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보건학박사 학위논문

Source Characterization of Particulate Matter

Using Molecular Markers in Incheon, Korea

인천지역의 Molecular Markers 를 이용한

입자상 물질의 오염원 평가

2013년 8월

서울대학교 대학원

보건학과 환경보건학 전공

최 종 규

Source Characterization of

Particulate Matter Using Molecular

Markers in Incheon, Korea

A Dissertation Submitted in Partial Fulfillment of

the Requirement for the degree of

Doctor of Philosophy in Public Health

To the Faculty of the Graduate School of Public Health at

Seoul National University

By

Jongkyu Choi

Data approved: June, 2013

Seung-Muk Yi

Kiyoung Lee

Domyung Paek

Yong Pyo KIM

Kyung-Duk Zoh

인천지역의 Molecular Markers 를

이용한 입자상 물질의 오염원 평가

지도교수 조 경 덕

이 논문을 보건학박사 학위논문으로 제출함

2013년 4월

서울대학교 대학원

보건학과 환경보건 전공

최 종 규

최종규의 보건학박사 학위논문을 인준함

2013년 6월

위 원 장 이 승 묵 (인)

부위원장 이 기 영 (인)

위 원 백 도 명 (인)

위 원 김 용 표 (인)

위 원 조 경 덕 (인)

i

TABLE OF CONTENTS

TABLE OF CONTENTS .............................................................................. i

LIST OF TABLES ...................................................................................... ⅲ

LIST OF FIGURES .................................................................................... ⅴ

ABSTRACT ................................................................................................ 1

Chapter 1. Introduction

1.1. Background ........................................................................................ 7

1.2. Objectives ...................................................................................... 22

1.3. Structure and Scope of Thesis ....................................................... 23

References ............................................................................................ 25

Supplementary materials ........................................................................ 38

Chapter 2. Chemical characteristics of PM2.5 aerosol in Incheon,

Korea

Abstract ................................................................................................... 43

2.1. Introduction ..................................................................................... 45

2.2. Materials and methods ..................................................................... 47

2.3. Results and discussion ..................................................................... 51

2.4. Conclusions ..................................................................................... 76

References ............................................................................................ 78

Supplementary materials ........................................................................ 86

ii

Chapter 3. Source Apportionment of PM2.5 at the coastal area in

Korea

Abstract ................................................................................................... 99

3.1. Introduction ................................................................................... 101

3.2. Materials and methods ................................................................... 103

3.3. Results and discussion ................................................................... 114

3.4. Conclusions ................................................................................... 134

References ............................................................................................ 136

Supplementary materials ...................................................................... 146

Chapter 4. Molecular Marker Characterization of Particulate

Matter and Its Organic Aerosols Using PMF

Abstract ................................................................................................. 155

4.1. Introduction ................................................................................... 158

4.2. Materials and methods ................................................................... 161

4.3. Results and discussion ................................................................... 165

4.4. Conclusions ................................................................................... 198

References ............................................................................................ 201

Supplementary materials ...................................................................... 214

Chapter 5. Conclusions ............................................................................ 239

국문초록 .................................................................................................... 245

iii

LIST OF TABLES

Table 1-1. Summary of other existing studies associated with the PM2.5 component analysis. ..................................................................................... 11 Table 1-2. Summary of other existing studies associated with the organic speciation of PM2.5. .......................................................................... 14 Table 1-3. Chemicals in particles from different emission sources. ............ 17 Table 1-4. Probable sources of organic speices classifed in previous receptor model. ............................................................................................. 18 Table S1-1. Chemical and physical properties of major organic classes and molecular markers in PM. ...................................................................... 38 Table 2-1. The major constituents (mean ± standard deviation) of PM2.5 in Incheon, Korea (unit: μg/m3 for PM2.5). ................................................... 59 Table 2-2. Quantification results (ng/m3) for organic species of fine particles at the study sites ............................................................................. 64 Table 2-3. Factor loadings from principal component analysis of PM 2.5 aerosol after varimax rotation ....................................................................... 74 Table S2-1. Method detection limits, RSD (%), and RPD (%) for target analytes. ........................................................................................................ 87 Table S2-2. Summary report on the Recovery (%) for target analytes ........ 90 Table S2-3. GC and MS Analysis Conditions .............................................. 91 Table S2-4. The average concentration of metal elements, WSOC, and SOC in PM2.5 samples (ng/m3 or ratio). ....................................................... 92 Table S2-5. Results of non-parametric mean comparison for PM2.5 species using Mann-Whitney t-test. ............................................................. 93 Table 3-1. Summary statistics and mass concentrations of PM2.5 and 29 species measured for PMF analysis .............................................................. 116

iv

Table 3-2. The source concentration (μg/m3) and contributions (%) of identified sources to PM2.5 mass concentrations ........................................ 117 Table 4-1. Summary statistics and mass concentrations of TSP and organic species measured for PMF analysis ................................................. 167 Table 4-2. Pearson correlation coefficient for organic compounds in TSP and PM2.5 ...................................................................................... 178 Table S4-1. Factor loadings from principal component analysis of organic aerosol in PM after varimax rotation ............................................. 214 Table S4-2. Factor loadings from principal component analysis of PM after varimax rotation ................................................................................. 216

v

LIST OF FIGURES

Fig. 1-1. The comparison of major constituents in PM2.5 with previous studies ........................................................................................................... 11 Fig. 1-2. Results of wind field analysis using CALMET model in Incheon, 2011 ............................................................................................... 20 Fig. 2-1. Mass balance based on the chemical composition of annual mean fine particle concentrations ................................................................. 52 Fig. 2-2. The relationship of WSOC with NO3

-, SO42-, and SOC in

PM2.5 aerosol samples ................................................................................... 60 Fig. 2-3. Comparison of OC aerosol contents (SOC, POC, WSOC, and OC/EC) in PM2.5 during non-episode and episode periods .......................... 62 Fig. S2-1. Location of the study sites in Incheon, Korea ............................. 94

Fig. S2-2. Comparison of PM2.5 constituents during non-event, smog episode, and yellow sand event .................................................................... 95 Fig. S2-3. Concentrations of carbonaceous aerosol components and organic species of PM2.5 in Incheon by month ............................................. 96 Fig. S2-4. Comparison of concentration and fraction of OC class’s component in PM2.5 during non-event, smog episode, and yellow sand event ............................................................................................................. 97

Fig. 3-1. Location of the study sites in the coastal area around the capital of Korea .......................................................................................... 104 Fig. 3-2. Source profiles obtained from PM2.5 samples (prediction ± standard deviation) at the sampling site ..................................................... 118 Fig. 3-3. Time series plot for each source contribution of PM2.5 at the sampling site. .............................................................................................. 119 Fig. 3-4. Seasonal comparisons of source contributions to the PM2.5 mass concentration (mean ± standard deviation) ....................................... 121

vi

Fig. 3-5. CPF plots for the average source contributions deduced from PMF analysis .............................................................................................. 128 Fig. 3-6. PSCF map for (a) secondary nitrate, (b) secondary sulfate, (c) biomass burning, and (d) soil source resolved by PMF for at the sampling site, Korea during 2009 and 2010 ............................................... 132 Fig. S3-1. (a) IM (the maximum mean values of each species obtained from the scaled residuals), IS (the maximum standard deviation values of each species obtained from the scaled residuals), and rotational freedom as a function of the factors chosen in PMF, (b) Q-value for the different factor solutions and the change of “FPEAK” parameter ............. 146 Fig. S3-2. Correlation between predicted and observed mass concen trations using multiple linear regression analysis ....................................... 147 Fig. S3-3. The source contributions of episode sample and normal sample to the PM2.5 mass concentration (mean ± standard deviation) ....... 147 Fig. S3-4. Total number of end points of 5-day backward wind trajectories started at four different altitudes (500, 1000, and 1500 m) during the sampling period ......................................................................... 148 Fig. S3-5. PSCF map for PM2.5 five local sources such as (a) combustion, (b) industry, (c) motor vehicle 1, (d) motor vehicle 2, and (e) sea salt source resolved by PMF for at the sampling site, Korea .......... 149 Fig. S3-6. Comparison of source profile for 8 to 10 sources of PM2.5 in Incheon, Korea ........................................................................................... 150 Fig. S3-7. Comparison of time series plot for 8 to 10 sources contri bution of PM2.5 in Incheon, Korea .............................................................. 151

Fig. S3-8. Factor loadings from principal component analysis of PM2.5 source and major species (e.g., Organic species, WSOC, and SOC) after the varimax rotation ........................................................................... 154 Fig. 4-1. Source profiles of obtained from TSP samples(prediction ± standard deviation) at the sampling site ..................................................... 173 Fig. 4-2. Time series plot for each source contribution obtained from TSP samples at the sampling site ............................................................... 174

vii

Fig. 4-3. The source contributions (%) of identified sources to TSP mass concentrations .................................................................................... 175 Fig. 4-4. Seasonal comparisons of source contributions to TSP mass concentration (mean ± standard deviation). ............................................... 177 Fig. 4-5. Source profiles obtained from organic data (prediction ± standard deviation) using 41organic marker species in Incheon, Korea .... 180 Fig. 4-6. The source contributions (%) of identified sources to OC mass concentrations calculated from PMF model using 41organic marker species ......................................................................................................... 181 Fig. 4-7. The source contributions (%) of identified sources to organic carbon mass concentrations calculated from PMF model using organic markers-41species ...................................................................................... 184 Fig. 4-8. Seasonal comparisons of source contributions to organic carbon mass concentrations calculated from PMF model using organic markers-41species ...................................................................................... 187 Fig. 4-9. Source profiles obtained from TSP samples (prediction ± standard deviation) using 63species in Incheon, Korea. ............................ 192 Fig. 4-10. Time series plot for each source contribution of TSP using 63species in Incheon, Korea ....................................................................... 193 Fig. 4-11. The source contributions (%) of identified sources to TSP mass concentrations calculated from PMF model using 63species ............ 195 Fig. 4-12. CPF plots for the average source contributions deduced from organic carbon based on molecular marker PMF analysis ......................... 197 Fig. 4-13. CPF plots for the average source contributions deduced from TPS-molecular marker PMF analysis ......................................................... 198 Fig. S4-1. Concentrations of carbonaceous aerosol components and organic species of TSP in Incheon by month ............................................. 218 Fig. S4-2. The diagonistic factor of PMF model using traditional 21items (a) IM, IS, and rotational freedom as a function of the factors chosen in PMF, (b) Q-value for the different factor solutions and the

viii

change of “FPEAK” parameter .................................................................. 219 Fig. S4-3. Correlation between predicted and observed mass concen trations using multiple linear regression analysis. This results was obtained from PMF analysis (22items) .................................................... 220 Fig. S4-4. Comparison of source profile for 8 to 10 sources of TSP using 22items in Incheon, Korea ................................................................ 221

Fig. S4-5. Comparison of timeseries plot for 8 to 10 source contri bution of TSP using 22items in Incheon, Korea ......................................... 222 Fig. S4-6. The diagonistic factor of PMF model using 41 molecular markers only. (a) IM, IS, and rotational freedom as a function of the factors chosen in PMF, (b) Q-value for the different factor solutions and the change of “FPEAK” parameter ...................................................... 223 Fig. S4-7. Comparison of source profile for 7 to 9 sources of organic carbon using 41 organic marker species in Incheon, Korea ....................... 224 Fig. S4-8. Comparison of timeseries plot for 7 to 9 source contribution of organic carbon using 41 organic marker species in Incheon, Korea ...... 225 Fig. S4-9. Plot of FLA/(FLA + PYR) against IcdP/(IcdP + BghiP) for PAH source diagnostics. Two dash lines represent the thresholds for petroleum combustion and coal/biofuel burning ........................................ 226 Fig. S4-10. The diagonistic factor of PMF model using traditional 21items couple with 41 molecular markers. (a) IM, IS, and rotational freedom as a function of the factors chosen in PMF, (b) Q-value for the different factor solutions and the change of “FPEAK” parameter ............. 227 Fig. S4-11. Comparison of source profile for 7 to 9 sources of TSP using 63items in Incheon, Korea ................................................................ 228 Fig. S4-12. Comparison of time series plot for 7 to 9 source contri bution of TSP using 63items in Incheon, Korea ......................................... 229

- 1 -

ABSTRACT

Source Characterization of Particulate Matter

Using Molecular Markers in Incheon, Korea

Jongkyu Choi

The Graduate School of Public Health

Seoul National University

Airborne particulate matter (PM) has adverse effects on human

morbidity and mortality, visibility, climate change, and materials. Even

though the main composition of fine particles has been reported in

several studies, only 10~20% of the organic compounds has been

quantified as individual organic species. In order to develop effective

strategy for reducing fine particle pollution, it is very important to

analyze the components and evaluate the source of particulate matter.

The purpose of this study is to evaluate the characteristics of particulate

matter and determine sources using molecular markers (MM).

To find out the characteristics of PM in Incheon, PM samples were

collected for 1 year and analyzed for its composition. One hundred and

twenty samples for fine particle (PM2.5) and TSP were collected in

Incheon area from 2009 to 2010. The collected samples were analyzed

for the main ingredients such as OC, EC, ions, heavy metals and other

- 2 -

major components. In addition, water-soluble organic carbon (WSOC)

and the main ingredients of organic aerosol (OA) were analyzed. The

results by this analysis were used as input data of the source

apportionment model, positive matrix factorization (PMF), which has

been widely used as a basic model, unlike MM.

The first study was performed to elucidate the characteristics, sources,

and distribution of PM2.5 and carbonaceous species in Incheon, Korea.

