DETERMINATION OF ORGANIC PARTICLE COMPOSITION ...etd.lib.metu.edu.tr/upload/12621762/index.pdfof...

DETERMINATION OF ORGANIC PARTICLE COMPOSITION IN ANKARA ATMOSPHERE AND INVESTIGATION OF THEIR CONTRIBUTION TO

RECEPTOR MODELING

A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES

OF MIDDLE EAST TECHNICAL UNIVERSITY

BY

EBRU KOÇAK

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR

THE DEGREE OF DOCTOR OF PHILOSOPHY IN

ENVIRONMENTAL ENGINEERING

DECEMBER 2017

Approval of the thesis:


RECEPTOR MODELING

submitted by EBRU KOÇAK in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Environmental Engineering Department, Middle East Technical University by,

Prof. Dr. Gülbin Dural Ünver ___________ Dean, Graduate School of Natural and Applied Sciences

___________ Prof. Dr. Kahraman Ünlü Head of Department, Environmental Engineering

Prof. Dr. Gürdal Tuncel ___________ Advisor, Environmental Engineering Dept., METU

Prof. Dr. İpek İmamoğlu ___________ Co-Advisor, Environmental Engineering Dept., METU

Examining Committee Members:

___________ Prof. Dr. F. Dilek Sanin Environmental Engineering Dept., METU

Prof. Dr. Gürdal Tuncel ___________ Environmental Engineering Dept., METU

Prof. Dr. Ayşegül Aksoy ___________ Environmental Engineering Dept., METU

Prof. Dr. Gülen Güllü ___________ Environmental Engineering Dept., Hacettepe University

Assoc. Prof. Dr. Eftade E. Gaga ___________ Environmental Engineering Dept., Anadolu University

Date: 25.12.2017

I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work.

Name, Last name: Ebru KOÇAK

Signature:

v

ABSTRACT


RECEPTOR MODELING

Koçak, Ebru Ph.D., Department of Environmental Engineering

Advisor: Prof. Dr. Gürdal Tuncel Co-advisor: Prof. Dr. İpek İmamoğlu

December 2017, 275 pages

There are two main objectives of this work. First one is to reveal general characteristics

of organic particulate composition in Ankara atmosphere, including their temporal and

spatial variability, dependence of composition to meteorology and sources. The second

one is to generate new source markers to improve the resolution receptor oriented source

apportionment studies. In this study, 45 particulate organic matters were measured at two

different stations in Ankara. First one was located at parking site of Department of

Environmental Engineering, METU. This site was classified as sub-urban station. The

second one was located at Faculty of Agriculture, Ankara University. This site was

classified as urban station. Sampling campaign was started at July 2014 and finished at

September 2015. 336 daily PM2.5 samples were collected from urban station and 275 daily

PM2.5 samples were collected from sub-urban station by using high volume samplers with

an average flow rate of 1.08 m3 min-1. Analyzed compounds were categorized into five

main groups: PAHs, n-alkanes, n-alkanoic acids, levoglucosan and EC-OC. Their

temporal and seasonal variations were examined. In this study, EPA PMF 5.0 was applied

vi

to determine sources of particulate organic matter and contribution of sources to each

compound. The optimized model results revealed presence of eight sources for urban and

sub-urban stations. Two combustion factor, a natural gas combustion factor, a road dust

factor, two food cooking factors, a biomass burning factor and a plant emissions factor

were identified for urban station. Two combustion factors, a biomass burning factor, a

vehicular emission factor, a natural gas combustion factor, a plant emission factor, a food

cooking factor and a secondary organic aerosol factor were identified for sub-urban

station.

Keywords: PM2.5, particulate organic matter, urban station, sub-urban station, PMF

vii

ÖZ

ANKARA ATMOSFERİNDEKİ ORGANİK PARTİKÜL MADDE KOMPOZİSYONUNUN BELİRLENMESİ VE RESEPTÖR MODELLEMESİ

ÇALIŞMALARINA KATKILARININ İNCELENMESİ

Koçak, Ebru Doktora, Çevre Mühendisliği Bölümü

Tez Danışmanı: Prof. Dr. Gürdal Tuncel Eş Danışman: Prof. Dr. İpek İmamoğlu

Aralık 2017, 275 sayfa

Bu çalışmanın iki temel amacı bulunmaktadır. İlk hedef, Ankara atmosferindeki organik

partikül madde düzeylerini ve zaman içeresinde gösterdikleri değişimleri ve bu

değişimlere katkıda bulunan faktörleri belirlemektir. İkinci hedef ise, organik partikül

maddelerin analizi ile bazı izleyici bileşiklerin belirlenmesi ve bunları kullanarak kaynak

belirleme çalışmalarının çözünürlüğünü arttırmaktır. Bu çalışmada, Ankara’da bulunan

iki farklı istasyondan toplanan örneklerde 45 partikül organik madde analizi

gerçekleştirilmiştir. İlk istasyon ODTÜ Çevre Mühendisliği Bölümü park alanından

bulunan, yarı-kentsel; ikinci istasyon ise, Ankara Üniversitesi Ziraat Fakültesi’nde

bulunan, kentsel olarak sınıflandırılan örnekleme istasyonudur. Örnekleme Temmuz

2014’ de başlamış ve Eylül 2015’de sonlandırılmıştır. Kentsel istasyondan 336 günlük

PM2.5 örneği, yarı-kentsel istasyondan 275 günlük PM2.5 örneği ortalama 1,08 m3 dak-1

hava akımı ile yüksek hacimli örnekleyici yardımı ile toplanmıştır. Analiz edilen maddeler

beş ana başlıkta toplanmaktadır: PAHlar, n-alkanlar, n-alkanoik asitler, levoglukosan ve

EK-OK. Bu maddelerin zamansal ve mevsimsel değişimleri incelenmiştir. Bunun yanı

viii

sıra, EPA PMF 5.0 modeli kullanılarak kaynak çözümleme çalışması gerçekleştirilmiş ve

optimize edilen model sonuçlarına göre kentsel ve yarı-kentsel istasyon için sekiz kaynak

ortaya konmuştur. Kentsel istasyon için iki yanma faktörü, bir doğalgaz yanma faktörü,

bir yol tozu faktörü, iki yemek pişirme faktörü, bir biyokütle yakılması faktörü ve bir

bitkisel emisyon faktörü belirlenmiştir. Yarı-kentsel istasyon için iki yanma faktörü, bir

biyokütle yanma faktörü, bir araç emisyon faktörü, bir doğalgaz yanma faktörü, bir

bitkisel emisyon faktörü, bir yemek pişirme faktörü ve bir ikincil organik aerosol emisyon

faktörü belirlenmiştir.

Anahtar Kelimeler: PM2.5, partikül organik madde, kentsel istasyon, yarı-kentsel istasyon,

PMF

ix

To my parents, my lovely daughter,

and my husband..

xi

ACKNOWLEDGMENTS

First and foremost, I would like to express my sincere appreciation to my advisor Prof.

Dr. Gürdal Tuncel for his guidance, advice and support throughout this study. He

always supported me even during my hardest times. It is an honor for me to have the

chance to work with him.

I would like to thank my co-advisor Prof. Dr. İpek İmamoğlu, for her constant guidance,

support and encouragement throughout the period of this study.

I would like to thank my committee members Prof Dr. Gülen Güllü, Assoc . P ro f .

Dr . Eftade Gaga, Prof. Dr. F. Dilek Sanin and Prof. Dr. Ayşegül Aksoy for their suggestions

and patience on me to finish this study.

This study was supported by two Scientific and Technological Research Council of

Turkey (TÜBİTAK) projects. First one’s project number was 112Y036. I would like to

acknowledge to Asst. Prof. Dr. Seda Aslan Kılavuz for her coordination of this project. I

also would thank to other project members Sena Uzunpınar, Ezgi Sert, Tayabeh Goli and

İlke Çelik for their helps to maintain samples from our stations properly. The second

TUBİTAK project’s number was 115Y484. I would like to thank Asst. Prof. Dr. Fatma

Öztürk and her staff at Bolu Abant İzzet Baysal University, for their assistance and

analyzes of EC/OC.

I would like to especially thank to Dr. Fadime Kara Murdoch for teaching me GC-MS

analysis and a lots of experimental information. I also would like to thank my lab mate

Dr. Hale Demirtepe for her friendship during my hard working lab days.

My special gratitude is extended to my friends, İlker Balcılar, Begüm Balcılar, Elif Küçük,

Selcen Ak, Nilüfer Ülgüdür, Tolga Piveneli, Berkay Çelebi and Seçil Ömeroğlu for their

xii

endless support and friendship during my study. They were always by my side in good

and bad times.

Especially, I owe my deepest gratitude to my parents Adnan and Nergis Sarıkaya and my

lovely sister Sevil Sarıkaya Eken, the ones who have the biggest share on my success.

This thesis would not have been possible without their endless love, support and

encouragement.

Last but not least, I would like to thank my unbelievably supportive husband Alp Kağan

Koçak for his help, understanding and patience. His faithful support during the final stages

of this Ph.D. is so appreciated. And my lovely daughter Ada Yağmur born and grown

along the completion of this thesis. They have always been an inspiration for me.

xiii

TABLE OF CONTENTS

ABSTRACT ....................................................................................................................... v

ÖZ ................................................................................................................................... vii

ACKNOWLEDGMENTS ................................................................................................ xi

TABLE OF CONTENTS ............................................................................................... xiii

LIST OF FIGURES ........................................................................................................ xix

CHAPTERS ....................................................................................................................... 1

1. INTRODUCTION ................................................................................................. 1

2. THEORITICAL BACKGROUND ........................................................................ 5

2.1 Overview of Air Pollution ............................................................................... 5

2.2 Particulate Matter ............................................................................................. 6

2.3 Particulate Matter Properties ........................................................................... 6

2.4 Gas/Particle Partitioning .................................................................................. 7

2.5 Particulate Matter- Health Effects ................................................................... 8

2.6 Particulate Matter- Climate Effects ................................................................. 8

2.7 Effect of Meteorological Events on PM2.5 ....................................................... 8

2.8 Air Quality Standards ...................................................................................... 9

2.9 Analytical Methods Used for the Determination of Atmospheric Organic Pollutants .................................................................................................................. 11

2.10 Polycyclic Aromatic Hydrocarbons (PAHs) ................................................. 13

2.11 n-Alkanes ....................................................................................................... 15

2.12 n-Alkanoic acids ............................................................................................ 17

2.13 Levoglucosan ................................................................................................. 19

2.14 EC/OC ............................................................................................................ 19

2.15 Secondary Organic Aerosols (SOA) .............................................................. 20

2.16 Source Apportionment- Receptor Models ..................................................... 21

2.16.1 Mass Closure ...................................................................................... 22

xiv

2.16.2 Chemical Mass Balance (CMB) ......................................................... 23

2.16.3 UNMIX ............................................................................................... 23

2.16.4 Positive Matrix Factorization (PMF) .................................................. 24

2.17 Determination of atmospheric aerosols and their sources ............................. 25

2.17.1 Residential combustion ....................................................................... 26

2.17.2 Road transport ..................................................................................... 27

2.17.3 Biomass Burning ................................................................................. 27

2.17.4 Food cooking ...................................................................................... 27

2.17.5 Natural sources .................................................................................... 28

3. MATERIAL AND METHODS ........................................................................... 29

3.1 Sampling Location ......................................................................................... 29

3.2 Sampling Procedures and Preparation of Filters for Analysis ....................... 32

3.3 Target Compounds ......................................................................................... 33

3.4 Method Optimization ..................................................................................... 34

3.4.1 General Principles of Gas Chromatography- Mass Spectrometry .......... 34

3.4.2 Optimization of a GC-MS Procedure for the Measurement of Organic Particulate Matter in PM2.5 ................................................................................... 36

3.4.2.1 Optimization of oven temperature and heating rate (ramp) .............. 36

3.4.2.2 Non- derivatized compounds (PAHs and n-Alkanes) ....................... 37

3.4.2.3 Derivatized compounds ..................................................................... 40

3.4.2.4 Calibration ......................................................................................... 44

3.4.3 Experimental Procedure ......................................................................... 50

3.4.4 EC/OC Analysis ..................................................................................... 52

3.4.5 Quality Assurance and Quality Control .................................................. 54

3.4.6 Recovery tests for measured compounds ............................................... 55

3.4.7 Limit of detection (LOD) and Limit of quantification (LOQ) ............... 63

3.4.8 SRM Analysis ......................................................................................... 65

3.4.9 Data Validation ....................................................................................... 69

3.4.10 Lab and Field Blanks .......................................................................... 75

3.4.11 Secondary Organic Aerosol Estimation .............................................. 76

3.4.12 Source Apportionment ........................................................................ 78

xv

3.4.12.1 Mass Closure ................................................................................... 78

3.4.12.2 Positive Matrix Factorization (PMF) .............................................. 79

3.4.13 Meteorological Data ........................................................................... 82

4. RESULTS AND DISCUSSION .......................................................................... 85

4.1 Summary statistics ......................................................................................... 85

4.2 Frequency Distributions ................................................................................. 89

4.3 Comparison of measured concentrations of molecular markers with literature 92

4.4 Meteorology of the study area ..................................................................... 102

4.5 Long Term Meteorology .............................................................................. 103

4.6 Meteorology during study period ................................................................ 105

4.7 Wind Direction ............................................................................................ 109

4.8 Temporal variations in concentrations of measured organic particulate matters112

4.8.1 Episodic changes in concentrations of measured compounds .............. 113

4.8.2 Weekend weekday differences in concentrations of measured species 118

4.8.3 Seasonal variations ............................................................................... 126

4.9 Effect of local meteorology on concentrations of measured molecular markers141

4.9.1 Effect of wind speed ............................................................................. 142

4.9.2 Effect of temperature ............................................................................ 144

4.9.3 Effect of mixing height ......................................................................... 148

4.9.4 Effect of ventilation coefficient ............................................................ 150

4.9.5 Effect of wind direction ........................................................................ 152

4.10 Spatial Correlations ..................................................................................... 160

4.11 Secondary Organic Aerosol (SOA) Estimation ........................................... 166

4.12 Source Apportionment ................................................................................. 172

4.12.1 Mass Closure .................................................................................... 172

4.12.2 PMF .................................................................................................. 176

4.12.2.1 PMF optimization parameters and results ..................................... 177

4.12.2.2 Diagnostic ratios for PAHs ............................................................ 180

4.12.2.3 Urban Station-Factors .................................................................... 182

..................................................................................................................................

................................................................................................................................

................................................................................................................................

xvi

4.12.2.4 Sub-urban Station-Factors ........................................................... 200

4.12.2.5 Contribution of factors to total PM2.5 mass and total organic aerosol particulate matter ............................................................................................ 217

4.12.2.6 Correlation of the factors between Urban and Sub-urban Station . 220

5. CONCLUSIONS ................................................................................................ 223

6. RECOMMENDATIONS ................................................................................... 227

REFERENCES ............................................................................................................... 229

APPENDICES

A. Chemical structure of the compounds ................................................................... 263

B. Sample Chromatograms ......................................................................................... 266

CURRICULUM VITAE ................................................................................................ 269

xvii

LIST OF TABLES

TABLES

Table 2.1. Air quality standards for PM2.5 for different countries. .................................. 10

Table 2.2. Physicochemical properties of PAHs .............................................................. 14

Table 2.3. Physicochemical properties of n-Alkanes ....................................................... 17

Table 2.4. Physicochemical properties of n-alkanoic acids ............................................. 18

Table 2.5. Physicochemical properties of Levoglucosan ................................................. 19

Table 3.1. Compounds to be analyzed in the study.......................................................... 33

Table 3.2. Compounds, their retention times and m/z values .......................................... 38

Table 3.3. Optimization of GC-MS oven temperature programs for PAHs and n-Alkanes

.......................................................................................................................................... 39

Table 3.4. Derivatization methods and Chromatographic result ...................................... 42

Table 3.5. Compounds, their retention times and m/z values .......................................... 43

Table 3.6. Optimization of GC-MS oven temperature programs for n-Alkanoic Acids and

Levoglucosan ................................................................................................................... 43

Table 3.7. Calibration conditions for GC-MS .................................................................. 45

Table 3.8. Calibration concentrations.............................................................................. 45

Table 3.9. Extraction methods applied to particulate organic matter .............................. 55

Table 3.10. Recovery results for Alkanes (Solvent: Toluene) ......................................... 57

Table 3.11. Details of the experimental setup .................................................................. 59

Table 3.12. Recovery results ............................................................................................ 61

Table 3.13. LOD and LOQ values for compounds .......................................................... 64

Table 3.14. SRM 1649b analysis results .......................................................................... 67

Table 3.15. Sample to blank ratio of the compounds ....................................................... 76

Table 3.16 Equalizing factors used in mass closure ........................................................ 79

Table 4.1 Urban Station-Statistics for each molecular tracer specie ............................... 85

Table 4.2. Sub-urban Station- Statistics for each molecular tracer species ..................... 87

xviii

Table 4.3. Comparison of average concentrations measured in this work with

corresponding data generated for other cities around the world. ..................................... 97

Table 4.4 Long-term (1950 – 2016) monthly average values of meteorological data at the

study area ........................................................................................................................ 104

Table 4.5. Urban Station- Monthly average values of meteorological parameters during

the study period. ............................................................................................................. 106

Table 4.6. Sub-urban Station-Monthly average values of meteorological parameters

during the study period. .................................................................................................. 106

Table 4.7. Comparison of long-term monthly average temperature with the temperature,

which prevailed during sampling period. ....................................................................... 107

Table 4.8 Spatial correlations of the compounds between the stations .......................... 163

Table 4.9. Urban Station-Monthly average PM2.5, EC, OC, OCsecondary, and

%OCsecondary/PM2.5 .......................................................................................................... 170

Table 4.10. Sub-urban Station-Monthly average PM2.5, EC, OC, OCsecondary and

%OCsecondary/PM2.5 .......................................................................................................... 171

Table 4.11. Mass Closure model results- Urban Station ................................................ 174

Table 4.12. Mass Closure model results- Sub-urban Station ......................................... 175

Table 4.13 Diagnostic ratios used in this study with their reported values for specific

processes (Tobiszewski and Namiesnik, 2012) .............................................................. 181

xix

LIST OF FIGURES

FIGURES

Figure 3.1 Location of the urban and suburban sampling stations .................................. 31

Figure 3.2. Schematic representation of GC-MS ............................................................. 34

Figure 3.3. Calibration curves of selected PAH ............................................................... 46

Figure 3.4. PAHs: Chromatogram-Calibration Point-3. a-Naphthalene, b-Acenaphtylene,

c-Acenapthene-d, d- Acenapthene, e-Fluorene, f-Phenanthrene, g-Anthrecene, h-

Fluoranthene, ı-Pyrene, j- Benzo[a]anthracene, k- Chrysene-d, l- Chrysene, m-

Benzo[k]fluoranthene, n- Benzo(e)pyrene, o- Benzo[a]pyrene, p- Perylene, r-

Indeno[1,2,3,-cd]pyrene, s- Dibenzo[a,h]anthracene, t- Benzo[ghi]perylene, u-

dibenzo[a,h]anthracene-d ................................................................................................. 47

Figure 3.5. Calibration curves of selected n-alkanes ....................................................... 47

Figure 3.6. n-Alkanes: Chromotogram-Calibration Point-3. a-decane, b-undecane, c-

dodecane, d- tridecane, e-tetradecane, f-pentadecane, g-hexadecane, h-heptadecane, ı-

octadecane, j- nonadecane, k- eicosane d 42, l- eicosane, m-heneicosane, n- docosane, o-

tricosane, p-tetracosane, r- pentacosane, s- hexacosane, t- heptacosane, u-octacosane d 58,

v- octacosane, w- nonacosane, y-triacosane, z- triacontane, aa-hentriacontane, ab-

dotricontane, ac-tetratriacontane, ad-pentatriacontane, ae-hexaticaontane d 74 .............. 48

Figure 3.7. Calibration curve of s selected n-Alkanoic Acids and Sterols ...................... 48

Figure 3.8. n-Alkanoic acids: Calibration Point-3 a- Decanoic-d19 Acid 98 atom % D, b-

Dodecanoic acid, c- Tridecanoic acid, d- Tetradecanoic acid, e- Pentadecanoic acid, f-

Hexadecanoic acid, g- Heptadecanoic acid, h- Linoleic acid, ı- Oleic acid, j- Octadecanoic

acid ................................................................................................................................... 49

Figure 3.9. Calibration curve of Levoglucosan ................................................................ 49

xx

Figure 3.10. Levoglucosan Calibration Point-3. a- Levoglucosan, b-D7-Levoglucosan . 50

Figure 3.11. Experimental procedure ............................................................................... 51

Figure 3.12. Schematic representation of EC / OC analyzer interior design ................... 52

Figure 3.13. A thermogram example of a day .................................................................. 53

Figure 3.14. Recovery study-experiment organization tree ............................................. 58

Figure 3.15. SRM 1649b analysis- n-Alkanes ................................................................. 68

Figure 3.16. SRM 1649b analysis-PAHs ......................................................................... 68

Figure 3.17. Sub-urban station- scatterplots of correlated organic particulate compounds

.......................................................................................................................................... 71

Figure 3.18. Urban station- Scatterplots of correlated organic particulate compounds ... 72

Figure 3.19. A correlation graph for the selected compound couples to determine outliers

(sub-urban station) ............................................................................................................ 73

Figure 3.20. A correlation graph for the selected compound couples to determine outliers

(urban station) .................................................................................................................. 74

Figure 4.1 Urban station- typical frequency distributions of selected organic molecular

tracer species (x-axis- ng m-3) .......................................................................................... 90

Figure 4.2 Suburban station- typical frequency distributions of selected organic molecular

tracer species (x-axis- ng m-3) .......................................................................................... 91

Figure 4.3. Comparison of n-Alkanes with the literature ............................................... 100

Figure 4.4. Comparison of PAHs with the literature ...................................................... 101

Figure 4.5. Comparison of n-alkanoic acids, levoglucosan and EC-OC with the literature

........................................................................................................................................ 102

Figure 4.6 Long-term (1950 – 2016) monthly average mixing height and ventilation

coefficient ....................................................................................................................... 104

Figure 4.7 Monthly variation of temperature, relative humidity, wind speed and mixing

height .............................................................................................................................. 108

Figure 4.8 Sub-urban station wind rose plots during sampling period .......................... 110

Figure 4.9 Urban station wind rose plots during sampling period ................................. 111

xxi

Figure 4.10 Ankara-Long term wind rose plot (1950-2016) .......................................... 112

Figure 4.11. Episodic time series of sub-urban station .................................................. 115

Figure 4.12. Episodic time series of urban station ......................................................... 116

Figure 4.13 a) Sub-urban sampling period wind rose plot b) Sub-urban February-March

wind rose plot ................................................................................................................. 117

Figure 4.14. WD and WE PAH concentration distribution at Urban Station ................ 119

Figure 4.15. WD and WE PAH concentration distribution at........................................ 120

Figure 4.16. WD and WE n-Alkanes concentrations distribution at Urban and Sub-urban

Stations ........................................................................................................................... 121

Figure 4.17 WD and WE n-Alkanoic acids concentrations distribution at Urban Station

........................................................................................................................................ 123

Figure 4.18 WD and WE n-Alkanoic acids concentrations distribution at .................... 123

Figure 4.19. WD and WE Levoglucosan concentrations distribution at Urban and Sub-

urban Stations ................................................................................................................. 124

Figure 4.20. WD and WE EC-OC concentrations distribution at Urban and Sub-urban

Stations ........................................................................................................................... 125

Figure 4.21. Seasonal n-Alkanes variation at Urban Station ......................................... 132

Figure 4.22. Seasonal n-Alkanes variation at Sub-urban Station................................... 133

Figure 4.23. Seasonal PAHs variation at Urban Station ................................................ 135

Figure 4.24. Seasonal PAHs variation at Sub-urban Station ......................................... 135

Figure 4.25. Seasonal n-Alkanoic acids variation at Urban Station .............................. 136

Figure 4.26. Seasonal n-Alkanoic acids variation at Sub-urban Station ........................ 137

Figure 4.27. Seasonal Levoglucosan variation at Urban and Sub-urban Station ........... 139

Figure 4.28. Seasonal EC-OC variation at Urban and Sub-urban Station ..................... 140

Figure 4.29. Relationship between wind speed and particulate bound organic molecular

markers ........................................................................................................................... 143

Figure 4.30. Relationship between temperature and particulate bound organic molecular

markers ........................................................................................................................... 147

xxii

Figure 4.31. Comparative plots for maximum temperature and mean temperature effect

on concentrations ............................................................................................................ 147

Figure 4.32. Relationship between mixing height and particulate bound organic molecular

markers ........................................................................................................................... 149

Figure 4.33. Relationship between ventilation coefficient and particulate bound organic

molecular markers .......................................................................................................... 151

Figure 4.34 Sub-urban Station CBPF Plots for PAHs for concentration >70th percentile

........................................................................................................................................ 154

Figure 4.35 Sub-urban Station CBPF Plots for n-alkanes for concentration >70th percentile

........................................................................................................................................ 155

Figure 4.36 Sub-urban Station CBPF Plots for n-alkanoic acids, OC and EC for

concentration >70th percentile ........................................................................................ 156

Figure 4.37 Urban Station CBPF Plots for PAHs for concentration >70th percentile .... 157

Figure 4.38 Urban Station CBPF Plots for n-alkanes for concentration >70th percentile

........................................................................................................................................ 158

Figure 4.39 Urban Station CBPF Plots for n-alkanoic acids, OC and EC for concentration

>70th percentile ............................................................................................................... 159

Figure 4.40. Correlation of selected organic compounds between urban and suburban

stations ............................................................................................................................ 165

Figure 4.41 Urban station OC and EC relationship ....................................................... 168

Figure 4.42. Sub-urban station OC and EC relationship ................................................ 169

Figure 4.43 Percentage of chemical compounds contributing to the PM2.5 mass at urban

and sub-urban stations .................................................................................................... 175

Figure 4.44 Mass closure-source contributions .............................................................. 176

Figure 4.45. Histograms of scaled residual distributions ............................................... 180

Figure 4.46 Urban Station- Species profiles- % of species and concentration of species

........................................................................................................................................ 183

xxiii

Figure 4.47 Urban Station Factor-1 Contribution, factor loadings of species, G-scores and

CBPF at 70th percentile .................................................................................................. 185






CBPF probability at 70th percentile ................................................................................ 191









Figure 4.55 Sub-urban Station- Species profiles- % of species and concentration of species

........................................................................................................................................ 201

Figure 4.56 Sub-urban Station Factor-1 Contribution, factor loadings of species, G-scores

and CBPF at 70th percentile ........................................................................................... 202









xxiv


and CBPF at 70th percentile ............................................................................................ 212





Figure 4.64 Urban Station- Contribution of Factors to Total PM2.5 ............................... 218

Figure 4.65 Sub-urban Station-Contribution of Factors to Total PM2.5 ......................... 219

Figure 4.66 Combustion factor correlation between stations ......................................... 222

Figure 4.67 Food cooking factor correlation between stations ...................................... 222

Figure 4.68 Food cooking factor correlation between stations-2 ................................... 222

Figure 4.69 Plant emission factor correlation between stations ..................................... 222

Figure 4.70 Biomass burning factor correlation between stations ................................. 222

xxv

LIST OF ABBREVIATIONS

AQAMR Air Quality Assessment and Management Regulation

AU Ankara University

BC Black carbon

BSTFA-TMCS N,O-bis-(trimethylsilyl) trifluoroacetamide Trimethylchlorosilane

CBPF Conditional Bivariate Probability Function

CI Chemical ionization

CMB Chemical Mass Balance

DCM Dichloromethane

EC Elemental carbon

EI Electron impact

GC-MS Gas Chromatography and Mass Spectrometry

ISO International Organization for Standardization

LOD Limit of detection

LOQ Limit of quantification

METU Middle East Technical University

OC Organic carbon

PAH Polycyclic aromatic hydrocarbon

PM Particulate matter

PMF Positive matrix factorization

QA/QC Quality Assurance / Quality Control

ROG Reactive Organic Gases

RT Retention time

SIM Selected Ion Monitoring Mode

SOA Secondary Organic Aerosols

SRM Standard Reference Material

US EPA United States Environmental Protection Agency

xxvi

VOC Volatile organic carbon

WD Weekday

WE Weekend

WHO World Health Organization

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CHAPTER 1

INTRODUCTION

Over the past several decades, increases in the rates of pulmonary disease have associated

with particulate air pollution (Delfino et al., 2013; Laumbach and Kipen, 2012). These

effects have been comprehensively reviewed in the literature (Davidson et al., 2005;

Harrison and Yin, 2000; Nel, 2005; Schlesinger and Cassee, 2003; Stanek et al., 2011;

Valavanidis et al., 2008). As a result of these studies, authorities have had to work on

taking precautions on particulate matter (PM) and it was included into “six criteria air

pollutants” list (EPA, 1970). There are two separate standards exist for particulate matter

with a mean aerodynamic diameter less than 10 μm (PM10) and particulate matter with a

mean aerodynamic diameter less than 2.5 μm (PM2.5). PM2.5 is of particular interest since

it focuses more closely on the fine particle fraction (0.1 – 2.5 μm) containing particles

which are able to travel deeper into the human lung (Ariola et al., 2006; Querol et al.,

2004). In addition to its range in sizes, PM composition shows diversity. This composition

includes inorganic ions such as nitrate, sulfate and ammonium, a wide array of organic

compounds and elemental carbon (EC). This diversity directly comes from the numerous

sources and photochemical processes of particulate matter (Moon et al., 2008; Teixeira et

al., 2012).

Source apportionment studies in urban atmosphere are essential for designing cost-

effective regulatory actions to improve air quality, because regulatory actions taken

without such scientific feedback can be very costly and sometimes ineffective. Receptor

modeling involves measurement of natural tracers of a number of sources and subsequent

resolution of sources using statistical tools. Tracer elements have been used as natural

tracers for sources since 1970s. Since source types identified by tracer elements are

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limited, using of particle-bound organic compounds as source tracer can open a new

horizon in receptor modelling.

Each molecular tracer originates from a unique source. However, in reality, they are

emitted from multiple sources which have own time-dependent source contribution.

Chemical composition of organic particulate matter and their sources have been studied

extensively in the literature (Arı, 2016; Dutton et al., 2010; Schauer et al., 1996a;

Simoneit, 1999; Song et al., 2006; Zhang et al., 2012). Among these studies, only a few

of them (Arı, 2016; Dutton et al., 2010) were done on daily basis, with higher number of

samples. Most of these studies were done with a less frequent sampling due to cost and

analysis time constraints. Dutton et al., (2009) collected 549 daily samples during a 1.5

yearlong study at Denver, USA and Arı et al., (2016) collected 191 daily samples at

Eskişehir atmosphere during 1 yearlong study. In Ankara atmosphere, there was a need

to provide comprehensive daily measurements regarding organic particulate matter

composition and their contribution to receptor modeling. The present study was carried

out to fill this gap in the literature.

The first goal of this study is to optimize a method that enable for daily measurement and

speciation of particulate organic matters, which are PAHs, n-alkanes, n-alkanoic acids and

levoglucosan, and to characterize chemically a long time series of daily PM2.5

measurements at two receptor site in Ankara to a level of detail that has not been done

before for an extended period of time. The second goal of this study is to use the daily

data to determine specific sources contributing to the PM2.5 measured at two receptor site

located in Ankara.

This study is a part of a three year-extensive work which was completed in 2016 through

a TÜBİTAK project (Project Number: 112Y036). This thesis’s subject was a part of this

project and covers the analysis of 45 organic compounds (PAHs, n-alkanes, n-alkanoic

acids, levoglucosan and EC-OC) from PM2.5 filters and their source apportionment.

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Daily measurements of organic particulate compounds were performed at two stations

placed at an urban and suburban sites located at Ankara University (AU) and Middle East

Technical University (METU) respectively. Sampling period was started at 1st of July

2014 and finished at 20th October 2015 with 275 daily samples for suburban station and

336 daily samples for urban station. Therefore 9 months of summer and 6 months of winter

data were collected. During the sampling campaign, high volume samplers were used for

PM2.5 sampling and GC-MS was used for the analyzes of PAHs, n-alkanes, n-alkanoic

acids and levoglucosan. Besides, EC-OC analyzes were carried out with thermo-optical

carbon aerosol analyzer at Bolu Abant İzzet Baysal University which was supported by

another TÜBİTAK Project (Project Number: 115Y484).

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THEORITICAL BACKGROUND

2.1 Overview of Air Pollution

Earth’s atmosphere is divided into five main layers: troposphere, stratosphere,

mesosphere, thermosphere and exosphere ( Seinfeld et al., 1998; Vallero, 2008). The two

layers at the bottom, troposphere and stratosphere are the most important layers in terms

of air pollution. Troposphere’s height extends up to 10-15 km from the Earth’s surface

and the most atmospheric aerosol mass is dominated there. Temperature of this layer

decreases with increasing altitude and this results vertical mixing of atmospheric

pollutants in the troposphere. The most important removal mechanism is water cycle in

the troposphere. Stratosphere’s height extends up to 50 km from Earth’s surface.

Temperature profile of stratosphere is opposite of troposphere, increasing with increasing

altitude. Ozone layer exists in the stratosphere and it is crucial for life due to its absorbance

capacity short wavelength radiation of the sun (Finlayson-Pitts and Pitts, 2000). Gases and

particles that are put into the air or emitted by various sources result the air pollution and

atmosphere, primarily troposphere, is contaminated. As a result, all living beings effected

from combination of these gases and particles emitted to atmosphere (Cropper, 2016). In

today’s world air quality becomes as a serious problem in most cities and therefore

attention has been increasing on this topic. Emission of air pollutants is reasoned by

different anthropogenic processes and biogenic sources. (Friedlander, 1973; Lelieveld et

al., 2001; U.S. EPA, 1970; Wark et al., 1998).

CHAPTER 2

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2.2 Particulate Matter

PM stands for particulate matter (also called particle pollution). It is the term for a

combination of solid particles and liquid droplets found in the air such as dust, dirt, soot,

smoke and liquid droplets and they held together by intermolecular forces and being

primarily larger than molecular dimensions (Seinfeld et al., 1998; US. EPA, 2011).

“Aerosol” is a commonly used term for particulate matter (Hinds, 2012). During its

lifetime atmospheric aerosol undergoes constant physical and chemical transformation

cycles which is typically of the order of one week in the lower troposphere. Atmospheric

aerosol particles have significant influence on regional air quality, global geochemical

cycles and the radiation budget of the earth. Particulate matter originates from both natural

and anthropogenic sources. Natural sources include soil from arid regions, natural

weathering of rock, volcanic emissions, sea spray, biomass burning from wildfires, plant

waxes and secondary organic aerosol formation. Anthropogenic sources could be divided

into many sub-categories. The main ones are; residential combustion-natural gas

combustion, road dust, food cooking, biomass burning by man-made fires and vehicular

emissions (Vallero, 2008; Seinfeld et al., 1998).

2.3 Particulate Matter Properties

Mass concentration is the most commonly used particulate matter property which is the

mass of particulate matter in a unit volume of air. Generally, µg m-3 or ng m-3 is used for

unit of mass concentration of particulate matter. Also number of particles per unit volume

of aerosol is used as mass concentration and unit is number cm-3 (Kulkarni et al., 2011).

The size of particle is the most important parameter due to being determinant for the

behavior of the particle. There are two separate standards exist for particulate matter with

a mean aerodynamic diameter less than 10 μm (PM10) and particulate matter with a mean

aerodynamic diameter less than 2.5 μm (PM2.5). How particles are formed is used to

determine their size. For instance, fine particles can be generated from combustion while

coarse ones are originated from mechanical processes (Vallero, 2008).

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PM2.5 is of particular interest since it focuses more closely on the fine particle fraction

(0.1–2.5 μm) containing particles which can be inhaled into human lungs and cause

deterioration of the respiratory system and other diseases (Ariola et al., 2006; Querol et

al., 2004). In addition, they are important in terms of pollutant transport perspective due

to their buoyancy. They can easily transport to far distances.

In addition to its range in sizes, PM composition shows diversity. This composition

includes inorganic ions such as nitrate, sulfate and ammonium, a wide array of organic

compounds and EC. This diversity directly comes from the numerous sources and

photochemical processes leading to PM observed in atmosphere PM resources are quite

diverse. (Moon et al., 2008; Teixeira et al., 2012).

2.4 Gas/Particle Partitioning

Gas/particle partitioning theory proposes that the phase distribution of an organic

compound is identified by its absorption into particle phase matrix, and that this phase

partitioning can be determined by using a coefficient, Kp,opm, defined as

Kp,opm,=(F/OPM)/A. Kp,opm is the gas/particle partitioning coefficient (m3µg-1). F is the

particle-associated mass concentration of the organic compound of interest (µg m-3). OPM

is the total organic particulate matter concentration which is partitioning (µg m-3). A is the

gas-phase mass concentration of the compound of interest (µg m-3). It is estimated that the

gas/particle partitioning coefficient Kp,opm depends on the vapor pressure of the organic

compounds (Schauer, 1998).

PAHs, n-alkanes, n-alkanoic acids and levoglucosan are semi volatile organic compounds

and thus they can exist in both gas and particle phases. Fate of organic compounds depends

on various atmospheric mechanisms such as volatilization, evaporation, dissolution,

dry/wet deposition and gas adsorption. Therefore, gas/particle partitioning has an

important role on transportation of semi volatile organic compounds in the atmosphere

(Eftade and Arı, 2011; M et al., 2014; Sangiorgi et al., 2014; Scahuer, 1998).

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2.5 Particulate Matter- Health Effects

Morbidity and mortality increase by exposure to particulate matter. The most important

two factors for the level of health impact are the location which the particle deposit in the

respiratory system and their chemical composition. Place of deposition depends on the

size of the particle and its shape. Since respiratory system has high humidity, hygroscopic

properties of a particle affect its growth and deposition. Especially small particles could

be carried into alveolar region (Hinds, 1999). Potential chemical compounds which can

cause health effects are metals, acids, organic compounds, soluble salts, peroxides and

black carbon (BC) (Harrison and Yin, 2000; Helble et al., 2000; Kampa and Castanas,

2008; Pöschl, 2005). There is no single compound cause health effect dominantly,

however there should be a combination of these compounds (Davidson, Phalen, Solomon,

et al., 2005).

2.6 Particulate Matter- Climate Effects

Tropospheric particulate matter has an impact on climate change. They can scatter

incoming solar radiation back to space under the impact of two mechanisms. The first one

is the direct scattering by particles themselves. The second mechanism is increased

concentration of cloud condensation nuclei which forms higher number of smaller cloud

droplets and enlarging cloud reflectivity. These two mechanisms could change the Earth’s

radiation balance and cool the Earth’s surface (Ebi and McGregor, 2008; Hinds, 1999;

Tagaris et al., 2007). Beside, some particles could modify the global climate by absorbing

solar radiation by altering the surface albedo after deposition in the Polar Regions

(Kaufman et al., 2002; Menon et al., 2002).

2.7 Effect of Meteorological Events on PM2.5

Atmospheric particulate matter reach receptors by being transported and some of them

could be transformed also. The location of the receptor and atmospheric activities affect

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particulate matter concentration and sensitivity of the receptors to this concentration

fluctuation establishes the extent of the effect. Beside emission characteristics such as

location, height, and duration as well as the concentration of the particulate matter are also

of importance (Vallero, 2008).

