dynamic analysis of ambient air pollution and...

DYNAMIC ANALYSIS OF AMBIENT AIR POLLUTION AND ASSESSMENT OF EFFECTS OF CLIMATE CHANGE ON AIR

QUALITY IN URBAN AREAS OF PAKISTAN

By

ANJUM RASHEED

DEPARTMENT OF ENVIRONMENTAL SCIENCES

FATIMA JINNAH WOMEN UNIVERSITY RAWALPINDI, PAKISTAN

APRIL, 2014

DYNAMIC ANALYSIS OF AMBIENT AIR POLLUTION AND ASSESSMENT OF EFFECTS OF CLIMATE CHANGE ON AIR

QUALITY IN URBAN AREAS OF PAKISTAN

A dissertation presented

by

Anjum Rasheed

to

The Department of Environmental Sciences

In partial fulfillment of the requirements

for the degree of

Doctor of Philosophy

in the subject of

Environmental Sciences

Fatima Jinnah Women University,

Rawalpindi, Pakistan

April, 2014

Declaration

I, hereby, declare that the work presented in this thesis is my own effort based on

actual experimentation except where otherwise acknowledged and that the thesis is

my own composition. No part of the thesis has previously been presented for any

degree.

(Anjum Rasheed)

Certificate This Thesis submitted by Anjum Rasheed is accepted in its present form by the

Department of Environmental Sciences, Fatima Jinnah Women University,

Rawalpindi, as satisfying the thesis requirement for the Degree of Doctor of

Philosophy in Environmental Sciences.

Supervisor: ______________

Dr. Uzaira Rafique

To my Beloved Mentors and Teachers

with Deepest Gratitude

for their

Affection and Kindness

i

ACKNOWLEDGEMENTS

I would like to express my sincere gratitude to all the people who provided

any sort of support in completion of my Ph.D. thesis. First of all, I would like to

acknowledge Higher Education Commission (HEC) of Pakistan for awarding me

Indigenous scholarship to do my Ph.D. and for providing me financial support to do my

research work at NCSU. I am thankful to Mr. Baqir Husnain, Project Manager, Indigenous

Ph.D. Fellowship Program and Mr. Jehanzeb Khan Project Director, International

Research Support Initiative Program (IRSIP) at HEC for being very facilitating. I thank

Dr. Samina Amin Qadir, Vice Chancellor, FJWU for always being very affectionate and

kind to facilitate me during my Ph.D. I acknowledge Dr. Walter Robinson, Head,

Department of Marine, Earth and Atmospheric Sciences, NCSU for providing academic

support and facilities at NCSU. I am extremely grateful to my supervisor Dr. Uzaira

Rafique for all her support throughout my Ph.D. She has been always understanding and

facilitating during my studies. I am highly grateful to my co-supervisor, Dr. Viney P.

Aneja, Professor, Department of Marine, Earth and Atmospheric Sciences, North Carolina

State University (NCSU), USA for his research inputs and valuable guidance to conduct

my research work. I am thankful to him for making himself available anytime despite his

busy schedule. It had been a great opportunity for me to learn from his expertise in the

field of air quality. My special thanks to him and Mrs. Poonam Aneja, for their kindness

and hospitality. I thank Dr. Anantha Aiyyer, Associate Professor, MEAS, NCSU for his

guidance and contribution to my research work. He is acknowledged for his help during

compilation of Weather Research and Forecasting (WRF) model. I am grateful to Mr.

Burhan Ahmed, Meteorologist, Pakistan Meteorological Department (PMD) for providing

me training on WRF model and GrADS and enabling me to complete my research work. I

would like to thank Mr. Qamar and Mr. Sajjad, PMD for helping me in installations of

WRF model and for guiding me in interpretation of weather patterns.

I am thankful to Mr. Asif S. Khan, Director General, Pakistan Environmental

Protection Agency for his kindness and for providing me air quality data of Pakistan. I am

extremely grateful to Mr. Zia Ul Islam, Director, Climate Change Division for his constant

and unconditional support during my Ph.D. I offer profound gratitude to him for giving

ii

me confidence and for inspiring me to proceed for higher studies. I am highly obliged to

Mr. Daisaku Kiyota, CTII, Japan for making me believe in myself and helping me to

tackle the difficult situations and people. I am thankful to Mr. Toshiharu Ochi, Green Blue

Ltd., Japan for his guidance, research input and valuable time. My sincere gratitude go to

all the JICA Experts Mr. Takashi Onuma, Mr. Kageyam, Mr. Kenichi Kuramoto, Mr.

Fujimura, Mr. Takahisa Sato, Mr. Akimoto and Mr. Hosono for their kindness and

cooperation during my Ph.D.

I am thankful to my teachers Dr. Uzaira Rafique, Dr. Shazia Iftikhar, Dr. Asma

Jabeen, Ms. Fareena Iqbal, Dr. Sheikh Saeed, Dr. Rohama Gill, Dr. Azra Yasmin, Dr.

Sofia Khalid, Dr. Abida Farooqi and Dr. Naeema for their guidance and support during

my studies at FJWU. I am extremely grateful to my teachers Mrs. Razia Mukhtar Naqvi,

Ms. Halima, Ms. Riffat Naqvi, Ms. Fiza, Ms. Adeeba, Ms. Waqar-un-Nisa, Ms.

Farkhanda, Ms. Maimoona, Ms. Shahida, Ms. Azra Jillani, Ms. Kanwal Jamshaid, Dr.

Uzaira Rafique and Dr. Viney P. Aneja for being wonderful and kind teachers. I am

thankful to Ms. Priya Pillai, and Mr. Praju for extending all their support during my stay at

NCSU and for being always very helpful. I also thank Ms. Khairun Nisa, NCSU for her

guidance during compilation of WRF model. I am grateful to Ms. Uzma, Ms. Sumreen,

Ms. Farah, Ms. Saima and Ms. Noshabah for their support and cooperation during my

studies.

I highly regard the love and care of my grandfather who had always wished for my

higher studies and a prosperous career; however, I regret not to share the happiness of

completion of my Ph.D. due to his death a day before the convocation. I am thankful to

my parents for their love and for providing me opportunity for higher education. My

special thanks to my father for always standing by my side and for supporting me

throughout my life. I am extremely grateful to my aunt, Mrs. Kausar Jabbar, for her love

and affection and for helping me in my studies. I am indebted to my brother Sohail

Rasheed for his care, kindness and financial support during my stay in US. I thank my

grandparents, Uncle Mushtaq, Uncle Khalid, Uncle Tariq, Uncle Asif and Ms. Shahida for

all their support and guidance during my studies. I thank my siblings Waqas, Saadia,

Maria and Maryam for giving me space to do my Ph.D. thesis.

I am thankful to Allah Almighty for enabling me to complete my Ph.D. and for providing me wonderful opportunities in my life.

iii

ABSTRACT

Air pollution is becoming a major environmental issue in Pakistan owing to rapid

urbanization and economic growth. In order to assess the extent of air quality within the

major urban environments, PM2.5 pollutant has been analyzed during the period 2007-2011

in Islamabad; and 2007 to 2008 in Lahore, Peshawar and Quetta. Seasonal and diurnal

variation of PM2.5 mass concentration and meteorological factors affecting the emissions,

secondary PM2.5 formation and accumulation of pollutants have been analyzed. Air quality

monitoring data and meteorological data were obtained from Federal and Provincial

Pakistan Environmental Protection Agencies. Ambient air quality data of Islamabad,

Pakistan, for six representative air pollutants (carbon monoxide (CO), oxides of nitrogen

(NO and NOy′), sulfur dioxide (SO2), ozone (O3), fine particulate matter (PM2.5), non-

methane hydrocarbons (NMHCs), and meteorology was collected for five years (2007-

2011).

In Islamabad, the annual average PM2.5 mass concentrations were 81.1±48.4 µg m-

3, 93.0±49.9 µg m-3, 47.8±33.2 µg m-3, 79.0±49.2 µg m-3, 66.1±52.1 µg m-3 during 2007 to

2011, respectively; and the highest hourly values observed were 303 µg m-3 during

December 2007, 495.0 µg m-3 during November 2008, 259.8 µg m-3 during September

2009, 456.0 µg m-3 during October 2010, and 379.0 µg m-3 during January 2011.

Comparison of the four cities during summer 2007 to spring 2008 shows that all the four

cities have PM2.5 concentration exceeding the Pakistan National Environmental Quality

Standards (annual average concentration of 25 µg m-3; and 24 hourly average

concentration of 40 µg m-3) for ambient air. During the same time period (i.e. summer

2007 to spring 2008), the highest seasonal PM2.5 mass concentration for Islamabad was

observed as 98.5 µg m-3 during spring 2008; 150.4±87.9 µg m-3; 104.1±51.1 µg m-3 and

72.7±55.2 µg m-3 for Lahore, Peshawar, and Quetta during fall 2007 respectively. Wind

speed and temperature have a negative correlation with the mass concentration of PM2.5.

Moreover, the relation of vapor pressure is weak but mostly negative. Diurnal profile for

all the cities suggests an association of PM2.5 with vehicular traffic.

Data analysis revealed annual average mass concentration of PM2.5 (~45 to ~95 µg

m-3) and NO concentration (~41 to ~120 µg m-3) exceed the Pakistan’s National

Environmental Quality Standards (NEQS). The annual O3 concentration is within the

iv

permissible limits; however, some of the hourly concentration exceeds the NEQS mostly

during summer months. Correlation studies suggest that carbon monoxide has as a

significant (p-value ≤0.01) positive correlation with NO and NOy′; whereas, with ozone, a

significant (p-value ≤0.01) negative correlation is observed. The regression analysis

estimates the background CO concentration to be ~250 to ~500 ppbv in Islamabad. The

higher ratio of CO/NO (~10) suggests that mobile sources are the major contributor to NO

concentration. On the other hand, the ratio analysis of SO2/NO for Islamabad (~0.011)

indicates that the point sources are contributing to SO2 in the city. NO and SO2 correlation

indicates a direct emission sources containing high sulfur content. The correlation of PM2.5

and NO suggests that a fraction of secondary PM2.5 is produced by chemical conversion of

NO into nitrates. The regional background O3 concentration for Islamabad has been

determined to be ~31ppbv. The study suggests that there is an increase in O3 concentration

with increases in photochemical conversion of NO to reservoir NOy′ species.

In order to investigate the contribution of local or transboundary sources of air

pollution towards the high ozone episodes in Islamabad, backward trajectories using

NOAA HYSPLIT model were computed. Furthermore, simulations of two selected high

ozone episodes were carried out by using Weather Research and Forecasting (WRF)

model to assess the influence of meteorological conditions on level and variation of ozone

during episode period. The HYSPLIT back trajectories have revealed that a number of

back trajectories are originated from west, south-west and eastern transboundary pollution

sources. It has been observed that local sources are also contributing towards pollution in

Islamabad when high concentrations are observed during stagnant conditions.

Furthermore, when air masses from west, south-west and south-east are advecting into the

city, stagnant conditions lead to accumulation of pollutants. It has been revealed that most

of the episodes occurred during stagnant conditions followed by advection from far-off

regions.

The study recommends that an extended air quality and climate modeling may be

conducted to get an insight into the tropospheric chemistry of the area leading to many

frequent high ozone episodes. There is also need to develop effective control strategies to

meet the ambient air quality standards through the use of an integrated assessment model.

v

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ............................................................................................... i

ABSTRACT .................................................................................................................... iii

LIST OF FIGURES ....................................................................................................... viii

CHAPTER 1: INTRODUCTION .............................................................................. 1

1.1. Urban Air Pollution ............................................................................................ 1

1.2. Major Air Pollutants and Tropospheric Chemistry .............................................. 2

1.2.1 Sulfur Dioxide............................................................................................. 2

1.2.2 Carbon Monoxide ....................................................................................... 3

1.2.3 Methane ...................................................................................................... 4

1.2.4 Non-Methane Hydrocarbons ....................................................................... 5

1.2.5 Hydroxyl Radical ........................................................................................ 6

1.2.6 Nitrogen Oxides .......................................................................................... 7

1.2.7 Particulate Matter in the Atmosphere........................................................... 8

1.2.8 Tropospheric Ozone Formation ................................................................. 10

1.3 Air Pollution and Meteorology ......................................................................... 11

1.4 Air Pollution and Climate Change .................................................................... 14

1.5 Air Pollution and Climate Change Scenario in Pakistan .................................... 16

1.6 Climate Modeling............................................................................................. 19

1.7 Significance of the Research Work ................................................................... 20

1.8 Objectives ........................................................................................................ 21

CHAPTER 2: METHODOLOGY............................................................................. 22

2.1. Description of Sampling Sites .............................................................................. 22

2.1.1. Islamabad ...................................................................................................... 22

2.1.2. Lahore ........................................................................................................... 23

2.1.3. Peshawar ....................................................................................................... 24

2.1.4. Quetta ............................................................................................................ 25

2.2. Experimental Methods ......................................................................................... 26

2.2.1. Data Collection .............................................................................................. 26

2.2.2. Ambient Particulate Monitor.......................................................................... 29

2.2.3. NOx Analyzer: ............................................................................................... 30

2.2.3. Ambient SO2 Monitor: ................................................................................... 31

vi

2.2.4. Ambient CO Monitor: .................................................................................... 34

2.2.5. Ambient O3 Monitor ...................................................................................... 35

2.2.6. Ambient Hydrocarbon Monitor ...................................................................... 36

2.2.7. Combined Wind Vane and Anemometer ........................................................ 37

2.3. Synoptic Analysis for PM2.5 High Episodes: ..................................................... 38

2.4. Back Trajectory Modeling ................................................................................ 39

2.5. Weather Research and Forecasting (WRF) Model Simulations ......................... 39

CHAPTER 3: RESULTS AND DISCUSSION ............................................................... 40

SECTION I: ....Analysis of Fine Particulate Matter (PM2.5) in Urban Areas of Pakistan: An Observational-Based Analysis ........................................................................................ 40

3.1. Spatial and Temporal Variation of PM2.5 .............................................................. 40

3.2. Diurnal Profile of PM2.5 ....................................................................................... 44

3.3. Effect of Meteorology on PM2.5............................................................................ 51

3.5. Analysis of High PM2.5 Episodes.......................................................................... 61

3.5.1. Islamabad Winter High PM2.5 Episode (December 1-9, 2007) ........................ 62

3.5.2. Lahore High PM2.5 Episode in Winter (February 1-25, 2008) ......................... 66

3.5.3. Peshawar High Winter PM2.5 Episode (December 1-22, 2007) ....................... 69

3.5.4. Quetta High PM2.5 Winter Episode (December 1-18, 2007): .......................... 72

3.5.5. Lahore High PM2.5 Episode in Summer (June 1-12, 2007).............................. 75

3.5.6. Quetta High PM2.5 Summer Episode (August 13-19, 2007) ............................ 80

3.6. Conclusion ........................................................................................................... 83

SECTION II: Measurements and Analysis of Air Quality in Islamabad, Pakistan ........ 85

4.1 Meteorology ..................................................................................................... 85

4.2 Average Concentration of Pollutants ................................................................ 85

4.3. Correlation of Air Pollutants ................................................................................ 90

4.4. Photochemistry of Ozone Formation .................................................................. 100

4.5. Diurnal Variation of Pollutants ........................................................................... 102

4.9. Conclusions ....................................................................................................... 111

SECTION III: . Back Trajectory Analysis and Simulation of Ozone High Episodes by WRF Model in Islamabad, Pakistan ....................................................................................... 113

5.1. Ozone Episodes in Islamabad City ..................................................................... 113

5.2. Back Trajectory Analysis ................................................................................... 113

5.3. Weather Research and Forecasting (WRF) Model Simulations ........................... 137

vii

5.3.1. High Ozone Episode during June 9-15, 2009 ............................................... 137

5.3.2. High Ozone Episode during August 15-19, 2011 ......................................... 155

5.4. Conclusions ....................................................................................................... 167

References .................................................................................................................... 168

Appendices................................................................................................................... 168

Appendix A: Research Publications .............................................................................. 168

Appendix B: Conference Presentations ......................................................................... 168

viii

LIST OF FIGURES

Figure 2.1. Physical Map of Pakistan showing the sampling sites in Islamabad,

Lahore, Peshawar and Quetta .................................................................................. 22

Figure 2.2. Monitoring Site at Central Laboratory for environmental Analysis and

Networking (CLEAN), Pak-EPA…………………………………………………….23

Figure 2.3. Monitoring Site of Punjab-EPA, Lahore ............................................... 24

Figure 2.4. Monitoring Site of KP-EPA, Peshawar ................................................ 25

Figure 2.5. Monitoring Site of Balochistan-EPA, Quetta ......................................... 26

Figure 2.6. Fixed Automated Air Quality Monitoring Station at Central Laboratory of

Environmental Analysis and Networking (CLEAN), Pak-EPA ................................ 27

Figure 2.7. Analyzers for Ambient Air Installed within the Automated Air Quality

Monitoring Station .................................................................................................. 28

Figure 3.1. Annual and Seasonal Average PM2.5 Mass Concentration (µg m-3) in

Islamabad during 2007-2011 (±1 standard deviation is also shown in the figure; No.

of data points given above the bars)………………………………………………….41

Figure 3.2. Comparison of Annual and Seasonal Average PM2.5 Mass Concentration

(µg m-3) in Islamabad, Lahore, Peshawar and Quetta during Summer 2007-Spring

2008 (±1 standard deviation is also shown in the figure; No. of data points given

above the bars) ........................................................................................................ 42

Figure 3.3. Integrated Average Diurnal Profile of PM2.5 Mass Concentration (µg m-3)

in Islamabad, Lahore, Peshawar and Quetta for 2007-2011 (±1 standard deviation is

also shown in the figure) .......................................................................................... 45

Figure 3.4(a). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in

Islamabad ................................................................................................................ 46

Figure 3.4(b). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in

Lahore ..................................................................................................................... 47

ix

Figure 3.4(c). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in

Peshawar ................................................................................................................ .48

Figure 3.4(d). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in

Quetta ..............................................................................................................……49

Figure 3.5. Workday-Weekend Variation of PM2.5 Mass Concentration in (a)

Islamabad; (b) Lahore; (c) Peshawar; and (d) Quetta .............................................. 51

Figure 3.6. Effect of Temperature on PM2.5 Mass Concentration (µg m-3) during

2007-2011 in (a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta ..................... 53

Figure 3.7. Effect of Solar Radiation on PM2.5 Mass Concentration (µg m-3) during

2007-2011 in (a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta ..................... 55

Figure 3.8. Effect of Wind Speed on PM2.5 Mass Concentration (µg m-3) during

2007-2011 in a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta ..................... 57

Figure 3.9. Effect of Vapour Pressure on PM2.5 Mass Concentration (µg m-3) during

2007-2011 in a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta ...................... 59

Figure 3.10(a). Time Series of PM2.5 Mass Concentration and Temperature in

Islamabad during December 1-9, 2007 .................................................................... 62

Figure 3.10(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Islamabad

during December 1-9, 2007 ..................................................................................... 63

Figure 3.10(c). Average Wind Speed (m s-1; contours) and Wind Direction (vectors)

in Islamabad during December 1-9, 2007 ................................................................ 64

Figure 3.10(d). Back Trajectory Analysis of High PM2.5 Episode in Islamabad during

December 1-9, 2007 ................................................................................................ 65

Figure 3.11(a). Time Series of PM2.5 Mass Concentration and Temperature in Lahore

during February 1-25, 2008 .................................................................................... 66

Figure 3.11(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Lahore

during February 1-25, 2008 ..................................................................................... 67


in Lahore during February 1-25, 2008 ..................................................................... 67

x

Figure 3.11 (d). Back Trajectory Analysis of High PM2.5 in Lahore during February

1-25, 2008…………………………………………………………………………….68

Figure 3.12(a). Time Series of PM2.5 Mass Concentration and Temperature in

Peshawar during December 1-22, 2007.................................................................... 69

Figure 3.12(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Peshawar

during December 1-22, 2007 .................................................................................. 70


in Peshawar during December 1-22, 2007 ................................................................ 70

Figure 3.12(d). Back Trajectory Analysis of High PM2.5 in Peshawar during

December 1-22, 2007 .............................................................................................. 71

Figure 3.13(a). Time Series of PM2.5 and Temperature in Quetta during December 1-

18, 2007 .................................................................................................................. 72

Figure 3.13(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Quetta

during December 1-18, 2007 ................................................................................... 73


in Quetta during December 1-18, 2007 .................................................................... 74

Figure 3.13(d). Back Trajectory Analysis of High PM2.5 in Quetta during December

1-18, 2007 ............................................................................................................... 75

Figure 3.14(a). Time Series of PM2.5 and Temperature in Lahore during June 1-12,

2007 ........................................................................................................................ 76

Figure 3.14(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Lahore

during June 1-12, 2007 ........................................................................................... 77


in Lahore during June 1-12, 2007 ............................................................................ 78

Figure 3.14(d). Back Trajectory Analysis of High PM2.5 in Lahore during June 1-12,

2007 ........................................................................................................................ 79

Figure 3.15(a). Time Series of PM2.5 and Temperature in Quetta during August 13-

19, 2007 .................................................................................................................. 80

xi

Figure 3.15(b). Averaged Diurnal Profile of PM2.5 Mass Concentration in Quetta

during August 13-19, 2007 ...................................................................................... 81


in Quetta during August 13-19, 2007 ....................................................................... 81

Figure 3.15(d). Back Trajectory Analysis of High PM2.5 in Quetta during August 13-

19, 2007…………………………………………………………………………...….82

Figure 4.1. Annual Averaged PM2.5 Mass Concentration in Islamabad during

2007… .................................................................................................................... 86

Figure 4.2. Annual Averaged Concentration of NO (µg m-3) in Islamabad during

2007-2011 ............................................................................................................... 86

Figure 4.3. Annual Averaged Concentration of CO (mg m-3) in Islamabad during

2007-2011 ............................................................................................................... 87

Figure 4.4. Annual Averaged Concentration of O3 (µg m-3) in Islamabad during

2007-2011 …..………………………………………………………………………..87

Figure 4.5. Number of Exceedances of Annual Average Concentration of CO (mg

m-3) in Islamabad during 2007-2011……………………………………………..…..88

Figure 4.6. Number of Exceedances of Annual Average Concentration of O3 (µg m-3)

in Islamabad during 2007-2011 ............................................................................... 88

Figure 4.7. Time Series of Ambient Concentrations of O3, NO, SO2, PM2.5 and CO in

Islamabad during 2007-2011 ................................................................................... 89

Figure 4.8. Time Series of Monthly Averaged Concentrations of O3, NO, SO2, PM2.5

and CO in Islamabad during 2007-2011 .................................................................. 90

Figure 4.9. Correlation between CO and PM2.5 ambient concentration during 2007-

2011…..………………………………………………………….…………...............91

Figure 4.10. Correlation between CO and NO in Islamabad during 2007-2011 ....... 92

Figure 4.11. Correlation between CO and NOy′ in Islamabad during 2007-2011…..92

Figure 4.12. Monthly average of SO2 concentration for 2007, 2008, 2010, and 2011

(I denotes ±1SD) ..................................................................................................... 95

Figure 4.13. Correlation between SO2 and NO in Islamabad during 2007-2011 ...... 95

xii

Figure 4.14. Correlation of PM2.5 and NO in Islamabad for the Period 2007-2011….