To do this, we analyzed the major components of PM2.5 such as OC, EC,

ionic, and metallic species in individual samples. Furthermore, organic

species and WSOC were evaluated to characterize the influence of

individual PM2.5 components. The average PM2.5 concentration (41.9 ±

9.0 μg/m3) exceeded the annual level set by the United States’ National

Ambient Air Quality Standards (15 μg/m3). The major fraction of PM2.5

consisted of ionic species (accounting for 38.9 ± 8.8%), such as NO3-,

SO42-, and NH4

+, as well as organic carbon (OC) (accounting for 18.9 ±

5.1%). We also analyzed the seasonal variation in PM2.5 and secondary

aerosols such as NO3- and SO4

2- in PM2.5. As an important aerosol

indicator, WSOC (mean 4.7 ± 0.8 μg/m3, 58.9 ± 10.7% of total OC)

showed a strong relationships with NO3-, SO4

2-, and SOC (R2 = 0.56,

0.67, and 0.65, respectively), which could represent favorable

conditions for SOC formation during the sampling period. Among the

individual organic aerosols measured, n-alkanes, n-alkanoic acids,

levoglucosan, and phthalates were major components, whereas

polycyclic aromatic hydrocarbons (PAHs), oxy-PAHs, hopanes, and

cholestanes were minors. The concentration of organic compounds

- 3 -

during smoggy periods was higher than that of non-event periods. The

concentration of n-alkane and n-alkanoic acid species during the

smoggy periods was 10-14 times higher than that of the normal period.

Using principal component analysis coupled with multiple linear

regression analysis, we identified motor vehicle/sea salt, secondary

organic aerosols, combustion, biogenic/meat cooking, and soil sources

as primary sources of PM2.5.

In the second study, on the basis of the analyzed chemical species in

the PM2.5 samples, the sources of PM2.5 were identified using a positive

matrix factorization (PMF) model. And finally nine sources of PM2.5

were determined. The major sources of PM2.5 were secondary nitrate

(25.4%), secondary sulfate (19.0%), motor vehicle 1 (14.8%) with a

lesser contribution from industry (8.5%), motor vehicle 2 (8.2%),

biomass burning (6.1%), soil (6.1%), combustion and copper

production emissions (6.1%), and sea salt (5.9%) respectively. From a

paired t-test, it was found that the samples during the yellow sand

periods were characterized by higher contribution from soil sources (p

< 0.05). Furthermore, the possible source areas of PM2.5 emissions were

determined by using the conditional probability function (CPF) and the

potential source contribution function (PSCF). CPF analysis identified

the motor vehicles and sea salt as possible local sources of PM2.5. PSCF

analysis indicated that the possible sources for secondary particles

(sulfate and nitrate) were related to the major industrial complexes in

China.

In the final study, MM-PMF was preformed to evaluate the sources of

- 4 -

PM and organic carbons. PMF model was carried out and three

different analysis items were categorized. For example, first, 22 items

such as OC, EC, ionic compounds and trace metals in TSP, second, 41

items in organic compounds, and third, 63 items in both TSP (22) and

organic compounds (41). The nine sources of TSP were identified by

the PMF analysis using 22 items. The major sources of TSP were motor

vehicle (17.4%), sea salt (14.0%), secondary sulfate (13.7%), soil

(12.8%), combustion (11.6%), and industry (10.8%) with the lesser

contributions from non-ferrous industry (6.8%), secondary nitrate

(5.4%), and road dust (3.6%). From the molecular marker-PMF

analysis including only organic marker compounds (41species), the

eight-sources were separated as follows: The resolved eight sources

included combustion (LMW-PAHs), biomass burning, vegetative

detritus (n-Alkane), benzo(a)pyrene, SOA1, SOA2, combustion

(HMW-PAHs), and motor vehicle. Among them, secondary organic

aerosol, PAHs, and motor vehicle were evaluated as three major

sources of organic carbon sources. The source contribution of organic

aerosol resolved by PMF model showed different characteristics

depending on the season. The vegetative detritus and motor vehicle

were increased during the summer season by the increase in

biogenic/photochemical activity. However, most of the other organic

sources were prominent in the winter season by the increase in the level

of air pollution emission and atmospheric stability. In addition, CPF

results identified possible locations for local source, which included

primary sources, biomass burning/soil, motor vehicle/non-ferrous

- 5 -

industry, vegetative detritus (n-alkane), benzo(a)pyrene, and

combustion (PAHs).

Through this study, various sources of PM were evaluated by using

MM-PMF analysis. Sea port and combustion sources were found as the

additional PM2.5 sources that did not appear in other areas. The

contribution of sea salt and soil pollutants in the coarse particle was

two times as high as those in the fine particle. However, secondary

organic and inorganic species generated by the oxidation reaction of

primary pollutants occupied a very large portion of fine particulate

matters. As a result, secondary oxidation reaction was considered as a

primary cause of PM2.5. Therefore, it is very important to explain the

process for finding out the sources of these secondary pollutants and to

evaluate those sources in detail. Even though local sources existed,

PSCF analysis indicated that a certain part of pollutants such as

secondary aerosol, soil, and biomass burning have been associated with

long-range transport. Another important fact was that SOA, motor

vehicle, and combustion (PAHs) were identified as a major source of

organic carbon. Finally, there was an increase by more than 10 times in

particulate organic pollutants in the fine dust during the smog period.

This study has some important implications; first, it is first attempt to

analyze and evaluate the organic constituents of particulate matter in

Korea. Second, we found the chemical composition of particulate

matter for more than one hundred organic and inorganic species. And

finally, we could identify the contribution and major sources for

particulate compounds through receptor model. This kind of

- 6 -

characterization process for particulate organic aerosols will be a key

foundation to understand the importance of the issue and be helpful to

provide possible solutions which are relevant to PM reduction measures

in the future.

Keywords: PM2.5, positive matrix factorization (PMF), molecular

marker (MM), GC×GC-TOFMS, conditional probability

function (CPF), potential source contribution function

(PSCF).

Student number: 2002-30844

- 7 -

Chapter 1

Introduction

1.1. Background

Atmospheric particulate matters (PM) are tiny pieces of solid or liquid

matter associated with the Earth's atmosphere. Ambient particles are

made of a large number of chemical compounds originating from both

natural and anthropogenic sources. Their emission sources

consequently determine their chemical composition, size, and shape

characteristics. Impact of particulate matter on human health also varies

depending on the sources and components. Some particulates occur

naturally, originating from volcanoes, dust storms, resuspension of soil

particles, forest and grassland fires, living vegetation, and sea spray.

Anthropogenic sources included human activities, such as the burning

of fossil fuels in vehicles, domestic heating, power plants and various

industrial processes also generate significant amounts of particulates.

These solid and liquid particles come in a wide range of sizes.

Particles less than 10 micrometers in diameter (PM10) tend to pose the

greatest health concern because they can be inhaled into and

accumulate in the respiratory system. Particles less than 2.5

micrometers in diameter are referred to as "fine" particles (PM2.5).

Sources of fine particles include all types of combustion (motor

- 8 -

vehicles, power plants, wood burning, etc.) and some industrial

processes. Particles with diameters between 2.5 and 10 micrometers are

referred to as "coarse". Sources of coarse particles include crushing or

grinding operations, and dust from paved or unpaved roads.

Especially, due to their small size, fine particle are can penetrate

deeply into the lungs of people who inhale them, where they can

accumulate, react, or be absorbed into the body. Epidemiological

studies have shown a significant association between elevated PM2.5

levels and a number of serious health effects, including premature

mortality, aggravation of respiratory and cardiovascular disease, lung

disease, decreased lung function, asthma attacks, and certain

cardiovascular problems such as heart attacks and cardiac arrhythmia

(Dockery and Pope, 1994; Schwartz, 1994; Pope and Dockery, 1999).

World Health Organization (WHO) estimates that PM2.5 concentration

contributes to approximately 800,000 premature deaths per year,

ranking it the 13th leading cause of mortality worldwide (WHO, 2002).

Worldwide, about 8% of lung cancer deaths, 5% of cardiopulmonary

deaths and about 3% of respiratory infection deaths are attributed to

PM exposure (WHO, 2009).

In 1997, the U.S. Environmental Protection Agency (EPA) revised its

particulate matter standards to include an annual standard for PM2.5 of

15μg/m3 and a 24-hour standard of 65μg/m3 (EPA, 1997). The WHO

has released guidelines for safe levels of PM2.5 (24-h maximum

thresholds of 25 μg/m3 (WHO, 2008), and the EU established a

- 9 -

directive on PM2.5 limits in 2008. In accordance with the actions of

these nations, Korea’s authority is expected to implement

environmental standards from 2015 (PM2.5 25μg/m3-year). The new

regulations propose keeping existing limits on emissions of PM10 and

including new limits on PM2.5.

1.1.1. Particulate matter

Numerous papers reported that PM has adverse effects on human

morbidity and mortality, visibility, climate change, and materials

(Wang et al., 2001; Zhang et al., 2003; Ianniello et al., 2011). These

effects have sparked interest in the chemical and physical properties of

PM, with increasing interest in its organic matter (OM) composition. In

particular, known as the toxic substances, fine particles in the

atmosphere are made up of a complex mixture of compounds, both

liquid and solid. Common constituents of ambient PM2.5 include:

sulfate (SO42-); nitrate (NO3

-); ammonium (NH4+); elemental carbon; a

great variety of organic compounds; and inorganic material (including

metals, dust, sea salt, and other trace elements). Ambient PM2.5 is

typically comprised of a mixture of primary and secondary particles.

Primary particles are emitted directly into the air as a solid or liquid

particle (e.g., elemental carbon and organic particles from diesel

engines or burning activities). Secondary particles (e.g., SO42- and NO3

-

) form in the atmosphere as a result of various chemical transformations

of gaseous precursors such as sulfur dioxide (SO2) and oxides of

nitrogen (NOX). In this way, these primary and secondary particles

- 10 -

result from a broad variety of sources and emissions activities. For

example, SO4 particles usually result from reactions of SO2 emissions

(from sources like power plants and industrial boilers) with ammonia

emissions (from sources like animal feeding operations and fertilizer

production, and to a lesser extent from mobile sources and power

plants). Nitrate particles usually result from reactions of NOX emissions

(from such as mobile sources, power plants, or other industrial sources)

with ammonia emission. However, in the absence of ammonia,

secondary compounds (sulfate and nitrate) take an acidic form as

sulfuric acid (liquid aerosol droplets) and nitric acid (atmospheric gas).

PM2.5 concentration in the capital region of Korea can be estimated

from the concentration of the Seoul metropolitan. According to the

previous studies, the annual average PM2.5 concentration in Seoul was

43g/m3(Kim et al., 2006; Heo et al., 2009). This value is almost higher

than the annual level listed in the world, but lower than the

concentrations found in industrialized cities in Asia (Heo et al., 2009;

Lee and Kang, 2001; Ho et al., 2006; Lee and Hopke, 2006; Kim and

Hopke, 2008; Tan et al., 2009; He et al., 2001; Ye et al., 2003; Khan et

al., 2010; Moon et al., 2008). For example, the average concentration

of PM2.5 was reported to be 62.4 ~ 127 g/m3 in some cities of China

(Tan et al., 2009; He et al., 2001; Ye et al., 2003). In addition, the main

composition of PM2.5 was found to be carbonaceous compounds and

ionic species.

- 11 -

Seoul(Korea)

Chongju(Korea)

Hongkong

St.Louis(U

SA)

Seattle(U

SA)

Guangzhou(China)

Chegongzhuang(China)

Tsinghua(China)

Hainan(China)

Tongji(China)

Yokohama(Japan)

Gosan(Korea)

Con

cnet

rati

on(u

g/m

3 )

0

20

40

60

80

100

120

140

OCECSO42-

NO3-

NH4+

ElementsResidueUnknown

Fig. 1-1. The comparison of major constituents in PM2.5 with previous studies

Table 1-1. Summary of other existing studies associated with the PM2.5 component analysis

No Study Site Study Duration Sample Method Reference

1) Seoul(Korea) 2003-2006 ADS Heo et al., 2009 2) Chongju(Korea) 1995-1996 ADS Lee et al., 2001 3) Hongkong 2000-2001 HVAS Ho et al., 2006 4) St.Louis(USA) 2001-2003 RAAS Lee and Hopke, 2006 5) Seattle(USA) 2000-2005 RAAS Kim and Hopke, 2008 6) Guangzhou(China) 2007-2008 HVAS Tan et al., 2009 7) Chegongzhuang(China) 1999-2000 LVAS He et al., 2001 8) Tsinghua(China) 1999-2000 LVAS He et al., 2001 9) Hainan(China) 1999-2000 LVAS Ye et al., 2003

10) Tongji(China) 1999-2000 LVAS Ye et al., 2003 11) Yokohama(Japan) 2007-2008 LVAS Khan et al., 2010 12) Gosan(Korea) 2001-2003 LVAS(ADS) Moon et al., 2008

Note) ADS: Annular Denuder system, HVAS: Hi-Volume Air Sampler LVAS: Low-Volume Air Sampler, RAAS: Reference-Volume Air Sampler

- 12 -

1.1.2 Patriculate Organic Carbon

Organic carbon is one of the major components in PM2.5 in the

atmosphere, accounting for 10~70% of the total dry fine particulate

mass in urban areas (Cao et al., 2004; Yang et al., 2005). The main

composition of fine particles has been reported several studies, but only

10~20% of the organic compounds present can be quantified as

individual organic species (Sin et al., 2005; Zheng et al., 2000). They

are comprised of thousands of chemical constituents from numerous

organic compound classes, such as aromatics (i.e., PAHs, alcohols

alkanes (i.e., n-alkanes, hopanes, and steranes), and carboxylic acids

(i.e., alkanoic acids) (Rogge et al., 1993a,b; Zheng et al., 1997;

Mazurek et al., 2002; Fine et al., 2004). Some organic compounds, for

example, PAHs, are listed as priority pollutants since they have been

proved to be carcinogenic and mutagenic (IARC, 1984; Guo et al.,

2009).

Organic carbon is emitted into the atmosphere as primary organic

aerosol (POA) from diverse sources including combustion of fossil

fuels, meat cooking, biomass burning and mobile sources, and is also

formed in the atmosphere as secondary organic aerosol (SOA) from the

oxidation of gas-phase precursors (Pankow, 1994; Kroll and Seinfield,

2008). SOA have recently gained much attention because current

models estimate that they account for a dominant fraction of the total

organic particulate mass (Sheesley et al., 2004; Zhang et al., 2009).

- 13 -

Nevertheless current estimates of global SOA production remain

extremely approximate due to the lack of observations capable to

discern between the various SOA sources (Sheesley et al., 2004;

Schichtel et al., 2008; Lee et al., 2010). The difficulty arises from the

lack of adequate validation of SOC tracers, as well as the incomplete

identification of molecular markers (Schichtel et al., 2008; Heo et al.,

2013).