Variation in meteorological parameters affects air quality. Under the effect of alterations

of temperature, ventilation factors such as wind speed, mixing height and ventilation

coefficient, pollutant concentrations change (Jacob and Winner, 2009). Meteorological

conditions and their effects on particulate matter concentrations and transportation are

examined in various studies (Chow et al., 1994; Jeong et al., 2004; C. Jeong et al., 2006).

Response of particulate matter to meteorological change is complicated because of the

diversity of particulate matter content. Studies link some useful correlation of particulate

matter with the meteorological variables. The winds play an important role on the seasonal

variation of particulate matter concentrations by transporting them. There is a negative

correlation between concentrations and wind speed data. By the help of the wind speed

and wind direction data, dominant pollution source directions could be determined

(Elminir, 2005). Stagnation periods are determined by high pressure, low winds, clear sky,

inversion conditions and dense fog presence. As a result of stagnation periods high

concentrations could be measured (Kim et al., 2000; Zubkova, 2003).

2.8 Air Quality Standards

When the air quality standards are examined it will be seen that standard references are

determined according to differentiate between a polluted and non-polluted atmosphere.

These are established by air quality standards which describe the maximum concentrations

of a pollutant. Pollutants are selected according to two damage levels: (1) primary

standards which are harmful for human health and safety; (2) secondary standards which

result damages to the flora, fauna, material and environment. And these standards were

determined based on scientific studies related with the effects reasoned by a specific

pollutant (Araújo et al., 2014).

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In last ten years, air quality standards of Turkey improved parallel to European Union

directives. In 2008, Air Quality Assessment and Management Regulation (AQAMR) was

renewed (T.C. Official Gazette, 2008).The absence of the PM2.5 limit value appears to be

a deficiency, but the PM10 limit value is set at 50 µg m-3 for 24 h and 40 µg m-3 for annual

emission (T.C. Official Gazette, 2008). Air quality standards for PM2.5 for different

countries are listed in Table 2.1.

Table 2.1. Air quality standards for PM2.5 for different countries.

Countries/Regions Averaging Time Standard (µg m-3) References WHO (World Health Organization)

24 h Annual

25 10

(WHO, 2006)

Canada 24 h 30 (S. W. Lee, 2010) USA 24 h

Annual 35 15

(US.EPA, 2008)

EU Annual Annual

25 (in 2010) 20 (in 2015)

(2008/50/EC, 2008)

Mexico 24 h Annual

65 15

(S. W. Lee, 2010)

When the atmospheric organic pollutants are the subject, PAH compounds of particular

toxicological and environmental concern are monitored using internationally recognized

methods. The list of priority PAHs shows variety in different countries. In the United

States, the EPA (Environmental Protection Agency) has listed 16 priority PAHs;

acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene,

benz(a)anthracene, chrysene, benzo(b)fluoranthene, benzo(k)fluoranthene,

benzo(a)pyrene, indeno(1,2,3-cd)pyrene, dibenzo(a,h)anthracene, benzo(ghi)perylene

and naphthalene. Among these benzo(a)pyrene (BaP) is a suitable marker due to its

stability and relatively constant contribution to the carcinogenic activity of particle-bound

PAH. US. EPA and WHO limit values for BaP are 1 ng m-3 and 8.7×10-2 µg m-3 (Hailwood

et al., 2001).

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2.9 Analytical Methods Used for the Determination of Atmospheric Organic

Pollutants

Estimation of emission source to the atmosphere depends on accurate and precise

detection of organic compound composition (Jacobson and Hansson, 2000; Mazurek,

2002; Schauer et al., 1999). In general, determination of organic compound concentrations

includes similar steps in literature: (1) organic solvent extraction, (2) gas chromatography

– mass spectrometry (GC-MS) detection. Extraction of particulate matter collected on a

filter medium is generally made by organic solvents such as hexane, benzene, methanol

and dichloromethane. The extract then could be directly measured or derivatized by

different agents based on the functional group present in the compounds to be analyzed.

For instance, methylation or silylation of n-alkanoic acids and silylation of polar

functional groups such as levoglucosan, cholesterol and stigmestrol are derivatized in

order to increase the elution and resolution in the analysis (Chow et al., 1994; Chung et

al., 2001; Hughes et al., 2000).

The method to be optimized to detect the species depends on many factors. They can be

sorted as: (1) sample location, (2) sample duration, (3) sampling conditions such as

temperature and humidity, (4) number of samples, (5) target compounds, (6) concentration

range, (7) recovery efficiency, (8) sensitivity, (9) accuracy, (10) precision, (11) required

level of training and (12) expertise required to operate the equipment, and (12) cost

(Doğan, 2013; Nair and Kuriakosei V., 1999).

Analytical methods of atmospheric organic pollutants generally follow this order:

sampling of the particulate matter on a filter, transporting to a laboratory and analyzing

the filters by GC-MS (US EPA, 1999). Various sampling devices and short definitions are

given below.

Streaker sampler: Trace elemental concentrations could be determined by this

sampler. A nucleopore filter stretched on a frame and mounted in a device which

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resulting a sucking orifice to be drawn along the length of the filter. The sucking

orifice is connected to a vacuum pump.

High volume sampler: PM2.5 or PM10 is collected on a glass filter by drawing air

through the filter at a flow rate of about 1m3 min-1. The most important difference

that distinguishes high volume sampler from other samplers is the amount of

particles on the filter that catches air in this amount. Especially for atmospheric

organic particulate matter studies, sufficient aerosol mass for high performance

gas chromatography is critical.

Cascade impactor: It collects the atmospheric particles in a manner that sizes of

the particles could be determined. In this sampler, air passes through a series of

circular orifices of decreasing diameter. The largest particles passing through the

orifices at each step impact and stack to the sticky surface, therefore smaller ones

pass to other step. They are typically designed with five steps by chosen cut points

of 8.0, 4.0, 2.0, 1.0, 0.5 µm aerodynamic diameter. Particles of which diameter is

less than the last stage are collected by a filter at the end. In general, nucleopore

type filter is used.

Dichotomous sampler: The main purpose of this sampler is differentiate the coarse

particles from fine particles. There are two particle size fractions: 0 to 2.5 µm and

2.5 to 15 µm. The principle is that particles accelerate through a nozzle after which

30 percent of flow stream is drawn off at right angles. The fine particulate matter

follows the right angle flow stream, while the coarse particulate matter continues

toward the collection nozzle.

Cyclone Sampler: It is used for only fine particulate matter samplings. This

sampler collects the particles on a 37 mm dimeter filter with a selecting flow rate

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and cut-off points (Heumann, 2003; Jacobson and Hansson, 2000; Kulkarni et al.,

2011).

GC-MS is an analytical method which combines the separation properties of gas-liquid

chromatography with detection characteristic of mass spectrometry to determine various

compounds within a sample. As against to GC which separate the volatile and thermally

stable substitutes in a sample, GC-MS splits the analyte to be determined on the basis of

its mass. GC needs the analyte to have vapor pressure between 30 and 300oC. Detection

of the compounds by GC shows an insufficient proof and analysis depends on retention

time (RT) only. Beside, GC-MS determines a compound by showing its mass to the

number electrostatic charges that the compound carries. The term m/z is measured.

Commonly used techniques are electron impact (EI) and chemical ionization (CI). By GC-

MS, wide range volatility compounds could be analyzed, analyze time could be shorter,

sensitivity is higher for the compounds that are hard to analyze (Jenke, 1996; International

Organization for Standardization, 2002; ISO/IEC 17025, 2005; Rowley, 2001).

2.10 Polycyclic Aromatic Hydrocarbons (PAHs)

PAHs having a small percentage in the aerosol mass are common atmospheric pollutants

and some of them are mutagenic and carcinogenic. (Fang et al., 2002; Omar et al., 2006).

Almost all of them are originated from anthropogenic emissions such as industrial

production, vehicle exhausts, waste incineration and wood burning (Cheung et al., 2010;

Dyke et al., 2003; Sofowote et al., 2011). Beside, these compounds are originated from

incomplete combustion of organic matter (Rogge, 1993). These produced PAH releases

into atmosphere in fine particulate matter attached to fine particles (Baek et al., 1991).

There are many studies in the literature about particulate bound PAHs. Some of them

summarized in this section. The concentration of ten PAH and two of their hydroxyl

derivatives were monitored in a sub-urban area of Italy (Barrado et al., 2012). Total PAH

and OH-PAH concentrations changed between 175 to 2125 pg m-3. The compounds have

higher concentrations in almost all samples are pyrene, fluorine, phenanthrene and

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chrysene. The positive correlation was observed between OH-PAHs and their parent

PAHs. When seasonal changes have been investigated, both PAHs and OH-PAHs were

higher for colder months and lower for the warmer months (Callén et al., 2014; Chen et

al., 2016; Wang et al., 2015). There are also studies conducting hourly measurements.

Forty-eight daily PM2.5 samples were collected at two year period in Shanghai, China (Gu

et al., 2010). PAH concentrations were analyzed with GC-MS to obtain diurnal and

seasonal changes. When daily variations were investigated, significant changes were

observed for PAHs. Rush hour (6:30-10:00) concentrations were the highest that

indicating the contribution of vehicle emissions. Seasonal variations were also observed.

Colder months have the highest concentrations for PAHs. There are various sources

responsible for PAHs emissions. Y. Ma et al. (2016) conduct a one-year PM2.5 bound PAH

observation at an urban site in Hong Kong. As a result, four sources were identified:

marine vessels, vehicle emissions, biomass burning and coal combustion. Similar results

were obtained in Gao et al.'s (2013) study. At Chen et al.'s (2016) study, three potential

sources were identified of PAHs: unburned petroleum and traffic emission, steel industry

and coal combustion, and petroleum and oil burning.

A long term study for PAH was performed at Czech Republic (Dvorska et al., 2012). Site

could be defined as central European background monitoring place. The 12-year data set

showed that the winter concentrations were six times more than the summer

concentrations. This study defends that source composition could not be identified by

commonly used PAHs only. Data set should be extended by other PAHs, e.g., coronene

as a tracer of road traffic, retene as an indicator for wood combustion.

Table 2.2. Physicochemical properties of PAHs

PAHs Molecular Formula

Molecular Weight

Melting Point (oC)

Boiling Point (oC)

Vapor pressure (mm Hg)

Fluorene (Fl) C13H10 166.22 114.8 295 0.0006

Phenanthrene (Phe) C14H10 178.23 98.24 340 0.000121

Anthracene (An) C14H10 178.23 215 339.9 5.53E-06

Fluoranthene (Flut) C16H10 202 107.8 384 9.22E-06

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PAHs Molecular Formula

Molecular Weight

Melting Point (oC)

Boiling Point (oC)


Pyrene (Pyr) C16H10 202 151.2 404 4.50E-06

Benzo(a)anthracene (BaA) C18H12 228 84 437.6 2.10E-07

Chrysene (Chr) C18H12 228.29 253.8 448

Benzo(b)fluoranthene (BbF) C20H12 252 168 481 5.00E-07

Benzo(k)fluoranthene(BkF) C20H12 252 217 480 9.65E-10

Benzo(e)pyrene C20H12 252 177.5 310-312 5.70E-09

Benzo(a)pyrene (BaP) C20H12 252 176.5 495 5.49E-09

Perylene C20H12 252 274 5.25E-09

Dibenzo(a,h)anthracene(DBA) C22H14 278 262 524

Indeno(1,2,3,-cd)pyrene (IP) C22H12 276 163.6 536 3.48E-07

Benzo(ghi)perylene (BghiP) C22H12 276 278.3 545

Retene C18H18 234 101 390

Picene C22H14 278

Coronene C24H12 300 437.3 525 2.17E-12

2.11 n-Alkanes

In today’s world, many sources could be responsible for n-alkanes emitted to the

atmosphere. There are two main responsible source for n-alkanes: anthropogenic (e.g.

combustion of fossil fuels, wood and agricultural debris) and biogenic (e.g. plant waxes,

pollen, bacteria, fungi and fungal spores) (Chow and Watson, 2002; Hoyle et al., 2011;

Schauer et al., 1996; Tsapakis et al., 2002).

Atmospheric aliphatic hydrocarbons (n-alkane) is highly resistant to biochemical

degradation and is present in very high concentrations in aerosol (Pietrogrande et al.,

2010; Young and Wang, 2002). Ambient n-alkane compounds are present in similar series

depending on the carbon atoms they carry in the atmosphere. There are various approaches

to determine the source of n-alkane compounds and all of them depends on the number of

carbon atmos. They are given below.

Alkane compounds from nine carbons up to 16 carbons (C9-C16) are generally used in

diesel and aircraft fuels. The n-alkane compounds over C16 are used for fuel oil and

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mechanical oils. These compounds are also used as anti-wetting compounds due to

hydrophobic structures and as anti-corrosion agents on the market. And n-alkanes carrying

20-32 carbon atoms (C20-C32) are more dominant in the plant waxes. Lastly, n-alkane

compounds containing 35 and more carbon atoms are present in the asphalt structure, but

these compounds are reduced as much as possible by thermal or catalytic cracking

processes applied to increase the number of petroleum octanes in petroleum refineries

(Gogou et al., 1996; Li, Peng, and Bai, 2010; Murzin, 2015). Atmospheric n-alkane

compounds are often source dependent and this makes them a good tracer for source

apportionment studies (Simoneit et al., 2004; Yadav et al., 2013; Yamamoto and

Kawamura, 2010).

According to Rogge et al. (1993) and Simoneit (1999). Cmax shows the highest

concentration of n-alkanes, and it is issued to determine to differentiate the biogenic from

anthropogenic sources. Higher molecular weight n-alkane compounds with carbon

number 27 or more are emitted from plant waxes into atmosphere. Anthropogenic

emissions, especially fossil fuel burning, include mainly lower carbon number n-alkane

compounds, C22-C25. The contribution of plant wax can be estimated by using the

concentration of next higher and lower carbon numbered n-alkanes. The following

formula was used to estimate the contribution of plant wax:

% ∑ 0.5 / ∑ ∗ 100 Equation 2.1

where, wax Cn is the concentration of plant wax and if there is any negative value coming

from this equation, it is taken as zero, Cn, Cn-1, Cn+1 are the concentrations of n-alkanes

with neighboring carbon numbers. Higher percent wax Cn values show higher contribution

from biogenic sources (Wang et al. 2006).

Ambient n-alkanes were investigated in seven different sites in Europe in Alves et al.

(2012) study. This study shows that percentile of source distribution could be change from

one location to another. Plant waxes have always been identified as the dominant source,

as source distribution varies between anthropogenic and biogenic depending on location.

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Fossil fuel contribution has been observed for rural areas and wood burning contribution

has also been determined as dominant source for both rural and urban sites. Plant waxes

was also found as dominant source especially during warm period in Dutton et al. (2009)

study. When temporal variation was the case fall and winter time concentrations were

higher than summer time values.

Table 2.3. Physicochemical properties of n-Alkanes

Alkanes Molecular Formula

Molecular Weight

Melting Point (oC)

Boiling Point (oC)


heneicosane C21H44 296.574 40.5 356.5 8.73E-05

docosane C22H46 310.601 44.4 368.6 1.28E-06

tricosane C23H48 324.627 47.6 380 1.74E-05

tetracosane C24H50 338.654 54 391.3 4.07E-06

pentacosane C25H52 352.68 54 401.9 1.51E-06

hexacosane C26H54 366.707 56.4 412.2 4.69E-07

heptacosane C27H56 380.733 59.5 442 2.81E-07

octacosane C28H58 394.76 64.5 431.6 1.60E-09

nonacosane C29H60 408.787 63.7 440.8 4.30E-10

triacontane C30H62 422.813 65.8 449.7 2.73E-11

hentriacontane C31H64 436.84 67.9 458 1.40E-11

dotriacontane C32H66 450.866 69.7 467 < 1

tritriacontane C33H68 464.893 72 474 4.02E-11

tetratriacontane C34H70 478.92 72.6 483 <1

pentatriacontane C35H72 492.946 75 790 5.39E-12

2.12 n-Alkanoic acids

The role of alkanoic acids as chemical compound in the troposphere has become an

important issue of growing interest in literature (Chebbi and Carlier, 1996). Organic acids

were measured as 30-70% of solvent extractable organic compounds in many countries

(Guo et al., 2015; He et al., 2006; Yao et al., 2004). Due to their hygroscopic

characteristics and potential of acting as cloud condensation nuclei, they have received

much attention (Guo et al., 2015; Novakov and Penner, 1993). Emission sources of n-

alkanoic acids in atmospheric aerosols show similarity to the sources of n-alkanes.

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Sources of n-alkanoic acids are now well recognized; there are two major sources as

anthropogenic and biogenic. Various anthropogenic sources of n-alkanoic acids in the

atmosphere were mentioned including fossil fuel, wood and detritus combustion (biomass

burning), food cooking (Rogge, 1993; Simoneit and Mazurek, 2007). Biomass burning is

an important source of n-alkanoic acids, because they are the major components of plant

tissues and surface waxes (He et al. 2004; Zhang et al. 2008). And also a lot of studies

show that meat cooking has a big role for source contribution to n-alkanoic acids.

Tetradecanoic acid, hexadecanoic acid and octadecanoic acid are the most dominant

n-alkanoic acids in meat source aerosol (He et al., 2004; Schauer et al., 1999; Zhao, et

al., 2007; Zhao et al., 2015) . On the other hand, fossil fuel combustion is another

fundamental source for n-alkanoic acids, with Cmax=16 (Simoneit and Mazurek, 2007).

Biogenic sources of those are similar to sources of n-alkanes. The main source

contributors are plant waxes, fungi, bacteria, spores from fungi and bacteria, pollen and

algea (Abdullahi et al., 2013). With strong anthropogenic sources, temporal variation of

n-alkanoic acids induced by seasonal variation in meteorology is modified by local

anthropogenic emissions around the urban station (Zheng et al., 2002).

Table 2.4. Physicochemical properties of n-alkanoic acids

n-Alkanoic acids

Molecular Formula

Molecular Weight

Melting Point (oC)

Boiling Point (oC)


Dodecanoic acid C12H24O2 200 43.2 298.9 1.60E-05

Tridecanoic acid C13H26O2 214 44.5 312.4 1.26E-05

Tetradecanoic acid

C14H28O2 228 53.9 326.2 1.40E-06

Pentadecanoic acid

C15H30O2 242 52.3 339.1 4.35E-07

Hexadecanoic acid

C16H32O2 256 61.8 351.5 3.80E-07

Heptadecanoic acid

C17H3402 270 61.3 363.8 6.23E-08

Linoleic acid C18H32O2 280 -8.5 365.2 8.68E-07

Oleic acid C18H34O2 282 45 360 5.46E-07

Octadecanoic acid

C18H36O2 284 69.3 383 7.22E-07

19

2.13 Levoglucosan

Besides anthropogenic sources such as fossil fuel combustion and automobile exhaust

emissions, it was recognized that biomass burning provides a considerable contribution to

the atmospheric aerosol in urban and suburban areas (Andreae, 1991; Lanz et al., 2008;

Liu et al., 2000; Zhang et al., 2008). Additionally, biomass burning has an effect on

atmospheric chemistry with its several days transport periods and thousands of kilometers

(Fraser and Lakshmanan, 2000). The major emission product of biomass burning is

levoglucosan which is in the form of cellulose and it is source specific and emitted in high

amount during biomass combustion (Poore, 2002; Puxbaum et al., 2007; Simoneit et al.,

1999). The emission of levoglucosan is not in small amounts but it is also resistant to

reactions and is also a specific substance for cellulose burning. Because of all these

features, levoglucosan has become an ideal tracer for biomass burning (Chow et al., 2006;

Fraser and Lakshmanan, 2000; Wang et al., 2007). The general trend of the levoglucosan

concentration shows linearity with the increasing biomass burning at a site. During winter

time its atmospheric concentration was detected higher than summer times. And also the

concentration was detected higher at urban site than the rural site (Perrone et al., 2012;

Yan et al., 2009).

Table 2.5. Physicochemical properties of Levoglucosan

Molecular Formula

Molecular Weight

Melting Point (oC)

Boiling Point (oC)


Levoglucosan C16H10O5 162 183 385 7.50E-08

2.14 EC/OC

Atmospheric carbons are an important component for particles whose aerodynamic radius

is less than 2.5μm (Zhang et al., 2013). The carbon in atmospheric particles can be

classified into two main categories: EC and OC (Pandis et al., 1992). Determination of

EC and OC concentrations is one of the important methods in determining aerosol source

and species (Seinfeld and Pandis, 1998).

20

EC is also known as black carbon and is exhausted into the atmosphere as a combustion

boost. Its structure is graphite with black color. The carbon in the EC is generated from

combustion reactions that occur at high temperatures and the isotopic structure does not

change much due to its inert nature. For this reason, almost all of the particulate phase EC

can be assumed to have reached to atmosphere from primary combustion sources (Pandis

et al., 1992b). OC part could exhausted from primary sources, on the other hand, the OC

part can undergo many modifications due to photochemical reactions in the atmosphere,

and it is formed from hundreds of organic compounds (Rogge et al., 1993). Particulate

OC contains basically hydrocarbons and other organic compounds of various oxidation

products (Castro et al., 1999; Lim and Turpin, 2002). If the ratio of OC to EC is calculated,

SOA can be simply calculated with a relatively simple empirical method (Dusek, 2000b).

The atmospheric aerosol has different sources, such as fossil fuel burning, biomass

burning, as well as burning reactions at high temperatures, and plant reactions and

photochemical reactions at low temperatures. Thus, EC appears to be a tracer of primary

human resources and has an inert character in atmospheric reactions (Klouda et al., 1990).

Organic carbon (OC), on the other hand, contains numerous compounds of both primary

and secondary origin (Slater et al., 2002).

2.15 Secondary Organic Aerosols (SOA)

Secondary organic aerosol is formed in the atmosphere due to chemical reactions which

transform more volatile compounds into lower volatile species, which then distinguished

into the particulate phase. The emission sources and components of SOA are not identified

completely. There are some models trying to estimate the mass fraction and composition

of SOA, however they underpredict the SOA fraction. Since the chemistry of SOA is so

complex in terms of molecular weight, vapor pressure, polarity and instability. Therefore,

recent studies have focused on the methodology to classify the SOA by category. (Aiken

et al., 2008).

21

The main factors affecting SOA formation in the atmosphere are; concentrations of

primary gas-phase compounds (Volatile organic carbon-VOC precursor compounds),

intensity of solar radiation, the chemical structure of the particles, the state of

meteorological conditions such as temperature and relative humidity. The predominant

gas-phase precursor compounds in the atmosphere are; isoprene, such as monoterpenes

(α-pinene and limonene) and sesquiterpenes (β-caryophyllene) from biogenic emissions,

anthropogenic emissions, and solvents (Sarwar and Corsi, 2007; Stone et al. 2010). Other

VOCs that are responsible for the formation of SOA are aromatics (o, m and p-xylene,

ethylbenzene, m and p-ethyltoluene, 1,3,5-trimethylbenzene, isopropyl benzene and some

other benzene derivatives of 10 carbons), cycloalkanes (methylcyclopentane,

methylcyclohexane, ethylcyclohexane and cyclohexane) and n-alkanes (nonan, n-heptane,

2-methylheptane, 3-methyl heptane, 2,4,4-trimethylheptane, n-octane and n-decane)

(Kourtidis and Ziomas, 1999). All of these organic compounds are known as Reactive

Organic Gases (ROG) and their sources are fossil fuel consumption, biomass burning,

solvent use, plant emissions and ocean emissions (Dusek, 2000a; Kourtidis & Ziomas,

1999).

2.16 Source Apportionment- Receptor Models

Determination of relationship between source emissions, atmospheric concentrations and

health and environmental effects could be enabled by source apportionment studies. There

are different source and receptor based models. Source based receptor models use

pollutant emission rates and meteorological data in a mathematical model. This model

transforms the emitted pollutants and create an estimation of the pollutant concentrations

as a function of space and time. On the other hand, receptor models use atmospheric

pollutant concentrations measured at a sampling site to determine the source types,

locations and contributions to overall health and environmental effect. In earlier studies,

inorganic and elemental data were used to determine the sources by source apportionment

techniques. In time, measurements of organic pollutant measurements were improved by

differentiating various carbon source contributions (Lin et al., 2010).

22

Receptor models are used as a mathematical and statistical procedure to identify and

quantify the sources of atmospheric air pollutants at a receptor location. Contrary to

photochemical and dispersion air quality models, receptor models do not use pollutant

emissions, meteorological data and chemical reactions to obtain the contribution of each

source. Alternatively, receptor models use the chemical and physical characteristics of

atmospheric aerosols measured at site and determine the source and contribution to

receptor concentration (US. EPA, 2010).

Receptor models derive contributions from various source types using multivariate

measurements taken at one or more receptor sites. These receptors can be placed indoor

or outdoor monitors or they could be mobile samplers of which activities are controlled

by an individual or group of people. A chemical compound called as “tracer” is used for

certain sources. There are many analytical methods such as x-ray spectra, gas

chromatographs, microscopic analysis, carbon-14 and other isotopic abundances supply

patterns to identify and quantify the source contribution (Watson, 1984).

2.16.1 Mass Closure

Mass closure is used as an alternative method instead of methods including multivariate

statistical tools. It depends on analysis of chemical compounds to be analyzed. The sum

of the masses of chemical compounds is used to construct the mass closure. To increase

the time-resolution of the results of mass closure, continuous sampling equipments can

be used during sampling campaign (Grover et al., 2006; Park et al., 2006). Mass closure

model includes estimation of the contribution of atmospheric particulate compounds to

PM2.5. Various component model of atmospheric particles and conversion factors can be

used in mass balance to predict the contribution of possible air pollutant sources.

Harrison et al. (2003) developed a pragmatic model for mass closure including four

major components; secondary aerosol, minerals, sea salt and carbonaceous aerosol.

Therefore, a simple but useful model can be constructed with a small number of source

component. All needed is seven chemical compounds (sulphate, nitrate, chloride,

calcium, iron, EC and OC) to be analyzed and mass calculation according to prescribed

23

definition of source components. This method can be summarized as a mixture model of

chemical ana l y s i s a n d s i m p l e statistical procedures. Differently from previous

study, Turpin and Lim (2001) recommended a conversion factor for organic compounds

as 1.6 and 2.1 for urban and non-urban aerosol respectively. There is also unexplained

part of the mass and this part is generally named as water content. The contribution of

water content can constitute up to 20-35% of the annual median PM2.5 concentrations

(Tsyro, 2005).

2.16.2 Chemical Mass Balance (CMB)

The CMB model predicts source contributions depend on the degree to which source

profiles can be joined to reproduce ambient concentrations. If appropriate chemical

compounds have been measured, CMB links primary particles to their source types and

identify the chemical form ıf secondary aerosol (Watson et al., 2002). The CMB model

needs source and receptor measurements of the compounds, source profiles, uncertainties,

sampling periods and locations that shows the effect and different spatial scales (Nguyen,

2014). There are advantages and disadvantages of the CMB model. It has a user-friendly

software, quantifies primary source contributions, provides uncertainties on source

contribution estimates depend on input data, uncertainties and collinearity of source

profiles; on the other hand, it does not directly determine presence of new or unknown

sources and it should be combined with profile aging model to predict secondary aerosol.

2.16.3 UNMIX

The UNMIX model is a version of Principle Component Analysis which is geometrically

limited to generate source profiles and contributions with physically meaningful attribute

of non-negativity (Hopke, 2009; Jorquera and Rappenglück, 2004). The model need

sample compound concentrations as input and determines the compounds in the factor and

contribution matrices. It includes meteorological data and stack configuration of

stationary sources into the model, does not require source measurements, is a commercial

software and appropriate for high-time resolution measurements. However there are some

24

weaknesses such as; only handle stationary sources, does not account uncertainty in the

measurement, meteorological data may not be always available or accurate (Henry, 2003).

2.16.4 Positive Matrix Factorization (PMF)

PMF was developed by Paatero and his colleagues in the mid-1990s (Anttila et al., 1995;

Paatero and Tapper, 1994) and it has been widely used in air quality researches (Kim et

al., 2003; Pandolfi et al., 2008; Song et al., 2006). The main purpose of PMF application

is to identify the number of factors which represents the data variability and to examine

correlation among the measured variables. Additionally, tracers of specific pollution

sources structure could also be identified and commented. PMF uses the least square

approach by integrating the non-negative constraints into the optimization process and

using the error estimates for each data. The advantages of PMF over other models are:

does not require prior knowledge of sources, assign value for missing data and below-

detection limit data. Therefore mass profile factors determined by PMF are better at

identifying the source structure than those derived by other receptor models (Paatero et

al., 2002). Furthermore, the mathematical algorithm of PMF prevents the development of

negative factor loadings and scores, which can be resulted from principle component

analysis (Reff et al.,2007). A mass balance equation could be written by using p

independent sources to all chemical species.

∑ Equation 2.2

where xij is the jth species concentration measured in the ith sample, gik is the mass

concentration from the kth source contributing to the ith sample, fkj is the jth species mass

fraction from the kth source eij is the residual associated with the jth species concentration

measured in the ith sample, and the p is the total number of the independent sources

(Hopke, 1985; Paatero, 1997).

25

PMF could be defined as a multivariate factor analysis tool that separates a matrix of

sample data into two matrices. First one is factor contribution (G) and the second one is

factor profiles (F). After run the program, these factor profiles have to be commented by

the user to determine the source types contributing to the sample by using measured source

profile information, and emissions or discharge inventories. Factor contributions and

profiles are obtained by minimizing Q value that is the objective function.

∑ ∑∑

Equation 2.3

Q is a parameter that should be controlled after each trial. PMF gives the two versions of

Q; (1) True-Q which is the goodness of fit parameter including all data; (2) Robust-Q is

the goodness of fit parameter determined excluding points of which uncertainty scaled

residual is greater than 4.

The differences of True-Q and Robust-Q demonstrates the influence of data points with

high scaled residuals. The reason of this situation could be explained by two ways: these

data points may have sudden increases in concentrations that are not permanent cases

during the sampling period; or uncertainties may be too high to result similar values in

True-Q and Robust-Q since the residuals scaled by the uncertainty (Norris et al., 2014).

2.17 Determination of atmospheric aerosols and their sources

There are three major characteristics of atmospheric aerosol pollutants; total mass

concentration, size distribution and chemical content. Mass concentration for particulate

matter is recorded as mass (µg) per unit volume (m3). In order to calculate mass

concentration all particles are passed from a known volume of air and collected on a filter

which is pre and post weighted. PM2.5 concentrations of which aerodynamic diameter is

less than 2.5 µm, are monitored to provide air quality standard and to prevent diseases

originating from the particles inhaled. And also coarse fraction of atmospheric aerosol,

PM10 of which aerodynamic diameter is less than 10 µm, is monitored due to its possible

26

toxic structure. Beside their health effects, size distribution of particulate pollutants has a

great importance to understand the transport and removal pattern of particles and chemical

composition identify the type of effects caused by particulate matter on humans,

vegetation and materials (Vallero, 2008).

Particulate matter is composed of a wide variety of components. These are elemental or

black carbon (EC and BC) and organic carbon (OC), sulfate, nitrate, trace metals, crustal

material, sea salt and organic compounds. In point of particulate matter sources, they can

be directly emitted from anthropogenic or natural sources or could be formed in the

atmosphere from combustion by-products such as volatile organic compounds, ammonia,

oxides of sulfur and oxides of nitrogen. And contribution of these depends on location,

season and time of day. Primary ones mainly originate from combustion and high-

temperature activities, and they could be come from resuspension of street dust for

example. Beside, secondary particulate matter is formed by photochemical transformation

under the presence of ozone and hydroxyl and other reactive molecules.

2.17.1 Residential combustion

Although interest has grown in biomass combustion in developed countries, Turkey still

continue to use coal and natural gas for residential combustion. Toxic compounds such

as PAHs and trace elements have been determined in combustion emissions and generally

they present in PM2.5 fraction. A review study of literature shows that coal combustion is

an important contributor to total PAHs emission with the percentage of 10.7 in China

(Calvo et al., 2013; Weijun Li et al., 2012). In Zhang’s study (2005) emission factors were

found 8820 mg kg-1 for PM10 and 6860 mg kg-1 for PM2.5 in residential coal combustion.

Different studies show that EC and OC have significant contribution to particulate

matter concentration from combustion processes (Cao et al., 2005; Y. Gao et al., 1997;

Zhang et al., 1993). OC/EC ratios were found higher during heating seasons with

increased primary emissions resulted from residential combustion (Cao et al., 2007; He

et al., 2001; Park et al., 2002).

27

2.17.2 Road transport

In urban areas road transport is one of the main sources of particulate matter. It makes a

large contribution to particulate matter concentrations and its impacts on human health

have been demonstrated in various study (Buckeridge et al., 2002; Fan et al., 2006; HEI,

2010; Masiol et al., 2012; Mauderly, 1994; Pant and Harrison, 2013; Rissler et al., 2012).

It could be categorized according to their formation type. Main mechanisms are

combustion of fuels, mainly gasoline and diesel and exhaust emissions. Beside, road could

be a source itself with only interactions between vehicles and road surface. This item is

also known as non-exhaust emission and includes tyre wear, brake wear, road surface wear

and resuspension (Hester and Harrison, 2016; Pant and Harrison, 2013a).

2.17.3 Biomass Burning

Biomass burning has an important potential contribution to particulate matter (Cheng et

al., 2013; Fine and Cass, 2001; Lee et al., 2008; Schauer et al. , 2001; Zhang et al., 2012).

Many trace substances emitted by biomass burning are reactants in atmospheric chemistry

(Schauer et al., 1999). The particulate matter emitted to the troposphere (urban, rural and

remote) from biomass burning occurs by natural and man-made fires. Vegetation is the

major biomass being burned, and it includes primarily biopolymers with minor amounts

of lipids and terpenoids. Once released to the atmosphere, biomass burning outputs are

mixed with the particulate matter emitted from many other natural and anthropogenic

pollution sources, then they become a form which is difficult to recognize and quantify

(Schauer et al., 1996b). The most important tracer of biomass burning is levoglucosan

since it is relatively stable in the atmosphere (Puxbaum et al., 2007; Schauer et al., 1999;

Schkolnik et al., 2005).

2.17.4 Food cooking

Food cooking operations contribute significantly to urban atmospheric particulate matter

concentrations. In a study made in Los Angeles stated that 20% of fine particulate matter

28

(PM2.5) was originated from food cooking (meat charbroiling and frying) processes (Calvo

et al., 2013; McDonald et al., 2003). In another study contribution of food cooking to OC

has been investigated and stated that 10% of OC reasoned from food cooking processes

(Robinson et al., 2006). More than 120 organic aerosol were determined coming from

meat cooking processes such as palmitic, stearic and oleic acids and cholesterol (Mohr et

al., 2009).

2.17.5 Natural sources

Natural sources could show high contribution to particulate matter emission. The sources

could be listed as: windblown (desert and local) dust, sea salt aerosols, volcanoes, primary

biological aerosol particles and wild-land fires (Calvo et al., 2013; D’Alessandro et al.,

2013; Liora et al., 2015). For the importance of this study, especially biological aerosol

particles are the concern. They manly include pollen, plant waxes, fungal spores, bacteria,

and viruses. For the n-alkanoic acids; homologs of which carbon number is less than 20

are thought to be derived in part from microbial sources while homologs of which carbon

number is higher than 22 are from vascular plant wax. Thus, there should be a correlation

between the n-alkanes and n-alkanoic acids due to plant wax (Simoneit et al., 2004;

Simoneit and Mazurek, 2007).

29

MATERIAL AND METHODS

3.1 Sampling Location

Two sampling stations had been set up to collect, not only to collect organic particles, but

also for measurement of volatile organic compounds and trace elements. However, this

study is confined to measurement of organic compounds associated with primary and

secondary particles. Locations of sampling stations used in this study are given in Figure

3.1.

The first sampling site was located within METU Campus, at the back of the

Environmental Engineering Department (39°53'12.9"N 32°46'58.8"E). This site was

chosen based on its location within a large campus limits and its relative lack of influence

from nearby major roadways or point sources. The campus population is around 15,000

during daytime and the main source is traffic emissions. Population decreases to 2,000 -

3,000 during night. At night, emissions are generally from heating central gas-powered

heating facility, which heats dormitories and university housing. Earlier studies at this

site demonstrated that some of the pollutants measured at METU campus is transported

from downtown Ankara (Kuntasal et al. 2013; Yatin et al. 2000). Two crowded highways,

namely Eskisehir and 1071 Malazgirt highways surround the campus. These two busy

roads are approximately 2.5 km from the sampling site. With its setting METU station

can be classified as a suburban station and will be referred to as “suburban station” in this

manuscript.

CHAPTER 3

30

Başbuğ Street and Turgut Özal Avenue are approximately 70 m and 330 m from our AU

station, respectively. The station is also approximately 380 m away from Fatih Caddesi,

which is another busy street in the city. In addition to these major roads, the station is

located at the most populated part of the Altındağ, which is a district with 368000 residents

at 2016 (TUİK, 2017). The station has the characteristics of a typical urban station and

will be referred to as AU station or “urban station” in the manuscript.

The second site was located at AU, Faculty of Agriculture, which is at Dışkapı district

(39°57'47.4"N 32°51'42.6"E). By comparison, this area could be classified as urban area

since there are many schools, housing and hospitals. The site was located between Turgut

Özal Avenue and Irfan Başbuğ Street, which are among the busiest roads in Ankara. Irfan

31

Figure 3.1 Location of the urban and suburban sampling stations

32

3.2 Sampling Procedures and Preparation of Filters for Analysis

Sample collection started on July 1, 2014 and organic molecular quantification has been

completed on 275 daily samples for sub-urban station and 336 daily samples for urban

station. In addition to these regular samples, 30 field and 30 laboratory blanks were also

collected. Samples were collected on 8”x10” quartz filters (Pallflex, Tissuquartz

2500QAT-UP) using high volume samplers (Thermo Scientific, HVAIR100), equipped

with a pre-impactor with 2.5 µm cut point and operating at 1.01-1.18 m3 min-1 flow rate.

Sampling was daily. Samplers were operated continuously and stopped only to change

filters at around 10:00 AM every day.

Field blanks were collected with regular intervals. Field blanks consisted of filters

prepared like sample filters, but retained on the sampler for approximately one minute

without operating the sampler. After sampling these blanks were processed and analyzed

like regular samples. In addition to field blanks, laboratory blanks were also regularly

generated and analyzed to eliminate contamination from laboratory operations. Two types

of lab blanks were analyzed. These were filter blanks, which included quartz filters taken

randomly from boxes. These filters were processed and analyzed without being sent to

field. The second type of laboratory was process blanks, which included carrying out

extraction procedure without filter.

Before sampling Quartz fiber filters were pre-cleaned by heating to 500oC for 5 hr. in a

Lenton Furnaces-ETC 4420 oven. They were then weighted to ± 0.00001 g using a

Sartorius, model A210P balance. Weighted filters were wrapped into prebaked Al-foil

until they were installed onto samplers. After sampling, filters were immediately wrapped

into Al-foil (the same piece of Al foil in which they were wrapped before sampling). In

the laboratory they were weighted again for gravimetric determination of PM2.5 mass

concentration and then stored in Al foil until extraction.

Strict adherence to filter collection, handling and transport methodology had been

followed throughout the study to minimize organic contamination.