................................................................................................................................ 96

Figure 4.15(a). Correlations between Measured Daily Averages of CO and PM2.5 in

Islamabad during 2007-2011…………………………………………………………97

Figure 4.15(b). Correlations between Measured Daily Averages of NO and SO2 in


Figure 4.15(c). Correlations between Measured Daily Averages of CO and O3 in


Figure 4.15(d). Correlations between Measured Daily Averages of CO and NOy′ in

Islamabad during 2007-2011……………………………………………………...….99

Figure 4.15(e). Correlations between Measured Daily Averages of NMHCs and O3 in


Figure 4.15(f). Correlations between Measured Daily Averages of CH4 and O3 in

Islamabad during 2007-2011…………………………….………………………….100

Figure 4.16. Variation of concentration of Ozone vs (NOy’-NO)/NOy’ in the summer

months for 2007-2011 during maximum photochemical activity of the day i.e., 9:00

a.m. to 3:00 p.m. ………………………………...………………………………….101

Figure 4.17(a). Diurnal profiles of ozone, nitric oxide, CO and non-methane

hydrocarbons (NMHCs) ………………………...………………………………….102

Figure 4.17(b). Seasonal and diurnal variation of averaged ozone concentration

during 2007-2011 (±1 standard deviation is also shown in the figure)…………..…103

Figure 4.18(a). Correlation of Ozone with Temperature during 2007-2011 at 9:00

a.m. – 3:00 p.m. ……………...……………………………………………………..104

Figure 4.18(b). Correlation of Ozone with Solar Radiation during 2007-2011 at 9:00

a.m. – 3:00 p.m. …...………………………………………………………………..105

Figure 4.18(c). Correlation of PM2.5 with Temperature during 2007-2011 at 9:00 a.m.

– 3:00 p.m. ………………………………………………………...………………..105

Figure 4.18(d). Correlation of PM2.5 with Solar Radiation during 2007-2011 at 9:00

a.m. – 3:00 p.m. ……...……………………………………………………………..106

xiii

Figure 4.19. Air Parcel Back Trajectories for PM2.5 and Ozone Episodes during 2007-

2011 ...................................................................................................................... 109

Figure 4.20. Air parcel 48-hour back trajectories analysis for some selected

PM2.5 and Ozone high pollution episodes during 2007-2011 .................................. 110

Figure 5.1. Back Trajectory Analysis of High Ozone Episode in Islamabad during

27th August – 2nd September, 2007………………………………………..………..114


September 7-19, 2007…………………………………...………………………….115


September 25-27, 2007………………..……………………………………………116


October 12-21, 2007…………….…………………………………………………..117


28th April – 1st May, 2008………..…………………………………………………118


10th May – 1st June, 2008………………………..………………………………….119


June 4-13, 2008.................………………………………………………………….120


June 20-24, 2008………...………………………………………………………….121


August 25-27, 2008…………………………………………………………………122


May 13-21, 2009……..……………………………………………………………..123


May 6-31, 2009……………………………...……………………………..……….124


August 7-9, 2009……………………………………………………………………125

xiv


August 22-25, 2009…………………………………………………………………126


August 27-30, 2009 ............................................................................................... 127


September 19-22, 2009 .......................................................................................... 128


June 11-13, 2010 ................................................................................................... 129


June 19-23, 2010………………………………………………………………..…..130


April 22-24, 2011…………………….……………………………………………..131


May 16-20, 2011 ................................................................................................... 132


May 22-25, 2011……………………………………………………………...….…133


June 2-25, 2011 ..................................................................................................... 134


July 4-6, 2011 ........................................................................................................ 135


July 10-13, 2011…………………………………………………………………….136

Figure 5.24(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed

(contours) Overlaid by Wind Direction (vectors) at 850hPa on June 09, 2009........ 138

Figure 5.24(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed


Figure 5.24(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed


xv

Figure 5.24(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed




























xvi


















(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 15, 2009. ....... 153






(contours) Overlaid by Wind Direction (vectors) at 850hPa on August 15, 2011.... 156





xvii



















Figure 5.34(a). Daytime Averaged Air Temperature (oC; shaded), Wind Speed










xviii







xix

LIST OF TABLES

Table 3.1 Linear Regression Analysis of PM2.5 and Meteorological Variables for Islamabad, Lahore, Peshawar, and Quetta………………………………...61

Table 4.1 Ratio Analysis based on average emissions and/or ambient data……….94

Table 4.2 Linear Regression Analysis of Ozone and PM2.5 with Meteorological Variables ………………………………………………………………..107

1

CHAPTER 1: INTRODUCTION

1.1. Urban Air Pollution

Air pollution is defined as “the presence of substances in the ambient

atmosphere resulting from the activity of man or from natural processes causing

adverse effects to man and the environment” (Weber, 1982). Presently, the urban air

quality has been degraded to a great extent due to indiscriminate industrialization,

vehicular emissions and biomass burning (Jenkin, 2004; Real and Sartelet, 2011). It is

important to understand the complexities of air quality in order to deal with the

climate change. Air pollution is caused by emissions from various natural and

anthropogenic sources and it depends on meteorological conditions and topography.

Air pollution is controlled mainly through controlling various emission sources. Rapid

urbanization and economic growth has led to air pollution with an increase in

pollutants like particulate matter, NOx, SO2, CO, NMHCs etc. These pollutants are

either emitted directly into the atmosphere or formed in the troposphere by

photochemical reactions. Some of these pollutants like CO, NOx and NMHCs are

precursors for photochemical production of ozone in the troposphere. It has been

observed that the atmospheric concentrations of various pollutants are increasing

continuously. For instance, the concentration of tropospheric ozone has increased by a

factor of 3-4 during last century. Similar changes have been observed in the

concentration of N2O with an increase of 15% during last 150 years. The

concentration of methane has been doubled during last 150 years due to increase in

anthropogenic sources. Hydrocarbons are also showing the similar pattern of growth

(IPCC, 2001). It has been estimated that approximately 3333 tons of organic

compounds and 890 tons of NOx are released into the basin of Los Angeles on daily

basis (Monks and Leigh, 2009).

A severe form of air pollution is known as smog which comes from

combination of smoke and fog. The photochemical smog such as experienced in Los

Angeles is caused by high concentration of oxidants such as ozone and peroxidic

compounds which are formed by photochemical reactions (Monks and Leigh, 2009).

2

Air pollutants have a long-range transport mechanism which leads to regional and

global impacts of air pollution (Zhao et al., 2007).

1.2. Major Air Pollutants and Tropospheric Chemistry

1.2.1 Sulfur Dioxide

Sulfur compounds in the atmosphere originate from natural as well as

anthropogenic sources. However, anthropogenic emission accounts much more than

the sulfur concentration released from natural sources. It has been estimated that

about 75% of the average global levels of sulfur and about 90% of sulfur in the

Northern Hemisphere are coming from anthropogenic activities. Natural sources of

sulfur are soil, volcanic eruption and biogenic activities in the ocean (Jackson, 2003).

Major anthropogenic sources of SO2 are industrial and transport sector (Harrison et

al., 1997). Brick kilns are also considered to be a major source of SO2 in Pakistan as

coal with high sulfur content is used as fuel (Biswas et al., 2008). Sulfur dioxide in

the atmosphere leads to formation of PM2.5 and acid deposition (Wang and Wang,

1995).

Sulfur dioxide does not dissociate photochemically in the atmosphere. The

oxidation of sulfur compounds causes major concerns like climate balance and

acidification. Furthermore, high sulfur concentrations lead to exchange of sulfates

from troposphere to stratosphere forming a layer in the stratosphere (Monks and

Leigh, 2009). The oxidation of SO2 is carried by various mechanisms in liquid- and

gas-phase. The gas-phase oxidation reactions of sulfur dioxide are mentioned as

follows (Monks and Leigh, 2009):

The gas-phase oxidation of SO2 leads to formation of sulfuric acid which gets

attached to the particulate matter due to its relatively low vapour pressure. A major

fraction of sulfuric acid is removed by wet deposition process through precipitation

3

and cloud droplets (Monks and Leigh, 2009). The aqueous phase oxidation of SO2

depends on the nature of phase such as clouds and fog and availability of light and

oxidants like ozone and H2O (Monks and Leigh, 2009). The wet and dry depositions

cause corrosion and damage to crops, forests and aquatic ecosystems (Monks and

Leigh, 2009). Sulfur dioxide and nitrogen dioxide contribute to acid rain by adding

sulfuric acid and nitric acid to the atmosphere (Monks and Leigh, 2009).

1.2.2 Carbon Monoxide

Carbon monoxide is produced through inefficient burning of fossil fuel. Major

sources of CO are combustion from internal engines, indoor cooking and heating and

iron smelting, burning of biomass and oxidation of non-methane hydrocarbons

(NMHCs) and methane (Ayres and Ayres, 2009; Wotawa et. al., 2001) with the

largest source coming from tropospheric photochemical reactions i.e., 25 times higher

than combustion (Weinstock and Niki, 1972). Burning of agricultural residues and

forest fires also contribute to the emission of carbon monoxide in the troposphere

(Kasischke and Bruhwiler, 2003; Crutzen and Andreae, 1990). Carbon monoxide is

used as an indicator of presence of other pollutants such as ozone in the troposphere

(Zhao et al., 2007). It is also a good tracer of pollutants coming out of sources like

incomplete combustion of fossil fuel particularly motor vehicles (Zhao et al., 2007).

Lifetime of carbon monoxide in the troposphere is from a few weeks to several

months due to which it is transported over long distances causing pollution to the

regions far away from the emission sources (Zhao et al., 2007; Liu et al., 2002). It has

been reported by Poisson et al. (2000) that the oxidation of NMHCs is responsible for

approximately 40 - 60% of the concentration of carbon monoxide over oceans and

about 30 - 60% of carbon monoxide in the atmosphere. CO is an important variable as

it can be used as an indicator of mobile source emissions (Warneck, 1988).

Furthermore, it is the precursor gas for ozone formation in the troposphere (Warneck,

1988). In relatively less polluted areas, i.e., low NOx regimes, hydroxyl radical reacts

with carbon monoxide to form peroxy radical:

4

In low-NOx conditions, HO2 reacts with ozone causing its destruction and also

leads to formation of OH radical:

In polluted areas, HO2 reacts with another peroxy radical leading to the formation of hydrogen peroxide (H2O2):

It may also react with organic peroxy radical (CH3O2) to form organic peroxides:

In high NOx-areas, peroxy radical catalyses the oxidation of NO to NO2 with subsequent formation of ozone:

1.2.3 Methane

Wetlands, oceans, geology and termites are the natural sources of methane in

the atmosphere (IPCC, 2001). Other than the natural sources, methane is emitted from

landfills, natural gas, coal combustion, petroleum industries, rice paddies, biomass

burning and enteric fermentation (Forster et al., 2007; Mathez, 2009; IPCC, 2001).

Methane is present in the atmosphere much more than any other organic specie

(Shallcross, 2009). The global average concentration of methane was 750 ppbv in the

pre-industrial era which has now increased to 1780 ppbv in the Northern Hemisphere

and 1650 ppbv in the Southern Hemisphere (Shallcross, 2009) with the global average

5

concentration as 1775 ppbv (Mathez, 2009). During 20th century, methane had been

increasing about 1.3% annually till early 1990s (Blake and Rowland, 1988), however,

the growth rate of global methane concentration slowed down to about 0.6% on

annual basis (Steele et al., 1992). The actual reason of slowing down of methane

growth rate in the atmosphere is not confirmed as it may be either because of

reduction in emission sources of methane or its removal by radical chemistry which

involves reaction with hydroxyl radical (Shallcross, 2009).

In low-NOx areas, methane reacts with hydroxyl radical to form peroxy radical CH3O2

which facilitates the oxidation of NO to NO2 which then leads to photochemical

production of ozone as given in reactions (1.11) and (1.12):

1.2.4 Non-Methane Hydrocarbons

The ambient concentrations of non-methane hydrocarbons (NMHC) and NOx

along with some catalysts contribute to the difference between urban and rural

photochemistry (Shallcross, 2009). Hydrocarbons are very important in the

tropospheric ozone formation contributing to about 40% of ozone in the troposphere

(Houweling et al., 1998). The NMHCs in the troposphere come from various natural

and anthropogenic sources mainly coming from automobile exhaust, oil-fired power

plants, biomass burning, combustion processes, oil-based paints, petrochemical

industries, solvent makers, evaporation of gasoline and leakages from natural gas and

liquified petroleum gas (An et al., 2008; Arsene et al., 2009; Harrison, 1999; Jackson,

2003; Watson et al., 2001). The natural sources of NMHCs include biogenic and

oceanic emissions (Arsene et al., 2009; Harrison, 1999). Reactive hydrocarbons

mainly come from biogenic sources (Hewitt, 1999).

It has been observed that aldehydes increase the rate of conversion of NO to

NO2 and consequently the formation of ozone (Wayne, 2000). Hydroxyl radical also

comes from the photolysis of aldehydes and ketones formed by the oxidation of

6

NMHCs (Monks and Leigh, 2009). Reaction of NMHCs with OH radical is very

important due to its role in photochemistry of the troposphere (Derwent, 1995;

Finlayson-Pitts and Pitts, 1999). The oxidation of NMHCs is initiated by hydroxyl

radicals which lead to oxidation of NO to NO2. NO2 is then photolyzed to form

tropospheric ozone. In these reactions, hydroxyl radical is catalytic and peroxy

radicals are the chain propagators. The final oxidized products of hydrocarbons are

water vapours, whereas, some other partially oxidized species like aldehydes, ketones

and carbon monoxide are also formed with ozone as a byproduct (Monks and Leigh,

2009). The schematic representation of these reactions is given below:

Reaction (1.20) then leads to photochemical formation of ozone through

reactions (1.11) and (1.12).

Major issue of concern is the health effects of NMHCs as some of these

compounds are carcinogenic (Jackson, 2003). There have been increased incidences

of lungs cancer, tumors of skin and bladder due to increased exposure to PAHs

through inhaled air (Monks and Leigh, 2009). Atmospheric hydrocarbons lead to

ozone formation by reacting with OH radicals in the presence of NOx and also

contribute to the formation of particulate matter through gas to particle conversion

(Arsene et al., 2009; Claeys et al., 2004; Atkinson and Arey, 2003).

1.2.5 Hydroxyl Radical

Hydroxyl radical is the most reactive specie in the troposphere and is an

indicator of radical-chain oxidation reactions in the atmosphere. A major source of

hydroxyl radical is photolysis of ozone involving water vapours in the atmosphere

(Monks and Leigh, 2009). The most critical aspect of hydroxyl is its abundant

concentration in the atmosphere and its high reactivity. In the areas with no pollution

7

i.e., low NOx, OH radical reacts with carbon monoxide or methane to form carbon

dioxide and peroxy radicals like CH3O2 and HO2. HO2 further reacts with ozone to

form OH radical. In this way, OH radical leads to reactions which contribute to

destruction of tropospheric ozone (Monks and Leigh, 2009).

1.2.6 Nitrogen Oxides

There are a number of anthropogenic and biogenic sources for nitrogen oxides

in the atmosphere which include biomass burning, vehicular and industrial emissions,

fossil fuel combustion, emissions from the power plants, lightening discharges,

agricultural activities and microbial activity in soil (Shallcross, 2009; Mathez, 2009;

Finlayson-Pitts and Pitts, 2000). The fate and influence of NOx depend on its sources

and sinks in the atmosphere. The lifetime of NOx is quite significant and depends on

[NO/NO2] ratio and concentration of hydroxyl radical (Shallcross, 2009). The sum of

total reactive nitrogen is termed as NOy which is defined as NOy = NOx + NO3 +

2N2O5 + HNO3 + HNO4 + HONO + PAN + nitrate aerosol + alkyl nitrate. Nitrogen

oxides are also converted into nitric acid and nitrate particulates which may be

removed from the atmosphere by wet and dry deposition processes (Monks and

Leigh, 2009). Peroxy Acetyl Nitrate (PAN) is a byproduct of oxidation linked to

urban air pollution. It is an important component of NOy as it transports nitrogen

oxides from urban polluted areas to the remote areas (Singh et al., 1992).

NO, NO2 and ozone are considered to be in a photostationary state under

suitable concentrations (Leighton, 1961) with the condition that these are not affected

by the local emission sources of NOx and that the constant solar radiation is available

(Shallcross, 2009). Night-time chemistry is significant due to its role in formation of

secondary pollutants. During night-time, gradual oxidation of NO2 by ozone leads to

formation of secondary pollutants (Shallcross, 2009). The following reactions show

the photostationary state of NOx and ozone:

8

1.2.7 Particulate Matter in the Atmosphere

Particulate matter with aerodynamic diameter less than 10µm are known as

coarse particulates (PM10), whereas, particulates with aerodynamic diameter less than

2.5 µm are called fine particulate matter (PM2.5). Composition of particulate matter

depends on the source characteristics (McMurry et al., 2004). Particulate matter (PM)

include nitrates, sulfates, elemental carbon, organic carbon, sea salt and soil dust.

Nitrates, sulfates and carbon particulates are present as PM2.5, whereas, the others are

coarse particles. Black carbon is emitted directly into the atmosphere as primary

pollutant, whereas, nitrates, sulfates and organic carbon are formed in troposphere by

oxidation of nitrogen oxides, sulfur dioxide and non-methane hydrocarbons

respectively (Jacob and Winner, 2009). Organic carbon and nitrates keep on changing

their phase between gas and particle form depending upon temperature variation

(Jacob and Winner, 2009). Significant anthropogenic sources of particulate matter

include coal combustion, motor vehicles, industrial activities, cement production,

incineration metallurgy, biomass burning and agricultural activities (Jackson, 2003;

Biswas et al., 2008). Particulate matter also include some of toxic compounds like

polycyclic aromatic hydrocarbons (Smith et al., 1996).

The adverse health effects of particulate matter include pre-mature mortality,

lungs and cardiovascular diseases (McMurry et. al., 2004; Pope et al., 2002). Fine

particulate matter is very significant in tropospheric chemistry, whereas, larger

particles provide surface for heterogeneous reactions and also perform the role of

cloud condensation nuclei (Monks and Leigh, 2009).

Particulate Matter also have impact the formation of clouds and weather

pattern. It has been observed that the areas with more haze have less rainfall

(Rosenfeld et al., 2007). Particulate Matter is important factor for climate change due

to its contribution to radiation transfer in the atmosphere (IPCC, 2001). Particulate

Matter also affect the hydrological cycle and rate of precipitation (Ramanathan et al.,

2001; Menon et al., 2002; Sarkar et al, 2006).

9

Black carbon is emitted into the atmosphere from combustion of fossil fuels

particularly diesel, forest fires and agricultural residue burning (Husain et al., 2007).

Black carbon consist of both elemental and organic carbon species (Andreae and

Gelencser, 2006). About 5-15% of PM2.5 in the atmosphere is the black carbon

(Husain et al., 2007). Black carbon aerosols contribute to the global warming to a

great extent due to their capacity to absorb the solar radiation (Jacobson, 2002). It has

been reported that the high black carbon concentrations have led to increased flooding

and droughts in South Asian region (Menon et al., 2002). Furthermore, such high

black carbon concentrations have also caused the loss of agricultural productivity by

10-20% in some countries of South and Southeast Asia (Chameides et al., 1999).

Black carbon particles can travel upto thousands of kilometers due to their residence

time of six days (Husain et al., 2007). Black carbon particulates contribute to global

warming through radiative forcing (Penner et al., 2003). Dust particles add heat to the

atmosphere through absorption of solar radiation (Haywood et al., 2001). Brick kilns

are another significant source of black carbon along with polycyclic aromatic

hydrocarbons (PAHs) and sulfur (Biswas et al., 2008; Smith et al., 1996) into the

atmosphere. Inefficient fuel usage in brick kilns like low quality coal, old tyres and

biomass leads to toxic emissions (Stone et al., 2010).

During past two decades, some areas of Northeastern Pakistan and Eastern

India have been experiencing severe fog in winter during which high concentration of

sulfur dioxide has been observed (Hameed et al., 2000). The average concentration of

PM2.5 in Lahore has been observed to be manifold higher than the average PM2.5

concentrations in New York, Seoul and Hong Kong (Biswas et al., 2008). Husain et al

(2007) has reported the average PM2.5 mass concentration as 190 µg m-3 with

variation of 89 - 476 µg m-3 in Lahore, 67% of which is carbonaceous in nature.

