3) and BaP (0.24ng/m
3). In 2006 the concentrations of
these compounds were less than 0.09ng/m3. In Muzdalifa and in 2004 season the
concentrations of these compounds were 0.19, 0.19 and 0.15 ng/m3 and in 2006 season
21
their concentrations were ≤0.05, ≤0.05 and 0.25ng/m3 respectively. The other PAHs
were found in <0.10 ng/m3 [32].
Rafia Afroz et al (2003) concludes from their research that in the early periods of
abundant resources and minimal development pressures, no or little attention was given
to increasing environmental pollution problems in Malaysia. The episodes of haze in
Southeast Asia in year 1983, 1984, 1991, 1994, and 1997 showed alarm bells to the
authorities looking the matters of environmental pollution. With this concern the
relevant authorities established Malaysian Air Quality Guidelines and Air Pollution
Index with the objectives of improving of ambient air quality. Air quality monitoring
was launched as a part of strategy to control and prevent air pollution in Malaysia. Air
pollution monitoring programs were launched in several cities in Malaysia for the
determination of various air pollutants. The measured results showed higher
concentrations of NOx and SPM. Other air pollutants like carbon monoxide, ozone,
sulfur dioxide and lead were also detected in several big cities in Malaysia. These air
pollutants were found coming mainly from road transportation, emissions from industrial
operations and open burning of waste substances. The road transportation emissions was
found dominant contributor of the most ambient air pollution and in the result caused
substantial health impacts [33].
Haidong Kan et al (2012) concludes from their research that China as the largest
developing country is heavily using fossil fuels and this is causing dramatic increase in
emissions of both ambient air pollutants and greenhouse gases (GHGs). China is now
experiencing the worst air pollution problem in the world, and is also the largest
contributor of atmospheric carbon dioxide emissions. Though the increased health
threats, recorded in Chinese population are lower in intensity than the developed
countries. The health impact of ambient air pollution and change in climate
simultaneously can assist out the Chinese government in attaining the sustainable
development [34].
22
Kai Z. et al (2007) concluded 25 years air quality index (API) data of Guangzhou city
and data was acquired from metrological station. Of all the significant pollutants total
suspended particles accounted for nearly 62%, sulfur dioxide 12.3% and oxides of
nitrogen 6.4% respectively. The average air pollution index of Guangzhou over past six
years was higher from Beijing and lower than Shenzhen, Zhuhai and Shantou. The air
pollutants levels have shown a declining trend in recent years but generally are worse
than the ambient air quality standards for United States of America, Hong Kong and
European Union. The sulfur dioxide and oxides of nitrogen concentration implies that
waste gas pollution from all kinds of transport emissions had turned a significant air
pollution problem [35].
O. Ozden et al (2008) measures air quality of the city Eskişehir, located 230 km
southwest to the capital of Turkey. Only five of the major air pollutants, most studied
worldwide and available for the region, were considered for the assessment. Available
sulphur dioxide (SO2), particulate matter (PM), nitrogen dioxide (NO2), ozone (O3), and
non-methane volatile organic carbons (NMVOCs) data from local emission inventory
studies provided relative source contributions of the selected pollutants to the region.
The contributions of these typical pollution parameters, selected for characterizing such
an urban atmosphere, were compared with the data established for other cities in the
nation and world countries. Additionally, regional ambient SO2 and PM concentrations,
determined by semi-automatic monitoring at two sites, were gathered from the National
Ambient Air Monitoring Network (NAAMN). Regional data for ambient NO2 (as a
precursor of ozone as VOCs) and ozone concentrations, through the application of the
passive sampling method, were provided by the still ongoing local airquality monitoring
studies conducted at six different sites, as representatives of either the traffic-dense-, or
coal/natural gas burning residential-, or industrial/rural-localities of the city. Passively
sampled ozone data at a single rural site were also verified with the data from a
continuous automatic ozone monitoring system located at that site. Effects of variations
in seasonal-activities, newly established railway system, and switching to natural gas
usage on the temporal changes of air quality were all considered for the assessment [36].
23
Jesus A. Araujo (2011) concludes that air pollution has been associated with significant
adverse health effects leading to increased overall morbidity and mortality of worldwide
significance. Epidemiological studies have shown that the largest portion of air
pollution-related mortality is due to cardiovascular diseases, predominantly those of
ischemic nature. Human studies suggest an association with atherosclerosis and
increasing experimental animal data support that this association is likely to be causal.
While both gasses and particles have been linked to detrimental health effects, more
evidence implicates the particulate matter (PM) components as major responsible for a
large portion of the pro atherogenic effects. Multiple experimental approaches have
revealed the ability of PM components to trigger and/or enhance free radical reactions in
cells and tissues, both ex vivo as well as in vivo. It appears that exposure to PM leads to
the development of systemic pro oxidant and pro inflammatory effects that may be of
great importance in the development of atherosclerotic lesions. This article reviews the
epidemiological studies, experimental animal, and cellular data that support the
association of air pollutants, especially the particulate components, with systemic
oxidative stress, inflammation, and atherosclerosis. It also reviews the use of
transcriptomic studies to elucidate molecular pathways of importance in those systemic
effects [37].
Mercedes A. Bravo et al (2012) determined that air quality modeling can improve
estimates bout epidemiological studies. The investigations show that concentrations of
particulate matter of 2.5 μm diameter and ozone for the eastern United States in year
2002 with the use of Community Multi Scale Air Quality (CMAQ) modeling system.
The monitoring results produced estimates for 370 counties for PM2.5 and 454 counties
for O3. The Epidemiology may achieve positive response from air quality modeling with
improved spatial and temporal scaling [38].
T.V.B.P.S. Rama Krishna et al (2005) used the industrial Source Complex Short Term
(ISCST-3) model to study the impact of an industrial complex, located at the outskirts of
Hyderabad city, India, on the ambient air quality. The emissions of sulfur dioxide from
38 elevated point sources and 11 area sources along with the meteorological data for 2
24
months (April and May 2000) representing the summer season and for 1 month (January
2001) representing the winter season. The 8-hour and 24-hour averaged model-predicted
concentrations have been compared with corresponding observed concentrations at three
receptors in April 2000 and at three receptors in May 2000 where ambient air quality is
monitored during the study period. A total of 90 pairs of the predicted and observed
concentrations have been used for model validation by computing different statistical
errors and through Quantile – Quantile (Q–Q) plot. The results show that the model-
predicted concentrations are in good agreement with observed values and the model
performance is found to be satisfactory. The spatial distribution of sulfur dioxide
concentrations over the study area is examined in the summer and winter months and
found that the levels of SO2 are within the limits in comparison to the National Ambient
Air Quality Standards except near the industrial area [39].
N.S. Leksmono et al (2006) investigated theoretical perspective and assumed that traffic
is not the sole cause of urban pollution. It presents air quality assessments in different
scenarios, which are modeled using ADMS-Urban to forecast levels of nitrogen dioxide.
Modeling was carried out using simple scenarios with a combination of traffic and
industrial emissions along with meteorological data. The obtained results from modeling
showed the significance of the oxides of nitrogen and nitrogen dioxide relationship and
meteorological data as parameters inputted into the model [40].
J.W.S. Longhurst et al (2006) states that ambient air is managed in Great Britain by an
effective and risk management process addressing to public health issues. After year
2001 some 129 Local Authorities declared Air Quality Management Areas (AQMAs).
This strategy strengthened the process of LAQM along with a simplification based on
the experience of first Round. Now, a double step process is adopted which comprises of
an updating and screening assessment. The Government determines a time scale for
assessment of air quality since year 2010.Though the mechanism of LAQM adopted in
this procedure is developed for Great Britain, but the generic elements of the process are
applicable to any other country experiencing similar air pollution problems both national
and local oriented situations to resolve them [41].
25
A. Molter et al (2010) derived exposure effects from urban monitoring networks that
represent the spatial variation of pollutants, while personal monitoring campaigns are
often not feasible. Therefore, many studies now rely on empirical modeling techniques
to predict pollution exposure. This research was conducted by an approach of LUR that
was used from an air dispersion model rather than monitored data. There are several
advantages to such an approach such as a larger number of sites to develop the LUR
model compared to monitored data. Furthermore, through this approach the LUR model
can be adapted to predict temporal variation as well as spatial variation. This work was
based on two LUR models for an epidemiologic study based in Greater Manchester by
using modeled NO2 and PM10 concentrations as dependent variables, and traffic
intensity, emissions, land use and physical geography as potential predictor variables.
The LUR models were validated through a set aside ―validation‖ dataset and data from
monitoring stations. The final models for PM10 and NO2 comprised nine and eight
predictor variables respectively and had determination coefficients (R²) of 0.71 (PM10:
Adj. R² = 0.70, F = 54.89, p < 0.001, NO2: Adj. R² = 0.70, F = 62.04, p < 0.001).
Validation of the models using the validation data and measured data showed that the R²
decreases compared to the final models, except for NO2 validation in the measured data
(validation data: PM10: R² = 0.33, NO2: R² = 0.62; measured data: PM10: R² = 0.56, NO2:
R² = 0.86) [42].
M.A. Botchev & J.G. Verwer (2003) states that implicit time stepping typically requires
solution of one or several linear systems with a matrix I−τJ per time step where J is the
Jacobian matrix. If solution of these systems is expensive, replacing I−τJ with its
approximate matrix factorization (AMF) (I−τR) (I−τV), R+V=J, often leads to a good
compromise between stability and accuracy of the time integration on the one hand and
its efficiency on the other hand. For example, in air pollution modeling, AMF has been
successfully used in the framework of Rosenbrock schemes. The standard AMF gives an
approximation to I−τJ with the error τ2RV, which can be significant in norm. In this
paper we propose a new AMF. In assumption that −V is an M-matrix, the error of the
new AMF can be shown to have an upper bound τ||R||, while still being asymptotically
26
. This new AMF, called AMF+, is equal in costs to standard AMF and, as both
analysis and numerical experiments reveal, provides a better accuracy. We also report on
our experience with another, cheaper AMF and with AMF-preconditioned GMRES [43].
U.W. Tang & Z.S. Wang Based (2007) developed a prototype system for modeling air
pollution of the Macao Peninsula. The system is based on road traffic air pollution
model, an urban landscape model, digital maps and a Geographic Information System
(GIS). It is compared with meso scale model systems with input/output resolution in
kilometers. By applying the model it was detected that four urban forms exist on the
Macao Peninsula that influence vehicle transport. The findings show that the urban
environment is influenced by historical areas with narrower roads, complex road
networks and a higher density of intersections. However, the street canyon develops
effects on these historical areas which result in higher carbon monoxide (CO) levels
[44].
A.A. Karim & P.F. Nolan (2011) developed a ―Computational Fluid Dynamics (CFD)
approach‖. This approach is used to quantify nitrogen dioxide dispersion emitted from
vehicular flow in Maidstone, UK. The simulations were made for the whole day in
January, 2008. ―The developed CFD model utilizes a modified k-ɛ turbulence model and
Arrhenius reaction kinetics. The predictions and field measurements were recorded over
a twelve hour time interval with mean hourly results. The outcome showed that the
reactive pollutant approach greatly improves the predictions in comparison to the results
obtained from experiments‖. Furthermore the impact of peroxy radicals at the time of
rush hours was found to be a major disturbance [45].
A. Karppinen et al (2000) developed a modeling system for assessing road traffic
volumes, emissions from road traffic sources and atmospheric dispersion of air pollution
in an urban area. The dispersion modeling was based on combined application of the
urban dispersion modeling system (UDM-FMI) and the road network dispersion model
(CAR-FMI). The modeling system works on a method, that allows interaction of air
pollutants emitted from a large number of urban road transport sources. The authors‘
27
presents an overview of the modeling system and its application for predicting oxides of
nitrogen and nitrogen dioxide levels in Helsinki metropolitan area in year 1993 [46].
Ana G. Ulke & M. Fatima Andrade (2001) states that production and transport of urban
air pollution studied at São Paulo, Brazil, were due to the importance of the mega city as
source of pollutants and the flow pattern and topography of the region. An Eulerian air
quality model was applied. An improved method for calculating vertical diffusivities
was introduced in the model and the impact on the behavior of pollutants was analyzed.
The approach includes both shear generated and buoyancy-driven turbulence in a
continuous formulation that adequately represents turbulence evolution in the
atmospheric boundary layer. Dispersion and transformation processes are well described
by model simulations. The application of the proposed parameterization leads to
increased predicted concentrations. Relative changes range from 1.2 to 2. Uncertainties
in the emissions result in some disagreement between measured and simulated
concentrations [47].
Sotiris Vardoulakis et al (2007) evaluated here widely used regulatory dispersion
models, WinOSPM, ADMS-Urban 2.0 and AEOLIUS. The model evaluation relied on
two comprehensive datasets, which included carbon monoxide, particulate matter and
oxides of nitrogen levels along with traffic information and relevant meteorological data
from two busy street canyons in Birmingham and London for a one year period. The
ability of the models to reproduce roadside nitrogen dioxide and oxides of nitrogen
levels ratios by using simplified chemistry schemes was evaluated for one of the sites.
Finally, advantages and limitations of the current regulatory street canyon modeling
practice in the UK [48].
A. Karppinen et al (2000) have developed a modeling system for predicting the traffic
volumes, emissions from stationary and vehicular sources, and atmospheric dispersion
of pollution in an urban area. A companion paper addresses model development and its
applications. This paper describes a comparison of the predicted NOx and NO2
concentrations with the results of an urban air quality monitoring network. We
28
performed a statistical analysis concerning the agreement of the predicted and measured
hourly time series of concentrations, at four monitoring stations in the Helsinki
metropolitan area in 1993. The predicted and measured NO2concentrations agreed well
at all the stations considered. The agreement of model predictions and measurements for
NOx and NO2 was better for the two suburban monitoring stations, compared with the
two urban stations, located in downtown Helsinki [49].
Yilmaz Yildirim & Mahmut Bayramoglu (2006) states that air pollution is a growing
problem coming from household heating system, high density of road transport, electric
power generation and continuously increasing industrial and commercial. Assessment
and prediction of air quality parameters in the urban ambient air are important to
evaluate the health impact. Artificial intelligent techniques are efficiently used for
modeling highly complex and non-linear phenomena. In this research work neuro-fuzzy
logic method was suggested to estimate the impact of meteorological factors on oxides
of sulfur and finer particular matter pollution concentrations within an urban area. The
model predicts satisfactorily the trends for oxides of sulfur and finer suspended particles
levels with performance level in between 75–90% and 69–80 %, respectively [50].
R. Perez-Roa et al (2006) performed an evaluation with eight air quality meteorological
stations uniformly spread over the city of Santiago, Chile. The measured results show
that with the ANN-KV model, CAMx achieved better predictions of peak carbon
monoxide levels. Typically root-mean-square errors are reduced to half of their original
values. The outcome of ANN-KV model was then used to predict carbon monoxide
ambient concentrations at another period i.e. summertime and also to predict ambient
concentrations of total carbon along with particulate matter at both periods. A much
improved outcome was observed with this combination. Furthermore, the ANN
formulation allowed the urban emission inventory to be critically assessed indicating
that the weekend emissions in Santiago [51].
John Gulliver & David Briggs (2011) stated that current methods of air pollution
modeling do not readily meet the needs of air pollution mapping for short-term exposure
29
studies. The main limiting factor is that for those few models that couple with a GIS
there are insufficient tools for directly mapping air pollution both at high spatial
resolution and over large areas (e.g. city wide). A simple GIS-based air pollution model
(STEMS-Air) has been developed for PM10 to meet these needs with the option to
choose different exposure averaging periods (e.g. daily and annual). STEMS-Air uses
the grid-based FOCALSUM function in Arc GIS in conjunction with a fine grid of
emission sources and basic information on meteorology to implement a simple Gaussian
plume model of air pollution dispersion. STEMS-Air was developed and validated in
London, UK, using data on concentrations of PM10 from routinely available monitoring
data. Results from the validation study show that STEMS-Air performs well in
predicting both daily (at four sites) and annual (at 30 sites) concentrations of PM10. For
daily modeling, STEMS-Air achieved r2 values in the range 0.19–0.43 (p < 0.001) based
solely on traffic-related emissions and r2 values in the range 0.41–0.63 (p < 0.001) when
adding information on ‗background‘ levels of PM10. For annual modeling of PM10, the
model returned r2 in the range 0.67–0.77 (P < 0.001) when compared with monitored
concentrations. The model can thus be used for rapid production of daily or annual city-
wide air pollution maps either as a screening process in urban air quality planning and
management, or as the basis for health risk assessment and epidemiological studies [52].
Liping Xia & Yaping Shao (2005) in a simple approach found quite efficient and
adequate road network data that are available and statistical constraints are applied to
confine the model behavior. It was established through traffic information database for
Hong Kong Island and used it for traffic flow simulation. It was shown that by
specifying three types of traffic routes and providing traffic flow data at selected
stations, the model is capable of simulating traffic flow on the road network. Using
empirical emission factors for a number of vehicle categories, the emission rates of
major air pollutants, CO, NOx and PM10, were determined. The estimated emission rates
were compared with measurements for several air quality monitoring stations [53].
Magne Aldrin & Ingrid Hobæk Haff (2005) present a general model where the logarithm
of hourly concentration of an air pollutant is modeled as a sum of non-linear functions of
30
traffic volume and several meteorological variables. The model can be estimated within
the framework of generalized additive models. Although the model is non-linear, it is
simple and easy to interpret. It quantifies how meteorological conditions and traffic
volume influence the level of air pollution. A measure of relative importance of each
predictor variable is presented. Separate models are estimated for the concentration of
PM10, PM2.5, NOx and NO2 at four different locations in Oslo, based on hourly data in
the period 2001–2003. The obtained results showed quite good fit particularly of the
largest particles [54].
Bingli Xu et al (2011) performed the integration of high dimensional geo-visualization,
geo-data management, geo-process modeling and computation, geospatial analysis, and
geo-collaboration is a trend in GI Science. The technical platform that matches the trend
forms a new framework unlike that of GIS and is conceptualized in this paper as a
collaborative virtual geographic environment (CVGE). This paper focuses on two key
issues. One is scientific research on CVGE including the concept definition and the
conceptual and system framework development. The other is a prototype system
development according to CVGE frameworks for air pollution simulation in the Pearl
River Delta. The prototype system integrates air pollution source data, air pollution
dispersion models, air pollution distribution/dispersion visualization in geographically
referenced environments, geospatial analysis, and geo-collaboration. Using the prototype
system, participants from geographically distributed locations can join in the shared
virtual geographic environment to conduct collaborative simulation of air pollution
dispersion. The collaborations supporting this simulation happen on air pollution source
editing, air pollution dispersion modeling, geo-visualization of the output of the
modeling, and geo-analysis [55].
Renjie Chen et al (2010) concluded that case-crossover studies conducted in China to
investigate the acute health effects of air pollution. We conducted a time-stratified case-
crossover analysis to examine the association between air pollution and daily mortality
in Anshan, a heavily-polluted industrial city in northeastern China. Daily mortality, air
pollution, and weather data in 2004 –2006 in Anshan were collected. Time-stratified
31
case-crossover approach was used to estimate the effect of air pollutants (PM10, SO2,
NO2 and CO) on total and cardiopulmonary mortality. Controls were selected as
matched days of the week in the same month. Potential effect modifiers, such as gender
and age, were also examined. We found significant associations between air pollution
and daily mortality from cardiovascular diseases in Anshan. A 10 μg/m3 elevation of 2-
day moving average (lag 01) concentration in PM10, SO2, NO2 and CO corresponded to
0.67% (95% CI: 0.29%, 1.04%), 0.38% (95% CI: − 0.06%, 0.83%), 2.11% (95% CI:
0.22%, 4.00%) and 0.04% (95% CI: 0.01%, 0.07%) increase of cardiovascular mortality.
The associations for total and respiratory mortality were generally positive but
statistically insignificant. The air pollution health effects were significantly modified by
age, but not by gender. Conclusively, our study showed that short-term exposure to air
pollution was associated with increased cardiovascular mortality in Anshan. These
findings may have implications for local environmental and social policies [56].
Nathan Rabinovitch et al (2004) stated that urban minority children with asthma are at
higher risk for severe exacerbations leading to hospitalizations and deaths. Because
multiple studies have reported associations between air pollution and asthma worsening,
elevated levels of air pollution are cited as a possible trigger for increased asthma
morbidity in urban areas. Few studies have prospectively followed panels of urban
children with asthma to determine whether air pollution levels are associated with
clinically relevant outcomes such as asthma exacerbations [57].
David B. Peden (2005) stated that the occurrence of asthma and allergic diseases has
continued to increase in the United States and worldwide, despite general improvements
in air quality over the past 40 years. This observation has led many to question whether
air quality is truly a significant risk factor in the development and exacerbation of
asthma and whether further improvement in air quality is likely to result in improved
health outcomes. However, epidemiologic studies have shown that levels of pollutants of
less than the current ambient air quality standards still result in exacerbations of asthma
and are associated with other morbidities as well. Specific locations, such as living near
a roadway, might pose a special exposure risk. Genetic factors almost certainly play a
32
role in determining susceptibility to pollutants, such as including those involved with
antioxidant defenses. The best studied of these in the context of air pollution risks are
glutathione-S-transfer as polymorphisms. Irrespective of whether pollutants contribute to
the development of asthma or the well-documented increases in asthma results in more
people having pollutant-induced disease, poor air quality in many places remains a
significant problem for patients with asthma and allergic disease. A number of public
health, pharmaceutical, and nutriceutical interventions might mitigate the effects of
pollutant exposure and deserve further study [58].
Devon Payne-Sturges & Gilbert C. Gee (2006) determined physical, built environment
and social aspects behind the air pollution impacts. Similar other research findings on
health and environment measure the same impacts. These findings provide evidences
about impacts ranging from blood mercury concentrations, and asthma morbidity and
mortality due to PM2.5. These measures and categories are derived from a review of
environmental health disparities from several disciplines. As a next step in a long-term
effort to better understand the relationship between social disadvantage, environment,
and health disparities, we hope that the proposed measures and literature review serve as
a foundation for future monitoring of environmental health disparities [59].