33

3.3 Target Compounds

In this study, forty-five organic molecular marker compounds were quantified in each

sample (complete list is given in Table 3.1). They included n-alkanes, PAHs, n-alkanoic

acids, levoglucosan and EC-OC. These compounds are markers for different emission

sources and used to apportion sources affecting composition of organic particles at our

stations.

Table 3.1. Compounds to be analyzed in the study

n-Alkanes PAHs n-Alkanoic acidsheneicosane docosane tricosane tetracosane pentacosane hexacosane heptacosane octacosane nonacosane triacontane hentriacontane dotriacontane tritriacontane tetratriacontane pentatriacontane

Fluorene (Fl) Phenanthrene (Phe) Anthracene (An) Fluoranthene (Flut) Pyrene (Pyr) Benzo[a]anthracene (BaA) Chrysene (Chr) Benzo[b]fluoranthene (BbF) Benzo[k]fluoranthene(BkF) Benzo(e)pyrene Benzo[a]pyrene (BaP) Perylene Dibenzo[a,h]anthracene(DBA) Indeno[1,2,3,-cd]pyrene (IP) Benzo[ghi]perylene (BghiP) Retene Picene Coronene

Dodecanoic acid Tridecanoic acid Tetradecanoic acid Pentadecanoic acid Hexadecanoic acid Heptadecanoic acid Linoleic acid Oleic acid Octadecanoic acid

Levoglucosan EC OC

34

3.4 Method Optimization

3.4.1 General Principles of Gas Chromatography- Mass Spectrometry

The GC-MS technique was used for separation of chemical mixtures and identification of

the components at a molecular level. GC-MS is one of the most widely-used devices for

analysis of organic compounds in ambient air samples (US EPA, 1999).

Figure 3.2. Schematic representation of GC-MS

There are two separate techniques such as gas chromatography (GC) and mass

spectrometry (MS) and they are successfully linked to form GC-MS. Gas chromatography

can separate volatile and semi-volatile compounds; however its detection selectively is a

deficit. This deficiency is avoided by using a mass spectrometer, which is known for its

selectivity, in identifying charged groups as a detector (Sneddon et al., 2007). This

35

combination is proved to be a very powerful in separating and identifying organic

compounds in mixtures, including air samples.

Monitoring the organic particulate matter in fine atmospheric particles is an important

challenge due to the hundreds of compounds present, and their wide range of chemical

properties. The general methodology in determination of organic compounds in air

samples or other mixtures with GCMS has the following sequence of processes (Cropper,

2016).

Sample Collection extraction preconcentration derivatization (if needed)

GCMS analysis

Compounds to be analyzed are partitioned between a mobile phase and a stationary phase

in GC. Mobile phase is a gas (such as, high purity Helium) and the stationary phase is a

high molecular weight liquid which is chemically attached to the inner walls of a long

capillary tube (such as dichloromethane). GC columns are generally made of silica and

could have lengths between 30 – 60 m and diameters between 0.2-0.25 mm, depending on

the type of the compounds to be analyzed. There is a polymeric covering on the outside

surface of the capillary columns.

Complex mixtures of organic compounds are first extracted into a high purity solvent. The

volume of high purity solvent used in extraction must be large (expressed in liters) for the

efficiency of extraction. Since concentrations of organic molecules in air is very low, they

are generally concentrated by evaporating solvent to a very low volume (2 ml or less) by

gentle blowing nitrogen gas. Few µL of extracts are then injected into GC column. An

inert carrier gas (Helium), is used to transport extracted organic compounds, which are

now in vapor phase, through the GC column. Different solubilities are reasoned to

different times to traverse the length of the column. For the specific set of the GC

conditions, the time in which the organic compound to travel the GC column shows

36

diversity, and this term is called as RT. In general, higher molecular weight compounds

have higher RT while lower molecular ones have smaller RTs (Clement and Taguchi,

1991; Sneddon et al., 2007). Organic compound molecules enter the ion source chamber

of the mass spectrometer under high vacuum. They are bombarded by the electrons in the

ion source chamber. A schematic representation of GC-MS instrument is given in Figure

3.2 Molecules are ionized and dissociate into different fragment ions. After the ions

transport to mass analyzer part, they are separated depending on their mass to charge ratio

(m/z). then they are determined by detector (Clement and Taguchi, 1991; Lin, et al., 2007;

Sneddon et al., 2007). After all, the abundance of ions versus their m/z is plotted in the

monitor by a software. The mass spectrum of a compound is a fingerprint graph from

which the original organic structure could be understood. By matching the GC retention

time of a compounds and its mass spectrum, which is obtained from a standard reference

material (SRM) analyze under the same conditions, a positive determination of the

compounds is gathered.

3.4.2 Optimization of a GC-MS Procedure for the Measurement of Organic

Particulate Matter in PM2.5

3.4.2.1 Optimization of oven temperature and heating rate (ramp)

The extracts were analyzed by GC-MS using an Agilent Technologies 7890A gas

chromatograph (GC) attached to an Agilent 5975 Mass Selective Detector (MS). The GC

was equipped with a 30-meter low-bleed non-polar J&W Scientific HP-5ms capillary

column with a 0.25 mm diameter bore coated in 5% phenyl-methylpolysiloxane (Agilent

#19091S-433).

Stages of the method optimization study were as follows: (1) identification of the m/z

values of compounds, (2) optimization of the temperature program and (3) development

of a SIM method to increase the sensitivity of analysis.

37

3.4.2.2 Non- derivatized compounds (PAHs and n-Alkanes)

Temperature programs were optimized with 1 ppm solutions of PAH and n-alkane

mixtures. These 1 ppm standard solutions were prepared from 10 ppm PAH-Mix 45

(Ehrenstorfer) and 500 ppm n-Alkanes-Mix 10 (Ehrenstorfer) stock solutions.

In the first step of optimization, all compounds were analyzed in scan mode “scan mode”

to determine mass to charge (m/z) ratios for the compounds that occur in our samples.

RTs and (m/z) ratios found for 14 n-alkane and 18 PAH compounds are given in Table

3.2. In the second step, a “Selected Ion Monitoring Mode-sim” method was developed

using the m/z ratios found in “scan” mode. This “sim” method was then used in all

analysis. Since in the “sim” mode analysis is performed only at m/z values assigned for

compounds, it significantly reduced analysis time, which is essential in a work where large

number of samples are analyzed.

38

Table 3.2. Compounds, their retention times and m/z values

Standards Retention time(min) m/z

n-alkanes

docosane 30.067 57.1, 43.1, 71, 85.1

tricosane 31.067 57.1, 43.1, 71, 85.1

tetracosane 32.672 57.1, 43.1, 71, 85.1

pentacosane 34.237 57.1, 43.1, 71, 85.1

hexacosane 36.184 57.1, 43.1, 71, 85.1

heptacosane 38.516 57.1, 43.1, 71, 85.1

octacosane 41.021 57.1, 43.1, 71, 85.1

nonacosane 43.65 57.1, 43.1, 71, 85.1

triacontane 46.359 57.1, 43.1, 71, 85.1

triacontane 49.359 57.1, 43.1, 71, 85.1

hentriacontane 51.877 57.1, 43.1, 71, 85.1

dotriacontane 54.629 57.1, 43.1, 71, 85.1

tetratriacontane 57.357 57.1, 43.1, 71, 85.1

pentatriacontane 60.047 57.1, 43.1, 71, 85.1

PAHs

Fluorene (Fl) 17.051 166

Phenanthrene (Phe) 21.184 178

Anthracene (An) 21.408 178

Fluoranthene (Flut) 27.63 202

Pyrene (Pyr) 28 202

Benzo[a]anthracene (BaA) 33.544 228

Chrysene (Chr) 33.5 228

Benzo[b]fluoranthene (BbF) 39.698 252

Benzo[k]fluoranthene(BkF) 39.865 252

Benzo(e)pyrene 41.439 252

Benzo[a]pyrene (BaP) 41.753 252

Perylene 42.352 252

Dibenzo[a,h]anthracene(DBA) 49.647 278.1

Indeno[1,2,3,-cd]pyrene (IP) 50.111 276.1

Benzo[ghi]perylene (BghiP) 51.268 276.1

Retene 30.225 219.1,234.1

Picene 50.895 278

Coronene 61.197 300, 150

39

Three different oven temperature programs were tried for non-derivatized compounds.

The first program was obtained from literature. However, coronene and n-

Hexatriacontane-d74 were not determined in this program. In the second program, all

compounds were detected, but analysis time for each sample was too long (total analysis

time was 121.5 min) for this study, where large number of samples were analyzed in the

course of 18 months. The third program was reasonably short (81 min) and included all

compounds in n-alkanes, PAHs group. Details of the temperature program optimized for

non-derivatized compounds is given in Table 3.3.

Table 3.3. Tested GC-MS oven temperature programs for PAHs and n-Alkanes

Inlet Temperature 250oC Interface Temperature 280oC MS Source 230oC, max 250 oC MS Quadropole 150oC, max 200oC GC Oven Temperature Program-1 Beginning temperature: 80 °C,1 min

Ramp-1: 7 °C min-1, 180 °C,1 min Ramp-2: 8 °C min-1, 240 °C,1 min Ramp-3: 2 °C min-1, 300 °C, 10 min Total time: 64.8 min

GC Oven Temperature Program-2 Beginning temperature: 80 °C,1 min Ramp-1: 5 °C min-1, 180 °C,3 min Ramp-2: 8 °C min-1, 240 °C,10 min Ramp-3: 2 °C min-1, 300 °C, 50 min Total time: 121.5 min

GC Oven Temperature Program-3 Beginning temperature: 80 °C,1 min Ramp-1: 5 °C min-1, 180 °C,3 min Ramp-2: 8 °C min-1, 240 °C,5 min Ramp-3: 2 °C min-1, 300 °C, 10 min Total time: 81.5 min

40

3.4.2.3 Derivatized compounds

Derivatized compounds in this study were n-alkanoic acids and levoglucosan. A separate

temperature program was optimized and used for these compounds. Method optimization

was also done using 1 ppm standard solutions, which were prepared from 20 ppm n-

alkanoic acid mix and levoglucosan stock solutions.

Temperature program, optimized for derivatized compounds was based on methods

reported in literature (Ali et al., 2013; R. Larsen et al., 2006). Two different derivatization

methods were tried, the most appropriate method has been selected and GC-MS oven

temperature program was optimized for the selected derivatization method. Silylation is

the most frequently suggested method of derivatization in literature.

Trimethylchlorosilane (TMCS), trimiethylsilylimidazole (TMSI), N-methyl-

trimethylsilyltrifluoroacetamide (MSTFA), N,O-bis-(trimethylsilyl) trifluoroacetamide

(BSTFA) and N-(t-butyldimethylsilyl)-N-methyltrifluoroacetamide (MTBSTFA) are

suggested reagents for derivatization. Derivatization using BSTFA-TMCS, where methyl-

silyl forms of compounds are generated, is the preferred method in literature. Recovery

of BSTFA-TMCS method is reported to be better than derivatization methods where other

reagents are used (Ali et al., 2013; Schummer et al., 2009). Derivatization using

BF3/methanol as reagent is another suggested method for derivatization of alkanoic acids

and levoglucosan. These two methods were tried and the method, which gave the best

recovery, was selected for this study.

Results of the two derivatization methods to determine pentadecanoic acid is given in

Table 3.4. As can be seen in Table, BSTFA+%1TMCS reagent gave better sensitivity for

pentadecanoic acid then BF3/methanol reagent. The two reagents were also tried for

derivatization of compounds other than pentadecanoic acid. Derivatization where

BSTFA+%1TMCS was used as reagent performed better than BF3/methanol method for

all tested compounds. Thus, derivatization method where using BSTFA+%1TMCS are

used as reagent was used throughout the study.

41

After selection of derivatization method, 1 ppm alkanoic acid mixture and levoglucosan

were derivatized and analyzed first in the “scan” mode to determine m/z ratios of

compounds in the mixture. A “sim” method was then developed using the m/z of

compounds found in scan mode (Table 3.5). Finally, a GC-MS temperature program was

determined using literature studies and encountered sensitivity problems during analysis

by SIM mode as the bases.

Three different oven temperature programs have been tried for derivatized compounds.

These are given in Table 3.6. The first program has been taken from literature. Since

beginning temperature time is so long, many unexpected peaks appeared in the first

temperature program. Therefore, beginning temperature time was decreased in the second

program. In this second program, long chain n-alkanoic acids could not be observed.

Therefore, ramp-2 waiting time was increased to 20 min in the third program. This last

program worked effectively and used throughout the study.

42

Table 3.4. Derivatization methods and Chromatographic result

Derivatization method Chromatographic result BF3/methanol: volume of prepared standard solution is decreased to 300 ul by N2 blowing. Add 2 mL BF3. Wait for 2 hr at 70 oC. And the volume is decreased to 500 ul under N3 blowing. Then it is well mixed, wait for phase separation. Take the upper phase and pass it through from Na2SO4 column, and then it is completely blow off under a gentle stream of ultrahigh purity nitrogen. Finally add 200 ul hexane and read at GC-MS.

BSTFA+%1TMCS: prepared standard solution is completely blow off under a gentle stream of ultrahigh purity nitrogen. Add 100 ul BSTFA+%1TMCS, well mixed for 1 min at vortex. Wait for 2 hr at 70 oC. Then well mixed at vortex again and read at GC-MS immediately.

43

Table 3.5. Compounds, their retention times and m/z values

Standards Retention time(min) m/z

Dodecanoic acid 24.412 73, 117, 257 Tridecanoic acid 26.575 271, 117, 73 Tetradecanoic acid 28.621 117, 73, 285 Pentadecanoic acid 30.553 117, 299.1,73.1 Hexadecanoic acid 32.589 73, 117, 313 Heptadecanoic acid 35.228 117, 73, 327 Linoleic acid 37.438 73, 337 Oleic acid 37.664 73, 117, 339 Octadecanoic acid 38.813 117, 73, 341 Levoglucosan 25.491 206, 220

Table 3.6. Tested GC-MS oven temperature programs for n-Alkanoic Acids and Levoglucosan

Inlet Temperature 250oC Interface Temperature 280oC MS Source 230oC, max 250oC MS Quadropole 150oC, max 200oC GC Oven Temperature Program-1 Beginning temperature: 50°C,15 min

Ramp-1: 10 °C min-1, 200 °C,3 min Ramp-2: 10 °C min-1, 310 °C,20 min Total time: 63 min

GC Oven Temperature Program-2 Beginning temperature: 50°C,10 min Ramp-1: 10 °C min-1, 200 °C,10 min Ramp-2: 10 °C min-1, 310 °C,10 min Total time: 56 min

GC Oven Temperature Program-3 Beginning temperature: 50 °C,1 min Ramp-1: 5 °C min-1, 100 °C,5 min Ramp-2: 10 °C min-1, 320 °C,20 min Total time: 73min

44

3.4.2.4 Calibration

A set of calibration standards were prepared separately for the PAHs, n-alkanes, n-

alkanoic acids and levoglucosan. PAHs were prepared from 10 ppm PAH-Mix 45

(Ehrenstorfer) and 100 ppm Internal Standards Mix 25 (acenaphthene-d10, chrysene-d12,

perylene-d12, phenanthrene-d10) (Ehrenstorfer) (see Figure3.4). n-Alkanes were

prepared from 500 ppm Alkanes-Mix 10 (Ehrenstorfer) and 1000 ppm internal standards;

n-eicosane-d42, n-octacosane-d58 and n-Hexatriacontane-d74 (Chiron) (see Figure 3.6).

n-Alkanoic acids were procured in solid phase and a 20 ppm (µg L-1) mix in DCM were

prepared. The mix include dodecanoic acid, tridecanoic acid, tetradecanoic acid,

pentadecanoic acid, hexadecanoic acid, heptadecanoic acid, linoleic acid, oleic acid,

octadecanoic acid (Ehrenstorfer), cholestrol (Cambridge) and stigmasterol (Extsynthese).

A 1 ppm mix internal standard mix, which included Decanoic-d19 Acid (98 atom % D)

and Heptadecanoic-d33 Acid (98 atom % D) (CDN Isotope), was also prepared (see

Figure 3.8). Levoglucosan was prepared from solid standard (LGC) and its concentration

was 20 ppm in DCM. 1 ppm internal standard was prepared from Levoglucosan-d7 (98 atom

% D) (see Figure 3.10).

In the literature there are studies detected the compounds to be analyzed in this work.

According to concentration values determined in the literature, calibration concentrations

were decided (Table 3.8). Additionally, deuterated internal standards were added to each

prepared calibration vial. Five standards, with increasing concentration of each analyte,

and one blank were prepared for each calibration set. Each calibration points were

analyzed 3 times on the GC-MS to determine an accurate value for the particular organic

compounds. Calibration curves were generated for each organic molecular marker from

all calibration runs of the standard solutions (Figure 3.3, 3.5, 3.7 and 3.9). Calibration

curves were used to convert peak areas to mass of individual compounds. All calibration

curves were manually integrated. Calibration curves for most of the molecular markers

are highly linear with coefficients (R2) greater than 0.97 for PAHs, 0.99 for n-alkanes,

45

0.95 for n-alkanoic and 0.99 for levoglucosan. The linearity of the calibration curves for

all standard compounds show high consistency and reproducibility of the GC-MS.

Table 3.7. Calibration conditions for GC-MS

PAHs and n-Alkanes n-Alkanoic acids and Levoglucosan Inlet temperature 250 °C Interface temperature 280 °C MS Source 230° C, max 250 °C MS Quadrapole 150 °C, max 200° C

Inlet temperature 250 °C Interface temperature 280 °C MS Source 230° C, max 250 °C MS Quadrapole 150 °C, max 200° C

GC oven temperature program Beginning temperature: 80 °C,1 min Ramp-1: 5 °C min-1, 180 °C,3 min Ramp-2: 8 °C min-1, 240 °C,5 min Ramp-3: 2 °C min-1, 300 °C, 10 min Total time: 81.5 min

GC oven temperature program Beginning temperature: 50 °C,1 min Ramp-1: 5 °C min-1, 100 °C,5 min Ramp-2: 10 °C min-1, 320 °C,20 min Total time: 73 min

Table 3.8. Calibration concentrations

Points* n-Alkanes PAHs n-Alkanoic acids and levoglucosan p1 0.025 0.0025 0.0025 p2 0.125 0.0125 0.25 p3 0.5 0.05 1.25 p4 2 0.2 2.5 p5 5 0.4 5

*concentration units are ppm

46

An acceptable initial calibration is required before any samples are analyzed, and then

intermittently calibration was checked by injecting one of the mid-point standards.

Additionally, RTs were monitored by using the internal standards before every set of

sample analysis.

Figure 3.3. Calibration curves of selected PAH

47

Figure 3.4. PAHs: Chromatogram-Calibration Point-3. a-Naphthalene, b-Acenaphtylene, c-Acenapthene-d, d- Acenapthene, e-Fluorene, f-Phenanthrene, g-Anthrecene, h-Fluoranthene, ı-Pyrene, j- Benzo[a]anthracene, k- Chrysene-d, l- Chrysene, m- Benzo[k]fluoranthene, n- Benzo(e)pyrene, o- Benzo[a]pyrene, p- Perylene, r- Indeno[1,2,3,-cd]pyrene, s- Dibenzo[a,h]anthracene, t- Benzo[ghi]perylene, u-

dibenzo[a,h]anthracene-d

Figure 3.5. Calibration curves of selected n-alkanes

48

Figure 3.6. n-Alkanes: Chromotogram-Calibration Point-3. a-decane, b-undecane, c-dodecane, d- tridecane, e-tetradecane, f-pentadecane, g-hexadecane, h-heptadecane, ı-

octadecane, j- nonadecane, k- eicosane d 42, l- eicosane, m-heneicosane, n- docosane, o- tricosane, p-tetracosane, r- pentacosane, s- hexacosane, t- heptacosane, u-octacosane d 58, v- octacosane, w- nonacosane, y-triacosane, z- triacontane, aa-hentriacontane, ab-

dotricontane, ac-tetratriacontane, ad-pentatriacontane, ae-hexaticaontane d 74

Figure 3.7. Calibration curve of s selected n-Alkanoic Acids and Sterols

49

Figure 3.8. n-Alkanoic acids: Calibration Point-3 a- Decanoic-d19 Acid 98 atom % D, b- Dodecanoic acid, c- Tridecanoic acid, d- Tetradecanoic acid, e- Pentadecanoic acid, f-

Hexadecanoic acid, g- Heptadecanoic acid, h- Linoleic acid, ı- Oleic acid, j- Octadecanoic acid

Figure 3.9. Calibration curve of Levoglucosan

50

Figure 3.10. Levoglucosan Calibration Point-3. a- Levoglucosan, b-D7-Levoglucosan

3.4.3 Experimental Procedure

Reagent-grade methylene chloride was used to extract the organic compounds of interest

from quartz filters. All glassware used in extraction process was cleaned with Alconox

laboratory cleaning detergent and water, rinsed with deionized water and hexane. They

were given a final rinse with methylene chloride (DCM) immediately prior to use.

Prior to extraction, each filter was spiked with 100 μl of surrogate mixture containing

known concentrations of isotopically labeled compounds not present in the atmosphere.

By selecting surrogate standards of similar structure to the molecular markers being

quantified, we were able to account for variability in recovery during the extraction

process. Spiked filters were extracted by sonicating (Cole-Parmer 8892) in DCM for 30

minutes, providing an extract volume of 40 mL per filter. In sonication, the analyzed

compound is extracted by the sound waves travelling in the solvent used. By the help of

the sound waves, the compound will be extracted from the solid media and transferred

into the solvent. The extracts were then concentrated using a rotary evaporator (Heidolph,

Hei-VAP Precision G1 Rotary Evaporator). Extracts were then passed through Na2SO4

51

and glass wool column to remove moisture and filter particles and finally concentrated

under a gentle stream of ultra-high purity nitrogen to a final volume of 100 μl. For each

sample, this procedure was run twice for derivatized and non-derivatized part.

For derivatization, BSTFA/TMCS (%1) was added to extract after ‘Na2SO4 and glass

wool column’ step and waited for 2-hours at 70oC. After derivatization, internal standard

(1-Phenyldodecane 97%) was spiked to vials. Vials were then ready for analysis by GC-

MS. The flow diagram of sample processing is depicted in in Figure 3.11.

Figure 3.11. Experimental procedure

Quartz fiber filters (8×10 inch2) was conditioned at 500oC for 5 hr.

Sampling is carried out.

Filter is punched to 2×47 mm diameter circle.

Filter is placed into glassbottle.

Each filter was spiked with 100 μl of surrogate .

40 ml DCM is added and30 min sonication is performed.

The extracts are then concentrated with rotary evaporator up to appx. 10 ml

Then extracts passed through Na2SO4 and glass wool column under a gentle stream of ultra-high purity nitrogen to a final volume of 100 μl.

For each day sample, this procedure is applied twice (for example: sample of 01.01.2015 is prepared as der-010115OU and 010115OU).

BSTFA/TMCS (%1) is added into derivatized sample extraction and wait at 70oC for 2 hrs.

Finally, internal standard (1-Phenyldodecane 97%) spiked to vials, they are ready to read at GC-MS.

52

3.4.4 EC/OC Analysis

EC and OC analysis of collected samples were performed at Bolu Abant İzzet Baysal

University. Filters were cut with 47 mm punch and weighed. Weighted filters were placed

in petri dishes and labeled before they were shipped to Bolu. Thermo gravimetric OC/EC

analyzer (Sunset Lab, Model 5) was used for analysis. The photograph of the analyzer is

presented in Figure 3.12. A 1.5 cm2 area was cut from quartz sample filter, which was

then placed in a glass sample paddle and fed to furnace. Once sample was introduced to

the furnace, temperature was gradually increased to 870°C under constant flow of Helium

(He) gas, to avoid oxidation of EC. OC which were desorbed from sample with increasing

temperature were converted to pyrolysis products and move to manganese dioxide (MnO2)

oxidizing furnace, where they were oxidized to CO2. The CO2 swept by the helium gas

from the oxidizing medium is mixed with hydrogen gas and converted to CH4 over a

heated nickel catalysis and the carbon found in the sample is determined by the flame

ionization detector (FID) (Öztürk and Keleş, 2016).

Figure 3.12. Schematic representation of EC / OC analyzer interior design

53

After the first step in the quartz sampling furnace is completed, the temperature in the

furnace is reduced to 550°C and the helium/oxygen carrier gas mixture was fed to the

furnace. With this oxidizing gas mixture, EC in sample is oxidized to CO2, which was

then analyzed like OC. A thermogram obtained from one of the samples is presented in

Figure 3.13. A typical thermogram contains four OC peaks (OC1, OC2, OC3, and OC4)

and six individual EC peaks (EC1, EC2, EC3, EC4, EC5, and EC6).

Figure 3.13. A thermogram example of a day

54

3.4.5 Quality Assurance and Quality Control

The Quality Assurance and Quality Control (QA/QC) include the quality control (QC)

procedures for combining obtained datasets and the quality assurance (QA) procedures to

clarify the overall quality of the datasets by assessing the conformity and the effectiveness

of the QC system, based on data quality objectives and general conditions

(UNFCCC/CCNUCC, 2014).

QA/QC is becoming more and more important for data assessment. Number of analyte

and number of samples are increasing with optimizations in instrumentation. In this study

>40 species were detected for two sampling sites and analyzed in more than 500 samples.

When such large number of samples are analyzed for a large number of analytes, one does

not have a chance for replicate analysis. This means that all necessary precautions should

be taken to get correct results in one analysis. QA and QC protocols originated from this

necessity and in time proved very useful in homogenization of results in large networks

of monitoring stations.

The QA-QC protocol followed in this study included the following six steps:

I. Recovery tests for measured compounds

II. Determination of detection limits and quantification limits

III. Standard reference material 1649b (SRM 1694b, NIST) analysis

IV. Proper calibration of the instrument

V. Data validation

VI. Lab and field blank analysis.

Each of these steps are discussed in following sections.

55

3.4.6 Recovery tests for measured compounds

Generally particulate organic matter from PM2.5 are collected using high volume samplers

(US EPA, 2008), and the sample preparation includes extraction. There are several

methods in the literature for the extraction of particulate organic matter from filter matrix.

Methods could be listed as sonication, probe sonication, soxhlet extraction and agitation

(Edney et al., 2003; Godri et al., 2011; Sheesley et al., 2004; Simoneit et al., 2004).

Summary of extraction methods applied to particulate organic matter are given in Table

3.9.

Soxhlet extraction is avoided in this study, because it requires large volumes of solvent

and it is time consuming. Approximately 500 samples were collected from two stations

in this work. If soxhlet extraction were used, solvent consumption would be prohibitive

for such large number of samples.

Table 3.9. Extraction methods applied to particulate organic matter

Method Solvent References Sonication DCM-Methanol Simoneit et al., 2004; Stone et al., 2010

Sonication Acetone- Hexane- Toluene Rissanen et al., 2006

Sonication DCM Amador-Muñoz et al., 2013; Dutton et al., 2010

Sonication DCM-Hexane Sheesley et al., 2004

Sonication Hexane-Benzene-Isopropanol

Mazurek et al., 1987

Sonication Methanol Gerlofs-Nijland et al., 2007; Happo et al., 2010; Jalava et al., 2005; Janssen et al., 2014; Verma et al., 2012

Probe sonication

Methanol Godri et al., 2011; Mudway, 2004

Soxhlet Methanol-DCM Sheesley et al., 2004; Škarek et al., 2007 Soxhlet DCM De Kok et al., 2005 Agitation Acetonitrile- DCM-

Hexane Edney et al., 2003

56

Sonication was selected as extraction method for this study, because it is a quick method

and relatively high extraction efficiencies were reported in literature. The next step was

the selection of the solvent. For the preliminary studies, toluene was used as solvent,

because relatively high recoveries were reported in literature, and it is not as harmful for

human health as other solvent types that can be used in extraction (Joseph et al., 2012).

However, trials for toluene was not successful, except for PAHs. Recoveries were either

too high (%160) or too low (%20). Since toluene was used successfully in other studies,

we first thought that efficiency of extraction of organics from filters may not be efficient.

Then filter spike step was eliminated and standard solution was directly injected to solvent

and other steps (ultrasonic extraction, rotary evaporation, Na2SO4 column and N2

blowing) were performed for n-alkanes only. Results were not any better. Recoveries of

n-alkanes using toluene as solvent are given in Table 3.10. It was concluded that the

problem was not extraction of filters, it was the solvent used in extraction. Once it was

realized that toluene cannot be used as solvent in extractions, a new optimization exercise

was designed, where different solvents were tried and extraction time was optimized.

Flow diagram of this optimization exercise is given in Figure 3.14.

57

Table 3.10. Recovery results for Alkanes (Solvent: Toluene)

n-Alkanes Spike to filter % Spike to solvent %

decane 33.27 43.01 undecane 50.15 57.00 dodecane 55.66 62.80 tridecane 58.58 65.58 tetradecane 60.38 67.47 pentadecane 61.84 69.46 hexadecane 63.48 72.60 heptadecane 63.73 73.71 octadecane 64.17 74.28 nonadecane 63.92 74.31 eicosane 64.23 75.09 heneicosane 64.16 75.92 docosane 63.34 75.71 tricosane 63.56 76.36 tetracosane 62.42 76.89 pentacosane 62.44 79.93 hexacosane 61.31 84.66 heptacosane 60.32 94.95 octacosane 59.96 107.33 nonacosane 59.92 117.22 triacontane 59.57 123.10 tritriacontane 59.08 120.08 hentriacontane 58.01 111.71 dotricontane 57.06 94.10 tetratriacontane 55.81 82.69 pentatriacontane 53.99 74.73

58

Figure 3.14. Recovery study-experiment organization tree

extr

acti

on m

etho

d: s

onic

atio

n

1st parameter: time

one step extraction: 30min

two step extraction: 30min+30min

2nd parameter: solvent

one solvent

Toluene

Hexane

DCM

two solvent

Toluene+Toluene

Hexane+Hexane

DCM+DCM

Hexane+Toluene

Toluene+DCM

DCM+Hexane

59

Four different solvents, namely toluene, hexane, dichloromethane and methanol were

tested for their recoveries. Although we knew that toluene had poor recovery values, it is

included in the exercise for consistency. In the first step, extraction was performed for

30 minute (one step extraction) and for 30 + 30 minutes (two step extraction) for each

solvent. In the second step, solvents have been mixed in 1:1 (v) ratio. Methanol was

excluded from the exercise, because crystals were formed after preconcentration by

blowing N2, when methanol was used. Nine different extraction sets have been prepared.

These sets are given in Table 3.11.

Table 3.11. Details of the experimental setup

Experiment Solvent-1 Time (min) Solvent-2 Time (min)

1 Toluene 30 - -

2 Toluene 30 Toluene 30

3 Hexane 30 - -

4 Hexane 30 Hexane 30

5 DCM 30 - -

6 DCM 30 DCM 30

7 Hexane 30 Toluene 30

8 Toluene 30 DCM 30

9 DCM 30 Hexane 30

Using the methods given in Figure 3.14 and the GC-MS program selected for derivatized

and non-derivatized compounds, spiked standards were extracted in triplicate. In all

extraction method blanks were also prepared and analyzed in the same way. Although all

equipment (amber glass bottles, pasteur pipettes and syringe used during the extraction

steps) were cleaned by hexane before experiments, empty amber bottles were also filled

with DCM and extracted with the same procedure. These were called “clean blanks” and

they were used to check any contamination coming from experimental procedures. Since

60

“clean blanks” did not include any organic compounds, they were not included into

recovery calculations. As can be seen from Table 3.12, when the solvent used was toluene

or hexane for 30 min and 30+30 min extractions, recoveries were either too low (<10%)

or too high (>400%). Too low recoveries mean failed recovery to solvent, too high

recoveries mean possible matrix effects (ME), i.e. ion suppression or ion enhancement

during extraction (Peters et al., 2007). Recovery limits that include allowance for a

relatively high range, 70-170%, can be appropriate for detecting a specie (US. EPA, 2014).

Same variability in recoveries were also observed when these two compounds were mixed

and when these compounds were mixed with other solvents. Since such low and high

recoveries could not be accepted, toluene and hexane was eliminated as extraction

solvents.

Recoveries of organic compounds obtained in different experiments are given in Table

3.12. Compared to other methods/durations of extraction 30 min extraction with DCM

extraction solvent gave the most repeatable results. Two-step extractions did not give any

additional advantage. Therefore, 30 min extraction with DCM as extraction solvent, which

provided high and consistent recovery results in one-step extraction (30 min) was selected

as the extraction solvent and used throughout the study.

61

Table 3.12. Recovery results

Compound Experiment No

1 2 3 4 5 6 7 8 9 recovery

(%) recovery

(%) recovery

(%) recovery

(%) recovery

(%) recovery

(%) recovery

(%) recovery

(%) recovery

(%) n-alkanes heneicosane 51.3 123.4 81.1 168.9 122.4 118.9 68.9 288.1 165.8 docosane 247.1 356.4 30.9 170.5 121.7 42.9 132.6 125.7 145.7 tricosane 27.1 128.7 17.0 139.3 104.2 221.1 28.9 128.9 154.6 tetracosane 24.3 134.4 122.2 125.2 108.0 120.8 145.7 321.8 168.9 pentacosane 123.2 287.9 46.8 144.6 109.8 187.7 85.6 125.8 85.6 hexacosane 80.0 160.5 78.6 107.6 117.8 266.7 78.4 68.4 125.6 heptacosane 90.5 154.6 108.1 110.7 106.5 249.4 82.4 85.4 145.8 octacosane 143.5 236.4 68.3 107.7 103.6 292.8 76.9 146.5 178.9 nonacosane 179.7 369.8 70.7 108.5 116.2 116.9 78.5 254.9 169.8 triacontane 321.7 421.8 59.9 142.5 104.1 272.1 125.4 214.6 145.2 triacontane 338.3 465.4 62.5 128.8 117.0 208.2 263.8 284.9 152.1 hentriacontane 415.7 456.8 84.4 89.0 124.1 265.9 287.2 263.1 175.1 dotricontane 411.7 489.8 153.0 148.2 110.0 222.3 245.3 325.7 253.4 tetratriacontane 345.7 463.7 141.6 149.4 115.8 291.7 345.6 368.7 264.1 pentatriacontane 302.6 456.8 91.0 153.1 122.5 211.1 288.8 346.5 284.1 PAHs Fluorene (Fl) 13.1 21.8 28.6 36.4 91.5 21.3 14.2 16.8 115.2 Phenanthrene (Phe) 58.2 61.7 26.8 34.8 93.4 162.5 18.8 125.6 125.6 Anthracene (An) 67.3 121.8 30.1 35.6 96.4 79.8 25.5 71.6 45.6 Fluoranthene (Flut) 47.9 45.9 32.4 38.9 97.8 7.5 54.8 65.9 36.4 Pyrene (Pyr) 30.4 128.7 35.4 45.8 95.5 66.0 32.8 55.69 68.4

62

Benzo[a]anthracene (BaA9)

38.3 65.5 38.9 47.8 94.4 15.1 14.5 25.4 75.6

Chrysene (Chr) 36.3 124.6 64.2 70.4 91.2 110.8 54.6 108.9 89.7 Benzo[b]fluoranthene (BbF)

26.1 65.8 108.5 125.9 124.6 264.7 14.7 245.9 165.4

Benzo[k]fluoranthene(BkF) 53.9 150.8 123.6 132.1 123.5 114.6 75.6 128.9 145.8 Benzo(e)pyrene 39.2 142.8 111.2 135.5 91.0 220.2 78.9 158.4 131.4 Benzo[a]pyrene (BaP) 37.7 138.6 106.3 97.4 123.5 280.3 72.5 290.6 165.7 Perylene 33.9 128.9 114.1 96.4 125.5 272.0 74.6 254.2 145.6 Dibenzo[a,h]anthracene (DBA)

35.6 135.9 146.2 158.9 126.7 155.6 81.2 145.6 189.6

Indeno[1,2,3,-cd]pyrene (IP)

36.3 168.8 158.8 165.4 124.4 211.2 75.6 145.2 148.8

Benzo[ghi]perylene (BghiP)

43.7 178.5 168.2 178.2 125.6 56.3 125.4 35.9 256.8

Retene 171.3 256.7 59.1 62.2 73.6 44.2 75.5 64.8 65.4 Picene 43.3 65.4 80.9 133.8 123.5 108.4 65.9 52.1 94.6 Coronene 51.5 298.1 67.0 168.1 93.4 156.4 49.6 42.9 74.6 n-Alkanoic Acids Dodecanoic acid 58.1 223.2 214.0 37.6 99.1 162.3 125.8 145.9 189.7 Tridecanoic acid 20.3 75.3 80.4 8.0 92.4 28.5 56.4 121.8 165.3 Tetradecanoic acid 114.0 54.9 239.7 81.1 98.1 718.5 150.9 156.7 125.9 Pentadecanoic acid 21.5 148.6 233.5 20.1 81.5 232.7 125.9 152.2 236.7 Hexadecanoic acid 49.2 43.3 183.9 239.8 115.5 173.5 158.4 145.9 228.9 Heptadecanoic acid 1.8 90.2 269.6 150.9 82.9 180.5 125.6 170.6 142.8 Linoleic acid 135.1 55.1 137.6 231.7 95.6 148.3 256.5 165.8 258.6 Oleic acid 10.3 343.8 364.9 60.0 98.7 187.9 312.4 190.6 356.4 Octadecanoic acid 195.9 217.0 373.8 344.9 96.4 288.9 256.8 300.8 321.4 Levoglucosan 141.6 120.4 97.2 158.4 85.6 65.7 87.2 189.4 194.6

63

3.4.7 Limit of detection (LOD) and Limit of quantification (LOQ)

In “Definition and procedure for the determination of the method detection limit”

document prepared by US. EPA (2016), LOD is defined as “the minimum measured

concentration of a substance that could be reported with 99% confidence that the measured

concentration is differentiated from the blank results”. LOQ, on the other hand, is defined

as “the lowest level that could be reliably obtained within the specified limits of precision

and accuracy during laboratory operating conditions” (Carlson et al.,2014).

Concentrations of compounds can be determined in the range between LOD and LOQ,

but not necessarily with the same precision and accuracy possible at the LOQ. At the site

of measurement approach, LOD gives much less confidence than LOQ. Therefore, LOQ

create a target performance level for analyses using a specific set of precision and accuracy

limitations (Wayman et al., 1999).

In this study, in order to evaluate the performance of analytical method, analyte recovery,

limits of detection (LOD) and limits of quantification (LOQ) were determined. All

calibration curves were generated with standard solutions, concentrations of molecular

markers in standard solutions include range of concentrations that are observed in

atmospheric samples.

There are several conceptual approaches to calculate the LOD and LOQ. In this study,

LOD and LOQ for each analyte were calculated based on the smallest concentrations

prepared to generate calibration curves. For each analyte, the smallest calibration

concentration was spiked seven times, manual integration has been performed on

chromatograms, and then standard deviations were calculated. In order to calculate the

LOD and LOQ, kD and kQ factors were used. Recommended values for kD and kQ are 3

and 10 respectively. LOD and LOQ values were determined as depicted in Table 3.13 by

multiplying standard deviation with kD and kQ, (Mocak et al.,1997).