Increased emissions of its precursor gases is of great concern (Ohara et al., 2007)

particularly in South Asian region where high altitude areas of Himalayas are

characterized by the severe pollution phenomenon of Atmospheric Brown Clouds

(UNEP, 2008). The high concentration of aerosols has huge impact on the air quality

as well as to the climate change (UNEP, 2008).

Adverse health effects of particulate matter include asthma, pneumonia,

exacerbation of chronic obstructive pulmonary disease, increased mortality rate and

10

decreased lungs function (USEPA, 1995; Saldiva et al., 1995). Approximately, annual

3, 60,000 premature deaths in Asia are caused by exposure to PM2.5 (WHO, 2008).

Fine particulate matter increases the risk of respiratory and cardiovascular diseases by

penetrating into lungs (Schwartz et al., 2002; Dockery et al., 1993).

1.2.8 Tropospheric Ozone Formation

Tropospheric ozone adversely affects the life on earth due to its reactive nature

(Mathez, 2009). Ozone has been designated as criteria pollutant by USEPA due to its

health effects (USEPA, 1993). It is very reactive and, therefore, does not stay in the

troposphere for long time (Mathez, 2009). Ozone is formed photochemically in the

atmosphere by carbon monoxide, nitrous oxide and hydrocarbons (Mathez, 2009) in

presence of HOx (Jacobson, 2002; Chan et al 1998; Crutzen, 1973; Chameides and

Walker, 1973). Previously, it was considered that the stratospheric ozone is the major

source of tropospheric, however, later it was found that the photochemical oxidation

of various gases in the atmosphere is a major source of ozone (Shallcross, 2009;

Fabian and Pruchniewz, 1977). Ozone titration by nitrogen oxides brings balance to

its atmospheric concentration (Shallcross, 2009). Tropospheric ozone, hydroxyl

radical and H2O2 are the indicators of oxidizing capacity of the troposphere

(Shallcross, 2009). Ozone formation may be either NOx- or VOC- sensitive and it

basically depends on VOC/NOx ratio (Sillman, 1999).

Ozone is a very reactive gas and, therefore, it poses high risk to human health

(Conti et al., 2005) and ecosystem (Paoletti et al., 2006). Tropospheric ozone

contributes to the global radiative forcing which makes it a significant greenhouse gas

(Forster et al., 2007). Stratosphere-Troposphere Exchange also contributes to the

tropospheric ozone concentration. (Davis et al., 2010; Trickl et al., 2010). Long range

transport and variation in lifetime of the ozone lead to its spatial and temporal

variation in the troposphere (Mickley et al., 2004).

Atmospheric dynamics is quite significant in variation of ozone concentration

in the troposphere (Delcloo and Backer, 2008). Ozone exchange in stratosphere-

troposphere and troposphere-PBL also contributes partially to the natural variation of

ozone in the troposphere (Stohl et al., 1995). Reduction in emission of precursor gases

11

has led to low tropospheric ozone concentrations in Europe (Delcloo and Backer,

2008; Bronniman et al., 2003). It may be explained by the fact that 30% and 15-20%

of the anthropogenic emissions of NOx and NMHCs respectively was reduced in

Europe and North America during 1990-2002 (Delcloo and Backer, 2008). About

45% of the emissions of carbon monoxide have been reduced in Europe during this

period, whereas, emissions are increasing in Asia with no check (Streets et al., 2003).

Being a precursor gas for formation of highly reactive hydroxyl (OH) radical,

ozone has a significant role in global climate change (Kumar et al., 2010). High ozone

concentrations have been observed in Asian region where it poses great risk to human

health and plants (Mauzerall and Wang, 2001; Desqueyroux et al., 2002; Tanimoto,

2009]. It has been reported (Ding et al., 2008) that there has been an approximately

2% increase in ozone concentration in Beijing during 1995-2000. In Southern China,

an annual increase of 0.5-0.9 ppbv (Wang et al., 2009) and a monthly increase of 5%

have been reported for Eastern China (Xu et al., 2008). High ozone concentration at

low altitude areas is linked to the in situ photochemical formation involving

precursors and radiation (Solberg et al., 2008).

Ozone has very low solubility in water and, therefore, ozone is not removed

by wet deposition process (Jacob and Winner, 2009). Ozone, along with its precursor

gases, is transported from polluted areas to far-off regions due to its lifetime of days

to weeks affecting the regional background ozone concentration which is of great

concern (Jacob and Winner, 2009).

1.3 Air Pollution and Meteorology

Meteorology affects the quality of air by interrupting the rate of dispersion

with change in wind speed, convection and mixing depth, precipitation scavenging,

dry deposition and rate of photochemical reactions. Meteorological conditions like

temperature, relative humidity, stagnation, wind speed, wind direction, precipitation,

convection, mixing depth and boundary layer mixing also affect the air quality (Lin et

al., 2001; Jacob and Winner, 2009). Interannual variability of ambient concentration

of pollutants in relation to the meteorological conditions has been reported. Climate

change has the tendency to increase the frequency of tropospheric ozone pollution

12

episodes. Weather conditions like high temperature and stagnation have been held

responsible for the highest ozone episode in northeastern USA (Lin et al., 2001).

Northeast US experienced the highest temperature during summer 1988 and very high

number of ozone exceedances during this period. Such exceptionally ozone episodes

due to an increase of global temperature would cause implications for air quality. It

has been observed that the stagnation causes less dispersal of air pollutants causing

higher concentration of pollutants (Holzer and Boer, 2001). Eastern US experienced

severe and persistent pollution episodes due to decreased ventilation by cyclones

tracking across Canada (Leibensperger et al, 2008; Mickley et al., 2004). Europe has

also experienced very high ozone episodes during heat wave of summer 2003 which

implies the role of temperature in ozone formation (Solberg et al., 2008; Vautard et

al., 2007).

Low wind speed restricts dispersion of air pollutants leading to their

accumulation in a particular area (Holzer and Boer, 2001). High temperature gives

rise to the formation of ozone due to increased rate of photochemical reactions and

increased biogenic emissions consequently affecting the formation of ozone (Kleeman

et al., 2010; An et al., 2008; Lin et al., 2001). Ozone has a strong correlation with

ambient temperature (Cox and Chu, I995) and a negative correlation with relative

humidity (Camalier et al., 2007). However, ozone concentration of less than 60 ppbv

has not shown correlation with temperature (Sillman and Samson, 1995). It has been

estimated that there will be a 10% rise in tropospheric ozone concentration with a rise

of 4oC in average annual temperature (US-EPA, 1989). Various models simulations

have confirmed that temperature is affecting the ozone formation more significantly

(Steiner et al., 2006; Dawson et al., 2007). It has been projected for four cities in

Canada with different weather conditions that high ozone episodes would increase by

50% in 2050s and 80% in 2080s (Cheng et al., 2007).

Major factors contributing to ozone formation in the troposphere are

photochemical processes, horizontal and vertical transportation and physical removal

mechanisms (Altshuller, 1989). Ambient meteorological conditions i.e., radiation

levels, temperature, and humidity influence the level of tropospheric ozone (Seinfeld

and Pandis, 1998; Chan et al., 1998). High ozone episodes are associated with

stagnation, high temperature, low relative humidity (Ellis et al., 2000) and anti-

13

cyclones (Cheng et al., 2007). Topography also plays a major role in high ozone

concentrations as subsidence inversion is formed in specific areas (Cheng et al., 2001;

Tanner and Law, 2002). The influence of wind direction varies due to the advection of

air masses, topography and nearby high ozone areas (Rohli et al., 2004). Tropospheric

ozone may travel to hundreds of kilometers downwind depending on the wind speed

and direction and topography (An et al., 2008).

Precipitation is a major sink for particulate matter which reduces its lifetime in

the troposphere (Jacob and Winner, 2009). High wind speed is associated with less

pollutants due to dispersal and transportation to other regions (Husain et al., 2007),

however, it may also bring pollutants from other areas. Low mixing height during

nighttime leads to buildup of pollutants (Husain et al., 2007). During winter season,

lower ambient temperature, low wind speed and the height of boundary layer i.e.,

~500-800 meters lead to accumulation of air pollutants (Ram et al., 2010; Nair et al.,

2007). Particulate matter is negatively correlated with relative humidity (Wise and

Comrie, 2005). Nitrate particles decrease with increase in temperature as they

exchange between gas and particle phase (Tsigaridis and Kanakidou, 2007). Sulfate

particles increase with increase in temperature (Dawson et al., 2007; Kleeman, 2007)

as a result of increased rate of sulfur oxidation. PM mass concentration decreases with

increase in precipitation due to removal by wet deposition process (Balkanski et al.,

1993).

Particulate matter and ozone are known as significant climate forcing variables

due to their interaction with the solar radiation (Forster et al., 2007). It has been

observed that about 80% of the variation in average ozone concentration is positively

correlated with temperature and negatively correlated with the relative humidity

(Camalier et al., 2007). Solar radiation and temperature have been found to be the

dominant meteorological variables affecting ozone concentrations during summer

season in Switzerland (Ordonez et al., 2005).

Particulate matter is less correlated with meteorological conditions than ozone

due to variation in its composition (Wise and Comrie, 2005). Particulate matter has a

strong negative correlation with wind speed (Cheng et al., 2007). Some of the areas

14

experience slowly moving anticyclones which lead to accumulation of air pollutants

(Hulme and Jenkins, 1998).

1.4 Air Pollution and Climate Change

According to IPCC (2007), “Climate Change is referred to a change in the

state of climate that can be identified (e.g., using statistical tests) by changes in the

mean and/or the variability of its properties, and that persists for an extended period,

typically decades or longer. It refers to any change in climate over time, whether due

to natural variability or as a result of human activity”. This definition differs from that

in the United Nations Framework Convention on Climate Change (UNFCCC), where

climate change refers to a change of climate that is attributed directly or indirectly to

human activity that alters the composition of the global atmosphere and that is in

addition to natural climate variability observed over comparable time periods”.

Air Quality has influenced the global climate to a great extent. Some of the

significant factors which cause climate change are greenhouse gas concentrations,

palaeogeography, variation in ocean-heat transport and changing orbital parameters.

Climate system consists of atmosphere, cryosphere, hydrosphere, biosphere and

lithosphere. Almost all the energy required by the climate system is provided by the

solar radiation. The spherical shape of Earth is responsible for seasonal cycle of

weather (Lockwood, 2009). Indiscriminate energy usage causes rapid increase in air

pollution and subsequently leads to climate change. Global warming started in 1910

proceeding in two phases i.e., during 1910-1940 and then starting again in 1970 till

today (Mathez, 2009). Global average temperature increase during last century is

about 1oC, whereas, the rate of increase during last three decades has been 0.27oC per

decade (Brohan et al., 2006). This global warming is causing sea level rise at the rate

of 2.6±0.04 mm per year which leads to sea level rise of 26 cm (10 inches) per

century (Alley et al., 2005). It has been estimated that the sea level may increase upto

20-60 centimeter (8-24 inches) by 2100 (Meehl et al., 2007).

Global average mixing ratio of CO2 has been increased by about 36% after

industrial revolution with an increase from 285 ppm in pre-industrial era to 379 ppm

in 2005 (Forster et al., 2007). Major sources of CO2 are combustion of fossil fuels,

15

biomass burning and deforestation (Forster et al., 2007; Houghton, 2003; Andreae and

Merlet, 2001). Chlorofluorocarbons are about ten times stronger than the CO2

greenhouse effect due to its role in stratospheric ozone depletion (Ramanathan and

Feng, 2009). Man-made fluorine gases have increased efficiency to absorb infrared

radiation. Perfluorocarbons (PFCs) are highly radiative efficient with a lifetime of

1,000 to 50,000 years (Forster et al., 2007). Nitrogen oxides are also important factors

for depletion of stratospheric ozone which consequently leads to an imbalance of

absorbed and emitted solar radiation (Crutzen, 1972). Nitrous oxide (N2O), another

greenhouse gas, increased from 270 ± 7 ppbv in pre-industrial era to 314 ppb in the

year 1998 (Forster et al., 2007).

It has been observed that the annual average minimum and maximum

temperature have increased and that the average minimum temperature has increased

more than the average maximum temperature (Vose et al., 2004). It has been

estimated that the global average temperature increase during 1910-1945 was 0.11oC

per decade, 0.01oC per decade from 1945 to 1975 and 0.22oC per decade during 1976-

2000 (Jones and Moberg, 2003). Global average temperature has been increased by

0.5oC during last 150 years (Houghton, 1994). Tropospheric ozone and particulate

matter are significant climate forcing agents due to their interaction with solar and

terrestrial radiation (Pulikesi et. al., 2006; Forster et al., 2007). The stratospheric

ozone depletion has led to an increase in the ground level radiation which

consequently leads to increase in the formation of tropospheric ozone due to increased

rate of photochemical reactions.

Global sea-surface temperature has also been increased about 0.7oC (1.3oF)

during past century, which is more than any other change in the climate system

(Trenberth et al., 2007; Barnett et al., 2005). It has been predicted that the average

temperature in Western United States will increase 0.8o-1.7oC by 2050 (Barnett et al.,

2008). It has been reported that the sixteen warmest years of the century have

occurred during last two decades with 2010 being the warmest year (WMO, 2011).

Ozone layer in the stratospheric protects the earth from harmful solar radiation

due to its capacity to absorb the radiation (Mathez, 2009). The atmospheric

compounds containing nitrogen, hydroxyl radicals and chlorine destroy the ‘good

16

ozone’ layer (Mathez, 2009). Volcanic eruption also emits chlorine and sulfur

compounds which ultimately reach stratosphere to destroy the ozone layer. Volcanic

eruption has been part of the natural processes balancing the concentration of ozone in

the stratosphere. However, after industrialization, anthropogenic activities are

contributing much more to the destruction of ozone layer (Bluth et al., 1993).

1.5 Air Pollution and Climate Change Scenario in Pakistan

Air pollution has become a major environmental concern for Pakistan. Major

causes for air pollution are rapid urbanization, economic growth and unplanned

industrialization. In urban areas of Pakistan, major sources of air pollutants are

automobiles exhaust, combustion of fossil fuels/biomass, coal-fired power plants,

industrial emissions; open burning of solid waste and aircrafts (IMF, 2010; Barber,

2008; World Bank, 2006). Industries leading to air pollution include power plants,

cement, steel, fertilizer, sugar mills along with a major contribution from the small-

scale industries like brick kilns and plastic moulding (IMF, 2010; ADB and CAI-Asia,

2006; Faruqee, 1997). Industrial emissions largely contain particulate matter, carbon

monoxide and carcinogens like soot and asbestos (IMF, 2010). The problem is

aggravated by old vehicles, diesel trucks and low level of fuel quality (World Bank,

2006). Vehicles with two-stroke engines contribute more to pollutants emissions due

to inefficiency of burning fuel. There has been a growth of 117% in production of

two-stroke vehicles in 2010-11 from year 2000-01 (Government of Pakistan, 2012).

Continued increase in the usage of diesel driven heavy duty vehicles and two-stroke

vehicles add up to emission of pollutants into the atmosphere (World Bank, 2006;

ADB and CAI-Asia, 2006). High sulfur content of 0.5-1% in diesel and 1-3.5% in

furnace oil leads to higher emissions of sulfur dioxide and particulates (ADB and

CAI-Asia, 2006). Particulate matter pollution is also aggravated by more reliance on

diesel fuel by the transport sector (Shyamsundar, et al., 2001). Compared to an

average vehicle of United States, an average Pakistani vehicle emits about 25 times

higher CO2 and CO, 20 times more non-methane hydrocarbons (NMHCs) and 3.5

times more sulfur dioxide (Barber, 2008). Usage of diesel-fueled electric generators at

a large scale due to extensive power outage in the country is a significant factor

adding up the urban air pollution (IMF, 2010).

17

The World Bank (2006) estimated an annual environmental degradation cost

of about Rs. 365 billion in Pakistan. Estimated environmental health cost due to urban

particulate pollution is about Rs. 65 billion causing about 22,000 premature annual

deaths among adults and 700 deaths among young children. Particulate matter is one

of the significant pollutants with adverse health impacts like pre-mature mortality,

lungs and cardiovascular diseases (NARSTO, 2004; Pope et al., 2002). An estimated

cost of about Rs. 45 billion is caused by lead exposure of which airborne lead

particulate is a major fraction. There is high incidence of IQ loss, mild mental

retardation, anemia and cardiovascular diseases caused by lead air pollution. Indoor

air pollution costs approximately Rs. 67 billion causing 28,000 deaths and 40 million

cases of respiratory diseases annually (World Bank, 2006).

Pakistan has become too vulnerable to climate change mainly due to its

geographical location despite the fact that it contributes least to the greenhouse gas

emissions (Ministry of Environment, 2011). Pakistan's ranking of global GHG

emitters is 135th with a contribution of 0.8% of total GHG emissions (Planning

Commission, 2010). In year 2008, Pakistan’s GHG emissions were 309 million

tonnes (mt) of CO2 equivalent, comprising of 54% CO2, 36% Methane (CH4), 9%

Nitrous Oxide (N2O) and 1% other gases. Energy is the largest sector contributing to

about 50% of the GHG emissions whereas, agriculture and industrial sectors have

share of 39% and 6% respectively. About 5% of the GHG emissions is being emitted

from other activities (Planning Commission, 2010). It has been projected that there is

an increased risk of flooding, glacier melting and landsliding in Pakistan due to

impacts of climate change (Planning Commission, 2010).

Average annual temperature increased by 0.6 °C over Pakistan during last

century with higher temperature increase over northern areas (0.8 °C) than that of

southern areas (0.5 °C). Another effect of climate change is an average increase in

precipitation by 25% in Pakistan. Future projections through Global Circulation

Models (GCMs) show that there will be an average increase in temperature over

Pakistan in the range of 1.3-1.5 °C by 2020s, 2.5-2.8 °C by 2050s, and 3.9-4.4 °C by

2080s compared to the average global temperature increase of 2.8-3.4 °C by the end

of the 21st century (Planning Commission, 2010). Due to increased trend of glacier

melting, fifty two lakes have been declared as potentially dangerous due to extreme

18

chances of outflow which may lead to flash floods (Ministry of Environment, 2011).

It is projected that the climate change will lead to extreme weather events like floods

and droughts in future. Pakistan is facing a number of climate change related threats

including rapid glacier melting, sea level rise, energy crisis and water and food

security (Ministry of Environment, 2011). Some other climate change issues include

loss of biodiversity, deforestation and ecosystem damage. The adverse health effects

caused by extreme changes in temperature and rainfall include heat strokes,

pneumonia, malaria (Planning Commission, 2010) dengue fever and other vector-

borne diseases (Ministry of Climate Change, 2012).

During last forty years, the Northern Pakistan has experienced an average

temperature rise of 1.5oC, whereas, it was recorded as 0.76oC in other areas

(Chaudhry et al., 2009). During 2001-2010, the average temperature in Northern

Areas of Pakistan increased by 1.3oC whereas temperature rise in the country on

average was 0.6oC (Rasul et al., 2008). Furthermore, the intensity and frequency of

heat waves have also been increased in the Himalayas, Karakoram and Hindukush

Mountain (Rasul et al., 2008). The water source in South Asian region is coming from

the glaciers in Himalayas and the Tibetan Plateau. Glacier melting will lead to an

irreversible water scarcity in the region (Mathez, 2009). There has been about four

times increase in severity of weather and storms in the South Asian region during past

three decades (Mathez, 2009).

Monsoon in Asian region has also been affected greatly by ‘Atmospheric

Brown Clouds’ and other pollutants coming from anthropogenic sources (Ramanathan

et al., 2007). These brown clouds also cause global warming leading to glacial

melting in Himalayas. This glacier melting is posing high risk to water availability in

future (Ramanathan et al., 2007). Summer monsoon in Asian region provides the

rainfall required for sustainability (Annamalai et al., 1999). It is evident that the dry

summers in eastern Mediterranian region and arid regions of North Africa are a result

of Asian summer monsoon (Rodwell, 1997; Hoskins, 1996). It is very important to

predict the variability in monsoon which would be significant for climate research and

future projections.

19

1.6 Climate Modeling

Long-range transport is another factor affecting the concentration of pollutants

in a region (Ohara, 2011). Air parcel back trajectories identify the actual source of

pollution which may be located in a far-off region (Dutkiewicz et al., 1987).

Backward and forward trajectories are very useful tool to trace the local and regional

transport of tropospheric ozone (Jiang et al., 2003). The climate models make future

projections of climate change; however, these models have practical limitations and

some uncertainties (Mathez, 2009). Multiple simulations by using the same model

may provide authentic information on natural variability and external elements

contributing towards climate change (Mathez, 2009). An important and more common

way to measure global warming is to measure the average increase in temperature

during a century and future projections (Mathez, 2009).

The variability of ozone concentration in the troposphere has been explained

by various global and regional scale chemical transport models. Ozone’s

concentration strongly depends on availability of precursor gases i.e., NOx, Non-

methane Hydrocarbons, carbon monoxide and meteorological conditions favouring its

formations in the troposphere (Delcloo and Backer, 2008).

Modeling studies have been carried out to examine the effects of climate

change more specifically on the regional ozone air quality by assuming the constant

primary emission into the atmosphere. Langner et al. (2005) examined the changes in

the accumulated ozone concentration from present to 2070 in Europe using regional

climate model driven by two different general circulation models (GCMs). It was

found that there was an increase in ozone concentration in central and southern

Europe, while a decreasing trend in the ozone concentration was observed over

Northern Europe affecting the regional pattern of precipitation.