Organic aerosols play an important role in climate change due to their

interaction with light (Jacobson, 2001; Schichtel et al., 2008).

Furthermore, the emissions from wood burning, fossil fuel combustion

and mobile sources are linked to adverse health outcomes (Salvi et al.,

1999; Pope and Dockery, 2006; Delfino et al., 2010; Janssen et al.,

2011). Hence, efforts to understand the sources of organic aerosols in

PM2.5 as well as the specific components of PM-bound carbon which

have been linked to adverse health effects are important elements

required to support appropriate mitigation plans for PM2.5.

14

Table 1-2. Summary of other existing studies associated with the organic speciation of PM2.5

Reference Rogge et al.,

1993b Schauer, J.J.andCass, G.R., 2000.

Yassaa et al., 2001

Fraser et al., 2002

Li et al., 2006

Zheng et al., 2005.

Park et al., 2006.

Bae, M.S. and Schauer, J.J., 2009.

Study Site Los Angeles,

USA

Fresno and Bakersfield,

California USA

Algiers City, Italy

Houston, USA

Philadelphia, USA

Beijing, China

Gwangju, Korea

St. Louis, USA

Study Periods 1982 2000 May - Sep,

1998 Mar 1997 – Mar 1998

2000 2000 Mar - May,

2001 June - July

2001

N-Alkane 50.2-68.9 (C23-34)

18.48-215.6 (C24-33)

14.3-142 (C18-32)

4.19-731.9 (C16-33)

34.0 (C25-32)

356.8 (C17-36)

19.0-257.4 (C24-36)

29.5

N-Alkanoic acid 256.1-294.3

(C9-30) 332.5-979.3

(C9-30) 24.7-169.1 (C14-30)

45.6-166.4 (C10-26)

128.1 (C10-30)

260.6 (C14-30)

31.0-223.2 (C14-30)

45.3

PAHs 3.7-11.9

(14 species) 34.4-139.6 (22 species)

5.5-43.4 (11species)

0.9-25.8 (25 pecies)

2.9 166.7

(27 species) 13.2-26.7

(22 species)3.8

Aliphatic dicarboxyl acid

199.1-312.2(C3-9)

44.1-77.4 (C4-9)

- 12.5-93.9 (C3-10)

29.1 (C3-9)

109.9 (C3-9)

0.8-84.0 (C4-9)

15.8

Aromatic polycarboxyl acid

91.7-115.9 22-32.1 - - 2.0 54.8 4.4-30.7 8.9

Hopane & Cholesterane - - - 0.29-6.7 5.4 18.1 - 2.8

Legoglucosan - 1,100-7,590 - 7.2-141.8 - 1296.7 18.1-1753.8 60.5

Cholesetrol N.D-2.7 - - - - - 0.5-2.6 0.8

Oxy-PAHs 0.4-2.3 - - - - 17.0 1.6-9.9 1.7

Resin acid - 22.1-296.4 - - - 76.3 15.6-27.2 58.4

Other compounds 19.9-42.1 190.0-1192.2 - 0.1-2.4 - 132 92-1866.3 70.0

- 15 -

1.1.3 Source Characterization of Particulate Matter

Receptor based source apportionment methods can provide

quantitative information about source contributions to support air

quality control development. The most common receptor models can be

categorized into univariate models such as chemical mass balance

(CMB) and multivariate models such as principal components analysis

(PCA), PMF, and UNMIX (Cooper and Watson, 1980; Henry et al.,

1984; Hopke, 1991; Ramadan et al., 2003).

A disadvantage with CMB source apportionment is the requirement of

a priori knowledge of the source profiles. Therefore, questions are

always raised as to the accuracy of the source profiles and the ability to

quantify errors associated with using source profiles that may not

represent the sources impacting receptor sites (Jaeckels et al., 2007).

On the other hand, PMF does not require source profiles as model

inputs but does require knowledge of source profiles to interpret the

factors derived from the model as air pollution sources (Jaeckels et al.,

2007). The PMF receptor model can quantify source profiles and

source contributions using observed chemical species at receptor sites.

This model has been used in many source apportionment studies with

OC, EC, ionic species, and trace elements over the past decade

(Polissar et al., 1998, Polissar and Hopke, 2001; Kim et al., 2003;

Larsen and Baker, 2003; Liu et al., 2003; Ogulei et al., 2005, 2006; Lee

and Hopke, 2006; Song et al., 2006; Brown et al., 2007; Gildemeister et

al., 2007; Kim et al., 2007; Shrivastava et al., 2007; Subramanian et al.,

- 16 -

2007). Some studies have also coupled PMF results with surface wind

direction and air-mass back trajectories to obtain reasonable prediction

of possible source locations (Ashbaugh et al., 1985; Hopke et al., 1995;

Ogulei et al., 2005, 2006; Kim et al., 2006; Du and Rodenburg, 2007;

Gildemeister et al., 2007, Rizzo and Scheff, 2007).

Advances in analytical techniques for organic compounds have

resulted in considerable progress in source apportionment like PMF and

CMB model (Jaeckels et al., 2007; Schnelle-Kreis et al., 2007;

Shrivastava et al., 2007). Some researchs characterizing carbonaceous

PM has been already conducted in Europe and North America (Schauer

et al., 1996; Sheesley et al., 2004; Jaeckels et al., 2007; Shrivastavaet

al., 2007). Despite these efforts, there is still no complete inventory of

the chemical compounds that compose the fine-particle organic aerosol

from any site in the world. Futhermore, the evaluation of the individual

organic matter in PM2.5 in Korea has yet been conducted throughout

much of the developing world and in the East Asia, specifically, to

assess the organic composition of the PM.

- 17 -

Table 1-3. Chemicals in particles from different emission sources (Wang et al., 2004: EPA-451/R-04-001)

Source Type Dominant Particle Size

Chemical Abundances in Percent Mass< 0.1% 0.1 to 1% 1 to 10% > 10%

Paved Road Dust Coarse Cr, Si, Pb, Zr SO42-, Na+, K+, P,

S, Cl, Mn, Ba, Ti EC, Al, K, Ca, Fe OC, Si

Unpaved RoadDust Coarse NO3

-, NH4+, P,

Zn, Sr, BaSO4

2-, Na+, K+, P,S, Cl, Mn, Ba, Ti EC, Al, K, Ca, Fe Si

Construction Coarse Cr, Mn, Zn, Sr, Ba SO42-, K+, S, Ti EC, Al, K, Ca, Fe Si

Agricultural Soil Coarse NO3-, NH4

+, Cr, Zn, Sr

SO42-, Na+, K+, S,

Cl, Mn, Ba, Ti EC, Al, K, Ca, Fe Si

Natural Soil Coarse Cr, Mn, Sr, Zn, Ba Cl, Na+, EC, P, S,Cl, Ti EC, Al, Mg, K, Ca, Fe Si

Lake Bed Coarse Mn, Sr, Ba K+, Ti SO42-, Na+, OC, Al, S, Cl, K, Ca, Fe Si

Motor Vehicle Fine Cr, Ni, Y NH4+, Si, Cl, Al, Si, P,

Ca, Mn, Fe, Zn, Br, Pb Cl-, NO3-, SO4

2-,NH4+, S OC, EC

VegetativeBurning Fine Ca, Mn, Fe, Zn,

Br, Rb, PbNO3

-, SO42-, NH4

+,Na+, S Cl-, K+, Cl, K OC, EC

Residual/Crude Oil Combustion Fine K+, OC, Cl, Ti, Cr, Co, Ga, Se NH4

+, Na+, Zn, Fe, Si V, OC, EC, Ni S, SO42-

Incinerator Fine V, Mn, Cu, Ag, Sn K+, Al, Ti, Zn, Hg NO3-, Na+, EC, Si,

S, Ca, Fe, Br, La, PbSO4

2-, NH4+,

OC, ClCoal-FiredPower Plant Fine Cl, Cr, Mn, Ga,

As, Se, Br, Rb, Zr NH4

+, P, K, Ti, V,Ni, Zn, Sr, Ba, Pb

SO42-, OC, EC,

Al, S, Ca, Fe Si

Oil-Fired Power Plant Fine V, Ni, Se, As,

Br, Ba Al, Si, P, K, Zn NH4+, OC, EC,

Na, Ca, Pb S, SO42-

Smelter Fine Fine V, Mn, Sb, Cr, Ti Cd, Zn, Mg, Na, Ca,K, Se Fe, Cu, As, Pb S

Antimony Roaster Fine V, Cl, Ni, Mn SO42-, Sb, Pb S None

Reported

Marine (Natural) Fine andCoarse

Ti, V, Ni, Sr, Zr, Pb, Ag, Sn, Sb, Pb

Al, Si, K, Ca, Fe,Cu, Zn, Ba, La NO3

-, SO42-, OC, EC Cl-, Na+,

Na, Cl

- 18 -

Table 1-4. Probable sources of organic speices classifed in previous receptor model Compound Important sources Reference

Benzo(e)pyrene, indeno[1,2,3-cd]pyrene, benzo[g,h,i]perylene,coronene, benzo(b+j+k)fluoranthenes Some PAHs

Metallurgical coke-production, Combustion, Vehicular exhaust

Robinson et al. (2006)

Dibenzothiphene Asphalt, coke and other sources Rogge et al. (1997)

22,29,30-Trisnorneohopane, 17a(H),21b(H)-29-norhopane, 17a(H),21b(H)-hopane, 22S+R-17a(H),21b(H)-30-homohopane, 22S+R-17a(H), 21b(H)-30-bishomohopane

Gasoline and diesel vehicle exhaust

Simoneit (1985), Rogge et al. (1993a)

Syringaldehyde, acetosyringone, syringic acid Hardwood combustion Rogge et al. (1998),

Simoneit (2002)

Levoglucosan, b-sitosterol Biomass burning, b-sitosterol is constituent of vascular plant wax

Hays et al. (2002, 2005), Simoneit (2002)

Hexacosanoic acid, octacosanoic acid, triacontanoic acid, dotriacontanoic acid

Biomass burning and primary biogenic

Rogge et al. (1993b), Schauer et al. (2001), Hays et al. (2002, 2005)

Sum of resin acids (predominantly dehydroabietic acid, and 7-oxodehydroabietic acid)

Softwood combustion Rogge et al. (1998), Simoneit (2002), Robinson et al. (2006b)

Benzothiazole Tire wear Rogge et al. (1993a)

Oleic acid Meat cooking, seed oil cooking, wood and other primary sources

Schauer et al. (1996, 2002), Hays et al. (2002)

Palmitoleic acid, cholesterol Meat cooking Rogge et al. (1991), Schauer et al.(1999a)

Cis-pinonic acid, nopinone, norpinionic acid

Biogenic secondary organic aerosol

Yu et al. (1999), Koch et al. (2000), Fick et al. (2003)

6,10,14-Trimethyl-2-pentadecanone Secondary oxidation product from plant or vehicular exhaust

Simoneit (1986), Alves et al. (2001)

Butanedioic acid, pentanedioic acid, hexanedioic acid

Secondary oxidation products and primary sources

Schauer et al. (1996), Sheesley et al. (2004)

1,2 Benzenedicarboxylic acid Anthropogenic SOA Fraser et al. (2003)

1,3-Benzenedicarboxylic acid Vehicles and other sources

Fraser et al. (2003), Simoneit et al.(2005)

Iso-hentriacontane and anteiso-dotriacontane

Cigarette smoke, Vegetative detritus Rogge et al. (1994a)

Palmitic and stearic acids Meat, vehicles and other sources

Alkyl cyclohexanes: undecyl, dodecyl, tridecyl, tetradecyl, pentadecyl, heptadecyl, hexadecyl, octadecyl, nonadecyl

Vehicle exhaust Schauer et al. (1999b, 2002)

n-Nonacosane, n-triacontane, n-hentriacontane,n-dotriacontane, n-tritriacontane

Vegetative detritus, road-dust, tire wear, biomass burning

Simoneit and Mazurek (1982), Rogge et al. (1993a, b), Hays et al. (2002, 2005)

※ This table was extracted from Shrivastava et al, 2007.

- 19 -

1.1.4 The Characteristics of the Study Area

Incheon, study site, is one of the industrialized urban city with an area

of 1032.41 km2 and a population over 2.85 million in the capital region

of South Korea. The precise geographical location of Incheon is

126°37′E, 37°28′N, which situates it at roughly the midpoint on the

west coast of the Korean Peninsula. Incheon has a continental climate,

but its coastal location also results in a partly maritime climate

compared to inland regions, leading to a narrower annual temperature

range. The annual average temperature is 12.5℃, with the highest daily

temperature recorded as 16.7℃ and the lowest as 9.2℃. The annual

precipitation is 1,368mm, which is less than other regions of a similar

latitude. The annual average wind speed is 2.8m/sec, the main wind

direction being north-northwest, followed by north and northwest.

Seasonal analysis of the wind field, which is the important factor of the

atmospheric diffusion, showed a different pattern, respectively.

According to result of the wind analysis (Fig 1-2), the northwesterly is

dominant wind in the winter and the fall. During these seasons, wind

speed was as low as 1~2 m/sec, which was a factor in reducing the

ability of atmospheric diffusion. On the other hand, the prevailing

westerly winds in spring and the summer was the prevailing south wind,

respectively. The results demonstrate the characteristics of the winter

monsoon, and the influence of sea breeze during the spring and summer.

- 20 -

Fig. 1-2. Results of wind field analysis using CALMET model in Incheon, 2011.

There were 10 industrial complexes (4 national complexs and 6 local

complexs) in Incheon, where 9,046 are in operation by the

manufacturer. In addition to these industrial complexes, a total of

20,922, small and medium-sized factories, are scattered throughout the

city. This city has also various infrastructures such as sea ports, airports,

power plant, metropolitan landfills, and three major high ways. Due to

rapid urbanization and industrialization, there has been a significant

increase in population and vehicle since the opening.

- 21 -

The past decade's population growth rate was 10% (2.85 million, 2012),

and the number of registered vehicle showed an increase of 6.3%

(983,000 vehicles, 2012).