64

Table 3.13. LOD and LOQ values for compounds

Compounds LOD (ppm) LOQ (ppm)

n-Alkanes heneicosane 0.0432 0.1441 docosane 0.4536 1.5121 tricosane 0.0353 0.1176 tetracosane 0.0366 0.1219 pentacosane 0.0350 0.1168 hexacosane 0.0330 0.1099 heptacosane 0.0329 0.1096 octacosane 0.0334 0.1114 nonacosane 0.0267 0.0890 triacontane 0.0194 0.0647 tritriacontane 0.0225 0.0751 hentriacontane 0.0191 0.0637 dotricontane 0.0214 0.0715 tetratriacontane 0.0072 0.0241 pentatriacontane 0.0130 0.0432 PAHs Fluorene (Fl) 0.0003 0.0010 Phenanthrene (Phe) 0.0005 0.0017 Anthracene (An) 0.0004 0.0014 Fluoranthene (Flut) 0.0007 0.0022 Pyrene (Pyr) 0.0007 0.0022 Benzo[a]anthracene (BaA) 0.0004 0.0013 Chrysene (Chr) 0.0004 0.0015 Benzo[b]fluoranthene (BbF) 0.0006 0.0019 Benzo[k]fluoranthene(BkF) 0.0007 0.0022 Benzo(e)pyrene 0.0004 0.0013 Benzo[a]pyrene (BaP) 0.0005 0.0018 Perylene 0.0004 0.0015 Dibenzo[a,h]anthracene(DBA) 0.0007 0.0022 Indeno[1,2,3,-cd]pyrene (IP) 0.0014 0.0046 Benzo[ghi]perylene (BghiP) 0.0007 0.0024 Retene 0.0005 0.0018 Picene 0.0005 0.0015

65

Compounds LOD (ppm) LOQ (ppm) Coronene 0.0007 0.0024 n-Alkanoic acids Dodecanoic acid 0.0124 0.0415 Tridecanoic acid 0.0106 0.0355 Tetradecanoic acid 0.0271 0.0904 Pentadecanoic acid 0.0164 0.0547 Hexadecanoic acid 0.0347 0.1157 Heptadecanoic acid 0.0131 0.0438 Linoleic acid 0.0274 0.0913 Oleic acid 0.0240 0.0799 Octadecanoic acid 0.0330 0.1099 Levoglucosan 0.0012 0.0410

3.4.8 SRM Analysis

SRM for ambient total suspended particulate matter (SRM 1649) was developed by

national Institute of Standards and Technology (NIST) (at that time NBS for National

Bureau of Standards). SRM 1649 included organic compounds on particles as well.

However, organic compounds included in the certification of SRM 1649 was very limited.

Due to these limitations, different laboratory studies were conducted and the list of the

target analytes was refined. As a result PAHs and n-alkanes were added to new

certification list and the new reference material became SRM 1649b matrix (Schantz et

al., 2005). The SRM 1649b was prepared from the same particulate material that was

certified in 1982 as SRM 1649 and re-issued in 1999 as SRM 1649a; however, the bulk

material was sieved to a smaller particle size fraction.

In studies that involve analysis, SRM can be used mainly for three purposes: (1) method

optimization, particularly for recovery tests, (2) calibration of the measurement systems,

(3) compatibility of the measurement QA protocols (NIST, 2016). In this study, SRM is

used for second and third purposes. Although we did not calibrate the instrument with

SRM 1649, accuracy of calibration curves prepared were checked using SRM 1649

66

results. In this study, SRM 1649b were routinely extracted and analyzed to check the

accuracy and reproducibility of the method.

The mass of the SRMs was accurately measured using a balance with 1 µg precision and

normalized to the mg kg-1 concentrations. SRM 1649b was subjected to the same

extraction procedure with the samples. Concentrations calculated from chromatograms

were compared with certified vales (which comes along with the reference material when

it was purchased). Overall, the experimental concentrations obtained in this study for both

SRM 1649b were in excellent agreement with certified concentrations. The agreement

between measured and certified values showed that calibrations were good.

Concentrations of molecular markers obtained by extraction of SRM 1649b and certified

values of the same reference material is given in Table 3.14. The same data are also given

in Figure 3.16 and Figure 3.15 for visual inspection.

67

Table 3.14. SRM 1649b analysis results

Compounds Found concentration (ppm)

Certified contents SRM 1649b (ppm)

Error (%)

PAHs

Naphthalene (Nap) 0.021 0.025 15.472

Anthracene (An) 0.009 0.009 2.216

Fluoranthene (Flut) 0.142 0.137 3.399

Pyrene (Pyr) 0.117 0.107 9.287

Benzo[a]anthracene (BaA)

0.047 0.047 1.737

Chrysene (Chr) 0.067 0.067 0.422

Benzo[b]fluoranthene (BbF)

0.132 0.134 1.107

Benzo[k]fluoranthene (BkF)

0.042 0.039 7.014

Benzo(e)pyrene (BeP) 0.061 0.066 7.377

Benzo[a]pyrene (BaP) 0.052 0.055 5.632

Perylene (Pe) 0.014 0.014 3.452

Dibenzo[a,h]anthracene (DBA)

0.006 0.006 2.322


0.093 0.088 6.185

n-Alkanes

eicosane 0.045 0.042 6.873

docosane 0.109 0.116 5.797

tricosane 0.353 0.357 1.007

tetracosane 0.556 0.602 7.651

pentacosane 1.389 1.450 4.152

hexacosane 1.383 1.472 6.057

heptacosane 1.341 1.383 2.985

octacosane 1.066 0.937 13.769

nonacosane 1.398 1.293 8.110

triacontane 0.566 0.558 1.573

hentriacontane 0.926 0.914 1.309

dotricontane 0.323 0.312 3.544

68

Figure 3.15. SRM 1649b analysis- n-Alkanes

Figure 3.16. SRM 1649b analysis-PAHs

0,0

0,2

0,4

0,6

0,8

1,0

1,2

1,4

1,6

Con

cent

rati

on (

ppm

)

Alkanes

SRM 1649b

Calculated concentration SRM 1649b

0,0

0,0

0,0

0,1

0,1

0,1

0,1

0,1

0,2

Nap An Flut Pyr BaA Chr BbF BkF BeP BaP Pe DBA BghiP

Con

cen

trat

ion

(pp

m)

PAHs

SRM 1649b

Calculated concentration SRM1649b

69

3.4.9 Data Validation

During preparation of a reliable data set data validation is necessary. Since peaks in

chromatograms are fitted manually, quality checks for peak fitting is particularly

important. When peaks are manually fitted human errors has to be minimized by post-

processing data validation protocol.

In this study, a four step data validation protocol was applied after the integration of peaks

in chromatograms. There are four steps which were routinely used in earlier studies in our

group (Civan, 2010; Doğan, 2013; Kuntasal, 2005).

- Control of organic particulates RT

- Control of concentration differentiation of every compound separately

- Control of concentration differentiations of internal standards at the same time

- Control from correlation plots

The first step is checking RTs whether there were any RT shifts in chromatograms or not.

This was an important step, since handling manual integration of large number of

chromatograms was a tedious task, human errors in integrations is likely. To eliminate

such kind of errors, variation in RT of each compound was compared with RT’s of

compounds’ in different sections of chromatograms. If any shift of RT was observed that

chromatogram was re-integrated.

In the second step, time series plots of concentration of each compound was prepared and

searched for outliers. The chromatogram was reanalyzed if there is a very high or very

low concentration for any of the compound. Actually outliers do not a mean a wrong

value, since our data set is lognormally distributed. Checking for outliers is just to make

sure that high concentrations are real. If high concentrations do not change after re-

integration, they were kept in data set.

In the third step, time series plots of nine correlated organic particulate compounds were

compared. Tetracosane, pentacosane and hexacosane were selected for n-alkanes. Their

70

sources are gasoline and diesel exhaust. Pyrene, chrysene and fluoranthene were selected

for PAHs and their sources are gasoline and diesel combustion. Tridecanoic acid,

pentadecanoic acid and heptadecanoic acid were selected for n-alkanoic acids and their

sources are food cooking. These compounds are expected to be correlated, as they are

emitted from same sources. Time-series plots of these correlated compounds’ are

prepared. Examples of this exercise is shown in Figure 3.17 and Figure 3.18, for suburban

and urban stations, respectively. If there is an abnormal peak (a peak that is not observed

in other correlated compound(s)) the chromatogram was re-integrated. As in the second

step, this abnormal peaks do not mean a faulty value. If there is no change in concentration

after re-integration that value kept as it is.

In the fourth and final step, binary scatterplots between correlated compounds are

investigated. In order to prepare correlation plots, data below the LOQ values were

replaced with one half of the LOQ value. After that, Pearson Correlation Coefficients (R)

of the compounds with respect to other compounds were determined. The plots of

correlations which have R values higher than 0.6 were drawn. Then, using Statgraphics

(Version 16.1.11) a linear trend line and a corresponding 95% confidence interval trend

line were drawn. Finally, the data points found outside the 95% confidence interval lines

were investigated again. Selected examples for both stations are depicted in Figure 19 and

Figure 20.

71

Figure 3.17. Sub-urban station- scatterplots of correlated organic particulate compounds

0

5

10

15

20C

once

ntra

tion

(ng

m-3

)Sub-urban Station n-Alkanes

tetracosane pentacosane hexacosane

0

5

10

15

Con

cent

rati

on (

ng m

-3)

Sub-urban station PAHs

Fluoranthene (Flut) Pyrene (Pyr) Chrysene (Chr)

0

5

10

15

20

Con

cent

rati

on (

ng m

-3)

Sub-urban Station n-Alkanoic acids

Tridecanoic acid Pentadecanoic acid Heptadecanoic acid

72

Figure 3.18. Urban station- Scatterplots of correlated organic particulate compounds

01020304050

Con

cent

rati

on (

ng m

-3)

Urban Station n-Alkanes

tetracosane pentacosane hexacosane

0

10

20

30

40

Con

cent

rati

on (

ng m

-3)

Urban station PAHs

Fluoranthene (Flut) Pyrene (Pyr) Chrysene (Chr)

01234567

Con

cent

rati

on (

ng m

-3)

Urban Station n-Alkanoic acids

Tridecanoic acid Pentadecanoic acid Heptadecanoic acid

73

Figure 3.19. A correlation graph for the selected compound couples to determine outliers (sub-urban station)

74

Figure 3.20. A correlation graph for the selected compound couples to determine outliers (urban station)

75

3.4.10 Lab and Field Blanks

Laboratory blanks were used to determine any contamination during processing and

weighing of filters. Laboratory blanks were kept in conditioning chamber and taken out

only for weighing. Thirty laboratory blanks were weighed and analyzed during sampling

campaigns.

Field blanks are conditioned and they are used to determine any contamination during

entire sampling process. Field blanks were prepared like regular filters transported to

stations in aluminum foil, like sample filters, placed onto sampler like sample filter, but

removed from the sampler without operating it (no air drawn through the filters). Field

blanks were then processed and analyzed exactly like sample filters. Thirty field blanks

were weighed and analyzed during sampling campaign.

Only four organic compounds, namely dodecanoic acid, hexadecanoic acid, octadecanoic

acid and levoglucosan, were detected in lab and field blanks. Concentrations of these four

compounds in blanks and sample filters are given in Table 3.15, along with their sample-

to-blank ratios. Approximately 1600 m3 of air, which was average air volume that passed

through sample filters in 24 hours (one sampling period), was assumed to pass through

the blank filter to convert measured concentrations of compounds in blank solutions into

corresponding concentrations in air. As can be seen from the Table, S/B ratio vary

between 11-499, indicating that laboratory and field blanks are not serious sources of

uncertainty in this study.

76

Table 3.15. Sample to blank ratio of the compounds

Compounds Sub-urban station Urban StationAvg

Blank Conc ng m-3

Avg Sample Conc ng m-3

S/B Avg Blank Conc ng m-3

Avg Sample Conc ng m-3

S/B

Dodecanoic acid

0.0534 1.194 21.14 0.0534 0.717 11.15

Hexadecanoic acid

0.5209 9.525 17.82 0.5209 12.39 20.75

Octadecanoic acid

0.0776 11.94 147.20 0.0776 9.37 103.55

Levoglucosan 0.0038 0.445 84.22 0.0038 2.66 499.4

3.4.11 Secondary Organic Aerosol Estimation

A common method used to calculate the primary and secondary parts of the OC in PM2.5

is the EC tracer method. If the sources of the primary OC and the EC are considered to be

the same, it is quite common for the EC to be used as a good tracer for OC releasing from

primary combustion sources. From there, the formation of secondary organic aerosol

(SOA) increases the outdoor concentration of the OC and the OC/EC ratio. In this case, if

there is a situation that exceeds the expected OC/ EC ratio in a region, the SOA formation

for this region can be mentioned (Cabada et al., 2004; Strader et al., 1999). The most

important challenge in the SOA computing approach given in the literature is the ability

to accurately determine the OCnon-combustion and (OC/EC)primary. There are some methods

presented in the literature for determining the primary OC/EC ratio. (1) The most

commonly used approach for calculating the primary value of (OC /EC) is to use detailed

emission inventories for the primary OC and EC, or to measure in environments such as

highway tunnels; (2) using the OC and EC measurement values made; (3) the modeling

of the primary emission and SOA data (Keywood et al., 2011).

Principle for using OC/EC ratios to determine secondary organic aerosol concentrations;

EC is only released from combustion sources, therefore be contained in the primary

aerosol and therefore have a linear relationship with the primary OC. Thus, the difference

77

between the calculated primary OC and the measured OC is largely regarded as the

secondary OC, or SOA.

After drawing linear regression analysis for EC and OC, cut point value of the y-axis uses

for OCnon-combustion and slope of the line uses for (OC/EC)primary (Cabada et al. 2004; Saylor

et al., 2006; Strader et al., 1999).

The EC tracer method is the most commonly used method for calculating the primary and

secondary parts of the OC in the measured fine particles (Cao et al., 2004; Castro et al.,

1999; Yu et al., 2004). Considering that all of the primary OC and EC are left to the

atmosphere from the same sources, it can be assumed that the EC component can be

regarded as a good tracer for the part released from the primary combustion sources of the

OC. In this direction, secondary aerosol formation directly increases the outdoor

concentration of the OC and the numerical value of the OC/EC ratio. As a result, it can be

said that the formation of secondary organic particles occurs in situations that exceed the

expected OC/EC ratio for emissions of primary resources in the sampling area (Cabada et

al. 2004; Strader et al.,1999). For the situations at which OC released from combustion

sources as well as non-combustion sources;

OCmasured = OCprimary + OCsecondary Equation 3.1

and

OCprimary = OCcombustion + OCnon-combustion Equation 3.2

By assuming that the primary rate of EC concentrations (OC/EC) measured in the

calculation of OCcombustion is constant;

OCcombustion = (OC/EC)primary x EC Equation 3.3

and

OCsecondary=OCmeasured – [OCnon-combustion+ (OC/EC)primary x EC] Equation 3.4

78

3.4.12 Source Apportionment

3.4.12.1 Mass Closure

Before PMF application, a much simplified method for chemical analysis of airborne

particles, which takes the major aerosol compounds into consideration, was investigated.

Table 3.16 shows the species which were analyzed and the adjustment factors used for

mass closure model. The reasons for studying these compounds are detailed below.

Sulphate is one of the major tracer for secondary aerosol so that it is included in

mass closure.

Nitrate is generally found as ammonium nitrate or sodium nitrate. Since the

sampling was made in fine fraction nitrate concentration is adjusted to an

equivalent mass of ammonium nitrate.

Chloride is the tracer for marine aerosol and for this reason its concentration is

adjusted to mass of sodium chloride.

Calcium and iron are used as a tracer of crustal mineral particles. Calcium

concentrations were adjusted to an equivalent mass of gypsum and iron

concentration was increased to soil and road dust by using different factors for

roadside, background and roadside increment.

EC comes from combustion sources. Since its form is graphitic carbon, its mass

was used as it is.

OC is a source from primary sources, on the other hand, the OC part can undergo

many modifications due to photochemical reactions in the atmosphere, and it is

constructed from hundreds of organic compounds.

79

Table 3.16 Equalizing factors used in mass closure

Compounds Convert to Adjustment factor* Sulphate (as SO4

-2) (NH4)2SO4 Hydrate

1.38 1.29

Nitrate (as NO3-) (fine) NH4NO3

Hydrate 1.29 1.29

Chloride (as Cl-) NaCl 1.65 Calcium (as Ca) CaSO4.2H2O 4.30 Iron (as Fe) Soil/road dust 5.50 (roadside)

9.00 (background) 3.50 (roadside increment)

EC (as C) EC 1.00 OC (as C) Organic compounds 2.1

* (Turpin and Lim 2001;Harrison et al., 2003)

3.4.12.2 Positive Matrix Factorization (PMF)

PMF, which was developed by Paatero and Tapper (1994) and Paatero (1997), became

the most widely used multivariate receptor modeling tool in short time, due to advantages

it offered over more conventional receptor modeling tools like factor analysis or principal

component analysis. PMF, like most other multivariate statistical tools, based on the idea

that concentrations of chemical parameters emitted from the same source exhibit similar

time-dependent changes in a receiving environment. Thus it is possible to group them

under minimum number of factors (sources) that explains largest variability in the

concentration data set. Thus, each factor is associated with a specific source type.

PMF is used to solve general receptor modeling problems using constrained, weighted

least squares minimization scheme. The general model assumes that p source, source

species or source region (factors) influence a receptor and that linear combinations of

effects from the p factor cause an increase in the observed concentrations of various

compounds.

The mathematical equation of the PMF model is given below. In this equation, xij is the

concentration of jth pollutant in the ith day in a receiving medium; gik: contribution of the

80

kth factor to the ith day's receptor; fkj is the contribution of the pollutant j at the kth factor;

eij: represents the unexplained part of the jth pollutants in the day.

∑ Equation 3.5

In the PMF it is assumed that only the xij are known and the aim is to determine the

contributions (gik) and profiles (fkj). It is assumed that all contributions (gik≥0) and the

profiles (fkj≥0) have only non-negative values. This assumption forms the limited part of

the smallest squares. Thus, only sources that do not contain negative chemical

concentrations and having no negative source contributions are observed.

In the weighted part of the PMF, the data with high uncertainty (missing values, values

below the detection limit or negative values) are reduced in order to reduce the influence

of the weights on the source determination. In the PMF analysis, some samples or

elements are extracted from the data set. In PMF analysis, however, the values assigned

to the missing data are loaded with high uncertainties and the weight of these values is

also reduced.

In the PMF minimum squares section, the objective function Q in the PMF is minimized

according to the resolution of the smallest squares given in the equation below. In this

equation, sij refers to the uncertainty of the jth chemical in the day ahead.

If the model is suitable for the dataset and if the uncertainties in the model accurately

reflect the uncertainties in the dataset; the Q value will be approximately equal to the

number of data in the concentration data set.

∑ ∑∑

Equation 3.6

81

This study used EPA PMF 5.0 version, which is more user friendly compared to other

PMF programs. The concentrations of target pollutants for each sample and their

corresponding uncertainties are used as input data in the PMF program. That is, the PMF

program must be prepared with files of concentrations and uncertainties. For the PMF

analysis, there should be no incomplete contaminants in the data set. In order to avoid

these deficiencies, various methods are used in the literature (Reff et al., 2007), 1) samples

with missing pollutants are completely removed from the dataset, 2) pollutants from

missing measurements are removed from the dataset, 3)the uncertainties of the

measurements are increased. For missing measurements, usually arithmetic mean, median

or geometric mean is used.

The pollutants whose concentrations are below the detection limit (LOD) are replaced

with half of the limit (LOD/2) and the uncertainties are increased by 5/6 of the LOD. Thus,

the uncertainties of the values below the specified LOD are increased. If less than 95% of

the number of samples of a pollutant is below the LOD, this pollutant is removed from the

data set.

EPA PMF has a user-friendly interface. There are four sub-categories in the analysis data

entry section. The statistics of the input data on the Concentration / Uncertainty screen are

automatically calculated and displayed for the signal / noise ratios (S/N) for each pollutant

(25 Percentile, 50 Percentile, 75 Percentile and maximum concentration values). The ratio

of the signal to the noise is calculated using the following equation:

∑

∑ Equation 3.7

The user examines the statistical results and assigns the categories of pollutants as strong,

weak or bad. Paatero and Hopke (2003) defined pollutants with a bad S/N value of less

than 0.5, a strong S / N value of 2 or more, and a weak S/N value of 0.5 to 1. In the PMF,

if pollutants are defined as bad, these pollutants are not included in the analysis, while

82

uncertainties of poorly categorized pollutants are tripled. However, if a pollutant is desired

to remain in the analysis, the uncertainty in the data file of the pollutant is increased by

five to ten times to include the pollutant analysis (Paatero and Hopke, 2003).

In the PMF, target object function is to minimize the Q value. So it would not be so wrong

to say that Q value is the most important indicator of model performance. The smallest Q

value between the various repetitions is selected and it is searched whether this value is

the local minimum or the global minimum. As a result of PMF analysis, two Q values are

obtained, robust and real. The result of subtracting outliers from the Robust Q-value data

set is a Q value calculated as a result, whereas the real Q is the Q value obtained as a result

of using the entire data. The Q values produced by the PMF should be close to or equal to

the theoretical Q value (approximately equal to the number of samples in the initial

concentration file).

In order to use the separated factors appropriately, the fundamental solution Fpeak is

implemented in the PMF, and the fundamental solution thus selected is returned to the real

solution with the help of the Fpeak option.

There are some uncertainties in the PMF analysis, such as from the dataset (such as

measurement errors or random sample errors) and the PMF model itself (such as

uncertainties in the solution). In order to minimize these errors, the bootstrap method can

be applied (Wehrens et al., 2000).

3.4.13 Meteorological Data

The meteorological data (wind speed and direction data) of the sampling days are provided

from the General Directorate of Meteorology. The conditional probability function (CPF)

for each region was calculated using the wind direction data and collected data. CPF is a

frequently used method in the literature to determine whether the effects of sources from

various wind directions have an impact on the station zone. The CPF for each wind sector

is calculated as follows:

83

∆∆

Equation 3.8

∆θ: wind sector, nΔθ represents the total hourly wind number from the wind sector Δθ and

mΔθ represents the number of hourly winds from the wind sector Δθ after a specified

threshold concentration (Ashbaugh et al., 1985; Xie and Berkowitz, 2006). CPF

calculations were made for each sector using the wind directions corresponding to the

hourly data.

The conditional bivariate probability function (CBPF) combine ordinary CPF and wind

speed data as a third variable. Pollutant concentration is allocated to the cells identified by

ranges of wind direction and wind speed rather than only wind direction sectors. It can be

formulized as:

∆ ,∆∆ ,∆

Equation 3.9

Where mΔθ, Δu is the number of samples in the wind sector Δθ with wind speed interval Δu

having concentration C greater than a threshold value x, nΔθ, Δu is the total number of

samples in that wind direction-speed interval. Bivariate case provides more detailed

information on the direction and nature of the sources since different source types could

have different wind speed characteristics. Bivariate polar plots were prepared through the

openair R package. The openair software is freely available as an R package (Uria-

Tellaetxe and Carslaw, 2014).

The mixing height and the ventilation coefficient are very important parameters due to

their effect on the pollutant concentrations. mixing height can be defined as the height at

which vertical mixing can occur. The low mixing ratio, together with the low aeration

coefficient, is a stable atmospheric indicator together and indicates that the vertical

mixture is very limited. This leads to higher concentrations of contaminants. In other

84

words, dilution will increase and contaminant concentrations will decrease as the mixing

height and the ventilation coefficient increase (Buzcu and Fraser 2006; Majumdar et al.,

2011).

The data required to calculate the mixing height for this study were obtained from the

Department of Meteorology Affairs of the Ministry of Forestry and Water Affairs. The

hourly mixing height was calculated using the PCRAMMET program developed by U.S.

EPA. The ventilation coefficient was calculated by multiplying the mixing height and the

corresponding wind speed. The ventilation coefficient is important because it provides

information on the aeration capacity of the atmosphere. It is also an important parameter

for the dilution and removal of the contaminant from the atmosphere (Chan et al., 2012).

85

RESULTS AND DISCUSSION

4.1 Summary statistics

During the sampling period (July 2014-September 2015), 275 and 337 samples were

collected at suburban and urban stations, respectively. Summary statistics of data

generated at urban and suburban stations, including average, standard deviation,

minimum, maximum, median and percent of observations, are given in Table 4.1 and

Table 4.2. Geometric mean value is used for log-normal distribution while median value

is used for right-skewed distributions. In this study almost all compounds show right

skewed distribution, but not necessarily lognormal distribution, as will be discussed in the

next section. Therefore, the median value for molecular tracer species was used in the

further data processing.

Table 4.1 Urban Station-Statistics for each molecular tracer specie

Compounds (ng m-3)

URBAN STATION

Avg Std Min Max Median %

observation

heneicosane 5.15 5.12 0.14645 24.83 2.95 91.1

docosane 5.64 4.26 0.02084 42.17 5.16 96.14

tricosane 7.02 5.38 0.02083 52.37 6.50 96.14

tetracosane 8.75 7.09 0.31336 44.96 7.83 97.63

pentacosane 9.20 6.36 0.35373 43.46 8.53 95.85

hexacosane 11.06 6.61 1.16994 35.55 10.94 98.22

heptacosane 12.30 7.07 0.71243 44.62 12.58 99.11

octacosane 11.44 4.77 1.20262 27.96 11.92 99.41

nonacosane 13.26 11.18 0.06038 152.46 13.25 99.11

CHAPTER 4

86

Compounds (ng m-3)

URBAN STATION

Avg Std Min Max Median%

observation

triacontane 12.83 6.07 2.52784 52.75 12.95 99.7

tritriacontane 12.34 7.79 0.92345 50.05 9.93 99.11

hentriacontane 8.32 4.96 1.21888 32.79 8.40 100

dotricontane 4.25 2.72 0.12520 16.60 4.40 98.52

tetratriacontane 4.47 2.48 0.24535 14.55 4.42 52.52

pentatriacontane 2.59 2.10 0.16724 13.70 1.75 29.38

Fluorene (Fl) 2.78 2.26 0.03907 7.48 2.78 7.12

Phenanthrene (Phe) 1.56 1.33 0.02823 8.48 1.37 97.63

Anthracene (An) 1.92 1.45 0.03107 5.72 2.17 98.52

Fluoranthene (Flut) 3.45 3.27 0.00386 19.19 2.59 99.11

Pyrene (Pyr) 2.75 2.83 0.00225 19.61 2.28 97.63

Benzo[a]anthracene (BaA) 3.59 3.83 0.01549 27.77 3.08 98.81

Chrysene (Chr) 4.15 4.34 0.04147 34.30 3.07 97.63

Benzo[b]fluoranthene (BbF) 6.97 7.85 0.00005 69.21 5.34 99.11

Benzo[k]fluoranthene(BkF) 4.10 5.97 0.02083 74.22 2.61 99.7

Benzo(e)pyrene 4.87 6.17 0.01225 55.08 3.37 100

Benzo[a]pyrene (BaP) 4.11 5.47 0.06025 56.07 2.46 99.11

Perylene 1.63 2.37 0.03599 13.54 0.68 72.4

Dibenzo[a.h]anthracene(DBA) 0.65 0.58 0.02689 2.52 0.55 18.69

Indeno[1.2.3.-cd]pyrene (IP) 6.19 4.06 0.37121 16.40 4.65 27

Benzo[ghi]perylene (BghiP) 2.06 1.63 0.02967 7.15 1.48 26.11

Retene 0.66 0.68 0.01506 3.28 0.39 18.69

Picene 2.94 0.92 1.72342 4.31 2.86 0.89

Coronene 6.01 5.52 0.43280 31.56 4.68 17.51

Dodecanoic acid 0.71 0.70 0.00011 3.01 0.46 83.09

Tridecanoic acid 0.63 0.73 0.01950 5.19 0.20 87.54

Tetradecanoic acid 2.59 2.63 0.07190 16.67 1.52 86.35

Pentadecanoic acid 0.91 0.91 0.02294 5.89 0.57 86.94

Hexadecanoic acid 12.37 13.04 0.09791 78.41 7.69 87.24

Heptadecanoic acid 0.53 0.52 0.03248 2.84 0.34 76.85

Linoleic acid 6.13 11.09 0.21998 110.39 2.10 65.88

87

Compounds (ng m-3)

URBAN STATION

Avg Std Min Max Median %

observation

Oleic acid 1.63 4.55 0.00797 60.44 0.37 62.91

Octadecanoic acid 9.37 9.64 0.01376 41.45 4.56 85.76

levoglucosan 2.67 3.15 0.01436 20.88 1.87 71.22

EC** 2.35 0.96 0.67 5.03 2.17 100.00

OC** 11.88 9.43 2.14 67.82 7.90 100.00

**units of EC and OC are µg m-3

Table 4.2. Sub-urban Station- Statistics for each molecular tracer species

Compounds (ng m-3) SUB-URBAN STATION

Avg Std Min Max Median % observation

heneicosane 1.81 1.22 0.04129 10.83 1.44 100

docosane 2.64 2.97 0.05446 24.52 1.88 94.91

tricosane 3.77 3.22 0.02909 27.31 2.88 98.18

tetracosane 3.32 1.93 0.01626 12.77 3.07 99.64

pentacosane 3.11 1.71 0.01206 13.09 3.14 100

hexacosane 3.42 1.75 0.01120 16.10 3.54 99.64

heptacosane 5.39 3.04 0.06962 18.50 5.51 100

octacosane 3.48 3.47 0.01967 19.56 1.84 99.27

nonacosane 5.39 3.51 0.09952 13.40 4.74 100

triacontane 5.21 4.18 0.07830 31.69 3.57 98.91

tritriacontane 5.87 4.93 0.15396 30.49 3.54 100

hentriacontane 3.95 5.17 0.28093 52.21 2.55 95.64

dotricontane 1.29 1.34 0.01932 7.01 0.56 95.27

tetratriacontane 1.42 1.77 0.02515 9.46 0.70 93.45

pentatriacontane 1.31 1.75 0.02315 14.57 1.03 38.91

Fluorene (Fl) 1.01 1.10 0.00367 3.97 0.49 12.73

Phenanthrene (Phe) 1.53 0.93 0.00748 3.82 1.45 93.82

Anthracene (An) 1.28 1.37 0.00562 8.08 0.39 94.55

Fluoranthene (Flut) 2.10 2.33 0.00052 12.27 1.37 98.55

Pyrene (Pyr) 1.58 1.25 0.00054 6.54 1.31 97.82

Benzo[a]anthracene (BaA) 1.18 0.84 0.00226 4.70 1.16 99.27

Chrysene (Chr) 1.65 1.24 0.00190 7.67 1.55 99.27

Benzo[b]fluoranthene (BbF) 2.00 2.16 0.00158 11.53 1.41 94.91

88

Compounds (ng m-3) SUB-URBAN STATION

Avg Std Min Max Median % observation

Benzo[k]fluoranthene(BkF) 1.73 1.70 0.00136 8.09 1.25 97.82

Benzo(e)pyrene 1.52 1.39 0.00185 7.61 1.21 93.09

Benzo[a]pyrene (BaP) 1.81 1.56 0.00250 8.39 1.34 96.73

Perylene 0.96 1.04 0.00270 5.99 0.57 74.55

Dibenzo[a.h]anthracene(DBA) 0.35 0.28 0.01494 1.02 0.21 17.82

Indeno[1.2.3.-cd]pyrene (IP) 1.74 0.95 0.00632 4.60 1.62 32.73

Benzo[ghi]perylene (BghiP) 0.82 0.87 0.04128 5.25 0.52 25.82

Retene 0.15 0.15 0.01955 0.78 0.10 14.55

Picene BDL BDL BDL BDL BDL 0

Coronene 1.81 1.56 0.10299 5.69 1.30 14.18

Dodecanoic acid 1.19 1.20 0.01067 9.31 0.71 94.55

Tridecanoic acid 0.85 0.70 0.00096 3.92 0.65 96.73

Tetradecanoic acid 3.86 2.91 0.04894 13.89 3.01 97.82

Pentadecanoic acid 1.20 0.93 0.00578 7.81 1.05 96.73

Hexadecanoic acid 9.51 8.62 0.07079 62.36 6.80 97.45

Heptadecanoic acid 1.52 2.74 0.00170 18.81 0.48 100

Linoleic acid 2.70 1.84 0.29625 12.13 2.27 69.09

Oleic acid 3.61 2.77 0.00409 18.31 2.78 71.27

Octadecanoic acid 11.94 11.87 0.04371 76.82 8.57 96

levoglucosan 0.45 0.62 0.00031 4.76 0.22 72.00

EC** 0.84 0.52 0.10 3.39 0.70 100.00

OC** 5.37 2.83 1.31 20.32 4.83 100.00 **units of EC and OC are µg m-3

89

4.2 Frequency Distributions

Frequency distribution is used to understand the statistical characteristics of pollutant

concentration data. It can be used to determine how frequently a crucial level is exceeded.

It also provides information on the type of statistical analysis that should be used in

evaluation process. There are different types of distributions that was fitted to atmospheric

pollutant concentration data. These include lognormal, Weibull, gamma and type V

Pearson. Most studies performed previously demonstrated that atmospheric data always

followed a right-skewed distribution, but not necessarily lognormal (Fang and Lu, 2002;

Rumburg et al., 2001).

STATGRAPH (Version 16.1.11) was used in order to determine the frequency

distributions of the data set of the organic molecular tracer species. Frequency

distributions of selected organic molecular tracers at urban and suburban stations are given

in Figure 4.1 and Figure 4.2 respectively. It is clear from figures that distributions of

molecular markers are right-skewed in both stations. Additionally, Kolmogorov–Smirnov

goodness of fit test was applied to test whether data fit to lognormal distribution. The test

demonstrated that, although all distributions were right-skewed only some fitted to

lognormal distribution with 95% confidence.

90

Figure 4.1 Urban station- typical frequency distributions of selected organic molecular tracer species (x-axis- ng m-3)

91

Figure 4.2 Suburban station- typical frequency distributions of selected organic molecular tracer species (x-axis- ng m-3)

92

4.3 Comparison of measured concentrations of molecular markers with

literature

In this section, molecular marker data generated in this study are compared with

comparable data generated in different studies around the world. Data used in comparison

are given in Table 4.3.

There are very few long-term studies with PM-bound organic molecular tracer species,

because long-term studies require substantial time and effort for sampling, analysis and

further processing of data. Although comparison of data generated in different studies can

be very useful to assess state of pollution in a given environment and provides same

preliminary information about prevailing atmospheric processes in the study area, one has

to be cautious about such comparison, because data generated in any study are affected a

variety of factors, including; sampling duration, location of sampling station, campaign

period, approach used in sampling, method of analysis and statistical tools used in data

evaluation. Among these sampling location is the most important one.

Some information about sampling sites and methodologies, which are included in

comparison exercise are given in following paragraphs.

In Denver (Dutton et al., 2010), sampling has been done daily (541 daily samples) from a

site which was located at a two story elementary school building. Sampling location was

near to a residential area and strong emission point sources were not close. A sharp-cut

cyclone with a flow rate 92 L min-1 was used to collect PM2.5 samples on quartz fiber

filters. DCM was used for the extraction of organic molecular tracer species from the

filters and GC-MS was used for the quantification of the compounds.

Stone et al. (2010) collected samples at a university campus at Lahore, Pakistan. Samplers

was situated on the roof of the Institute for Environmental Engineering and Research.

PM2.5 samples (63 daily samples between January 2007 and January 2008) were collected

93

by medium volume sampler with a flow rate of 16 L min-1 on quartz fiber filters. Sampling

site was defined as an urban area. Dichloromethane and methanol was used for extraction

and GC-MS was used as the analytical tool.

In Korea (Simoneit et al. 2004), atmospheric air pollutants were collected on quartz fiber

filters with high volume samplers. Dichloromethane/methanol was used for extraction

and organic tracers were analyzed by GC-MS. Sampling sites were classified as “urban”.

In Wang et al. (2016)’s study, samples were collected at Guangzhou which is a mega city

located in the center of a potentially polluted region in China. Sampling station was

located at South China Institute of Environmental Sciences, which is located at an “urban”

area. A high volume sampler was used to collect the PM2.5 samples on quartz-fiber filter

at a flow rate of 1.05 m3 min-1. Forty-eight samples were collected during summer and

winter sampling campaigns.

In Munich (Qadir et al., 2013), PM2.5 samples were collected from a monitoring site

located in Lothstrasse, Munich. The site is in the inner city of Munich near to a primary

road from which 41,000 vehicles pass daily. Sampling campaign was made at two stage;

first one was started at October 2006 and samples were collected every third day until

February 2007. The second campaign was started at October 2009 and continued until

February 2010.

In Milan (Perrone et al., 2012), samples were collected at Northern Italy, with low-volume

samplers at a flow rate of 38 L min-1. Samples were collected at three different

microenvironments, namely urban, suburban and a remote. Quartz fiber filters were used

for daily PM2.5 sampling. Approximately 80 samples were collected. Chemical analysis

was performed by GC-MS.

In Eskişehir (Arı, 2016), samples were collected in 2013 and 2014. In this study, both

trace elements, anions, and organic compounds were analyzed. 191 daily samples were

94

collected. GC-MS was used during analysis of the samples and PMF was applied to

identify the source contribution to particulate matter mass.

Concentrations of almost all n-alkanes measured at urban site in this study, except

nonacosane, hentriacontane and dotriacontane, are the highest observed in the foreign

countries. This could be due to the location of the urban station as it is the surrounded by

crowded roads and located at a highly populated region. For sub-urban station the

concentration of n-alkanes observed considerably high with respect to other studies but

lower than corresponding concentrations measured at our urban station. Concentrations

range from 1.31 to 5.87 ng m-3 for sub-urban station and 2.59 to 13.26 ng m-3 for urban

station. The main contributors to the total n-alkane concentration are tritriacontane,

heptacosane and nonacosane. Their contributions to ∑alkane concentration were 11.4%,

10.5% and 10.5%, respectively. Highest contributing alkanes at suburban station were

nonacosane, triacontane and tritriacontane. Their contributions to ∑n-alkane

concentration were 10.3%, 10.0% and 9.6%, respectively.

Concentrations of PAHs measured in our stations range from 0.15 to 2.10 ng m-3 for

suburban station and 0.66 to 6.97 ng m-3 for urban station. Main contributors to the ∑PAH

concentration at urban station are Flut, BbF and Coronene (with average contributions of

9.0%, 8.6% and 7.8%, respectively). For urban station; BbF, IcdP and Coronene were the

main contributors (their average contributions were 11.5%, 10.2% and 9.9%,

respectively). Concentrations of BaP, which is identified as carcinogenic and which is a

U.S. EPA priority atmospheric pollutant (regulatory standard is 1 ng m-3), are 1.81 and

4.11 ng m-3 at sub-urban and urban stations, respectively.

These concentrations measured at our urban and suburban stations are significantly higher

than values reported for Denver, Lahore, Korea, Munich and Milan and lower than value

reported in Ghangzhau and Eskişehir. This could depend on the distances of these stations

from the emission sources, depletion processes and meteorological parameters during

sampling campaign.

95

For n-alkanoic acids, concentrations observed at our urban and sub-urban stations are

generally comparable to concentrations reported in other studies. Values range from 0.85

to 11.94 ng m-3 for suburban station and 0.53 to 12.37 ng m-3 for urban station. The main

contributors to the total concentration are octadecanoic and hexadecanoic acids (their

contributions were 32.8% and 26.1%, respectively) at suburban station and hexadecanoic

and octadecanoic acids at urban station (their contributions to ∑acids were 35.5% and

26.9%, respectively). Hexadecanoic acid concentrations reported for Denver and Lahore

are 18.1 and 48.9 ng m-3, respectively, which are comparable to values we measured in

this work. Since main source of hexadecanoic acid is wood smoke and cooking emissions

(Dutton et al.,2009; Fine et al., 2001; Rogge et al., 1993), differences and similarities in

hexadecanoic acid concentrations in different studies may signify relative importance of

these sources in those work.