It has been reported through regional model simulations that global warming

may lead to increase in concentration of aerosols as a result of increased formation of

aerosol precursors. Aerosol concentration is also expected to be influenced by

perturbations of precipitation frequencies and patterns (Aw and Kleeman, 2003). It is,

20

therefore, very important to make predictions and future projections about the impact

of air quality on climate change.

1.7 Significance of the Research Work

This dissertation is compilation of three research papers based on my Ph.D.

research work. The proposed research project focuses primarily on the assessment of

the sensitivity of air quality to climate change. The research work analyzes the

complex role of global change processes in the ambient air quality of Pakistan.

Ambient concentrations of criteria pollutants i.e., ozone (O3), oxides of nitrogen

(NOx), sulfur dioxide (SO2), non-methane hydrocarbons (NMHC), Total

Hydrocarbons (THC), Carbon Monoxide (CO) and Particulate Matter (PM10 and

PM2.5) have been analyzed in an effort to characterize air pollution in the urban

environment of Pakistan. This will be coupled with meteorological measurements to

gain an insight in the diurnal and seasonal variations of these pollutants. For this

purpose, Weather Research and Forecasting Model (WRF) has been used. WRF

model will be helpful in determining the mesoscale meteorological factors involved in

the variation of air quality and in response to convection. Forward and back

trajectories and ratio analysis; coupled with delta pollutant analysis will offer insight

into the origin, source, and magnitude of pollutants formed within the urban

environment.

This research work was aimed to find out the current scenario of air pollution

in the urban environment of Pakistan. It focused on seasonal variation of fine

particulate matter (PM2.5) in major cities of Pakistan and the effect of meteorological

conditions on the average mass concentration of PM2.5. The research work analyzed

the background concentration of ozone in Islamabad and the role of anthropogenic

precursor gases in its build up. A peculiar aspect of this research work is to calculate

the backward-trajectories in order to track the possible emission sources in other

regions, other than the local sources, responsible for ozone and PM2.5 pollution within

Islamabad city. Furthermore, synoptic analysis has been conducted for meteorological

condition using WRF model.

21

Understanding the nature and extent of climate change is a vital and important

area of research. It is also a great challenge for the modern society to cope with the

current air quality issues and the climate change. This study will enable to make

future projections on air quality and variability and intensity of weather patterns in

order to assess the impacts of climate change on Pakistan through simulations using

GCMs and other models. Although, substantial research is being done in the field of

air quality in Pakistan, there is insufficient research in projecting the impacts of

climate change and developing the adequate adaptation and mitigation options.

Research in the area of climate change would be helpful in promoting innovation to

address the environmental challenges faced by the country. Air quality and global and

regional climate models simulations can be used by the government authorities to

develop policies. Prediction of the impacts of climate change on air pollution is quite

complex and this area needs to be studied with strong emphasis on the future scenario

of climate change so that the control measures may be adopted and Environmental

Protection Act and rules and regulations made thereunder may be implemented.

1.8 Objectives

Main objectives of this research work are given below:

• Analysis of seasonal and diurnal variation of fine particulate matter in major

cities of Pakistan and assessment of effect of meteorological factors on the

atmospheric concentrations of PM2.5 and tropospheric ozone

• Observational based analysis of criteria pollutants in urban environment of

Pakistan and correlation analysis of tropospheric ozone with its precursor

gases

• Back-Trajectory analysis using HYSPLIT model in order to find out the

possible sources of pollution and the transportation mechanism of air pollutant

depending on meteorology.

• Assessment of potential for possible effects of climate change on air quality in

this region by simulation of high pollution episodes by Weather Research and

Forecast Model

22

CHAPTER 2: METHODOLOGY

2.1. Description of Sampling Sites

The air quality monitoring was carried out in four major cities of Pakistan i.e.,

Islamabad, Lahore, Peshawar and Quetta. The sampling sites (circles) are mentioned

in Figure 2.1:

Figure 2.1. Physical map of Pakistan showing the sampling sites (circles) in

Islamabad, Lahore, Peshawar and Quetta

2.1.1. Islamabad

Islamabad is the Federal Capital of Pakistan and is located at 33°26′N 73°02′E

with Margalla Hills surrounding the city from two sides (Siddique et al., 2012). It has

a population of approximately 1.15 million inhabitants. The average elevation of

Islamabad is 457-610 metres with lot of variation having the highest elevation of 1604

meters. Islamabad has a semi-arid sub-tropical climate having warm to hot humid

summers with monsoon season and a cold winter. The total area of Islamabad is 906

Km2 with an urban area of 220.15 Km2 (Capital Development Authority, 2012). The

23

air monitoring station is located at Central Laboratory for Environmental Analysis

and Networking (CLEAN), Sector H-8/2, Islamabad (Figure 2.2).

Figure 2.2. Monitoring Site at Central Laboratory for Environmental Analysis and Networking (CLEAN), Pak-EPA

2.1.2. Lahore

Lahore is located at 31°32′N 74°22′E with a municipal area of 332 km2, which

has been extended to 1 000 km2 due to urbanization. Lahore is the second largest city

of Pakistan with a population of about 9.01 million (Bureau of Statistics, 2012). There

are approximately 2.7 million motor vehicles and 1986 industries in the city (Bureau

of Statistics, 2012). Vehicular and industrial emissions are the main sources of air

pollution in the city (Stone et al., 2010). Lahore City is located at an elevation of 217

meters. The city is characterized by hot semi-arid climate with monsoon season and

dry and warm winters. The monitoring site is located in Township, a typical

residential area of Lahore city (Figure 2.3).

24

Figure 2.3. Monitoring Site of Punjab-EPA, Lahore

2.1.3. Peshawar

Peshawar is the capital city of Khyber-Pakhtunkhwa (KP) province and lies on

the Iranian plateau at 34°01′N 71°35′E. It has a population of about 3.6 million. The

city has an area of 1 257 Km2 and is located at an elevation of 359 meter. Semi-arid

climate is a characteristic of Peshawar. The air monitoring station is installed at the

rooftop of KP-EPA building located at Khyber Road, Peshawar (Figure 2.4).

25

Figure 2.4. Monitoring Site of KP-EPA, Peshawar

2.1.4. Quetta Quetta is the provincial capital of the Balochistan Province. Quetta is a bowl

shaped valley with an elevation of 1 680 meters surrounded by mountain ranges with

peak height above 3 000 meters (Muhammad et al., 2006). Quetta has an area of 2 653

km2 with the population of about 1.4 million inhabitants (Muhammad et al., 2006).

Quetta has a semi-arid climate and it does not experience monsoon rainfalls. Air

monitoring station is installed at Balochistan-EPA located at Samungli Road, Quetta

(Figure 2.5).

26

Figure 2.5. Monitoring Site of Balochistan-EPA, Quetta

2.2. Experimental Methods

2.2.1. Data Collection The air quality data for this research work was obtained from Pakistan

Environmental Protection Agency (Pak-EPA). Hourly air quality monitoring data for

five years (2007-2011) was collected using automated fixed and mobile air

monitoring stations (see Figures 2.6 and 2.7) for ambient concentration of six major

pollutants. The air monitoring stations are equipped with ambient air quality

analyzers, combined wind vane, anemometer (Koshin Denki Kogyo Co, Ltd. Model

KVS 501), thermo hygrometer (Koshin Denki Kogyo Co, Ltd. Model HT-010), solar

radiation meter (Koshin Denki Kogyo Co, Ltd. Model SR-010) and data logging

system (Horiba, Ltd. Model Special).

Data Logging systems at Federal and each Provincial EPA retrieve the air

quality data from air monitoring stations through data processing software. The

ambient air quality parameters like carbon monoxide (CO), oxides of nitrogen (NOx

27

i.e., NO and NO2), sulfur dioxide (SO2), ozone (O3), fine particulate matter (PM2.5),

and hydrocarbons (total hydrocarbons, non-methane hydrocarbons and methane) were

determined using specific and prescribed analyzers in air monitoring stations. The

detail of data points used for all cities for calculation of diurnal profiles and regression

analysis is given in Table 2.1.

Figure 2.6. Fixed Automated Air Quality Monitoring Station at Central Laboratory of Environmental Analysis and Networking (CLEAN), Pak-EPA

(Source: JICA TC-EMS, 2010)

28

Figure 2.7. Analyzers for Ambient Air within the Automated Air Quality Monitoring

Station

In Pakistan, the 24-hour National Environmental Quality Standards (NEQS) for PM2.5

has been set at 40 µg m-3, and the annual and hourly average have been set at 25 µg

m-3. A revised 24-h limit of 35 µg m-3 and annual and hourly average of 15 µg m-3

have become effective from 1st January, 2013 (Pak-EPA, 2010). On the basis of

severity of health effects of PM2.5, World Health Organization has set the guideline of

25 µg m-3 as 24-hour mean and 10 µg m-3 as annual mean (WHO, 2006). The

permissible limit of ozone is 180 µg m-3 for 1-hour average. However, a revised

standard value of 130µg m-3 is now effective from January, 2013 (Pak-EPA, 2010).

World Health Organization has set the guideline value for ozone levels at 100 µg m-3

for an 8-hour daily average (WHO, 2006). The annual average standard value for SO2

is 80 µg m-3, however, the 24-hour average value is 120 µg m-3. The annual and 24-

(Source: JICA TC-EMS, 2010)

29

hour average standard value for NO is 40 µg m-3, however, the annual average

standard value for NO2 is 40 µg m-3 and 24-hour average value is 80 µg m-3. The 8-

hour standard limit for CO is 5 mg m-3 and 1-hour limit is 10 mg m-3 (Pak-EPA,

2010). In this study, the concentrations of ambient air pollutants have been compared

with the NEQS applicable before January, 2013.

Seasons have been specified as winter (December - February), spring (March -

May), summer (June - August) and fall (September - November). Seasonal average

has been calculated in order to find out the variation of PM2.5 mass concentration in

various seasons.

Details of air quality analyzers are as follows:

2.2.2. Ambient Particulate Monitor PM2.5 was measured by Dust Analyzer (Horiba Ltd; Model APDA-370) with 0~5

mg/m3 range through β-ray absorption method (ISO6349). Specifications of the

analyzer are given as follows (JICA, 2007):

Specifications Principle: Beta Ray attenuation

Application: PM10 (PM2.5 by using filter paper)

Range: 0-5mg/m3(Auto-ranging) within ±10% of indication

Dust Collection: Filtration System; Use a glass fiber filter paper roll for over 1

or half month running

Suction Flow Rate: 1m3/hr (Automatic flow control)

Measurement time: 1 hour

Analog output: 0-1 V

Digital output: RS232 serial port

Self check and Diagnostics: Diagnostic messages or signals, flow rate, mass

concentration, mechanical movement, date, time, pressure,

daily average, source radiation intensity

30

Working temperature range: 10-40oC

Power source: AC 220V, 1-ph. 50Hz

Measuring Principle The Horiba dust monitor (APDA-370) measures and records the concentration

of particulate matter in ambient air automatically by beta ray attenuation principle. In

this procedure, beta rays are emitted by Carbon-14 (14C) in order to provide a constant

source of high-energy electrons. Sensitive scintillation detector is used to determine a

zero reading. The tape with beta rays is moved to the sample nozzle where a specific

amount of dust is captured by a vacuum pump. When this sample is put between

detector and beta source, the concentration of particulate matter is measured by

attenuation of beta rays (Horiba, 2009(a)).

2.2.3. NOx Analyzer: Chemiluminescence (ISO7996) method was used to determine NOx, NO and

NO2 concentrations using ambient NOx monitor (Horiba Ltd; Model APNA-370) with

detection limit of 0.5ppb and range of 0~1ppm. Thermal converter in NOx monitor is

known to introduce error in accurate concentration determination of ambient NO2 and

NOx. Since all other reactive oxidized nitrogen compounds also get converted to NO

during thermal conversion, thus, we propose using NOy′ to denote NOx. NOy´ may be

used as a surrogate for ambient NOy (= NO + NO2, HONO + HNO3 + PAN + NO3 + -

----) concentration. Specifications of NOx analyzer are as follows (JICA, 2007):

Specifications Principle: Chemiluminescence

Application: NOx

Range: 0-1 ppm (Auto-ranging and manual each channel)

Low detection limit: 1ppb

Zero drift: 2ppb/day

Span drift: 2%FS/day

Working temperature range: 5-40oC


31

Measuring Principle When ozone (O3) is added to the sample gas containing nitrogen oxide (NOx),

a part of nitrogen monoxide (NO) in the sample gas is oxidized to nitrogen dioxide

(NO2). Some concentration of NO2 is in the excited state (NO2*) and emits light in the

de-excitation state. This phenomenon of light emission is called chemiluminescence.

NO + O3 → NO2* + O2

NO2* → NO2 + hv

This reaction is extremely fast and involves only NO — affected little by the

other co-existent gases. When NO concentration is low, the intensity of light produced

is proportional to the NO concentration. The method using this reaction to measure

NO concentration is known as the chemiluminescence method (CLD method). In

APNA-370, sample is moved into the analyzer separately in two ways. At one side,

NOx concentration is determined by reducing NO2 to NO with a NOx converter; the

other is used for NO concentration measurement directly.

The gases are moved to the reference gas line, NO and NOx every 0.5 seconds

with solenoid valves, and are introduced to the reaction chamber in turn. On the other

hand, the open air is separately sucked through the air filter, dried by a self-

reproducing-type silica gel dryer, and used to form ozone in an ozonizer. Then, the

generated ozone is introduced into the reaction chamber. In the reaction chamber, the

sample and ozone react, and the light emission involved in the reaction is detected by

the photodiode. This instrument calculates NO, NO2 and NOx concentrations from the

outputs obtained by the photodiode, which are proportional to the NOx and NO

concentrations, and outputs the results as continuous signals (Horiba, 2009(b)).

2.2.3. Ambient SO2 Monitor: Sulfur dioxide was measured by SO2 monitor (Horiba Ltd; Model APSA-370) with

detection limit of 1ppb, range of 0~0.5ppm through U.V. fluorescence method

(ISO10498). Specifications of SO2 monitor are as follows (JICA, 2007):

Specifications: Principle: U.V. Fluorescence

Application: SO2 in ambient air

32

Range: 0-0.5 ppm (Auto-ranging)

Low detection limit: 1ppb


Span drift: ±2%FS/day

Sample gas flow rate: 0.7 litre/min.

Suction Flow Rate: 1m3/hr (Automatic flow control)

Measurement time: 1 hour

Working temperature

range:

5-40oC


Measurement Principle Sulfur dioxide emits light at various wavelengths i.e., ≤320 nm, range: 240 nm

to 420 nm due to irradiation with ultraviolet rays. This measuring principle is called

the fluorescence method as it determines SO2 through intensity of fluorescence light.

It measures SO2 with minimum influence of moisture. It does not require any

supplemental gas and gives linear output. When excitation light is irradiated and

absorbed:

I = I0e-aLx ...............(1)

Where:

I: intensity of the excitation light that passes through the cell

I0: initial intensity of the excitation light

a: absorption coefficient for the excitation light

L: cell length

Therefore, the amount of the excitation light absorbed in the cell, ∆I, is:

∆I = I0 − I = I0 (1 − e-aLx) ........(2)

33

The number of the SO2 excited in Process [I], SO2*, is proportional to the above ∆I.

[SO2*] = ∆I/hν1 ...(3)

SO2* produced by Process [I] is transferred from excited state to the ground state in

three ways.

• Fluorescence Process:

• Dissociation Process:

• Quenching Process:

These three processes lead to transferring the sulfur dioxide from excited state into

ground state. Accordingly, the number of the SO2* that goes through the fluorescence

process is:

Therefore, the fluorescence intensity detected with the photomultiplier is

expressed by the following equation, using the geometric constant of the cell, G:

34

If the SO2 concentration is low (1000 ppm or lower), the following equality is true

and the fluorescence intensity is proportional to the SO2 concentration, x (Horiba,

2009(c)):

2.2.4. Ambient CO Monitor: Ambient CO Monitor (Horiba Ltd; Model APMA-370), using non-dispersive

infrared-ray method (ISO4224) with detection limit of 0.1ppm and measuring range

of 0~50 ppm, was used to measure CO ambient concentration. The specifications of

the equipment are given as follows (JICA, 2007):

Specifications Principle: Non-dispersive infrared ray

Application: CO

Range: 0~50 ppm (auto-ranging and manual)

Low detection limit: 0.1ppm

Zero drift: 0.3ppm/day

35

Span drift: ±2% FS/day


Measuring Principle In CO monitor, carbon monoxide is determined by modulation effect caused

by absorption of infrared radiation when zero gas and sample are moved to its cell.

The detector’s output becomes zero due to which there is no zero drift. The output

starts increasing with increase in the measured gas concentration. APMA-370 CO

monitor provides the detections more accurately as there is no interference component

(Horiba, 2009(d)).

2.2.5. Ambient O3 Monitor Ambient Ozone Monitor (Horiba Ltd; Model APOA-370) with detection limit of 0.5

ppb, range of 0~1ppm and working on the principle of UV photometry method was

used to determine ozone concentration in ambient air. The specifications of O3

monitor are mentioned below (JICA, 2007):

Specifications Principle: U.V. Photometry

Range: 0~1 ppm (auto-ranging and manual)

Low detection limit: 0.5ppb


Span drift: 2% FS/day


Measuring Principle The ultraviolet absorption method is based on ozone's characteristic of

absorbing ultraviolet rays of specific wavelength. In this analysis method, the sample

gas which has passed through the filter is divided into two flow paths. The sample gas

in one path is introduced to the de-ozonizer, where its ozone is eliminated, and then

sent to the cell as “reference gas.” The sample gas in the other path is sent to the cell

directly, as “sample gas,” by switching a solenoid valve. The measurement cell is

exposed to direct radiation by a low-pressure mercury lamp which generates

36

ultraviolet rays with central wavelength of 253.7 nm, and a detector, which involving

a photodiode and electric system to obtain electric signals, measures ultraviolet

absorption by ozone.

The “sample gas” and the “reference gas” are sent to the cell alternately,

switched by 1 Hz with the solenoid valve. The deference in ozone content between

the reference gas and the sample gas can be obtained from the deference in the

measured ultraviolet absorption (Horiba, 2009(e)).

2.2.6. Ambient Hydrocarbon Monitor Ambient Hydrocarbon (HC) monitor was used to measure the ambient

concentrations of non-methane hydrocarbons (NMHCs), methane (CH4) and total

hydrocarbons (THC). Basic specifications of hydrocarbon monitor are as follows

(JICA, 2007):

Specifications (1) NMHC-THC Monitor

Principle: Converter oven

Application: Hydrocarbons

Detector: Hydrogen flame ionization

Range: 0~50 ppmC (auto-ranging and manual)

Low detection limit: 0.1ppmC

Zero drift: 0.5ppmC/day

Span drift: 1.0ppmC/day

Analog output: 2 signals (methane, Non-methane hydrocarbon)


(2) Hydrogen Generator

Application: For hydrogen gas supply to NMHC-THC monitor

Principle: Electrolysis of pure water (no use of any liquid caustic

electrolytes)

Gas purity: H2 purity more than 99.99%

37

Flow rate: 90 ml/min. (STP)


(3) Built-in Zero Gas Generator

Application: Supply of zero-free HC (for FID unit)

Flow rate: 100cc/min.

Pressure: 0.3 bar

Measuring Principle Hydrocarbon produces a high temperature flame, when it becomes in contact

with the hydrogen flame. The produced energy ionizes the hydrocarbon molecules at

the tip of nozzle. Direct current voltage between two electrodes opposite to each other

produces an ion current which is proportional to the carbon number of the ionized

hydrocarbon. When this ion current is passed through high resistance, it is converted

into voltage and then the concentration of total hydrocarbons is measured. In APHA-

370, the sample is used to measure the concentration of CH4 and THC separately. The

concentration of non-methane hydrocarbon (NMHCs) is detected by subtracting CH4

from THC (Horiba, 2009(f)).

2.2.7. Combined Wind Vane and Anemometer Anemometer measures the wind speed, whereas, the wind vane is used to determine

the direction of winds. The detail of the equipments is given below (JICA, 2007):

Specifications (1) Wind Direction Sensor

Type: Wind Vane (no heater)

Measuring Method: Opto-electronic transducer

Measuring range: 0 to 360o

Starting threshold: 0.5 m/s

Accuracy: Within ± 3o

Operating temperature range: -20 to 60 degC

(2) Wind Speed Sensor

38

Type: Propeller

Measuring Method: Opto-electronic transducer

Measuring range: 0.5 to 90 m/s

Starting threshold: 0.5 m/s

Accuracy: Within ± 3%F.S.


Thermo-Hygrometer

Specifications (1) Temperature Sensor

Materials: Pt resistance

Measuring range: -20 to 80 degC

Accuracy (at 0degC): Within ± 0.5 degC

(1) Humidity Sensor

Measuring Method: Thin film capacitor

Measuring range: 0 to 100% RH

Accuracy (at 20degC): Within ± 3% RH

Solar Radiation Meter

Specifications Method: Black-carbon thermopile

Spectral range: 400 to 2800 nm

Sensitivity: 7 mV/kw/m2


2.3. Synoptic Analysis for PM2.5 High Episodes: Synoptic analysis of high PM2.5 episodes in four cities of Pakistan was

conducted using reanalysis data of National Center for Environmental Prediction

(NCEP) / National Center for Atmospheric Research (NCAR). The data include air

temperature, U- and V-wind components, precipitation and relative humidity. Grid

39

Analysis and Display System (GrADS) was used to graphically display the

meteorological pattern during high PM2.5 episodes at four monitoring sites. Various

plots were developed using wind vectors, shaded contours and smoothed contours.