Hak-Sung Kim et al (2014) monitored using the meteorological satellite data of the
National Oceanic and Atmospheric Administration (NOAA) and ground-based
measurements at Cheongwon, a background observation site in central Korea. The nine
cases, measured over 14 days, of natural dust particles caused by dust storms originating
from northern China and Mongolia were observed at Cheongwon in the spring, autumn,
and winter of 2010. In addition, seven cases, measured over 18 days, of anthropogenic
dust particles originating from eastern China were observed over the course of a year. In
those cases of natural and anthropogenic dust particles observed at Cheongwon, the level
of particle matter (PM) with a diameter of ≤10 μm (PM10) (100 μgm−3
day−1
) or PM2.5
(50 μgm−3
day−1
) exceeded the air quality standards of Korea for 5 and 6 days,
respectively. At the same time, CO concentrations rose higher due to long-range
transport, while CO levels in the cases of anthropogenic dust particles (954 ppb) rose
33
higher compared with the cases of natural dust particles (812 ppb). While gusty north–
northwesterly winds were blowing in the front side of high-pressure systems, an increase
in CO concentrations along with the influx of dust storms was observed at Cheongwon
in the three natural dust particle cases with continental background airflow (n-CBAs)
[60].
Erika Zarate &, Alain Clappier (2010) used for Air Modeling And Analysis (SAMAA)
for the prediction of air quality Nantes city. Within this scope, the emission module
AIREMIS was used for the urban traffic plan, in order to study three traffic scenarios.
The SAMAA was also applied to define the correct location to fix measurement stations
in the surroundings of an industrial plant. The sulfur dioxide plume was simulated for
various meteorological configurations [62].
Julian D. Marshalla et al (2008) found implicit assumption about comparisons of annual
mean levels and their spatial changes. In between the years variability was detected
small. The mean ranges for all monitors were 9 percent (4–13 percent), NO; 4 percent
(2–9 percent), NO2; 6 percent (1–13 percent), CO; and, 4 percent (2–8 percent), O3. Key
meteorological parameters during those years also exhibit small between-year
variability: ten percent or less for mean temperature and annual precipitation. For NO,
NO2, and CO, monitors‘ concentration rankings changed by at most two increments in
82 out of 84 cases (98 percent) [62].
W. Loibl & R. Orthofer (2001) compared results with data from the air monitoring
network which indicates that the model reflects well the overall oxides of nitrogen air
quality situation in Austria. There are no significant differences in between model results
and observed results and the uncertainties are within an acceptable range. The deviations
between model results and monitoring data are within ±15 μg/m3, which is very low
considering many assumptions needed to establish the model. In various cases, the
deviations between model results and monitoring data could be explained by the fact that
monitoring sites do not represent the overall air quality concentrations in the
neighborhood. This modeling is useful to support ambient air monitoring networks. In
34
addition to this the monitoring systems give accurate short-term sate about the relevant
air quality situation at a designated location [63].
W. K. Modey & D. J. Eatough (2003) determined PM2.3 composition along with semi-
volatile organic compounds (SVOC) and ammonium nitrate. The mean fine particulate
composition during six month time interval recorded as PM2.3 was 19.4μg/m3;
ammonium sulfate was 4.3μg/m3, non-volatile organic material was 6.9μg/m
3; elemental
carbon was 0.4μg/m3 and SVOC were 6.1μg/m
3. The episodes of finer particles
observed throughout the six month interval [64].
N. Canha et al (2014) worked on passive sampling mechanism to obtain the fine
particulate matter concentrations in classrooms of the urban and rural primary schools.
The experiments were carried out during a year by passive deposition covering the
seasonal variability of the particles masses and chemical substances. In order to
determine the main polluting sources, correlations between air pollutant parameters and
their supplementing factors were studied. Higher particle masses concentrations were
recorded in autumn, with a mean of 1.54. The major element in the collected particles
was calcium, representing 73 percent of the analyzed mass of the particles inside the
urban classrooms. In the rural environments calcium remained the major component but
with lower contribution to the overall particles composition of 46 percent [65].
H. Hong & L. W. Zhen (2012) found that larger can be detected and then classified into
six groups, i.e., 0.3–0.49, 0.5–0.99, 1–1.99, 2–4.99, 5–9.99. The levels of all particle
categories in numbers and were measured every second. The measurements were taken
in mornings and afternoons on each day of a week. The weather variations varied
through the week and levels of particulate were also found different. Therefore, it was
not considered proper to use all collected data for investigating the statistical
distribution. On a single day, the weather conditions in morning and afternoon are
generally similar and their influences on PM variations [66].
M. M. Kamal (2006) applied the Malaysian Air Quality Index (MAQI) to distribute the
level of air pollution. The Environment Department recorded the ambient air quality for
35
five major air pollutants lower atmospheric ozone, particulates, carbon monoxide, oxides
of sulfur dioxide and oxides of nitrogen. For each of these pollutants, it was determined
to set national air quality standards to protect public health. For this setting Artificial
Neural Network (ANN) was adopted [67].
P. Lalitaporn (2013) found assessment levels of PM10, carbon monoxide and nitrogen
dioxide to compare with respective satellite values. The observed data showed that
satellite observations are able to capture the trend and seasonal variability of the
emissions and ground concentrations. The model simulations were conducted using
CMAQ model [68].
A. J. Cohen et al (2011) developed GMAPS model at the World Bank and used to
generate estimates of concentrations of PM10 in all world cities with populations of
>100000, and in national capitals. The estimated model is based on available
measurements of PM10 and TSP from population-oriented monitoring stations in cities
worldwide for the period 1985 to 1999, retrieved in October 2001. In all cases, data from
a monitoring site were included if and only if it was clearly identified as a residential or
mixed residential site (see section 2.3 for definition). For instance, city averages reported
for many Chinese cities (National Environmental Protection Agency of China 2000)
were not included in the model estimation because the location of these sites could not
be ascertained. In principle, the monitoring data used for calculation of annual averages
should be collected throughout the year, since seasonal patterns in the data are fairly
common. More than 85% of cities in Europe and the United States collect measurements
of PM throughout the year. The representativeness of the data for cities in other parts of
the world could not be confirmed. In addition, in many countries where PM was
measured throughout the year, it was only measured on every sixth day. The methods for
measuring concentrations of PM also varied both gravimetric and automatic methods
[69].
Martin L. Williams et al (2005) mentioned the highest concentrations of the ―classical‖
indicators such as PM10 and sulfur dioxide are found in Africa, Asia and Latin America.
36
The highest levels of secondary pollutants such as ozone and nitrogen dioxide are
measured in Latin America and in some larger cities and urban air sheds in the
developed countries. Trends in air quality development differ in respect of the four
indicator pollutants. In Europe, PM10 levels had decreased by the end of last century but
have tended to rise again, which may be partially explained by changing weather
conditions. Even though large Asian cities have seen a slight reduction in PM10 levels
over the last few decades, PM (PM10 and PM2.5) is still the major air pollutant in Asia.
Many of the large cities in Latin America, as well as Mexico City, still experience high
levels of PM [70].
Mark Podrez (2015) used emission and meteorological parameters as input to AERMOD
to determine hourly oxides of nitrogen levels at a single receptor location. The model
estimated predicted levels as input to a spreadsheet. The modeled and measured oxides
of nitrogen levels were found equal to or higher than 20μg/m3. This threshold avoids the
measurement uncertainty that can be important while measuring oxides of nitrogen [71].
Hwa-Lung Yu et al (2015) collected the hourly aerosol data at the Hsin-Chuang
supersite since March 2002, including sulfate, nitrate, EC, OC, polycyclic aromatic
hydrocarbon (PAH), PM10, and PM2.5 measurements. In addition to aerosol observations,
the Taiwan Environmental Protection Agency (TWEPA) has also regularly monitored
the concentrations of criteria pollutants, including ozone, NO, NO2, CO, SO2, PM2.5, and
PM10, and meteorological variables, including wind speed (WS), ambient pressure,
temperature, and RH by using its island-wide monitoring network. The TWEPA Hsin-
Chuang station is located approximately 200 m from the Hsin-Chuang aerosol supersite.
In this study, we analyzed data obtained hourly at both the supersite and the TWEPA
station at Hsin-Chuang, including data related to ambient pollutants and meteorological
observations. Because of the high similarity (i.e. correlation coefficients of 0.91 and 0.92
for PM10 and PM2.5, respectively) between the PM measures at the two stations, the
supersite PM10 and PM2.5 records were used to represent the PM levels at Hsin-Chuang.
In this study, hourly air quality and meteorological datasets for all of 2009 were
investigated to reveal the major common underlying processes involved in yearlong
37
temporal variations in aerosols. Because the diurnal pollution dynamics can highly
depend upon various environmental conditions, this study uses PM10 as an indicator for
the general condition of ambient environment. In this study, the air qualities with three
scenarios are considered, i.e. PM10 at normal, extreme high, and extreme low levels. We
obtained hourly data from 3 selected days with the median (35.83μg m−3
), maximal
(226.2μg m−3
), and minimal (7.40μg m−3
) daily PM10 levels in 2009, were further
analyzed. The selected days corresponded to those in which normal conditions, an ADS
event, and a rainfall event occurred: August 17 (Saturday), April 25 (Monday), and
September 29 (Tuesday), respectively [72].
Kowsalya Vellingiri et al (2015) in this study monitored the concentrations of particulate
matter of 2.5 microns and 10 microns at a central urban area of Yongsan (YS), Seoul,
Korea during 2013. The daily average concentrations of both PM2.5 and PM10 were
26.6 ± 12.6 and 45.0 ± 20.4 μg/m3 respectively. The measured PM2.5levels showed slight
increase in the annual standard value (25μg/m3) set by the Korean Ministry of
Environment (KMOE), while that of PM10 was lower than its permissible value
(50 μg/m3). Comparison of the monthly mean values (μg/m
3) of both particulate matter
levels showed maximum concentrations in January (PM2.5: 36.9 and PM10: 59.7) and
minimum concentrations in September (PM10: 28.1) and October (PM2.5: 14.9) [73].
Bangtian Zhou et al (2015) collected air samples of particulate matter at Peking
University from March 2012 to April 2013. Seventeen indoor air samples were also
collected over this time interval. The winter particulate matter levels were found
decreased in comparison to those reported a decade ago. However the summer
particulate matter levels showed increased levels over the same time period. The
increasing summer particulate matter levels likely resulted from a shift in the major
source of particulates matter primary coal burning to road transport associated secondary
pollutants formation. A multiple regression model showed 62% of daily particulate
levels variations, and the wind direction was the most important factor in influencing
particulates levels [74].
38
Jing Cai et al (2015) found toxicity of CO mainly because of its reaction with
hemoglobin to form carboxyhemoglobin (COHb) in the blood that results in hypoxia.
The direct impact of carbon monoxide on blood and other tissues is in the form of
changes in body organ function The World Health Organization fixes 10mg/m3 for eight
hours so that the COHb concentration of 2.5 percent is not crossed. The normal COHb
concentration in a nonsmoker is about 0.5–1.5 percent. Inhalation of concentrations of
carbon monoxide less than 10mg/m3 induces impacts on humans at or near 2 percent
level of COHb [75].
Vanessa Silveira et al (2015) evaluated the average mean seasonal trends of pollutants
and monthly means were calculated. The higher levels of carbon monoxide, PM10,
oxides of sulfur and oxides of nitrogen were observed during winter months. The
observed data were in accordance with the local meteorological situations. The cause of
these higher concentrations during spring can be associated to cloud cover [76].
A. McCreddin et al (2015) correlated Gaussian dispersion to predict personal exposure
to PM10 with background data. The statistical models were used to predict personal
exposure to nitrogen dioxide using land use regression, greater variation between
background air quality and predicted. The correlation between measured personal
exposure and background concentration was found to be R2 = 17.1 percent for the
sampled residents [77].
Ezaz Ahmed et al (2015) studied the pollution phenomena of particulate matter
concentration in atmospheric air in central area in Seoul, Korea during the period from
year 2004 to 2013. The average levels of each parameter for the entire research study
period were found to be 26.6 ± 2.59, 54.0 ± 15.0, and 75.3 ± 16.6 μg/m3 respectively.
The seasonal average of PM2.5 ranged in between 22.9 ± 7.10 (fall) to 30.2 ± 7.58 μg/m3
(winter). In contrast, PM10 and TSP showed a summer minimum (40.1 ± 12.6 and
55.6 ± 17.8 μg/m3, respectively) and a spring maximum (67.1 ± 16.7 and
93.7 ± 21.1 μg/m3, respectively) [78].
39
Dalia Salameh, et al, (2015) studied seasonal and spatial characteristics of PM2.5 and its
chemical composition in the Mediterranean Basin over a period of one year from 2011 to
2012 in five European Mediterranean cities i.e. Barcelona, Marseille, Genoa, Venice,
and Thessaloniki. The PM10 annual average concentration ranged from 23 to 46μg/m 3
,
while the respective PM2.5 ranged from 14 to 37μg/m 3
, with the highest concentrations
observed in Thessaloniki and Venice. Both cities presented an elevated number of
exceedances of the PM10 daily limit value, as 32% and 20% of the days exceeded
50μg/m 3
, respectively. Similarly, exceedances of the WHO guidelines for daily PM2.5
concentrations of 25μg/m 3 were also recorded higher with 78% of the days during that
period, followed by Venice with 39%. The lowest PM levels were measured in Genoa
[79].
Nimesha Fernando et al (2015) determined that the current atmospheric carbon dioxide
concentration (CO2) has reached the level of 400 μmol CO2 mol−1
and is predicted to be
±550 μmol CO2 mol−1
by the middle of the 21st century according to the
Intergovernmental Panel on Climate Change (IPCC) under ―mid-range‖ emission
scenario. This will have a direct impact on the growth and development and yield
formation of crops, particularly for C3 plants including wheat and rice. It is predicted
that grain yield will increase by 15–17% under an atmospheric CO2 concentration
(a[CO2]) of about 550 μmol CO2 mol−1
. However, the positive influence of [CO2] on
plant growth and grain yield is counteracted by inferior grain quality [80].
M.D. Mueller et al (2015) developed a method to generate two-week NO2 concentration
maps with a high spatial resolution (10 m by 10 m) for the city of Zurich, Switzerland,
based on statistical modeling. Our models utilize data from a dense passive diffusion
sampler network consisting of 49 sites that measured 14-day mean NO2 concentrations
in the year 2008. The regression analysis is based on Generalized Additive Models
(GAMs) and a stepwise forward selection algorithm that leads to models relying on a
small number of explanatory variables (2-3). The explanatory variables included in the
regression analysis are spatially resolved information on traffic and heating systems
related NOX and NO2 emissions, respectively, sky view factors, and topography
40
(elevation). Deviance explained of the 26 models ranges from 0.66 to 0.79. 81 % of the
modeled and 77 % of the predicted NO2 concentrations, respectively, deviate less than
25 % from the observations. The modeling approach outlined in this paper augments the
value of point measurements obtained from urban routine passive diffusion sampler
networks by providing spatially resolved concentration fields. The derived maps allow a
detailed assessment of NO2 levels in cities and can be used in applications such as public
health protection [81].
Suresh Tiwari et al (2015) determined high resolution concentrations of nitric oxide
(NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), and Ozone (O3) at a urban site
(urban background) in New Delhi, India for a period of two years from September 2010
to August 2012. During this study period, the mean concentrations of NO, NO2, NOx, O3
(ppb), and CO (ppm) were observed to be 17.2, 12.5, 29.3, 23.6, and 1.97, respectively.
This data was then employed to investigate the relationships between NO, NO2, and O3
as a function of NOx. The highest mean concentrations of NO were observed from
midnight 00:00 to 05:00 h local time (LT) in the morning as a result of increase in traffic
emissions and a reduction in boundary layer height during the night. The total levels of
oxidant [OX], which are considered to be the sum of O3 and NO2, were determined. A
study of variation of [OX] and NOx identified two distinct contributions to ambient OX
concentrations, i.e., NOx independent and NOx dependent. The NOx dependent
contribution corresponds to the local production of ozone, and the NOx independent
contribution corresponds to regional concentrations, which at this site is the background
level of ozone. The monthly and diurnal variations of [OX] are discussed. Wind
directions were used to identify possible regional source of [OX]. The analysis suggests
that [OX] concentrations were about six times higher with winds originating from the
Northwest direction (NW) compared to those from the East [82].
Mauro Masiol et al (2015) focused on the Veneto region that lies in the eastern part of
the Po Valley (Italy). This is one of the hotspots in Europe for air quality, where efforts
to meet the European standard for PM2.5 according to current and future legislation have
been generally unsuccessful. Recent data indicating that ammonium, nitrate and sulfate
41
account for about one third of total PM2.5 mass show that secondary inorganic aerosol
(SIA) plays a key role in the exceeding the standards. A sampling campaign for PM2.5
was carried out simultaneously in six major cities (2012–2013). The water soluble
inorganic ions were quantified and data processed to: (1) investigate the seasonal trends
and the spatial variations of the ionic component of aerosol; (2) identify chemical
characteristics at the regional-scale and (3) assess the potential effects of long-range
transport using back-trajectory cluster analysis and concentration-weighted trajectory
(CWT) models. Results indicated that PM2.5 and SIA ions have an increasing gradient in
concentrations from North (mountain) to South (lowland) and from East (coastal) to
West (more continental), whereas K+ and Ca
2+ levels are quite uniformly distributed.
Similar seasonal trends in PM2.5 and ions are seen across the region. Simultaneous daily
changes were observed and interpreted as a consequence of similar emission sources,
secondary pollutant generation and accumulation/removal processes. Sulfate and nitrate
were not directly related to the concentrations of their precursor gases and were
generally largely, but not completely, neutralized by ammonium. The clustering of back-
trajectories and CWT demonstrate that the long-range movement of the air masses has a
major impact upon PM2.5 and ion concentrations: an area spreading from Eastern to
Central Europe was identified as a main potential source for most ions. The valley sites
are also heavily influenced by local emissions in slow moving northerly air masses.
Finally, two episodes of high nitrate levels were investigated to explain why some sites
are experiencing much higher concentrations than others. This study identifies some key
features in the generation of SIA in the Po Valley, demonstrating that SIA generation is
a regional pollution phenomenon and mitigation policies are required at regional,
national and even European scales [83].
Chinmay Jena et al (2015) compared different anthropogenic NOX emission inventories
and examined the associated variation in simulated surface ozone (O3) in India. Six
anthropogenic NOX emission inventories namely Emission Database for Global
Atmospheric Research (EDGAR), Intercontinental Chemical Transport Experiment-
Phase B (INTEX-B), Regional Emission Inventory in Asia (REAS), MAC City, Indian
National Emission Inventory and Top-Down NOX emission inventory for India (Top-
42
Down) are included in the comparison. We include these emission inventories in
regional chemical transport model WRF-Chem to simulate tropospheric column NO2
and surface O3 mixing ratios for the month of summer (15-March to 15-April) and
winter (December) in 2005. Predicted tropospheric column NO2 using different NOX
emission inventory are evaluated with the OMI satellite observations. All emission
inventories show similar spatial features, however uncertainty in NOX emissions
distribution is about 20–50% over rural regions and about 60–160% over the major point
sources. Compared to OMI, the largest bias in simulated tropospheric NO2 columns is
seen in the REAS (−243.0 ± 338.8 × 1013
molecules cm−2
) emission inventory, followed
by EDGAR (−199.1 ± 272.2 × 1013
molecules cm−2
), MAC City (−150.5 ± 236.3 × 1013
molecules cm−2
), inventories during winter. Simulations using different NOX emission
inventories produces maximum deviation in daytime 8-h averaged O3 of the order of 9–
17 ppb (15–40%) in summer and 3–12 ppb (5–25%) in winter over most of the land area.
The simulation suggests that choice of NOX emission inventories have significant effect
on surface O3 concentration for air quality studies over India [84].
M. Masiol and Roy M. Harrison (2015) determined among other emission sources in the
Greater London area, the international airport of Heathrow to be a major source of air
pollution and is one of the UK locations where European air quality Limit Values are
currently breached. However it is very difficult to differentiate between pollutants
arising from airport operations and those from the large volumes of road traffic
generated by the airport, as well as the nearby M4 and M25 motorways, A4 and A30
major roads, the conurbation of London and other external sources. In this study, eight
years (January 2005–December 2012) of measurements of various air pollutants (NO,
NO2, NOx, O3, CO, PM10 and PM2.5) were investigated from 10 sites: eight sites are
located within a distance of 2.5 km from the airport, while two sites representative of the
regional background and of background air quality in London (Harwell (60 km WNW)
and North Kensington (17 km ENE), respectively) were included. A series of statistical
tools was thus applied to: (1) investigate the time series by analyzing hourly data as
diurnal, weekly, seasonal and annual patterns; (2) reveal the effects of the atmospheric
circulation upon air pollution by analyzing background-corrected polar plots and (3)
43
quantify the impact of the airport upon air quality in the local area using the inter-site
differences of measured concentrations. The results show different diurnal patterns in
emissions of NOx from the airport and from the motorways. The concentration increment
arising from passage of air across the airport during airport activity (6 am–10 pm) and
with wind speed > 3 m s−1
is ca. 1–9 μg m−3
of NO2 and 2–20 μg m−3
of NOx at
background stations. Such results are slightly lower than in a previous study analyzing
the 2001–2004 period. Air quality impacts of the M25 and M4 motorways are
substantial only at the Hillingdon site (30 m from M4). Concentration increments of
particulate matter can take either small positive or negative values [85].