Levoglucosan is an important organic compound, particularly in source apportionment

studies, because it is the only marker for emissions from biomass burning. Levoglucosan

concentrations measured at urban and sub-urban stations were 0.44 and 2.67 ng m-3,

respectively. These values are significantly smaller than corresponding concentrations

reported in literature. For example, levoglucosan concentrations reported for Eskişehir,

Munich and Milan were 63.6, 145 and 395 ng m-3, respectively. This is consistent with

the expected use of wood for heating. In Puxbaum et al.’s study (2007), levoglucosan

values were detected for six background site across Europe and the values ranged between

5.2 ng m-3 and 309 ng m-3. High concentrations reported for Milan and Munich may be

due to the importance of local wood burning in these cities. Besides these high values,

there are also some low concentrations as well. For example, levoglucosan values

reported for UK was only 9 ng m-3 (Yin et al., 2010).

EC and OC concentrations observed at both stations are comparable to concentrations

reported in other studies. EC concentrations reported for other locations range between

0.60 to 6.00 µg m-3, while observed concentrations are between 0.70 and 2.17 µg m-3 at

both suburban and urban stations. OC values vary from 1.90 to 13.7 µg m-3 in other

96

studies while observed concentrations 4.83 and 7.90 µg m-3 for sub-urban and urban

stations, respectively. When the OC to EC ratios in PM2.5 were examined, they were in

the range of 4.2-5.3 in Denver, Korea and Ghangzhau, but it was much lower (0.95) in

Munich. OC to EC ratios show a relation among a different type of combustion sources

(Na et al., 2004). Low OC/EC ratio was related with traffic sources such as light duty

gasoline-2.2 and heavy-duty gasoline-0.8, whereas higher ratios were related with

domestic heating such as wood combustion-4.15 and natural gas combustion-12.7, forest

fire-14.5 and road dust- 13.1.

97

Table 4.3. Comparison of average concentrations measured in this work with corresponding data generated for other cities around the world.

Compounds Dutton et al., 2010

Denver USA

Stone et al.,

(2010) Lahore

Pakistan

Simoneit et al., 2004

Korea

Wang et al., 2016

Ghangzhau China

Qadir et al., 2013 Munich

Germany

Perrone et al., 2012 Milan Italy

Arı, 2016 Eskişehir Turkey

Sub-urban station

Urban station

n-Alkanes (ng m-3)

Heneicosane C21 - 0.57 1.42 1.03 1.48 1.63 15.9 1.81 5.15

Docosane C22 1.45 0.7 1.62 1.68 2.24 3.48 22.3 2.64 5.64

Tricosane C23 2.16 3.16 2.26 2.89 2.60 5.01 24.1 3.77 7.02

Tetracosane C24 1.13 3.4 2.31 5.64 2.60 7.10 23.5 3.32 8.75

Pentacosane C25 1.65 7.43 3.68 8.1 1.81 6.42 21.4 3.11 9.20

Hexacosane C26 0.92 3.08 2.5 8.16 1.61 6.40 20.0 3.43 11.06

Heptacosane C27 1.29 7.49 5.55 7.38 2.49 6.21 15.8 5.39 12.30

Octacosane C28 0.86 4.4 2.39 5.59 1.74 4.78 14.5 3.48 11.44

Nonacosane C29 1.91 17.7 6.21 8.82 3.48 6.19 12.4 5.39 13.26

Triacontane C30 0.7 - 1.55 5.73 1.64 3.30 9.7 5.21 12.83

Hentriacontane C31 - 5.59 4.84 12.8 3.65 5.98 13.2 3.96 8.32

Dotriacontane C32 0.47 1.31 4.6 1.09 2.40 10.0 1.29 4.25

Tritriacontane C33 - 3.81 2.2 6.28 - - 6.7 5.87 12.35

Tetratriacontane C34 0.58 1.73 0.71 3.13 0.31 - 4.9 1.42 4.47

Pentatriacontane C35 0.45 2.25 0.52 2.6 0.26 - 0.5 1.31 2.59

PAHs (ng m-3)

Fluorene (Fl) 0.36 - 2.25 - 1.1 1.01 2.78

Phenanthrene (Phe) - - 0.4 3 - 2.9 1.53 1.56

Anthracene (An) - - 0.79 - 1.9 1.28 1.92

-

-

-

-

-

98


Denver USA

Stone et al.,

(2010) Lahore

Pakistan


Korea

Wang et al., 2016

Ghangzhau China


Germany



Sub-urban station

Urban station

Fluoranthene (Flut) - - 0.67 2.6 - 7.8 2.10 3.45

Pyrene (Pyr) 0.1 - 0.64 2.73 - 5.4 1.58 2.75

Benzo[a]anthracene (BaA) 0.06 - - 1.56 0.33 1.21 5.1 1.18 3.59

Chrysene (Chr) - - 0.43 2.66 0.94 1.33 5.0 1.65 4.15

Benzo[b]fluoranthene (BbF) 0.22 - - 4.16 1.78 6.4 1.20 6.97

Benzo[k]fluoranthene(BkF) 0.22 - - 3.41 1.64 - 5.9 1.73 4.11

Benzo(e)pyrene (beP) 0.18 - 0.43 3.62 0.45 0.93 1.52 4.87

Benzo[a]pyrene (BaP) 0.18 - 0.23 4.86 0.56 1.61 4.9 1.81 4.11

Perylene 0.01 - 0.05 1.15 0.08 - 0.96 1.63

Dibenzo[a.h]anthracene(DBA) 0.02 - - 3.88 - - 2.2 0.35 0.65

Indeno[1.2.3.-cd]pyrene (IP) 0.08 - 0.3 3.24 0.71 0.91 1.74 6.19

Benzo[ghi]perylene (BghiP) 0.21 - - 1.47 0.76 1.11 3.1 0.82 2.06

Retene 0.42 - - - 0.14 - 0.15 0.66

Picene 0.01 - - - - - - 2.94

Coronene 0.1 - 0.12 2.6 0.48 - 1.81 6.01

n-Alkanoic acids and Levoglucosan (ng m-3) Dodecanoic acid 3.08 1.79 - - - - 1.19 0.71

Tridecanoic acid 0.22 0.65 - - - - 0.85 0.63

Tetradecanoic acid 4.58 2.89 3 - - - 3.86 2.60

Pentadecanoic acid 0.99 1.81 - - - - 1.20 0.91

Hexadecanoic acid 18.08 48.85 0.75 - - - 9.51 12.37

Heptadecanoic acid 0.62 1.85 - - - - 1.52 0.53

-

-

-

- -

-

-

-

- -

-

-

-

-

99


Denver USA

Stone et al.,

(2010) Lahore

Pakistan


Korea

Wang et al., 2016

Ghangzhau China


Germany



Sub-urban station

Urban station

Linoleic acid - - - - - - 2.70 6.13

Oleic acid 2.04 - - - 1.28 - 3.62 1.63

Octadecanoic acid 11.28 - 10.4 - - - 11.94 9.37

Levoglucosan - - - - 145 395 0.44 2.67

EC (µg m-3) 0.65 - 0.98 1.9 2.00 - 0.70 2.17

OC (µg m-3) 3.13 - 4.20 10.2 1.90 - 4.83 7.90

*”-“ means that this compound was not analyzed in that study

-

-

-

-

-

-

100

Figure 4.3. Comparison of n-Alkanes with the literature

0

5

10

15

20

25

30

conc

entr

atio

ns (

ngm

-3)

n-alkanes

Dutton et al., 2010 Denver USA Stone et al., (2010) Lahore Pakistan

Simoneit et al., 2004 Korea Wang et al., 2016 Ghangzhau China

Qadir et al., 2013 Munich Perrone et al., 2012 Milan

Sub-urban station Urban station

Arı, 2016 Eskişehir

101

Figure 4.4. Comparison of PAHs with the literature

0

1

2

3

4

5

6

7

8

9co

ncen

trat

ions

(ng

m-3

)PAHs






102

Figure 4.5. Comparison of n-alkanoic acids, levoglucosan and EC-OC with the literature

4.4 Meteorology of the study area

Meteorological parameters have an important role in air pollution by affecting air

pollutants during emissions, transport, formation and deposition. There are two major

groups of meteorological parameters to be considered, (1) those that affect dispersion of

pollutants and (2) those that are important in transport and transformations of pollutants.

While stability of atmosphere and related parameters are essential in dispersion of

pollutants in atmosphere, meteorological parameters affecting the pollutant dispersion

include; wind speed, wind direction and atmospheric stability. It is important to know

0,01

0,1

1

10

100

1000

log

conc

entr

atio

ns (

ngm

-3)

n-alkanoic acids, levoglucosan and EC-OC






103

these parameters and their temporal variation for proper evaluation of measurement

results.

How much and how meteorological parameters affects the atmospheric pollutant depends

on the type of the pollutant (Latini et al., 2002; Zhang et al., 2015). There are many studies

in the literature show that concentration of atmospheric pollutants attribute to local

meteorological conditions beside emission properties (Marcazzan et al., 2001; Donkelaar

et al., 2010). In this study, meteorological data was taken from General Directorate of

Meteorology. Sub-urban station’s data was taken from Ankara-Etimesgut Airport (No:

17129) Station and Urban station’s data was taken from Ankara-Keçiören (No: 17130)

Station. These two stations are the closest stations to the sampling locations.

4.5 Long Term Meteorology

Monthly average temperature, relative humidity, wind speed, mixing height and

ventilation coefficient data are given in Table 4.4. Temperature changes between 0.43 and

23.59oC. Relative humidity values vary between 43.94 and 76.80 %. The minimum wind

speed was recorded in November (1.91 m s-1) and the maximum wind speed was recorded

in July (2.73 m s-1). Mixing height varied between 700 and 1600 m, while ventilation

coefficient is between 1.91 and 2.73 m2 s-1. The relationship between mixing height and

ventilation coefficient could be seen from Figure 4.6. These values represent long-term

meteorological features in central Anatolia.

104

Table 4.4 Long-term (1950 – 2016) monthly average values of meteorological data at

the study area

Months Temperature (oC)

Relative Humidity

(%)

Wind Speed (m s-1)

Mixing Height

(m)

Ventilation Coefficient

(m2 s-1) January 0.43 76.80 2.07 765.01 1510.29

February 1.94 71.99 2.23 911.65 1934.99

March 6.03 64.18 2.31 1158.28 2510.40

April 11.40 58.79 2.29 1350.40 3023.93

May 16.03 57.21 2.22 1462.62 3417.60

June 20.13 51.78 2.35 1534.89 3934.55

July 23.59 44.47 2.73 1621.42 4696.53

August 23.40 43.94 2.61 1644.43 4586.00

September 18.78 48.38 2.17 1369.55 3067.73

October 12.91 59.45 1.98 1067.66 2107.28

November 7.05 69.18 1.91 795.32 1491.97

December 2.59 76.58 2.02 718.99 1412.07

Figure 4.6 Long-term (1950 – 2016) monthly average mixing height and ventilation

coefficient

02004006008001000120014001600

0500

1000150020002500300035004000

Mix

ing

Hei

ght (

m)

Ven

tila

tion

coe

ffic

ient

(m

2s-1

)

Long term mixing heght and ventilation coefficient

VC MH

105

4.6 Meteorology during study period

Monthly average temperature, relative humidity, wind speed, mixing height and

ventilation coefficient values recorded during sampling period are given in Table 4.5 and

Table 4.6 for urban and sub-urban stations respectively. The temperature in the both urban

and suburban stations varies between 1 and 25oC during entire sampling period. Average

winter and summer temperatures were 6.5 and 19.2oC respectively. The relative humidity

values changes between 40.7 and 83.7% for urban station and 40.6 and 90.1% for sub-

urban station. Winter and summer average humidity values were 72.9 and 52.5% for urban

station and 79.9 and 59.1% for sub-urban station. Winter and summer average wind

speeds were 2.4 and 2.6 m s-1, respectively for urban station and 2.0 and 2.6 m s-1,

respectively for sub-urban station. Additionally, the maximum and minimum values are

2.01-3.17 m s-1 for urban station and 1.59 and 2.93 m s-1 for sub-urban station. Monthly

variation of temperature, relative humidity, wind speed and mixing height are also

presented in Figure 4.7 for visual inspection. The values measured during sampling

period are not much different from the long-term values (see Table 4.7). Annual average

temperature is 12±8oC for long term and both stations. Annual relative humidity is

60±12% for long term, 63-70±13% for urban and suburban stations respectively. Wind

speed is approximately 2-2.5 m s-1 for long term and both stations. Mixing height is about

1270±500 m for long term and both stations. For this reason, the year we sampled can be

considered as a typical year for this region.

106

Table 4.5. Urban Station- Monthly average values of meteorological parameters during the study period.


Relative Humidity

(%)

Wind Speed (m s-1)

Mixing Height (m)

Ventilation Coefficient

(m2 s-1) January 1 78.55 2.29 581 1332 February 3 71.31 3.17 915 2902 March 7 65.73 2.26 1044 2357 April 9 54.96 2.63 1175 3092 May 17 54.17 2.38 1453 3459 June 18 67.69 2.39 1452 3475 July 25 40.70 3.02 1689 5108 August 25 42.38 2.89 1664 4801 September 21 55.30 2.34 1349 3154 October 14 68.63 2.15 831 1787 November 8 69.27 2.01 807 1620 December 6 83.70 2.24 609 1363

Table 4.6. Sub-urban Station-Monthly average values of meteorological parameters during the study period.


Relative Humidity

(%)

Wind Speed (m s-1)

Mixing Height

(m)

Ventilation Coefficient (m2 s-1)

January 1 84.97 2.12 639 1353 February 3 76.74 2.78 1031 2865 March 7 73.48 2.20 1281 2818 April 9 61.99 2.93 1601 4686 May 17 62.48 2.50 1982 4946 June 18 71.82 2.26 1815 4096 July 25 46.79 2.76 2265 6261 August 25 48.77 2.71 2325 6291 September 21 62.60 2.47 1902 4693 October 14 75.95 1.68 1165 1953 November 8 77.92 1.59 990 1570 December 6 90.10 1.80 651 1174

107

Table 4.7. Comparison of long-term monthly average temperature with the temperature, which prevailed during sampling period.

Months Temperature (oC) Sampling Period

Temperature (oC) Long Term

January 1 0.43 February 3 1.94 March 7 6.03 April 9 11.40 May 17 16.03 June 18 20.13 July 25 23.59 August 25 23.40 September 21 18.78 October 14 12.91 November 8 7.05 December 6 2.59

108

Figure 4.7 Monthly variation of temperature, relative humidity, wind speed and mixing height

109

4.7 Wind Direction

The summer, winter and total wind rose plots prepared for the sampling locations are

given in Figure 4.8 and Figure 4.9. Wind rose plots were prepared by using a WRPLOT

View (Lakes Environmental, Canada). Dominant wind directions affecting sub-urban and

urban stations are E and NE respectively. These dominant wind sectors are important,

because sources that are most effective on suburban and urban stations are expected to be

in these directions. For the sub-urban station city center is located at the east of the station

and for the urban station east sector is important, because city have been enlarging to the

east of the station and population to the east of the station is increasing day by day. This

simple analysis demonstrates that there are potential source areas located to the east of

each station and emissions in these potential source areas are expected to have impact on

measured organic particulate matter concentrations at our stations. Figures also indicate

that winter is calmer than summer season for both stations.

The wind rose, which was prepared using long term data (1950-2016) is depicted in Figure

4.10. The wind pattern in long-term rose is not significantly different from the wind

patterns observed during sampling period. The long-term wind pattern is exactly

mimicked at urban station. However, there is a small difference at suburban station, where

dominant wind sector is E and NE.

110

Figure 4.8 Sub-urban station wind rose plots during sampling period

111

Figure 4.9 Urban station wind rose plots during sampling period

112

Figure 4.10 Ankara-Long term wind rose plot (1950-2016)

4.8 Temporal variations in concentrations of measured organic particulate

matters

The composition of PM2.5 change by regional and by seasonal factors and the degree of

spatial and temporal variability differs by compounds. In this study, temporal variations

in concentrations of measured organic markers are examined at three levels. Firstly, time

series plots of the compounds were investigated and possible relations between

compounds were determined. Episodic variations in the time series observed in

particulate organic matters’ concentrations are discussed. Secondly, weekend-weekday

differences in concentrations are investigated. Finally, seasonal variations in organic

markers’ concentrations and the role emissions and meteorology on seasonal variations

are discussed.

113

4.8.1 Episodic changes in concentrations of measured compounds

Most of the atmospheric pollutant concentrations does not follow an average pattern, on

the contrary; there is a large number of low concentrations and few high concentrations

that appear as episodes in time series plots. This situation causes a log normal or right-

skewed distributions for atmospheric pollutants. Therefore, episodic changes are expected

when time series of atmospheric pollutants are examined. Sudden increase in emissions

or variations in meteorology can be the reasons for episodic changes. Particularly sudden

changes in wind direction is generally is the source of right skewed distributions and

episodes in time series plots of pollutant concentrations. When the wind blows from the

direction of a strong source of pollution, concentration of pollutants released from this

particular source increases and concentrations of pollutants decrease rapidly when the

wind alters direction and begins to blow from another sector, where there is no source.

The second meteorological parameter affecting the temporal behaviors of the compounds

is precipitation. For most of the parameters analyzed in this study, rain events linked to

minimum concentrations. However, there were few cases not following the general trend

and observed concentrations were high with the increasing precipitation. Such cases are

probably due to challenge in matching precipitation data with concentrations. Aerosol

particulate matter samples were collected daily and precipitation data was also daily

averages. Since their start and finish time do not same, effect of rain on the observed

concentrations can be seen one day after or before the recorded day (Öztürk, 2009).

Time-series plots for selected compounds, at urban and sub-urban stations, are given in

Figure 4.11 and Figure 4.12 respectively. Daily variations in the concentrations together

with the rainfall are depicted in the figures. In these plots episodes could be easily

identified by sudden increases in concentrations, which are followed by equally sudden

decreases. It is also clear from the figures that not all but relatively sufficient minimum

concentrations correspond to high rainfall. Such behavior of particulate bound organic

matters is due to scavenging by rainfall. These investigations proposed that washout

114

mechanism is an important factor to identify the daily concentration variations in the

atmosphere.

Both urban and sub-urban stations were exposed to different pollution sources due to their

separate and not-so-close locations. Urban station was under the influence of vehicular

emission and domestic heating. There are two main highway around the station and it is

close to settlement area. Sub-urban station, on the other hand, was mainly under the effect

of vehicular emission, road dust and food cooking. However, the magnitude of emissions

from these sources at suburban station is not expected to be as high as the magnitude of

their emissions at urban site. Therefore, distribution of vehicular emission, domestic

heating, road dust and food cooking sources around the stations directly or indirectly affect

the episodic variations in concentrations of particulate organic matter. When the wind

direction changes and starts to blow from one of these sources, high concentrations of

different tracers were recorded at both urban and suburban stations.

115

Figure 4.11. Episodic time series of sub-urban station

116

Figure 4.12. Episodic time series of urban station

117

Time series plots of Fluoranthene and Benzo(a)pyrene measured at suburban station

(shown in Figure 4.11) is a good example, which shows the role of meteorology on

generating episodes in pollutant concentrations. These compounds have similar episodic

pattern at sub urban station. There is a strong peak in both PAHs between March and April

2015. The wind rose prepared for this period is depicted in Figure 4.13. The wind rose

shown in the figure is different from the wind rose prepared for the study period and

discussed earlier in the manuscript. In the rose prepared for the study period, there were

28% contribution from E sector, 11% contribution from W and 12% contribution form

NW sectors. However, in the wind rose prepared for the episode (Figure 4.13),

contributions of W and NW are close to the contribution of E sector, which is dominating

wind direction both in the wind rose prepared for sampling period and long-term wind

rose which covers years between 1950 and 2016. In Figure 4.13 contribution of E sector

is 20% while contributions of W and NW are 16.5 and 12.3%, respectively. Since there

are roads in W and NW sectors at Suburban station, increase concentrations of

fluoranthene and benzo(a)pyrene with increasing contributions of W and NW sectors is

not surprising.

Figure 4.13 a) Sub-urban sampling period wind rose plot b) Sub-urban February-March wind rose plot

a b

118

4.8.2 Weekend weekday differences in concentrations of measured species

Median weekday (WD) to weekend (WE) concentrations and WD/WE concentration

ratios of PAHs at urban and suburban stations are depicted in Figure 4.14 and Figure 4.15,

respectively. There are no dramatic differences between WD and WE PAH

concentrations at both stations. The WD to WE ratio varies between 1.0 ± 0.2, both in

suburban and urban stations. The only exceptions to this general pattern was observed in

fluorene concentrations (where WD/WE ratio is 1.6) and Picene (where the ratio is 0.4) at

urban station and in perylene (WD/WE ratio is 1.4) at suburban station. However, there

are some differences in behaviors of PAHs at suburban and urban stations, which are

worth noting.

Most if not all PAHs have higher WE concentrations at suburban station. The difference

is more pronounced in fluoranthene, pyrene, benzo[k]fluoranthene, benzo[e]pyrene,

benzo[a]pyrene and perylene concentrations. WD to WE concentration ratio varied

between 0.48 for perylene and 2.37 for dibenzo(a,h)anthracene. WD-WE difference in

PAH concentration is smaller at urban station. For example, WD/WE ratio is <0.5 for

perylene at suburban station, whereas it is close to, or even slightly higher, than unity at

urban station. The ratio in urban station varied between 0.52 for picene and 2.74 for

fluorene. Statistical significance of WD/WE ratios were tested with Mann-Whitney W-

test, which compares medians of WD and WE concentrations of each PAH compound.

The test demonstrated that WD and WE median concentrations of all PAHs at urban

station is not statistically significant at 95% confidence interval. Since there is not much

industry in Ankara, measured PAH concentrations originate from incomplete combustion

in either traffic or in space heating. Earlier studies at Ankara demonstrated that emissions

from traffic decrease and emissions from space heating increase during WE (Genç et al.,

2010). In urban stations expected decrease in PAH concentrations at WE owing to reduced

traffic intensity is compensated by increased PAH emissions from space heating. This

pattern resulted comparable PAH concentration during WD and WE.

119

Since traffic intensity at the university (around our suburban station), much smaller than

traffic intensity around urban station, PAH emissions from space heating is expected to

have a larger share at suburban station. In addition to this, the decrease in traffic load at

WE is more dramatic at the university. Since there are no classes at the WE, traffic activity

drops, particularly around our station, which is located at the peripheral of the campus.

Thus, higher PAH concentrations observed at suburban station reflects increased PAH

emissions from space heating at WE.

Figure 4.14. WD and WE PAH concentration distribution at Urban Station

0

0,5

1

1,5

2

2,5

3

0

1

2

3

4

5

6

wee

kday

/wee

kend

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entr

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g m

-3)

Urban Station PAHs WD and WE

weekday weekend weekday/weekend ratio

120

Figure 4.15. WD and WE PAH concentration distribution at

Sub-urban Station

Variation n-alkane concentrations between WD and WE are shown in Figure 4.16.

Concentrations of n-alkanes do not show a significant WD–WE difference in both

stations. WD/WE ratio varied between 0.77 for pentatriacontane and 1.02 for nonacosane

at urban station and between 0.31 for tetratriacontane and 1.08 for octacosane at suburban

station. Mann-Whitney W test was applied to WD and WE alkane concentrations at both

stations. The test demonstrated that WD and WE concentrations are not different with

95% statistical significance level, except for heneicosane, for which WD and WE median

concentrations are different (p<0.05). This indicates that the sources responsible for these

compounds do not show a significant effect neither in WD nor WE. The sources of n-

alkanes can be both anthropogenic and biogenic. Anthropogenic ones can be fossil fuel

burning, traffic emissions and biogenic ones can be plant waxes. Plant waxes do not show

significant variation between WD and WE. Percentile of anthropogenic source

contributions can change from one location to another. Almost similar WD and WE

concentration variation shows that traffic emission does not have a significant effect on n-

0

0,5

1

1,5

2

2,5

00,20,40,60,8

11,21,41,61,8

wee

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/wee

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Sub-urban Station PAHs WD and WE


121

alkane concentrations in WD. On the other hand, fossil fuel burning emission rate

increases during WE with increasing demand of domestic heating. Therefore, WD and

WE variations are similar in urban and sub-urban station.

Figure 4.16. WD and WE n-Alkanes concentrations distribution at Urban and Sub-urban Stations

0

0,2

0,4

0,6

0,8

1

1,2

02468

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wee

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Urban Station n-Alkanes WD and WE

weekday weekend weekday/weekend

0

0,2

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Sub-urban Station n-Alkanes WD and WE


122

WD and WE median concentrations of n-alkanoic acids are given in Figure 4.17 and

Figure 4.18 for urban and suburban stations, respectively. Concentrations of n-alkanoic

acids, like other compound groups, do not show significant differences between WD and

WE. WD to WE concentration ratios varied between 0.86 for linoleic acid and 2.13 for

oleic acid at urban station and between 0.87 for dodecanoic acid and 1.08 for tridecanoic

acid at suburban station. Emission sources of n-alkanoic acids in atmospheric aerosols

show similarity to the sources of n-alkanes; there are two major sources as anthropogenic

and biogenic. Anthropogenic ones are fossil fuel, wood and detritus burning, food cooking

and biogenic source is plant waxes. Biogenic sources do not show important variation

between WD and WE. It is known that food cooking has a big role for source contribution

to n-alkanoic acids. Hexadecanoic acid and octadecanoic acids are the most important two

tracers for food cooking. Their WD concentrations were detected slightly higher than WE

concentrations. There are many restaurants and households around the urban station and

there is a canteen near to sub-urban station. Therefore, the catering sector’s contribution

would be higher in WD at urban and sub-urban station. When the general trends are

examined through n-alkanoic acids, there are no significant differences between WD and

WE concentrations. This indicates that the sources responsible for these compounds do

not show a significant effect neither in WD nor WE.

WD and WE median levoglucosan concentration are given in Figure 4.19. Levoglucosan

concentrations are higher during WE at both stations (WD/WE ratio is 0.7 at urban and

0.6 at suburban stations. Observed pattern is not surprising for levoglucosan. The only

source of levoglucosan in atmosphere is wood burning. In Ankara wood is burned for

cooking and for heating at households and burned more during WE, when everybody is at

home. Higher levoglucosan concentrations at WE in both stations reflects this pattern of

wood combustion at Ankara.

123

Figure 4.17 WD and WE n-Alkanoic acids concentrations distribution at Urban Station

Figure 4.18 WD and WE n-Alkanoic acids concentrations distribution at

Sub-urban Station

0

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2,5

02468

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Urban station- n-Alkanoic acids WD and WE


0

0,2

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Sub-urban station- n-Alkanoic acids WD and WE


124

Figure 4.19. WD and WE Levoglucosan concentrations distribution at Urban and Sub-urban Stations

WD and WE concentrations of EC and OC is depicted in Figure 4.20. Unlike other marker

groups, WE concentrations of EC and OC are higher than their corresponding WE

concentrations both in urban and suburban stations. Diesel exhaust is the main source of

particularly EC in atmosphere. Higher EC concentration during WD is due to higher

number of diesel vehicles in traffic during WE. The difference between median WD and

WE EC concentrations is statistically significant at 95% confidence interval. OC depicted

a slightly different pattern. Although WD OC concentrations are mathematically higher

than WE OC concentrations also at urban station, the difference was not high and not

statistically significant with 95% confidence. Smaller WD-WE pattern in OC is, because

unlike EC, OC has a variety of sources, some like traffic has higher source strength during

WD, some like combustion have higher source strength in WE and some like cooking

does not have well-defined WD-WE pattern.

0,58

0,6

0,62

0,64

0,66

0,68

0,7

0,72

0,74

0

0,5

1

1,5

2

2,5

3

METU AU

conc

entr

arti

on (

ng m

-3)

Levoglucosan WD-WE


125

Figure 4.20. WD and WE EC&OC concentrations distribution at Urban and Sub-urban Stations

1,1

1,11

1,12

1,13

1,14

1,15

1,16

1,17

1,18

1,19

0

2

4

6

8

10

12

EC OC

WD

/WE

Con

cent

rati

on (

µg

m-3

)WD-WE EC&OC Urban

WEEKDAY WEEKEND WEEKDAY /WEEKEND ratio

1,05

1,1

1,15

1,2

1,25

1,3

1,35

0

1

2

3

4

5

6

EC OC

WD

/WE

Con

cent

ratio

n (µ

g m

-3)

WD-WE EC&OC Sub-urban

WEEKDAY WEEKEND WEEKDAY /WEEKEND ratio

126

4.8.3 Seasonal variations

Seasonal variations in concentrations of molecular markers (and also of all other

pollutants, for that matter) are worth discussing, because such variations can provide

information on their sources and meteorological parameters causing temporal variability

in their concentrations. Seasonal variations in concentrations of particle-bound organics

and meteorology are discussed in following paragraphs.

Seasonal variation in concentrations of all pollutants are driven by three factors. These

are (1) seasonal variation in source strengths of pollutants, (2) seasonal variation in

meteorology and (3) seasonal variation in photochemistry. The first factor, namely

seasonal variation in source strength, is pollutant-specific. Some of the pollutants have

higher emissions and some lower emissions in certain seasons. For example; organic

compounds that are emitted from solvents are emitted more in summer. The second factor,

which is related to variations in meteorology applies to all pollutants. Most of the

meteorological parameters (temperature, wind speed, relative humidity and mixing

height) show seasonal variations. Among these, the most important one is the mixing

height, which is the layer under which pollutants are homogeneously distributed. The

thickness of this layers varies between 500 m and 5 km at Ankara (Genç et al., 2010).

Since the mixing height determines the volume in which pollutants are dispersed, it

directly affect pollutant concentrations. Wind speed, which is horizontal ventilation

mechanism in an urban environment also affect seasonal variations in concentrations of

pollutants. Effect of temperature on concentrations of species can be both direct and

indirect. For example, concentrations of photo chemically active species are high in

summer due to increased photon concentration. These species are also correlated with

temperature, because temperature is also high in summer. This is indirect effect of

temperature, because variations in their concentrations are, actually, not due to increase

or decrease in temperature. Temperature can also directly affect concentrations of some

of the pollutants. For example, concentrations of volatile organic compounds that are used

in solvents, like toluene, increase in summer months due to enhanced evaporation at

127

higher temperatures in summer. The third factor, which is photochemistry, is also

compound specific. Compounds that are photochemically active are generated or

destroyed more effectively in summer, when solar flux is high.

Seasonal variation in meteorology that prevailed during our sampling period are given in

Table 4.5 and Table 4.6 for urban and suburban stations, respectively. Summer and winter

temperatures ranged between 9oC (in April) - 25oC (in August) and 1oC (in January) -

14oC (in October), respectively. Average temperatures were 19.2C during summer and

6.5C during winter. As can be seen in tables, Ankara is very cold during winter months,

implying that concentrations of compounds for which combustion is a source are expected

to be higher during winter season, due to increased combustion emissions with decreasing

temperature.

Lowest and the highest wind speed recorded at urban station during summer season were

2.3 m s-1 in September and 3 m s-1 in July. Low and high wind speed values recorded

during winter season varied between 2m s-1 (in November) and 3.2 m s-1 (in February).

Average summer and winter wind speed at urban station were 2.3C and 2.6C,

respectively. Wind speed recorded at suburban station were not significantly different

from those measured at urban station. Average values were 2.6 m s-1 in summer and 2.0

m s-1 in winter. Low annual and seasonal wind speed is one of the well-known

characteristics of Ankara meteorology. This clearly reflects in variations in monthly

average wind data depicted in Table 4.5 and Table 4.6.

Since wind speed is a measure of horizontal ventilation in Ankara, extremely low wind

speed values favors accumulation of pollutants, which results in relatively high pollutant

concentrations and frequent episodes. Summer average wind speed are slightly higher

than that in winter at suburban station and the two average values are comparable at urban

station. This pattern may contribute to lower concentrations of molecular markers during

summer at suburban station.

128

As discussed earlier in this chapter, mixing height is the most important meteorological

parameter that affect temporal variation of pollutants. In urban station; mixing height

varied between 1175 m (in April) - 1689 m (in July) in summer and between 581 (in

January) - 1044 m (in March). Average summer and winter mixing heights at urban

station were 1460 m and 800 m, respectively. At Suburban Station; summer mixing height

ranged between 1601 m (April) and 2325 m (August) with an average value of 1980 m.

It changed between 640 m (in January) and 1300 m (in March) in winter season. Winter

average mixing height at suburban station was 960 m.

There are two important points worth noting in this variability of mixing height. First,

summer mixing height is higher than winter mixing height at both stations. Since mixing

height is driven by temperature profile in atmosphere at a particular time, lower mixing

height in winter is not surprising and observed in all studies where it is calculated (mixing

height is lower at night and the highest during noon-time for the same reason). Of course

this seasonal pattern, naturally contributes to observed lower concentrations of all

pollutants in summer season. The second important point observed in this study is lower

mixing heights observed at urban station. Mixing height is also affected from surface

coating at a given location. Humans significantly altered surface characteristics, and thus

albedo, at urban areas, which affects mixing height. Today different codes are used to

calculate mixing height at urban and rural settings in atmospheric modeling studies.

Difference observed in this study (higher mixing height at suburban and lower mixing

height at urban stations), is a good example of this difference resulting from human

interference to nature.

Since atmospheric concentrations of species are primarily affected from horizontal and

vertical ventilation of urban airshed, a parameter that combines these two mechanism is

expected to be more closely related with short- and long-term variability in pollutant

concentrations. The parameter that is used for this purpose is “Ventilation Coefficient”.

Ventilation coefficient is the product of wind speed, which is a measure of effectiveness

horizontal ventilation over the city and mixing height, which is the measure of vertical

129

ventilation (Ashrafi et al., 2009; Chan et al. 2012; Iyer and Ernest Raj 2013; Krishnan and

Kunhikrishnan 2004).

For Urban Station; ventilation coefficient ranged between 3100 m2 s-1 (April) and 5100

m2 s-1 (July) in summer season with an average of 3800 m2 s-1. For winter season,

minimum and maximum ventilation coefficients were 1332 m2 s-1 (January) and 2902 m2

s-1 (February) with an average of 1893.5 m2 s-1. Like in mixing height, higher ventilation

coefficient was calculated at suburban station in both seasons. Summer and winter

ventilation coefficients varied between 4100 – 6300 m2 s-1 (average 5200 m2 s-1) and 1200

– 2900 m2 s-1 (average 2000 m2 s-1). In Ankara seasonal variation in ventilation coefficient

is not significantly different from that of mixing height due to unusually low wind speed.

As discussed previously, annual average wind speed at Ankara is approximately 2 m s-1.

This is considered as light wind speed. Please note that wind speed < 0.3 m s-1 is

considered as “calm” in meteorology. Although seasonal variation in ventilation

coefficient is not very different from seasonal variation in mixing height, it is better

correlated with daily concentrations of molecular markers, due to episodic short term

increases in wind speed, which is accounted for in ventilation coefficient, but entirely

ignored in mixing height.

This discussion of seasonal variations in temperature, wind speed, mixing height and

ventilation coefficient suggests meteorology favors lower concentrations of pollutants in

summer season, because wind speed, mixing height and ventilation coefficient are all

higher in summer. This pattern suggests that Ankara is better ventilated during summer

months by enhanced horizontal and vertical ventilation mechanisms. This, in turn, favors

lower concentrations of all pollutants during summer months. This also means that if

summer and winter concentrations of a pollutant (any pollutant, not necessarily organic

compounds) is comparable or lower than its summer concentration, then that compound

should have stronger source during summer months, because if emissions of a pollutant

are similar in summer and winter seasons, or if winter emissions are higher than summer

130

emissions, winter concentrations of that particular pollutants will be higher with the

contribution of meteorology.

In order to differentiate the sources of n-alkanes C-max is used and details of this method

was explained in Section 2.11. Carbon number lower than 27 are mainly coming from

fossil fuel combustion while carbon number higher than 27 are typically from biogenic

sources (Simoneit, 1999). Also % of wax n-alkanes is a method to determine the

contributions of biogenic versus anthropogenic sources. Higher %wax Cn shows the

more contributions from biogenic sources (Wang et al., 2006).

Summer and winter concentrations of measured n-alkane compounds are given in Figure

4.21 and Figure 4.22. In both stations, winter concentrations of measured alkanes are

higher during winter months. Mann-Whitney W-test demonstrated that summer and

winter median concentrations were different in 95% confidence interval. Summer-to-

winter concentration ratio varies around 0.4. There are only two alkanes that have higher

concentrations in summer months in urban station. These were heneicosane (C21) and

tritriacontane (C33). Other than these, all alkanes had at least a factor of two higher

concentrations in winter. Higher winter concentrations can be due to meteorology, as

discussed previously, or it may indicate higher emissions in winter. Anthropogenic and

biogenic alkane sources can be differentiated, because alkane molecules emitted from

anthropogenic sources are lighter than biogenic alkanes. Alkanes, which are smaller than

C27 are emitted from anthropogenic sources, whereas, compounds, which are heavier than

C27 are generally wax from biogenic sources (Rogge et al., 1993) For alkanes at urban

station highest concentrations were associated with C33 in summer and with C27 in winter.

Since alkane profile at urban station shift from heavier to lighter compounds from summer

to winter seasons, it can be concluded that contribution of anthropogenic sources to total

alkane concentration is higher in winter. Higher wax concentrations in summer season is

also reported in literature (Dutton et al., 2009).

131

Another approach to differentiate anthropogenic and biogenic sources of alkane

compounds is known as “%wax Cn” method. Where a parameter called %wax Cn is

calculated using the Equation 2.1, which is explained in Section 2.11 (Simoneit, 1999)

Higher %wax Cn values is an indication of high contributions from biogenic sources.

At Urban station; the highest % wax Cn corresponded to tritriacontane (C33) with 83% and

48% during summer and winter seasons, respectively. Please note that tritriacontane was

one of the two compounds that have higher concentrations in summer. The other one was

heneicosane (C21) which is a light alkane with anthropogenic sources. High concentration

of this compound is probably due to a strong source contributing to its concentration in

summer season. However high %wax value of tritriacontane (C33), which was the second

alkane with higher summer concentration and which was biogenic in nature, indicates that

contribution of biogenic sources (or wax) on total alkane mass is higher in summer season.

Please note that this conclusion, which bases and %wax values support the conclusion

previously reached from highest concentrations of alkane compounds in summer and

winter (Cmax approach). Average plant wax contribution to total n-alkane concentration

was 24% (range. 3-83%) in summer season and 15% (range: 2-48%) in winter. These

wax contributions to total alkane mass calculated by both Cmax and %wax approaches were

very similar.

This discussion indicates that although contribution of biogenic sources is higher in

summer, approximately 85-90 % of the n-alkanes were still from anthropogenic sources.

Results indicated that middle alkanes with moderate chain length (C23-C33) had higher

concentrations and their concentration accounted for approximately 90% of the total n-

alkanes in both seasons.