2.4. Back Trajectory Modeling Backward air trajectories were generated by using the Hybrid-Single Particle

Lagrangian Integrated Trajectory (HYSPLIT) model which has been developed by the

National Oceanic and Atmospheric Administration’s (NOAA) Air Resources

Laboratory (ARL). Archived three-dimensional meteorological data is used by

HYSPLIT model to compute the trajectories. Gridded Meteorological Data Archives

from Global Data Assimilation System (GDAS) of National Center for Environmental

Prediction (NCEP) / National Center for Atmospheric Research (NCAR) was used to

calculate the back trajectories. The trajectories were computed for the heights of

500m AGL, 1000m AGL and1500m AGL for a period of 24 hours. Label interval was

set to be six hours to track the path of trajectory. Mixed layer depth was also

determined for each back trajectory. The back trajectories were calculated for

Pakistan with a buffer zone including some part of China, India, Afghanistan, Iran and

Arabian Sea.

2.5. Weather Research and Forecasting (WRF) Model Simulations Weather Research and Forecasting (WRF) Model simulation was run for

synoptic analysis of Islamabad city during selected high ozone episodes. The

simulation was run over 18-Km resolution with 27 vertical levels, the lowest of which

was 1013hPa. The 18-Km grid covers the Capital Territory of Islamabad. Six-hourly

National Center for Environmental Prediction (NCEP) / National Center for

Atmospheric Research (NCAR) Final (FNL) Operational Global Analysis gridded

data (0.5o ×0.5o) was used as boundary and initial conditions for WRF simulations.

Default parameterization scheme has been used to run the WRF model. The

simulations for two episodes begin at 00:00 UTC and end at 18:00 UTC. 6:00 UTC

input was set for synoptic analysis of daytime, whereas 18:00 UTC was set as start

time for analysis of night-time meteorology during high ozone episodes.

40

CHAPTER 3: RESULTS AND DISCUSSION

SECTION I: ANALYSIS OF FINE PARTICULATE MATTER (PM2.5) IN URBAN AREAS OF PAKISTAN: AN

OBSERVATIONAL-BASED ANALYSIS

3.1. Spatial and Temporal Variation of PM2.5

Spatial and temporal variation in mass concentration of PM2.5 in different cities

gives an insight of the factors affecting the pollution levels with respect to

geographical location and time. Figure 3.1 provides the average annual and seasonal

PM2.5 mass concentration in Islamabad during 2007-2011. In Islamabad, average

annual PM2.5 mass concentration is 81.1±48.4 µg m-3, 93.0±49.9 µg m-3, 47.8±33.2 µg

m-3, 79.0±49.2 µg m-3 and 66.1±52.1 µg m-3 during 2007-2011 respectively. The

highest hourly average concentrations were observed as 303 µg m-3 during December

2007, 495.0 µg m-3 during November 2008, 259.8 µg m-3 during September 2009,

456.0 µg m-3 during October 2010 and 379.0 µg m-3 during January 2011. Main

sources of air pollution in Islamabad are rapid urbanization, vehicular and industrial

emissions, construction activities, emissions from brick kilns located on the outskirts

of Islamabad (Qadir et al., 2012; Siddique et al., 2012) and forest fires in the Margalla

Hills. There were about eighty wildfire incidents in the Margalla Hills during summer

months of 2006-2010 (The Express Tribune, 2011) contributing to the PM2.5 pollution

during summer months. Some dust storms also contributed to the PM2.5 burden in

Islamabad city during the monitoring period. A dust storm of 148 Km h-1 had hit

Islamabad on July 14, 2007 (Pakistan Weather Portal, 2013) and another storm with

an intensity of 130 Km h-1 struck the city on June 23, 2010 (GEO Pakistan, 2010).

Advection of air pollution from nearby city Rawalpindi also increases the pollution

level in Islamabad. Biomass combustion for space heating in the suburban areas of

Islamabad increases the mass concentration of PM2.5 during winter season. Moreover,

Margalla Hills also restrict the dispersion of pollutants leading to buildup of pollutants

level in Islamabad.

41

Figure 3.1. Annual and Seasonal Average PM2.5 Mass Concentration (µg m-3) in

Islamabad during 2007-2011 (±1 standard deviation is also shown in the figure; No. of data points given above the bars)

Figure 3.2 gives a comparison of annual and seasonal PM2.5 mass concentration

among Islamabad, Lahore, Peshawar and Quetta during summer 2007-Spring 2008.

Data shows that Lahore is the most polluted city among all the urban environments

with the highest PM2.5 mass concentration in each of the seasons. Furthermore, the

average annual and seasonal PM2.5 mass concentrations in all the cities, during whole

year, remained above the Pakistan NEQS.

The annual average PM2.5 mass concentration in Islamabad has been observed to

be 81.2±47.4 µg m-3 and the highest seasonal average PM2.5 mass concentration for

Islamabad has been observed as 98.5 µg m-3 during spring 2008. Fuel burning for

space heating in surrounding rural areas is a source of sulfate particulates. In addition,

atmospheric dispersion of PM2.5 is decreased during winter season due to lower

mixing depth and lower wind speed. Winter fog is another phenomenon restricting the

dispersion of pollutants away from the city. Lower concentration of PM2.5 in summer

42

than in winter may also be explained by the heavy monsoon rainfall during the

months of July and August (Sadiq and Qureshi, 2010).

Figure 3.2. Comparison of Annual and Seasonal Average PM2.5 Mass Concentration (µg m-3) in Islamabad, Lahore, Peshawar and Quetta during Summer 2007-Spring

2008 (±1 standard deviation is also shown in the figure; No. of data points given above the bars)

The annual average PM2.5 mass concentration for Lahore was observed to be

118.3±79.1 µg m-3 and the highest seasonal averaged PM2.5 mass concentration of

150.5±87.9 µg m-3 was observed during Fall, 2007. Primary sources of PM2.5 in

Lahore are diesel emission, biomass burning, coal combustion, two-stroke vehicles

and industrial activities (Stone et al., 2010; Dutkiewicz, et al., 2009). Peak values of

PM2.5 in winter season may be attributed to the primary emissions from combustion-

related sources, which increase during winter season due to indoor space heating.

Lodhi et al. (2009) has also documented about four times higher PM2.5 mass

concentration during winter than those observed in other seasons. Mixing height of

about 250 m during night time and 1000 m during day time in winter season (Husain

et al., 2007) may be another factor for accumulation and increase in concentration of

pollutants within this area. Lower mixing height traps the pollutants within a

particular area restricting the dispersion of pollutants to other areas. Long-lasting

43

episodes of stagnation accompanied by fog in Lahore are quite favorable for increased

formation and accumulation of sulfate particulates (Biswas et al., 2008). Wet

deposition mechanism is more pronounced in Lahore during summer season (Sadiq

and Qureshi, 2010).

The annual average PM2.5 mass concentration for Peshawar was found to be

86.2±50.0 µg m-3 and the highest seasonal PM2.5 mass concentration observed during

fall, 2007 was 104.1±51.1 µg m-3. Vehicular and industrial emissions are the primary

sources for PM2.5 mass concentration in Peshawar (Khan et al., 2008). Moreover,

increased number of brick kilns situated in and around the city (Jan et al., 2013) has

led to high level of PM2.5 because these kilns use low quality of coal and tyres as fuel.

Wood burning during winter season for space heating has a great influence on the

ambient mass concentration of PM2.5 during winter season (Sandradewi et al., 2008).

High PM2.5 mass concentration during winter may be attributed to the atmospheric

stability under low temperature and lower inversion layer. Such conditions lead to

accumulation of PM2.5 which has a higher residence time as compared to PM10. The

scavenging effect of monsoon rainfall may also be considered a factor for less PM2.5

mass concentration in summer than in winter.

The annual average PM2.5 mass concentration in Quetta was found to be 63.3±52.0

µg m-3 and the highest seasonal average PM2.5 mass concentration was observed to be

72.7±55.2 µg m-3 during Fall, 2007. Major sources of air pollution in Quetta include

emissions from motor vehicles, increased usage of two-stroke vehicles, dairy farms,

diesel-driven heavy vehicles, stone crushing, coal-fired power plants, industrial

activities and brick kilns (Quetta District Government, 2011; Muhammad et al., 2006;

Faiz et al., 1996). The topography of Quetta is favorable for accumulation of PM2.5

and its precursor gases subsequently leading to photochemical formation of secondary

PM2.5 as well. Quetta remains engulfed by a thick layer of smog during winter season

(Quetta District Government, 2011). High winter PM2.5 mass concentrations are

largely due to the combination of meteorology and increased primary combustion

emissions from space heating. During summer months, low pressure prevails over

Quetta due to which dust storms generated in deserts of Iran are transported to Quetta

(Muhammad et al., 2006). Higher PM2.5 concentrations in Quetta during summer 2007

44

may also be attributed to dust storm which was struck on July 23, 2007 (Wikipedia,

2013).

All of these cities are under the influence of extensive anthropogenic activities

leading to high PM2.5 mass concentration. Higher PM2.5 mass concentration during the

Fall season is due to burning of agricultural residue in the surrounding areas during

the months of September, October and November (SUPARCO, 2009). PM2.5 has

higher mass concentrations during winter season in all the cities compared with the

summer season. In winter season, elevated levels may be associated with increased

coal and biomass burning for heating purposes within the cities and nearby rural

areas. Inversion layers suppress the vertical transport of pollutants which leads to

elevated levels of pollutants during winter season. The difference between winter and

summer PM2.5 mass concentrations seems to be due to the combination of increased

burning of fossil fuel i.e., coal for space heating purposes in winter season; and

meteorological conditions i.e., shallow planetary boundary layer (Husain et al., 2007),

low precipitation level in cities other than Quetta and comparatively stable conditions.

3.2. Diurnal Profile of PM2.5

3.2.1. Annual Averaged Diurnal Profile

Figure 3.3 shows the integrated average hourly diurnal profile of PM2.5 mass

concentration in Islamabad, Lahore, Peshawar and Quetta during 2007-2011. Diurnal

profile of all four cities follows a similar pattern of variation – in general the first peak

occurs between 7.00 a.m. to 10.00 a.m.; and the second maximum in PM2.5 mass

concentration occurs during 9:00 p.m. to 1:30 a.m. This diurnal pattern shows that

even the lowest levels are above the Pakistan hourly NEQS for PM2.5.

45

Figure 3.3. Integrated Average Diurnal Profile of PM2.5 Mass Concentration (µg m-3) in Islamabad, Lahore, Peshawar and Quetta for 2007-2011 (±1 standard deviation is

also shown in the figure)

Night-time high PM2.5 mass concentration is because of increased movement of

heavy duty vehicles in the city and the development of the night time inversion layer

(diurnal meteorological changes). During winter season, high night time concentration

of PM2.5 may also be attributed to the increased use of biomass burning (fuelwood)

for heating purposes. The early morning elevation in PM2.5 mass concentration is

owing to an increase of the traffic density.

3.2.2. Seasonal Averaged Diurnal Profile

The seasonally averaged diurnal profile has been determined for each city

separately in order to find out any variation in PM2.5 mass concentration during

different seasons. Figure 3.4(a) shows the seasonal averaged diurnal profile of

Islamabad. It has been observed that the winter season has the highest values followed

by fall, spring and summer. The level of PM2.5 starts increasing at 8:00 a.m. with

increase in the traffic movement. Later, the concentration goes down during 4:00 p.m.

to 8:00 p.m. which may be due to formation of other secondary pollutants by PM2.5.

Another reason for low concentration during afternoon might be breakdown of

temperature inversion layer which builds up again in evening leading to higher

46

ambient concentrations of PM2.5. The seasonally averaged diurnal profile shows that

the concentration of PM2.5 is highest during winter season in Islamabad. Nighttime

high mass concentration of PM2.5 during winter season is in agreement to the usage of

coal for heating purposes. The diurnal profile for all seasons also indicates the

contribution of vehicular emissions towards the ambient PM2.5 mass concentration in

Islamabad during the study period.

Figure 3.4(a). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in

Islamabad

Figure 3.4(b) provides a seasonal diurnal profile of PM2.5 for Lahore. Similar trend

of PM2.5 mass concentration as that in Islamabad has been observed in Lahore with

highest values in winter and lowest values during summer season. The average hourly

concentration of PM2.5 has been observed to be higher than the standard limit of 25 µg

m-3 round the clock. PM2.5 peaks have been seen during 8:00 a.m. to 2:00 p.m. and

8:00 p.m. to 2:00 a.m. Daytime high concentrations may be due to traffic rush hours

and midnight high values of PM2.5 may be attributed to heavy duty vehicles

contributing largely to PM2.5 concentration in the city. Furthermore, alarmingly higher

mass concentration of PM2.5 during winter season implies that the increased coal

combustion during night time for indoor heating and long-lasting fog play a major

47

role in formation and buildup of PM2.5 within the city. The winter inversion layer at

night time also restricts the dispersion of pollutants.

Figure 3.4(b). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in

Lahore

Figure 3.4(c) shows the seasonally averaged diurnal profile of PM2.5 for Peshawar

city. The diurnal profile shows that the PM2.5 mass concentration remains above the

hourly standard limit of PM2.5. The concentration has been observed to be higher

during 8:00 a.m. to 10:00 a.m. in morning and during midnight. Morning time is peak

hours for traffic movement and evening rush hours and heavy duty vehicles during

night lead to high values in midnight. In Peshawar, the high PM2.5 mass

concentrations have been observed during fall and winter months. The diurnal profile

shows that the peak values of PM2.5 have been found during winter season from 8:00

a.m. to 10:00 a.m. and from 18:00 hrs to 22:00 hrs. Whereas, high PM2.5 mass

concentration during rest of the time have been observed during fall season. As

observed for other two cities, winter PM2.5 concentrations are higher than the summer

season.

48

Figure 3.4(c). Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in Peshawar

In general there is a bimodal distribution in the diurnal PM2.5 seasonal

variation. The level of PM2.5 concentration has a peak during the morning associated

with increase in the traffic movement (i.e. mobile emissions and dust entrainment);

and a second peak associated with rush hour traffic and reduction in the planetary

boundary layer height. Midnight high values of PM2.5 may be attributed to heavy duty

vehicles carrying goods into and through the city. The seasonally averaged diurnal

profile shows that the concentration of PM2.5 is highest during winter season in

Islamabad, Lahore, and Peshawar associated with usage of coal for heating purposes

and long-lasting fog.

Seasonal diurnal profile of PM2.5 in for Quetta has been shown in Figure

3.4(d). The diurnal profile of PM2.5 in Quetta is different from other cities’ PM2.5

pattern in a way that here the mass concentration of PM2.5 is not as higher than that of

other seasons. However, there are exceedances of standard limit of PM2.5. The

variation of PM2.5 concentration during the day and night is similar to other cities’

profile.

49

Figure 3.4(d): Seasonal Averaged Diurnal Profile of PM2.5 Mass Concentration in

Quetta

The concentration of PM2.5 goes down at midnight and starts increasing at 6:00

a.m. and again goes down at about 10:00 a.m. In later hours, the PM2.5 concentration

starts increasing after 6:00 p.m. due to high traffic movement in evening. Quetta,

being at high altitude (1680 m, MSL), has increased PM2.5 mass concentration owing

to high usage of biomass burning (fuel wood combustion) for space heating purpose

in winter compared to other cities. However, meteorological conditions in Quetta vary

with precipitation in winter due to western disturbance and no summer monsoon

rainfall (Chaudhary, 1992). Winter rainfall may be considered as a factor contributing

towards lower PM2.5 mass concentration in winter than summer season. During

summer months, stagnation and dry weather conditions prevail in Quetta leading to

enhanced accumulation of PM2.5 and its precursor gases within the city. The dust

storms generated in the desert of Iran are another major contributor towards the

pollution in Quetta during summer (Muhammad et al., 2006).

50

3.2.3. Workday-Weekend Variation in Diurnal Profile

The diurnal profile of PM2.5 mass concentration during workdays and weekends

has been analyzed separately in order to find out any variation in concentrations due

to change in traffic movement. Figure 3.5 shows the diurnal profile of annual

averaged PM2.5 mass concentration during workdays and weekends in Islamabad,

Lahore, Peshawar and Quetta. Diurnal profile is almost similar for workdays and

weekends in Islamabad with little variation during noon and midnight. At noon, the

peak of PM2.5 mass concentration starts later during weekend, whereas, at midnight,

the concentration goes higher than workdays. Daytime mass concentration of PM2.5 in

Lahore remains higher during workdays than that on weekends. However, the

midnight PM2.5 mass concentration increases during weekends. Similar pattern of

PM2.5 diurnal profile is observed in Peshawar with daytime higher concentration

during workdays and nighttime higher values during weekends. Quetta has a different

scenario as it has higher PM2.5 mass concentration during workdays than that

observed during weekends throughout the day. The maximum of peaks during

workdays are pronounced during workdays when traffic density is higher than the

weekends. Midnight higher PM2.5 mass concentration during weekends is in

agreement to the late night outdoor activities during weekends.

51

Figure 3.5 Workday-Weekend Variation of PM2.5 Mass Concentration in

(a) Islamabad; (b) Lahore; (c) Peshawar; and (d) Quetta

3.3. Effect of Meteorology on PM2.5

The PM2.5 mass concentration in Islamabad, Lahore, Peshawar and Quetta has been

correlated with meteorological variables in order to find out any possible association

between ambient PM2.5 mass concentration and meteorology in these cities. The

available data during 2007-2011 for these cities has been used for regression analysis.

3.3.1. PM2.5 and Temperature

Figure 3.6(a) shows the seasonal correlation of PM2.5 with temperature in

Islamabad. The figure shows that PM2.5 has a negative correlation (p ≤ 0.01; r = -0.2)

52

with temperature in winter season and a positive association (p ≤ 0.01; r = 0.065) with

temperature during summer season. About 4% of the variance in PM2.5 in winter and

0.4% variance during summer can be explained by its linear relationship with

temperature. Figure 3.6(b) gives the relationship between PM2.5 and temperature in

Lahore. The figure shows that 26% of the variance in PM2.5 can be explained by

temperature in winter months. During winter, PM2.5 has a strong correlation with

temperature (p ≤ 0.01; r = -0.5). Figure 3.6(c) shows that PM2.5 has a negative

correlation (p ≤ 0.01; r = -0.1) with temperature in winter season and a positive

correlation (p ≤ 0.01; r = 0.1) during summer months. Negative correlation observed

between PM2.5 and temperature during winter in Islamabad, Lahore and Peshawar

suggests that thermal inversion layers and fog may play a major role in elevation of

PM2.5 levels. Positive correlation between PM2.5 and temperature may also be an

indication of increased agricultural and biogenic emissions of ammonia and oxides of

nitrogen (Tai et al., 2010). Similar positive correlation of PM2.5 and temperature

during summer and a negative correlation during winter season have been found by

Barmpadimos et al. (2012).

53

Figure 3.6 Effect of Temperature on PM2.5 Mass Concentration (µg m-3) during 2007-

2011 in (a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta

However, the overall PM2.5 mass concentration trend with temperature is negative

for Islamabad, Lahore, and Peshawar. Tiwari et al. (2013) have also reported a

negative correlation of PM2.5 with temperature which depends on composition of the

particulate matter. This is perhaps due to the semi-volatile components such as nitrate

and organics are expected to decrease as they shift from the particle phase to the gas

phase at higher temperature (Sheehan and Bowman, 2001; Aw and Kleeman, 2003;

Tsigaridis and Kanakidou, 2007; Dawson et al., 2007; Kleeman, 2008).

Figure 3.6(d) shows the relationship between PM2.5 and temperature in Quetta. The

regression analysis shows that about 4% of the variation in PM2.5 is associated with

54

temperature in winter season and 10% of the PM2.5 variation is associated with

temperature in summer months. Quetta, being a high elevation site, the association

between PM2.5 and temperature is unlike other cities. Here, PM2.5 is positively

correlated (r = 0.2) with temperature in winter season, whereas, these two variables

are negatively correlated (r = -0.3) in summer. The correlation between PM2.5 and

temperature is statistically significant (p ≤ 0.01) during both the seasons. Negative

correlation observed between PM2.5 and temperature during summer suggests that

there may be more abundance of nitrate particles which get converted from particle to

gas phase due to high summer temperatures (Dawson et al., 2007). The positive

correlation of PM2.5 with temperature during winter may be due to the fact that the

winter precipitation in the form of rainfall and snow works as a scavenger for PM2.5

and perhaps plays an important role in decrease of PM2.5 mass concentration with

decrease in temperature during winter months justifying the positive relationship

between these two variables. The regression analysis of both the variables shows that

the variables other than temperature i.e., precipitation, cloud cover, wind speed, wind

direction and topography also play a major role in PM2.5 mass concentration during

both the seasons as low fraction of PM2.5 is dependent on temperature.

3.3.2. PM2.5 and Solar Radiation

Figure 3.7(a) shows the correlation between PM2.5 and solar radiation in Islamabad.

The regression analysis provides the information that about 0.4% of variation of PM2.5

during summer is associated with solar radiation and 0.7% variation in PM2.5 during

summer months depends on solar radiation. There is a correlation of r = 0.06 between

PM2.5 and solar radiation in winter and a correlation of r = 0.1 during summer. Figure

3.7(b) shows that PM2.5 has a significant (p ≤ 0.05) correlation with solar radiation (r

= -0.24) during winter season in Lahore, however, it doe s not have a good association

(r = 0.03) with solar radiation during summer season. The regression line shows that

6% of the PM2.5 variation is associated with solar radiation in winter and a negligible

fraction of about 0.1% has dependence on solar radiation.