Peter Molnar et al (2015) investigated the effects on historical NOx estimates on time
trends, spatial distributions, exposure contrasts, the effect of relocation patterns and the
effects of back extrapolation. Historical levels of nitrogen oxides (NOx) from 1975 to
2009 were modeled with high resolution in Gothenburg, Sweden, using historical
emission databases and Gaussian models. Yearly historical addresses were collected and
geo-coded from a population-based cohort of Swedish men from 1973 to 2007, with a
total of 160,568 address years. Of these addresses, 146,675 (91%) were within our
modeled area and assigned a NOx level. NOx levels decreased substantially from a
maximum median level of 43.9 μg m−3
in 1983 to 16.6 μg m−3
in 2007, mainly due to
lower emissions per vehicle km. There was a considerable variability in concentrations
within the cohort, with a ratio of 3.5 between the means in the highest and lowest
quartile. About 50% of the participants changed residential address during the study, but
the mean NOx exposure was not affected. About half of these moves resulted in a
positive or negative change in NOx exposure of >10 μg m−3
, and thus changed the
exposure substantially. Back extrapolation of NOx levels using the time trend of a
background monitoring station worked well for 5–7 years back in time, but extrapolation
more than ten years back in time resulted in substantial scattering compared to the ―true‖
dispersion models for the corresponding years [86].
Min Liu et al (2015) found that carbon dioxide (CO2) is the most important
anthropogenic greenhouse gas contributing to global climate change. Understanding the
44
temporal and spatial variations of CO2 concentration over terrestrial ecosystems provides
additional insight into global atmospheric variability of CO2 concentration. Using 355
site-years of CO2 concentration observations at 104 eddy-covariance flux tower sites in
Northern Hemisphere, we presented a comprehensive analysis of evolution and variation
of atmospheric CO2 concentration over terrestrial ecosystem (ACTE) for the period of
1997–2006. Our results showed that ACTE exhibited strong seasonal variations, with an
average seasonal amplitude (peak-trough difference) of 14.8 ppm, which was
approximately threefold that global mean CO2 observed in Mauna Loa in the United
States (MLO). The seasonal variation of CO2 were mostly dominant by terrestrial carbon
fluxes, i.e., net ecosystem protection (NEP) and gross primary production (GPP), with
correlation coefficient(r) were −0.55 and −0.60 for NEP and GPP, respectively.
However, the influence of carbon fluxes on CO2 were not significant at inter annual
scale, which implied that the inter-annual changing trends of atmospheric CO2 in
Northern Hemisphere were likely to depend more on anthropogenic CO2 emissions
sources than on ecosystem change. It was estimated, by fitting a harmonic model to
monthly-mean ACTE, that both annual mean and seasonal amplitude of ACTE increased
over the 10-year period at rates of 2.04 and 0.60 ppm yr−1
, respectively. The uptrend of
annual ACTE could be attributed to the dramatic global increase of CO2 emissions
during the study period, whereas the increasing amplitude could be related to the
increases in Northern Hemisphere biospheric activity. This study also found that the
annual CO2 concentration showed large variation among ecosystems, with the high value
appeared in deciduous broadleaf forest, evergreen broadleaf forest and cropland. We
attribute these discrepancies to both differential local anthropogenic impacts and carbon
sequestration abilities across ecosystem types [87].
Yun Gon Lee et al (2015) examined the quiescence of Asian dust events in South Korea
and Japan during the spring of 2012, presenting a synoptic characterization and
suggesting possible causes. Synoptic observation reports from the two countries
confirmed that spring 2012 had the lowest number of dust events in 2000–2012. The
monthly dust frequency (DF) in March 2012 over the dust source regions, i.e., deserts in
northern China and Mongolia, indicated a significant decrease compared to the 12 year
45
(2000–2011) March climatology. The DF in April 2012 was comparable to the 12 year
climatology values, but in May 2012 it was slightly lower. The daily Ozone Monitoring
Instrument Aerosol Index and the Navy Aerosol Analysis and Prediction System
simulations revealed stagnant dust movement in March and May 2012. Anomalous
anticyclones north of the source regions decreased the dust outbreaks and enhanced the
southeasterly winds, resulting in few dust events over the downwind countries (i.e.,
South Korea and Japan). By contrast, in April 2012, a strong anomalous cyclone east of
Lake Baikal slightly increased the dust outbreaks over northeastern China. However, the
major dust outbreaks were not transported downwind because of exceptional dust
pathways, i.e., the southeastward pathway of dust transport was unusually blocked by
the expansion of an anomalous anticyclonic circulation over the Sea of Okhotsk, with
dust being transported northeast [88].
46
CHAPTER NO. 3
AIR POLLUTION
3.1. INTRODUCTION
In this chapter, the history of air pollutants and their health impacts, national and
international legislation, international agreements related with air pollution and national
environmental quality standards are provided.
3.2 HISTORY OF AIR POLLUTION
The atmosphere is a complicated natural gaseous substances system that is very
important to support life on planet Earth. Ambient air pollution has long been recognized
as a threat to human health as well as to the Earth's ecosystems. When industrialization
began in United Kingdom during the eighteenth and nineteenth century, the air pollution
levels stated to rise. Most of the industries were using coal as fuel along with burning of
coal in homes for the household heating purpose multiplied the air pollutants emissions
into the atmosphere. This situation brought the concept of urban smog and later on got
the familiarity with the name of London Fog. More or less the same situation was felt in
rest of the developing countries of that time [89].
The seriousness of exposure to high levels of ambient air pollution was well understood
in the mid of the twentieth century. The cities and towns in the United States of America
and Europe faced notorious episodes of air pollution. That resulted in large number of
deaths and hospital admissions. Subsequent clean air legislation and other regulatory
actions led to the reduction of ambient air pollution in many regions of the world,
particularly in the developed countries of North America and Europe. However, studies
conducted over the last decade, using sensitive designs and methods of analysis, have
identified adverse health effects caused by combustion derived air pollution. Even at the
low ambient concentrations that generally prevail in cities in North America and
Western Europe, Health Effects take place [90].
46
47
Pollution from the combustion of fossil fuels is largely emitted into the outdoor air. The
contribution of different combustion sources is the cumulative mixture containing
certain air pollutants, such as SOx, NOx, CO2, CO and particulate. However exposures
of air pollutants to human take place in both within and outside the house. An
individual‘s contact with ambient urban air pollution is based on the length of time spent
within the house and outside the house.
A recent study states that in 1600 cities of the 91 countries, only 12 percent residents in
these cities are breathing within WHO‘s permissible air quality guidelines. While rest 88
percent population in these cities is living in an environment which is heavily polluted
with heavy concentrations of air pollutants. On the basis of critical ambient air pollution
in the cities, Delhi is ranked at first position, Karachi is at fifth, Peshawar is sixth and
Rawalpindi is ranked at seventh position. In Karachi the average P.M2.5 concentration is
117µg/m3
. According to the country wise air pollution ranking, Pakistan is at top, Qatar
is second, Afghanistan is third, Bangladesh is fourth, Iran is fifth, Egypt is sixth,
Mongolia is seventh, United Arab Emirates is eighth, India is nine and Bahrain is ranked
at tenth position [91].
Recent scientific research findings suggest out that urban air pollution develops wide
ranging impacts on human health and may range from eye irritation to loss of life.
Recent assessments suggest that the impacts on public health may be considerable. The
quantification of the impact of air pollution on public health has gradually become a
critical component for the control of air pollution. Quantifying the magnitude of the
impact of air pollution in cities worldwide is not an easy job. However, presents
considerable challenges owing to the limited availability of information on both effects
on health and on exposures to air pollution in many parts of the world.
3.3 MAJOR AIR POLLUTANTS AND THEIR HEALTH EFFECTS
The effects of air pollution have got unique characteristics on which basis the impact of
it on the natural environment and human health is judged. To understand this
phenomenon, it is essential to consider the geographical location of the air pollution
48
sources as well as the distribution sources. The geographical location is referred away
through spatial and temporal scales. Under the spatial scales the air pollutants may cause
effect within the localized area or within an urban area and if it is beyond this region
then it may be within some more extended area up to a certain region or having long
ranging impacts up to hemispheric scale or global scale. The other important factor to
consider is regarding the life span of the air pollutants or pollutant species. The air
pollutant life spans ranges from few seconds to many hundred years. That is why their
suspension in the ambient air is crucial and this is that time in which these pollutants
potentially cause major effects on the natural environment as well as on human health.
As the scientists started to concentrate on the emitted air pollutants, they determined
many facts and clues about the types, sources and characteristics about the air pollutants.
The scientific, laboratories and statistical computational techniques helped a lot in
determining various aspects of this atmospheric menace. The effects of air pollutants on
human health may be observed through a lengthy process of physical, chemical,
physiological and behavioral processes. The observational process initially starts with
the air emissions into the ambient environment. The people inhale the emitted air
pollutants while carrying out their daily life activities. The inhalation by the people
remains uninterrupted whether they are in the indoor or outdoor environments. The
inhaled air pollutants cause different adverse health effects depending upon the type of
pollutants, doses of inhalations and the persons‘ susceptibility to that substance. The
common impacts may range from lung‘s malfunctioning, asthma problems, respiratory
disorders, increased hospital visits and even pre mature deaths. People living in highly
polluted areas experience more frequent sickness as against those who live in
comparatively clean environments.
The emitted pollutants add up in the atmosphere through two broadly classified types of
sources. One is referred as stationary or immobile sources of emitting air pollutants. In
this type of sources all the industries where combustion take place for one process or the
other and generate air pollutants and they are accordingly add up in the atmosphere.
Varieties of industries are operating and new are being established with the passage of
49
time. Working of power plants, refineries, cement plants, fertilizer plants, sugar
industries, textile industries, commercial products manufacturing industries etc are a few
to mention here. The other type is referred as mobile sources of emitting air pollutants.
As the name employees these sources are automobiles or vehicles which when operate,
generate sufficient mass of air emissions. These may include all vehicles on roads or
highways, trains and airplanes.
With the stationary and mobile sources different types of air pollutants are emitted. Each
pollutant has got its own characteristics and effect on natural environment as well on
human health. Some significant air pollutants and their health impacts are given below.
3.3.1 Particulate Matter (P.M)
Particulate matter may be found in solid or liquid state in the atmosphere in suspended
form. Particulates are not found in uniform sizes rather they exist in irregular shapes and
dia. Depending on the diameter of two types of particulates are important one is referred
as finer and the other is coarser. The finer particulates are those whose size is less than
2.5 microns (µ) and are denoted as P.M2.5. Finer particulates are emitted from
automobiles or industries when fuel is burned and combustion take place. While coarser
particulates are those, whose size ranges between 10 to 2.5 microns (µ) and are denoted
as P.M10. The coarser particulates are emitted when activities like construction, sea
spray, or vehicles ply on the road surfaces [3]. For detailed studies of particulate
emissions and their behaviors different categories are focused. Under these categories,
the particles having less than 2.5 µm are termed as ‗respirable particles‘. The particles
ranging in between 10 µm and 2.5 µm are referred as ‗inhalable particles‘. The particles
lesser than 44 µm are termed as ‗suspended particles‘. The particles greater than 44 µm
are termed as ‗settleable particles‘. While the particles, which are up to 100 µm, are
referred as ‗total suspended particles‘. The particulate matter has got many impacts on
human health. These finer particulates on their inhalation can cause asthma, respiratory
disorders and unnatural deaths.
50
The recent research founds that particulate (PM) are primarily made up of anthropogenic
emissions. Though, the PM10 is recognized as having considerable adverse impacts on
the bronchiolar region of respiration system that is the primary site of asthma and
associated airway inflammation. One component of the PM10 fraction of particular
interest is endotoxin. When endotoxin is inhaled, stimulates alveolar macrophages and
respiratory epithelial tissue to release cytokines that initiate an inflammatory cascade.
Human exposure shown decline in airflow, development of neutrophilic alveolitis, and
enhanced cytokine release by activated macrophages and airway epithelial cells by
inhalation of endotoxin [92].
In Spain, the PM10 levels reported during various episodes were found 22.0μg/m3 at the
rural area and 49.5μg/m3 at the urban site. In the Metropolitan area of Athens, Greece the
daily PM10 concentrations ranged between 32.3μg/m3 and 60.9μg/m
3 during four year of
studies. In Kolkata, India, the 24 hour average concentrations of PM10 was found in the
range 68.2-280.6μg/m3 for residential area and 62.4-401.2μg/m
3 for industrial area. The
total PAH concentration (ng/m3) in the organic extracts ranged in between 2.22-3.36 in
Arafat, 4.76-5.17 in Muzdalifa and in the reference site it was 0.18. Comparison of these
concentrations with the concentrations in industrial (2.8), urban (5.5) and rural (2.0) area
in Flanders, Belgium shows that Muzdalifa has concentration similar to the urban site
which was the highest in Flanders [93].
3.3.2 Carbon Monoxide (CO)
Carbon monoxide is a gaseous substance that is emitted from the burning of fossil fuels.
It is an odorless gas and cannot be seen through naked eyes. Burning of fossil fuels emits
the carbon monoxide. Improperly tuned automobiles emit higher concentrations of
carbon monoxide as against the properly tuned vehicles. In addition to this furnaces and
heating systems may also considerable amount of carbon monoxide.
The carbon monoxide has got numerous impacts on human health. Its entrance inside the
body through inhalation makes difficult for body parts to get the required dosages of
oxygen [3]. As it enters into the human body and come into interaction with human
51
blood, the hemoglobin is converted into carboxyhemoglobin (COHb). As hemoglobin
has got much higher affinity towards carbon monoxide as against that of oxygen so the
blood do not get sufficient amounts of oxygen for human body metabolisms. This
transformation of hemoglobin to carboxyhemoglobin results in effects on brain,
cardiovascular system, muscles and development of fetus as well. Larger dose exposures
may cause dizziness, tiredness and headaches. The elderly people suffering from cardio
disorders may experience complications in their body system.
The researchers found a frequent peak in the dust concentration between 08:00 am and
10:00 a.m. On 4 February peak levels in ambient air pollution was carbon monoxide
3.5mg/m3, nitrogen dioxide was 140μg/m
3 and sulfur dioxide was 45μg/m
3 were in the
city. Highest levels ozone levels of around 100 ppb were found in between 7th
September and 15th
September. At the time take off and landing of airplanes from 5 until
9 September the boundary layer height could be found at about 3,200 m (5 September),
higher than 3,200 m (6 and 7 September) and at 3,500 m (9 September) near the top of
layer [94].
3.3.3 Oxides of Sulfur (SOx)
Oxides of sulfur (SOx) are the combine name of all oxides of sulfur out of which two
most important are namely sulfur dioxide (SO2) and sulfur trioxide (SO3). Out of these
sulfur dioxide is most important as an air pollutant. Sulfur dioxide is considered a
corrosive gas that cannot be seen by naked eye nor can it be smelled at lower
concentrations. However at higher concentrations it smells like a ―rotten egg‖. The main
sources of emissions of sulfur dioxide are oil and coal power plants. In addition to this, it
is emitted from manufacturing units which produce chemicals, paper or fuel. Fossil fuels
particularly coal and oil contains sulfur content in varying from one to five percent. On
its combustion it is converted into sulfur oxide. Presently in the developed countries that
fossil fuel is used that is free from sulfur or with negligible concentration of sulfur
content. This is a primary pollutant and as it is emitted into the atmosphere it involves in
52
many chemical reactions and formulates secondary pollutants which results in acid rain
incidents.
The life span of oxides of sulfur ranges from four to ten days in the atmosphere. The
impacts of sulfur dioxide are wide ranging and results in complicating health of
asthmatic patients. Its exposure causes ear, eyes and nose irritations. Higher atmospheric
concentrations of it may harm the plants, vegetation and even the buildings [95].
The emission file provided only a constant emission rate for each stack, though this is
reasonable given the continuous operation of these industries. Not all of the stacks
emitted SO2 and so the 100 most significant SO2 sources were selected, representing 98
% of the SO2 emissions. Stack heights ranged from 12 to 140 m, with the most
significant SO2 sources being emitted from stacks of 100 m or greater in height.
Emission temperatures ranged from 54 to 300°C. Plume rise and trajectory are calculated
by a numerical model within ADMS that takes into account entrainment and penetration
of inversions. The extent of the estate at the time of the modeling period was 10 km2.
Stack and monitoring station locations were verified by matching to an image base map
through ArcView GIS: all values were found to be offset by +344 m (easting) and −278
m (northing) compared with the base map UTM coordinates (WGS 1984 UTM Zone
47N) and was adjusted accordingly [96].
3.3.4 Oxides of Nitrogen (NOx)
Oxides of nitrogen comprise of nitrous dioxide (N2O), nitrogen dioxide (NO2) and nitric
oxide (NO). They are formulated when nitrogen (N2) reacts with oxygen (O2). Out of
these oxides of nitrogen, nitrogen is of more importance from air pollutants point of
view. The life span varies from one to seven days for nitric oxide and nitrogen dioxide
while for nitrous oxide the life span is up to one hundred seventy years. The nitrogen
dioxide is produced with the burning of fossil fuels and has got strong smells at higher
concentration levels. It is produced by the combustion process in power plants as well as
automobiles. Though it is primary pollutant but once it enters into the atmosphere reacts
with already present other substances to produce newer substances called secondary
53
pollutants. These secondary pollutants may result in the formation of ozone, acid rain or
particulates.
The impacts of emissions of nitrogen dioxide have been found on humans as well as on
natural environment. On humans it can develop respiratory disorders or respiratory
infections. In case of natural environment it can cause acid rain which is injurious to
vegetation, ecosystems and building structures.
The researchers found that eastern part of India is affected by increasing air pollution
levels as a result of concentrated industrial activities. The impact of NOX emissions
resulting from various air pollution sources, viz. industries, vehicles and domestic, was
estimated using Industrial Source Complex Short-Term Gaussian dispersion model. The
contribution of NOx concentration from industrial, vehicular and domestic sources was
found to be 53, 40 and 7%. Further statistical analysis was carried out to evaluate the
model performance by comparing measured and predicted NOx concentrations. The
model performance was found good with an accuracy of about 68% [97].
3.3.5 Ozone (O3)
Ozone is a gas that can be present at two locations. Due to atmospheric reactions it is
formulated due to emissions from anthropogenic sources and formation of secondary
near the surface of the earth i.e. in the troposphere and naturally available in the upper
layers in the atmosphere i.e. in the stratosphere. It is also referred as bad ozone and bad
ozone. It is referred as bad ozone because it is found harmful when it is present in the
troposphere as it causes smog and considered as a significant air pollutant. It is
considered good ozone when it is present in the stratosphere as it acts as a shield to
protect the earth and its environment from the harmful ultraviolet radiations of the Sun.
As greenhouse gas, ozone contributes 3-7 percent in the greenhouse effect [98].
Ozone is formulated when nitrogen oxides and volatile organic compounds reacts in the
presence of sunlight in the troposphere. Nitrogen oxides are released due to combustion
54
of fossil fuels. The concentration ozone in troposphere has been increased from 237 ppb
in 1750 to 337 ppb in recent years indicating 42 percent increase.
Ozone can be hazardous by causing health hazards to humans when found in the ambient
air. Health related problems may be asthma, sore throats, breathing shortness and
coughing. Presence in higher concentration may also damage the vegetation cover.
3.3.6 Methane (CH4)
Methane is a greenhouse gas in the earth‘s atmosphere. The emissions of methane take
place from stagnant water bodies as a natural source while it is released from industries,
agricultural practices, waste management and oil and gas exploration fields as an
anthropogenic source. It has been found through various investigations that methane has
considerably increased from 700 ppb in 1750 to 1762 ppb in recent years indicating 151
percent increase [99].
3.3.7 Lead (Pb)
Lead is a toxic metal and is emitted in the atmosphere due to automobile emissions
mainly. The sources may be power plants, paint manufacturers and fertilizer factories.
As for as the impacts of lead exposures are concerned, the higher concentrations may be
harmful for humans particularly children as it can lead to lowering the intelligence
quotient (I.Q) levels as well as malfunctioning of the kidneys. In adults the lead
exposure may affect on the cardio systems and cause heart related disorders.
3.3.8 Greenhouse gases (GHG)
In the earth‘s atmosphere there exist some gases which have the capability to absorb and
emit solar radiation within the infrared range causing an effect called as greenhouse
effect. These gases are referred as greenhouse gases (GHG). If this greenhouse effect
has not been there the earth‘s average surface temperature would have been nearly 30°C
colder than it is existing now [100]. The primary greenhouse gases are water vapours,
carbon dioxide, nitrous oxide, methane and ozone [101]. With the industrialization and
55
transportation activities the heavy build up of about 40 percent of greenhouse gases has
taken place in the earth‘s atmosphere. This can be imagined from increase of carbon
dioxide from 280 ppm in 1750 to 400 ppm in year 2015 indicating about 41 percent
increase. It is predicted that if greenhouse gas emissions continued at the present rate,
the earth‘s surface temperature could historically increases early as year 2047 [102].
The greenhouse gases (GHGs) warm the surface of the earth and the atmosphere with
remarkable implications for precipitations, melting of glaciers and snow caps in ocean,
including sea level increase phenomenon. About thirty years ago, it was observed that
the increase in tropospheric ozone from atmospheric air pollution (NOx, CO and others)
is an important greenhouse effect. In addition to this the recognition of
chlorofluorocarbons (CFCs) on stratospheric ozone and its climate effects linked
chemistry and climate very closely. About 20 years ago, atmospheric air pollution was
primarily considered to be an urban problem. However the current findings have
revealed that atmospheric air pollution is transferred across continents and oceans due to
long-range transport. On the other hand, absorption of solar radiation by black carbon
and some other organic substances increase the atmospheric heating and tend to amplify
greenhouse warming of the atmosphere [103].
3.3.9 Toxic Air Pollutants
Some trace air pollutants like arsenic, dioxin, formaldehyde, benzene and asbestos are
also found present in the ambient air. They are emitted by variety of industrial,
commercial and municipal solid waste burning activities in the urban environment [104].
Though they are in smaller concentrations in the ambient air but even that smaller
concentration is capable to cause numerous health impacts. Some of these are suspected
of carcinogenic, some cause birth defects, some cause skin, eye irritation and breathing
problems [105].