At Sub-urban station; Cmax was C27 for summer and C33 for winter seasons accounting for

13.5 % of total n-alkanes. The highest % wax Cn corresponded to tritriacontane (C33) with

91% and 78% during summer and winter seasons, respectively. There are only two n-

alkanes that have higher concentrations in summer months in urban station. These were

pentacosane (C25) and pentatricontane (C35). Other than these, all n-alkanes had at least a

132

factor of two higher concentrations in winter. Higher winter concentrations can be due to

meteorology, as discussed previously, or it may indicate higher emissions in winter. Since

n-alkane profile at sub-urban station shift from lighter to heavier compounds from summer

to winter seasons, it can be concluded that contribution of biogenic sources to total n-

alkane concentration is higher in winter. However, the average plant wax contribution to

total n-alkanes was 31% in both seasons. These numbers indicate that, approximately

70% of the n-alkanes were still originate from anthropogenic sources in both seasons.

Results indicated that middle alkanes with moderate chain length (C23-C33) had higher

concentrations and their concentration accounted for approximately 90% of the total n-

alkanes in both seasons.

Figure 4.21. Seasonal n-Alkanes variation at Urban Station

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Figure 4.22. Seasonal n-Alkanes variation at Sub-urban Station

Summer and winter median concentrations of PAHs are given in Figure 4.23 and Figure

4.24 for urban and suburban stations, respectively. Winter median concentrations of all

PAH compounds were higher than their corresponding concentrations in summer season,

in both stations. The only exception to this general pattern was observed in summer and

winter concentrations of benzo(ghi)perylene, which has approximately a factor of two

higher concentrations during summer season at urban station. Its concentration at

suburban station is higher during winter months like all remaining PAH compounds

measured in this work. The reason for higher summer concentration of

benzo(ghi)perylene is not clear. One possible reason is small number of data in summer

data set. Benzo(ghi)perylene was detected in only 6% of the summer samples at suburban

station.

Higher winter concentrations of all PAH compounds both in urban and suburban stations

is partly due to the effect of meteorology, which was discussed previously in this section

and partly due to their winter sources, namely combustion. Main source of most PAH

compounds is incomplete combustion in traffic, and in coal burning for space heating.

Traffic emissions decrease only slightly in summer and cannot account for large

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differences in PAH concentrations between summer and winter. Higher space heating

emissions in winter, together with meteorology can account for observed differences.

Space heating is not a significant PAH source in cities, which are heated with natural gas.

In such cities traffic is the dominating PAH source. However, it is an important source in

Ankara, because only 75% of households in Ankara is heated with natural gas (Kentair,

2013). Coal combustion is still main mode of heating, particularly at low-income districts

of the city. Gaga (2004), collected surface snow samples at approximately 100 locations

covering more or less whole Ankara, to assess dry deposition fluxes of 16 PAH

compounds. PAH distribution maps clearly demonstrated substantially high PAH

deposition fluxes at low income, slum area of the city. Their findings also support the

conclusion that, higher winter PAH concentrations is combined effect of meteorology and

emissions, reached in this study.

Another point worth noting about seasonal variations of PAH compounds is smaller

differences observed at suburban station. Summer-to-winter ratios of individual

compounds varied around 0.3 at urban station and around 0.7 at suburban station. Median

summer-to-winter ratios of all measured PAHs is 0.3 at urban station and 0.6 at suburban

station, indicating that summer and winter concentrations are closer to each other at

suburban station. This also supports contribution of space heating to measured PAH levels

in winter. Contribution of space heating to pollutant concentrations is limited at suburban

station, because the station is not in the immediate vicinity of residential areas and all

settlement areas around the university is heated by natural gas. Impact of coal combustion

for heating is much higher at urban station, because the station is in the middle of

residential area and it is close to low-income districts where coal combustion is common.

135

Figure 4.23. Seasonal PAHs variation at Urban Station

Figure 4.24. Seasonal PAHs variation at Sub-urban Station

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Summer and winter concentrations of n-alkanoic acids at urban and suburban stations are

given in Figure 4.25 and Figure 4.26, respectively. As in all groups of molecular markers,

n-alkanoic acid concentrations are higher in winter. Unlike in PAHs summer-to-winter

concentration ratios of individual acids are not significantly different at urban and

suburban stations. This may suggest that seasonal variations are determined by

meteorology, because if there were anthropogenic contribution to seasonal differences,

summer-winter difference is expected to be higher at urban station. A typical example of

this is observed in seasonal variation in PAH compounds, which was discussed in previous

paragraph.

Figure 4.25. Seasonal n-Alkanoic acids variation at Urban Station

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137

Figure 4.26. Seasonal n-Alkanoic acids variation at Sub-urban Station

Summer and winter median levoglucosan concentrations are given in Figure 4.27 for both

urban and suburban stations. Levoglucosan is an excellent tracer for wood burning. As

in all other organic compounds its winter concentrations are higher than their summer

concentrations in both stations. However, difference between summer and winter

concentrations are significantly larger at urban station. Summer-to-winter concentration

ratio is 0.45 at suburban station and 0.3 at urban one. This difference in seasonal ratios

indicate anthropogenic contribution to levoglucosan concentrations at urban station.

Since wood burning is common mode of space heating at low-income districts of the city,

which surrounds our urban station, observed anthropogenic contribution to levoglucosan

concentration at urban station is not surprising.

Seasonal variation in EC and OC concentration at urban and suburban stations are given

in Figure 4.28. EC concentrations in both stations and OC concentration at urban station

are higher in winter season. The only exception to this general pattern is OC concentration

at suburban station, where it is higher during summer season. OC at suburban station is

one of the few organic compounds that has higher concentration in summer. This is

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probably due to enhanced biogenic emissions in summer. Since our suburban station is

surrounded by trees, whereas urban station was in the middle of a residential areas,

biogenic emissions are expected to affect suburban station more. EC concentrations are

higher in winter at both stations. Summer-to-winter concentration ratio are approximately

0.8 both at urban and suburban stations. This similarity in summer-to-winter ratios

indicate that seasonal variation in EC concentrations is governed by meteorology, rather

than emissions. This should be expected, because main source of EC in both stations is

diesel traffic, which does not show a significant seasonal variation.

The main outcome of this section is that seasonal variations in concentrations of molecular

markers can provide information on the presence anthropogenic contributions,

particularly coal combustion on measured concentrations of particle-bound organics.

Meteorology alone can cause summer-to-winter concentration ratio to be as low as 0.5,

but not smaller than that. If there is no anthropogenic contribution affecting winter

concentrations of organic compounds this ratio is comparable at both stations. Summer-

to-winter concentration ratios < 0.5 is due to contribution of anthropogenic sources in

winter season. In Ankara most likely winter source is combustion of coal at slum area.

Natural and anthropogenic sources effective in summer season can increase the summer-

to-winter ratio. However, in Ankara higher summer concentrations is observed only for

EC and one two n-alkanes at suburban station.

139

Figure 4.27. Seasonal Levoglucosan variation at Urban and Sub-urban Station

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Figure 4.28. Seasonal EC-OC variation at Urban and Sub-urban Station

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141

4.9 Effect of local meteorology on concentrations of measured molecular

markers

The impact of meteorology on pollutant concentrations were discussed in general terms

in previous sections. In Section 4.8.3, where seasonal variations in concentrations of

molecular markers were discussed, it was demonstrated that meteorology account for at

least a fraction of seasonal variation observed in all groups of particle-bound organic

compounds. In this section relation between concentrations and wind speed, mixing

height and ventilation coefficient in short-term basis is discussed.

Meteorological parameters affect pollutant concentrations by affecting horizontal and

vertical ventilation processes. Horizontal ventilation refers to horizontal displacement of

air mass from urban atmosphere. Wind speed is a good indicator of horizontal ventilation.

Vertical ventilation, on the other hand, refer to vertical displacement of air over the city

and it is related with stability of the atmosphere. Mixing height is a good indicator for the

effectiveness of vertical ventilation process, because among all meteorological parameters

that are routinely monitored at meteorological stations, it is the only one related with

stability of atmosphere.

If there is no horizontal ventilation due to very low wind speed “calm” conditions occur

and if there is no vertical ventilation due to very stable atmosphere prevailing condition is

called “inversion”. Both calm and inversion conditions results in accumulation of

pollutants, but the worst case occurs when both horizontal and vertical ventilation stops,

which is called “stagnant” conditions. Extremely high concentrations of pollutants

recorded during stagnant conditions are called “episode” (Mudakavi, 2010; Vallero,

2008). Local meteorology also affects residence times of pollutants and their transport

from one location to another.

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4.9.1 Effect of wind speed

Wind speed is one of the important local meteorological parameter, which shows the

effectiveness of horizontal ventilation of the city. Variation of concentrations of selected

organic compounds with wind speed is depicted in Figure 4.29. Concentrations of

pollutants are expected to decrease with increasing wind speed due to effective horizontal

removal from urban atmosphere.

Organic compounds measured in this study did not show a consistent trend with wind

speed. Concentrations of some, like tetracosane (Figure 4.29a) and dodecanoic acid at

suburban station (Figure 4.29i) decrease with increasing wind speed, whereas others like

pentacosane (Figure 4.29b) and levoglucosan (Figure 4.29h) did not show any statistically

significant change with wind speed. However, majority of the measured organic

compounds showed a pattern, which is similar to those observed for tetracosane and

dodecanoic acid, namely decreasing concentrations with wind speed. Lack of consistent

trend in all compounds is probably due to very low wind speed measured during our

sampling period, which is typical for Ankara. As discussed previously in the manuscript

annual average wind speed at Ankara is approximately 2 m s-1, which is relatively close

to calm in meteorology (wind speed < 0.3 m s-1) and is considered as light wind speed.

Generally, it is difficult to see a consistent pattern in such low wind speed.

Another reason for lack of decreasing concentrations with increasing wind speed can be

increasing emissions during periods with high wind speed. For example, wind speed was

found to be higher during summer in both stations. In summer plant wax emissions

increase, which increase concentrations of some alkanes in atmosphere. Although this

has nothing to do wind speed, concentrations of some alkanes (> C27) appears to be

increasing with wind speed (Zheng et al., 2000). wind speed also enhances resuspension

of road dust which is enriched with organic compounds (Kasner et al., 2013).

143

Figure 4.29. Relationship between wind speed and particulate bound organic molecular markers

144

4.9.2 Effect of temperature

Temperature can affect the concentrations of compounds differently from other

meteorological parameters. There are direct and indirect effects of temperature on the

concentrations. Direct effect of the temperature includes increased emissions of some

organic compounds with increasing temperature. For example, plant wax emissions

increase with temperature. Indirect effect of the temperature is also very common. This

include increase or decrease in concentrations of pollutants that are due to some other

reason, but appears correlated with temperature. For example, photochemically produced

compounds have higher concentrations in summer due to increased solar flux. However,

since temperature is high in summer, concentration of that particular specie appears to be

correlated with temperature. There are also some grey areas, where relation between

concentration and temperature can be considered as both direct and indirect. For example,

concentrations of PAHs and alkanes are high in winter because coal combustion increase.

Since temperature is low in winter PAH or alkane concentrations appears inversely

correlated with temperature. However, this can also be considered as direct effect,

because coal combustion increase in winter, because temperatures are low.

Relation of temperature and molecular marker concentrations are given in Figure 4.30. In

most of the figures concentrations of organic compounds decrease with increasing

temperature. For some of these compounds observed decrease is not statistically

significant at 54% confidence level, probably because relation between concentrations and

temperature is not linear. For all compounds shown in the figure, lower concentrations

are observed at high temperatures. This is because coal combustion, which occurs only

at winter when temperature is low, is an important source for most of these compounds.

For some of the compounds, on the other hand, concentrations increase with increasing

temperature. These compounds generally have biogenic sources. Emissions from

biogenic sources increase in summer with increasing temperature. For example,

pentatriacontane is one of these compounds where concentrations increase with increasing

145

temperature. Main source of pentatriacontane is plant wax. Emission of wax from plants

increase with increasing temperature (Strader et al., 1999).

The relationship between temperature and concentrations were prepared by using mean

daily temperature values. It is clearly known that concentration of compounds having

photochemical characteristics or having biogenic sources increases with increasing

temperature. Therefore, maximum daily temperature values can be more meaningful

during investigation of the effect of temperature on concentrations. However, similar

results were gathered as can be seen from Figure 4.31. For example, pentacosane,

hentriacontane and pentatriacontane measured at sub-urban station had an increasing trend

with mean temperature and similar pattern were observed with maximum temperature.

PAHs measured at urban and sub-urban stations had a decreasing trend with both mean

and maximum temperature. These observations suggest that the compounds depicted

increasing trend with increasing temperature have biogenic sources instead of having

photochemical characteristics.

146

Figure 4.30. Relationship between temperature and particulate bound organic molecular markers

147

Figure 4.31. Comparative plots for maximum temperature and mean temperature effect on concentrations

148

4.9.3 Effect of mixing height

Definition of mixing height and methods to calculate it were given in “Materials and

Method” section. It is defined as the altitude in which pollutants are vertically dispersed

and mixed due atmospheric turbulence (Seibert et al., 2000). In that sense, mixing height

can be considered as the volume in which pollutants are homogenously distributed. Since

depth (which is the mixing height) determines dilution of pollutants, mixing height has a

profound influence on pollutant concentrations in an urban atmosphere. In this study,

mixing height is calculated using PCRAMMET software developed by the US EPA,

which is a preprocessor used to generate hourly mixing height data for local dispersion

models. Hourly mixing height data generated by PCRAMMET were then converted to

daily values to be used in this work.

Changes of concentrations with mixing heights are given in Figure 4.32. The general

trend is decreasing concentrations of particle-bound organic compounds with increasing

mixing height, as expected. However, there are few compounds that showed the opposite

trend. For example, pentacosane, hentriacontane, pentatriacontane and heneicosane and

octadecanoic acid have increasing concentrations with increasing mixing height. These

are heavy alkanes associated with biogenic emissions. Since biogenic emissions are

expected to be higher in summer, when mixing height is at its maximum, their

concentrations appear to increase with mixing height. In addition to summer sources of

some of the molecular markers, photochemical production, which is enhanced during

summer, can also results in an apparent increase in concentration of that compound with

mixing height.

149

Figure 4.32. Relationship between mixing height and particulate bound organic molecular markers

150

4.9.4 Effect of ventilation coefficient

Since ventilation coefficient is discussed in detail in previous sections, only a brief

explanation will be given in this section. Ventilation coefficient is the product of wind

speed and mixing height. Since it includes both vertical and horizontal ventilation

indicators, it is expected to be better correlated with pollutant concentrations than wind

speed and mixing height alone. Variation of concentrations of organic markers with

ventilation coefficient is depicted in Figure 4.33. Variation of concentrations of organic

compounds shown in the figure are not significantly different from variations of

concentrations with mixing height. This is because average wind speed is very low in

Ankara and it does not show a substantial difference between summer and winter (summer

and winter average wind speed at our urban station is 2.3 m s-1 in summer and 2.6 m s-1 in

winter). This means that ventilation coefficient is driven by variability in mixing height

and explains why variation of concentrations of organic markers with ventilation

coefficient is similar to variation of concentrations with mixing height. As in mixing

height, the general trend is decreasing concentrations of molecular markers with

ventilation coefficient, as expected. However, there are few compounds like, pentacosane,

hentriacontane, pentatriacontane and heneicosane and octadecanoic acid, which behave

differently. Concentrations of these compounds increase with increasing ventilation

coefficient. The reasons of such behavior is discussed in previous section where relation

between organic concentrations of organic compounds and mixing height was discussed.

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Figure 4.33. Relationship between ventilation coefficient and particulate bound organic molecular markers

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4.9.5 Effect of wind direction

During source apportionment studies, wind direction data could be important information

to identify the sources of atmospheric air pollutants. Their concentrations may show a

sharp directional route according to wind directions. When the air comes from certain

directions (sources) pollutant concentrations are high, on the other hand, concentrations

connected with other directions are low. Relationship between pollutant concentrations

and wind direction could be explained by using pollution roses. By using this method,

some mistakes could be made. For example, one or two very high detected concentrations

in a sector within a few hours could lead to high sector averages. And this does not mean

that this sector’s contribution is high to this pollutant’s concentration.

There are different approaches to avoid such misleading. The first method is called

Conditional Probability Function (CPF). This method provides probability of sources

being a certain wind sector instead of concentrations in that sector. The other method is

called Conditional Bivariate Probability Function (CBPF). This method couples ordinary

CPF with wind speed as a third variable, allocating the observed pollutant concentration

to cells defined by ranges of wind direction and wind speed rather than to only wind

direction sectors.

CBPF plots of the compounds were depicted in Figure 4.34-Figure 4.39. They were

prepared for concentrations >70th percentile for each compound. Four direction, the

increasing wind speed from origin to outside and probability values with color legend

could be seen from the polar plots. Blue represents the “0” probability and red represents

the highest probability for that compound. It is obvious that the analyzed group of

compounds follow a certain pattern in themselves. In general, the increase in wind speed

results in an accompanying decrease in concentration. However, please note that

increasing wind speed carries more polluted air mass to both receptor sites from the

sources. This may be related to the distance of the source from the measuring site (Uria-

Tellaetxe and Carslaw, 2014).

153

Plots prepared for sub-urban station indicate high probability of PAHs for winds blowing

from WNW, WSW and SSE that is toward the extensional area of the city, which also

host some main roads affected by heavy traffic. Lighter n-alkanes of which sources mainly

traffic related have high probability at directions of WNW, WSW and E. The heavier n-

alkanes of which sources mainly biogenic ones have high probability at directions of NW

and NE. The most important tracers for food cooking is octadecanoic acid, and its CBPF

plot represents that even lower wind speed can lead to high probability of octadecanoic

acid.

Plots prepared for urban station depict high probability of PAHs for winds blowing from

WNW, WSW and NNE that is toward the Keçiören and Altındağ, which host main roads

affected by heavy traffic and combustion sources. The direction of probability for n-

alkanes shift from E to N with lighter n-alkanes to heavier ones. CBPF plots for n-alkanoic

acids were dominated by N and NE direction. Low wind speed indicates that emissions

were from ground levels sources (for example, dodecanoic and tetradecanoic acids) while

higher wind speed indicates that emission from long distances.

154

Figure 4.34 Sub-urban Station CBPF Plots for PAHs for concentration >70th percentile

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Figure 4.35 Sub-urban Station CBPF Plots for n-alkanes for concentration >70th percentile

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Figure 4.36 Sub-urban Station CBPF Plots for n-alkanoic acids, OC and EC for concentration >70th percentile

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Figure 4.37 Urban Station CBPF Plots for PAHs for concentration >70th percentile

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Figure 4.38 Urban Station CBPF Plots for n-alkanes for concentration >70th percentile

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Figure 4.39 Urban Station CBPF Plots for n-alkanoic acids, OC and EC for concentration >70th percentile

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4.10 Spatial Correlations

Spatial correlation refers to correlation of parameters between two different stations.

These two stations can be in two different locations in the same city or at two different

cities or in two separate rural locations. In our case it was correlation between our urban

and suburban stations. The idea behind spatial correlations is to understand local vs

regional nature of sources affecting stations. The types of sources affecting different

stations may be the same, but physical locations of emissions driving concentrations of

pollutants may be different. For example, concentrations of many organic compounds

measured in this study is strongly affected from traffic emissions. However, compounds

measured at urban station comes from traffic emissions at Turgut Özal Boulevard, Irfan

Başbuğ street and Fatih street, whereas same compounds measured at suburban station

comes from traffic emissions at Eskisehir highway, Mevlana boulevard and Malazgirt

boulevard. Although their sources are identical, concentrations of these compounds

measured at our stations will not be correlated.

In earlier studies in our group it was demonstrated that transport of emissions from the

city to METU campus has a significant contribution on both organic and inorganic

pollutants measured at the university (Kuntasal et al., 2013; Yurdakul et al., 2017).

Molecular markers transported in this way are expected to show correlation between the

two stations.

Correlation between concentrations of organic markers at urban and suburban stations are

given in Table 4.8. Twenty-six out of 45 organic compounds showed correlation between

urban and suburban stations with statistical significance > 95%. Please note that

statistically significant correlation does not necessarily mean strong correlation. This is

particularly true for data sets containing large number of data points, because probability

of chance correlation (p in most statistical packages) is a function of N (number of data

points) and the value of “r” (correlation coefficient). Correct notation for “p < 0.05”,

which is used for 95% confidence interval in most statistical packages, is “P[r,n] < 0.05”

because of these dependences. Correlation can become statistically significant at low

161

values of r (correlation coefficient) if “n” is large. This is the case in this work where

there are large number of data points were available for most compounds. Values of “r”

varied between 0.63 (coronene) and 0.30 (nonacosane). Correlation between urban and

suburban concentrations of selected molecular markers are given in Figure 4.40.

Compounds that show the strongest correlation between the two stations are coronene,

EC, OC, benzo[a] anthracene. The highest number of correlations were observed for PAH

group. Concentrations of 11 PAH compounds (out of 16 measured in this study) measured

at urban and suburban stations were correlated. Similarly, 8 n-alkane and 6 n-alkanoic

acid were also correlated in both stations.

Logarithmic fit between concentrations of compounds at both stations generally provided

higher r and R2 values than linear fit, indicating the relation between concentrations of

compounds at two stations may be more complex than a linear one. The statistical tests to

test the statistical significance of correlations between concentrations of compounds at

urban and suburban stations are designed for data with Gaussian distribution. However,

it was demonstrated in earlier sections in this manuscript that distributions of molecular

markers measured in this study are right-skewed. Five compounds that did not show a

statistically significant correlation between their concentrations at urban and suburban

stations depicted high r values when logarithmic fit was applied. These were pentacosane,

vanillin, retene, linoleic acid and fluoranthene. When these compounds are added,

concentrations of a total of 31 compounds (out of 45 measured) showed statistically

significant correlation between the two stations.

The R2 values indicate the fraction variance of a compound at one stations, which can be

accounted by the variability in the other station. R2 values shown in Table 4.8 suggest

that variance in concentrations of molecular markers measured at suburban station is

partly accounted for by the variability of data at urban station. Transport of some organic

compounds from the city to our suburban station was suggested in previous sections of

this manuscript. However, most of those suggestions was based qualitative information.

This outcome of spatial correlations between concentrations of species measured at two

162

stations is the first clear evidence for such transport of pollutants from populated, high-

emission areas in the city to our suburban station at METU.

Correlations also indicated that the magnitude of transport is not the same for all organic

compounds. Approximately 30% - 40% of the variances of benzo[a]anthracene, coronene,

chrysene, pentacosane, EC and OC are accounted by the variability of data at suburban

station, whereas only small fraction (<10%) of variances of Benzo[k]fluoranthene,

heneicosane, dibenheneicosanezo[a,h]anthracene and hentriacontane can be accounted for

by variability in their concentrations at urban station. This is not surprising and can be

attributed to availability of local sources around suburban station and different reactivity’s

of these compounds. Most (but not all) of the compounds which depicted relatively high

correlation between the two stations are PAHs. Please note that PAHs in the atmosphere

are products of incomplete combustion either in vehicle engine or during combustion of

coal for space heating. Contribution of traffic to observed PAH concentrations are

expected in both stations. However, contribution of coal combustion to observed PAH

levels are different. Urban station is surrounded by low-income districts, like Keçiören,

Altındağ where coal burning is common, but main mode of heating around in all districts

around our suburban station is natural gas. Because of this difference in emission profiles,

transport of PAH compounds measured at METU from the city is expected.

Levoglucosan, which is a very specific tracer for wood burning is not correlated between

two stations indicating that local sources around stations are responsible from

levoglucosan concentrations measured at both urban and suburban station.

163

Table 4.8 Spatial correlations of the compounds between the stations

Compounds r p* R2

PM2.5

OC 0.562 0.000 0.32

EC 0.502 0.000 0.25

Fluorene (Fl)

Phenanthrene (Phe) 0.267 0.000 0.07

Anthracene (An)

Fluoranthene (Flut)

Pyrene (Pyr) 0.295 0.000 0.87

Benzo[a]anthracene (BaA) 0.536 0.000 0.29

Chrysene (Chr) 0.433 0.000 0.19

Benzo[b]fluoranthene (BbF) 0.214 0.002 0.46

Benzo[k]fluoranthene(BkF) 0.323 0.000 0.10

Benzo(e)pyrene 0.368 0.000 0.68

Benzo[a]pyrene (BaP) 0.248 0.000 0.62

Perylene 0.305 0.000 0.93

Dibenzo[a,h]anthracene(DBA)

Indeno[1,2,3,-cd]pyrene (IP)


Retene

Picene

Coronene 0.635 0.001 0.40

heneicosane

docosane 0.413 0.000 0.17

tricosane 0.330 0.000 0.11

tetracosane

pentacosane

hexacosane 0.201 0.003 0.40

heptacosane 0.469 0.000 0.22

164

octacosane 0.261 0.000 0.68

nonacosane 0.131 0.047 0.18

triacontane

tritriacontane -0.393 0.000 0.16

hentriacontane

dotriacontane 0.144 0.036 0.02

tetratriacontane

pentatriacontane

Dodecanoic acid 0.442 0.000 0.19

Tridecanoic acid 0.472 0.000 0.22

Tetradecanoic acid 0.324 0.000 0.10

Pentadecanoic acid

Hexadecanoic acid 0.177 0.012 0.03

Heptadecanoic acid 0.313 0.000 0.10

Linoleic acid

Oleic acid 0.372 0.000 014

Octadecanoic acid

levoglucosan

165

Figure 4.40. Correlation of selected organic compounds between urban and suburban stations

166

4.11 Secondary Organic Aerosol (SOA) Estimation

In this study, OC analysis have been carried out. PAHs, n-alkanes, n-alkanoic acids, and

levoglucosan have been determined from daily collected PM2.5 samples. Since the

chemistry of SOA is so complex, the composition of SOA was not identified in this study.

Therefore, an estimation method was used to identify the mass fraction of SOA and

contribution to total PM2.5 mass.

Since no direct chemical analysis method for the determination of either of the primary

and secondary OC components is currently available, the results in this study makes the

EC tracer method an even more practicable tool in the estimation of secondary organic

aerosol contribution to PM2.5 concentrations.

EC and OC have been analyzed and data was ready for the SOA estimation. EC tracer

method was used to identify the primary and secondary parts of the OC in PM2.5. If the

primary sources of EC and OC are considered to be same, EC could be used as good

marker for OCprimary. The OCprimary was calculated by using Equation 3.2 and 3.3 (see

Section 3.4.11). OCsecondary then was calculated by subtracting OCprimary value from

OCmeasuered.

There are two parameters that should be assumed, (OC/EC)primary and OCnon-combustion in

EC tracer method.

There are two approaches to assign a value for (OC/EC)primary; (1) detailed

emission inventory, and (2) a strong EC-OC correlation. Since there is no emission

inventory for Ankara and the EC-OC correlation is weak, a combined approach

has been used. There are different (OC/EC)primary values recorded in the literature.

(OC/EC)primary values may vary greatly depending on the characteristics of the

region being measured and the sampling time (Cabada et al., 2004). (OC/EC) primary

for this study was accepted as 0.85, because (OC/ EC)primary values measured in

studies performed in different regions in the literature ranged from 0.7 to 1.0.

167

The second parameter has to be decided was OCnon-combustion. The cut points of y-

axis obtained from annual and seasonal EC vs OC plots have been used for

assigning a value to OCnon-combustion. It was chosen as 2.5 for urban station and 1.5

for sub-urban station, not to be too far from the y-axis cutoff points obtained from

the annual and seasonal plots (Figure 4.41 and Figure 4.42). It is expected that

there will be a very high correlation between the OC and EC concentrations when

a large percentage of the measured total aerosol is coming from primary sources.

Figure 4.41 and Figure 4.42 depict a fairly low correlation between EC and OC in

both station. For this reason, it can be said that the high percent of the aerosol

measured in this region can be released from secondary sources.

Monthly averages of PM2.5, EC, OC, OCsecondary and %OCsec/PM2.5 values were depicted

in Table 4.9 and Table 4.10. It can be seen that the OCsecondary value was 6.13 µg m-3, about

12% of the total PM2.5 at urban station while OCsecondary value was 3.27 µg m-3, about 8%

of the total PM2.5 at sub-urban station. OC secondary values were approximately 60% of total

OC at both stations. When the temperature profile is examined from the Table 4.9 and

Table 4.10, it can be seen that summer values are lower than the winter ones. Increase in

temperature generally cause an increase in SOA concentrations. However, there is a

critical temperature that is found as 16oC in Strader et al.’s study (1999). As temperature

increases from 16oC, the predicted SOA concentrations starts to decrease. This situation

could be explained by the indirect effect of temperature on SOA. With increasing

temperature gas phase and aerosol phase SOA concentration increase. But, increasing

temperature leads to increase in vapor pressure of the secondary compounds and this

results a transport of higher percentage of SOA to gas phase. Therefore, this indirect effect

of temperature causes an optimum maximum temperature for SOA concentration increase

in summer season. For our case, this could be the reason for higher winter concentrations

than summer ones.

168

Figure 4.41 Urban station OC and EC relationship

y = 5,5469x - 1,9276

R² = 0,3204

0

20

40

60

80

0 1 2 3 4 5 6

OC

(µ

g m

-3)

EC(µg m-3)

Urban Station- EC vs OC

y = 2,1405x + 3,1646R² = 0,3575

0

5

10

15

20

25

0 1 1 2 2 3 3 4 4 5 5

OC

(µ

g m

-3)

EC(µg m-3)

Urban Station- -summer EC vs OC

y = 7,6857x - 4,4943R² = 0,4001

01020304050607080

0 1 2 3 4 5 6

OC

(µ

g m

-3)

EC(µg m-3)

Urban Station- winter EC vs OC

169

Figure 4.42. Sub-urban station OC and EC relationship

y = 3,9192x + 2,0814R² = 0,5135

0

5

10

15

20

25

0 1 1 2 2 3 3 4 4

OC

(µg

m-3

)

EC (µg m-3)

Sub-urban Station- EC vs OC

y = 1,6608x + 3,82R² = 0,1444

0

2

4

6

8

10

12

0 1 1 2 2 3

OC

(µg

m-3

)

EC (µg m-3)

Sub-urban Station- -summer EC vs OC

y = 4,8317x + 0,9346R² = 0,6376

0

5

10

15

20

25

0 1 1 2 2 3 3 4 4

OC

(µg

m-3

)

EC (µg m-3)

Sub-urban Station- winter EC vs OC

170

Table 4.9. Urban Station-Monthly average PM2.5, EC, OC, OCsecondary, and %OCsecondary/PM2.5

Months PM 2.5

(µg m-3)

EC

(µg m-3)

OC

(µg m-3)

OCsecondary

(µg m-3)

% OCsec/PM2.5

July.14 82.81 1.43 8.05 4.33 5.23

Aug.14 79.43 2.35 8.29 3.97 5.00

Sep.14 60.34 2.65 7.56 2.81 4.65

Oct.14 48.37 3.04 15.86 10.78 22.28

Nov.14 78.14 2.80 20.49 16.22 20.76

Dec.14 51.27 2.90 18.92 13.96 35.92

Jan.15 30.54 1.90 14.12 10.00 32.75

Feb.15 55.88 1.71 6.41 3.29 5.89

Mar.15 41.20 2.54 9.73 5.07 12.31

Apr.15 87.44 2.59 10.15 5.70 6.52

May.15 87.92 2.60 7.71 3.00 3.41

Jun.15 98.00 2.54 5.50 1.04 1.06

July.15 51.82 1.47 7.11 3.35 6.47

Aug.15 59.19 1.85 8.67 4.60 7.77

Sept.15 76.95 2.07 8.11 3.85 5.00

Total avg. 65.95 2.30 10.44 6.13 11.67

Summer avg. 75.99 2.17 7.91 3.63 5.01

Winter avg. 50.90 2.48 14.25 9.89 21.65

171

Table 4.10. Sub-urban Station-Monthly average PM2.5, EC, OC, OCsecondary and %OCsecondary/PM2.5

Months PM 2.5

(µg m-3)

EC

(µg m-3)

OC

(µg m-3)

OCsecondary

(µg m-3)

% OCsec/PM2.5

July.14 53.34 0.41 5.52 3.67 6.87

Aug.14 50.74 0.62 5.14 3.11 6.14

Sep.14 37.42 0.85 4.51 2.29 6.12

Oct.14 31.52 1.10 5.31 3.03 9.60

Nov.14 31.92 1.34 7.38 4.74 14.86

Dec.14 31.49 1.46 7.35 4.96 15.74

Jan.15 43.90 0.82 6.72 4.80 10.95

Feb.15 38.99 0.89 4.88 2.62 6.73

Mar.15 36.16 0.65 3.74 2.26 6.26

Apr.15 61.74 0.76 3.89 1.74 2.82

May.15 66.85 0.74 4.47 2.34 3.51

Jun.15 27.24 0.80 4.65 2.47 9.07

July.15 52.33 0.63 5.99 3.95 7.56

Aug.15 72.00 0.93 6.08 3.79 5.27

Sept.15 64.63 0.72 5.42 3.33 5.15

Total avg. 46.68 0.85 5.40 3.27 7.78

Summer avg. 54.03 0.72 5.07 2.97 5.83

Winter avg. 35.66 1.04 5.89 3.74 10.69

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4.12 Source Apportionment

4.12.1 Mass Closure

Mass closure is a simple method to determine components of aerosol population over an

urban area, which found frequent use in recent years (Putaud et al., 2004; Terzi et al.,

2010; Vecchi et al., 2004). Natural tracers of expected components are used to quantify

that particular component in the atmosphere. Diagnostic ratios reported in literature were

used to convert concentrations of tracer species into component mass or concentration.

For example, Ca and Fe concentrations were used to quantify mineral dust concentration

in atmosphere. Seven components were assumed to contribute to aerosol population in

Ankara atmosphere and concentrations of these seven components were estimated using

mass closure approach. Seven aerosol components used in this work include sulphate,

nitrate, chloride, calcium, iron, EC and OC. Results are presented in Table 4.11 and Table

4.12 for urban and rural stations, respectively. Contributions of aerosol components are

also presented in Figure 4.43 and Figure 4.44.

Among these seven components, SO42-, which is a marker to calculate (NH4)2SO4 mass;

NO3-, which is a marker to calculate NH4NO3 mass; Cl-, which is a marker to calculate

sea salt (NaCl); Ca, which is a marker to calculate gypsum concentration and Fe, which is

a marker to calculate soil component in aerosol population were not measured in this

study. Volatile organic compounds and trace elements were measured at the same stations

and in the same time period as different components of the same umbrella project. Data

for SO42-, NO3

-, Cl-, Ca and Fe were taken from (Goli, 2017).

Atmospheric concentrations of (NH4)2SO4 was calculated by multiplying SO42-

concentration by 1.38, which is the ratio of (NH4)2SO4–to-SO42- masses. The mass of

NH4NO3 was calculated from NO3- data, by using 1.2 as the factor to convert nitrate to

ammonium nitrate. Concentration of Cl- ion was used to estimate salt concentration. In

areas close to a sea, sea salt, which forms by bursting bubbles at the sea surfaces is the

173

main source of NaCl. However, in sampling locations like Ankara, which is not close to

the sea, origin of salt is road-salting, where NaCl or mixture of NaCl and sand is applied

to roads for de-icing in winter season. Although Na is a better marker for salt, we used

Cl- ions as a marker specie for salt, because in places like Ankara, which are far from

coast, a significant fraction of Na can originate from soil. Gypsum, which is CaSO4 was

calculated using Ca as marker and 4.9 was used as conversion factor. Iron was used as

marker for litophilic particles (soil and road dust particles) conversion factor for urban

and suburban stations were 5.5 and 9.0, respectively. The concentration of iron was

increased to represent the mass contribution of mineral dusts at background level. At

Harrison’s study (2003), the factors required for calculating crustal dust particles in the

roadside and background sites were fitted as 5.5 and 9.0. EC data was used directly

without making any conversion, because EC occurs as carbon particles in atmosphere.

OC, however; multiplied with 2.1 to convert carbon into organic molecules (Turpin and

Lim, 2001). Different factors are being used to convert OC mass to organic compound

mass. Ratios vary between 2.0 and 4.0, but most widely used factors change between 2.5

and 3.0. Since we are trying to obtain a crude estimate of organic compound mass, small

differences between factors used do not change conclusions significantly.

Results that are shown in Table 4.11 and Table 4.12, revealed some interesting features of

aerosol composition in Ankara atmosphere. The component that had the highest

contribution to PM2.5 mass is organic component. It accounts for approximately 40% of

PM2.5 mass at urban and 23% of PM2.5 mass at suburban stations. Higher contribution

of organic component to PM2.5 mass at urban site is not surprising and reflects higher

concentrations of OC and other organic species at urban station (see Figure 4.43 and

Figure 4.44). These values are not very different from values reported for other urban and

suburban atmospheres around the world, which is approximately 30% - 35% at urban and

15% at rural atmosphere (Bressi et al., 2013; Putaud et al., 2004).

The second highest contributing component to PM2.5 mass is secondary inorganic ions and

their sulfate forms. Contribution of this secondary component is 16% (ammonium sulfate

174

and ammonium nitrate together) at urban and approximately 22% at suburban station.

Such high contributions of secondary inorganic ions were also reported in most studies in

literature (Fang et al., 2002; Liu et al., 2005). Contribution of litophilic component

(gypsum plus soil) is 8.1% at urban and 15.5% at suburban station. Higher contribution

of soil type material to PM2.5 mass at suburban station is due to larger surface area of

exposed soil at suburban site. Contribution of EC to PM2.5 concentrations is 1.7% at

suburban station and 4% at urban site. This factor-of-two difference is due to stronger

influence traffic emissions at urban station. Salt is the smallest contributing component

and has similar contributions at both stations. Considering large distance between Ankara

and coasts, this small contribution should be expected.

Please note that values given in tables and discussion in this section is for PM2.5 size

fraction only. Contribution of these components to coarse fraction (d > 2.5 µm) aerosol

is expected to be different.

Table 4.11. Mass Closure model results- Urban Station

Analyte Concentration (µg m-3)

Converted to Mass Closure Model Concentration (µg m-3)

% Contribution to PM2.5

Sulphate 5.11 Ammonium sulphate

7.0 11.7

Nitrate 2.04 Ammonium nitrate

2.6 4.4

Chloride 0.34 Sodium chloride 0.6 0.9

Calcium 0.44 Gypsum 1.9 3.2

Iron 0.53 Soil/road dust 2.9 4.9 EC 2.39 EC 2.4 4.0

OC 11.24 Organic compounds

23.6 39.3

175

Table 4.12. Mass Closure model results- Sub-urban Station

Analyte Concentration (µg m-3)

Converted to Mass Closure Model Concentration (µg m-3)

% Contribution to PM2.5

Sulphate 5.70 Ammonium sulphate

7.9 16.4

Nitrate 2.23 Ammonium nitrate

2.9 6.0

Chloride 0.29 Sodium chloride 0.5 1.0

Calcium 0.55 Gypsum 2.4 4.9

Iron 0.56 Soil/road dust 5.1 10.6 EC 0.83 EC 0.8 1.7

OC 5.31 Organic compounds

11.1 23.2

Figure 4.43 Percentage of chemical compounds contributing to the PM2.5 mass at urban and sub-urban stations

Urban-Percent components of PM2.5

Ammonium sulphate Ammonium nitrate

Sodium chloride Gypsum

Soil/road dust EC

Organic compounds

Sub-urban- Percent components of PM2.5

Ammonium sulphate Ammonium nitrate

Sodium chloride Gypsum

Soil/road dust EC

Organic compounds

176

Figure 4.44 Mass closure-source contributions

4.12.2 PMF

PMF was used in this study to determine the sources of particulate bound organic

compounds and their contributions to total PM2.5 mass. The details of the method were

discussed previously in Section 2.16.4. PMF was run separately to data generated at

urban and suburban stations. Eight factors were extracted in both PMF exercises,

indicating that aerosol population at Ankara includes eight organic components. Structure

and physical meaning of these eight factors are discussed in following sections.