55

Figure 3.7 Effect of Solar Radiation on PM2.5 Mass Concentration (µg m-3) during 2007-2011 in

(a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta

Figure 3.7(c) shows that PM2.5 is negatively correlated (p ≤ 0.01; r = -0.2) with

solar radiation during winter season but there is a weak association (r = 0.06) between

PM2.5 and solar radiation during summer months. About 3% of the variation in PM2.5

in winter may be explained by its linear relationship with solar radiation and 0.3% of

PM2.5 variation during summer is associated with solar radiation which is almost

negligible. Figure 3.7(d) shows that there is positive correlation of PM2.5 and solar

radiation during both winter and summer in Quetta. PM2.5 has a statistically

insignificant correlation (r = 0.06) with solar radiation during winter season and a

good correlation (p ≤ 0.05; r = 0.2) during summer season with 3% of variation in

PM2.5 caused by solar radiation.

56

3.3.3. PM2.5 and Wind Speed

Wind speed and mixing depth have strong effect on particulate matter (Jacob and

Winner, 2009). High wind speed results in dispersion of PM2.5 mass concentration and

stagnation leads to accumulation of PM2.5 in a particular area. The stagnant conditions

caused by low wind speed i.e., high pressure systems deteriorate the ambient air

quality (Leibensperger et al., 2008; Liao et al., 2006). Figures 3.8(a), 3.8(b), 3.8(c)

and 3.8(d) show the effect of wind speed on level of PM2.5 in Islamabad, Lahore,

Peshawar and Quetta respectively. It has been observed that there is a statistically

significant (p ≤ 0.01) negative correlation between PM2.5 and wind speed during

winter and summer seasons in all the cities. In Islamabad, PM2.5 has a negative

correlation with wind speed in both the seasons (winter: r = -0.298 and summer: r = -

0.17). About 9% of the PM2.5 variation in winter and 3% of PM2.5 variation is

associated with the linear relationship of PM2.5 with wind speed in Islamabad. Lower

correlation between PM2.5 and wind speed shows the contribution of local emission

sources towards the ambient concentration of PM2.5. PM2.5 has a correlation of r = -

0.3 during winter months and a correlation of r = -0.239 in summer in Lahore. About

10% of PM2.5 variation in winter and about 6% of PM2.5 variation is due to wind

speed in Lahore city.

57

Figure 3.8 Effect of Wind Speed on PM2.5 Mass Concentration (µg m-3) during 2007-2011 in

a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta

In Peshawar, PM2.5 has a negative correlation of r = -0.3 with wind speed during

winter months with dependence of about 10% of PM2.5 variation on wind speed.

There is a correlation of r = -0.185 between PM2.5 and wind speed in summer season

and 3% of PM2.5 variation is associated with wind speed in the city. PM2.5 and wind

speed have a negative correlation of r = -0.13 in winter and a correlation of r = -0.25

in summer months in Quetta. About 1% of PM2.5 variation in winter and 6% variation

in summer is explained by its relationship with wind speed in Quetta. Correlation of

PM2.5 with wind speed is stronger in winter than in summer in all cities except Quetta.

It indicates the contribution of local sources to high PM2.5 mass concentration in

Quetta during winter season. As Quetta is situated in a valley, there is stagnation in

58

the air leading to accumulation of PM2.5 mass concentration. Islamabad and Peshawar

have also mountains which hinder the transport of PM2.5. DeGaetano and Doherty

(2004) showed a negative correlation of wind speed with fine particulate matter.

3.3.4. PM2.5 and Vapour Pressure

Figure 3.9(a) represents the correlation between PM2.5 and vapour pressure for

Islamabad. PM2.5 has a negative correlation with vapour pressure (p ≤ 0.01; r = -0.2)

during winter months, however, it has a positive correlation with vapour pressure (p ≤

0.01; r = 0.05) during summer season. In winter season, about 4% of PM2.5 variation

is associated with vapour pressure whereas, about 0.3% of PM2.5 variance is

associated with its linear relationship; with vapour pressure during summer season.

Figure 3.9(b) shows that there is a correlation of PM2.5 with vapour pressure during

both the seasons in Lahore. There is negative correlation (p ≤ 0.01; r = -0.23) between

PM2.5 and vapour pressure during winter season and a positive correlation (p ≤ 0.01; r

= 0.07) during summer months. Regressions analysis shows that about 5% of the

variation in PM2.5 mass concentration is associated with vapour pressure in winter,

whereas, only about 0.5% PM2.5 variation is caused by vapour pressure during

summer.

59

Figure 3.9(c) gives the correlation of PM2.5 and vapour pressure in Peshawar. PM2.5

has statistically significant (p ≤ 0.01) positive correlation with vapour pressure during

both the seasons i.e., r = 0.36 in winter and r = 0.22 in summer season. About 13% of

the variation in PM2.5 is related to its linear relationship with vapour pressure during

winter months and about 5% of PM2.5 variation is associated with vapour pressure in

summer season. Figure 3.9(d) shows that PM2.5 has a positive correlation (r = 0.05)

with vapour pressure during winter season, and a negative correlation (p ≤ 0.05; r = -

0.12) during summer season in Quetta. As winter months are more humid, the positive

correlation between these two variables during winter show that anthropogenic

emissions like nitrates and sulfates are contributing more towards the PM2.5 pollution

Figure 3.9 Effect of Vapour Pressure on PM2.5 Mass Concentration (µg m-3) during 2007-2011 in a) Islamabad, (b) Lahore, (c) Peshawar, and (d) Quetta

60

(Tai et al., 2010). The negative correlation of PM2.5 with vapour pressure shows the

contribution of dust particles and elemental and organic carbon to PM2.5 level during

summer months (Tai et al., 2010; Wise and Comrie, 2005). Regression analysis shows

that there is weak association of PM2.5 variation with vapour pressure i.e., 0.3% in

winter and 1% in summer.

3.4. Linear Regression Analysis

Linear regression analysis has been conducted to determine the meteorological

variables that explain the most variance in the overall data. Summary of linear

regression analysis is given in Table 3.1. The regression analysis provides the extent

to which meteorological conditions affect the concentration of PM2.5 in the

atmosphere. The regression analysis shows that the four meteorological variables

accounted for 20% variance in PM2.5 mass concentration in Islamabad. Meteorology

seems to affect the PM2.5 mass concentration in Islamabad, Lahore and Peshawar

more during winter than in summer season. The meteorological factors accounted for

approximately 19% variance in PM2.5 mass concentration in Lahore city. About 13%

variance in average PM2.5 concentration in Peshawar is explained by the

meteorological conditions. Annual average mass concentration of PM2.5 in Quetta

seems to be less affected (6%) by meteorology as compared to other cities. It has been

observed that the effect of meteorology on PM2.5 is more prominent during winter

season in Islamabad (21%), Lahore (20%) and Peshawar (18%), however, Quetta

(located at high elevation) city has a different scenario. Here, the Meteorological

parameters have a contribution of about 8% variance in PM2.5 mass concentration

during winter months, whereas, about 24% of variance in PM2.5 is explained by

meteorology during summer season.

61

Table 3.1. Linear Regression Analysis of PM2.5 and Meteorological Variables for

Islamabad, Lahore, Peshawar, and Quetta

City Season Correlation Coefficient (r) Variance Explained

Islamabad

Winter 0.457 21%

Summer 0.211 4%

Annual 0.447 20%

Lahore

Winter 0.453 20%

Summer 0.251 6%

Annual 0.432 19%

Peshawar

Winter 0.424 18%

Summer 0.372 14%

Annual 0.357 13%

Quetta

Winter 0.279 8%

Summer 0.489 24%

Annual 0.239 6%

3.5. Analysis of High PM2.5 Episodes

Two high PM2.5 episodes for each city during winter and summer seasons have

been selected for synoptic analysis. These episodes were analyzed as a function of

62

meteorology in order to assess the impact of climate on air quality. The description of

high PM2.5 episodes along with the synoptic analysis is given as follows:

3.5.1. Islamabad Winter High PM2.5 Episode (December 1-9, 2007)

For Islamabad, December 1-9, 2007 was selected as a high PM2.5 episode due to

very high concentrations throughout this period. The time-series of PM2.5 mass

concentration and temperature during this period is given in Figure 3.10(a). The

maximum concentration of PM2.5 was observed to be 303.33 µg m-3 on December 6,

2007 which is quite high. The average concentration 130.44 µg m-3 of PM2.5 during

the episode period remains above the standard limit of 25 µg m-3. The average

temperature was observed in the range of 3-21oC.

Figure 3.10(a): Time Series of PM2.5 Mass Concentration and Temperature in Islamabad during December 1-9, 2007

Figure 3.10(b) gives the diurnal profile of PM2.5 during December 1-9, 2007. The

averaged concentration of PM2.5 is much higher than the standard value of PM2.5

throughout the day. Peak values of PM2.5 have been observed during midnight and

63

mid-day. It is contrary to the seasonal averaged diurnal profile of Islamabad where the

PM2.5 concentration was not as high during mid-day.

Figure 3.10(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in

Islamabad during December 1-9, 2007

Figure 3.10(c) shows the wind speed and wind direction in Islamabad city during

high PM2.5 episode. Wind speed has been observed to be very low and there is no

advection of air in the city. It implies the contribution of local pollution sources.

Furthermore, very low wind speed leads to accumulation of pollutants within the city

during this period. Figure 3.10(d) shows the back trajectory for PM2.5 episode during

December 1-9, 2007 which indicates the emission sources in Western India and

Afghanistan as possible transboundary sources for this episode.

64

Figure 3.10(c): Average Wind Speed (m s-1; contours) and Wind Direction

(vectors) in Islamabad during December 1-9, 2007

65

Figure 3.10(d). Back Trajectory Analysis of High PM2.5 Episode in Islamabad during

December 1-9, 2007

66

3.5.2. Lahore High PM2.5 Episode in Winter (February 1-25, 2008)

February 1-25, 2008 was selected as high PM2.5 winter episode for Lahore. Figure

3.11(a) gives the time-series of PM2.5 mass concentration and temperature during this

period. The maximum concentration of PM2.5 has been observed on February 1, 2001

to be too high i.e., 345 µg m-3. The average concentration 132.02 µg m-3 of PM2.5

during the episode period was also observed to be much higher than the standard

limit. The average temperature during February 1-25, 2008 was observed to be

28-48oC. Figure 3.11(b) shows the diurnal profile of PM2.5 during this period. The

mass concentration of PM2.5 remained too high throughout the day with values going

down during 10:00 a.m. to 4:00 p.m. After 4:00 p.m., the PM2.5 mass concentration

goes up again reaching its peak at 9:00 p.m.

Figure 3.11(a): Time Series of PM2.5 Mass Concentration and Temperature in

Lahore during February 1-25, 2008

67

Figure 3.11(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in Lahore

during February 1-25, 2008


(vectors) in Lahore during February 1-25, 2008

68

Average wind speed (Figure 3.11(c)) during high PM2.5 episode is observed

to be 1.5 m s-1 and no advection has been observed during this time. Figure

3.11(d) represents the back trajectory for high PM2.5 episode during February 1-25,

2008 in Lahore indicating the pollution sources being transported from western side.

Figure 3.11(d). Back Trajectory Analysis of High PM2.5 in Lahore during February 1-25, 2008

69

3.5.3. Peshawar High Winter PM2.5 Episode (December 1-22, 2007)

High PM2.5 episode in winter season for Peshawar was selected for duration of

December 1-22, 2007. Figure 3.12(a) gives the time-series of PM2.5 mass

concentration and temperature during this period. The maximum concentration of

PM2.5 in Peshawar has been observed to be 321.83 µg m-3 on December 8, 2007. The

average concentration of PM2.5 during this period observed as 118 µg m-3 was much

higher than the standard limit. The average temperature during December 1-22, 2007

was observed in the range of 5-22oC. Figure 3.12(b) shows the diurnal profile of

PM2.5 during this period. The mass concentration of PM2.5 remained too high

throughout the daytime with values going down at 11:00 a.m. and then starts

increasing at 4:00 p.m.

Figure 3.12(a): Time Series of PM2.5 Mass Concentration and Temperature in

Peshawar during December 1-22, 2007

70

Figure 3.12(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in

Peshawar during December 1-22, 2007

Average wind speed (Figure 3.12(c)) during high PM2.5 episode is observed to be

about 1.5 m s-1 which is quite low and there is no advection of air into Peshawar

during this time.


(vectors) in Peshawar during December 1-22, 2007

71

Figure 3.12(d) shows the back trajectory for PM2.5 episode during December 1-22,

2007 in Peshawar. The back trajectory analysis shows that the local emission sources

as well as Western Punjab, India are responsible for this high PM2.5 episode in

Peshawar.

Figure 3.12(d). Back Trajectory Analysis of High PM2.5 in Peshawar during December 1-22, 2007

72

3.5.4. Quetta High PM2.5 Winter Episode (December 1-18, 2007):

December 1-18, 2007 was selected as high PM2.5 episode for Quetta during winter

season. Figure 3.13(a) shows the time series of PM2.5 and temperature during the

episode period. The maximum concentration of PM2.5 in Quetta reached 283 µg m-3 on

December 6, 2007 which is very high concentration. The average concentration of

68.67 µg m-3 during this period remained higher than the NEQS. The temperature in

Quetta during the episode period was in the range of -1 to 18oC. Figure 3.13(b) shows

the diurnal profile of PM2.5 mass concentration during this period. The mass

concentration of PM2.5 starts increasing at 6:00 a.m. due to increase in traffic

movement and then goes down at about 10:00 a.m. The other peak is seen from 10:00

p.m. to midnight.

Figure 3.13(a): Time Series of PM2.5 and Temperature in Quetta

during December 1-18, 2007

73

Figure 3.13(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in Quetta

during December 1-18, 2007

The wind vectors show that the northerly winds are advecting into the city,

however, wind speed is very low i.e., 1 m s-1 (Figure 3.13(c)). Figure 3.13(d)

represents the back trajectory for PM2.5 episode in Quetta during December, 2007. The

trajectory shows the contribution of possible emission sources in South-western India

and local sources located in Sindh province.

74


(vectors) in Quetta during December 1-18, 2007

75

Figure 3.13(d). Back Trajectory Analysis of High PM2.5 in Quetta during December 1-18, 2007

3.5.5. Lahore High PM2.5 Episode in Summer (June 1-12, 2007)

June 1-12, 2007 was selected as high summer PM2.5 episode for Lahore. Figure

3.14(a) shows the time series of PM2.5 and temperature during this period. The

76

maximum concentration of PM2.5 was observed as 222.5 µg m-3 on June 12, 2007 and

the average concentration of 80.87 µg m-3 during this period also exceeded the NEQS.

The average temperature during summer high PM2.5 episode was observed to be in the

range of 28-49oC. Figure 3.14(b) shows the diurnal profile of PM2.5 mass

concentration during this period. The hourly average mass concentration of PM2.5

remained high with lower values observed during 2:00 p.m. to 4:00 p.m.

Figure 3.14(a): Time Series of PM2.5 and Temperature in Lahore

during June 1-12, 2007

77

Figure 3.14(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in Lahore


Southerly winds are being advected into the city, however, due to very low wind

speed; there is no dispersion of pollutants (see Figure 3.14(c)). Figure 3.14(d)

represents the back trajectory for high PM2.5 episode during June 1-12, 2007 in

Lahore. The trajectory analysis shows that north-western wind patterns may be a

possible source for this pollution episode.

78


(vectors) in Lahore during June 1-12, 2007

79

Figure 3.14(d). Back Trajectory Analysis of High PM2.5 in Lahore during June 1-12, 2007

80

3.5.6. Quetta High PM2.5 Summer Episode (August 13-19, 2007)

August 13-19, 2007 was selected as high PM2.5 episode for Quetta during summer

season. Figure 3.15(a) shows the time series of PM2.5 and temperature during the

episode period. The maximum concentration of PM2.5 in Quetta reached 210.5 µg m-3

on August 16, 2007 and the average concentration observed as 79.2 µg m-3 is above

the NEQS. The average temperature of Quetta during the episode period was in the

range 19-35oC. Figure 3.15(b) shows the diurnal profile of PM2.5 during this period.

Peak values of PM2.5 are observed during 9:00 a.m. to 11:00 p.m. and at midnight.

There is convection of southerly and north-westerly winds in the study area. The

wind speed has been observed to be 3 m s-1(Figure 3.15(c)).

Figure 3.15(a): Time Series of PM2.5 and Temperature in Quetta

during August 13-19, 2007

81

Figure 3.15(b): Averaged Diurnal Profile of PM2.5 Mass Concentration in Quetta



(vectors) in Quetta during August 13-19, 2007

82

Figure 3.15(d) shows the back trajectory for PM2.5 episode in Quetta during August

13-19, 2007 which indicates that the long-range transport of emissions coming from

Afghanistan and Turkmenistan contribute towards this high PM2.5 episode during

summer season in Quetta.

Figure 3.15(d). Back Trajectory Analysis of High PM2.5 in Quetta during August 13-19, 2007

83

3.6. Conclusion

This study attempts to characterize the PM2.5 pollution in four major urban centers

in Pakistan. The average PM2.5 mass concentrations are significantly higher in Lahore

than the other three cities. All the four cities have average PM2.5 concentration higher

than the Pakistan NEQS. These high concentrations of PM2.5 may be attributed to

primary sources e.g. coal and fossil fuel combustion coupled with the meteorological

conditions controlling the formation of secondary PM2.5 and their dispersion within

the troposphere. Analysis for high pollution episodes conducted using the NOAA

HYSPLIT model indicates that air trajectories influencing Lahore, Islamabad,

Peshawar and Quetta commonly originate from western India, especially in summer

as part of the prevailing monsoon circulation and eastern Afghanistan. These source

areas (states of Gujarat, Rajasthan and Punjab) have high concentration of industrial

activity and are likely sources of enhanced PM2.5 concentration, in addition to the

local sources. High mass concentration in winter may be a result of increased local

coal combustion activities due to more usage of coal and biomass for heating

purposes. Furthermore, in low temperatures, nitrate may have been exchanged from

gas-phase into particles which suggests that the ratio of nitrates particulate formation

in winter to sulfate formation in summer is high. It may also indicate that the primary

NOx emissions are more than SO2 emissions in these cities indicating higher

contribution from vehicular emissions. Surface inversion layers and fog contribute to

a great extent to hinder the dispersion of PM2.5 in urban areas of Pakistan especially in

Lahore where winter fog is extended and at its maximum. Lower mixing height in

winter season may be another factor as it does not allow dispersion of pollutants for

prolonged low temperature periods. Significant daily variation has also been observed

in PM2.5 mass concentration with peaks in morning till noon and another peak with

maximum values from evening to midnight. Among meteorological factors,

temperature and wind speed show a negative correlation with PM2.5 indicating the

dispersion of pollutants with the wind and accumulation of pollutants at low

temperatures.

84

Particulate pollution has become an issue of great concern in Pakistan, and its

control is a challenge for regulatory agencies. There is a need to strictly enforce the

vehicular and industrial emission standards in order to control the elevated pollution

levels.

85

SECTION II: MEASUREMENTS AND ANALYSIS OF

AIR QUALITY IN ISLAMABAD, PAKISTAN

Ambient air quality data of Islamabad for five years (2007-2011) was analyzed for

determination of average concentration of representative six air pollutants. The hourly

data for each pollutant collected was analyzed for average annual, seasonal and

diurnal variation. Analysis of various pollutants has also been conducted in order to

find out the role of precursors, possible emission sources, meteorology, origin of air

masses (based on back-trajectory analysis), and background concentrations.

4.1 Meteorology

The climate of Islamabad has a semi-arid climate with warm to hot humid

summers followed by monsoon season and a cold winter. In general, May and June

are the hottest months with average high temperature of ~38oC (100.4oF) observed in

June. In winter season, the average low temperature of ~2oC (35.6oF) may be

observed in January. Fog occurs in Islamabad during the winter season. Monsoon

season brings heavy rainfall and thunderstorm during July-September. In Islamabad,

temperatures vary from cold to mild, routinely dropping below zero. In the hills

(Margalla Hills) there is sparse snowfall. The highest temperature recorded was 46.5

oC (115.7 oF) in June, while the lowest temperature was −4 oC (24.8 oF) in January.

On 23 July 2001, Islamabad received a record breaking 620 millimetres (24 in.) of

rain fell in just 10 hours. It was the heaviest rainfall in 24 hours in Islamabad and at

any locality in Pakistan during the past 100 years (Hameed, 2007).

4.2 Average Concentration of Pollutants The average concentration of pollutants in Islamabad computed for PM2.5, NO,

CO and O3 concentrations are presented in Figures 4.1, 4.2, 4.3 and 4.4. Since the CO

standard is either 1-hour or an 8-hour standard, and the ozone is 1-hour average;

Figures 4.5 and 4.6 provide the numbers of exceedances of the ambient concentrations

for carbon monoxide and ozone during 2007 - 2011.

86

Figure 4.1. Annual Averaged PM2.5 Mass Concentration in Islamabad during 2007-

2011

Figure 4.2. Annual Averaged Concentration of NO (µg m-3) in Islamabad during 2007-2011

87

Figure 4.3. Annual Averaged Concentration of CO (mg m-3) in Islamabad during

2007-2011

Figure 4.4. Annual Averaged Concentration of O3 (µg m-3) in Islamabad during

2007-2011

88

Figure 4.5. Number of Exceedances of Annual Average Concentration of

CO (mg m-3) in Islamabad during 2007-2011

Figure 4.6. Number of Exceedances of Annual Average Concentration of O3 (µg m-3)

in Islamabad during 2007-2011

The annual average mass concentration of PM2.5 exceeds the Pakistan’s

National Environmental Quality Standard (NEQS) of 25 µg m-3 in each year (2007-

89

2011). In Islamabad, the annual average PM2.5 mass concentration is 81.1±48.4 µg m-

3, 93.0±49.9 µg m-3, 47.8±33.2 µg m-3, 79.0±49.2 µg m-3, 66.1±52.1 µg m-3 during

2007 to 2011 respectively; and the highest hourly values observed were 303 µg m-3

during December 2007, 495.0 µg m-3 during November 2008, 259.8 µg m-3 during

September 2009, 456.0 µg m-3 during October 2010, and 379.0 µg m-3 during January

2011. Such high mass concentrations of PM2.5 may be attributed to primary sources

such as black carbon aerosols (Husain et al., 2007; Viidanoja et al., 2002), and

secondary formation (i.e. gas-to-particle conversion) also contribute to PM2.5 (Raja et.

al., 2010). High PM2.5 is associated with adverse human health effects (Petrovic et al.,

2000).