There is an increasing threat of polycyclic aromatic hydrocarbons (PAHs) in the ambient
air they are accumulating in the atmosphere. Some of these polycyclic aromatic
hydrocarbons have been found among the certain carcinogens substances. Polycyclic
56
aromatic hydrocarbons and their derivatives are produced by the incomplete combustion
of organic material coming from natural combustion like volcanic eruptions and forest
fires. However the bulk share is contributed by the anthropogenic emissions. The
polycyclic aromatic hydrocarbons levels vary from place to place particularly in urban
and rural environments. These polycyclic aromatic hydrocarbons are mainly influenced
by domestic and vehicular emission sources [106].
3.4 LEGISLATION FOR AIR POLLUTION
After realizing the consequences of the air pollution and its health impacts, legislation
was initiated. The 1875 Public Health Act of United Kingdom contained a smoke
abatement section in order to minimize the urban air pollution. United Kingdom
remained forefront in initiating preceding rules and regulation to check, monitor and
regulate the air pollution. In year 1956 Clean Air Act was promulgated and modified
later on in year 1968 and in year 1993. In year 1995, the United Kingdom passed its
Environment Protection Act and introduced National Air Quality Strategy to set air
quality standards for the regulation of most significant air pollutants.
In the United States of America, the initial legislation was initiated through Air Pollution
Act 1955. Consequently with the scientific and technological advancements relevant
modifications and amendments were followed in 1967, 1977 and in 1990. Under these
provisions of the law the United States Environmental Protection Agency (USEPA) was
created. The states and local governments have enacted the similar legislation for better
regulation and enforcement.
The Canadian government has also framed legislation to check the air emissions. In this
respect the Canadian Environmental Protection Act 1999 was enacted. Later on the
country introduced some more regulatory frameworks like the Canadian Ambient Air
Quality Standards (CAAQS), Base Level Industrial Emission Requirements (BLIERs),
In China having higher ambient air pollution in big cities started the Airborne Pollution
Prevention and Control Action Plan (APPCAP) which aims to reduce air pollution from
the levels of year 2012 to 25 percent by the year 2017.
57
Realizing the importance and need of the environmental protection and pollution
problems, the Government of Pakistan introduced Pakistan Environmental Protection
Ordinance in 1983. This ordinance was made as a comprehensive law as Pakistan
Environmental Protection Act 1997 with the approval of the President of Pakistan. In
view of the Pakistan Environmental Protection Act 1997, the required National
Environmental Quality Standards (NEQS), Initial Environmental Examination (IEE) and
Environmental Impact Assessment (EIA) guidelines and Ambient Air Quality Standards
were introduced. In addition to that federal and provincial Environmental Protection
Agencies were also established.
3.5 INTERNATIONAL LEGISLATION
Since the issue of air pollution is of such a nature that once at any place it is emerged
then it cannot be restricted to geographical boundaries. Therefore trans-boundary
impacts of air pollution are considered very much certain. Due to this fact at world level
international bindings were introduced and are at place. Some of these are discussed
below.
3.5.1 Convention on Long Range Trans-boundary Air Pollution (CLRTAP)
The convention on long range trans-boundary air pollution has been made by the United
Nations Economic Commission for Europe (UNECE) in 1979. This convention is meant
for the human environment against the air pollution. The main purpose of this
convention was to limit and reduce gradually the emerging localized as well as trans-
boundary air pollution from the region. The main emphasis has been given to air
pollution in general and persistent organic pollutants (POPs) in particular like; aldrin,
chlordane, dieldrin, endrin, heptachlor, etc. [107].
3.5.2 Framework Convention on Climate Change (FCCC)
In May 1992, this Framework Convention on Climate Change (FCCC) was adopted in
the United Nations Headquarters in New York. The purpose behind this convention on
climate change was to set an overall framework to tackle the challenge posed by the
58
climate change. Under this convention‘s framework, nations of the world share
information on greenhouse gas emissions, adoptable policies and best practices.
Furthermore, the agreed nations launch suitable strategies for consideration of
greenhouse gases and adopting them according to the accepted impacts [108].
3.5.3 Kyoto Protocol
The Kyoto Protocol is the ratification of the United Nations Framework Convention on
Climate Change (UNFCCC). This is a commitment of member nations to reduce the
greenhouse emissions in a phased out way. The Kyoto Protocol‘s first commitment
period started in year 2008 and ended in year 2012. The second commitment phase
started at the end of year 2012 under the name of Doha Agreement. Up to now as in July
2015, thirty six member nations of United Nations have accepted the Doha Agreement
whereas for its acceptance 144 nations acceptance is needed [109].
3.5.4 Montreal Protocol
The Montreal Protocol is an international agreement aimed to protect the ozone layer by
phasing out variety of ozone depleting substances (ODS) with the implementation of this
agreement, it is being observed now that, the ozone hole is slowly started to restore. If
this recovery is continued, then it is certain that the ozone hole will recover to 1980
levels between 2050 and 2070. The findings are showing that with the implementation of
Montreal Protocol, the atmospheric concentrations of famous chlorofluorocarbons
(CFCs) are decreasing. The United States EPA has predicted that this decrease in ozone
depleting substances from atmosphere will prevent over 280 million cases of skin
cancer, 1.5 million skin cancer deaths and 45 million cataracts cases in the United States
only [110].
3.5.5 U.S – Canada Bilateral Agreement on Acid Rain
The U.S – Canada Bilateral Agreement on Acid Rain is a bilateral agreement and is also
called as Acid Rain Treaty. This treaty was made in March 1991 with the objectives of
preventing trans-boundary air pollution and resultant acid rain in their territories. This
59
agreement came into force immediately as the President of the United States and the
Prime Minister of Canada signed it. The signing parties agreed that air pollution can
cause irreparable damage to the natural environment, impact human health and hamper
the economic development. That is why it is essential for both the parties to check and
limit the emitting air pollutants and cooperate and coordinate if any issue of trans-
boundary air pollution arises [111].
3.6 AMBIENT AIR QUALITY
The ambient air is that outdoor environment in which the humans and organism perform
breathing. This ambient air comprises of extremely variable and complicated mixture of
different substances occurring in gases, liquid or solid states. Hundreds of substances
may be found in the ambient air. However, the relevant government authorities like
environmental protection agencies or environmental protection councils who so ever are
mandated have to develop critical or priority parameters which are used for the
monitoring of the air quality. If these selected parameters represent lower concentrations
of the relevant pollutants then it is understood that the ambient air quality is good and
conducive for humans and organisms survival. If the focused parameters are found
higher in concentrations then it is understood that the ambient air quality is not up to the
mark and needs improvement.
3.7 AMBIENT AIR QUALITY STANDARDS
In order to determine the acceptable levels of each air pollutant parameter it is essential
to detect out the tolerance limits of that respective air pollutant on the human health and
natural environment. The scientists and researchers after extensive investigation and
research have found out permissible levels of each air pollutant and they are now chosen
as air quality standards. The World Health Organization (WHO) has got the mandate to
monitor, investigate and coordinate with member nations about the life threatening
issues around the world. In the area of air pollution too the WHO has set ambient air
quality guidelines for the facilitation of the member nations. Accordingly the member
60
nations adopt these WHO guidelines and set them according to their circumstances and
situations [112].
3.8 MONITORING OF AMBIENT AIR QUALITY
Monitoring has become effective methodology to detect out the variance or increment in
the emitted air pollutants in the atmosphere. If it is found in an area that the air
pollutants‘ concentrations are higher than the permissible levels, then the state of the
ambient air needs immediate improvement. As the increase is detected the respective
regulatory agency initiates measures like rescheduling of traffic flow if the traffic is the
cause of increase or closure of industrial activities for short duration in the zones. The
emitting air pollutants are targeted so that the population exposures can be effectively
reduced. The main air emitting sources are focused and the respective management of
the polluting organization is advised to incorporate most preferable options for the
control of emissions or if still the situation is not controlled then closure of the industries
in that affected zone is adopted for such time period till the situation of ambient air
quality is restored or brought down up to the acceptable levels.
3.9 STATUS OF AIR POLLUTION IN MAJOR CITIES OF THE
WORLD
Every day, hundreds of millions of people step outside into an environment that has
become unsafe for human survival. Outdoor air pollution kills 3.3 million people every
year, mostly in cities; more than HIV, malaria and influenza combined. Hospitals don‘t
record air pollution as a cause of death. It manifests through an increase in already
prevalent human heart and lung diseases. Therefore its impact can only be assessed by
taking samples directly from the air.
The WHO takes information from monitoring stations in more than 1,600 cities on every
populated continent as shown in Figure 3.1. The figure comes from Beijing-based
environmental group ―Air Quality Index China‖, which worked with environmental
protection agencies in more than 70 countries. It continuously collects data from more
61
than 5,900 feeds coming from more than 8,000 air-quality-monitoring stations in more
than thousand cities. Only feeds from government agencies are used for the update
and the map refreshes after every 15 minutes.
Figure 3.1: Status of Air Pollution in major cities of the world
The cities marked with red colour indicate severe ambient air pollution status, the cities
marked with brown colour indicate moderate ambient air pollution status and the cities
marked with green colour indicate cleaner status. This sounds comprehensive air
pollution status of the cities in the world but less than a third of cities of over 100,000
people. On the other hand not all the big cities are polluted cities as they monitor and
maintain the air quality within the permissible levels.
Of the 1,622 cities covered in the WHO data, 510 are in two countries – the US and
Canada. Just 16 are in Africa (half of these in relatively wealthy South Africa and
Egypt). Latin America‘s 604 million people are among the most heavily urbanized on
earth. Their air is monitored in 109 cities. Across the Middle East, data is collected in
just 24 cities.
Oxford Street in London was named the most polluted road in the world for NO2. The
silver lining to Delhi‘s smog is forcing politicians and government agencies to act. The
62
government is seeking to introduce tougher regulations on vehicle makers and build a
bypass around the city. In recent research in China it was found that 94% of adults
believe air pollution is a critical problem and the government should prioritize to solve
it. As a result, substantial action has already been taken. The Beijing government has
launched a program of alternate driving days based on license plate numbers and coal
power plant shutdowns. Not the least of China‘s responses has been the development of
mass monitoring of ambient air pollution. Hourly warning systems have lead Chinese
pedestrians wearing masks to protect themselves on days when an alert is sounded.
3.10 STATUS OF AIR POLLUTION IN THE CITIES OF
PAKISTAN
Like other mega and medium size cities of the world, Pakistan‘s cities also experience
higher concentration of air pollutants in the urban environment. The random
measurements carried out by various researchers and organizations indicate that air
pollution level is considerably higher than the WHO air quality guidelines as well as
ambient air quality standards of Pakistan. Pakistan Environmental Protection Agency
(Pak-EPA) has conducted a study to investigate the ambient air quality in the major
cities of Pakistan. Some of these studies were technically and financially supported by
the Japan International Cooperation Agency (JICA). All these studies revealed that the
number one problem relevant to the ambient air quality is the Particulate Matter (PM).
PM (both TSP and PM10) concentration in all major cities is extremely high. According
to the Pak-EPA/JICA three cities investigation report1, very high concentration of TSP
and PM10 has been recorded at Lahore, Rawalpindi and Islamabad. The concentration of
SPM in these cities is 4.4 to 7.5 times higher than WHO Guidelines. The second
emerging air pollutant in Pakistan is nitrogen oxide. Pak-EPA has measured NO2 in
major cities Karachi, Lahore, Peshawar, Islamabad and Quetta to determine its
concentration level so that the future strategy could be chased out to safeguard the public
from its adverse effect. The highest concentration of NO2 was found in Karachi and then
descending to Lahore, Quetta, Peshawar and Islamabad. It reflects the high density of
traffic locations in all five cities by averaging the all NO2 values, Karachi and Lahore
63
have shown the similar average concentration of NO2 i.e., 76µg/m3. The average
concentration of NO2 in Quetta, Peshawar and Islamabad were 69.50, 47.28 and
30.41µg/m3 respectively. The least minimum value of NO2 in Islamabad was found in
the residential area around embassy road, which was 11.65µg/m3. The highest
concentration of NO2 399.65µg/m3 was found at Karimabad Junction in Karachi.
64
CHAPTER NO. 4
MATERIALS AND METHODS
4.1 INTRODUCTION
The materials and methods adopted for examination and prediction of air pollutants were
described. The selection of sampling locations, criteria of sites, instruments employed
and their calibration techniques were discussed. The method of data collection, analysis
and interpretation is provided.
4.2 RESEARCH WORK AREA
In this research study four cities of Sindh province were considered for measuring
ambient air quality. The air quality data collection locations in four cities, Karachi,
Hyderabad, Nawabshah and Sukkur and are schematically shown in figure 4.1, figure
4.2, figure 4.3 and figure 4.4 respectively. The logic behind selecting these cities is that
the Karachi is a mega city having very high population and large establishment of
industrial and vehicular settlements. Hyderabad is taken in this research study as it
accommodates moderate population settlement, vehicular and industrial establishments.
While both Nawabshah and Sukkur has got lower population and vehicular and
industrial settlements. These cities have shown enormous growth trends in population,
number of vehicles and establishment of industries in Karachi in particular. The reasons
behind the population growth can be attributed to high population growth rate and shift
in rural population to the urban area for better livelihood sources and job opportunities.
As for as increased number of industries in these cities is concerned it is due to the
economic development of the country as a whole and being coastal city in case of
Karachi.
64
65
Figure 4.1: Schematic air quality measurement sampling location map of Karachi
Figure 4.2: Schematic air quality measurement sampling location map of
Hyderabad
66
Figure 4.3: Schematic air quality measurement sampling location map of
Nawabshah
Figure 4.4: Schematic air quality measurement sampling location map of Sukkur
67
4.3 DATA COLLECTION LOCATIONS
Selection of data collection locations is most important decision in any research study.
Since this was an extensive research study so it was highly important to select suitable
numbers of the data collection points as well as proper location of it. The numbers of
location selected in this study are Karachi 20 locations, Hyderabad 15 locations,
Nawabshah 10 locations and Sukkur 10 locations. The names of these locations are
given in Table 4.1. From all the selected locations the air quality parameters were
recorded for thirty times at different timings. At each location about ninety minutes time
was needed to monitor and record the stable result of the relevant parameter.
Table 4.1: List of names of data collection locations
S.
No
Name of Location
Karachi Hyderabad Nawabshah Sukkur
1 Al Asif Square Hala Naka New Naka Old Sukkur
2 North Nazimabad Hyderabad By-Pass PMU Lab e Mehran
3 Nursary Chowk Nasim Nagar Shalimar Bus
Stand
High Court Road
4 Star Gate City Gate Mohni Bazar Eid Gah Road
5 Tower Market Tower Sabzi Mandi Station Road
6 Maripur Road Tilk Incline Railway Station SITE Area
7 Shershah Station Road Bucheri Road Canal Road
8 Civil Hospital Gari Khata Chowk Habib Sugar Mills Hamdard Society
9 Numaish Chorangi Badin Stop Society Chowk Airport Road
10 Do Talwar SITE Area QUEST Shikarpur Road
11 Clifton Press Club ….. …..
12 Sea View Gul Center ….. …..
13 Korangi Crossing Hussainabad ….. …..
14 Brooks Chorangi Latifabad No. 07 ….. …..
15 National Refinery
Chorangi
Latifabad No. 12 ….. …..
16 Dawood Chorangi ….. ….. …..
17 Millinium Mall ….. ….. …..
18 Johar Complex ….. ….. …..
19 Karachi University ….. ….. …..
20 Gulshan e Hadeed ….. ….. …..
68
4.4 QUALITY ASSURANCE IN AIR QUALITY DATA
COLLECTION
Ambient air quality is analyzed through air quality analysis equipment. The analysis is
based on measuring the air pollutants through respective sensors. These sensors are fixed
in the relevant equipment in the environmental monitoring stations. The monitoring
stations may be fixed in temporary structures, permanent structures or random analysis is
performed. In this research work of air quality data measurement, quality assurance air
pollution system guidelines were adopted to achieve the authenticated data. Performing
an ambient air monitoring program and its execution is not considered to be an easy task.
However, appropriate analysis and maintenance of equipment can help in smooth
working [113]. For this smooth working following arrangement can be found suitable.
4.4.1 Life Span of Equipment
Every equipment has got expected life span and if it is not within the specified life span
period then the air quality data obtained may not be relied. According to the international
standards (U.S EPA, 2013) for amortization purpose a life span of seven to ten years is
considered acceptable. Though the equipment life span may be prolonged but in doing
so there is possibility of additional downtime, more up-keep and chances of invalid data
generation.
4.4.2 Primary Set-up and Acceptance Testing
It is important for carrying out the air quality measurement to perform primary set-ups
and seek acceptable testing in the laboratory facility before using the equipment for site
location data recording. The relevant equipment are set ready by receiving operations
processes. All the accessories are checked upon and verified from any inequality. If the
tested results indicate smooth functioning then the equipment are very much ready to be
carried away for field or site locations data recording else the equipment may need
proper repair or maintenance.
69
4.4.3 Equipment Calibrations
The equipment calibration is another essential job to be carried out before using the
equipment for air quality data recording. The calibration methods are specified by the
manufacturers and are based on several functions. Some of the functions of calibrations
may include; mass flow controlling, permeation devices, voltage standards, photometers,
pressure standards and temperature standards. When all such admissible functions are
performed only then the equipment is found satisfactory and ready for field operations.
4.5 EQUIPMENT USED FOR DATA COLLECTION AND
METHOD
Selection of equipment is key issue to obtain reliable and authenticate results of the
decided parameters. For this ambient air quality research relevant portable and latest
equipment were chosen. All the selected air quality parameters were recorded after
applying proper calibration of the subject equipment and by following the standard
methods and procedures for analysis. The details of the equipment used are described
below one by one.
4.5.1 Particulate Matter (P.M) Meter
For the measurement of P.M2.5 and P.M10, the Aerocet 5315 equipment was used and is
shown in figure 4.1. This equipment is small, light weight, battery operated and
completely portable. This equipment is capable to measure six different size categories
of the dust particles i.e. P.M1, P.M2.5, P.M4, P.M7, P.M10 and TSP. This equipment
records the readings as particles counts or mass particles concentrations. The operating
principle is based on counting of individual particles using scattered laser light and
calculate the equivalent mass concentration by using proprietary algorithm. The
equipment is capable to measure the particles concentrations from 0.0µg/m3 to
1000µg/m3.
70
Figure 4.5: Model Aerocet 5315 Particulate Matter (P.M) Meter
4.5.2 Carbon dioxide and carbon monoxide analyzer
For the measurement of carbon dioxide and carbon monoxide, IAQ Air Quality Meter
Model 7545 equipment shown in figure 4.2 was used. The recorded data is displayed on
the screen and can be easily transferred to a computer. The equipment is based on low
drift carbon dioxide and carbon monoxide sensors for stable and accurate readings. This
equipment is smaller in portable, chargeable battery operated and user friendly.
Figure 4.6: Model IAQ 7545 Carbon dioxide and Carbon Monoxide
Analyzer
71
4.5.3 Oxides of Nitrogen Analyzer
For the measurement of oxides of nitrogen, Model T-200 of Teledyne systems
equipment was used in this research work. The equipment uses the chemiluminescence
detection principle, which allows it to detect the oxides of nitrogen at very
concentrations accurately from the ambient air. The equipment is capable to determine
NO, NO2 and NOx. The equipment offers an advanced colour display, user friendly
touch screen and interface. The measurement range is also supported by independent
ranges to auto range.
Figure 4.7: Model T-200 Oxides of Nitrogen Analyzer
4.5.4 Oxides of Sulfur Analyzer
For the measurement of oxides of sulfur, Model T-101 of Teledyne equipment was used.
The principle of working of this equipment is based on proven UV fluorescence to
measure oxides of sulfur at levels commonly required for measuring air quality. The
instrument is equipped with advanced colour display, user friendly touch screen and
interface. The equipment set-ups control and access to stored data is available through
the front panel. The Data measurement range is also supported by independent ranges to
auto range.
Figure 4.8: Model T-101 Oxides of Sulfur Analyzer
72
CHAPTER NO. 5
RESULTS AND DISCUSSIONS
5.1 INTRODUCTION
In this chapter, the results obtained using at various locations of selected cities is
illustrated. The results are tabulated in tables and represented through figures. The
results are divided into four sections based of cities like Karachi, Hyderabad,
Nawabshah and Sukkur.
5.2 RESULTS AND DISCUSSIONS OF KARACHI CITY
In this research study of ambient air quality the measured data of each location at
different timings of the day was used for the calculation of average value of that
location. This average value of air pollutants at twenty locations of Karachi city are
given in following figures.
Figure-5.1 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Al Asif Square location of Karachi city. The Al Asif Square location is
at such a position where heavy traffic congestion is found even at late night as buses,
coaches and wagons depart from this area to far away country-wide locations. This area
is also unpaved and loose dirt and dust can be seen here and there. In addition to this
solid waste is also thrown without proper care. All such circumstances cause much
higher levels of particulate and gaseous pollutants. Particularly the average recorded
value of P.M2.5 and P.M10 are 80.5µg/m3
and 325.56µg/m3
as against the permissible
NEQS level. The average concentration of carbon dioxide and carbon monoxide was
also found higher 423.13ppm and 10.91ppm respectively than the NEQS level. However
the average concentration of NOx and SOx was found 79.36µg/m3
and 63.80µg/m3
respectively as against the NEQS which are at the borderline and may increase if the air
pollution causing sources start emitting some more pollutants.
72
73
Figure 5.1: Minimum, maximum and average value of air pollutants at Al Asif
Square
Figure 5.2 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at North Nazimabad location of Karachi city. The location of North
Nazimabad is at a position where moderate traffic as well as residential settlement exists.
Here at this location traffic congestion is moderate during the day time up to the late
hours. At this location the average recorded value of P.M2.5 and P.M10 are 71.33µg/m3
and 293.00µg/ m3
respectively as against the permissible NEQS level. The average
concentration of carbon dioxide and carbon monoxide was found higher 417.73ppm and
10.55ppm respectively than the NEQS level. The average concentration of NOx was
found higher i.e. 85µg/ m3 than the permissible NEQS level. However the average SOx
concentration was found 72.06µg/ m3 as against the NEQS which is lower than the
permissible NEQS level.