Sub-urban

Urban

0

10

20

30

40

50

Sec

onda

ry A

eros

ol

Min

eral

s

Sea

Sal

t

Car

bona

ceou

s A

eros

ol

Une

xpla

ined

% C

ontr

ibut

ion

% Source Contribution

177

4.12.2.1 PMF optimization parameters and results

Before running the PMF program, there are some QC parameters to be optimized.

Optimization of PMF performance parameters is discussed in this section. The most

important performance indicator in PMF is the objective function Q. The aim is to

minimize Q by adjusting various parameters, such as F-peak, Scaled residuals etc. The

PMF model gives two Q values: one of them is Robust Q-value (Qrobust) which is

calculated by excluding outliers and the second one is True Q-value (Qtrue) which is

calculated by including all data points. If the ratio of True-Q to Robust-Q is higher than

1.5, there are too many outliers that affect the model outputs. Contribution of these

outliers to model fit can be (and should be) avoided by assigning them large uncertainties.

At each trial computed Qrobust is compared with a third Q value, namely theoretical Q

(Qtheoretical). Theoretical Q vale, as its name implies, a theoretical value, which depends

on number of samples, number of parameters and number of factors extracted in PMF.

Theoretical Q is calculated using the following relation.

Equation 4.1

Where, n is the number of parameters, m is number of samples used in PMF and p is the

number of factors extracted. The criteria for the performance of PMF is the closeness of

the robust and theoretical Q values. The Qrobust/Qtheoretical = 1.0 indicates an excellent

model fit. Generally, ratios <2.0 is considered as good fit and ratios < 3.0 are considered

acceptable fit.

Theoretical Q value cannot be modified. It has a constant value for a given number of

factors. However, Qrobust can be minimized by optimizing, scaled residuals, correlations

between g-scores, F-peak parameter, measurement uncertainties. The ultimate test for

accuracy of the PMF run is the comparison of concentrations of species calculated by the

model with their measured values. Furthermore, robustness of the factors extracted can

178

be tested by bootstrapping. These parameters, which were used to optimize PMF solution

is briefly discussed in this section.

Distribution of observed vs predicted concentrations around a mean value is also an

indication of the goodness of model sit. Such distribution for each specie is referred to as

scaled residuals. For each compound scaled residuals is expected to vary between ± 3.

When outliers are eliminated by assigning high uncertainty to those particular

concentrations quality of the model fit improves and Q decrease. Some examples for

distribution of scaled residuals ire given in Figure 4.45. Distributions of scaled residuals

is fairly narrow as they should be in Figure 4.45a, but residuals shown in Figure 4.45b are

broadly distributed and there are some values > ± 3, which should be eliminated to

improve the fit.

Another parameter should be optimized is correlations between the factor G-scores. PMF

software gives correlation plots between G-scores of factors. A high correlation between

G-scores of two factors is an indication that these two profiles are auto-correlated. This

physically means that the factors represent similar emission sources. In such a case PMF

cannot separate these two sources from each other. This problem is common for log-

normally distributed air pollution data and should be solved by trying different value of

the F-peak parameter. For this study F-peak value was varied in range between -0.5 and

+0.5 and its value was fixed at the point, where Qrobust and Qtrue reached to a minimum.

The minimum values for Qrobust and Qtrue were reached at F-peak = -0.5.

Another step in optimization process is to check stability of factors by bootstrapping. In

bootstrapping a random block of samples is removed from data set and the vacancy is

filled by using same number of samples in the remaining data twice. This process is

repeated 100 times (actually n is user specified, but n =100 is used in most PMF

applications. After each run, factor profiles are correlated with the base factor profiles

generated by the base run. In this study, correlation value was defined as 0.6 (r) and the

correlations were achieved in 80 out of 100 runs, then factors were accepted as robust.

179

For the PMF model, a set of 337 samples and 46 species for urban station and 275 samples

and 45 species for sub-urban station were used as input to the model. Compounds were

classified as bad, weak and strong based on their signal to noise ratio (S/N) and/or the

percentages of the samples below the detection limit (BDL). In this study, the compounds

with S/N higher than 3.0 were categorized as strong in the PMF model. The compounds

with S/N between 1.5 and 3 were categorized as weak. Weak compounds were included

in PMF partitioning of sources, but they are not included in the fit. The compounds with

S/N ratio lower than 1.5 or with BDL>80% were categorized as bad in quality and not

included to PMF analysis. According to these, the following compounds were considered

as bad: fluorone, coronene, retene and vanillin for sub-urban station and fluorone, picene

and coronene for urban-station. For sub-urban station PM2.5, dibenzo(a, h)anthracene,

indeno(1,2,3,-cd)pyrene, benzo(ghi)perylene, docosane and pentatriacontane; for urban-

station PM2.5, dibenzo(a,h)anthracene, indeno(1,2,3,-cd)pyrene, benzo(ghi)perylene,

retene, pentatriacontane and vanillin were identified as weak compounds. An extra

modelling uncertainty of 15% was used to gather model output. The model was run with

different number of factors ranging from 6 to 9 obtaining the optimum solution with 8

factors for both stations. The values of Qrobust and Qtrue were 20,210 and 20,234 for sub-

urban station, respectively and 24,200 and 24,222 for urban station, respectively. Ratio of

Robust-Q to Theoretical-Q was obtained as 2.0 and 1.9 for sub-urban and urban stations,

respectively.

180

Figure 4.45. Histograms of scaled residual distributions

(a) indeno(1,2,3-cd)pyrene- a good example and (b) dotriacontane- a bad example

4.12.2.2 Diagnostic ratios for PAHs

Different sources generate PAHs with different molecular weights (Ma et al. 2010) and

this feature is used to identify sources using PAH concentration ratios. Low molecular

PAHs are usually generated from low temperature processes such as grass, wood and coal

burning. On the other hand, high temperature processes, for example fuel combustion,

lead to higher molecular weight PAH species. In this study the used diagnostic ratios and

related information listed in Table 4.13. For example Benzo(a) anthracene-to – (Benzo(a)

anthracene + Chrysene) ratio can be used to differentiate between coal combustion,

vehicular emissions and petrogenic sources the ratio is < 0.2 in emissions from petrogenic

sources, between 0.2 and 0.35 in emissions from coal combustion and > 0.35 in traffic

emissions (Tobiszewski and Namiesnik, 2012). Similarly, fluoranthene – to -

(fluoranthene+pyrene) ratio is <0.4 in emissions from petrogenic sources, it is somewhere

181

between 0.4 and 0.5 in emissions from fossil fuel combustion and > 0.5 in biogenic

emissions (Opuene et al.,2009). In addition to these indeno[1,2,3,-cd]pyrene – to –

Indeno[1,2,3,-cd]pyrene+ Benzo[ghi]perylene) and anthracene – to –

Anthracene+Phenanthrene ratios are also used for apportionment of sources of particle-

bound organic compounds. Diagnostic ratios do not provide conclusive evidence about

sources, but they provide useful supporting information in source apportionment studies.

In PMF diagnostic ratios was used as one of the information sources to identify factors

and assign physical meaning to them.

Table 4.13 Diagnostic ratios used in this study with their reported values for specific processes (Tobiszewski and Namiesnik, 2012)

Diagnostic ratios Value range Source

Benzo(a) anthracene /

(Benzo(a) anthracene + Chrysene)

0.2-0.35

>0.35

<0.2

Coal combustion

Vehicular emission

Petrogenic

Fluoranthene/

(Fluoranthene+Pyrene)

<0.4

0.4-0.5

>0.5

Petrogenic

Fossil Fuel Combustion

Grass, wood, coal

combustion

Indeno[1,2,3,-cd]pyrene/( Indeno[1,2,3,-

cd]pyrene+ Benzo[ghi]perylene)

<0.2

0.2-0.5

>0.5

Petrogenic

Petroleum combustion

Grass, wood and coal

combustion

Antracene/(Anthracene+Phenanthrene) <0.1

>0.1

Petrogenic

Pyrogenic

182

4.12.2.3 Urban Station-Factors

As discussed before, eight factors were resolved by PMF analysis. In order to assign each

factor to a specific source, plots of F-loading and percentage of concentrations of organic

compounds explained by each factor which are regular outputs of PMF, were prepared for

each factor. Additionally, monthly median values of factor scores (G-scores) were

calculated and plotted. Factor loading (F-loading) for each compound at each factor is

concentration of each measured compound in each factor. Factor loadings have

concentration units. However, percentage of concentrations accounted by factors is more

informative than factor scores in assigning factors to physical sources. For example, EC

concentration (F-loading) is high in most of the sources, but it does not mean that EC is

an indicator specie for all of those sources. For this reason, F loading was used along with

Explained Variations and G scores in the in identification of sources.

The source profiles resolved by PMF model and the individual contribution of each factor

to total mass are presented in Figure 4.46. Eight factors extracted by PMF were identified

as: combustion, combustion-2, natural gas combustion, road dust, food cooking, food

cooking-2, biomass burning and plant emission. How factors were associated with these

physical sources are discussed in following sections.

183

Figure 4.46 Urban Station- Species profiles- % of species and concentration of species

184

Factor-1

Fractions of concentrations of compounds explained by factor 1, Factor 1 loadings and

monthly median concentrations of Factor 1 G-scores are depicted in Figure 4.47. The first

factor was explained 70 % of perylene, more than 40 % dibenzo(a,h) anthracene and

benzo(k)fluoranthene concentrations. It also contains a high percentage of

benzo(e)pyrene (30%), retene (30%) and chrysene (30%)concentrations. Perylene and

dibenzo(a,h)anthracene are good markers for combustion (Harrison 2016). An interesting

point for factor is that it does not contribute significantly to concentrations of n-alkanes

and weighted heavily by PAHs. Monthly median concentrations of G-score values for

this factor, which is illustrated in Figure 4.47c, shows that there is a clear seasonality with

higher scores in winter. This seasonal pattern is consistent with combustion factor. Factor

1 loading in Figure 4.47 also depicts a dominant nature of PAHs in this factor. In addition

to PAH compounds there is also high mass concentrations of PM2.5, EC, OC and

hexadecanoic acid in factor 1. It is well documented that PM2.5, EC and OC are good

tracers of combustion (Dan et al., 2004; Zheng et al., 2005). In addition, diagnostic

Fluoranthene-to-(Fluoranthene+Pyrene) diagnostic ratio was between 0.4-0.5, which is

the range associated with fossil fuel combustion (Tobiszewski and Namiesnik, 2012).

Similarly, Also the ratio of Indeno[1,2,3,-cd]pyrene-to-Benzo[ghi]perylene) ratio was

0.25, indicating that there are important contributions from fuel combustion (Wang et al.,

2016). Based on these arguments, Factor 1 was identified as combustion factor.

The CBPF graph of factor is depicted in Figure 4.47d. Emissions from NW and SW sectors

have an effect on Factor 1. Turgut Özal boulevard passes from NW of the urban station.

Therefore, combustion related pollutants can be transported to the receptor site even with

low wind speed. The city center is located on SW of the station. Therefore, combustion

emissions from the city center have an effect on the formation of this profile.

185

Figure 4.47 Urban Station Factor-1 Contribution, factor loadings of species, G-scores and CBPF at 70th percentile

020406080

100

PM

2.5

OC

EC

phen

anth

rene

…an

thra

cene

(A

n)fl

uora

nthe

ne…

pyre

ne (

Pyr

)be

nzo[

a]an

thra

c…ch

ryse

ne (

Chr

)be

nzo[

b]fl

uora

n…be

nzo[

k]fl

uora

n…be

nzo(

e)py

rene

benz

o[a]

pyre

ne…

pery

lene

dibe

nzo[

a,h]

ant…

inde

no[1

,2,3

,-…

benz

o[gh

i]pe

ryl…

rete

nehe

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tria

cont

ane

tetr

atri

acon

tane

pent

atri

acon

tane

dode

cano

ic a

cid

trid

ecan

oic

acid

tetr

adec

anoi

c ac

idpe

ntad

ecan

oic…

hexa

deca

noic

aci

dhe

ptad

ecan

oic…

lino

leic

aci

dol

eic

acid

octa

deca

noic

aci

dle

vogl

ucos

anva

nill

in

Fra

ctio

n of

con

cent

rati

ons

(%)

Factor-1 Combustion

0

1

2

3

4

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

ace…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

th…

Ben

zo[k

]flu

oran

th…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nthr

…In

deno

[1,2

,3,-

…B

enzo

[ghi

]per

yle…

Ret

ene

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

icon

tane

tetr

atri

acon

tane

pent

atri

acon

tane

Dod

ecan

oic

acid

Tri

deca

noic

aci

dT

etra

deca

noic

aci

dP

enta

deca

noic

aci

dH

exad

ecan

oic

acid

Hep

tade

cano

ic a

cid

Lin

olei

c ac

idO

leic

aci

dO

ctad

ecan

oic

acid

levo

gluc

osan

Van

illi

n

Fac

tor

load

ings

Factor-1 Combustion

0

1

2

3

4

5

6

G-s

core

Factor-1 Combustion

186

Factor-2

The second factor accounts for 71% of pyrene, 65% of benzo(b)fluoranthene and 65% of

chrysene concentrations. This factor also explains approximately 26 - 46% of the

concentrations of dotriacontane, nonacosane and hexacosane. Presence of both PAH and

n-alkane compounds in factor 2 suggests that this factor is attributable to anthropogenic

combustion sources. There is no PM2.5 or EC in Factor 2. Natural gas combustion is a

likely source of Factor 2, because (1) as in all fossil fuel combustion sources, incomplete

combustion of natural gas generate PAHs (Yarwood, 2002) and (2) complete or

incomplete combustion of natural gas results in low emissions of atmospheric particulate

matter (Rogge et al., 1993b). This situation agrees with the distribution of the particulate

bound organic matter at Factor-2. Factor 2 accounts for approximately 15% of chrysene

concentration, which is a good marker for natural gas combustion (Jaeckels et al., 2007).

Seasonal variation of G scores corresponding to this factor was depicted in Figure 4.48c.

High G score values in winter and low ones in summer support natural gas source for this

factor. In general, natural gas does not include n-alkanes. However, they can come out

from the gas pipeline as pollutants or can be synthesized during the combustion process.

Rogge et al. (1993) showed that n-alkanes, both low molecular weight and high molecular

weight, could be found in the particle phase collected on the filter. Based on these

arguments, natural gas combustion was assigned as the source of factor 2.

CBPF plot of Factor 2 is depicted in Figure 4.48d. It is seen that concentrations of

pollutants can increase with increasing wind speed -or at least not decrease due to

increasing mixing. This mixing can result in PAH enrichment in air and result higher

probability at higher wind speed from NE.

187


0

20

40

60

80

100

PM

2.5

OC

EC

phen

anth

rene

(P

he)

anth

race

ne (

An)

fluo

rant

hene

(F

lut)

pyre

ne (

Pyr

)be

nzo[

a]an

thra

ce…

chry

sene

(C

hr)

benz

o[b]

fluo

rant

…be

nzo[

k]fl

uora

nt…

benz

o(e)

pyre

nebe

nzo[

a]py

rene

…pe

ryle

nedi

benz

o[a,

h]an

th…

inde

no[1

,2,3

,-…

benz

o[gh

i]pe

ryle

…re

tene

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

iaco

ntan

ete

trat

riac

onta

nepe

ntat

riac

onta

nedo

deca

noic

aci

dtr

idec

anoi

c ac

idte

trad

ecan

oic

acid

pent

adec

anoi

c ac

idhe

xade

cano

ic a

cid

hept

adec

anoi

c ac

idli

nole

ic a

cid

olei

c ac

idoc

tade

cano

ic a

cid

levo

gluc

osan

vani

llin

Fra

ctio

n of

con

cent

ratio

ns (

%)

Factor-2 % Natural gas combustion

0

1

2

3

4

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

ace…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

th…

Ben

zo[k

]flu

oran

th…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nthr

…In

deno

[1,2

,3,-

…B

enzo

[ghi

]per

yle…

Ret

ene

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

icon

tane

tetr

atri

acon

tane

pent

atri

acon

tane

Dod

ecan

oic

acid

Tri

deca

noic

aci

dT

etra

deca

noic

aci

dPe

ntad

ecan

oic

acid

Hex

adec

anoi

c ac

idH

epta

deca

noic

aci

dL

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

anV

anil

lin

Fac

tor

load

ing

Factor-2 Natural gas combustion

0

1

2

3

4

G-s

core

s


188

Factor-3

Fraction of concentrations of organic marker compounds explained by factor 3, factor

loadings and monthly variation of factor 3 scores are given in Figure 4.49. Third factor

accounts for 74% of PM2.5 concentrations, 57% of tritriacontane concentration and also

explains concentrations of n-alkanes. Factor 3 also accounts for 48% of indeno(1,2,3-

cd)pyrene and 31% of EC concentration. It is clear from figure that this factor is

dominated by high molecular weight n-alkanes. As discussed previously in manuscript,

higher molecular weight n-alkanes originate from biogenic sources. However, high

contribution of PM2.5 to factor 3 suggests the presence of sources other than biogenic ones,

because very high contribution of biogenic emissions to PM2.5 mass contribution is not

expected. There are studies, which showed that tire wear extract includes n-alkanes with

a range of C19 to C41 (Rogge et al. 1993). n-Alkanes are used in tire manufacturing to

protect tires from oxidants and UV light. Rogge et al. (1993) demonstrated to occurance

of n-alkanes in road dust. Seasonality of factor 3 is depicted in Figure 4.49c. It is clear

from the figure that Factor 3 scores are high in summer and low in winter, which supports

road dust source for factor 3. As discussed before, the road dust can resuspend by wind

or passing traffic. Although wind speed does not change significantly between summer

and winter, resuspension of dry road dust is easier and occurs at lower wind speed in

summer months. However, soil is mud or ice-covered in winter which makes

resuspension of road dust more difficult. Based on these arguments, Factor 3 was

identified as road dust.

Figure 4.49d shows CBPF plot of Factor-3. This plot clearly shows highest road dust

concentrations when the wind is blowing from NE. Given that the receptor site is the west

side of the street and the highest concentrations are recorded when the wind is blowing

away from the NE is strong evidence of street recirculation.

189


0

20

40

60

80

100

PM

2.5

OC

EC

phen

anth

rene

…an

thra

cene

(A

n)fl

uora

nthe

ne (

Flu

t)py

rene

(P

yr)

benz

o[a]

anth

rac…

chry

sene

(C

hr)

benz

o[b]

fluo

rant

…be

nzo[

k]fl

uora

nt…

benz

o(e)

pyre

nebe

nzo[

a]py

rene

…pe

ryle

nedi

benz

o[a,

h]an

th…

inde

no[1

,2,3

,-…

benz

o[gh

i]pe

ryl…

rete

nehe

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tria

cont

ane

tetr

atri

acon

tane

pent

atri

acon

tane

dode

cano

ic a

cid

trid

ecan

oic

acid

tetr

adec

anoi

c ac

idpe

ntad

ecan

oic

acid

hexa

deca

noic

aci

dhe

ptad

ecan

oic

acid

lino

leic

aci

dol

eic

acid

octa

deca

noic

aci

dle

vogl

ucos

anva

nill

in

Fra

ctio

n of

con

cent

rati

ons

(%)

Factor-3 % Road dust

01234567

PM

2.5

OC

EC

Phe

nant

hren

e…A

nthr

acen

e (A

n)F

luor

anth

ene…

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

…C

hrys

ene

(Chr

)B

enzo

[b]f

luor

…B

enzo

[k]f

luor

…B

enzo

(e)p

yren

eB

enzo

[a]p

yren

…P

eryl

ene

Dib

enzo

[a,h

]a…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

er…

Ret

ene

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

tritr

iaco

ntan

ehe

ntri

acon

tane

dotr

icon

tane

tetr

atri

acon

tane

pent

atri

acon

tane

Dod

ecan

oic

acid

Tri

deca

noic

aci

dT

etra

deca

noic

…P

enta

deca

noic

…H

exad

ecan

oic…

Hep

tade

cano

ic…

Lin

olei

c ac

idO

leic

aci

dO

ctad

ecan

oic…

levo

gluc

osan

Van

illi

n

Fac

tor

load

ing

Factor-3 Road dust

0

1

2

G-s

core

s

Factor-3 Road dust

190

Factor 4

The fourth factor was enriched in tricosane and n-alkanoic acids. It explains 53% of the

concentrations of tricosane and pentadecanoic acid, 37% of the concentration of

hexadecanoic acid, 32% of the concentrations of EC and OC. Contribution of factor 4 to

n-Alkanes concentrations ranges between 8-15%. The seasonality of this factor is

depicted in Figure 4.50c. Factor 4 scores do not change from one month to another.

Which means there is no seasonality in G-scores of this factor. n-Alkanoic acids are the

most significant markers of the food cooking (Schauer et al., 1996a). Rogge et al. (1991)

showed that both n-alkanes and n-alkanoic acids emitted from frying of meat could make

an important contribution to atmospheric particulate matter and tricosane and oleic acid

are the best markers for this source. Factor 4 was identified as food cooking based on this

information.

Figure 4.50d shows CBPF plot of Factor-4. This plot clearly shows the highest food

cooking emissions when the wind is blowing from E and NE. Receptor site was located

in the campus and east side of the station is residential area. Therefore, the moderate and

high probability was expected from the east side of the station.

191

Figure 4.50 Urban Station Factor-4 Contribution, factor loadings of species, G-scores and CBPF probability at 70th percentile

0

20

40

60

80

100

PM

2.5

OC

EC

phen

anth

rene

(P

he)

anth

race

ne (

An)

fluo

rant

hene

(F

lut)

pyre

ne (

Pyr

)be

nzo[

a]an

thra

ce…

chry

sene

(C

hr)

benz

o[b]

fluo

rant

…be

nzo[

k]fl

uora

nt…

benz

o(e)

pyre

nebe

nzo[

a]py

rene

…pe

ryle

nedi

benz

o[a,

h]an

th…

inde

no[1

,2,3

,-…

benz

o[gh

i]pe

ryle

…re

tene

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

iaco

ntan

ete

trat

riac

onta

nepe

ntat

riac

onta

nedo

deca

noic

aci

dtr

idec

anoi

c ac

idte

trad

ecan

oic

acid

pent

adec

anoi

c ac

idhe

xade

cano

ic a

cid

hept

adec

anoi

c ac

idli

nole

ic a

cid

olei

c ac

idoc

tade

cano

ic a

cid

levo

gluc

osan

vani

llin

Frac

tion

of

conc

entr

atio

ns (

%)

Factor-4 % Food cooking

0

1

2

3

4

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

ace…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

th…

Ben

zo[k

]flu

oran

th…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Pery

lene

Dib

enzo

[a,h

]ant

hr…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

e…R

eten

ehe

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic a

cid

Pen

tade

cano

ic a

cid

Hex

adec

anoi

c ac

idH

epta

deca

noic

aci

dL

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

anV

anil

lin

Fac

tor

load

ings

Factor-4 Food cooking

0

1

2

G-s

core

s


192

Factor 5

The fifth factor is another combustion factor with an explained variation of 57% of

tridecanoic acid, 42% of bezo(a)pyrene, ̴ 30-35% of pentacosane, hexacosane and

tetracosane. The dominant n-alkanoic acid in this factor was tridecanoic acid and it has a

low molecular weight with a carbon number of 13. Similar to sources of n-alkanes, the

predominance of n-alkanoic acids with low molecular weight which carbon number is

lower than 16 is attributable to sources of anthropogenic origin, particularly incomplete

combustion of fossil fuels (Rogge et al., 1993). Benzo(a)pyrene is classified as the most

important toxic compound in the PAH group. Coal combustion was shown as the most

significant source of this compound in the literature (Commins, 1969; Commins and

Hampton, 1976; Mastral et al., 1996). In addition, diagnostic ratio of Benzo(a)pyrene/(

Benzo(a)pyrene + Chrysene) was 0.5, which is also an indicator for incomplete

combustion from vehicular emissions (Akyüz and Çabuk, 2010; Tobiszewski and

Namiesnik, 2012). G-score distribution over the sampling period is depicted in Figure

4.51c. There is a clear seasonal pattern with higher scores in winter. Therefore, this factor

was identified as combustion-2. CBPF plot is depicted in Figure 4.51d. Concentration

pattern shows high probability at NE and SE directions which implies combustion sources

can be detected at low concentrations.

193


0

20

40

60

80

100

PM

2.5

OC

EC

phen

anth

rene

(P

he)

anth

race

ne (

An)

fluo

rant

hene

(F

lut)

pyre

ne (

Pyr)

benz

o[a]

anth

rac…

chry

sene

(C

hr)

benz

o[b]

fluo

rant

…be

nzo[

k]fl

uora

nt…

benz

o(e)

pyre

nebe

nzo[

a]py

rene

…pe

ryle

nedi

benz

o[a,

h]an

th…

inde

no[1

,2,3

,-…

benz

o[gh

i]pe

ryle

…re

tene

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

iaco

ntan

ete

trat

riac

onta

nepe

ntat

riac

onta

nedo

deca

noic

aci

dtr

idec

anoi

c ac

idte

trad

ecan

oic

acid

pent

adec

anoi

c ac

idhe

xade

cano

ic a

cid

hept

adec

anoi

c ac

idli

nole

ic a

cid

olei

c ac

idoc

tade

cano

ic a

cid

levo

gluc

osan

vani

llin

Frac

tion

of

conc

entr

atio

ns (

%)

Factor-5 % Combustion-2

0

1

2

3

4

PM

2.5

OC

EC

Phe

nant

hren

e…A

nthr

acen

e (A

n)F

luor

anth

ene…

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

a…C

hrys

ene

(Chr

)B

enzo

[b]f

luor

a…B

enzo

[k]f

luor

a…B

enzo

(e)p

yren

eB

enzo

[a]p

yren

…P

eryl

ene

Dib

enzo

[a,h

]a…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

er…

Ret

ene

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

icon

tane

tetr

atri

acon

tane

pent

atri

acon

tane

Dod

ecan

oic

acid

Tri

deca

noic

aci

dT

etra

deca

noic

…Pe

ntad

ecan

oic…

Hex

adec

anoi

c…H

epta

deca

noic

…L

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c…le

vogl

ucos

anV

anil

linF

acto

r lo

adin

gs

Factor-5 Combustion-2

0

1

2

3

4

G-s

core

s


194

Factor 6

The sixth factor profile is characterized by high contribution of levoglucosan. Factor 6

explains 76% of the levoglucosan concentration. Levoglucosan is a very specific tracer

for wood burning (Simoneit et al.,1999). It has been used as a specific organic marker in

PMF source profiles in source apportionment studies (Jaeckels et al., 2007). Therefore,

this factor was identified as biomass burning. The seasonality of G scores is depicted in

Figure 4.52. Biomass burning has strong contributions in winter months and it is very low

in summer season (except August). These G-scores are strong evidence that this factor is

a biomass combustion factor. In addition, Fluoranthene/ (Fluoranthene + Pyrene)

diagnostic ratio of this factor is another indicator for wood combustion. The ratio was 1.0,

which represents grass, wood and coal combustion.

CBPF plot for Factor 6 is depicted in Figure 4.52d. Despite low concentrations of biomass

burning tracers, the sources are clearly visible when analyzed the CBPF approach. The

direction of the highest concentration is SW. Since wood burning is common mode of

space heating at low-income districts of the city, which surrounds our urban station,

observed high probability from SW direction is not surprising.

195


0

20

40

60

80

100

PM2.

5O

CE

Cph

enan

thre

ne (

Phe

)an

thra

cene

(A

n)fl

uora

nthe

ne (

Flu

t)py

rene

(P

yr)

benz

o[a]

anth

race

…ch

ryse

ne (

Chr

)be

nzo[

b]fl

uora

nt…

benz

o[k]

fluo

rant

…be

nzo(

e)py

rene

benz

o[a]

pyre

ne…

pery

lene

dibe

nzo[

a,h]

anth

r…in

deno

[1,2

,3,-

…be

nzo[

ghi]

pery

le…

rete

nehe

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tria

cont

ane

tetr

atri

acon

tane

pent

atri

acon

tane

dode

cano

ic a

cid

trid

ecan

oic

acid

tetr

adec

anoi

c ac

idpe

ntad

ecan

oic

acid

hexa

deca

noic

aci

dhe

ptad

ecan

oic

acid

lino

leic

aci

dol

eic

acid

octa

deca

noic

aci

dle

vogl

ucos

anva

nill

in

Frac

tion

of

conc

entr

atio

ns (

%)

Factor-6 % Biomass burning

0

1

2

PM

2.5

OC

EC

Phe

nant

hren

e…A

nthr

acen

e (A

n)F

luor

anth

ene…

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

a…C

hrys

ene

(Chr

)B

enzo

[b]f

luor

a…B

enzo

[k]f

luor

a…B

enzo

(e)p

yren

eB

enzo

[a]p

yren

e…P

eryl

ene

Dib

enzo

[a,h

]an…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

ery…

Ret

ene

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

icon

tane

tetr

atri

acon

tane

pent

atri

acon

tane

Dod

ecan

oic

acid

Tri

deca

noic

aci

dT

etra

deca

noic

…Pe

ntad

ecan

oic…

Hex

adec

anoi

c…H

epta

deca

noic

…L

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

anV

anil

linF

acto

r lo

adin

gs

Factor-6 Biomass burning

0

1

2

Janu

ary

Feb

ruar

y

Mar

ch

Apr

il

May

June

July

Aug

ust

Sep

tem

ber

Oct

ober

Nov

embe

r

Dec

embe

r

G-s

core

s


196

Factor 7

Diagnostic plots for factor 7 is depicted in Figure 4.53. Factor 7 was identified as plant

emissions since this factor explains 61% of tetratriacontane and 55% of pentatriacontane

concentrations. n-Alkanes with the carbon number higher than 27 can be classified as

biogenic, mainly plant waxes (Li et al., 2010). Plant waxes include mostly of aliphatic

species which has higher molecular weights, particularly n-alkanes and n-alkanoic acids

(Rogge, 1993). The G score plot is depicted in Figure 4.53. It is seen that there is not a

strong seasonal differences. However, there is a point to be investigated. G scores are

higher in especially March and April months. Wind speeds of these months were recorded

higher than the average wind speed (2.38 m s-1) of the sampling period. The indirect effect

of the wind speed can lead to such G score distribution. When we go back and look at the

relationship between the wind speed and concentrations, there are some n-alkanes having

a positive relationship with wind speed (see Figure 4.29). The CBPF plot of Factor 7 is

depicted in Figure 4.53d. The bivariate polar plot reveals three sources (or group of

sources) which are coming from WSW, E and NNE. Ground-level, non-buoyant plumes

from plant emissions, tend to have moderate concentrations under low wind speed

conditions. By considering narrower intervals of CBPF more detail can be resolved.

197


0

20

40

60

80

100

PM

2.5

OC

EC

phen

anth

rene

(Ph

e)an

thra

cene

(A

n)fl

uora

nthe

ne (

Flu

t)py

rene

(P

yr)

benz

o[a]

anth

race

…ch

ryse

ne (

Chr

)be

nzo[

b]fl

uora

nt…

benz

o[k]

fluo

rant

…be

nzo(

e)py

rene

benz

o[a]

pyre

ne…

pery

lene

dibe

nzo[

a,h]

anth

r…in

deno

[1,2

,3,-

…be

nzo[

ghi]

pery

le…

rete

nehe

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itria

cont

ane

hent

riac

onta

nedo

tria

cont

ane

tetr

atri

acon

tane

pent

atri

acon

tane

dode

cano

ic a

cid

trid

ecan

oic

acid

tetr

adec

anoi

c ac

idpe

ntad

ecan

oic

acid

hexa

deca

noic

aci

dhe

ptad

ecan

oic

acid

lino

leic

aci

dol

eic

acid

octa

deca

noic

aci

dle

vogl

ucos

anva

nilli

n

Frac

tion

of

conc

entr

atio

ns (

%)

Factor-7 % Plant emissions

0

1

2

3

4

PM2.

5 O

C E

CP

hena

nthr

ene

(Phe

)A

nthr

acen

e (A

n)F

luor

anth

ene

(Flu

t)P

yren

e (P

yr)

Ben

zo[a

]ant

hrac

e…C

hrys

ene

(Chr

)B

enzo

[b]f

luor

ant…

Ben

zo[k

]flu

oran

t…B

enzo

(e)p

yren

eB

enzo

[a]p

yren

e…P

eryl

ene

Dib

enzo

[a,h

]ant

h…In

deno

[1,2

,3,-

…B

enzo

[ghi

]per

yle…

Ret

ene

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

icon

tane

tetr

atri

acon

tane

pent

atri

acon

tane

Dod

ecan

oic

acid

Tri

deca

noic

aci

dT

etra

deca

noic

aci

dP

enta

deca

noic

aci

dH

exad

ecan

oic

acid

Hep

tade

cano

ic a

cid

Lin

olei

c ac

idO

leic

aci

dO

ctad

ecan

oic

acid

levo

gluc

osan

Van

illi

n

Fac

tor

load

ings

Factor-7 Plant emissions

0

1

2

3

4

5

G-s

core

s


198

Factor 8

Fraction of species concentrations accounted for by factor 8, factor loadings and temporal

variations of G scores are depicted in Figure 4.54. This factor was dominated by n-

alkanoic acids. Approximately 80% of dodecanoic acid, 73% of octadecanoic acid, 70%

of tetradecanoic acid and 55% of linoleic acid concentrations were accounted for by factor

8. Alkanoic acids are the most significant tracers of food cooking (Robinson et al., 2006;

Rogge et al., 1993). High Factor 8 scores values were observed in August, September,

October and November while lower ones are observed in remaining months. The higher

G scores could be due to two main reasons. First one is, the high emission rates of those

species and the other one is the effect of meteorological parameters (e.g. lower mixing

height and ventilation coefficient). Based on these arguments this factor was identified as

food cooking-2.

Figure 4.54d shows CBPF plot of Factor-8. This plot clearly shows the highest food

cooking emissions when the wind is blowing from E and ESE. Receptor site was located

in the campus and east side of the station is residential area. Therefore, this result is not

surprising.

199


0

20

40

60

80

100

PM

2.5

OC

EC

phen

anth

rene

…an

thra

cene

(A

n)fl

uora

nthe

ne (

Flu

t)py

rene

(P

yr)

benz

o[a]

anth

rac…

chry

sene

(C

hr)

benz

o[b]

fluo

rant

…be

nzo[

k]fl

uora

nt…

benz

o(e)

pyre

nebe

nzo[

a]py

rene

…pe

ryle

nedi

benz

o[a,

h]an

th…

inde

no[1

,2,3

,-…

benz

o[gh

i]pe

ryl…

rete

nehe

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tria

cont

ane

tetr

atri

acon

tane

pent

atri

acon

tane

dode

cano

ic a

cid

trid

ecan

oic

acid

tetr

adec

anoi

c ac

idpe

ntad

ecan

oic

acid

hexa

deca

noic

aci

dhe

ptad

ecan

oic

acid

lino

leic

aci

dol

eic

acid

octa

deca

noic

aci

dle

vogl

ucos

anva

nill

in

Frac

tion

of

conc

entr

atio

ns (

%)

Factor-8 % Food cooking-2

0123456

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

ace…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

t…B

enzo

[k]f

luor

ant…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nth…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

e…R

eten

ehe

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic a

cid

Pen

tade

cano

ic a

cid

Hex

adec

anoi

c ac

idH

epta

deca

noic

aci

dL

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

anV

anil

lin

Fac

tor

load

ings

Factor-8 Food cooking-2

0

1

2

3

G-s

core

s

Factor-8 Food cooking-2

200

4.12.2.4 Sub-urban Station-Factors

Eight factors were resolved by PMF analysis for sub-urban station. In order to assign each

factor to a physical source, fractions of marker concentrations explained by factors and F

loading, which are regular PMF outputs were plotted for each factor. Additionally,

seasonal variation of G score, which is another important output of PMF was also used.

The source profiles resolved by PMF model and the individual contribution of each factor

to total mass are depicted in Figure 4.55. Factors resolved by PMF were identified as:

biomass burning, combustion, combustion-2, vehicular emission, natural gas combustion,

plant emission, food cooking, and secondary organic aerosol. How factors are assigned to

these factors will be are discussed in following sections.

Factor 1

Fractions of concentrations of marker compounds explained by factor 1, Factor loadings

and monthly median concentrations of factor 1 G-scores are depicted in Figure 4.56. First

factor explained 78% of pentatriacontane and 62% of levoglucosan concentrations. Also

30-45% of indeno(1,2,3,-cd)pyrene, oleic acid, dibenzo(a,h)anthracene concentrations

were accounted for by this factor. Levoglucosan is a good marker for biomass burning

emissions (Jaeckels et al., 2007). Pentatriacontane is a high molecular weight n-alkane of

which source is biogenic such as plant wax. Therefore, its contribution to biomass burning

is expected. Under the light of these information, particularly with the presence of

levoglucosan in this factor, Factor-1 was identified as biomass burning. The seasonality

of G scores is depicted in Figure 4.56c. Winter scores are higher than summer ones. This

also supports biomass burning as the source generating factor 1. Ratio of Indeno[1,2,3,-

cd]pyrene/(Indeno[1,2,3,-cd]pyrene+ Benzo[ghi]perylene) was 1.0, suggesting grass,

wood and coal combustion source. CBPF plot is depicted in Figure 4.56d. The bivariate

polar plot reveals biomass source was dispersed through all directions. Biomass burning

emissions tend to have moderate concentrations under low wind speed conditions. By

considering narrower intervals of CBPF more detail can be resolved.

201

Figure 4.55 Sub-urban Station- Species profiles- % of species and concentration of species

202

Figure 4.56 Sub-urban Station Factor-1 Contribution, factor loadings of species, G-scores and CBPF at 70th percentile

0

20

40

60

80

100

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

acen

e…C

hrys

ene

(Chr

)B

enzo

[b]f

luor

anth

en…

Ben

zo[k

]flu

oran

then

…B

enzo

(e)p

yren

eB

enzo

[a]p

yren

e (B

aP)

Per

ylen

eD

iben

zo[a

,h]a

nthr

ace…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

ene…

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

icon

tane

tetr

atri

acon

tane

pent

atri

acon

tane

Dod

ecan

oic

acid

Tri

deca

noic

aci

dT

etra

deca

noic

aci

dPe

ntad

ecan

oic

acid

Hex

adec

anoi

c ac

idH

epta

deca

noic

aci

dL

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

an

Frac

tion

of

conc

entr

atio

ns (

%)

Factor-1 Biomass Burning

0

1

2

3

4

PM

2.5

OC

EC

Phe

nant

hren

e…A

nthr

acen

e (A

n)F

luor

anth

ene…

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

a…C

hrys

ene

(Chr

)B

enzo

[b]f

luor

a…B

enzo

[k]f

luor

a…B

enzo

(e)p

yren

eB

enzo

[a]p

yren

e…P

eryl

ene

Dib

enzo

[a,h

]ant

…In

deno

[1,2

,3,-

…B

enzo

[ghi

]per

y…he

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic…

Pent

adec

anoi

c…H

exad

ecan

oic…

Hep

tade

cano

ic…

Lin

olei

c ac

idO

leic

aci

dO

ctad

ecan

oic

acid

levo

gluc

osan

Fac

tor

load

ings

Factor-1Biomass burning

0

1

2

3

G-s

core

s


203

Factor 2

Fractions of concentrations of species explained by factor 2, factor 2 loadings and monthly

median factor 2 G scores are given in Figure 4.57. Factor 2 explains 74% of the

benzo(e)pyrene, 62% of benzo(a)pyrene and 46% of fluoranthene concentrations. The

factor also accounts for significant fractions of concentrations of n-alkanes like docosane

(34%), tricosane (17%) and n-alkanoic acids like hexadecanoic acid (31%) and

octadecanoic acid (21%). PAH species are highly enriched in this factor, suggesting that

the factor is related with a fossil fuel combustion source (Hester and Harrison 2016; Lee

et al., 2001). High winter G score values, shown in the figure, and fluoranthene - to -

(fluoranthene+pyrene) ratio was calculated as 0.42 both support combustion source for

Factor 2. Thus factor 2 was identified as combustion.