Incheon is often enveloped in sea fog (annual average, 42day) in

contacted with the coast, and is affected by long-range transport of

pollutants from industrial complexes in China as well as yellow sand

dust formed through desertification. Furthermore, local pollutants

emitted from heavy traffic, numerous industries, and urban facilities

have reduced the ambient air quality. The concentration of PM10 near

the capital is higher than that of large cities around the world,

exceeding the annual PM10 standards (50 g/m3/year; Korea Ministry

of Environment, 2009).

- 22 -

1.2 Objectives

This study was carried out to evaluate the characteristics of a variety

of particulate contaminants and their source using molecular marker. In

this study, fine particle (PM2.5) and TSP samples has been examined in

Incheon during the 2009-2010years. The collected samples were used

to analyze the main ingredient such as OC, EC, ions, heavy metals and

other major components. In addition, WSOC and individual species of

OA were also analyzed. The results of this analysis are used as an input

data of the source apportionment model.

The objectives of this study were (1) to analyze/evaluated the

characteristics of PM (PM2.5, TSP) and organic aerosol collected at the

coastal areas near Incheon city in Korea, (2) to quantify the source

contributions to PM and organic carbon at the coastal area in Korea,

using a PMF model, (3) to identify the actual local sources and the

likely locations of the regional sources by the conditional probability

function (CPF) or the potential source contribution function (PSCF),

and (4) to determine the spatial and seasonal variations of source

contributions.

- 23 -

1.3 Structure and Scope of Thesis

Chapter 2: Chemical characteristics of PM2.5 aerosol in

Incheon, Korea

The first study was performed to elucidate the characteristics, source,

and distribution of PM2.5 and carbonaceous species in Incheon, Korea.

We analyzed the major components of PM2.5 such as OC, EC, ionic,

and metallic species in individual samples. Furthermore, organic

species and WSOC were evaluated to characterize the influence of

individual PM2.5 components. To identify the concentration of PM2.5

aerosols during each season and during times of increased pollution, the

variation in all of the measured compounds were compared and

analyzed. In addition, various organic compounds in PM2.5 samples

were measured using GC×GC-TOFMS. Finally, using PCA with

multiple linear regression analysis (MLRA), we identified the source

and contribution of the aerosol components in the study area.

Chapter 3: Source Apportionment of PM2.5 at the

coastal area in Korea

The objectives of the second study are (1) to analyze the chemical

composition of PM2.5 collected at the coastal areas near Incheon city in

Korea, (2) to quantify the source contributions to PM2.5 at the coastal

area in Korea, using a PMF model, (3) to identify the actual local

sources and the likely locations of the regional sources by the CPF and

the PSCF, and (4) to determine the spatial and seasonal variations of

- 24 -

source contributions. Finally, each source contribution to measured

PM2.5 was compared with a dataset of individual organic species

measured.

Chapter 4: Molecular Marker Characterization of

Particulate Matter and Its Organic Aerosols Using PMF

Finally, in this chapter, PMF model was carried out and categorized by

3 different kinds of analysis items, for example, first, 22 items such as

OC, EC, Ionic compounds and trace metals in TSP, second, 63 items in

both TSP(22) and organic compounds(41), and third, 41 items in

organic compounds. On the basis of these results, we also compared the

results of the coarse particles (TSP) with those of fine particles (PM2.5).

Source apportionment was performed by using only organic

contaminants to evaluate the sources of organic pollutants and to

establish the management strategy. Finally, their temporal and seasonal

contributions were also evaluated from the source apportionment. The

local pollution sources were estimated using the CPF model.

- 25 -

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Supplementary Materials Table S1-1. Chemical and physical properties of major classes and molecular markers in PM

Species CAS No. MW Melting point Boiling point Solubility Vapor pressure Henry’s

coefficient Log kow IARC EPA

g ℃ ℃ (g/L) (mmHg) atm*m3/mol

Na+ 7440-23-5 23 296.4 679.41 4.45E+02 3.64E-19 2.45E-02 -0.77 NH4

+ 7664-41-7 17.0 -77.7 -33.3 1E+03 3.52E+01 3.45E-06 0.23 K+ 7440-09-7 19 63.4 759 5.78E+02 1.25E-15 2.45E-02 -0.77 Cl- 7782-50-5 70.9 -101 -34 4.24E+01 4.16E-08 7.73E-03 0.54 NO3- - 62 189.1 492.6 9.09E+01 8.08E-10 2.45E-02 0.21 SO4

2- - 96 270.5 623.9 1,000 2.33E+03 2.54E-11 -2.20 Mg 7439-95-4 188.6 483.0 3.25E+02 2.95E-31 2.45E-02 -0.57 Al 7429-90-5 26.9 198.0 503.9 5.94E+04 8.74E-10 2.45E-02 0.33 Si 63231-67-4 28.1 146.3 439.9 4.14E+01 1.36E-07 6.14E-02 0.53 P 14265-44-2 40.0 162.0 468.2 2.05E+02 2.33E+04 2.44E-02 -0.27 Ca 7440-70-2 40.0 188.6 483.0 3.99E+05 1.04E-43 2.45E-02 -0.57 Ti - 47.9 188.6 483.0 8.55E+04 4.24E-09 2.45E-02 0.23 V 7440-62-2 50.9 1,910 3,407 8.64E+01 4.24E-09 2.45E-02 0.23 2B Cr 7440-47-3 51.9 190 2642 8.67E+01 0 2.45E-02 0.23 Mn 7439-96-5 54.9 1,244 2,905 8.71E+01 0 2.45E-02 0.23 Fe 7440-48-4 55.8 118.6 482.9 6.24E+08 4.24E-09 2.45E-02 -0.77 Co 7440-48-4 58.9 1,495 2,870 8.75E+01 4.24E-09 2.45E-02 0.23 2B Ni 7440-02-0 58.7 1,445 2,730 4.22E+01 4.24E-09 2.45E-02 -0.57 2B A Cu 7440-50-8 63.5 1,083 2,595 4.21E+02 0 2.45E-02 -0.57 Zn 7440-66-6 65.4 419 908 3.44E+02 7.99E-23 2.45E-02 -0.47 As 7440-38-2 74.9 817 614 3.47E+01 1.14E+04 7.73E-01 0.68 1 A Se 7782-49-2 78.9 221 685 8.14E+01 6.67E-03 9.74E-03 0.24 Sr 7440-24-6 87.6 777 1,382 8.04E+01 5.75E-41 2.45E-02 0.23 A Cd 7440-43-9 112.4 321 765 1.23E+02 8.98E-18 2.45E-02 -0.07 1 B1 Sn - 118.7 186.4 486.6 9.91E-09 7.909 2.45E-02 1.29 Sb 7440-36-0 121.8 -84.0 27.9 2.29E+01 0 2.45E-02 0.73 Pb 7439-92-1 207.2 327.4 1,740 9.581 7.28E-11 2.45E-02 0.73 - -

- 39 -

Table S1-1. Chemical and physical properties of major classes and molecular markers in PM

Species CAS No. MW Melting point Boiling point Solubility Vapor pressure

Henry’s coefficient

Log kow IARC EPA

g ℃ ℃ (g/L) (mmHg) atm*m3/mol

Heptadecane (C17H36) 629-78-7 240.5 21-23 302 2.70E-08 3.24E-03 3.85E+01 8.69 Octadecane (C18H38) 209-790-3 254.5 28-30 317 9.36E-08 1.46E-03 5.12E+01 9.18 Nonadecane (C19H40) 629-92-5 268.5 32-34 330 2.97E-08 6.73E-04 6.79E+01 9.67 Eicosane (C20H42) 112-95-8 282.5 36.7 342.7 9.41E-09 3.10E-04 9.01E+01 10.16 Docosane (C22H46) 629-97-0 310.6 42 224 9.25E-11 1.69E-05 2.80E+02 12.13 Tetracosane (C24H50) 646-31-1 338.7 52 391.3 9.25E-11 1.69E-05 2.80E+02 12.13 Hexacosane (C26H54) 630-01-3 366.7 56.4 412.2 9.07E-12 5.26E-06 4.94E+02 13.11 Heptacosane (C27H56) 593-49-7 380.7 59.5 422 2.83E-12 9.84E-07 6.55E+02 13.60 Nonacosane (C29H60) 630-03-5 408.8 63.7 440.8 2.76E-13 9.56E-07 1.15E+03 14.58 Dotriacontane (C32H66) 544-85-4 450.9 69 467 8.29E-15 2.00 E-07 2.70E+03 16.06 Tritriacontane (C33H68) 630-05-7 464.9 71–73 485.3 2.57E-15 6.94E-08 3.59E+03 16.55 Tetratriacontane (C34H70) 14167-59-0 478.9 72.6 496.9 7.09E-16 7.76E-08 4.76E+03 17.04 Hexanoic acid (C6H12O2) 142-62-1 116.2 -3.4 205 10.610 0.278 1.70E-06 2.05 Heptanoic acid (C7H14O2) 111-14-8 130.2 -7.5 223 1.955 0.117 2.26E-06 2.54 Nonanoic acid (C9H18O2) 112-05-0 158.2 12.5 254 0.353 0.0214 3.98E-06 3.52 Decanoic acid (C10H20O2) 334-48-5 172.3 31.6 269 0.048 0.009 5.28E-06 4.09 Undecanoic acid C11H22O2) 112-37-8 186.3 28.6 284 0.021 0.005 7.01E-06 4.51 Dodecanoic acid C12H24O2) 143-07-7 200.3 43.2 298.9 1.28E-02 1.14E-03 9.31E-06 5.00 Tridecanoic acid (CH13H26O2) 638-53-9 214.3 40-45 236 1.88E-03 6.70E-04 1.24E-05 5.49 Tetradecanoic acid (C14H28O2) 544-63-8 228.4 54.4 250.5 4.67E-04 2.60E-04 1.64E-05 5.98 Pentadecanoic acid (C15H30O2) 1002-84-2 242.4 51-53 257 1.92E-04 1.34E-04 2.18E-05 6.47 Hexadecanoic acid (C16H32O2) 57-10-3 256.4 62.9 351-352 4.07E-05 5.52E-05 2.89E-05 6.96 Heptadecanoic acid (C17H34O2) 506-12-7 270.4 61.3 227 1.94E-05 2.84E-05 3.84E-05 7.45 Octadecanoic acid (C18H36O2) 57-11-4 284.5 69.6 383 9.34E-06 8.31E-06 5.10E-05 7.94

1) http://www.atsdr.cdc.gov/, 2) http://www.chemspider.com, and IARC: International Agency for Research on Cancer

- 40 -

Table S1-1. Chemical and physical properties of major organic classes and molecular markers in PM

Species CAS No. MW Melting point Boiling point Solubility Vapor pressure

Henry’s coefficient

Log kow IARC EPA

g ℃ ℃ (g/L) (mmHg) atm*m3/mol

Nonadecanoic acid (C19H38O2) 646-30-0 298.5 69.4 300 1.95E-06 7.46E-04 6.77E-05 8.44 Eicosanoic acid (C20H40O2) 506-30-9 312.5 75.4 328 2.99E-07 1.45E-05 8.98E-05 8.93 Heneicosanoic acid (C21H42O2) 2363-71-5 326.6 150.8 416.9 1.93E-07 1.69E-07 1.19E-04 9.42 Tricosanoic acid (C23H46O2) 2433-96.7 354.6 166.6 440.1 1.89E-08 3.01E-08 2.10E-04 10.40 Tetracosanoic acid (C24H48O2) 557-59-5 368.6 174.5 451.7 5.93E-09 1.21E-07 2.79E-05 10.89 Butanedioic acid (C4H6O4) 110-15-6 118.1 83.3 280.4 8.08E-08 1.08E-04 5.41E-12 -0.75 Pentanedioic acid (C5H8O4) 110-94-1 132.0 92.83 295.8 3.96E-08 3.27E-04 7.18E-12 -0.26 Hexanedioic acid (C6H10O4) 124-04-9 146.1 102.0 310.3 1.67E-08 1.28E-05 9.53E-12 0.23 Nonanedioic acid (C9H16O4) 123-99-9 188.1 127.2 348.5 5.684 2.29E-05 2.23E-11 1.70 Naphthalene 91-20-3 128.2 80.5 218 8.7E-07 8.70E-02 4.6×E-4 3.29 2B C Acenaphthene (C12H10) 83-29-9 154.2 95 96.2 1.93E-3 4.47E-03 7.91×E-5 3.98 3 Acenaphthylene (C10H8) 208-96-8 152.2 92-93 265-275 3.93E-3 2.90E-02 1.45×E-3 4.07 3 Fluorene (C13H10) 86-73-7 166.2 116-117 295 1.68-1.98E-3 3.20E-04 1.0×E-4 4.18 3 Phenanthrene (C14H10) 85-01-8 178.2 100 340 1.2E-3 6.80E-04 2.56×E-6 4.45 3 Anthracene (C14H10) 120-12-7 178.2 218 340,342 0.76E-3 1.77E-05 1.77×E-5 4.45 3 Fluoranthene (C16H10) 206-44-0 202.3 11 375 0.2-0.26E-3 5.00E-06 6.5×E-6 4.9 3 Pyrene (C16H10) 129-00-0 202.3 156 393,404 0.08E-3 2.50E-06 1.14×E-5 4.88 3 Benzo[b]fluoranthene (C20H12) 205-99-2 252.3 168.3 No data 1.2E-6 5.00E-07 1.22×E-5 6.04 2B Benzo[k]fluoranthene (C20H12) 207-08-9 252.3 215.7 480 7.6E-3(25) 9.59E-11 3.78×E-5 6.06 2B Benzo[a]pyrene (C20H12) 50-32-8 252.3 179 310-312 2.3E-3 5.60E-09 4.9×E-7 6.06 1 Benzo[ghi]perylene (C22H12) 191-24-2 276.3 273 550 2.6E-4(25) 1.03E-10 1.44×E-7 6.50 3 Chrysene (C18H12) 218-01-9 228.3 255-256 448 2.8E-3 6.30E-07 1.5×E-6 5.16 2B Indeno[1,2,3-cd]pyrene (C22H12) 193-39-5 276.3 163.6 530 0.06E-3 1.0E-11~-06 6.95×E-8 6.58 2B

- 41 -

Table S1-1. Chemical and physical properties of major organic classes and markers in aerosols