Figure 5.2: Minimum, maximum and average value of air pollutants at North
Nazimabad
0100200300400500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Al Asif Square Karachi Minimum
Maximum
Average
NEQS Level
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at North Nazimabad Karachi Minimum
Maximum
Average
NEQS Level
74
Figure 5.3 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Nursary location of Karachi city. The location of Nursary is at a
position where moderate traffic as well as residential settlement exists. At Nursary
location traffic congestion was found moderate during the day time up to the late hours.
At this location the average recorded value of P.M2.5 and P.M10 are 74.80µg/m3 and
271.36µg/m3 respectively as against the permissible NEQS level. The average
concentration of carbon dioxide and carbon monoxide was found higher 418.03ppm and
09.97ppm respectively than the NEQS level. The average concentration of NOx was
found higher i.e. 83.60µg/m3 than the permissible NEQS level. However the average
SOx concentration was found 69.9µg/m3 as against the NEQS which is lower than the
permissible NEQS level.
Figure 5.3: Minimum, maximum and average value of air pollutants at Nursary.
Figure 5.4 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Star Gate location of Karachi city. The location of Star Gate is at a
position where moderate traffic as well as residential settlement having tall buildings
exists. At Star Gate location traffic congestion is moderate during the day time up to the
late hours. At this location the average recorded value of P.M2.5 and P.M10 are
67.53µg/m3 and 228.2µg/m
3 respectively as against the permissible NEQS level. The
average concentration of carbon dioxide and carbon monoxide was found higher
413.46ppm and 09.50ppm respectively than the NEQS level. The average concentration
of NOx was found 73.40µg/m3 that is within the permissible NEQS level. Also the
average SOx concentration was found 57.26µg/m3 as against the NEQS which is lower
than the permissible NEQS level.
0
200
400
600
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Nursary, Karachi MinimumMaximumAverageNEQS Level
75
Figure 5.4: Minimum, maximum and average value of air pollutants at Nursary
At these four locations given in table 5.1 in Appendix-A indicate that average
concentration of P.M2.5 and P.M10 at all locations was higher. Similarly the average
concentration of carbon dioxide and carbon monoxide was higher at all four locations.
However in case of NOx it is higher than the permissible NEQS at North Nazimabad,
Nursary and Star Gate locations and lower than NEQS at Al Asif Square location. While
the average concentration of SOx was found lower than NEQS at all the four locations.
Figure 5.5 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Tower location of Karachi city. The Tower location is at such a
position where light traffic congestion is found even at late night as mini buses,
rickshaws, cars and motor cycles ply in the area. The average recorded value of P.M2.5
and P.M10 are 85.63µg/m3 and 307.50µg/m
3 as against the permissible NEQS level. The
average concentration of carbon dioxide and carbon monoxide was also found higher
417.00ppm and 10.34ppm respectively than the NEQS level. The average concentration
of NOx was found higher 82.50µg/m3 as against the NEQS level. However, the average
concentration of SOx was 70.90µg/m3 that is less than NEQS permissible level.
Figure 5.5: Minimum, maximum and average value of air pollutants at Tower
0
200
400
600
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Star Gate Karachi Minimum
Maximum
Average
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Tower Karachi MinimumMaximumAverageNEQS Level
76
Figure 5.6 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Maripur Road location of Karachi city. The Maripur Road location is
at such a position where heavy traffic congestion is found even at late night as trailers,
long tankers, car carriers, trucks, mini buses and cars ply on this road of the area. The
average recorded value of P.M2.5 and P.M10 are 102.90µg/m3 and 334.46µg/m
3 as against
the permissible NEQS level. The average concentration of carbon dioxide and carbon
monoxide was found higher 425.05ppm and 12.05ppm respectively than the NEQS
level. The average concentration of NOx was also found higher 81.83µg/m3 as against
the NEQS level. However, the average concentration of SOx was 66.60µg/m3 that is less
than NEQS permissible level.
Figure 5.6: Minimum, maximum and average value of air pollutants at Maripur
Road.
Figure 5.7 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Shershah location of Karachi city. The Shershah location is at such a
position where moderate traffic congestion is found up to late night as long vehicles,
trucks, mini buses and cars ply on this road of the area. The average recorded value of
P.M2.5 and P.M10 are 91.13µg/m3
and 289.56µg/m3
as against the permissible NEQS
level. The average concentration of carbon dioxide and carbon monoxide was found
higher 425.00ppm and 09.15ppm respectively than the NEQS level. The average
concentration of NOx was also found higher 80.10µg/m3
as against the NEQS level.
However, the average concentration of SOx was 55.96µg/m3
that is less than NEQS
permissible level.
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Maripur Road Karachi MinimumMaximumAverageNEQS Level
77
Figure 5.7: Minimum, maximum and average value of air pollutants at Shershah
Figure 5.8 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Civil Hospital location of Karachi city. The Civil Hospital location is
at such a position where moderate traffic congestion is found up to late night as mini
buses, rickshaws, motor cycles and cars ply on this road of the area. Here it is also found
that the solid waste is openly dumped and later on this waste is burnt that emit out
smoldering smoke. The average recorded value of P.M2.5 and P.M10 are 62.4µg/m3 and
254.46µg/m3 as against the permissible NEQS level. The average concentration of
carbon dioxide was found higher 408.53ppm as against the NEQS level. The average
concentration of carbon monoxide, NOx and SOx was 08.34ppm, 57.23µg/m3 and
50.53µg/m3 respectively than their respective permissible NEQS level.
Figure 5.8: Minimum, maximum and average value of air pollutants at Civil
Hospital.
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Shershah Karachi MinimumMaximumAverageNEQS Level
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Civil Hospital Karachi MinimumMaximumAverageNEQS Level
78
The average concentration of P.M2.5, P.M10, and carbon dioxide at all four locations as
shown in Appendix-A in Table 5.2 is higher than the permissible NEQS levels. The
average concentration of carbon monoxide at Tower, Maripur Road and Shershah
locations are higher and at Civil Hospital location it is lower than the permissible NEQS
levels. The average concentration of NOx at Tower, Maripur Road and Shershah
locations was higher and lower at Civil Hospital location than the permissible NEQS
levels. However the average concentration of SOx was lower than permissible NEQS
level at all the four locations.
Figure 5.9 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Numaish Chorangi location of Karachi city. The Numaish Chorangi
location is at such a position where moderate traffic congestion is found up to late night
as mini buses, rickshaws, motor cycles and cars ply on this road of the area. The average
recorded value of P.M2.5 and P.M10 are 64.73µg/m3 and 254.46µg/m
3 as against the
permissible NEQS level. The average concentration of carbon dioxide was found higher
408.53ppm as against the NEQS level. The average concentration of carbon monoxide,
NOx and SOx was 08.34ppm, 57.23µg/m3 and 50.53µg/m
3 respectively than their
respective permissible NEQS level.
Figure 5.9: Minimum, maximum and average value of air pollutants at Numaish
Chorangi
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Numaish Chorangi Karachi Minimum
Maximum
Average
NEQS Level
79
Figure 5.10 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Do Talwar location of Karachi city. The Do Talwar location is at
position where moderate traffic congestion is found up to late night as mini buses,
rickshaws, motor cycles and cars ply on this road of the area along with tall residential
buildings. The average recorded value of P.M2.5 and P.M10 are 52.20µg/m3 and
219.00µg/m3 as against the permissible NEQS level. The average concentration of
carbon dioxide was found higher 409.43ppm as against the NEQS level. The average
concentration of carbon monoxide, NOx and SOx was 08.21ppm, 54.83µg/m3 and
46.73µg/m3 respectively than their respective permissible NEQS level.
Figure 5.10: Minimum, maximum and average value of air pollutants at Do Talwar
Figure 5.11 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Clifton location of Karachi city. The Clifton location is at a place
where moderate traffic congestion is found up to late night as mini buses, rickshaws,
motor cycles and cars ply on this road of the area along with tall residential buildings.
The average recorded value of P.M2.5 and P.M10 are 56.63µg/m3 and 216.80µg/m
3 as
against the permissible NEQS level. The average concentration of carbon dioxide was
found higher 413.76ppm as against the NEQS level. The average concentration of
carbon monoxide, NOx and SOx was 07.87ppm, 55.53µg/m3 and 46.50µg/m
3
respectively than their respective permissible NEQS level.
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Do Talwar Karachi MinimumMaximumAverageNEQS Level
80
Figure 5.11: Minimum, maximum and average value of air pollutants at Clifton.
Figure 5.12 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Sea View location of Karachi city. The Sea View location is a location
alongside the sea where one side sea side is there and on the other side open residential
settlement exists. At this location low traffic congestion is found up to late night as
rickshaws, motor cycles and cars ply on this road of the area. The average recorded
value of P.M2.5, P.M10, carbon dioxide, carbon monoxide, NOx and SOx are 28.16µg/m3,
96.73µg/m3, 378.30ppm, 3.3ppm, 18.76µg/m
3 and 23.56µg/m
3 respectively as against
the permissible NEQS level.
Figure 5.12: Minimum, maximum and average value of air pollutants at Sea View
The average concentration of P.M2.5, P.M10, and carbon dioxide at three locations of
Numaish Chorangi, Do Talwar and Clifton locations in Table 5.3 in Appendix-A was
higher than the permissible NEQS levels. The average concentration of carbon monoxide,
NOx and SOx at all these four locations was lower than the permissible NEQS levels.
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Clifton Karachi Minimum
Maximum
Average
NEQS Level
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Sea View Karachi MinimumMaximumAverageNEQS Level
81
The results show that the Sea View location is free from the air pollutants because on
one hand the traffic load is less and on the other hand the wind from sea side transport
the emitted and persisting air pollutants towards the north direction over the city side.
Figure 5.13 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Korangi Crossing location of Karachi city. The Korangi Crossing
sampling location is at a position where on one side there exist different types of
industries and on the other side scattered residential settlement exists along with
moderate traffic congestion like mini buses, rickshaws, motor cycles and cars ply in this
area. The average recorded value of P.M2.5 and P.M10 are 63.30µg/m3 and 250.06µg/m
3
as against the permissible NEQS level. The average concentration of carbon dioxide and
carbon monoxide was found 418.26ppm and 12.4ppm respectively and was higher than
the NEQS level. The average concentration of NOx and SOx was 57.63µg/m3 and
43.73µg/m3 respectively than their respective permissible NEQS level.
Figure 5.13: Minimum, maximum and average value of air pollutants at Korangi Crossing
Figure 5.14 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Brooks Chorangi location of Karachi city. The Brooks Chorangi
sampling location is at a position where there exist different types of industries along
with moderate traffic congestion like trailers, mini buses, rickshaws, motor cycles and
cars ply in this area. The average recorded value of P.M2.5 and P.M10 are 95.46µg/m3 and
287.03µg/m3 as against the permissible NEQS level. The average concentration of
carbon dioxide and carbon monoxide was found 427.30ppm and 10.40ppm respectively
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Korangi Crossing Karachi Minimum
Maximum
Average
NEQS Level
82
and was higher than the NEQS level. The average concentration of NOx and SOx was
77.73µg/m3
and 47.80µg/m3
respectively than their respective permissible NEQS level.
Figure 5.14: Minimum, maximum and average value of air pollutants at Brooks
Chorangi
Figure 5.15 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at National Refinery Chorangi location of Karachi city. The National
Refinery Chorangi sampling location is at a position where there exist different types of
small to mega industries along with moderate traffic congestion like trailers, mini buses,
rickshaws, motor cycles and cars ply in this area. The average recorded value of P.M2.5
and P.M10 are 96.23µg/m3 and 289.16µg/m
3 as against the permissible NEQS level. The
average concentration of carbon dioxide and carbon monoxide was found 419.26ppm
and 11.01ppm respectively and was higher than the NEQS level. The average
concentration of NOx 82.73µg/m3 was higher and SOx 43.86µg/m
3 was lower than their
respective permissible NEQS level.
Figure 5.15: Minimum, maximum and average value of air pollutants at National
Refinery Chorangi
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Brooks Chorangi Karachi MinimumMaximumAverageNEQS Level
0100200300400500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at National Refinery Chorangi Karachi MinimumMaximumAverageNEQS Level
83
Figure 5.16 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Dawood Chorangi location of Karachi city. The Dawood Chorangi
sampling location is at a position where there exist different types of small to mega
industries along with moderate traffic congestion like trailers, mini buses, rickshaws,
motor cycles and cars ply in this area. The average calculated from recorded data of
P.M2.5 and P.M10 are 96.36µg/m3
and 287.83µg/m3
as against the permissible NEQS
level. The average concentration of carbon dioxide and carbon monoxide was found
438.00ppm and 12.40ppm respectively and was higher than the NEQS level. The
average concentration of NOx 83.73µg/m3
was higher and SOx 38.80µg/m3
was lower
than their respective permissible NEQS level.
Figure 5.16: Minimum, maximum and average value of air pollutants at Dawood
Chorangi
The average concentration of P.M2.5, P.M10, carbon dioxide and carbon monoxide at all
four locations of Korangi Crossing, Brooks Chorangi, National Refinery Chorangi and
Dawood Chorangi as given in Table 5.4 in Appendix-A were higher than the permissible
NEQS levels. In case of NOx the average concentration of it was high at National
Refinery Chorangi and Dawood Chorangi and low at Korangi Crossing and Brooks
Chorangi than the permissible NEQS level. However SOx at all these four locations was
lower than the permissible NEQS levels.
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Dawood Chorangi Karachi Minimum
Maximum
Average
NEQS Level
84
Figure 5.17 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Millennium Mall location of Karachi city. The Millennium Mall
sampling location is at a position where tall multi-storied residential buildings exists
along with heavy traffic congestion like mini buses, rickshaws, motor cycles and cars ply
in this area. The average recorded value of P.M2.5 and P.M10 are 84.10µg/m3
and
279.03µg/m3
as against the permissible NEQS level. The average concentration of
carbon dioxide and carbon monoxide was found 416.26ppm and 9.17ppm respectively
and was higher than the NEQS level. The average concentration of NOx was
80.36µg/m3
higher and SOx was 39.30µg/m3
lower than their respective permissible
NEQS level.
Figure 5.17: Minimum, maximum and average value of air pollutants at
Millennium Mall.
Figure 5.18 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Johar Complex location of Karachi city. The Johar Complex sampling
location is at a position where tall multi-storied residential buildings exists along with
heavy traffic congestion of mini buses, rickshaws, motor cycles and cars ply in this area.
The average recorded value of P.M2.5 and P.M10 are 77.86µg/m3 and 266.36µg/m
3 as
against the permissible NEQS level. The average concentration of carbon dioxide was
found 422.83ppm higher than and carbon monoxide was found 8.92ppm lower than their
respective NEQS level. The average concentration of NOx was 64.93µg/m3 and SOx
was 39.76µg/m3 both lower than their respective permissible NEQS level.
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Millennium Mall Karachi MinimumMaximumAverageNEQS Level
85
Figure 5.18: Minimum, maximum and average value of air pollutants at Johar
Complex
Figure 5.19 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Karachi University location of Karachi city. The Karachi University
sampling location is at a position where tall multi-storied residential buildings exists
along with heavy traffic congestion of mini buses, rickshaws, motor cycles and cars ply
in this area. The average recorded value of P.M2.5 and P.M10 are 68.53µg/m3 and
258.83µg/m3 as against the permissible NEQS level. The average concentration of
carbon dioxide was found 419.66ppm higher than and carbon monoxide was found
8.29ppm lower than their respective NEQS level. The average concentration of NOx was
54.2µg/m3 and SOx was 40.30µg/m
3 both lower than their respective permissible NEQS
level.
Figure 5.19: Minimum, maximum and average value of air pollutants at Karachi
University
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Johar Complex Karachi MinimumMaximumAverageNEQS Level
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Karachi University Karachi Minimum
Maximum
Average
NEQS Level
86
Figure 5.20 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Gulshan e Hadeed location of Karachi city. The Gulshan e Hadeed
sampling location is at a position where residential buildings exists along with moderate
traffic congestion of mini buses, rickshaws, motor cycles and cars ply in this area. The
average recorded value of P.M2.5 and P.M10 are 65.40µg/m3 and 234.66µg/m
3 as against
the permissible NEQS level. The average concentration of carbon dioxide was found
409.70ppm higher than and carbon monoxide was found 7.79ppm lower than their
respective NEQS level. The average concentration of NOx was 43.60µg/m3 and SOx
was 29.13µg/m3 both lower than their respective permissible NEQS level.
Figure 5.20: Minimum, maximum and average value of air pollutants at Gulshan e
Hadeed
The average concentration of P.M2.5, P.M10, and carbon dioxide at all four locations of
Millennium Mall, Johar Complex, Karachi Universityand Gulshan e Hadeed as given in
Table 5.5 in Appendix-A were higher than the permissible NEQS levels. In case of
carbon monoxide and NOx they are higher at Millennium Mall and low at the rest of
three sampling locations in comparison to their respective permissible NEQS level.
However SOx at all these four locations was lower than the permissible NEQS levels.
5.3 RESULTS AND DISCUSSIONS OF HYDERABAD CITY
The calculated average value of measured air pollutants at fifteen locations of
Hyderabad city are given in following figures.
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Gulshan e Hadeed Karachi Minimum
Maximum
Average
NEQS Level
87
Figure 5.21 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Hala Naka of Hyderabad city. The Hala Naka sampling location is at a
position where residential buildings with narrow roads exists along with heavy traffic
congestion like mini buses, buses, trucks, vans, rickshaws, motor cycles and cars ply in
this area. The average recorded value of P.M2.5 and P.M10 are 43.40µg/m3 and
224.46µg/m3 that are higher as against the permissible NEQS level. The average
concentration of carbon dioxide was 402ppm and was higher than the permissible NEQS
level. However the average concentration of carbon monoxide, NOx and SOx were
3.88ppm, 33.53µg/m3 and 49.00µg/m
3 respectively and were lower than the permissible
NEQS level.
Figure 5.21: Minimum, maximum and average value of air pollutants at Hala Naka
Figure 5.22 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Hyderabad By-Pass location. Hyderabad By-Pass sampling location is
at a position where very heavy traffic plies for up-country destinations with small strip
of residential buildings. The heavy traffic congestion includes long trailers and tankers,
passenger coaches, mini buses, buses, trucks, vans, and cars that ply in this area. The
average recorded value of P.M2.5 and P.M10 are 84.90µg/m3 and 335.83µg/m
3 that are
higher as against the permissible NEQS level. The average concentration of carbon
dioxide was 408.30ppm and was higher than the permissible NEQS level. However the
average concentration of carbon monoxide, NOx and SOx were 4.28ppm, 35.96µg/m3
and 47.06µg/m3 respectively and were lower than the permissible NEQS level.
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Hala Naka Hyderabad MinimumMaximumAverageNEQS Level
88
Figure 5.22: Minimum, maximum and average value of air pollutants at
Hyderabad By-Pass
Figure 5.23 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at Nasim Nagar sampling location of Hyderabad city. The Nasim Nagar
sampling location is at a position where moderate traffic with residential buildings
settlements exists. The traffic congestion includes vans, rickshaws, motor cycles and
cars that ply in this area. In this area it was also observed that the roads are unpaved,
dust and solid waste was found scattered here and there in the vicinity. The average
recorded value of P.M2.5 and P.M10 are 39.16µg/m3 and 240.16µg/m
3 that are higher as
against the permissible NEQS level. The average concentration of carbon dioxide was
408.53ppm and was higher than the permissible NEQS level. However the average
concentration of carbon monoxide, NOx and SOx were 3.84ppm, 36.16µg/m3 and
48.63µg/m3 respectively and were lower than the permissible NEQS level.
Figure 5.23: Minimum, maximum and average value of air pollutants at Nasim
Nagar
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at By-Pass Hyderabad Minimum
Maximum
Average
NEQS Level
0
200
400
600
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of air pollutants at NN Hyderabad MinimumMaximumAverageNEQS Level
89
Figure 5.24 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the City Gate sampling location of Hyderabad city. The City Gate
sampling location is at a position where moderate traffic with residential buildings
settlements exists. The traffic congestion includes passenger buses and coaches, vans,
rickshaws, four seat three wheelers, motor cycles and cars that ply in this area. In this
area it was also observed that the roads are unpaved, dust and solid waste was found
scattered here and there in the vicinity. The average recorded value of P.M2.5 and P.M10
are 40.16µg/m3 and 342.70µg/m
3 that are higher as against the permissible NEQS level.
The average concentration of carbon dioxide was 416.63ppm and was higher than the
permissible NEQS level. However the average concentration of carbon monoxide, NOx
and SOx were 5.12ppm, 30.56µg/m3 and 50.16µg/m
3 respectively and were lower than
the permissible NEQS level.
Figure 5.24: Minimum, maximum and average value of air pollutants at City Gate
Figure 5.25 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Market Tower sampling location of Hyderabad city. The Market
Tower sampling location is at a position where thick traffic with residential buildings
settlements exists. The traffic congestion includes passenger buses, vans, rickshaws, four
seat three wheelers, motor cycles and cars that ply in this area. In this area it was also
observed that dust and solid waste was found scattered here and there in the vicinity. The
average recorded value of P.M2.5 and P.M10 are 37.50µg/m3 and 218.53µg/m
3 that are
higher as against the permissible NEQS level. The average concentration of carbon
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of air pollutants at CG Hyderabad Minimum
Maximum
Average
NEQS Level
90
dioxide was 407.06ppm and was higher than the permissible NEQS level. However the
average concentration of carbon monoxide, NOx and SOx were 6.50ppm, 37.60µg/m3
and 45.66µg/m3 respectively and were lower than the permissible NEQS level.