CBPF plot is given in Figure 4.57d and it reveals moderate concentrations at SW and

higher concentrations at SSE. The wind speed dependence of the combustion sources can

be seen complex; higher wind speed results in lower concentrations at north side of the

station, while higher wind speed results in higher concentrations at south side of the

station. This could be due to re-circulation of the pollutants.

204


0

20

40

60

80

100

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

acen

…C

hrys

ene

(Chr

)B

enzo

[b]f

luor

anth

e…B

enzo

[k]f

luor

anth

e…B

enzo

(e)p

yren

eB

enzo

[a]p

yren

e…P

eryl

ene

Dib

enzo

[a,h

]ant

hra…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

en…

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

icon

tane

tetr

atri

acon

tane

pent

atri

acon

tane

Dod

ecan

oic

acid

Tri

deca

noic

aci

dT

etra

deca

noic

aci

dP

enta

deca

noic

aci

dH

exad

ecan

oic

acid

Hep

tade

cano

ic a

cid

Lin

olei

c ac

idO

leic

aci

dO

ctad

ecan

oic

acid

levo

gluc

osan

Frac

tion

of

conc

entr

atio

ns (

%)

Factor-2 % Combustion

00,5

11,5

22,5

3

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

ac…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

t…B

enzo

[k]f

luor

ant…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nt…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

…he

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic a

cid

Pen

tade

cano

ic a

cid

Hex

adec

anoi

c ac

idH

epta

deca

noic

aci

dL

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

an

Fac

tor

load

ings

Factor-2 Combustion

0

1

2

3

4

5

6

G-s

core

s

Factor-2 Combustion

205

Factor 3

Diagnostic plots for factor 3 is given in Figure 4.58. Factor is loaded with n-alkanes and

PAH compounds. It explains high fractions of anthracene (78%), octacosane (65%),

heptadecanoic acid (50%) and tridecanoic acid (50%) concentrations. The factor also

accounts for 34% - 50% of concentrations of other n-alkanes. PAHs and low molecular

weight n-alkanes are good markers for fossil fuel combustion (Abdel-Shafy and Mansour,

2016). Combustion source is also supported by seasonal variation of factor 3 scores,

which is higher during winter months and anthracene-to- (anthracene + phenanthrene)

ratio (0.8), which suggests a pyrogenic source. Therefore, this factor was identified as a

second combustion factor and named as combustion-2.

CBPF plot for Factor 3 is depicted in Figure 4.58d. Please note that two combustion source

CBPF plots reveals same directions for high concentration probability. Higher wind speed

results in higher concentrations at south side of the station. By considering narrower

intervals of CBPF, lower concentration intervals can be resolved.

206


0

20

40

60

80

100

PM2.

5O

CE

CP

hena

nthr

ene

(Phe

)A

nthr

acen

e (A

n)F

luor

anth

ene

(Flu

t)P

yren

e (P

yr)

Ben

zo[a

]ant

hrac

en…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

th…

Ben

zo[k

]flu

oran

th…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nthr

…In

deno

[1,2

,3,-

…B

enzo

[ghi

]per

ylen

…he

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic a

cid

Pen

tade

cano

ic a

cid

Hex

adec

anoi

c ac

idH

epta

deca

noic

aci

dL

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

an

Fra

ctio

n of

con

cent

rati

ons

(%)

Factor-3 % Combustion-2

0

1

2

3

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

ac…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

t…B

enzo

[k]f

luor

ant…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nt…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

…he

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic a

cid

Pent

adec

anoi

c ac

idH

exad

ecan

oic

acid

Hep

tade

cano

ic a

cid

Lin

olei

c ac

idO

leic

aci

dO

ctad

ecan

oic

acid

levo

gluc

osan

Fac

tor

load

ings


0

1

2

3

4

G-s

core

s


207

Factor 4

Concentrations of organic markers explained by factor 4, factor 4 loadings and seasonal

variation in Factor 4 scores are given in Figure 4.59. Factor 4 is weighted with PAHs. It

explains 69% of benzo(k)fluoranthene and 65% of benzo(b)fluoranthene concentrations.

Fractions of pyrene, phenanthrene and chrysene concentrations explained by factor 4

varies between 30-36%. The factor also accounts for approximately 10% of EC

concentration. PAH dominated factor are related with fossil fuel combustion (Rogge et

al.,1993a). However, fossil fuel combustion may be both for to space heating or in

gasoline powered or diesel engine. Presence of EC in this factor suggests a traffic source.

Furthermore, Benzo(a)anthracene-to-(Benzo(a)anthracene+Chrysene) ratio was 0.38,

which is a typical value for traffic emissions (Akyüz and Çabuk, 2010; Yunker et al.,

2002). Factor 4 scores are high in winter month. Although this gives the impression of

heating - related combustion source, it should be noted that high winter concentrations are

expected even if source strength does not change seasonally, due to meteorology. Thus,

Factor 4 was identified as vehicular emissions.

CBPF plot is depicted in Figure 4.59d. It reveals that both low and high wind speed

conditions lead to moderate and high concentrations from vehicular emissions especially

from north side of the station. Dumlupınar Boulevard and Ankara Highway are located on

north side of the station. Therefore, vehicular emissions from these roads have an effect

on the formation of this profile.

208


0

20

40

60

80

100

PM

2.5

OC

EC

Phen

anth

rene

(P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyre

ne (

Pyr

)B

enzo

[a]a

nthr

ac…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

t…B

enzo

[k]f

luor

ant…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nt…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

…he

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic a

cid

Pen

tade

cano

ic a

cid

Hex

adec

anoi

c ac

idH

epta

deca

noic

aci

dL

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

an

Frac

tion

of

conc

entr

atio

ns (

%)

Factor-4 % Vehicular emission

0

1

2

3

PM

2.5

OC

EC

Phen

anth

rene

(P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyre

ne (

Pyr

)B

enzo

[a]a

nthr

ace…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

t…B

enzo

[k]f

luor

ant…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nth…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

e…he

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic a

cid

Pen

tade

cano

ic a

cid

Hex

adec

anoi

c ac

idH

epta

deca

noic

aci

dL

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

an

Fac

tor

load

ings

Factor-4 Vehicular emission

0

1

2

3

4

G-s

core

s

Factor-4 Vehicular emission

209

Factor 5

Concentrations of measured species explained by factor 5, Factor 5 loadings and seasonal

variation of factor 5 scores are given in Figure 4.60. It is clear from the figure that it is an

n-alkane factor. Factor 5 explains 53% of hentriacontane, 43% of dotriacontane, 33-37%

of tetratriacontane, triacontane and tritriacontane concentrations. Factor 5 also accounts

for approximately 30% of OC and 40% of EC concentration. Higher molecular weight n-

alkanes with a carbon number 27 and more, are related with vegetable waxes (Li et al.,

2010). Therefore, Factor-5 was called as plant emissions. The G score plot is given in

Figure 4.60. The higher G score values are calculated at September, October and

November. Defoliation has been increasing at these months. Therefore, plant emissions

could increase and the n-alkanes with higher molecular weights could reach to receptor.

Presence of EC and OC in this factor suggest that it is not a pure plant factor and there is

some anthropogenic diesel source is mixed with plant emissions. The factor also accounts

for approximately 30% of PM2.5 mass.

Presence of light alkenes in the factor also support this mixed source, because unlike heavy

alkanes (C > 27), which are good markers for biogenic emissions, light alkanes (C < 27)

are markers for combustion source (Schauer et al., 2001). Although it includes some diesel

traffic contribution, this factor is named as plant emissions to differentiate it from other

traffic factor.

CBPF plot is given in Figure 4.60d and it is obvious that low wind speed condition leads

to high concentrations. Please note that this station was located in the campus and

therefore there are many contributors to this factor.

210


0

20

40

60

80

100

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

ace…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

t…B

enzo

[k]f

luor

ant…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nth…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

e…he

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic a

cid

Pent

adec

anoi

c ac

idH

exad

ecan

oic

acid

Hep

tade

cano

ic a

cid

Lin

olei

c ac

idO

leic

aci

dO

ctad

ecan

oic

acid

levo

gluc

osan

Frac

tion

of

conc

entr

atio

ns (

%)

Factor-5 % Plant emissions

0

1

2

3

4

5

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Fluo

rant

hene

(F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

ac…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

t…B

enzo

[k]f

luor

ant…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nth…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

e…he

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic a

cid

Pen

tade

cano

ic a

cid

Hex

adec

anoi

c ac

idH

epta

deca

noic

aci

dL

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

an

Fac

tor

load

ings


0

1

2

3

G-s

core

s


211

Factor 6

Explained concentrations, factor loadings and G scores for factor 6 are depicted in Figure

4.61. Factor-6 is dominated by n-alkanoic acids. It explains 74% octadecanoic acid, 54%

of hexadecanoic acid and 33% of tetradecanoic acid concentrations. As discussed in

Urban Station Factor 4 and 8, n-alkanoic acids are the most significant tracers of food

cooking emissions. They are released by oxidation and decarboxylation reactions during

food cooking (Rogge et al., 1991). Tetradecanoic acid, hexadecanoic acid and

octadecanoic acid, which are heavily loaded in Factor 6, are the most dominant n-alkanoic

acids in particles emitted from food cooking (Schauer et al., 1999; He et al., 2004; Y.

Zhao, et al., 2007; X. Zhao et al., 2015). Therefore, Factor 6 was identified as food

cooking.

CBPF plot is depicted in Figure 4.61d and it is obvious that food cooking emissions is

spread to almost all directions with moderate probability. Due to location of this station

this result was expected. Since there are departments around the station and so their

canteens.

212


0

20

40

60

80

100

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

ac…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

t…B

enzo

[k]f

luor

ant…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nt…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

…he

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic a

cid

Pen

tade

cano

ic a

cid

Hex

adec

anoi

c ac

idH

epta

deca

noic

aci

dL

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

an

Frac

tion

of

conc

entr

atio

ns (

%)


01234567

PM

2.5

OC

EC

Phe

nant

hren

e…A

nthr

acen

e (A

n)F

luor

anth

ene…

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

…C

hrys

ene

(Chr

)B

enzo

[b]f

luor

…B

enzo

[k]f

luor

…B

enzo

(e)p

yren

eB

enzo

[a]p

yren

…P

eryl

ene

Dib

enzo

[a,h

]a…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

er…

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

icon

tane

tetr

atri

acon

tane

pent

atri

acon

tane

Dod

ecan

oic

acid

Tri

deca

noic

aci

dT

etra

deca

noic

…P

enta

deca

noic

…H

exad

ecan

oic…

Hep

tade

cano

ic…

Lin

olei

c ac

idO

leic

aci

dO

ctad

ecan

oic…

levo

gluc

osanF

acto

r lo

adin

gs


0

1

2

3

G-s

core

s


213

Factor 7

Diagnostic plots for Factor 7 are given in Figure 4.62. Factor 7 is dominated by PAH

compounds, suggesting that it is related with combustion source. Factor accounts for 70%

of Perylene, 40-45% of benzo(a)anthracene, pyrene and chrysene concentrations. An

interesting point about this factor there is no EC, OC or PM2.5 in this factor. Although

presence of PAHs in a factor is enough to identify it as a combustion emissions and when

we say combustion we generally understand coal combustion for heating or combustion

in vehicular engine, lack of PM2.5 and EC in this factor indicate that this factor represents

natural gas combustion rather than coal combustion and traffic emissions. Composition

of Factor 7 is very similar to the composition of Factor 2 in Urban station PMF. This

factor was identified as natural gas combustion.

CBPF plot is depicted in Figure 4.62d. It is seen that concentrations of pollutants can

increase with increasing wind speed-or at least not decrease due to increasing mixing. This

mixing can result in PAH enrichment in air and result higher probability at higher wind

speed from SSE.

214


0

20

40

60

80

100

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

ace…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

th…

Ben

zo[k

]flu

oran

th…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nthr

…In

deno

[1,2

,3,-

…B

enzo

[ghi

]per

yle…

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

trit

riac

onta

nehe

ntri

acon

tane

dotr

icon

tane

tetr

atri

acon

tane

pent

atri

acon

tane

Dod

ecan

oic

acid

Tri

deca

noic

aci

dT

etra

deca

noic

aci

dP

enta

deca

noic

aci

dH

exad

ecan

oic

acid

Hep

tade

cano

ic a

cid

Lin

olei

c ac

idO

leic

aci

dO

ctad

ecan

oic

acid

levo

gluc

osan

Fra

ctio

n of

con

cent

rati

ons

(%)


0

0,5

1

PM

2.5

OC

EC

Phe

nant

hren

e…A

nthr

acen

e (A

n)F

luor

anth

ene…

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

…C

hrys

ene

(Chr

)B

enzo

[b]f

luor

…B

enzo

[k]f

luor

…B

enzo

(e)p

yren

eB

enzo

[a]p

yren

…P

eryl

ene

Dib

enzo

[a,h

]a…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

er…

hene

icos

ane

doco

sane

tric

osan

ete

trac

osan

epe

ntac

osan

ehe

xaco

sane

hept

acos

ane

octa

cosa

neno

naco

sane

tria

cont

ane

tritr

iaco

ntan

ehe

ntri

acon

tane

dotr

icon

tane

tetr

atri

acon

tane

pent

atri

acon

tane

Dod

ecan

oic

acid

Tri

deca

noic

aci

dT

etra

deca

noic

…P

enta

deca

noic

…H

exad

ecan

oic…

Hep

tade

cano

ic…

Lin

olei

c ac

idO

leic

aci

dO

ctad

ecan

oic…

levo

gluc

osan

Fac

tor

load

ings


0

1

2

3

4

G-s

core

s


215

Factor 8

Fraction of concentrations, Factor loadings and seasonal variation of G-scores

corresponding to Factor-8 are depicted in Figure 4.63. Factor 8 explains the 61% variance

of benzo(g,h,i)perylene and 50% of fluoranthene and 50% of dibenzo(a)anthracene. And

also OC has a contribution of 30%. Factor-8 was identified as Secondary Organic Aerosol.

Up to this factor, all species represents a specific source according to the literature.

However, the explained variation and G score plots change the course and a different

approach was enhanced for this factor. G scores show high seasonality with higher G

scores values in summer and lower ones in winter. This could be due to increasing

photochemical reactions at summer months. At this point SOA come in sight. Specific

secondary organic aerosol markers, which are composed of organic species formed during

the photochemical reactions occurred in the atmosphere under low pressure, were not

included in this study. Since, many of the studies showed that secondary organic aerosol

is not an important emission source for the fitting of the compound in the PMF. However,

this factor’s G scores showed that values were higher in summer season and this is the

expected seasonal distribution of secondary organic aerosol (Zheng et al., 2002).

However, this assignment of SOA to this factor is a speculation, and further data,

particularly on specific tracers of SOA is needed for positive identification of SOA as the

source of organic compounds in Factor 8. CBPF plot of Factor 8 is depicted in Figure

4.63d. The sources of the secondary organic aerosol are numerous and complex, therefore

this polar plot can give an idea of the direction of this source profile. Concentrations are

high from E and NE at which city center is located.

216


0

20

40

60

80

100

PM

2.5

OC

EC

Phe

nant

hren

e (P

he)

Ant

hrac

ene

(An)

Flu

oran

then

e (F

lut)

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

ace…

Chr

ysen

e (C

hr)

Ben

zo[b

]flu

oran

t…B

enzo

[k]f

luor

ant…

Ben

zo(e

)pyr

ene

Ben

zo[a

]pyr

ene…

Per

ylen

eD

iben

zo[a

,h]a

nth…

Inde

no[1

,2,3

,-…

Ben

zo[g

hi]p

eryl

e…he

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itri

acon

tane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic a

cid

Pen

tade

cano

ic a

cid

Hex

adec

anoi

c ac

idH

epta

deca

noic

aci

dL

inol

eic

acid

Ole

ic a

cid

Oct

adec

anoi

c ac

idle

vogl

ucos

an

Frac

tion

of

conc

entr

atio

ns (

%)

Factor-8 Secondary organic aerosol

012345

PM

2.5

OC

EC

Phe

nant

hren

e…A

nthr

acen

e (A

n)F

luor

anth

ene…

Pyr

ene

(Pyr

)B

enzo

[a]a

nthr

…C

hrys

ene

(Chr

)B

enzo

[b]f

luor

…B

enzo

[k]f

luor

…B

enzo

(e)p

yren

eB

enzo

[a]p

yre…

Per

ylen

eD

iben

zo[a

,h]a

…In

deno

[1,2

,3,-

…B

enzo

[ghi

]per

…he

neic

osan

edo

cosa

netr

icos

ane

tetr

acos

ane

pent

acos

ane

hexa

cosa

nehe

ptac

osan

eoc

taco

sane

nona

cosa

netr

iaco

ntan

etr

itria

cont

ane

hent

riac

onta

nedo

tric

onta

nete

trat

riac

onta

nepe

ntat

riac

onta

neD

odec

anoi

c ac

idT

ride

cano

ic a

cid

Tet

rade

cano

ic…

Pen

tade

cano

ic…

Hex

adec

anoi

c…H

epta

deca

noi…

Lin

olei

c ac

idO

leic

aci

dO

ctad

ecan

oic…

levo

gluc

osanF

acto

r lo

adin

gs


0

1

2

3

Fac

tor

load

ings


217

4.12.2.5 Contribution of factors to total PM2.5 mass and total organic aerosol

particulate matter

At urban station, there are five factors contributing to total PM2.5 mass (see Figure 4.64).

The first one is road dust with a percent of 74. Studies on atmospheric particulate matter

showed that organic aerosols at urban atmosphere is frequently accounted for by the road

dust, which is followed by vehicular emission and food cooking (Fuzzi et al., 2015).

Vehicular emission does not only imply particles emitted from exhaust, but it also includes

particles emitted from tires and worn brake linings. Both anthropogenic and biogenic

sources have a contribution on road dust concentration by removal mechanisms from

atmosphere. Such road dust can be resuspended by wind and become a component of

urban aerosol population. Please note that road dust is has significantly different

composition from regular litophilic particles in atmosphere. It includes litophilic, alumina

silicate particles, but it also includes fine particles emitted from exhaust and particles

generated by wearing tires and brake linings. Road dust is enriched by various organic

and inorganic compounds, particularly trace elements emitted from exhaust (Jadoon et al.,

2017; Jia et al., 2017; Padoan et al., 2017; Zhao et al., 2017). Size distribution of road dust

is also quite unique, because it includes both coarse dust particles and very small particles

emitted from vehicle exhaust (Ha et al., 2012; Y. Li et al., 2016; Padoan et al., 2017; Qiang

et al., 2014). Other components contributing to PM2.5 are food cooking, plant emission

and combustion. Contribution of these sources was also expected at urban site. The

missing three factors that do not have contribution on total PM2.5 are natural gas

combustion, biomass burning and combustion-1. The lack of PM2.5 in natural gas

combustion factor allowed us to differentiate it from other combustion sources, because

fossil fuel combustion sources, both combustion of coal for heating and combustion of

gasoline and diesel fuel in vehicle engines emit fine particles and should have PM2.5 mass

associated with their factors in PMF. However, gas combustion does not emit particles in

significant quantities. The lack of PM2.5 contribution from biomass burning and

combustion 1 factors is probably because of small contributions of these factors to PM2.5

mass concentrations.

218

Figure 4.64 Urban Station- Contribution of Factors to Total PM2.5

The highest contribution to PM2.5 mass at suburban station comes from secondary organic

aerosol factor with 56%, which is followed by plant emission (27%), biomass burning

(15%) and combustion-1 (2%) factors (see Figure 4.65). The difference between

contribution of sources at urban and suburban stations is interesting. Since the receptor

site was away from the main roads and districts with dense settlement (and consequently

having high population), combustion source and road dust do not have significant impact

on this station. Secondary organic particles had a significant contribution at suburban

station, but not at urban site. It was underlined several times in the manuscript that EC is

emitted from directly from primary sources such as incomplete combustion of fossil and

biomass fuels but organic species have both primary and secondary sources. SOA are

formed when the saturation levels are higher than the threshold values under low vapor

pressure conditions or by the adsorption onto atmospheric aerosol particles. To detect and

identify the specific SOA are difficult. Seasonal variations of particle concentrations of

factor scores can be one way of identifying factors (or individual compounds) that are

combustion-25%

road dust74%

food cooking-26%

plant emission5%

food cooking-110%

Urban Station- Contribution of Factors to Total PM2.5mass

219

associated with secondary organic particles (SOP). Since production of SOP is

photochemical in nature, factor representing SOP should have higher scores in summer.

This is one of the reasons in identifying factor 8 at suburban PMF as SOP factor.

There are four factors, at suburban station that did not contribute significantly to PM2.5

mass. These four factors which do not have contribution to total PM2.5 mass are

combustion-2, vehicular emission-1, food cooking, vehicular emission-2.

Figure 4.65 Sub-urban Station-Contribution of Factors to Total PM2.5

biomass burning

15%

combustion-12%

plant emission27%

secondary organic aerosol

56%

Sub-urban Station-Contribution of Factors to Total PM2.5mass

220

4.12.2.6 Correlation of the factors between Urban and Sub-urban Station

Sources of PM2.5 was resolved by PMF and eight factors were revealed. Four of these

source profiles, namely combustion, biomass burning, plant emission and food cooking

were observed in both urban and sub-urban stations. Each station had two factors, which

were not observed in other station. At urban station these source profiles are; natural gas

combustion which has the most significant tracers as benzo(b)fluoranthene and chrysene

and road dust which has the largest contribution to PM2.5 mass (74%) at urban station. At

sub-urban station these source profiles were vehicular emission which accounts for 69%

of benzo(k)fluoranthene concentration and secondary organic aerosol which has a

specific G score variation over sampling campaign.

There are two combustion factors at both stations. The scatterplot graphs between two

combustion factors of urban and sub-urban station are depicted in Figure 4.66 and

Figure 4.67. Relatively high R2 with 0.58 and 0.30 and P-value<0.05 shows that

the two combustion factors are related with >95% statistical significance. There is one

food cooking factor profile at sub-urban station and two food cooking factor profiles at

urban station. The scatterplot graphs between food cooking factors of urban and sub-

urban station are given in Figure 4.68 and 4.69. Food cooking factor at suburban station

is related with food cooking 1 factor at urban station with >95% statistical

significance. However, suburban food cooking factor does not correlate with the

second food-cooking factor at urban station. This indicate either certain food cooking

emissions are transported from the city to the university, or similar ingredients are

used in cooking at the city and around our suburban station. Transport scenario is

probably a more likely explanation of observed correlation.

The scatterplot graph between plant emission factors of urban and sub-urban station is

given in Figure 4.70. Relatively high R2 with 0.44 and P < 0.05 shows that the two plant

emission factors are related with >95% statistical significance. Which is reasonable and

probably do not indicate transport from city as in other factors discussed above, because

plants emit same markers bot at the city and around our suburban station.

221

The scatterplot graph between biomass burning factors of urban and sub-urban station is

depicted in Figure 4.71. Biomass burning factors in both urban and sub-urban stations do

not show a statistically significant correlation (R2= 0.03 and P-value<0.05). There could

be two different reasons (1) burning of different biomass, such as different type of wood

can generate different profiles at two stations. At urban stations PAHs and at suburban

station are more heavily loaded at biomass burning factors. Different types of wood may

be burning around urban and suburban stations, because urban station is located at the idle

of low income districts, where wood is burned in stoves for space heating. Around

suburban station, wood is definitely not burned in stoves for heating. Wood burning

around METU is probably for leisure and at fireplaces. Probably characteristics and

emissions of these two wood types are different. (2) Particulate organic matters observed

at sub-urban can be transported from the high emission areas in the city and characteristics

of these particles may change during transport via photochemistry. Both of these

scenarios are speculation at this moment and has to be investigated in later studies. Please

note that levoglucosan is the dominant marker in biomass burning factors in both stations,

which does not cast any doubt for biomass-burning source of these factors.

223

CONCLUSIONS

In this thesis the chemical composition of PM2.5 bound particulate organic matter have

been analyzed by optimizing a method which enable for daily measurement and speciation

of the chemical species. There were two stations and samples were collected daily in this

study. The number of the samples were 337 from urban station and 275 from sub-urban

station. After gathering data set including PAHs, n-alkanes, n-alkanoic acids,

levoglucosan and EC-OC, specific sources contributing to total PM2.5 have been resolved

by EPA PMF 5.0.

This study is the first and most extensive study in terms of number of daily samples

collected and number of particulate organic compounds analyzed in ambient atmosphere

covered in Turkey.

According to the statistical analysis of the chemical composition of particulate organic

matters urban station value are found higher than sub-urban station as it is expected. At

urban station, the median values of the n-alkanes, PAHs and n-alkanoic acids are in the

range of 1.75-13.25 ng m-3, 0.39-5.34 ng m-3 and 0.20-7.69 ng m-3 respectively, and

levoglucosan, EC-OC median values are 1.87 ng m-3 and 2.17-7.90 ng m-3; at sub-urban

station the median values of the n-alkanes, PAHs and n-alkanoic acids are in the range of

0.56-4.75 ng m-3, 0.1-1.62 ng m-3 and 0.71-8.57 ng m-3 respectively, and levoglucosan,

EC-OC median values are 0.22 ng m-3 and 0.70-4.83 ng m-3. The only particulate organic

compound that needed to be followed according to U.S.EPA is benzo(a)pyrene with a

limit of 1 ng m-3. Winter B(a)P concentrations are higher than the limit values for both

stations. B(a)P concentration was found as 5.5 ng m-3 and 1.5 ng m-3 at urban and sub-

urban stations respectively. Episodic changes in concentrations at both stations showed

that sudden increases in pollution sources or sudden changes in wind direction can affect

the pollutant’s temporal variations.

CHAPTER 5

224

The CBPF plots revealed that analyzed group of compounds follow a certain pattern in

themselves. Increasing wind speed carries more polluted air mass to both receptor sites

from the sources. This may be related to the distance of the source from the measuring

site.

Through the 45 compounds including PM2.5, 26 of them correlated between stations with

a P-value<0.05 while 19 of them do not show correlation with a P-value>0.05. The highest

correlation was observed at coronene, OC and benzo[a]anthracene with R2(%) 40.3, 31.6

and 28.7 respectively.

Secondary organic aerosol estimation showed that OCsecondary value is about 12% of the

total PM2.5 at urban station while this value decreases to 8% at sub-urban station. Winter

OCsecondary values were found more two times higher than the summer values at both

stations.

According to pragmatic mass closure model, four major sources were determined;

secondary aerosol, minerals, sea salt and carbonaceous aerosol. Secondary aerosol was

evaluated to account for 16.1 and 22.4 % of PM2.5 source at urban and sub-urban station

respectively. Minerals were evaluated to account for 8.1 and 15.5% of PM2.5 source at

urban and sub-urban station respectively. Sea salt was evaluated to account for 0.9 and

1.0% of PM2.5 source at urban and sub-urban station respectively. Carbonaceous aerosol

was evaluated to account for 43.3 and 24.9% of PM2.5 source at urban and sub-urban

station respectively. Higher contribution of organic component to PM2.5 mass at urban

site is not surprising and reflects higher concentrations of OC and other organic species at

urban station

In this study, EPA PMF 5.0 was used to perform source apportionment and determine the

sources of particulate organic matter. The optimized model results revealed presence of

eight sources for urban and sub-urban stations. Two combustion factor, a natural gas

combustion factor, a road dust factor, two food cooking factors, a biomass burning factor

225

and a plant emissions factor were determined for urban station. Two combustion factors,

a biomass burning factor, a vehicular emission factors, a natural gas combustion factor, a

plant emission factor, a food cooking factor and a secondary organic aerosol factor were

determined for sub-urban station. Four of these source profiles, namely combustion,

biomass burning, plant emission and food cooking are observed in both urban and sub-

urban stations. Each station had two factors, which are not observed in other station. These

source profiles are for urban station; natural gas combustion and road dust; for sub-urban

station vehicular emission and secondary organic aerosol. At urban station, there are five

factors contributing to total PM2.5: road dust (74%), food cooking (10%), food cooking-2

(6%), plant emission (5%), and combustion-2 (5%). At sub urban station there are four

factors contributing to total PM2.5: secondary organic aerosol (56%), plant emission

(27%), biomass burning (15%), and combustion (2%).

227

RECOMMENDATIONS

The comprehensive chemical composition of particulate organic compounds and their

emission profiles reported in this study. The results reveals that particulate organic

compounds are very important tracers for ambient atmosphere in Ankara.

The further expansion of this work needs detailed actions in different areas. Firstly, the

sources determined here are the major pollutant emissions in Ankara and in much in the

Turkey. Many of these sources may be applied to various urban and sub-urban areas of

the Turkey, however additional pollution sources are required to increase the resolution

of the PMF model. Secondly, to increase the resolution of the PMF model, trace elements

may be included to data set. Therefore, the common tracers for specific sources could be

more helpful to determine the source profiles. Additionally, proper collection of high

quality ambient atmosphere samples may be prepared to increase the accuracy of predicted

emission profiles. Another point is that in current study EPA PMF 5.0 was used for source

apportionment. There are different source apportionment models such as UNMIX which

require compound concentrations as input. Comparison of these two models can give

valuable information for the limitations and strengths of the applied models. Finally,

relationship of particulate matter and health effect may be correlated by the help of an

interdisciplinary study.

CHAPTER 6

229

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APPENDICES

A. Chemical structure of the compounds

Table A.1 Chemical structure of the compounds

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B. Sample Chromatograms

Figure B1. Sample chromatogram for urban station

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Figure B2. Sample chromatogram for suburban station

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CURRICULUM VITAE

PERSONAL INFORMATION Name Ebru Surname Koçak Date of Birth 12.10.1985 Nationality T.C. Marital Status Married Contact Address Middle East Technical University

Department of Environmental Engineering Universiteler Mah. Dumlupinar Blv. No:1 06800 Çankaya Ankara/TURKEY

Contact Phone +90 312 210 2652 E-mail [email protected]

EDUCATION May 2012 M.Sc., Environmental Engineering Middle East Technical University

June 2009 B.Sc., Environmental Engineering

Middle East Technical University ACADEMIC EMPLOYMENT AND PROFESSIONAL EXPERIENCE February 2011-Present Teaching Assistant

Department of Environmental Engineering, Middle East Technical University, Ankara, Turkey

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Assisted in the following courses; 1. ENVE 102 Environmental Chemistry 2. ENVE 208 Environmental Chemistry

Laboratory 3. ENVE 307 Air Pollution 4. ENVE 322 Transport Processes in

Environmental Engineering 5. ENVE 402 Wastewater Reuse 6. ENVE 417 Unit Operations and Process

Laboratory 7. ENVE 424 Instrumental Analysis for

Environmental Engineering 8. ENVE 428 Pollution Prevention

September 2010-Present Teaching Assistant

Department of Environmental Engineering, Aksaray University, Aksaray, Turkey

September 2009-September 2010 Yolsu Engineering Services LTD. CO., Ankara

Environmental Engineer-Project Engineer 2nd National Water Supply and Sanitation Project for 5 Rayons in Nakhchivan AR (Sadarak, Kangarli, Ordubad, Julfa and Shahbuz)

March 2009-July 2009 Aktif Çevre, International Environment Investments Engineering Services and Consulting LTD. CO., Ankara Volunteer – Environmental Engineer Menderes Geothermal Elektrik Üretim A.Ş., Aydın İli, Köşk İlçesi, Yavuzköy Köyü, Kuruçeşme Mevkii, 9.5 MW, Geothermal Power Plant Environmental Management Plant (prepared for World Bank)

February 2008-March 2009 Demo Organization LTD. CO., Ankara

Volunteer – Environmental Engineer Environmental Impact Assessment

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June-July 2008 BOTAŞ-Head of Engineering Studies and Contracts Department, Ankara Engineering Intern

June-July 2007 Baymina 770 MWe Combined Cycle Power

Plant, Ankara Engineering Intern

SPECIALTIES AND AREAS OF INTEREST Air Pollution Air pollution and control

Air pollution meteorology Atmospheric chemistry Air quality modelling: source and receptor models Climate change

PUBLICATIONS PEER-REVIEWED JOURNAL ARTICLES Koçak Ebru, Aslan Kılavuz Seda, İmamoğlu İpek, Tuncel Süleyman Gürdal (2017).

PM2.5 bound organic molecular marker speciation methods and observations from

daily measurements in Ankara, Turkey. Fresenius Environmental Bulletin,

26(1/2017), 263-272.

Koçak Ebru, Demirer Göksel Niyazi (2013). Biogas production from broiler manure

wastewater treatment plant sludge and greenhouse waste by anaerobic co

digestion. Journal of Renewable and Sustainable Energy, 5(4), 043126.

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CONFERENCE PRESENTATIONS

PUBLICATIONS IN INTERNATIONAL CONFERENCE PROCEEDINGS

Koçak Ebru, Öztürk Fatma, Aslan Kılavuz Seda, İmamoğlu İpek, Tuncel Süleyman

Gürdal (2017). Determination of ambient elementel and organic carbon

concentrations in urban and suburban atmospheres in Ankara: estimation of

secondary organic aerosol. 8th Atmospheric Sciences Symposium (ATMOS

2017), 1-4 November 2017, İstanbul, Turkey.

Koçak Ebru, Aslan Kılavuz Seda, İmamoğlu İpek, Tuncel Süleyman Gürdal (2016).

PM2.5 bound PAH speciation methods and evaluation of seasonal concentrations.

1st International Black Sea Congress on Environmental Sciences (IBCESS), 31

August- 3 September 2016, Giresun, Turkey.


PM2 5 Bound organic molecular marker speciation methods and observations

from daily measurements in Ankara, Turkey. 18th International Symposium on

Environmental Pollution and its Impact on Life in the Mediterranean Region, 26-

30 September 2015, Crete, Greece.


Concentration determination and source apportionment of particulate organic

matter in PM2 5 from Ankara atmosphere method development and sampling.

Awareness Raising Workshop; Pesticide residues in closed cropping and persistent

organic pollutants in the Turkish environment, 10-12 March 2014, Kuşadası,

Turkey.

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Timur Esemen, Daniel Klein, Thomas Dockhorn, Göçmez Selçuk, Koçak Ebru, Demirer

Göksel Niyazi (2011). Agricultural water reclamation Modular design of adapted

wastewater treatment facilities for Turkey. 8th IWA International Conference on

Water Reclamation & Reuse, 26-29 October 2011, Barcelona, Spain.

Koçak Ebru, Demirer Göksel Niyazi (2010). Agricultural reuse of water and nutrients

from wastewater treatment in Turkey. International Sustainable Water and

Wastewater Management Symposium, USAYS, 26-28 October 2010, Konya,

Turkey.

PUBLICATIONS IN NATIONAL CONFERENCE PROCEEDINGS


Ankara’da PM2.5 fraksiyonundaki partiküllerde PAH derişimlerinin ve

kaynaklarının belirlenmesi. VII. Ulusal Hava Kirliliği ve Kontrolü Sempozyumu,

1-3 November, Antalya, Turkey.


Ankara’da PM2.5 fraksiyonundaki partiküllerde n-alkanoik asit derişimlerinin

incelenmesi. 12. Ulusal Çevre Mühendisliği Kongresi, 5-7 October 2017, Ankara,

Turkey.


Ankara atmosferinde toplanan PM2.5 örneklerinde alkan konsantrasyon

seviyelerinin mevsimsel değişimlerinin değerlendirilmesi. 6. Ulusal Hava

Kirliliği Ve Kontrolü Sempozyumu, 7-9 October 2015, İzmir, Turkey

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Kaynak belirleme çalışmaları ile belirlenen kaynak gruplarının partiküler organik

bileşikler kullanılarak arttırılması örnekleme çalışmaları ve metot geliştirme.

V.Hava Kirliliği ve Kontrolü Sempozyumu (HKK2013), 18-20 September 2013,

Eskişehir, Turkey.

Koçak Ebru, Demirer Göksel Niyazi, Göçmez Selçuk, Timur Esemen, Daniel Klein,

Dckhorn Thomas (2011). Türkiye’de arıtılmış atıksu ve besiyerlerin tarımda

yeniden kullanılması. 9. Ulusal Çevre Mühendisliği Kongresi, 5-8 October 2011,

Samsun, Turkey.

RESEARCH PROJECTS

PROJECT COORDINATOR

Ankara Atmosferinde Toplanan PM2.5 Örneklerinde Organik Karbon (OC) ve Elementel

Karbon (EC) Seviyelerinin Belirlenmesi, The Scientific and Technological Research

Council of Turkey (TÜBİTAK), 115Y484, 2017.

RESEARCHER

“Atmosferdeki parçacık sayısal konsantrasyonu ile kütlesel konsantrasyonu arasındaki

ilişkinin kurulması ve parçacık sayısal boyut dağılımlarının atmosferik olaylardan ne

kadar etkilendiğinin incelenmesi”, The Scientific and Technological Research Council of

Turkey (TÜBİTAK), 115Y252, 2017.

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“Ankara atmosferinde toplanan PM2.5 örneklerinde organik karbon (OC) ve elementel

karbon (EC) seviyelerinin belirlenmesi”, The Scientific and Technological Research

Council of Turkey (TÜBİTAK), 115Y484, 2016.

“Atmosferden PM10, PM2.5 Ve PM1 Parçacıkları Eş Zamanlı Olarak Toplayabilecek,

Ekonomik Bir Örnekleme Sisteminin Geliştirilmesi”, The Scientific and Technological

Research Council of Turkey (TÜBİTAK), 114Y160, 2015.

“Doğu Karadeniz bölgesi aerosolünde ölçülen elementlerin kaynak bölgelerinin

incelenmesi”, The Scientific and Technological Research Council of Turkey (TÜBİTAK),

112Y378, 2014.

“Hava kalitesine yönelik, kaynak belirleme çalişmalari ile saptanabilen kaynak

gruplarinin farkli doğal izleyiciler (tracer) kullanilarak arttirilmasi”, The Scientific and

Technological Research Council of Turkey (TÜBİTAK), 112Y036, 2013.

“Agricultural Reuse of Water and Nutrients from Wastewater Treatment in Turkey”,

Intensified Cooperation (IntenC): Promotion of German-Turkish Higher Education

Research, TÜBİTAK - International Bureau of the BMBF, METU (Turkey) and Technical

University of Braunschweig, 108Y242, 2012.

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