Annual mean concentration of NO is also higher than the NEQS of 40 µg m-3

during 2007-2010, indicating the contribution of vehicular NO emissions. The hourly

average concentration of carbon monoxide for all the years is below the NEQS of 10

mg m-3. On some occasions, the hourly average ozone concentration exceeds the

NEQS primarily during the day during summer months (e.g. number of exceedances

of ozone concentrations during 2007 to 2011 were 121, 277, 324, 107 and 462

respectively). Figure 4.7 and Figure 4.8 give the time-series representation of air

pollutants in Islamabad during 2007-2011.

Figure 4.7. Time Series of Ambient Concentrations of O3, NO, SO2, PM2.5 and CO in

Islamabad during 2007-2011

90

Figure 4.8. Time Series of Monthly Averaged Concentrations of O3, NO, SO2, PM2.5

and CO in Islamabad during 2007-2011

4.3. Correlation of Air Pollutants

Figure 4.9 shows the correlation of CO with PM2.5 during 2007 to 2011. As

diesel combustion (from heavy duty vehicles and electric generators) is considered to

be a major source of both carbon monoxide and particulate matter, the correlation

between PM2.5 and CO was used to determine the possibility of similar source for

these two pollutants. Figure 4.9 shows that PM2.5 is significantly correlated (r = 0.61;

p-value ≤ 0.01) with carbon monoxide. From this plot, it may be inferred that the

sources other than automobiles (i.e. electric generators) also contribute towards

primary and secondary PM2.5 in the troposphere (since CO is primarily emitted from

the automobiles).

91

Figure 4.9. Correlation between CO and PM2.5 ambient concentration

during 2007-2011

Both carbon monoxide and the nitrogen oxides have many anthropogenic

sources in common including mobile sources (i.e. automobiles) and point sources (i.e.

energy production). It is therefore interesting to examine the relationships of these

species in ambient air, especially in an urban environment where the photochemical

transformations, including removal mechanisms, may be negligible; and then check

these relationship against emission inventories. Mobile sources often have the

characteristic of high CO/NO ratios and low SO2/NO ratios; whereas, higher SO2/NO

ratios and lower CO/NO ratios are associated with point sources (energy production).

Based on ambient data, Figure 4.10 and 4.11 provides the relationship between CO

and NO, and between CO and reactive nitrogen species, NOy′, in Islamabad during

2007 to 2011. A linear regression of hourly average CO and NO, and CO and NOy′

was performed which shows a significant (p-value≤0.01) correlation between CO and

NO concentrations ([CO]=10.13[NO]+511.3; r2=0.76), and a significant (p-

value≤0.01) correlation between CO and NOy′ concentrations

([CO]=9.84[NOy′]+256.8; r2=0.78). From this ratio analysis, relative background

concentrations may be determined by examining the intercept of the regression lines.

The regression curves reveal a background CO concentration of ~300 to ~600 ppbv in

92

the Islamabad urban area. This is similar to Raleigh, NC, USA, urban site value of

470 ± 52 ppbv (Aneja et al., 1997); however, CO background concentration in New

Delhi, India, has been observed as approximately 1693 ppbv (Aneja et al., 2001).

Figure 4.10. Correlation between CO and NO in Islamabad during 2007-2011

Figure 4.11. Correlation between CO and NOy′ in Islamabad during 2007-2011

93

Moreover, relative source strengths like mobile sources versus point sources

may also be suggested by examining the slope of the regression lines, and compared

with emissions inventory. Klimont et al. (2013) and ECCAD (2014) have provided an

emissions inventory (developed for the year 2010) for CO, SO2, and NOx. Table 1

compares and contrasts the emissions from this inventory by examining the

relationship between ambient CO and NOx, and between ambient SO2 and NOx for

2007 to 2011 in Islamabad, Pakistan. It also compares and contrasts with CO and NOx

relationship observed in Denver, CO, US (Parrish et al., 1991); Boulder, CO, US

(Goldan et al., 1995); Raleigh, NC, US (Aneja et al., 1997); and New Delhi, India

(Aneja et al., 2001). Based on ratio analysis of CO and NOx, Parrish et al. (1991)

reported values of 8.4, 7.8, and 10.2 for mobile sources in the Eastern US,

Pennsylvania area, and Western US, respectively. Given the average ratio of about 10

(i.e., the slope of the regression line) in Islamabad, it appears that mobile sources

contribute more to the concentrations of CO and NOx than point sources.

Monthly averages of sulfur dioxide concentration (1 µg m-3 SO2 = 0.38 ppbv)

is plotted in Figure 4.12. Sulfur dioxide concentrations are below Pakistan’s 24-hour

average NEQS value of 120 µg m-3 during the measurement period. A linear

regression of hourly average SO2 and NO concentrations (Figure 4.13) was performed

([SO2]=0.01[NO]+1.73; r=0.4). The ratio analysis of SO2/NO for Islamabad (slope

~0.01) (Table 1) indicates that point sources are contributing to SO2 in the city; also

corroborated by the emissions inventory for Islamabad (Klimont et al., 2014;

ECCAD, 2014).

94

Table 4.1. Ratio Analysis based on average emissions and/or ambient data

Region CO/NOx SO2/NOx

Eastern US a, b

Mobiles Point Sources

4.3

8.4 0.95

0.94

0.05 1.8

Pennsylvania area a, c


2.6

7.8 0.8

1.7

0.05 2.3

Western US a, d


6.7

10.2 1.2

0.41

0.05 1.1

Denver Metropolitan area a, e


7.3

10.5 0.18

0.19

0.05 0.44

Raleigh, NC f

New Delhi, India g

16.3

50

0.73

0.58

This study

Based on 2010 Emission Inventory h,i


Based on Ambient Data

4.94 0.63

10

0.34 7.0

0.01

a. Parrish et al, JGR, 1991 b. East of 95.5oW Longitude, South of 45oN latitude c. 76.5o - 81oW Longitude, 39o – 42o N latitude d. West of 104oW Longitude, South of 49oN latitude e. 104o -105.5oW Longitude, 39.5-41oN latitude f. Aneja et al, Chemosphere, 1997 g. Aneja et al, Environment International, 2001 h. Klimont et al, Environmental Research Letters, 2013 i. ECCAD, Emissions of atmospheric Compounds & Compilation of Ancillary Data, 2014.

95

Figure 4.12: Monthly average of SO2 concentration for 2007, 2008, 2010, and 2011 (I denotes ±1SD)

Figure 4.13. Correlation between SO2 and NO in Islamabad during 2007-2011

96

Figure 4.14 provides the correlation between PM2.5 and NO in Islamabad. The

association between PM2.5 and NO is significantly positive (p-value≤0.01; r=0.5)

suggesting that there is a contribution of NO in secondary production of PM2.5. Other

precursors (e.g. SO2) and primary sources (e.g. diesel generators) also lead to the

PM2.5 burden in the city as well.

Figure 4.14. Correlation of PM2.5 and NO in Islamabad for the Period 2007-2011

From the correlation among pollutants like CO, NO, SO2, and PM2.5 (Figure

4.15), it may be inferred that the pollution measured in Islamabad is primary in nature

having more association amongst species with direct emissions. The correlation

between PM2.5 and CO concentrations is an indication of direct emissions, most likely

from transport sector and fresh emissions from the industrial areas within the city

(Figure 4.15a). The CO concentrations are also owing to the chemical conversion of

VOCs via photochemistry; and some fraction of the PM2.5 also originates from gas-to-

particle conversion of SO2 and NOx. For the NOx emissions from the transport sector,

the nitric oxide (NO) is greater than 90 percent of the emissions (Vallero, 2008) and

readily converts to nitrogen dioxide (NO2) in the presence of sunlight.

97

The strong correlation between NO and SO2 indicates the contribution of

direct emission sources, such as emissions from transportation, industries, generator

sets (diesel combustion), and power plants. In polluted environments, as in case of

Islamabad, CO reacts with hydroxyl radicals and subsequently with NO to form ozone

through complex series of photochemical reactions (Figure 4.15c). Both carbon

monoxide and the nitrogen oxides have many anthropogenic sources in common

including mobile sources (i.e. automobiles), local industries, and point sources (i.e.

energy production) (Figure 4.15d). Nitric oxide (NO) emissions are readily oxidized

to nitrogen dioxide (NO2) in the presence of sunlight. Its subsequent complex

reactions with either volatile organic compounds (VOCs') and/or methane (CH4) lead

to the formation of tropospheric ozone (Figures 4.15e and 4.15f). Major source of

VOCs is a combination of automotive exhaust owing to incomplete combustion in the

vehicles because of relaxed maintenance and even adulteration of the fuel; and

industries and generator sets in and around Islamabad.

Figure 4.15(a): Correlations between Measured Daily Averages

of CO and PM2.5 in Islamabad during 2007-2011

98

Figure 4.15(b): Correlations between Measured Daily Averages

of NO and SO2 in Islamabad during 2007-2011

Figure 4.15(c): Correlations between Measured Daily Averages

of CO and O3 in Islamabad during 2007-2011

99

Figure 4.15(d): Correlations between Measured Daily Averages

of CO and NOy′ in Islamabad during 2007-2011

Figure 4.15(e): Correlations between Measured Daily Averages

of NMHCs and O3 in Islamabad during 2007-2011

100

Figure 4.15(f): Correlations between Measured Daily Averages

of CH4 and O3 in Islamabad during 2007-2011

4.4. Photochemistry of Ozone Formation

In the troposphere, ozone is formed in presence of sunlight by the precursors

involving NOx, methane, CO and VOCs/volatile hydrocarbons. VOCs/volatile

hydrocarbons and carbon monoxide react with NO in the prescence of sunlight to

form NO2; which is photolyzed to produce ozone. The correlation of ozone with its

precursors has been determined in order to find out the possible source contributions.

The correlation has been done for the summer (June, July and August) months to

compare and contrast ozone production efficiency during the summer season (Aneja

et al., 1996). To account for maximum photochemical activity during the day, and the

degree of conversion of NO to the reservoir NOy′ species, the time for this correlation

has been set as 9:00 a.m. to 3:00 p.m. O3 is plotted against (NOy′ – NO)/ NOy′ in

Figure 4.16.

101

Figure 4.16. Variation of concentration of Ozone vs (NOy’-NO)/NOy’ in the summer

months for 2007-2011 during maximum photochemical activity of the day i.e., 9:00 a.m. to 3:00 p.m.

This plot represents the relationship between ozone and the degree of

conversion of NO to reservoir NOy′ species. It is observed that ozone increases with

increase in the degree of photochemical conversion of NO to reservoir NOy′ species.

Ozone concentration is expected to be low in fresh air masses becauses it is primarily

formed by the same photochemiocal process which leads to the formation of NOy′

species such as HNO2, HNO3, PAN etc. Thus, with an increase in the ratio (NOy′ –

NO)/ NOy′, there is a consequent increase in the ambient concentration of ozone.

Results of this study show that aged airmasses have higher ozone concentration i.e.,

increasing (NOy′ – NO)/ NOy′. An exponential fit of the data yields [O3] = 30.9

exp(0.77(NOy′-NO)/ NOy′) . The intercept of O3 is ~31ppbv which represents the

nominal regional background concentration of ozone in ambient air which is not

influenced by the direct emissions. The regional background O3 concentration for

Islamabad is, therefore, ~31ppbv i.e., air advecting into Islamabad contains ~31ppbv

of ozone. This is similar to the nominal local background O3 concentration of

~28ppbv for Southeastern United States i.e. Raleigh, North Carolina, USA (Aneja et

al., 2000).

102

4.5. Diurnal Variation of Pollutants

Figure 4.17(a) shows the diurnal profile of ozone along with its precursors in

order to assess the influence of precursor pollutants on its production within the

troposphere. Ozone precursors (NOx, hydrocarbons, VOCs, and CO) build up during

the morning rush hour, and the ozone concentration starts increasing with a peak

between 12:00 noon to 16:00. At night time, the concentration of ozone decreases (no

photochemical activity) and goes to a minimum; where as high concentration of NO

and NMHCs occurs. Both NO and NMHCs increase owing to a combination of

evening automobile rush hour and trucks carrying freight through the city in evening

and night-time, no formation of ozone at night, and collapse of the planetary boundary

layer. In the morning, NMHCs initiate reactions for photochemical production of

ozone resulting in their minimum concentration and an ozone peak during mid-

afternoon.

Figure 4.17(a). Diurnal profiles of ozone, nitric oxide, CO and non-methane

hydrocarbons (NMHCs)

103

Figure 4.17 (b) shows the diurnal variation of average ozone concentration

during the four seasons. The diurnal profile is similar in all the four seasons due to the

fact that the tropospheric ozone formation takes place at daytime in presence of

sunlight. The maximum ozone concentration occurs during summer season due to

high temperature and high solar intensity; and the minimum concentration levels were

observed during winter season. The lower formation of ozone in winter is because of

low temperature and low solar intensity. With the beginning of spring season, the

formation of ozone increases.

Figure 4.17(b). Seasonal and diurnal variation of averaged ozone concentration

during 2007-2011 (±1 standard deviation is also shown in the figure)

The lower formation of ozone in winter is because of low temperature and low

solar intensity. The lower formation of ozone in winter is because of low temperature

and low solar intensity. Another reason of low concentration of ozone during winter

season is that the long-range transport of precursor gases is limited in winter, which

also contributes to some extent to its lower formation rate. And with the beginning of

spring season, the formation of ozone is increased.

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4.6. Effect of Meteorology on Air Pollutants

The formation of tropospheric ozone is strongly dependent on meteorological

conditions especially atmospheric temperature and solar radiation (NRC, 1991).

Figures 4.18(a) and 4.18(b) provide the relationship of ozone with temperature and

solar radiation. Ozone has been observed to be positively correlated with temperature

(p ≤ 0.01; r = 0.694) and solar radiation (p ≤ 0.01; r = 0.601). About 48% of variance

in ozone concentration during daytime is explained by temperature, whereas, solar

radiation affects about 36% of the variation in ozone concentration. The positive

correlation of ozone with temperature and solar radiation is due to their role in

photochemical formation of ozone. Similar relationship of ozone with temperature

and solar radiation has been reported by NRC (1991), and Jacob and Winner (2009).

Figure 4.18(a). Correlation of Ozone with Temperature during 2007-2011

at 9:00 a.m. – 3:00 p.m.

105

Figure 4.18(b). Correlation of Ozone with Solar Radiation during 2007-2011

at 9:00 a.m. – 3:00 p.m.

Figure 4.18(c). Correlation of PM2.5 with Temperature during 2007-2011

at 9:00 a.m. – 3:00 p.m.

106

Figure 4.18(d). Correlation of PM2.5 with Solar Radiation during 2007-2011

at 9:00 a.m. – 3:00 p.m.

Figures 4.18(c) and 4.18(d) represent the correlation of PM2.5 with temperature

in Islamabad. The figure shows that PM2.5 has a negative correlation (p ≤ 0.01; r = -

0.44) with temperature during 9:00 a.m. to 3:00 p.m. About 19% of variance in PM2.5

can be explained by its linear relationship with temperature. The regression analysis

of PM2.5 and solar radiation shows that about 13% of variation in ambient PM2.5

concentration in Islamabad during daytime is associated with solar radiation. There is

a statistically significant correlation of r = -0.36 between PM2.5 and solar radiation.

Tiwari et al (2013) has also reported a negative correlation of PM2.5 with temperature

which depends on composition of the particulate matter. This is perhaps due to the

semi-volatile components such as nitrate and organics are expected to decrease as

they shift from the particle phase to the gas phase at higher temperature (Sheehan and

Bowman, 2001; Aw and Kleeman, 2003; Tsigaridis and Kanakidou, 2007; Dawson et

al., 2007; Kleeman, 2008).

4.7. Linear Regression Analysis

Linear regression analysis has been conducted to determine the meteorological

variables that explain the most variance in the overall data. Summary of linear

regression analysis is given in Table 4.2. The regression analysis clearly explains the

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extent to which meteorological conditions affect the concentration of PM2.5 in the

atmosphere.

Variable Season Statistical Analysis Weighted Variables

Wind Speed Temperature Vapour Pressure

Solar Radiation

O3

Annual

Correlation Coefficient (r) 0.792

0.346

0.717

0.131

0.622

Variance Explained (%) 63%

12%

49%

4%

40%

Winter


0.59

0.654

-0.116

0.493


35%

44%

0.1%

29%

Spring


0.341

0.793

-0.176

0.636


12%

65%

2%

46%

Summer


0.362

0.727

-0.157

0.626


13%

43%

0.7%

35%

Fall


0.344

0.757

0.024

0.597


12%

61%

3%

48%

Variable Season Statistical Analysis Weighted Variables

Wind Speed Temperature Vapour Pressure

Solar Radiation

O3

Annual


0.346

0.717

0.131

0.622


12%

49%

4%

40%

Winter


0.59

0.654

-0.116

0.493


35%

44%

0.1%

29%

Spring


0.341

0.793

-0.176

0.636


12%

65%

2%

46%

Summer


0.362

0.727

-0.157

0.626


13%

43%

0.7%

35%

Fall


0.344

0.757

0.024

0.597


12%

61%

3%

48%

Table 4.2. Linear Regression Analysis of Ozone and PM2.5 with Meteorological Variables

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The regression analysis shows that the four meteorological variables

accounted for 20% variance in PM2.5 mass concentration in Islamabad. Meteorology

seems to affect the PM2.5 mass concentration in Islamabad, Lahore and Peshawar

more during winter than in summer season. The meteorological factors accounted for

approximately 19% variance in PM2.5 mass concentration in Lahore city. About 13%

of the variance in average PM2.5 concentration in Peshawar is explained by the

meteorological conditions. Annual average mass concentration of PM2.5 in Quetta

seems to be less affected (6%) by meteorology as compared to other cities. It has been

observed that the affect of meteorology on PM2.5 is more prominent during winter

season in Islamabad (21%), Lahore (20%) and Peshawar (18%), however, Quetta city

has a different scenario. Here, the Meteorological parameters have a contribution of

about 8% variance in PM2.5 mass concentration during winter months, whereas, about

24% of variance in PM2.5 is explained by meteorology during summer season.

4.8. Back Trajectory Analysis

The back trajectory analysis using the National Oceanic and Atmospheric

Administration (NOAA) HYSPLIT was conducted in order to study the atmospheric

transport of air pollutants and their precursors and to find out the potential source

regions for air pollution episodes in Islamabad during 2007-2011. Four pollution

episodes for PM2.5 and ozone (Figure 4.19 and Figure 4.20) were selected for back

trajectory analysis which show that the important source areas (during high pollution

episodes) reaching Islamabad are located in eastern Afghanistan and western India.

109

(a): Back Trajectory for High PM2.5 Episode during 1st – 30th November, 2007’

(b): Back Trajectory for High PM2.5 Episode during 1st – 30th January, 2011

(c): Two-days Back Trajectory for High O3 Episode during 19th – 22nd September, 2009’

(d): Three-days Back Trajectory for High O3 Episode during 10th – 13th July, 2011

Figure 4.19. Air Parcel Back Trajectories for PM2.5 and Ozone Episodes during 2007-2011

110

Figure 4.20. Air parcel 48-hour back trajectories analysis for some selected PM2.5 and Ozone high pollution episodes during 2007-2011

111

Major pollution sources from Afghanistan particularly Kabul and Jalalabad

include vehicular and industrial emissions, biomass burning, use of diesel electric

generators and burning of tyres. The source regions in Western India are located in

the states of Gujarat, Rajasthan and Punjab (i.e., Southeast of Islamabad) and are

known to have high concentration of industries and mechanized farming that are

sources of particulate and gaseous emissions. During winter, the monsoon flow

reverses and local sources of emissions in Islamabad due to burning coal and wood

are more important.

4.9. Conclusions

The ambient air quality for criteria pollutants has been characterized for

Islamabad, Pakistan, during 2007-2011. The annual and hourly average concentrations

show that the annual average concentrations of PM2.5 and NO are higher than the

Pakistan NEQS. Transportation is a major source of such high concentrations of NO.

The hourly average concentrations of ozone exceeds the NEQS primarily during the

summer season. Carbon monoxide and sulfur dioxide are within the safe limit.

Seasonal profile of ozone concentration shows that summer is the peak season for

photochemical production of ozone, while the winter season has the minimum

concentration of ozone amongst the four seasons. The back trajectory analysis using

the National Oceanic and Atmospheric Administration (NOAA) HYSPLIT show that

during summer months, important source areas of trajectories reaching Islamabad are

located in eastern Afghanistan and western India. The source regions in the Indian

states of Gujarat, Rajasthan and Punjab (i.e. Southeast of Islamabad) have high

concentration of industries and mechanized farming that are sources of particulate and

gaseous emissions.

This study reveals that the background concentration of carbon monoxide in

Islamabad (~300 to ~600 ppbv) is larger than Western US background CO

concentration (~200ppbv). The ratio of CO/NO (~10) indicates that the mobile

sources contribute predominantly to the ambient concentration of these compounds;

while the ratio of SO2/NO (~0.01) indicates that the point sources primarily contribute

to SO2 pollution within the city. The ratios of measured concentrations of [CO] to

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[NO], and [SO2] to [NO] observed in Islamabad provide a test for emission

inventories. The ratios of these pollutants in the available Islamabad emission

inventories are consistent with ratios obtained from ambient values for these

pollutants.