Species CAS No. MW Melting point

Boiling point

SolubilityVapor

pressure Henry’s

coefficientLog kow

IARC EPA

g ℃ ℃ (g/L) (mmHg) atm*m3/mol

17α(H),21β(H)-(22R)-Homohopane (C31H54) 60305-22-8 426.8 133.8 423.9 N.A 1.71E-07 1.15E+01 11.27 17α(H),21β(H)-30-Norhopane (C29H50) 53584-60-4 398.7 124.4 407.7 1.28E-09 5.19E-07 6.52 10.36 17α(H),21β(H)-Hopane (C30H52) 13849-96-2 412.8 127.1 412.3 4.61E-10 3.91E-07 8.66 10.78 17α(H)-22,29,30-Trisnorhopane (C27H46) 53584-59-1 370.7 112.8 387.8 1.14E-08 2.09E-06 3.70 9.45 ααα 20R Cholestane (C27H48) 481-21-0 372.7 115.5 392.5 1.85E-09 8.79E-06 8.39 10.36 ααα(20R,24R)-24-Ethylcholestane 62446-14-4 464.7 234.8 547.5 2.15E-05 2.82E-15 1.39E-08 5.56

αββ 20R Cholestane (C27H48) 69483-47-2 372.7 137.1 392.5 1.85E-09 8.79E-06 8.39 10.36

αββ(20R,24R)-24-Ethylcholestane (C29H52) 71117-92-5 400.7 130.3 417.9 1.85E-09 3.84E-07 8.39 10.36 9,10-Anthracenedione (C14H8O2) 84-65-1 208.2 127.6 363.6 3.92E-03 3.83E-08 3.18E-09 3.34 9H-Fluorenone (C13H8O) 486-25-9 180.2 99.48 331.7 5.72E-05 3.74E-03 6.77E-07 3.55 Benzofuran (C8H6O) 271-89-6 118.1 -18 174 5.35E-01 1.29 5.25E-04 2.54 Benzo[a]fluorenone (C17H12) 238-84-6 216.3 185.4 398.3 2.16E-05 3.87E-07 6.61E-08 4.73 Cholesterol (C27H46O) 57-88-5 386.7 126.7 432.7 4.13E-07 1.79E-07 1.67E-04 8.74 Levoglucosan (C6H10O5) 498-07-7 162.1 -91.7 313.8 7.81E+02 3.47E-07 1.42E-13 -1.25 Retene (C18H18) 483-65-8 234.3 117.4 367.7 1.50E-05 2.64E-06 1.10E-04 6.35 Squalene (C30H50) 112-02-4 410.7 58.9 452.9 6.65E-13 1.14E-06 2.42E+02 14.12 Dibutyl phthalate 84-74-2 278.3 6.0 337.9 2.35E-03 2.28E-04 1.22E-06 4.61 Benzothiazole 96-16-9 135.2 56.6 248.9 1.684E+03 7.4E--02 3.74E-07 2.17 Dehydroabetic acid 1740-19-8 300.4 159.7 403.3 1.35E-04 2.88E-07 1.78E-07 6.52 1,2-Benzenecaboxylic acid (C8H4O4) 88-99-3 166.1 126.6 351.4 1.53E+01 7.67E-07 2.18E-12 1.07

- 42 -

- 43 -

Chapter 2

Chemical characteristics of PM2.5

aerosol in Incheon, Korea

Abstract

We examined the characteristics, sources, and distribution of PM2.5

and carbonaceous species in particulate samples collected from June

2009 to May 2010 in Incheon, Korea. The average PM2.5 concentration

(41.9 ± 9.0 μg/m3) exceeded the annual level set by the United States’

National Ambient Air Quality Standards (15 μg/m3). The major fraction

of PM2.5 consisted of ionic species (accounting for 38.9 ± 8.8%), such

as NO3-, SO4

2-, and NH4+, as well as OC (accounting for 18.9 ± 5.1%).

We also analyzed the seasonal variation in PM2.5 and secondary

aerosols such as NO3- and SO4

2- in PM2.5. While SO42- concentrations

were higher in spring and summer, the concentration of PM2.5 and NO3-

were the highest in winter. SO42- concentrations were higher during the

spring and summer, but PM2.5 and NO3- were highest during the winter.

As an important aerosol indicator, WSOC (mean 4.7 ± 0.8 μg/m3, 58.9

± 10.7% of total OC) showed a strong relationship with NO3-, SO4

2-,

and SOC (R2 = 0.56, 0.67, and 0.65, respectively), which was indicative

- 44 -

of favorable conditions for SOC formation during the sampling period.

Among the individual organic aerosols measured, n-alkanes, n-alkanoic

acids, levoglucosan, and phthalates were major components, whereas

polycyclic aromatic hydrocarbons (PAHs), oxy-PAHs, hopanes, and

cholestanes were minor components. The concentration of organic

compounds during smoggy periods was higher than during non-event

periods. The n-alkane and n-alkanoic acid species during the smoggy

periods were 10-14 times higher than during the normal period. Using

principal component analysis coupled with multiple linear regression

analysis, we identified the primary sources of PM2.5 to be motor

vehicle/sea salt, secondary organic aerosols, combustion, biogenic/meat

cooking, and soil sources.

- 45 -

2.1 Introduction

Airborne particles are chemically and physically nonspecific, and may

originate from various natural or anthropogenic sources (Russel and

Allen, 2004). Airborne particles also play an important role in human

health, visibility degradation, and global climate change (Chlarlson et

al., 1992; Laden et al., 2000; Ito et al., 2006). In particular, fine

particles that can more readily penetrate into the lung are associated

with an increased incidence of respiratory and cardiovascular disease

(Dockery et al., 1993; Schwartz et al., 2002). Fine particles consist of

numerous compounds including nitrate, sulfate, inorganic compounds,

and organic species. OC in particles is a mixture of hundreds of

compounds, and can be formed by various sources and complex

atmospheric processes. OC and EC also play an important role in

climate change, influencing the properties of particles and the

nucleation of organic material cloud condensation (Ram and Sarin,

2010). Thus, further characterization of the chemical composition of

PM2.5 is required to understand the effects of PM2.5 on the global

climate and human health.

Many studies on the chemical speciation of PM2.5 have been

conducted in the United States (Rogge et al., 1993; Yassaa et al., 2001;

Fraser et al., 2002; Zheng et al., 2002; Shrivastava et al., 2007; Chen et

al., 2010). However, few studies have been performed in Asia,

including Korea (Yang et al., 2005; Wang et al., 2009; Samy et al.,

- 46 -

2010; Park and Cho, 2011). A large number of studies evaluating the

source of major components and individual organic matter in PM2.5

using element analysis, CMB, and PMF models have been performed

in the US. However, evaluation of the individual organic matter in

PM2.5 in Korea has yet to be completed.

Located in a coastal area close to South Korea’s capital city of Seoul,

Incheon is often enveloped in sea fog and is affected by long-range

transport of pollutants from industrial complexes in China as well as

yellow sand dust formed through desertification. Furthermore, local

pollutants emitted from heavy traffic, numerous industries, and urban

facilities have reduced the ambient air quality. The concentration of

PM10 near the capital is higher than that of large cities around the world,

exceeding the annual PM10 standards (50 μg/m3/year; Korea Ministry

of Environment, 2009).

The present study was performed to elucidate the characteristics,

source, and distribution of PM2.5 and carbonaceous species in Incheon,

Korea. We analyzed the major components of PM2.5 such as OC, EC,

ionic, and metallic species in individual samples. Furthermore, organic

species and WSOC were evaluated to characterize the influence of

individual PM2.5 components. To identify the concentration of PM2.5

aerosols during each season and during times of increased pollution, the

variation in all of the measured compounds were compared and

analyzed. In addition, various organic compounds in PM2.5 samples

were measured using GC×GC-TOFMS. Finally, using PCA with

multiple linear regression analysis (MLRA), we identified the source

and contribution of the aerosol components in the study area.

- 47 -

2.2 Materials and methods

2.2.1. PM2.5 samples

Field sampling for ambient air particles was conducted in Incheon

(37.28 N, 126.39 W, 10 m elevation). The sampling site was located on

the roof of Nam-Gu Council building in both residential and

commercial areas of the central city (Fig. S2-1). Samples were

collected every third day from June 2009 to May 2010 with

approximately 24 h sampling times.

A detailed description of the sampling and measurement methods has

been reported previously (Heo, 2009). Briefly, PM2.5 samples were

collected using a four-channel system consisting of a two-channel

annular denuder system (ADS) and two channel-filter packs, similar to

the EPA Compendium Method IO-4.2 (1999). After sampling, reagent-

grade deionized water was used to extract the ion components from

annular denuders and filters. Ionic species in the extracted solutions

were analyzed using ion chromatography (Dionex DX-120). Using the

filter pack systems, PM2.5 samples were collected on Teflon filters (Pall

Life Sciences) to measure the gravimetric mass of PM2.5 (Mettler-

Toledo, precision: 10-6 g). The filters were also used to measure trace

elements using ICP/MS (Perkin Elmer). The other filter system (quartz

filter) was used to analyze OC, EC, and individual organic aerosol

species. The quartz filters were prebaked at 550ºC for 10 h in a furnace

to remove residual carbon species. OC/EC was analyzed using the

- 48 -

NIOSH thermal/optical transmittance (TOT) method (Chow et al.,

1993; Birch and Cary, 1996).

To quantify WSOC, the residual samples of the quartz filter were

extracted with 20 mL deionized water and sonicated for 60 min. The

extracts were filtered with a syringe filter (Millipore), and then used to

analyze WSOC with a TOC (Shimadzu) analyzer.

2.2.2 Quantification of organic compounds

The filter extraction and measurement procedures to quantify particle-

phase organic compounds have been discussed previously (Mazurek et

al., 1987; Schauer, et al., 2002; Sheesley et al., 2004; Bae and Schauer,

2009). Briefly, to obtain sufficient material for analysis, half of the

quartz filter samples were made into composite samples on a monthly

or episode sample basis. Pyrene-d10, tetracosane-d50, and hexanoic

acid-d6 were added to each sample as a surrogate standard prior to

extraction. Filters were extracted with 50 mL dichloromethane and

sonicated twice, followed by hexane extraction (50 mL) under the same

conditions. The extract was concentrated in a two-stage process. First,

the two extracts were combined and reduced in volume to

approximately 5 mL using Turbovap II under a gentle stream of

nitrogen. Then the samples were filtered into a graduated test tube

through a PTFE syringe filter (0.2 m). Next, the samples were reduced

in volume using Turbovap II under nitrogen blow down to a final

volume of 1 mL. After the final extraction, each sample was spiked

- 49 -

with a set of deuterated internal standards, namely, tetracosane-d50 and

6-PAHs (naphthalene-d8, acenaphthene-d10, phenanthrene-d10,

chrysene-d12, perylene-d12).

Half volume of final extract (1 mL) was used to analyze PAHs,

hopane/steranes, and n-alkanes. In order to convert the polar

compounds into their trimethylsilyl and mehtylated derivatives for

analysis of organic acids, cholesterol, and levoglucosan, the other half

of extract was derivatized using the mixture of bis(trimethylsilyl)-

trifluoroacetamide (BSTFA), diazomethane, and chlorotrimethylsilane.

After the derivatization reaction, samples were concentrated to the pre-

derivatized final volume.

To identify the various organic compounds in PM2.5 samples, the

extracted samples were analyzed using a LECO Pegasus 4D GC×GC-

TOFMS within 18 h of extraction (Hamilton et al., 2004; Lee and Lane,

2010). A detailed description of the GC/MS operation conditions is

presented in the supplemental materials (Table S2-3). The

quantification of compounds was performed by estimating and

comparing the response factors for standard compounds in mass

fragment, retention times and chemical structure. Pegasus II software

(LECO) was used for data acquisition, and the US National Institute of

Standards and Technology (NIST) library was used for species

identification. Hundreds of certified standard solutions were prepared

to quantify the organic compounds (NIST 1494, 2266, 2277, 1649b;

PAHs standards and some organic makers from Accustandard,

ChemService, and Chiron Co.).

- 50 -

2.2.3 Quality assurance and control and statistical

analysis

Quality assurance and control (QA/QC) procedures were performed

for data certification. More detailed QA/QC data are provided in the

supplemental materials (Table S2-1, S2-2). For QA during sample

analysis, blank filters were examined simultaneously with the samples

using the same methods as described above. Background contamination

was periodically monitored (every 20 samples) using field blanks

which were simultaneously processed with the field samples and filter

blanks. Background contamination was less than 5% for all analytes.

The relative percent difference (RPD, %) between sample

concentrations was also used to evaluate measurement accuracy for

each pollutant, and was typically within ±10% of the standard value.

The relative standard deviation (RSD, %) is expressed as a percentage

of the standard deviation divided by the mean. The RSDs of ionic

species, metallic elements, and individual organic species averaged

approximately 0.8%, 1.4%, and 1.4%, respectively. The method

detection limit (MDL) was calculated as three times the value of the

standard deviation, obtained from seven consecutive analyses of low-

level samples. The MDL values of ionic species, metallic elements, and

individual organic species were estimated to be 0.01~0.05 g/m3,

0.0005~0.004 g/m3, and 0.003~0.079 ng/m3, respectively.

The recoveries of ionic species and metallic elements were determined

- 51 -

by spiking a standard solution into a blank filter once every 20 samples,

and the recovery (%) of organic species was calculated based on the

extraction recovery of the spiked surrogate organic standards. The

recoveries were estimated to be 91%, 98%, 80%, 81%, and 83% for

ionic species, metallic elements, alkanes, alkanoic acids, and PAHs,

respectively.

In addition, t-tests, ANOVA, and PCA were used to compare the

seasonal differences in PM2.5 and estimate source apportionment using

the SPSS program.

2.3 Results and Discussion

2.3.1. PM2.5 mass balance

We obtained a total of 120 samples over a 1-year period and analyzed

the chemical species including OC, EC and ionic and metallic

compounds. In addition, more than 100 individual organic compounds

in the aerosol phase including n-alkanes, n-alkanoic acids, aliphatic

dicarboxylic acids, PAHs, oxidized PAHs (oxy-PAHs), and some

organic markers were identified and quantified.