Figure 5.25: Minimum, maximum and average value of air pollutants at Market
Tower
The average concentration of P.M2.5, P.M10, and carbon dioxide at all five locations of
Hala Naka, Hyderabad By-Pass, Nasim Nagar, City Gate and Market Tower as shown in
Table 5.6 in Appendix-A were higher than the permissible NEQS levels. In case of
carbon monoxide, NOx and SOx at all the five locations they are within their respective
permissible NEQS level. Figure 5.26 shows measured air pollutants‘ calculated
minimum, maximum and average recorded value at the Tilk Incline sampling location of
Hyderabad city. The Tilk Incline sampling location is at a position where high rise
residential buildings surrounds the narrow roads along with heavy traffic congestion like
mini buses, four seat wheelers, rickshaws, motor cycles and cars ply in this area. The
average recorded value of P.M2.5 and P.M10 are 38.70µg/m3 and 257.00µg/m
3 that are
higher as against the permissible NEQS level. The average concentration of carbon
dioxide was 406.06ppm and was higher than the permissible NEQS level. However the
average concentration of carbon monoxide, NOx and SOx were 7.18ppm, 37.40µg/m3
and 48.60µg/m3 respectively and were lower than the permissible NEQS level.
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
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atio
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vel
Type of air pollutants
Concentration of air pollutants at MT Hyderabad Minimum
Maximum
Average
NEQS Level
91
Figure 5.26: Minimum, maximum and average value of air pollutants at Tilk
Incline
Figure 5.27 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Station Road sampling location of Hyderabad city. The Station
Road sampling location is at a position where high rise residential buildings surrounds
the narrow roads along with heavy traffic congestion like mini buses, four seat wheelers,
rickshaws, motor cycles and cars ply in this area. The average recorded value of P.M2.5
and P.M10 are 36.73µg/m3 and 289.10µg/m
3 that are higher as against the permissible
NEQS level. The average concentration of carbon dioxide was 411.23ppm and was
higher than the permissible NEQS level. However the average concentration of carbon
monoxide, NOx and SOx were 8.78ppm, 29.96µg/m3 and 43.13µg/m
3 respectively and
were lower than the permissible NEQS level.
Figure 5.27: Minimum, maximum and average value of air pollutants at Station
Road
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
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vel
Type of air pollutants
Concentration of air pollutants at TI Hyderabad Minimum
Maximum
Average
NEQS Level
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
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vel
Type of air pollutants
Concentration of air pollutants at SR Hyderabad Minimum
Maximum
Average
NEQS Level
92
Figure 5.28 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Gari Khata Chowk sampling location of Hyderabad city. The Gari
Khata Chowk sampling location is at a position where high rise residential buildings
surrounds the narrow roads along with heavy traffic congestion like mini buses, four seat
wheelers, rickshaws, motor cycles and cars ply in this area. The average recorded value
of P.M2.5 and P.M10 are 36.33µg/m3 and 300.26µg/m
3 that are higher as against the
permissible NEQS level. The average concentration of carbon dioxide was 410.70ppm
and was higher than the permissible NEQS level. However the average concentration of
carbon monoxide, NOx and SOx were 8.52ppm, 39.30µg/m3 and 49.43µg/m
3
respectively and were lower than the permissible NEQS level.
Figure 5.28: Minimum, maximum and average value of air pollutants at Gari
Khata Chowk
Figure 5.29 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Badin Stop sampling location of Hyderabad city. The Badin Stop
sampling location is at a position where residential buildings surrounds the main bus
terminal and narrow roads along with heavy traffic congestion like mini buses, four seat
wheelers, rickshaws, motor cycles and cars. From this location passenger coaches, buses
and vans depart to other cities and towns throughout the day up to late night. The
average recorded value of P.M2.5 and P.M10 are 38.96µg/m3 and 292.23µg/m
3 that are
higher as against the permissible NEQS level. The average concentration of carbon
dioxide was 414.90ppm and that of carbon monoxide was 9.31ppm and found higher
than their respective permissible NEQS level. However the average concentration of
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
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vel
Type of air pollutants
Concentration of air pollutants at GKC Hyderabad Minimum
Maximum
Average
NEQS Level
93
NOx and SOx were 38.80µg/m3 and 47.63µg/m
3 respectively and were lower than their
respective permissible NEQS level.
Figure 5.29: Minimum, maximum and average value of air pollutants at Badin Stop
Figure 5.30 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the SITE Area sampling location of Hyderabad city. The SITE Area
sampling location is at a position where different types of industrial units operate round
the clock. In the middle of the SITE area two main roads pass through on which heavy
traffic like large trailers and tankers, passenger buses and coaches, mini buses,
rickshaws, motor cycles and cars move on. The average recorded value of P.M2.5 and
P.M10 are 38.96µg/m3 and 299.50µg/m
3 that are higher as against the permissible NEQS
level. The average concentration of carbon dioxide was 409.46ppm and that of carbon
monoxide was 10.04ppm and found higher than their respective permissible NEQS level.
However the average concentration of NOx and SOx were 40.23µg/m3 and 48.96µg/m
3
respectively and were lower than their respective permissible NEQS level.
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
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vel
Type of air pollutants
Concentration of air pollutants at BS Hyderabad Minimum
Maximum
Average
NEQS Level
94
Figure 5.30: Minimum, maximum and average value of air pollutants at SITE
Hyderabad
The average concentration of P.M2.5, P.M10, and carbon dioxide at all five locations of
Tilk Incline, Station Road, Gari Khata Chowk, Badin Stop and SITE Area as given in
Table 5.7 in Appendix-A were higher than the permissible NEQS levels. In case of
carbon monoxide it was lower at Tilk Incline, Station Road and Gari Khata Chowk and
higher at Badin Stop and SITE Area sampling locations in comparison to their respective
permissible NEQS level. However NOx and SOx at all these five locations were lower
than the permissible NEQS levels.
Figure 5.31 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Press Club sampling location of Hyderabad city. The Press Club
sampling location is at a position where high rise residential buildings surrounds the
narrow roads along with heavy traffic congestion like vans, four seat wheelers,
rickshaws, motor cycles and cars ply in this area. The average recorded value of P.M2.5
was 34.40µg/m3 which is lower and P.M10 was 260.00µg/m
3 that was higher as against
their respective permissible NEQS level. The average concentration of carbon dioxide
was 403.50ppm and was higher than the permissible NEQS level. However the average
concentration of carbon monoxide, NOx and SOx were 7.14ppm, 30.03µg/m3 and
44.70µg/m3 respectively and found lower than their respective permissible NEQS level.
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of air pollutants at SITE Hyderabad Minimum
Maximum
Average
NEQS Level
95
Figure 5.31: Minimum, maximum and average value of air pollutants at Press Club
Hyderabad.
Figure 5.32 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Gul Center sampling location of Hyderabad city. The Gul Center
sampling location is at a position where high rise residential buildings surrounds the
narrow roads along with heavy traffic congestion vans, rickshaws, motor cycles and cars
ply in this area. The average recorded value of P.M2.5 and P.M10 are 38.53µg/m3 and
247.70µg/m3 that are higher as against the permissible NEQS level. The average
concentration of carbon dioxide was 405.50ppm and was higher than the permissible
NEQS level. However the average concentration of carbon monoxide, NOx and SOx
were 7.33ppm, 38.43µg/m3 and 47.70µg/m
3 respectively and found lower than their
respective permissible NEQS level.
0
50
100
150
200
250
300
350
400
450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of air pollutants at PC Hyderabad Minimum
Maximum
Average
NEQS Level
96
Figure 5.32: Minimum, maximum and average value of air pollutants at Gul
Center
Figure 5.33 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Hussainabad sampling location of Hyderabad city. The
Hussainabad sampling location is at a position where residential buildings surrounds the
narrow roads along with moderate traffic like vans, rickshaws, motor cycles and cars ply
in this area. The average recorded value of P.M2.5 was 32.70µg/m3 which was lower and
P.M10 was 274.4µg/m3 that was higher as against their respective permissible NEQS
level. The average concentration of carbon dioxide was 402.73ppm and was higher than
the permissible NEQS level. However the average concentration of carbon monoxide,
NOx and SOx were 7.31ppm, 34.40µg/m3 and 44.30µg/m
3 respectively and found lower
than their respective permissible NEQS level.
Figure 5.33: Minimum, maximum and average value of air pollutants at Hussainabad
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of air pollutants at GC Hyderabad Minimum
Maximum
Average
NEQS Level
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
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vel
Type of air pollutants
Concentration of air pollutants at HA Hyderabad Minimum
Maximum
Average
NEQS Level
97
Figure 5.34 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Latifabad No. 07 sampling location of Hyderabad city. The
Latifabad No. 07 sampling location is at a position where residential buildings surrounds
the narrow roads along with moderate traffic like vans, rickshaws, motor cycles and cars
ply in this area. The average recorded value of P.M2.5 was 28.76µg/m3 which was lower
and P.M10 was 248.26µg/m3 that was higher as against their respective permissible
NEQS level. The average concentration of carbon dioxide was 405.16ppm and was
higher than the permissible NEQS level. However the average concentration of carbon
monoxide, NOx and SOx were 6.86ppm, 35.76µg/m3 and 42.26µg/m
3 respectively and
found lower than their respective permissible NEQS level.
Figure 5.34: Minimum, maximum and average value of air pollutants at Latifabad
No. 07
Figure 5.35 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Latifabad No. 12 sampling location of Hyderabad city. The
Latifabad No. 12 sampling location is at a position where residential buildings surrounds
the narrow roads along with moderate traffic like vans, rickshaws, motor cycles and cars
ply in this area. The average recorded value of P.M2.5 was 28.70µg/m3 which was lower
and P.M10 was 225.30µg/m3 that was higher as against their respective permissible
NEQS level. The average concentration of carbon dioxide was 403.63ppm and was
higher than the permissible NEQS level. However the average concentration of carbon
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
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vel
Type of air pollutants
Concentration of air pollutants at L 07 Hyderabad Minimum
Maximum
Average
NEQS Level
98
monoxide, NOx and SOx were 6.62ppm, 27.13µg/m3 and 39.30µg/m
3 respectively and
found lower than their respective permissible NEQS level.
Figure 5.35: Minimum, maximum and average value of air pollutants at Latifabad
No. 12
The average concentration of P.M2.5 was higher at Gul Center and lower at Press Club,
Hussainabad, Latifabad No. 07 and Latifabad No. 12 as against the permissible NEQS
level. The P.M10 and carbon dioxide at all five locations of Press Club, Gul Center,
Hussainabad, Latifabad No. 07and Latifabad No. 12 as given in Table 5.8 were higher
than the permissible NEQS levels. In case of carbon monoxide, NOx and SOx they were
lower at all five sampling locations of Gul Center, Press Club, Hussainabad, Latifabad
No. 07 and Latifabad No. 12 in comparison to their respective permissible NEQS level.
5.4 RESULTS AND DISCUSSIONS OF NAWABSHAH CITY
The average value of air pollutants at ten locations of Nawabshah city are given in
following figures.
Figure 5.36 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the New Naka sampling location of Nawabshah city. The New Naka
sampling location is at a position where residential buildings surrounds the narrow roads
along with moderate traffic congestion like vans, trucks, passenger buses, rickshaws,
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of air pollutants at L12 Hyderabad Minimum
Maximum
Average
NEQS Level
99
motor cycles and cars ply in this area. The average recorded value of P.M2.5 was
31.13µg/m3 which is lower and P.M10 was 226.06µg/m
3 that was higher as against their
respective permissible NEQS level. The average concentration of carbon dioxide and
carbon mono oxide were 390.43ppm and 2.15ppm respectively and found lower than the
respective permissible NEQS level. Similarly the average concentration of NOx and
SOx were 5.53µg/m3 and 16.46µg/m
3 respectively and found lower than their respective
permissible NEQS level.
Figure 5.36: Minimum, maximum and average value of air pollutants at New Naka
Figure 5.37 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Peoples‘ Medical University (PMU) sampling location of
Nawabshah city. The PMU sampling location is at a position where residential buildings
surrounds the narrow roads along with moderate traffic congestion like vans, rickshaws,
motor cycles and cars ply in this area. The average recorded value of P.M2.5 was
32.83µg/m3 which is lower and P.M10 was 260.50µg/m
3 that was higher as against their
respective permissible NEQS level. The average concentration of carbon dioxide was
413.46ppm and was higher than the permissible NEQS level. However the average
concentration of carbon monoxide, NOx and SOx were 2.27ppm, 13.90µg/m3 and
23.46µg/m3 respectively and found lower than their respective permissible NEQS level.
0
50
100
150
200
250
300
350
400
450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
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vel
Type of air pollutants
Concentration of air pollutants at NN Nawabshah Minimum
Maximum
Average
NEQS Level
100
Figure 5.37: Minimum, maximum and average value of air pollutants at PMU
Figure 5.38 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Shalimar Bus Stand sampling location of Nawabshah city. The
Shalimar Bus Stand sampling location is at a position where residential buildings
surrounds the narrow roads along with moderate traffic congestion like vans, passenger
coaches and buses, rickshaws, motor cycles and cars ply in this area. The average
recorded value of P.M2.5 was 32.16µg/m3 which is lower and P.M10 was 259.23µg/m
3
that was higher as against their respective permissible NEQS level. The average
concentration of carbon dioxide was 409.90ppm and was higher than the permissible
NEQS level. However the average concentration of carbon monoxide, NOx and SOx
were 4.62ppm, 12.66µg/m3 and 24.1µg/m
3 respectively and found lower than their
respective permissible NEQS level.
Figure 5.38: Minimum, max. & average value of air pollutants at Shalimar Bus Stand
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
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vel
Type of air pollutants
Concentration of air pollutants at PMU Nawabshah Minimum
Maximum
Average
NEQS Level
050
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ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
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vel
Type of air pollutants
Concentration of air pollutants at SBS Nawabshah MinimumMaximumAverageNEQS Level
101
Figure 5.39 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Mohni Bazar sampling location of Nawabshah city. The Mohni
Bazar sampling location is at a position where residential buildings surrounds the narrow
roads along with moderate traffic congestion like vans, rickshaws, motor cycles and cars
ply in this area. The average recorded value of P.M2.5 and P.M10 are 38.16µg/m3 and
258.83µg/m3 that are higher as against the permissible NEQS level. The average
concentration of carbon dioxide was 413.23ppm and was higher than the permissible
NEQS level. However the average concentration of carbon monoxide, NOx and SOx
were 5.04ppm, 16.43µg/m3 and 27.63µg/m
3 respectively and found lower than their
respective permissible NEQS level.
Figure 5.39: Minimum, max. & average value of air pollutants at Mohni Bazar
Figure 5.40 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Sabzi Mandi sampling location of Nawabshah city. The Sabzi
Mandi sampling location is at a position where residential buildings surrounds the
narrow roads along with moderate traffic congestion like vans, rickshaws, motor cycles
and cars ply in this area. The average recorded value of P.M2.5 was 32.10µg/m3 which is
lower and P.M10 was 252.90µg/m3 that was higher as against their respective permissible
NEQS level. The average concentration of carbon dioxide was 410.76ppm and was
higher than the permissible NEQS level. However the average concentration of carbon
monoxide, NOx and SOx were 5.16ppm, 20.43µg/m3 and 30.73µg/m
3 respectively and
found lower than their respective permissible NEQS level.
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of air pollutants at MB Nawabshah Minimum
Maximum
Average
NEQS Level
102
Figure 5.40: Minimum, max. and average value of air pollutants at Sabzi Mandi
The average concentration of P.M2.5 was higher at Mohni Bazar location and lower at
New Naka, PMU, Shalimar Bus Stand and Sabzi Mandi locations as against the
permissible NEQS level. The P.M10 was found higher than the permissible levels at all
locations. The carbon dioxide was lower at New Naka location but at the other four
locations it was higher than the permissible NEQS levels. In case of carbon monoxide,
NOx and SOx they were lower at all five sampling locations of New Naka, PMU,
Shalimar Bus Stand, Mohni Bazarand Sabzi Mandi in comparison to their respective
permissible NEQS level. Figure 5.41 shows measured air pollutants‘ calculated
minimum, maximum and average recorded value at the Railway Station sampling
location of Nawabshah city. The Railway Station sampling location is at a position
where main railway junction exists along with residential buildings surrounds the narrow
roads. In addition to this moderate traffic like vans, trucks, passenger buses, rickshaws,
motor cycles and cars ply in this area. The average recorded value of P.M2.5 was
33.63µg/m3 which is lower and P.M10 was 397.06µg/m
3 that was higher as against their
respective permissible NEQS level. The average concentration of carbon dioxide was
406.00ppm and was higher than the permissible NEQS level. However the average
concentration of carbon monoxide, NOx and SOx were 4.67ppm, 14.53µg/m3 and
29.80µg/m3 respectively and found lower than their respective permissible NEQS level.
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of air pollutants at SM Nawabshah Minimum
Maximum
Average
NEQS Level
103
Figure 5.41: Minimum, max. and average value of air pollutants at Railway Station
Figure 5.42 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Bucheri Road sampling location of Nawabshah city. The Bucheri
Road sampling location is at a position where residential settlement exists along with
moderate traffic congestion like vans, passenger buses, rickshaws, motor cycles and cars
ply in this area. The average recorded value of P.M2.5 was 31.10µg/m3 which is lower
and P.M10 was 239.73µg/m3 that was higher as against their respective permissible
NEQS level. The average concentration of carbon dioxide was 405.50ppm and was
higher than the permissible NEQS level. However the average concentration of carbon
monoxide, NOx and SOx were 4.83ppm, 12.66µg/m3 and 19.76µg/m
3 respectively and
found lower than their respective permissible NEQS level.
Figure 5.42: Minimum, max. and average value of air pollutants at Bucheri Road
050
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ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
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vel
Type of air pollutants
Concentration of air pollutants at RS Nawabshah Minimum
Maximum
Average
NEQS Level
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
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vel
Type of air pollutants
Concentration of air pollutants at BR Nawabshah Minimum
Maximum
Average
NEQS Level
104
Figure 5.43 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Habib Sugar Mills sampling location of Nawabshah city. The
Habib Sugar Mills sampling location is at a position where on one side the Habib Sugar
Mills exists and on the other side residential settlement exists along with moderate traffic
congestion like vans, passenger buses, rickshaws, motor cycles and cars ply in this area.
The average recorded value of P.M2.5 was 34.09µg/m3 which is lower and P.M10 was
335.86µg/m3 that was higher as against their respective permissible NEQS level. The
average concentration of carbon dioxide was 405.06ppm and was higher than the
permissible NEQS level. However the average concentration of carbon monoxide, NOx
and SOx were 1.68ppm, 23.60µg/m3 and 31.26µg/m
3 respectively and found lower than
their respective permissible NEQS level.
Figure 5.43: Minimum, maximum and average value of air pollutants at Habib
Sugar Mills
Figure 5.44 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Society Chowk sampling location of Nawabshah city. The Society
Chowk sampling location is at a position where residential settlement exists along with
moderate traffic congestion like rickshaws; motor cycles and cars ply in this area. The
average recorded value of P.M2.5 was 32.96µg/m3 which is lower and P.M10 was
288.20µg/m3 that was higher as against their respective permissible NEQS level. The
average concentration of carbon dioxide was 409.83ppm and was higher than the
permissible NEQS level. However the average concentration of carbon monoxide, NOx
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
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vel
Type of air pollutants
Concentration of air pollutants near HSM Nawabshah Minimum
Maximum
Average
NEQS Level
105
and SOx were 3.13ppm, 22.46µg/m3 and 32.26µg/m
3 respectively and found lower than
their respective permissible NEQS level.
Figure 5.44: Minimum, maximum and average value of air pollutants at Society
Chowk
Figure 5.45 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Quaid E Awam University of Engineering, Science & Technology
(QUEST) sampling location of Nawabshah city. The QUEST sampling location is within
the premises of the university outside of the energy and environment engineering
department with very low traffic like motor cycles and cars ply in this area. The average
recorded value of P.M2.5 and P.M10 were 19.40µg/m3 and 121.30µg/m
3 that are higher as
against the permissible NEQS level. The average concentration of carbon dioxide and
carbon monoxide were 396.36ppm and 0.40 that was lower than the respective
permissible NEQS level. Similarly the average concentration of NOx and SOx were
6.83µg/m3 and 13.16µg/m
3 respectively and found lower than their respective
permissible NEQS level.
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of air pollutants at SC Nawabshah Minimum
Maximum
Average
NEQS Level
106
Figure 5.45: Minimum, maximum and average value of air pollutants at QUEST
As it is evident from Table 5.10 in Appendix-A in the appendix the average
concentration of P.M2.5 was found lower as compared to permissible NEQS levels at all
these five locations. The P.M10 was found higher than the permissible levels at all the
five locations. The carbon dioxide was lower at QUEST location but at the other four
locations it was higher than the permissible NEQS levels. In case of carbon monoxide,
NOx and SOx they were lower at all five sampling locations of Railway Station, Bucheri
Road, Habib Sugar Mills, Society Chowk and QUEST in comparison to their respective
permissible NEQS level.
5.5 RESULTS AND DISCUSSIONS OF SUKKUR CITY
The average value of air pollutants at ten locations of Sukkur city are given in following
figures. Figure 5.46 shows measured air pollutants‘ calculated minimum, maximum and
average recorded value at the Old Sukkur sampling location of Sukkur city. The Old
Sukkur sampling location is at a position where residential buildings surrounds the
narrow roads along with moderate traffic congestion like vans, trucks, passenger buses,
rickshaws, motor cycles and cars ply in this area. The average recorded value of P.M2.5
was 30.76µg/m3 which is lower and P.M10 was 182.76µg/m
3 that was higher as against
their respective permissible NEQS level. The average concentration of carbon dioxide
was 402.50ppm and was higher than the permissible NEQS level. However the average
concentration of carbon monoxide, NOx and SOx were 3.05ppm, 27.60g/m3 and
35.90µg/m3 respectively and found lower than their respective permissible NEQS level.
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of air pollutants at QUEST Nawabshah Minimum
Maximum
Average
NEQS Level
107
Figure 5.46: Minimum, max. and average value of air pollutants at Old Sukkur
Figure 5.47 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Lab e Mehran sampling location of Sukkur city. The Lab e Mehran
sampling location is at a position where residential buildings surrounds the roads along
with moderate traffic congestion like vans, trucks, passenger buses, rickshaws, motor
cycles and cars ply in this area. The average recorded value of P.M2.5 was 31.86µg/m3
which is lower and P.M10 was 180.63µg/m3 that was higher as against their respective
permissible NEQS level. The average concentration of carbon dioxide was 400.60ppm
and was higher than the permissible NEQS level. However the average concentration of
carbon monoxide, NOx and SOx were 3.33ppm, 27.70g/m3 and 36.43µg/m
3 respectively
and found lower than their respective permissible NEQS level.