Keeping in view the current air quality conditions in Islamabad, Pakistan, which are

degrading the atmospheric environmental conditions, there is an urgent need to

develop effective strategies for pollution control. There is also a need for regulatory

agency to enforce the emission standards for industries and motor vehicles in order to

meet the ambient air quality standards. Extensive spatial and temporal air quality

monitoring and modeling with an integrated assessment are significantly required in

developing comprehensive solution to the air quality concerns of Islamabad, Pakistan.

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SECTION III: BACK TRAJECTORY ANALYSIS AND SIMULATION OF OZONE HIGH EPISODES BY WRF

MODEL IN ISLAMABAD, PAKISTAN

5.1. Ozone Episodes in Islamabad City Industrialization and rapid urbanization in Islamabad have led to increased

pollution within Islamabad city. It is very important to monitor the high ozone

episodes in order to track the photochemical smog in Islamabad. The high ozone

episodes were identified by analysis of hourly ozone concentrations and exceedances

than the standard limit of 66 ppbv. Ozone concentrations during these episodes have

been observed to be too high. High ozone episodes have been observed to be lasting

for many days which is quite alarming.

5.2. Back Trajectory Analysis

The air mass back trajectories identify the actual source of pollution which

may be located in a far-off region (Dutkiewicz et al., 1987). Back trajectories were

computed in order to identify the origin of air parcels carrying the air pollutants. The

trajectories were calculated at the altitudes of 500m, 1000m and 1500m AGL for

some of the ozone high episodes. Low-ending trajectories represent air parcels nearer

the ground level, and consequently nearer the ground-based samplers. It has been

observed that the trajectory heights are not constant during the observed days. High-

ending trajectories may represent more accurate boundary layer flow above the local

terrain. Trajectory heights are not constant throughout the trajectories’ duration and

vary significantly from the selected height. Back trajectories were calculated for

different durations so that a possible source origin may be identified. The back

trajectory analysis for high ozone episodes in Islamabad during 2007-2011 is as

follows:

114

Figure 5.1 shows the HYSPLIT back trajectory of air mass from Islamabad for the

ozone high episode during 27th August – 2nd September, 2007. The trajectory was run

for 96 hours so that a particular source of pollution may be identified. It is observed

that the air mass at 500m AGL was transported from South India. Whereas, the air

parcel at 1000m and 1500m AGL are originated from Iran and are passing through

Afghanistan. It implies that the emission sources in Afghanistan and Rajasthan, India

may be possible sources of high ozone in Islamabad during this period.

Figure 5.1. Back Trajectory Analysis of High Ozone Episode in Islamabad during 27th August – 2nd September, 2007

115

Another high ozone episode lasted for about a month in Islamabad on 7th – 19th

September, 2007 (Figure 5.2). The air mass is advecting from Afghanistan in the

westerly direction. The winds, originated from Uzbekistan and Tajikistan are

transported through the cities of Kabul, Gardez and Khost.

Figure 5.2. Back Trajectory Analysis of High Ozone Episode in Islamabad during September 7-19, 2007

116

Figure 5.3 shows the HYSPLIT back trajectory of air parcel from Islamabad during

September 25-27, 2007 as this period is characterized by very high ozone

concentrations. Total run time for back trajectory was 96 hours. The air masses at

500m and 1000m AGL altitudes have been observed to be coming from Afghanistan

through the cities of Jalalabad and Asadabad.

Figure 5.3. Back Trajectory Analysis of High Ozone Episode in Islamabad during September 25-27, 2007

117

Figure 5.4 shows the air mass transport during high ozone episode by HYSPLIT back

trajectory during October 12-21, 2007. Total run time for this back trajectory was 96

hours. The air parcel was transported from Afghanistan at 500m, 1000m and 1500m

altitudes showing contribution of transboundary air pollution affecting the air quality

of Islamabad city.

Figure 5.4. Back Trajectory Analysis of High Ozone Episode in Islamabad during October 12-21, 2007

118

The HYSPLIT back trajectory given in Figure 5.5 has been computed for ozone high

episode during 28th April – 1st May, 2008. The back trajectory was calculated for 48

hours prior to the episode. It is revealed from the trajectory that the air masses at all

altitudes have similar backward direction coming from china.


28th April – 1st May, 2008

119

The HYSPLIT back trajectory given in Figure 5.6 has been computed for ozone high

episode during 10th May – 1st June, 2008. Total run time for the trajectory is 96 hours.

The back trajectory reveals that the air masses during the episode are most likely

representative of local pollution source. However, there is indication of air mass

transport from Afghanistan and India also at 500m and 1000m AGL respectively.


10th May – 1st June, 2008

120

Figure 5.7 shows the HYSPLIT back trajectory of air mass from Islamabad during

high ozone episode of June 4-13, 2008. The back trajectory was run for four days. It

has been observed that the air mass at 500m and 1000m AGL height are originated

from Uzbekistan in north-west and then transported towards Pakistan through

Jalalabad, Afghanistan. The air parcel at 1500m AGL is transported through Ghazni,

Afghanistan.

Figure 5.7. Back Trajectory Analysis of High Ozone Episode in Islamabad


121

The back trajectory for high ozone episode of June 20-24, 2008 is shown in Figure

5.8. Total run time for back trajectory was 120 hours. The air mass at 1000m and

1500 AGL altitudes are coming from Afghanistan. The air parcel at 500m AGL shows

the contribution of emission sources in west India towards high pollution in Islamabad

during this episode. The air mass at 1000m AGL has been observed to be transported

from the Arabian Sea, whereas, the air mass at 1500m AGL is coming from south-

west originated from Afghanistan.

Figure 5.8. Back Trajectory Analysis of High Ozone Episode in Islamabad during June 20-24, 2008

122

Figure 5.9 shows the back trajectory for high ozone episode on August 25-27, 2008.

The back trajectory was run for 96 hours. The air mass during the episode period is

originated from the Arabian Sea at 500m AGL and the air parcels at 1000m and

1500m AGL are transported from the southern parts of Pakistan. Stagnant conditions

also prevail before the high ozone episode as evident from the trajectory.


August 25-27, 2008

123

The back trajectory for a high ozone episode in Islamabad during May 13 - 21, 2009

is given in Figure 5.10. Total run time for back trajectory is 72 hours. It has been

observed that the air mass at 500m and 1000m AGL are originated in Iran and then

transported towards Islamabad through Afghanistan. However, the air parcel at

1500m shows long-range transport originating from Iraq and carrying the pollutants

on its way from Iran and Afghanistan.


May 13-21, 2009

124

The back trajectory for high ozone episode of May 6-31, 2009 is shown in Figure

5.11. Total run time for the trajectory is 72 hours. It has been revealed from the back

trajectory that the air mass at 500m AGL originates from Indian Punjab carrying coal-

fired power plants. There seems to be high stagnation at this altitude. The air mass at

1000m AGL is being transported from Iran through Afghanistan, whereas, the air

parcels at 1500m AGL show a long-range transport showing its origin from Turkey

and then being transported through Turkmenistan, Uzbekistan and Afghanistan.

Figure 5.11. Back Trajectory Analysis of High Ozone Episode in Islamabad during May 6-31, 2009

125

Figure 5.12 shows the back trajectory of high ozone episode occurred on August 7-9,

2009. The back trajectory was run for three days from the start of high ozone episode.

It is evident from the back trajectory that the air masses are originated from the

Arabian Sea.

Figure 5.12. Back Trajectory Analysis of High Ozone Episode in Islamabad


126

Figure 5.13 shows the back trajectory for a high ozone episode in Islamabad during

August 22-25, 2009. Total run time for back trajectory is 96 hours. It is observed from

the back trajectory that the movement of air masses towards Islamabad is diverse with

respect of altitude. The lower altitude air masses at 500m AGL are coming from

Karachi in north-east. The air mass at 1000m AGL originates from Kazakhstan and is

transported through Uzbekistan and Afghanistan. However, the air parcels at higher

altitude of 1500m AGL is originated from Balochistan province of Pakistan in south-

west.

Figure 5.13. Back Trajectory Analysis of High Ozone Episode in Islamabad during August 22-25, 2009

127

The back trajectory for high ozone episode of August 27-30, 2009 is shown in Figure

5.14. Total run time for the back trajectory is 96 hours from the onset of high ozone

event. It is observed from the back trajectory that the high altitude air masses at

1500m AGL are originated from Turkmenistan crossing Afghanistan to reach

Islamabad. The air masses at 1000m AGL are coming from the South possibly

originated from the Arabian Sea. The air parcels at 500m AGL are originated from

Afghanistan.

Figure 5.14. Back Trajectory Analysis of High Ozone Episode in Islamabad during August 27-30, 2009

128

Figure 5.15 shows the back trajectory for a high ozone episode in Islamabad on 19th –

22nd September, 2009. Total run time for back trajectory is 96 hours. It is observed

from the back trajectory that the high altitude air masses at 1000m and 1500m AGL

are originated from Iran and are transported through Afghanistan. The air masses at

500m AGL are originated from Afghanistan.


September 19-22, 2009

129


11th – 13th June, 2010. Total run time for back trajectory is 120 hours. The back

trajectory shows stagnant conditions in Islamabad prior to the episode. The air masses

before this episode are originated from the Arabian Sea and then are transported

towards Islamabad passing through southern India.


June 11-13, 2010

130


5.17. Total run time for the back trajectory is 72 hours. The back trajectory shows that

the air mass at 500m AGL is originated from Turkmenistan and is transported through

Afghanistan. The air parcels at 1000m and 1500m AGL are originated from

Afghanistan. The air mass at 1500m AGL seems to carry pollutants from the Kabul

city and there is high level of stagnation at this altitude.

Figure 5.17. Back Trajectory Analysis of High Ozone Episode in Islamabad during June 19-23, 2010

131

Figure 5.18 shows the back trajectory for a high ozone episode in Islamabad occurred

on 22nd-24th April, 2011. Total run time for back trajectory is 72 hours. It is revealed

from the trajectory that the air parcel at 1500m AGL is originated in Kazakhstan and

is transported through Turkmenistan, Uzbekistan, Tajikistan and Afghanistan.

Whereas, the air masses at 500m and 1000m AGL are originated from Tajikistan and

transported to Islamabad through Afghanistan.

Figure 5.18. Back Trajectory Analysis of High Ozone Episode in Islamabad during April 22-24, 2011

132


May 16-20, 2011. Total run time for back trajectory is 72 hours. It is revealed from

the back trajectory that the air masses at altitudes 500m, 1000m and 1500m AGL are

all originated from Turkmenistan and are moved towards Islamabad through

Uzbekistan and Tajikistan.


133


22nd – 25th May, 2011. Total run time for back trajectory is 96 hours. It is revealed

from the back trajectory that the air masses at altitudes 500m and 1000m AGL are

originated from Indian Kashmir, whereas, the air mass at 1500m AGL is being

transported from Southern India passing through Indian Punjab towards Islamabad.


134


5.21. Total run time for the back trajectory is 72 hours. It is observed that the air mass

at 500m AGL is originated from Turkmenistan and the air parcels at 1000m and

1500m AGL have their origin in Afghanistan. These air masses are transported to

Islamabad through West India indicating the pollution sources from India contributing

to high ozone event as well.


June 2-25, 2011

135


July 4-6, 2011. The back trajectory for this episode is run for 120 hours in order to

identify the possible pollution sources. It is revealed from the back trajectory that the

air masses prior to the high ozone event are transported from India bringing pollutant.


July 4-6, 2011

136

The back trajectory for high ozone episode of July 10-13, 2011 is shown in Figure

5.23. Total run time for the back trajectory is 96 hours. It is evident from the

trajectory that the emission sources in India are contributing to this high ozone

episode as the direction of air mass transport is from East.

Figure 5.23. Back Trajectory Analysis of High Ozone Episode in Islamabad during July 10-13, 2011

137

5.3. Weather Research and Forecasting (WRF) Model Simulations

Two high ozone episodes have been selected for simulation by Weather

Research and Forecasting (WRF) model. The first selected episode occurred during

June 9-15, 2009 and the second high ozone event was observed during August 15-19,

2011. The synoptic analysis has been conducted for meteorological conditions at

850hPa and 950hPa in order to appropriately assess the role of meteorology in high

level of ozone during selected durations for Islamabad city.

5.3.1. High Ozone Episode during June 9-15, 2009

The detailed synoptic analysis for each day of the episode is given as follows:

June 09, 2009

WRF Simulation has shown that at 850hPa, warm dry northerly wind flow is

quite significant over the study area at daytime. Temperature profile at the same level

ranges from 25 to 30oC. The synoptic simulation shows that there is almost stagnation

over Islamabad Northerly winds ranging between 6-8 m s-1 are flowing across eastern

and western side of the study area. At 925hPa, north-westerly winds are advecting

into Islamabad with a speed of 6 m s-1.

At night time, two wind components i.e., north-westerly dry winds and south-

westerly moist winds are advecting into the city at 850hPa. These two wind

components are converging in the study area due to mountain barriers in Islamabad.

Wind speed is observed to be 2 m s-1 which shows stagnation in the area. At night

time, south-westerly winds are transported by advection in Islamabad. The night time

temperature at 925hPa ranges 35-40oC.

138

Figure 5.24(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 09, 2009

Figure 5.24(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 09, 2009

139

Figure 5.24(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on June 09, 2009

Figure 5.24(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on June 09, 2009

140

June 10, 2009

At daytime, the temperature ranges between 30 to 35oC at both 850hPa and

925hPa levels. Wind pattern has also been observed similar for both pressure levels

i.e., south-easterly winds are advecting into the study area with the wind speed of 2 m

s-1.

At night time, the temperature of Islamabad at both pressure levels is 30-35oC.

South-westerly and north-easterly wind components are dominant at 850hPa with a

wind speed of 2 m s-1 which implies the stagnant conditions in the city. At pressure

level 925hPa, only north-easterly wind component is dominant in the area with very

high wind speed of 10 m s-1.

June 11, 2009

The daytime temperature of Islamabad ranged between 30o to 35oC at 850hPa.

Wind vectors show that there is advection of south-easterly winds with very low wind

speed of 2 m s-1. Such low wind speed shows that there is stagnation in the area. At

925hPa, south-easterly and north-westerly winds components are converging in the

area. However, the wind speed is quite low i.e., 2 m s-1.

At night time, north-easterly winds are advecting in the city with a wind speed

of about 6 m s-1 and temperature range of 30o to 35o C at the pressure level of 850hPa.

At 925hPa, the temperature ranges between 25o to 30oC. There is advection of

easterly winds in the study area with a wind speed of 10 m s-1.

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142



143



144



145

June 12, 2009

Daytime temperature ranges 30o-35oC in Islamabad on June 12, 2009 for both

pressure levels. At 850hPa, north-westerly and northerly wind components advecting

into the city but there is no convergence. Wind vectors are widely spaced which

means that there is stagnation in the area. At 925hPa, only north-westerly winds are

advected into Islamabad with a wind speed of 4 m s-1.

At night time, north-westerly winds are advecting in Islamabad with a wind

speed of about 4 m s-1 and temperature ranging 30o-35o C at 850hPa. The night time

temperature at 925hPa is 35o-40oC. North-westerly and northerly winds components

are advecting into the city with a wind speed of about 8 m s-1.

June 13, 2009

On June 13, 2009, daytime temperature ranges 30o-35oC at both the pressure

levels. South-easterly wind component is dominantly advecting in Islamabad at

850hPa and 925hPa. The wind speed is about 2 m s-1 implying stagnation in the area.

At night time, northerly winds are advecting into the city with a high wind

speed of 14 m s-1 leading to dispersion at 850hPa. At the pressure level of 925hPa,

two winds components i.e., northerly and north-easterly winds are advecting with

convergence in the study area. The wind speed at this level is 8 m s-1.

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147



148



149


Figure 5.28 (d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed

(contours) Overlaid by Wind Direction (vectors) at 925hPa on June 13, 2009

150

June 14, 2009

Daytime temperature on June 14, 2009 at 850hPa ranges 30o-35oC at both the

pressure levels. Northerly and southerly wind components are being converged in

Islamabad at both the pressure levels with a wind speed of 10 m s-1.

At night time, easterly winds are advecting in the study area with a speed of 10

m s-1 at both the pressure levels.

June 15, 2009

At daytime, south-easterly winds are advecting in the city at the pressure

levels 850hPa and 925hPa. Wind speed at 850hPa is 6 m s-1, however, it is 8 m s-1at

925hPa. temperature on June 14, 2009 at 850hPa ranges 30o-35oC at both the pressure

levels. Northerly and southerly wind components are being converged in Islamabad at

both the pressure levels with a wind speed of 10 m s-1.

At night time, south-easterly winds are advecting in the study area at 850hPa

and easterly winds are transported to the study area at 925hPa. Wind speed at both the

pressure levels is 10 m s-1 which is quite high.

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152



153



154



155

5.3.2. High Ozone Episode during August 15-19, 2011

Another high ozone episode during August 15-19, 2011 was selected for

simulation by WRF model in order to do synoptic analysis so that a link between air

pollution and meteorology may be assessed. The synoptic analysis is given below:

August 15, 2011

On August 15, 2011, south-westerly winds are dominant at both the pressure

levels during daytime. The wind speed at 925hPa is about 4 m s-1 whereas; there is

stagnation at 850hPa.

At 850hPa, south westerly winds are advecting in the study area and the wind

speed is about 4 m s-1 during night-time. However, southerly and south-easterly wind

components are dominant in this area at the pressure level of 925hPa. The wind speed

at this level is about 8 m s-.

August 16, 2011

During daytime of August 16, 2011, the wind pattern is same for two pressure

levels with south-westerly winds and a speed of 2 m s-1. The atmosphere remained

quite stagnant during this time. During night-time, south-easterly winds are dominant

in the area at 850hPa and 925hPa. The wind speed is about 4 m s-1 at 850hPa and 6 m

s-1 at 925hPa.

156

Figure 5.31(a). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 15, 2011

Figure 5.31(b). Daytime Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 15, 2011

157

Figure 5.31(c). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 15, 2011

Figure 5.31(d). Night-time Averaged Air Temperature(oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 925hPa on August 15, 2011

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160

August 17, 2011

On August 17, 2011, south-easterly winds are dominant at both the pressure

levels during day and night-time. There is stagnation in the area with wind speed of

about 2 m s-1.

August 18, 2011

On August 18, 2011, stagnant conditions are prevailing at 850hPa during day

and night. At pressure level of 925hPa, there is stagnation during daytime, however,

south-easterly wind component is advecting in the study area during night time with a

wind speed of 4 m s-1.

August 19, 2011

During daytime of August 19, 2011, diminished south-easterly winds upto 2 m

s-1 are passing through the study area at 850hPa. Overall condition is stagnant. At

925hPa, south-easterly winds are advecting with stagnant conditions.

At night-time, there is advection of south-easterly winds in the area with a

speed of 4 m s-1 at 850hPa and 2 m s-1 at 925hPa.

161



162



163

Figure 5.34(a). Daytime Averaged Air Temperature (oC; shaded), Wind Speed (contours) Overlaid by Wind Direction (vectors) at 850hPa on August 18, 2011


164



165



166



167

5.4. Conclusions

The HYSPLIT back trajectories have revealed that the transbounadry

predominantly back trajectories are originated from Afghanistan, Iran and India. The

most frequent path of trajectories is west, east and south. Furthermore, simulations of

two selected high ozone episodes were carried out by using Weather Research and

Forecasting (WRF) model to assess the influence of meteorological conditions on

level and variation of ozone during episode period. High ozone concentrations have

been observed during warm dry conditions. High ozone concentrations have also been

observed during precipitation days due to intrusion of transboundary pollution

dominating the air quality. The synoptic analysis through WRF simulations has given

an insight into the meteorological condition. It has been revealed that most of the

episodes have occurred during stagnant conditions implying the role of accumulation

of pollutants towards poor air quality of Islamabad. Advection of air masses from

south-east and south-west are also playing a major role in elevating the pollution level

of the city.

168

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APPENDICES

Appendix A

Research Publications

• Rasheed, A., Aneja, V.P., and Aiyyer, A., Rafique, U., Measurements and analysis of air quality in Islamabad, Pakistan', AGU Earth’s Future, ISSN: 2328-4277, DOI: 10.1002/2013EF000174 (In press).

• Rasheed, A., Aneja, V.P., Aiyyer, A., and Rafique, U., ‘Measurement and analysis of fine particulate matter (PM2.5) in urban areas of Pakistan' Aerosols and Air Quality Research IF-2.827 (submitted).

190

Appendix B

Conference Presentations

• Presentation on 'Ambient Air Quality Analysis in Urban Areas of Pakistan’ at the International Conference held on April 9-10, 2014 at Shah Abdul Latif University, Khairpur Mir’s, Pakistan.

• Presentation on 'Ambient Air Quality of Islamabad-A Monitoring Based Analysis' at the International Workshop on ‘Atmospheric Composition and the Asian Monsoon (ACAM)’ held on June 9-12, 2013, Kathmandu, Nepal.

• Presentation on 'Ambient Air Quality of Islamabad-A Monitoring Based Analysis' at the international workshop on 'Changing Chemistry in Changing Climate: Monsoon-2013' held on May 1-3, 2013 at Indian Institute of Tropical Meteorology, Pune, India.

• Presentation of research paper entitled 'Characterization of Fine Particulate Matter (PMfine) in Urban Areas of Pakistan-An Observational Based Analysis' in session 'Atmospheric Chemistry: Gas-Particle Interactions and Climate Change - II' in The Southeastern Regional Meeting of the American Chemical Society held on November 14-17, 2012, Raleigh, N.C., U.S.A.

• Presentation on 'Characterization of Fine Particulate Matter (PMfine) in Urban Areas of Pakistan' on PAMS Access Day on September 6, 2012 organized by the College of Physical and Mathematical Sciences, North Carolina State University, Raleigh N.C., U.S.A.