The average PM2.5 concentration during the sampling period was 41.9

± 9.0 μg/m3 (Fig.2-1).This value is almost three times higher than the

annual level listed in the US National Ambient Air Quality Standards

(15 μg/m3), but lower than the concentrations found in industrialized

- 52 -

cities in Asia (He et al., 2001; Lee and Hopke, 2006). The PM2.5

concentration was comparable to the average PM2.5 concentration (43

g/m3) in Seoul (Kim et al., 2006; Heo et al., 2009) but lower than that

of Chegongzhuang and Beijing, China (98~122 and 127 g/m3) (He et

al., 2001; Zhao et al., 2009).

Che

mic

al c

ompo

siti

on (

%)

0

10

20

30

40

50

60

70

80

90

100

EC (4.2+-0.6)

SO42-(12.1+-2.8)

NO3-(10.9+-4.0)

NH4+ (8.7+-1.9)

Other ion(7.2+-2.8)Unresolved

Organics(95.0+-26.3)

Alkanoic acid(30.2+-8.1)

Fine Particle Mass41.9ug/m3

Elutable Organics393.4 ng/m3

Resoluble Organics

Other organic species

(22.1+-12.2)

OC(18.9+-5.1) Resolved-Organic

(5.0+-1.5)

Alkane(43.6+-21.3)

Unknown(30.8+-11.4)

PAHs(2.5+-2.3), Hopane & cholestane(0.3+-0.2)Aliphatic dicarboxylic acid

(1.3+-1.4)Metal (6.3+-1.6)

Fig. 2-1. Mass balance based on the chemical composition of annual

mean fine particle concentrations.

Mass balances that describe the chemical composition of PM2.5

aerosols at the sampling sites are shown in Fig.2-1. On average, the

mass of fine particles consisted of mainly OC, EC, and ionic and

metallic compounds with composition ratios of 18.9 ± 5.1%, 4.2 ±

0.6%, 38.9 ± 8.8%, and 6.3 ± 1.6%, respectively. This suggests that

- 53 -

ionic species such as NO3-, SO4

2-, and NH4+, and organic matter, are the

main contributors to PM2.5 mass and play an important role in dictating

the chemical properties of fine particles.

In addition, about one fourth (22.1± 3.3%) of the OC in carbonaceous

materials existed as EC, and the resolved portion during the analysis

accounted for only 5.0±1.5% of the total OC mass. Resolved organic

species consisted mainly of n-alkanes, n-alkanoic acids, levoglucosane,

and phthalates, whereas PAHs, hopanes, chloestanes, and oxy-PAHs

were the minor part of resolved organic species (Fig.2-1). The sum of

the identified OCs accounted for a very small portion of the total OC,

which is consistent with studies performed previously in the US and

China (Rogge et al., 1993; Yang et al., 2005; Li et al., 2006; Wang et al.,

2009; Ding et al., 2009).

2.3.2. Seasonal variation in PM2.5

The average concentration of PM2.5 and its major components are

summarized in Table 2-1. The average PM2.5 concentration ranged from

36.3 to 50.9 μg/m3, with a higher value during the winter than in the

summer or fall (p < 0.05).The increased PM2.5 concentrations during the

winter were probably caused by a combination of increased emissions

from specific heating sources and the low mixing conditions of the air.

The low PM2.5 concentrations during the summer are likely due to high

dispersion and better deposition conditions provided by both the

increased mixing height and precipitation (Lee and Kang, 2001; Kim et

- 54 -

al., 2007; Yang et al., 2011).

In addition, the average SO42- concentration was higher during the

spring and summer than in the other seasons, which might be the result

of increased photochemical reactivity during these seasons. NO3-

concentration exhibited very strong seasonal variability, with the

highest concentration during the winter and lowest in the summer. The

formation of NO3- depends on temperature, NOX and NH3

concentrations, and relative humidity (Song et al., 2001; Heo et al.,

2009). The increased NO3- concentration during the winter season is

explained by the low temperatures and high humidity, which favors a

shift from the gas phase as nitric acid (HNO3) to the particle phase as

ammonium nitrate (NH4NO3) (Seinfeld and Pandis, 1998). This trend

might be associated with the thermal instability of ammonium nitrate

(Querol et al., 2004).

Variation in SO42- and NO3

- concentrations are closely related to

differences in aerosol acidity. The PM2.5 aerosol acidity can be

estimated using ionic balances of the relevant inorganic species in the

form of [H+] = (2×[SO42-]+[NO3

-])-([NH4+])(Lee et al, 1999). Chu

(2004) defined this term as an ammonium availability index (J). In Eq.

(1), J indicates the molar ratio of the observed cation concentration of

NH4+ to the amount required to fully neutralize the observed anion

concentration of SO42- and NO3

-.

%100][][2

][J

324

4

NOSO

NH (1)

- 55 -

When J < 100%, there is an NH4+ deficit, indicating that SO4

2- and

NO3- are acidic. When J = 100%, the aerosols are neutral, which

indicates sufficient neutralization of SO42- and NO3

-, and when J >

100%, there are sufficient NH4+ ions to fully neutralize SO4

2- and NO3-.

The mean value of J from our samples was 133% (108%, 115%, 134%,

and 132% during the spring, summer, fall, and winter, respectively).

This value suggests that there are sufficient NH4+ ions to neutralize

SO42- and NO3

- ions. As shown in Fig.S2-2, most SO42- and NO3

- in the

sample were fully balanced by NH4+ with a strong positive correlation

(slope 0.89; R2 = 0.85).

2.3.3 Carbonaceous compounds

Carbonaceous compounds in the atmosphere consist mainly of EC and

OC. EC is directly emitted from the source, while OC is divided into

two forms: primary organic carbon (POC), which originates from direct

particle emissions, and secondary organic carbon (SOC), which is

formed through both atmospheric oxidation of reactive organic gases

and subsequent gas-to-particle conversion processes (Russell and Allen,

2004; Subramanian et al., 2007). The average concentrations of OC and

EC at this sampling site were 7.9 ± 2.2 and 1.8 ± 0.3 μg/m3, respect

tively. The mean OC concentration peaked during the winter (Table 2-

1), which is probably due to the increased number of emission sources

from commercial/residential combustion under unfavorable atmo

- 56 -

spheric dispersion conditions (p < 0.05). The mean EC concentration

was also higher during the winter than summer, but its seasonal

variation was not significantly different from OC (p > 0.05,

summarized in Table 2-1). The small variation in EC concentration

compared to OC may be explained by the fact that there were various

sources of OC, including direct emissions and secondary formation of

OC via gas-to-particle conversion, whereas EC is mostly emitted from

primary combustion sources (Lonati et al., 2005).

In previous studies, EC, which is known to exist in an inert state in the

atmosphere, was often used as a tracer of primary OC because OC/EC

is typically co-emitted (Rogge et al., 1993). Therefore, the OC/EC ratio

can yield insight regarding emission and transformation characteristics

of carbonaceous aerosols, as well as help to identify the origins of

carbonaceous PM2.5 (Turpin and Huntzicker, 1995; Lee and Kang,

2001; Russell and Allen, 2004). The average ratio of OC/EC at this site

was 4.8 ± 0.6, 3.5 ± 0.6, 3.9 ± 0.2, and 5.9 ± 0.2 in PM2.5 in the spring,

summer, fall, and winter, respectively. An OC/EC ratio of greater than

2.0 is indicative of SOA (Kim et al., 2007).

Using EC as a tracer, we could indirectly estimate SOC concentrations

in particulate matter. The concentration of SOC was calculated from Eq.

(2), in which the minimum of OC/EC ratio was considered to be (Yuan

et al., 2005):

ECECOCOCTOC min)/(SOC (2)

where SOC (μg/m3) is the concentration of SOC, OCTOC (μg/m3) is the

- 57 -

concentration of total OC, and (OC/EC)min is the minimum OC/EC ratio

observed during the sampling period. It should be noted that the OC/EC

ratio is dependent on the sampling duration and analysis methods,

which may differ between studies.

The estimated mean SOC concentration was 4.6±1.9 μg/m3 (0.7~14.7

μg/m3), accounting for 58.2% of OC and 11.0% of PM2.5 mass,

respectively. The highest SOC concentration was 7.3 ± 0.6 μg/m3

during the winter, followed by 4.4 ± 0.9 (spring), 4.0 ± 0.8 (fall), and

2.7 ± 0.7 μg/m3 (summer).

To investigate the formation of carbonaceous particles in the

atmosphere, we examined the relationship between secondary aerosol

components (WSOC versus NO3-, SO4

2-, and SOC). In general, SOA

compounds are water-soluble because they contain polar functional

groups (e.g., hydroxyls, carbonyls, and carboxyls), which are formed as

a result of atmospheric oxidation (Saxena and Hildemann, 1996). Thus,

WSOC can be used as an indicator of SOA formation because the

major fraction of SOA is associated with WSOC (Kim et al., 2011),

although some WSOCs can also be produced by primary emissions

such as biomass burning (Kim et al., 2011). The concentration of

WSOC ranged from 1.9 to 10.6 μg/m3 (mean 4.7±0.8 μg/m3),

accounting for 25~89% (mean 58.9 ± 10.7%) of the measured OC

concentration. This fraction is similar to that of other Asian sites

(Rengarajan et al., 2011; Phthak et al., 2011; Park and Cho, 2011). Fig.

2-2 shows the relationship between WSOC in aerosol samples,

including WSOC versus NO3-, SO4

2-, and SOC. WSOC concentrations

- 58 -

during the sampling period were moderately correlated with NO3- ions

(R2 = 0.56). Also, WSOC and SOC had a strong regression coefficient

(R2 = 0.65). Another SOC component, the SO42- ion, also showed a

strong correlation with WSOC (R2 = 0.67) but a weak correlation with

SOC (R2 = 0.12). These results suggest that the dominant components

of the WSOC fraction are SOAs formed through similar pathways as

NO3- and SO4

2-, which have been discussed in previous studies (Kim et

al., 2006; Rengarajan et al., 2011).

59

Table 2-1. The major constituents (mean ± standard deviation) of PM2.5 in Incheon, Korea (unit: μg/m3 for PM2.5)

Class Species/ParameterSEASON EVENT TYPE

Spring Summer Autumn Winter Non-Event

(Year) Smog

Episode Yellow Sand

PM2.5

PM 2.5 43.8 ± 7.3 36.3 ± 6.6 36.7 ± 11.5 50.9 ± 2.1 41.9 ± 9.0 73.4 ± 14.7 37.9 ± 9.6 OC 7.2 ± 1.3 5.7 ± 0.5 7.9 ± 1.4 10.9 ± 0.8 7.9 ± 2.2 13.8 ± 4.7 8.0 ± 2.8 EC 1.5 ± 0.1 1.6 ± 0.2 2.0 ± 0.3 1.8 ± 0.1 1.8 ± 0.3 2.7 ± 0.6 1.6 ± 2.8 NH4

+ 3.7 ± 0.2 2.9 ± 0.6 3.3 ± 0.6 4.7 ± 0.4 3.7 ± 0.8 8.0 ± 2.3 2.7 ± 1.2 NO3

- 4.8 ± 1.1 3.3 ± 1.6 3.5 ± 1.8 6.7 ± 0.3 4.6 ± 1.7 10.0 ± 3.7 3.1 ± 3.3 SO4

2- 5.5 ± 1.6 5.1 ± 1.1 4.9 ± 1.8 4.9 ± 0.3 5.1 ± 1.2 11.6 ± 3.7 3.7 ± 2.4 ∑Other ion a) 3.1 ± 0.8 2.0 ± 0.7 2.5 ± 0.8 4.6 ± 0.4 3.0 ± 1.2 5.8 ± 3.4 3.1 ± 0.9 ∑ Metal b) 3.0 ± 0.7 2.0 ± 0.3 2.1 ± 0.6 3.4 ± 0.9 2.6 ± 0.7 3.6 ± 1.1 3.9 ± 2.3 ∑ Organic aerosol c) 0.4 ± 0.1 0.3 ± 0.0 0.5 ± 0.1 0.4 ± 0.2 0.4 ± 0.1 4.1 ± 3.8 0.8 ± 0.4

Air Quality Data

CO (ppm) 0.8 ± 0.2 0.6 ± 0.0 0.8 ± 0.1 1.2 ± 0.0 0.8 ± 0.3 1.7 ± 0.8 0.6 ± 0.5 SO2 (ppb) 8.7 ± 0.4 8.8 ± 0.5 8.3 ± 1.6 12.5 ± 0.9 9.6 ± 2.0 12.8 ± 2.6 7.2 ± 1.0 O3 (ppb) 25.7 ± 5.4 31.3 ± 4.6 22.3 ± 5.3 11.2 ± 0.4 22.7 ± 8.5 13.3 ± 14.0 24.7 ± 7.8 NO (ppb) 13.7 ± 7.4 7.2 ± 2.3 20.9 ± 6.1 40.8 ± 5.0 20.7 ± 13.9 63.0 ± 5.0 9.5 ± 7.6 NO2 (ppb) 32.9 ± 0.6 27.2 ± 1.1 35.2 ± 6.6 39.9 ± 4.6 33.8 ± 5.9 47.5 ± 11.5 26.2 ± 10.3 Wind speed (m/sec) 2.8 ± 0.7 1.7 ± 0.1 1.8 ± 0.5 1.6 ± 0.5 2.0 ± 0.6 1.1 ± 0.4 2.7 ± 1.0 Temperature (℃) 11.6 ± 10.0 22.2 ± 2.4 14.5 ± 8.1 -1.2 ± 3.0 11.8 ± 10.5 9.2 ± 5.1 8.3 ± 10.4 Humidity (%) 56.9 ± 5.5 68.1 ± 1.9 57.8 ± 1.8 59.5 ± 3.0 60.6 ± 5.4 74.1 ± 12.0 58.8 ± 12.2 Radiation (W/m2) 159.3 ± 8.4 179.4 ± 26.2 129.5 ± 32.3 85.3 ± 17.3 138.4 ± 41.8 65.3 ± 55.5 126.8 ± 53.4 Pressure (mmHg) 756.7 ± 4.5 749.4 ± 1.1 758.3 ± 4.2 761.3 ± 1.4 756.4 ± 5.3 758.7 ± 2.2 755.5 ± 4.5

a) Other ions: Na+, NH4+, K+, and Cl-

b) Metal: Mg, Al, Si, P, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Sr, Cd, Sn, Sb, and Pb c) Organic aerosol: individual organic species (see Table 2)

- 60 -

NO3- , SO4

2-, and SOC (ug/m3)

0 5 10 15 20

WS

OC

(ug/

m3 )

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

1. NO3- vs WSOC

2. SO42- vs WSOC

3. SOC vs WSOC

Y1= 0.35X + 3.04

R2= 0.56

Y3= 0.49X + 2.26

R2= 0.65

Y2= 0.24X + 3.46

R2= 0.67

Fig. 2-2. The relationship of WSOC with NO3-, SO4

2-, and SOC in

PM2.5 aerosol samples.