Figure 5.47: Minimum, max. and average value of air pollutants at Lab e Mehran
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Old Sukkur Minimum
Maximum
Average
NEQS Level
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Lab e Mehran Minimum
Maximum
Average
NEQS Level
108
Figure 5.48 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the High Court Road sampling location of Sukkur city. The High Court
Road sampling location is at a position where residential and office buildings surrounds
the narrow roads along with heavy traffic congestion vans, rickshaws, motor cycles and
cars ply in this area. The average recorded value of P.M2.5 and P.M10 are 35.90µg/m3 and
203.13µg/m3 that are higher as against the permissible NEQS level. The average
concentration of carbon dioxide was 404.36ppm and was higher than the permissible
NEQS level. However the average concentration of carbon monoxide, NOx and SOx
were 3.46ppm, 33.16µg/m3 and 41.33µg/m
3 respectively and found lower than their
respective permissible NEQS level.
Figure 5.48: Minimum, maximum and average value of air pollutants at High
Court Road
Figure 5.49 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Eid Gah Road sampling location of Sukkur city. The Eid Gah Road
sampling location is at a position where residential and office buildings surrounds the
narrow roads along with heavy traffic congestion of vans, rickshaws, motor cycles and
cars ply in this area. The average recorded value of P.M2.5 and P.M10 are 39.80µg/m3 and
198.90µg/m3 that are higher as against the permissible NEQS level. The average
concentration of carbon dioxide was 407.03ppm and was higher than the permissible
NEQS level. However the average concentration of carbon monoxide, NOx and SOx
were 3.84ppm, 25.60µg/m3 and 41.96µg/m
3 respectively and found lower than their
respective permissible NEQS level.
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at High Court Road Sukkur Minimum
Maximum
Average
NEQS Level
109
Figure 5.49: Minimum, max. and average value of air pollutants at Eid Gah Road
Figure 5.50 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Station Road sampling location of Sukkur city. The Station Road
sampling location is at a position where residential surrounds the narrow roads along
with heavy traffic congestion of vans, rickshaws; motor cycles and cars ply in this area.
The average recorded value of P.M2.5 and P.M10 are 38.70µg/m3 and 245.73µg/m
3 that
are higher as against the permissible NEQS level. The average concentration of carbon
dioxide was 407.00ppm and was higher than the permissible NEQS level. However the
average concentration of carbon monoxide, NOx and SOx were 4.10ppm, 36.06µg/m3
and 44.450µg/m3 respectively and found lower than their respective NEQS level.
Figure 5.50: Minimum, maximum and average value of air pollutants at Station
Road
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Concentration level
Concentration of Air Pollutants at Eid Gah Road Sukkur Minimum
Maximum
Average
NEQS Level
0
100
200
300
400
500
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Station Road Sukkur Minimum
Maximum
Average
NEQS Level
110
In Table 5.11 in the appendix the average concentration of P.M2.5 was found lower at
Old Sukkur and Lab e Mehran location as compared to permissible NEQS levels while it
was higher at High Court Road, Eid Gah Road and Station Road. The P.M10 was found
higher than the permissible levels at all the five locations. The carbon dioxide was higher
than the permissible NEQS levels. In case of carbon monoxide, NOx and SOx they were
lower at all five sampling locations of Old Sukkur, Lab e Mehran, High Court Road, Eid
Gah Road and Station Road in comparison to their respective permissible NEQS level.
Figure 5.51 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the SITE Area sampling location of Sukkur city. The SITE Area
sampling location is at a position where different industries and residential settlements
exists along with moderate traffic congestion like vans, trucks, passenger buses,
rickshaws, motor cycles and cars ply in this area. The average recorded value of P.M2.5
and P.M10 are 40.40µg/m3 and 212.26µg/m
3 that are higher as against the permissible
NEQS level. The average concentration of carbon dioxide was 408.03ppm and was
higher than the permissible NEQS level. However the average concentration of carbon
monoxide, NOx and SOx were 4.10ppm, 30.86µg/m3 and 37.50µg/m
3 respectively and
found lower than their respective permissible NEQS level.
Figure 5.51: Minimum, maximum and average value of air pollutants at SITE
Area.
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at SITE Area Sukkur MinimumMaximumAverageNEQS Level
111
Figure 5.52 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Canal Road sampling location of Sukkur city. The Canal Road
sampling location is at a position where residential settlements exists along with
moderate traffic congestion like vans, trucks, passenger buses, rickshaws, motor cycles
and cars ply in this area. The average recorded value of P.M2.5 and P.M10 are 35.70µg/m3
and 208.83µg/m3 that are higher as against the permissible NEQS level. The average
concentration of carbon dioxide was 406.20ppm and was higher than the permissible
NEQS level. However the average concentration of carbon monoxide, NOx and SOx
were 4.07ppm, 25.83µg/m3 and 43.33µg/m
3 respectively and found lower than their
respective permissible NEQS level.
Figure 5.52: Minimum, maximum & average value of air pollutants at Canal Road
Figure 5.53 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Hamdard Society sampling location of Sukkur city. The Hamdard
Society sampling location is at a position where residential settlements exists along with
moderate traffic congestion like vans, rickshaws, motor cycles and cars ply in this area.
The average recorded value of P.M2.5 was 33.40µg/m3 which is lower and P.M10 was
261.06µg/m3 that was higher as against their respective permissible NEQS level. The
average concentration of carbon dioxide was 408.56ppm and was higher than the
permissible NEQS level. However the average concentration of carbon monoxide, NOx
and SOx were 3.96ppm, 24.23µg/m3 and 35.30µg/m
3 respectively and found lower than
their respective permissible NEQS level.
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Canal Road Sukkur Minimum
Maximum
Average
NEQS Level
112
Figure 5.53: Minimum, max. & average value of air pollutants at Hamdard Society
Figure 5.54 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Airport Road sampling location of Sukkur city. The Airport Road
sampling location is at a position where residential settlements exists along with
moderate traffic congestion like vans, rickshaws, motor cycles and cars ply in this area.
The average recorded value of P.M2.5 was 33.06µg/m3 which is lower and P.M10 was
210.10µg/m3 that was higher as against their respective permissible NEQS level. The
average concentration of carbon dioxide was 403.60ppm and was higher than the
permissible NEQS level. However the average concentration of carbon monoxide, NOx
and SOx were 4.03ppm, 23.60µg/m3 and 39.33µg/m
3 respectively and found lower than
their respective permissible NEQS level.
Figure 5.54: Minimum, max. and average value of air pollutants at Airport Road
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Hamdard Society Sukkur Minimum
Maximum
Average
NEQS Level
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Airport Road Sukkur Minimum
Maximum
Average
NEQS Level
113
Figure 5.55 shows measured air pollutants‘ calculated minimum, maximum and average
recorded value at the Shikarpur Road sampling location of Sukkur city. The Shikarpur
Road sampling location is at a position where residential settlements exists along with
moderate traffic congestion like vans, trucks, passenger buses, rickshaws, motor cycles
and cars ply in this area. The average recorded value of P.M2.5 and P.M10 are 35.70µg/m3
and 281.80µg/m3 that are higher as against the permissible NEQS level. The average
concentration of carbon dioxide was 406.00ppm and was higher than the permissible
NEQS level. However the average concentration of carbon monoxide, NOx and SOx
were 4.40ppm, 36.90µg/m3 and 44.76µg/m
3 respectively and found lower than their
respective permissible NEQS level.
Figure 5.55: Minimum, max. & average value of air pollutants at Shikarpur Road.
In Table 5.12 in Appendix-A in the appendix average concentration of P.M2.5 was found
lower at Hamdard Society and Airport locations as compared to permissible NEQS
levels while it was higher at SITE area, Canal Road and Shikarpur Road. The P.M10 was
found higher than the permissible levels at all the five locations. The average value of
carbon dioxide was also found higher than the permissible NEQS levels. In case of
carbon monoxide, NOx and SOx they were lower at all five sampling locations of SITE
Area, Canal Road, Hamdard Society, Airport Road and Shikarpur Road locations in
comparison to their respective permissible NEQS level.
050
100150200250300350400450
ug/m3 ug/m3 Ppm Ppm ug/m3 ug/m3
P.M2.5 P.M10 CO2 CO NOx SOx
Co
nce
ntr
atio
n le
vel
Type of air pollutants
Concentration of Air Pollutants at Shikarpur Road Sukkur Minimum
Maximum
Average
NEQS Level
114
CHAPTER NO. 6
MODEL DEVELOPMENT AND PREDICTION OF
AIR QUALITY
6.1 INTRODUCTION
In this chapter, the models used by researchers for prediction of air quality in various
countries are described. The justification and selection of OpenAir model is provided,
which was used for prediction of future air pollutants level in the selected cities up to
year 2050.
6.2 AIR QUALITY MODELS
In this research study Relevant Models were explored. Currently two types of models are
in use one focuses on point sources and the other focuses on non point sources. Again
the range of models play decisive role. The range of models is based on local, regional
and global.
1. Air Force Dispersion Assessment Model (ADAM) dispersion model.
2. AERMOD is a steady-state plume model that incorporates air dispersion.
3. The HYbrid ROADway Model (HYROAD) integrates three modules that
simulate the effects of traffic, emissions and dispersion.
4. System for Air Modeling And Analysis (SAMAA) is a software for air quality
modeling and analysis.
5. Receptor modeling is a method for determining the sources of air pollution based
on air monitoring.
6.2.1 Air Force Dispersion Assessment Model (ADAM)
Air Force Dispersion Assessment Model (ADAM) is a modified box and Gaussian
dispersion model. This model is used by the US Environmental Protection Agency,
Office of Air Quality Planning and Standards (OAQPS). The release scenarios include
continuous and instantaneous, area and point options.
114
115
6.2.2 AERMOD Modeling System
AERMOD is a steady-state plume model that incorporates air dispersion based on
planetary boundary layer, turbulence structure and scaling concepts and both simple and
complex terrain. It is used by the US Environmental Protection Agency, Office of Air
Quality Planning and Standards (OAQPS).
6.2.3 Hybrid Roadway Model (HYROAD)
The HYbrid ROADway Model (HYROAD) integrates three modules that simulate the
effects of traffic, missions and dispersion. It is designed to determine hourly
concentrations of carbon monoxide (CO) or other gas-phase pollutants, particulate
matter (PM) and air toxics from vehicle emissions at receptor locations that occur within
500 meters of the roadway intersections. It is used by the US Environmental Protection
Agency, Office of Air Quality Planning and Standards (OAQPS).
6.2.4 System for Air Modeling and Analysis (SAMAA)
This is the system for Air Modeling and Analysis is software for air quality modeling
and analysis. This is used by the decision makers to define mitigation measures and
anticipate their qualitative & quantitative effects on air quality. It simulates air pollution
events at medium and regional scale in order to better understand the evolution of air
pollution in cities and their surroundings. It is very innovative because of its underlying
GIS, its modular approach, and its user-friendly interface for measuring local and hourly
emissions of primary pollutants.
6.2.5 Receptor Modeling
The receptor modeling is a method for determining the sources of air pollution based on
air monitoring data. It utilizes measurements at an individual monitoring site (the
receptor) to calculate the relative contributions from major sources to the pollution at
that site. These models can be applied to investigating the sources of individual air
pollution ―episodes‖ or, as with the emission inventory, to create effective control
116
strategies. Receptor-based models are most commonly used to investigate the sources of
air pollution.
6.2.6 Artificial Neural Network (ANN)
Artificial Neural Network (ANN) model is used by researchers for predicting the
ambient air quality.It determines the status of ambient air quality, ranging from good to
hazardous. The working of ANN is based on latest OpenAir Model [114] as shown in
Figure 6.1 which offers wide range of data analysis and statistical abilities. It has
Excellent graphics output. There are over 4000 packages that offer suitable analysis
techniques. By using the software methodologies of openair an algorithm as shown in
Figure 6.2 was developed for the prediction of air pollution in the selected four major
cities of Sindh.
Figure 6.1: Worksheet of OpenAir software for assessment and prediction of air
quality
117
Figure 6.2: Algorithm for prediction of air pollutants in major cities of Sindh
province
6.3 AIR POLLUTANTS GROWTH RATES
After measuring air quality data from selected locations, the data was used for
calculating growth rate with the help of OpenAir software. Figure 6.3 shows growth rate
of P.M2.5 of Karachi city. After using the algorithm it was found that the growth rate of
P.M2.5 varies from 1% to 4%. If it is assumed that this is the annual growth rate of P.M2.5
in Karachi then future prediction short term and long term can be made. Here in Figure
6.4 prediction results starting from year 2015 to year 2050 is shown. If the same growth
rate is assumed for Hyderabad, Nawabshah and Sukkur then similar prediction can be
made and the comparison is drawn.
118
Figure 6.3: Growth rate of Particulate Matter (P.M2.5) in Karachi
Figure 6.4: Short and long term Prediction & Comparison of P.M2.5 in major cities
of Sindh
Figure 6.5 shows growth rate of P.M10 of Karachi city. After using the algorithm it was
found that the growth rate of P.M10 varies from 0.5% to 1.5%. If it is assumed that this is
the annual growth rate of P.M10 in Karachi then future prediction short term and long
term can be made. Here in Figure 6.6 prediction results starting from year 2015 to year
0
20
40
60
80
100
120
140
2015 2020 2025 2030 2035 2040 2045 2050
Co
nce
ntr
atio
n le
vel (
µg/
m3)
Year
P.M2.5 of major cities of Sindh
Karachi P.M2.5 µg/m3 Hyderabad P.M2.5 µg/m3Nawabshah P.M2.5 µg/m3 Sukkur P.M2.5 µg/m3
119
2050 is shown. If the same growth rate is assumed for Hyderabad, Nawabshah and
Sukkur then similar prediction can be made and the comparison is drawn.
Figure 6.5: Growth rate of Particulate Matter (P.M10) in Karachi
Figure 6.6: Short and long term prediction & comparison of P.M10 in major cities
of Sindh
Figure 6.7 shows growth rate of carbon dioxide of Karachi city. After using the
algorithm it was found that the growth rate of carbon dioxide varies from 1.0% to 6%. If
it is assumed that this is the annual growth rate of carbon dioxide in Karachi then future
0
100
200
300
400
500
600
2015 2020 2025 2030 2035 2040 2045 2050
P.M
10
ug/
m3
Year
Comparison of PM10 in selected cities
Karachi P.M10 ug/m3 Hyderabad P.M10 ug/m3
Nawabshah P.M10 ug/m3 Sukkur P.M10 ug/m3
120
prediction short term and long term can be made. Here in Figure 6.8 prediction results
starting from year 2015 to year 2050 is shown. If the same growth rate is assumed for
Hyderabad, Nawabshah and Sukkur then similar prediction can be made and the
comparison is drawn.
Figure 6.7: Growth rate of carbon dioxide (CO2) in Karachi
Figure 6.8: Short and long term prediction & comparison of CO2 in major cities of
Sindh
300
350
400
450
500
550
600
650
700
750
800
850
2015 2020 2025 2030 2035 2040 2045 2050
CO
2 p
pm
Year
Comparison of CO2 in selected cities
Karachi CO2 ppm Hyderabad CO2 ppm
Nawabshah CO2 ppm Sukkur CO2 ppm
121
Figure 6.9 shows growth rate of carbon monoxide of Karachi city. After using the
algorithm it was found that the growth rate of carbon monoxide varies from 1.0% to 4%.
If it is assumed that this is the annual growth rate of carbon monoxide in Karachi then
future prediction short term and long term can be made. Here in Figure 6.10 prediction
results starting from year 2015 to year 2050 is shown. If the same growth rate is assumed
for Hyderabad, Nawabshah and Sukkur then similar prediction can be made and the
comparison is drawn.
Figure 6.9: Growth rate of carbon monoxide (CO) in Karachi
Figure 6.10: Short and long term prediction & comparison of CO in major cities of
Sindh.
0
2
4
6
8
10
12
14
16
18
20
2015 2020 2025 2030 2035 2040 2045 2050
CO
pp
m
Year
Comparison of CO in selected cities
Karachi CO ppm Hyderabad CO ppm
Nawabshah CO ppm Sukkur CO ppm
122
Figure 6.11 shows growth rate of oxides of nitrogen of Karachi city. After using the
algorithm it was found that the growth rate of oxides of nitrogen varies from 1.0% to
4%. If it is assumed that this is the annual growth rate of oxides of nitrogen in Karachi
then future prediction short term and long term can be made. Here in Figure 6.12
prediction results starting from year 2015 to year 2050 is shown. If the same growth rate
is assumed for Hyderabad, Nawabshah and Sukkur then similar prediction can be made
and the comparison is drawn.
1/10/2016K.C.Mukwana Ph.D Final Seminar
Supervised by: Prof. Dr. Saleem Raza Samo63
Figure 6.11: Growth rate of oxides of nitrogen (NOx) in Karachi
Figure 6.12: Short and long term prediction & comparison of NOx in major cities
of Sindh.
0
50
100
150
200
250
2015 2020 2025 2030 2035 2040 2045 2050
NO
x u
g/m
3
Year
Comparison of NOx in selected cities
Karachi NOx ug/m3 Hyderabad NOx ug/m3
Nawabshah NOx ug/m3 Sukkur SOx ug/m3
123
Figure 6.13 shows growth rate of oxides of sulfur of Karachi city. After using the
algorithm it was found that the growth rate of oxides of sulfur varies from 1.0% to 4%. If
it is assumed that this is the annual growth rate of oxides of sulfur in Karachi then future
prediction short term and long term can be made. Here in Figure 6.14 prediction results
starting from year 2015 to year 2050 is shown. If the same growth rate is assumed for
Hyderabad, Nawabshah and Sukkur then similar prediction can be made and the
comparison is drawn.
1/10/2016K.C.Mukwana Ph.D Final Seminar
Supervised by: Prof. Dr. Saleem Raza Samo64
Figure 6.13: Growth rate of oxides of sulfur (SOx) in Karachi
Figure 6.14: Short and long term prediction & comparison of SOx in major cities of
Sindh
0
20
40
60
80
100
120
140
2015 2020 2025 2030 2035 2040 2045 2050
SOx
ug/
m3
Year
Comparison of SOx in selected cities
Karachi SOx ug/m3 Hyderabad SOx ug/m3
Nawabshah SOx ug/m3 Sukkur SOx ug/m3
124
6.4 VALIDATION OF THE DEVELOPED MODEL
The OpenAir Model is an advanced version of ANN and possesses in-built validation
called EcoExpert. The EcoExpert is integrated into general computational Framework
for environmental data management. The EcoExpert process the data and find out
chemical, physical or human anomaly.
125
CHAPTER NO. 7
CONCLUSIONS AND SUGGESTIONS FOR
FUTURE WORK
7.1 INTRODUCTION
In this chapter, the major findings of the study, general conclusion and suggestion for
future work is described. The conclusions are made separately for each city and then
overall conclusion is provided along with suggestions.
7.2 MAJOR FINDINGS AND CONCLUSION
Out of twenty selected locations of Karachi, four places namely Al Asif Square, North
Nazimabad, Nursary and Star Gate were found more affected due to higher level of air
pollutants where the average concentration of P.M2.5, P.M10, CO and CO2 were higher
than permissible limits. The level of P.M2.5, P.M10, and CO2 at Numaish Chorangi, Do
Talwar and Clifton was higher than the permissible NEQS levels. The concentration of
NOx was higher at North Nazimabad, Nursary and Star Gate locations and SOx were
found lower than NEQS. It was revealed that the Sea View location was free from the air
pollutants, may be due to lower traffic load and sea breeze which may transport air
pollutants towards city. The details are given as under:
The results shows that PM2.5 is 2 to 3 (Maripur road) times higher than
permissible level (35µg/m3)
Similarly PM10 found 2-3 times higher than permissible levels (150µg/m3) .
The results shows that in Karachi the area of Maripur road, Shershah, Brooks
Chorangi, Dowood Chorangi and Al Asif Square ambient air is more
contaminated (2.5 times higher than NEQS) while the area of Sea View, Clifton
and Gulshan Hadeed are comparatively less polluted.
125
126
In Karachi the gaseous concentration of NOx (Higher only at 8 locations out of 20 as
against NEQS) was found higher but still within permissible levels & SOx was found
within permissible levels at all locations – Most probably due to CNG.
In Hyderabad city, the concentration of P.M2.5, P.M10, and CO2 at five locations namely
Hala Naka, Hyderabad By-Pass, Nasim Nagar, City Gate and Market Tower were
higher, whereas, the air pollutants level were lower than the permissible NEQS levels at
all other locations. More details are discussed in following paragraph:
In case of Hyderabad, the Hyderabad By-Pass area possess much higher
particulate matter (85 µg/m3) concentration due to excessive and speedy
movement of lighter and heavier vehicles up & down country.
The City Gate, Station Road and SITE ( PM2.5 35 to 58 µg/m3) and PM10 150 to
320 µg/m3 ) shows higher concentrations of particulate while the gaseous
concentration is not that high.
The rest of the places in Hyderabad shows moderate to low concentrations except
particulate concentrations.
In Nawabshah city, the concentration of P.M2.5 was higher only at Mohni Bazar and
P.M10 was higher than the permissible levels at all locations of Nawabshah city. The
level of CO, NOx and SOx were found within permissible NEQS level at all locations of
the city.
In Sukkur city, the concentration of P.M2.5, P.M10, and CO2 was found higher at High
Court Road, Eid Gah Road and Station Road than permissible levels at all five locations.
The level of CO, NOx and SOx were within NEQS.
OpenAir Model was used for determination of growth rate and future prediction of air
pollutants. It was discovered from predicted model results that the growth rate of
pollutants, such as P.M2.5, P.M10, CO2, CO, NOx and SOx varies from 1.0% to 4.0%,
0.5% to 1.5%, 1.0% to 6.0%, 1.0% to 4.0%, 1.0% to 4.0% and 1.0 to 4.0% respectively.