2.3.4. PM2.5 and its major constituents during periods of

air pollution

We analyzed variation in PM2.5 and its chemical components during

normal and air pollution periods. Pollution samples were obtained

during smoggy (4 days, visibility below 100 m) and yellow sand (8

days) periods. The smog episode is defined as a time of period when a

heavy smog and fog was observed with the naked eye under the low

visibility below 100 m. Yellow sand was also defined as a condition

which PM10 concentration over 200 μg/m3 was observed and its

concentration was persistent at least 2 hr. These two specific weather

- 61 -

conditions were forecasted and monitored by the weather agency.

The concentration profiles of each species in the fine particles during

the smoggy periods are summarized in Table2-1 and Fig. S2-4. PM2.5,

OC, and EC concentrations during the smoggy periods were 73.4 ±

14.7, 13.8 ± 4.7, and 2.7 ± 0.6 μg/m3, respectively, 1.5 to 2 times higher

than those during the normal (non-event) periods (p < 0.05). There

were also significant differences in the concentrations of ionic species

such as NO3- and SO4

2- between smoggy and normal (non-event)

periods (p < 0.05). The concentration of metal species from soil source

showed the highest (2.9 ± 1.7 m-3) during the yellow sand period (Kim

et al., 2007; Heo et al., 2009). Therefore, PM levels may increase

during smoggy and yellow sand periods.

To determine the degree of SOC formation and its relationship with

other variables, we investigated WSOC, SOC, and other aerosol

indicators. Fig. 2-3 shows the concentration profiles of WSOC and

SOC, and includes the ratio of H+/OC, WSOC/OC, and OC/EC during

the entire sampling period and specific episode periods. Compared to

the normal samples, there was a significant increase in secondary

carbonaceous compounds such as SOC and WSOC during smoggy

periods and in the winter season, with average concentrations of 8.6 ±

4.0, and 7.3 ± 2.4 μg/m3 during the smog periods and 7.3 ± 0.6 and 5.9

± 0.1 μg/m3 during the winter, respectively (p < 0.05). Interestingly, the

ratio of H+/OC and WSOC/OC peaked during the summer (one-way

ANNOVA test, p < 0.05). This result can be explained by the fact that

WSOC is formed through heterogeneous hydrolysis reactions under

- 62 -

acidic conditions (Chen et al., 2010; Park and Cho, 2011; Pathak et al.,

2011; Rengarajan et al., 2011). Thus, SOA was formed more easily in

acidic samples than neutralized samples due to the acid-catalyzed

process (Takahama et al., 2006; Tanner et al., 2009; Rengarajan et al.,

2011).

SOCPOC

WSOCH+/O

C

WSOC/OC

OC/EC

Con

cnet

rati

on (

ug/m

3 )

0

2

4

6

8

10

12

14

0

2

4

6

8

10

12

14Spring Summer Fall Winter Non-event Smog episode Yellow sand

Rat

io (

OC

ver

sus

H+

, WS

OC

, EC

)

Fig. 2-3. Comparison of OC aerosol contents (SOC, POC, WSOC, and OC/EC) in PM2.5 during non-episode and episode periods.

Thus, even though WSOC and SOC peaked during the winter season

due to the increase in primary organic sources (e.g., combustion, motor

vehicle, and biomass burning emissions) and favorable meteorological

conditions such as a low mixing height and a high ratio of H+/OC, the

formation of SOAs occurred primarily during the summer season. Also,

we verified the increase in secondary organic aerosols based on the

WSOC/OC ratio.

- 63 -

2.3.5. Speciation of organic aerosols by GC×GC-TOFMS

Various organic compounds in PM2.5 samples were measured using

GC×GC-TOFMS. The concentrations of organic compounds during

each season and episode period are summarized in Table 2-2. The

annual average concentration of the total quantified compounds was

393.44 (± 78.91) ng/m3, accounting for 5.0 (± 1.5) % of OC and 0.9 (±

0.3)% of PM2.5. Based on the annual average concentrations, n-alkane

(171.42 ± 27.39 ng/m3), n-alkanoic acids (118.78 ± 15.96 ng/m3),

levoglucosan (28.15 ± 9.86 ng/m3), and phthalates (15.82 ± 2.39 ng/m3)

were major components, whereas PAHs, oxy-PAHs, hopanes, and

cholestanes were minor components. This molecular distribution is

similar to previous studies (Rogge et al., 1993; Simoneit et al., 2004;

Wang et al., 2009). PM2.5 composition during a certain periods such as

smog and yellow sand episodes was compared with the normal

condition. Due to the small number of samples, statistical difference for

a certain period of pollution was verified by non-parametric statistical

methods (Mann-whitney t-test). The results are summarized in Table

S2-5. Most of the PM2.5 ingredients from smog samples were higher in

concentration than those of normal samples at the statistically

significant level (p < 0.05). Yellow sand samples also showed a

significantly high level of NO3-, alkanoic acids, PAHs, and hopanes.

OC, SOC, and DCA items showed a high level in summer, while NO3-,

WSOC, alkanoic acid, DCA, and PAHs items were in high

concentrations in winter.