127
The results of OpenAir Model indicates that if the present trend of release of air
pollutants will be continued then the Ambient Air Quality may be turned to
heavily polluted and cause local & regional air pollution episode.
It is concluded from this study that the level of P.M2.5, P.M10 were found higher and CO2
and CO was almost within permissible levels in all selected cities, whereas, the level of
NOx and SOx were found higher at most of the places in Karachi only. The model
results predicted that concentration of pollutants will be at alarming level up to year
2050 if the growth rate of population, industrialization and transportation is continued.
The findings of this work provide a baseline data and future predictions of air pollution
level of four major cities of Sindh province. It will help the regulatory authorities to
make effective policies for reduction of air pollutants and take measures for replacement
of fossil fuels with environmental friendly fuels.
7.3 SUGGESTIONS FOR FUTURE WORK
From this research study following suggestions are made:
It is suggested that in-depth study may be carried out to determine the status of
O3, HC and PAN substances as there are enough evidences that these pollutants
may be prevailing in the ambient air of these cities.
It is suggested that the regulatory agency as well as all the stakeholders may
realize their responsibility and play their role in the prevention of ambient air
pollution.
Fixed and mobile air quality monitoring may be established to get the level of
pollution round the clock and in case of excess concentrations the relevant
remedial measures may take place by the respective EPA.
―Polluters pay the Price‖ policy may be launched to create realization among the
individuals to industrial set-up level
128
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health‖, fact sheet, Media center.
[2] Development Statistics of Sindh, Bureau of Statistics, (2011), ―National
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[3] Salma S, S. Rehman and M.A. Shah, (2012), ―Rainfall trends in different climate
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[4] Marie S. O‘Neill, Michael Jerrett, Ichiro Kawachi and Jonathan I. Levy, (2003),
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[5] Wellenius GA, Schwartz J, and Mittleman MA. (2006), ―Particulate Air
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[6] Zanobetti A and Schwartz J. (2005), ―The Effect of Particulate Air Pollution on
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APPENDIX – A
Air Quality Data Tables
Table No. 5.1: Minimum, maximum and average value of air pollutants at Al Asif
Square, North Nazimabad, Nursary and Star Gate locations.
Location Description P.M2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
Al Asif Square
Minimum 65 289 413 9.5 67 53
Maximum 104 353 433 12.00 95 80
Average 80.50 325.56 423.13 10.91 79.36 63.80
North
Nazimabad
Minimum 57 266 400 9.5 73 57
Maximum 83 323 440 11.8 98 81
Average 71.33 293.00 417.73 10.55 85.00 72.06
Nursary
Minimum 64 235 402 8.5 70 56
Maximum 89 305 435 11.4 93 83
Average 74.80 271.36 418.03 9.97 83.60 69.9
Star Gate
Minimum 58 194 395 7.8 63 47
Maximum 81 257 426 11.2 85 67
Average 67.53 228.2 413.46 9.50 73.40 57.26
NEQS 35 150 397 9 80 120
144
Table No. 5.2: Minimum, maximum and average value of air pollutants at Tower,
Maripur Road, Nursary and Civil Hospital locations.
Location Description P.M2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
Tower
Minimum 73 285 402 9.2 70 62
Maximum 97 327 433 11.7 93 82
Average 85.63 307.50 417.10 10.34 82.5 70.90
Maripur Road
Minimum 86 310 409 10.2 72 58
Maximum 120 368 439 13.8 92 77
Average 102.90 334.46 425.05 12.05 81.83 66.60
Shershah
Minimum 78 265 410 7.9 65 43
Maximum 110 316 445 10.5 92 71
Average 91.13 289.56 425.00 9.15 80.10 55.96
Civil Hospital
Minimum 52 235 389 7.1 43 40
Maximum 74 282 424 9.4 72 61
Average 62.4 254.46 408.53 8.34 57.23 50.53
NEQS 35 150 397 9 80 120
145
Table No. 5.3: Minimum, maximum and average value of air pollutants at Numaish
Chorangi, Do Talwar, Clifton and Sea View locations.
Location Description P.M
2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
Numaish
Chorangi
Minimum 55 227 395 7.9 51 40
Maximum 78 270 423 10.4 74 61
Average 64.73 247.56 409.46 8.96 61.76 47.26
Do Talwar
Minimum 41 185 398 7.2 44 38
Maximum 64 241 423 9.4 70 56
Average 52.20 219.00 409.43 8.21 54.83 46.73
Clifton
Minimum 42 186 395 7.2 44 35
Maximum 65 246 426 8.8 64 58
Average 56.63 216.80 413.76 7.87 55.53 46.50
Sea View
Minimum 21 75 365 2.4 11 17
Maximum 39 124 394 4.3 27 36
Average 28.16 96.73 378.30 3.3 18.76 23.56
NEQS 35 150 397 9 80 120
146
Table No. 5.4: Minimum, maximum and average value of air pollutants at Korangi
Crossing, Brooks Chorangi, National Refinery Chorangi and
Dawood Chorangi locations.
Location Description P.M2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
Korangi
Crossing
Minimum 50 230 400 9.6 43 34
Maximum 72 269 433 12.4 74 58
Average 63.30 250.06 418.26 10.97 57.63 43.73
Brooks
Chorangi
Minimum 81 274 411 9.2 67 37
Maximum 114 312 441 11.5 88 58
Average 95.46 287.03 427.30 10.40 77.73 47.80
National
Refinery
Chorangi
Minimum 77 274 401 9.2 72 30
Maximum 124 311 434 12.4 95 57
Average 96.23 289.16 419.26 11.01 82.73 43.86
Dawood
Chorangi
Minimum 80 268 405 9.2 73 31
Maximum 121 311 438 12.4 96 48
Average 96.36 287.83 422.83 10.83 83.73 38.80
NEQS 35 150 397 9 80 120
147
Table No. 5.5: Minimum, maximum and average value of air pollutants at
Millennium Mall, Johar Complex, Karachi University and
Gulshan e Hadeed locations.
Location Description P.M
2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
Millennium
Mall
Minimum 70 227 399 8.2 70 31
Maximum 104 310 430 10.5 91 49
Average 84.10 279.03 416.26 9.17 80.36 39.30
Johar Complex
Minimum 66 245 405 8.0 49 31
Maximum 90 285 441 10.4 75 52
Average 77.86 266.36 422.83 8.92 64.93 39.76
Karachi
University
Minimum 58 240 402 7.5 40 29
Maximum 81 285 433 9.7 72 50
Average 68.53 258.83 419.66 8.29 54.2 40.30
Gulshan e
Hadeed
Minimum 56 211 394 6.9 33 20
Maximum 74 267 425 8.7 56 39
Average 65.40 234.66 409.70 7.79 43.60 29.13
NEQS 35 150 397 9 80 120
148
Table No. 5.6: Minimum, maximum and average value of air pollutants at Hala
Naka, Hyderabad By-Pass, Nasim Nagar, City Gate and Market
Tower locations.
Location Description P.M2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
Hala Naka
Minimum 28 189 383 3.1 23 41
Maximum 53 277 421 4.5 42 55
Average 43.40 224.46 402.73 3.88 33.53 49.00
Hyderabad By-
Pass
Minimum 71 298 393 2.8 29 39
Maximum 94 425 426 5.2 44 55
Average 84.90 335.83 408.30 4.28 35.96 47.06
Nasim Nagar
Minimum 31 206 386 3.0 29 40
Maximum 45 277 411 4.7 43 56
Average 39.16 240.16 408.53 3.85 36.16 48.63
City Gate
Minimum 26 303 403 4.4 24 40
Maximum 51 399 433 5.8 36 59
Average 40.16 342.7 416.63 5.12 30.56 50.16
Market Tower
Minimum 25 186 391 4.5 29 38
Maximum 46 261 432 11.8 46 56
Average 37.50 218.53 407.06 6.50 37.60 45.66
NEQS 35 150 397 9 80 120
149
Table No. 5.7: Minimum, maximum and average value of air pollutants at Tilk
Incline, Station Road, Gari Khata Chowk, Badin Stop and SITE
Area locations.
Location Description P.M2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
Tilk Incline
Minimum 30 210 391 5.1 26 43
Maximum 46 343 430 11.0 45 55
Average 38.70 257.00 406.06 7.18 37.40 48.60
Station Road
Minimum 21 212 388 5.7 23 37
Maximum 47 391 431 11.4 38 50
Average 36.73 289.10 411.23 8.78 29.96 43.13
Gari Khata
Chowk
Minimum 27 213 380 5.1 31 42
Maximum 46 360 430 11.5 46 56
Average 36.33 300.26 410.70 8.52 39.30 49.43
Badin Stop
Minimum 25 208 386 7.0 31 37
Maximum 44 349 414.63 10.8 44 53
Average 38.96 292.23 414.90 9.31 38.80 47.63
SITE Area
Minimum 28 218 388 7.3 33 39
Maximum 46 360 429 11.5 45 55
Average 38.96 299.50 409.46 10.04 40.23 48.96
NEQS 35 150 397 9 80 120
150
Table No. 5.8: Minimum, maximum and average value of air pollutants at Press
Club, Gul Center, Hussainabad, Latifabad No. 07 and Latifabad
No. 12 locations.
Location Description P.M2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
Press Club
Minimum 25 213 388 4.6 23 39
Maximum 43 311 417 10.2 37 52
Average 34.40 260.00 403.50 7.14 30.03 44.70
Gul Center
Minimum 29 165 387 5.2 32 40
Maximum 45 293 411 10.2 43 54
Average 38.53 247.70 405.50 7.33 38.43 47.70
Hussainabad
Minimum 24 195 387 6.2 24 36
Maximum 41 354 422 8.8 42 53
Average 32.70 274.4 402.73 7.31 34.40 44.30
Latifabad No. 07
Minimum 22 208 388 4.1 28 35
Maximum 36 297 411 8.4 42 49
Average 28.76 248.26 405.16 6.86 35.76 42.26
Latifabad No. 12
Minimum 22 181 385 5.2 21 29
Maximum 39 271 417 7.8 34 47
Average 28.7 225.30 403.63 6.62 27.13 39.30
NEQS 35 150 397 9 80 120
151
Table No. 5.9: Minimum, maximum and average value of air pollutants at New
Naka, PMU, Shalimar Bus Stand, Mohni Bazarand Sabzi Mandi
locations.
Location Description P.M2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
New Naka
Minimum 17 134 352 1.2 3 10
Maximum 45 294 411 2.9 8 23
Average 31.13 226.06 390.43 2.15 5.53 16.46
PMU
Minimum 21 143 396 1.8 10 17
Maximum 41 329 434 3.6 19 29
Average 32.83 260.5 413.46 2.27 13.90 23.46
Shalimar Bus
Stand
Minimum 21 195 398 3.4 9 15
Maximum 43 337 422 6.3 17 29
Average 32.16 259.23 409.9 4.62 12.66 24.1
Mohni Bazar
Minimum 22 197 402 4.0 11 22
Maximum 60 390 430 6.9 21 33
Average 38.16 258.83 413.23 5.04 16.43 27.63
Sabzi Mandi
Minimum 24 198 401 4.1 15 25
Maximum 46 347 419 6.5 26 36
Average 32.1 252.9 410.76 5.16 20.43 30.73
NEQS 35 150 397 9 80 120
152
Table No. 5.10: Minimum, maximum and average value of air pollutants at
Railway Station, Bucheri Road, Habib Sugar Mills, Society
Chowk and QUEST locations.
Location Description P.M2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
Railway Station
Minimum 19 380 392 3.5 11 24
Maximum 43 409 412 5.9 18 35
Average 33.63 397.4 406.0 4.67 14.53 29.8
Bucheri Road
Minimum 18 131 389 3.8 10 15
Maximum 53 320 425 6.5 16 24
Average 31.1 239.73 405.5 4.83 12.66 19.76
Habib Sugar
Mills
Minimum 24 217 419 1.2 17 27
Maximum 45 591 387 2.2 33 36
Average 34.9 335.86 405.06 1.68 23.6 31.26
Society Chowk
Minimum 21 219 401 1.4 19 27
Maximum 61 390 419 4.7 28 38
Average 32.96 288.2 409.83 3.13 22.46 32.26
QUEST
Minimum 11 96 389 0.2 3 10
Maximum 27 159 405 0.7 11 16
Average 19.4 121.3 396.36 0.40 6.83 13.16
NEQS 35 150 397 9 80 120
153
Table No. 5.11: Minimum, maximum and average value of air pollutants at Old
Sukkur, Lab e Mehran, High Court Road, Eid Gah Road and
Station Road locations.
Location Description P.M2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
Old Sukkur
Minimum 20 141 386 1.8 19 21
Maximum 42 235 418 4.2 38 46
Average 30.76 182.76 402.5 3.05 27.6 35.9
Lab e Mehran
Minimum 21 145 387 2.4 19 28
Maximum 42 219 413 4.0 35 47
Average 31.86 180.63 400.6 3.33 27.7 36.43
High Court
Road
Minimum 26 161 387 2.8 23 32
Maximum 47 241 419 4.2 44 52
Average 35.9 203.13 404.36 3.46 33.16 41.33
Eid Gah Road
Minimum 32 151 388 3.1 18 32
Maximum 48 231 424 4.6 37 55
Average 39.8 198.9 407.03 3.84 25.6 41.96
Station Road
Minimum 29 182 388 3.6 23 34
Maximum 48 317 422 4.8 47 57
Average 38.70 245.733 407.00 4.106 36.066 44.40
NEQS 35 150 397 9 80 120
154
Table No. 5.12: Minimum, maximum and average value of air pollutants at SITE
Area, Canal Road, Hamdard Society, Airport Road and Shikarpur
Road locations.
Location Description P.M2.5
µg/m3
P.M10
µg/m3
CO2
ppm
CO
ppm
NOx
µg/m3
SOx
µg/m3
SITE Area
Minimum 30 154 387 3.4 21 24
Maximum 51 283 423 4.8 41 48
Average 40.4 212.26 408.03 4.10 30.86 37.5
Canal Road
Minimum 23 161 386 3.2 18 33
Maximum 43 241 423 4.8 36 54
Average 35.7 208.83 406.20 4.07 25.833 43.33
Hamdard
Society
Minimum 23 169 393 3.4 17 23
Maximum 48 339 423 4.5 33 47
Average 33.40 261.06 408.56 3.96 24.23 35.30
Airport Road
Minimum 23 146 387 3.2 16 28
Maximum 42 267 417 4.8 33 52
Average 33.06 210.10 403.60 4.03 23.60 39.33
Shikarpur Road
Minimum 27 213 388 3.6 28 38
Maximum 45 377 418 5.2 45 55
Average 35.70 281.80 406 4.40 36.90 44.76
NEQS 35 150 397 9 80 120
155
APPENDIX – B
156
APPENDIX – C
Published research paper based on this Ph.D research work in an HEC recognized ISI
indexed International Journal
157
158
159
160
161
162
163
APPENDIX – D
List of research papers from this research study
S. No Title of Research paper Remarks
1 Assessment of Air Pollutants in
Karachi & Hyderabad cities and
their possible reduction options
Published in ISI indexed (HEC Recognized)
American–Eurasian Journal of Agricultural
& Environmental Sciences, ISSN 1818-6769,
© IDOSI Publications, 2015
2 Measurement of PM pollution at
Hyderabad city Sindh
Oral Presentation in 3rd
International
Conference on Reimagining Pakistan‘s cities
for the 21st century‖ organized by Pakistan
Urban Forum held on 4-8 December 2015 in
Lahore
3 Appraisal of Ambient Air
Pollutants‘ Concentration in
Karachi and their future
Predictions
In Process
164
APPENDIX - E
BRIEF CURRICULUM VITAE OF Ph.D SCHOLAR
Personal Information:
Name: Kishan Chand Mukwana Father‘s name: Isardas
Nationality: Pakistani Date & Place of Birth: 1st December 1970,
Mirpurkhas
Marital Status: Married Phone: +92 22 2670736
Mobile: +92 314 2810129 Email: [email protected]
Education:
Degree Institution Year of
Completion
M.E (Environmental
Engineering)
Institute of Environmental Engineering &
Management, Mehran University of Engineering &
Technology, Jamshoro, Sindh, Pakistan
1997
B.E (Mining
Engineering)
Mehran University of Engineering & Technology,
Jamshoro, Sindh, Pakistan
1993
Intermediate
(Pre-
Engineering)
Shah Abdul Latif Government College, Mirpurkhas,
Sindh, Pakistan
1987
Work Experience:
S.
No
Duration Designation
1 Dec 2006
to date
Assistant Professor (BPS-19), Energy & Environment Engineering
Department, Quaid-E-Awam University of Engineering, Science &
Technology, Nawabshah
2 2011 to
2013
Incharge Chairman, Energy & Environment Engineering Department
3 09.08.2006
to
03.12.2006
Deputy Director (EIA), EPA Government of Sindh, EPA Head office
Karachi
4 25.09.2002
to
08.09.2006
Administrative Incharge, EPA Government of Sindh, Regional office
Hyderabad
5 04.04.1998
to
24.09.2002
Assistant Director, , EPA Government of Sindh, Regional office
Hyderabad
165
HEC recognized research papers publications:
S.
No.
Title of Research Paper
1 K. C. Mukwana, S.R. Samo, A.Q. Jakhrani & M.M. Tunio, ―Assessment of Air
Pollutants in Karachi and Hyderabad Cities and Their Possible Reduction
Options‖, American-Eurasian J. Agric. & Environ. Sci. 89-95, 2015, ISSN
1818-6769, © IDOSI Publications, 2015
2 M.R. Luhur, A.L. Manganhar, K.H. Solangi, A.Q. Jakhrani, K.C. Mukwana &
S.R. Samo, ―A review of the state-of-the-art in aerodynamic performance of
horizontal axis wind turbine‖, Wind and structure, Volume 22, No. 1, 2016, 1-
16.
3 A.A. Mahessar, A.L. Qureshi, K. C. Mukwana, & A.N. Laghari, ―Impact of
Urban and Industrial effluent of Hyderabad city on fresh water Pinyari canal‖
QUEST RJ, Volume 14, No. 2, July-December 2015, ISSN 1605-8607.
4 Zeenat M. Ali, Z.A. Bhatti, K. C. Mukwana, & M.M. Tunio, ―Ground water
Quality of Khairpur Mir‘s Sindh: A Case Study‖ QUEST RJ, Volume 14, No. 2,
July-December 2015, ISSN 1605-8607.
5 S.A. Channa, A.Q. Jakhrani, K. C. Mukwana, S.H. Jakhrani, ―Analysis of
Physicochemical and Biological Quality Parameters of Phuleli Canal Water and
wastewater adjacent to Hyderabad city‖ QUEST RJ, Volume 14, No. 2, July-
December 2015, ISSN 1605-8607.
6 K.C. Mukwana, A. Mashoori, S.R. Samo, A.Q. Jakhrani & A.N. Laghari
―Wastewater Quality Analysis of Dubai Port World, Port Muhammad Bin
Qasim, Karachi‖ International Journal of Applied Environmental Sciences,
ISSN 0973-6077, Volume 10, Number 4 (2015), pp. 1527-1532.
7 A.A. Mahessar, A. L, Qureshi, K.C. Mukwana, &A. Q. Jakharani, ―Study of
Environmental Impacts and Threats to the Ramsar Haleji Lake, Sindh, Pakistan‖
International Journal of Applied Environmental Sciences, ISSN 0973-6077,
Volume 10, Number 5 (2015), pp. 1577-1590
8 Zeenat M. Ali, K. C. Mukwana, Hussain Saleem, Samina Saleem & Abdul
Jabbar Laghari, ―Exploitation of Available Water Reservoirs for Downstream
Users of River Indus, International Journal of Scientific & Engineering
Research, Volume 6, Issue 1, January-2015, ISSN 2229-5518
9 K.C. Mukwana, K.A. Samo, A.Q. Jakhrani, ―Assessment of Hospital Waste
Management System and generation rates at SMBBMU Hospital Larkana‖,
QUEST RJ, Volume 13, No. 2, July-December 2014, ISSN 1605-8607.
10 K.C. Mukwana, S. R. Samo, M.M. Tunio, A.Q. Jakhrani, M.R. Luhar, ―Study
of Energy Potential from Municipal Solid Waste of Mirpurkhas city‖, QUEST
RJ, Volume 13, No. 2, July-December 2014, ISSN 1605-8607.
166
11 M.M. Tunio, S.R. Samo, K.C. Mukwana, ―Fabrication and analysis of Portable
Batch – Type Bio Gas Plant‖, QUEST RJ, Volume 13, No. 2, July-December
2014, ISSN 1605-8607.
12 G.A. Maitlo, K C. Mukwana & S. R. Samo, ―Performance Evaluation of
Constructed Wetland‖ QUEST RJ, Volume 12, No. 2, July – December 2013,
ISSN 1605 – 8607.
13 S.R. Samo, A.R. Jatoi, F.H. Mangi, A.N. Laghari & K.C. Mukwana, ―Design
of a slow sand bed filtration system for purification of canal water‖, Volume 14,
No. 1, JAN-JUN 2015. ISSN 1605-8607.
14 S.R. Samo, K.C. Mukwana & M.M. Tunio, ―Experimental study of
Desalination Technologies and timer – based solar PV tracking system‖,
QUEST RJ, volume 13, No. 1, January – June 2014, ISSN 1605-8607.
15 A.K. Ansari, K.C. Mukwana & H.R. Ursani. ―Impact of Right Bank Outfall
Drain Effluents on river Indus‖ MUET Research Journal, Volume 20, Issue
Number 2, April 2001.
Professional Organizations membership:
a) Life Member, Pakistan Engineering Council (PEC)
b) Life Member, Mehran University of Engineering & Technology (MUET)
Alumni Association
c) Life Member, Quaid E Awam University of Engineering, Science &
Technology (QUEST) Alumni Association
d) Life Member, Japan International Cooperation Agency (JICA), OSAKA
Japan
e) Life Member, Pakistan Society of Development Economists (PSDE),
Islamabad