- 64 -

Table 2-2. Quantification results (ng/m3) for organic species of fine

particles at the study sites

Organic species/ Season-episode SEASON EVENT TYPE

Spring Summer Autumn WinterNon

-eventSmog

episode Yellow

sand

Heptadecane (C17) 0.97 1.93 2.29 0.96 1.54 16.06 15.95

Octadecane (C18) 0.42 0.08 0.11 0.85 0.37 1.34 N.D

Nonadecane (C19) 0.69 0.63 0.33 1.37 0.76 5.39 5.59

Eicosane (C20) 1.44 2.06 0.97 3.23 1.92 6.21 20.55

Heneicosane (C21) 8.34 1.73 11.72 16.23 9.51 66.32 1.02

Docosane (C22) 3.11 1.39 4.62 5.21 3.58 32.00 5.28

Tricosane (C23) 19.34 19.85 48.69 28.36 29.06 213.76 23.09

Tetracosane (C24) 8.40 6.51 20.84 10.26 11.50 119.88 9.00

Pentacosane (C25) 34.15 27.27 68.64 31.44 40.38 279.05 84.69

Hexacosane (C26) 14.96 15.91 36.17 16.56 20.90 138.25 25.97

Heptacosane (C27) 15.74 11.51 22.71 20.15 17.53 196.00 12.00

Octacosane (C28) 25.03 16.43 35.55 31.72 27.18 464.66 16.14

Triacontane (C30) 5.38 2.58 10.17 3.73 5.47 785.63 26.71

Dotriacontane (C32) 1.50 0.47 2.92 1.23 1.53 172.85 11.46

Tetratriacontane (C34) N.D 0.80 N.D N.D 0.20 41.03 1.35

Σ n-Alkane 139.48 109.17 265.73 171.31 171.42 2538.44 258.81

CPI(C9-C30) 1.48 1.44 1.55 1.46 1.49 1.00 1.64

Hexanoic acid (C6) 22.20 27.51 20.80 11.21 20.43 135.01 120.47

Heptanoic acid (C7) N.D N.D N.D N.D N.D N.D N.D

Nonanoic acid (C9) 0.63 N.D N.D 0.61 0.31 0.04 N.D

Decanoic acid (C10) 0.62 0.39 N.D 0.16 0.29 N.D N.D

Undecanoic acid (C11) N.D N.D N.D N.D N.D N.D N.D

Dodecanoic acid (C12) 5.08 5.33 5.33 5.60 5.33 26.65 18.91

Tridecanoic acid (C13) N.D 0.22 N.D N.D 0.06 N.D N.D

Tetradecanoic acid (C14) 4.69 7.01 8.47 4.28 6.11 39.47 15.10

Pentadecanoic acid (C15) 2.30 4.04 4.58 2.05 3.24 14.32 8.23

Hexadecanoic acid (C16) 21.56 28.41 39.25 20.63 27.46 211.16 89.68

Heptadecanoic acid (C17) 1.63 2.74 3.19 1.29 2.21 21.75 4.54

Octadecanoic acid (C18) 23.89 31.33 45.78 22.66 30.92 320.15 114.96

Nonadecanoic acid (C19) N.D 3.29 N.D 0.02 0.83 0.93 N.D

Eicosanoic acid (C20) 2.23 4.82 6.07 3.27 4.10 11.47 6.88

Heneicosanoic acid (C21) 18.95 3.60 4.08 15.04 10.42 457.03 0.40

Tricosanoic acid (C23) 1.18 1.56 2.11 1.39 1.56 2.06 3.30

Tetracosanoic acid (C24) 2.98 6.70 8.93 3.43 5.51 8.84 8.11

Σ n-Alkanoic acid 107.93 126.94 148.61 91.63 118.78 1248.87 390.58

CPI(C11-C24) 2.54 5.44 8.15 3.03 4.35 1.25 15.40

Butanedioic acid 0.76 10.62 7.31 0.60 4.82 4.05 8.20

Pentanedioic acid 0.32 0.29 0.63 0.20 0.36 2.04 0.52

Hexanedioic acid 0.24 0.06 0.10 0.04 0.11 0.53 0.52

Nonanedioic acid 0.04 N.D N.D 0.03 0.02 0.13 N.D

∑ Aliphatic dicarboxylic acid 1.36 10.97 8.05 0.87 5.31 6.76 9.25

- 65 -

Table 2-2. Quantification results (ng/m3) for organic species of fine

particles at the study sites

Organic species/ Season-episode SEASON EVENT TYPE

Spring Summer Autumn WinterNon

-eventSmog

episode Yellow

sand

Naphthalene 0.09 0.04 0.05 0.07 0.06 0.29 0.26

Acenaphthene 0.01 N.D N.D N.D N.D N.D N.D

Acenaphthylene N.D N.D N.D 0.09 0.02 N.D N.D

Fluorene 0.01 N.D N.D 0.03 0.01 0.05 0.03

Phenanthrene 0.94 0.01 N.D 2.64 0.90 0.44 0.04

Anthracene 0.74 N.D N.D 2.07 0.70 0.49 N.D

Fluoranthene 1.33 0.17 0.65 4.11 1.57 2.33 0.58

Pyrene 0.55 0.10 0.36 1.51 0.63 1.31 0.44

Benzo[a]fluoranthene 0.71 0.44 0.97 1.14 0.82 0.74 N.D

Benzo[b]fluoranthene 0.99 0.49 1.07 3.30 1.47 1.66 N.D

Benzo[k]fluoranthene 0.82 0.41 0.89 2.74 1.22 1.38 N.D

Benzo[a]pyrene 1.13 0.41 1.26 0.20 0.75 2.44 0.09

Benzo[e]pyrene 0.25 0.18 0.12 0.14 0.18 1.28 0.04

Benzo[ghi]perylene 0.34 0.09 N.D 1.02 0.36 N.D N.D

Chrysene 1.02 0.25 0.45 1.83 0.89 1.66 N.D

Indeno[1,2,3-cd]pyrene 0.09 N.D N.D 0.46 0.14 N.D N.D

Σ PAHs 9.05 2.60 5.84 21.36 9.71 14.07 1.49

17α(H),21β(H)-(22R)-Homohopane 0.09 0.12 N.D 0.21 0.11 1.84 N.D

17α(H),21β(H)-(22S)-Homohopane 0.08 0.17 N.D 0.18 0.11 2.61 N.D

17α(H),21β(H)-30-Norhopane 0.16 0.25 0.83 0.10 0.34 4.20 N.D

17α(H),21β(H)-Hopane 0.57 0.47 0.39 0.62 0.51 12.05 N.D

17α(H)-22,29,30-Trisnorhopane N.D N.D N.D N.D N.D 3.12 N.D

ααα 20R Cholestane N.D 0.08 N.D 0.20 0.07 1.20 N.D

ααα(20R,24R)-24-Ethylcholestane N.D N.D N.D N.D N.D 5.15 0.58

αββ 20R Cholestane N.D 0.04 N.D 0.08 0.03 0.16 N.D

αββ(20R,24R)-24-Ethylcholestane 0.04 N.D N.D N.D 0.01 0.31 N.D

αββ(20R,24S)-24-Ethylcholestane 0.03 N.D N.D N.D 0.01 0.29 N.D

Σ Hopanes 0.92 1.02 1.21 1.12 1.07 23.82 N.D

Σ Cholestanes 0.07 0.12 N.D 0.28 0.12 7.11 0.58

9,10-Anthracenedione 0.52 N.D N.D 1.32 0.46 N.D N.D

9H-Fluoren-9-one 0.19 0.03 N.D 0.67 0.22 N.D N.D

Benzofuran N.D N.D N.D N.D N.D 0.10 0.08

11H-Benzo[a]fluoren-11-one 0.04 0.03 0.06 0.08 0.05 0.34 0.04

7H-Benzo[c]fluoren-7-one 0.16 0.05 0.01 0.27 0.12 0.06 0.14

naphtho[1,2-c]furan 0.06 0.05 0.14 0.02 0.07 0.74 0.06

ΣOxy PAHs 0.98 0.15 0.21 2.36 0.92 1.25 0.32

Cholesterol N.D 1.16 2.44 1.03 1.16 16.16 4.73

Levoglucosan 36.80 1.42 8.24 66.13 28.15 17.42 0.10

Retene 0.24 0.14 0.24 0.49 0.28 1.50 0.09

Squalene 15.66 16.32 20.42 34.55 21.74 113.02 93.57

Dibutyl phthalate 15.77 14.30 15.39 17.83 15.82 83.61 11.19

- 66 -

Table 2-2. Quantification results (ng/m3) for organic species of fine

particles at the study sites

Organic species/ Season-episode SEASON EVENT TYPE

Spring Summer Autumn WinterNon

-eventSmog

episode Yellow

sand

Benzothiazole 23.47 0.42 3.54 19.56 11.75 22.65 0.38

Phenanthrene-2methyl N.D N.D N.D 0.11 0.03 N.D N.D

Phenanthrene-3methyl N.D N.D N.D 0.19 0.05 N.D N.D

Phenanthrene-1methyl N.D N.D N.D 0.10 0.02 0.27 N.D

Phenanthrene-1,7dimethyl 0.02 0.03 N.D 0.04 0.02 0.26 N.D

Pyrene-1methyl 0.03 N.D N.D 0.53 0.14 1.74 N.D

Pyrene-4methyl N.D 0.01 N.D 0.18 0.05 N.D N.D

Chrysene-1methyl N.D N.D N.D 0.01 N.D N.D N.D

1,2-Benzenecaboxylic acid 6.09 7.32 2.36 6.63 5.60 40.54 18.06

Dehydroabietic acid 1.08 0.63 1.56 1.89 1.29 7.58 1.34

Σ Other organic species 99.15 41.75 54.18 149.27 86.09 304.76 129.47

- 67 -

2.3.5.1 n-Alkanes

Of the organic aerosol components, n-alkanes from C17 to C34 were

measured and analyzed. C25 had the highest concentration (40.38 ±

11.43 ng/m3), followed by C23 and C28 n-alkane based on the average

annual concentration. The concentration of total n-alkanes ranged from

109.17 to 265.73 ng/m3 (average 171.42 ± 27.93 ng/m3) during each

season (Table 2-2), indicating significantly higher levels during the fall

than other seasons (p < 0.05), possibly due to leaf decay and

senescence. The concentration of n-alkanes at all urban sites varied

depending on the location and season. Furthermore, the concentrations

are known to peak at different times, such as the fall (Li et al., 2006) or

winter (Rogge et al., 1993; Guo et al., 2009). These concentrations

were comparable to n-alkane level of PM2.5 concentrations in urban

areas of St. Louis, USA (2.7~317 ng/m3) and on roads in Qingdao,

China (121~369 ng/m3) (Bae and Schauer et al., 2009; Guo et al., 2009),

but higher than those in southern California (50.2~68.9 ng/m3) (Rogge

et al., 1993) and Los Angeles and Philadelphia (7.1~124.6 ng/m3) (Li et

al., 2006; Wang et al., 2009).

The average n-alkane concentration during the smoggy period was

2538.44±1142.63 ng/m3, 14 times higher than during non-event periods

(Table 2-2, Fig. S2-4). The increased n-alkane emission rate during

smoggy periods was higher than that of PM2.5. There was also a

moderate increase in n-alkanes during yellow sand periods. Most of

measured n-alkanes during the smog days increased by not only local

pollutants emitted from heavy traffic, numerous industries, and urban

- 68 -

facilities under the unfavorable mixing condition, but also affected by

long-range transport of pollutants from industrial complexes in China.

Rogge et al. (1993) reported that the major source of n-alkanes is

generally anthropogenic (e.g., the combustion of fossil fuels, organic

debris, and meat cooking) and biogenic (e.g., plant wax, wood burning,

and microorganisms such as bacteria and fungi). The n-alkanes from

anthropogenic sources without odd numbers of carbon showed low

carbon preference index (CPI) values (odd to even ratio) near 1, while

that of the biogenic source with strong odd numbers had higher CPI

values (e.g., 6~9). In this study, CPI values (C19~C28) ranged from 1.44

to 1.55 during each season with the lowest CPI value (1.00) from

smoggy samples, implying that the majority of n-alkanes originated

from anthropogenic sources, especially during smoggy episodes

(Simoneit et al., 1986).

2.3.5.2 n-Alkanoic acids

The secondary dominant components were a series of n-alkanoic acids,

which ranged from 91.63 (± 21.00, winter) to 148.61 (± 11.73, fall)

ng/m3, peaking at C16 (27.46 ± 4.34 ng/m3) and C18 (30.92 ± 5.25

ng/m3), respectively. C16 and C18 alkanoic acids may originate from

emission sources such as meat cooking, fuel combustion, and plant wax

(Rogge et al., 1991; Simoneit, 1986). N-alkanoic acids had a seasonal

pattern with lower concentrations in the winter and higher

concentrations in the fall, similar to n-alkane.

The average n-alkanoic acid concentration during smoggy periods was

- 69 -

1248.87 ng/m3, 10 times higher than during non-episode periods (Table

2-2, Fig. S2-4). Although it is lower than during smoggy periods, the

average n-alkanoic acid concentration during yellow sand periods was

three times as high as those of non-episode periods. Similar to that of n-

alkanes, the n-alkanoic acid concentrations during the smoggy and

yellow sand period were likely influenced by both local pollutants

emission and regional emissions from industrialized cities in eastern

Asia.

The CPI (C11-C24) value estimated using the sum of even to odd ratios

showed a predominantly strong even number of carbons for n-alkanoic

acids homologs with a high CPI (8.15) during the fall, followed by

summer (5.44), winter (3.03), and spring (2.54), but the differences

were not statistically different (p > 0.05). Similar differences can be

found in previous studies (Simoneit et al., 2004; Wang et al., 2009).

This result suggests that particles from biogenic emissions such as plant

wax dominated during the entire sampling time. It is also noteworthy

that the CPI value was lowest during the smog period and highest

during the yellow sand period among all samples, indicating that the n-

alkanoic acids of smoggy samples were derived from anthropogenic

emissions, while those of yellow sand samples were derived from

biogenic sources.

2.3.5.3 Dicarboxylic acids

Dicarboxylic acid (DCA), an important marker of SOA, is formed by

photo-oxidation reactions of (S)VOC and exists mostly in the particle

- 70 -

phase (Shrivastava, 2007; Samy, 2010). DCA can also be emitted

directly from primary sources such as motor vehicles, meat cooking,

and wood combustion (Simoneit, 1986; Kawamura and Kaplan, 1987;

Rogge et al., 1993). In this study, the concentration of total DCA

(C4~C9) was highest (10.97±1.24 ng/m3) during the summer and

represented more noticeable seasonal differences during the spring and

winter (p < 0.05, Table 2-2, Fig. S2-4). Increased amounts of sunlight

during the summer can cause secondary oxidation and result in the

formation of SOAs, similar to DCA. The annual DCA concentration

was 5.31 (± 1.86) ng/m3, lower than those reported by Bae and Schauer

(2009), Li et al. (2006), and Fraser et al. (2002) (15.8, 29.1, and 41.5

ng/m3, respectively). The most abundant species in the DCA group was

butanedioic acid (C4, glutaric acid) with an average concentration of 4.82

(± 1.87) ng/m3, followed by glutaric acid (C5) and malic acid (C6). However,

some types of DCAs were not detected in this study; these compounds

may have been depleted due to losses of volatile trimethylsilyl (TMS)

derivatives (Bi et al., 2008).

2.3.5.4 PAHs and oxy-PAHs

PAH emission sources are mainly incomplete combustion processes

including motor vehicle exhaust and coal combustion (Rinehart et al.,

2006). In this study, 16 individual PAHs were analyzed with a total

PAH concentration of 9.71 ± 2.96 ng/m3, which varied seasonally from

2.60 to 21.36 ng/m3 (the dominant species was benzo(b/k)fluoranthene).

The PAH level of PM2.5 aerosols in Incheon was comparable to the

- 71 -

levels in Seoul, on Jeju Island (Korea), and in California (USA) (Park

et al., 2000; Lee et al., 2006; Rinehart et al., 2006). PAH concentrations

were highest in winter and lowest in summer (Table 2-2, Fig. S2-4).

Those two seasons showed statistically significant differences in

concentrations (p < 0.05). The clear seasonal pattern in PAH

concentration is attributed to the increased emission sources during

cold weather, especially from fossil fuel consumption for heating (Lee

et al., 2006).

Oxy-PAHs are derived from PAH oxidation in the presence of O3,

NO2, and N2O5 (Nikolaou et al., 1984). The oxy-PAHs had similar

trends to PAHs in that both were increased during the winter. The mean

level of 9,10-anthracenedione was 0.46 ± 0.20 ng/m3, and was

identified as a dominant oxy-PAH in ambient air and a major

combustion source.

2.3.5.5 Other organic aerosol compounds

Hopanes/cholestanes are common molecular markers of motor vehicle

exhaust, and are found in crude oil, source rocks, and refinery

petroleum products. Based on this study, six hopane homologs were

identified as a 17α(H) series with C27~C31. The total concentration of

hopane homologs was as low as 1.07 ± 0.31 ng/m3 and ranged from

0.92 to 1.21 ng/m3 without seasonal variation (Table 2-2, Fig. S2-4).

The predominant hopane homologs of the samples were

17α(H),21β(H)-hopane and 17α(H),21β(H)29-norhopane, with average

concentrations of 0.51 and 0.34 ng/m3, respectively.

- 72 -

The concentration of levoglucosan ranged from 1.42 to 66.13 ng/m3

(average, 28.15 ± 9.86 ng/m3) in aerosol samples with a peak during the

winter, which is similar to levels (2.8~102 ng/m3) reported in spring

2005 (Wang et al., 2009). Levoglucosan is likely derived from wood

combustion for heating and cooking (Simoneit et al., 2004). Although

the concentration was low, cholesterol was detected from 2.44 ng/m3 to

16.16 ng/m3 during the smog period. Cholesterol is thought to be a

molecular marker for aerosols released from meat cooking (Rogge et al.,

1991).

2.3.6. Source identification by PCA

PCA is generally used to identify the sources of particle matter in the

atmosphere (Yang et al., 2005; Larsen and Baker, 2003). It is an

effective analytical tool to minimize a set of original variables and

extract a small number of latent factors to analyze relationships between

observed variables and samples. Because the time resolution for the data

of individual organic compounds and other species are different, other

species were reclassified into the same manner to measure individual

organic compounds. In this way, all measured data were combined into

monthly and episodic periods (smog and yellow sand), and then analyzed

using PCA.

During the analysis, the factors were determined by selecting principal

components (PCs) with eigenvalues greater than 1 according to

previous studies (Henry et al., 1984; Henry, et al., 1987; Yidana et al.,

2008). They included some important chemical markers explaining and

- 73 -

representing each source characteristics of PM2.5. Table 2-3 lists five

factor loadings for individual aerosol components after Varimax

rotation of the data matrix. Factors 1 through 5 explained 39.0%, 19.2%,

11.1%, 7.6%, and 7.0% of the total variance in the data sets,

respectively. They were represented as motor vehicle/sea salt, SOAs,

combustion, biogenic/meat cooking, and soil sources.

- 74 -

Table 2-3. Factor loadings from principal component analysis of PM 2.5 aerosol after varimax rotation

Note: Bold values represent factor loadings higher than 0.60. Factor loadings less than 0.3 were omitted. Rotation Method: Varimax with Kaiser Normalization. a) Soil metals: Mg, Al, Si, Ca, Ti, and Fe. b) Non-soil metals: P, V, Cr, Mn, Ni, Cu, Zn, As, Se, Sr, Cd, Sn,

Sb, and Pb.

Data Source

Estimate Source

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Mortor

Vehicle/Sea salt

SOA Biogenic

Cook emission

Wood combustion/

PAH Soil

Variance (%) 39.0% 19.2% 11.1% 7.6% 7.0%

PM2.5

analyts

OC 0.398 0.841 - - - EC 0.694 0.438 - - -0.384 OC/EC - 0.566 - - 0.544 SOC - 0.900 - - - POC 0.694 0.441 - - -0.383 WSOC 0.499 0.699 - - - WIOC - -0.307 - - 0.584 H+ - 0.891 - - - K+ - 0.701 - 0.320 0.413 Cl- 0.846 - - 0.342 - NO3

- - 0.896 - - - SO4

2- - 0.808 0.373 - - Soil-metal a) - - - - 0.841 Non-soil metal b) 0.652 0.536 - - -

Organic speciation

data

n-alkane - - 0.944 - - n-alkanoic acid 0.749 - 0.611 - - aliphatic dicarboxylic acid

- - - -0.632 -

PAHs - 0.490 - 0.772 - hopanes 0.897 - - - - cholestanes 0.921 0.326 - - - oxy- PAHs - - - 0.900 - cholesterol - - 0.941 - - levoglucosan - 0.423 - 0.833 - retene 0.652 - 0.613 - - squalene 0.417 - 0.581 - - 1,2-benzene carboxylic acid

0.956 - - - -

- 75 -

Factor 1 was characterized by high amounts of EC, POC, Cl-, non-soil

metal, n-alkanoic acids, hopanes, cholestanes, retene, resin acids, and

1,2-benzenecarboxylic acid. The high loading values in EC, hopanes,

cholestanes, and Cl- suggest that factor 1 is related to motor

vehicles/sea salt. In addition, non-soil metals were moderately observed

in this factor. Factor 2 was characterized by high amounts of OC, SOC,

WSOC, H+, K+, NO3-, and SO4

2-. NO3- and SO4

2- are important markers

for SOA. The high acidity of this source facilitates the formation of

water-soluble SOA through acid-catalyzed atmospheric reactions.

Based on the high K+ levels, we considered the source of biomass

burning to be mixed with SOA. As a third factor, the biogenic and meat

cooking sources were distinguished by levoglucosan coupled with large

n-alkanes and n-alkanoic acids. These alkanes and alkanoic acids are

associated with open burning and primary biogenic emissions. Thus,

these factors represent a mixture of biomass burning and primary

biogenic emissions. The fifth source (combustion sources) included

PAHs, oxy-PAHs, and levoglucosan. Incomplete combustion processes

as well as wood combustion can produce PAHs and levoglucosan. The

soil source was distinguished based on soil metals (Mg, Al, Si, Ca, Ti,

and Fe) and water-insoluble compounds (WIOCs). This may be

because soil from road traffic, construction sites, and wind-blown soil

dust could be resuspended in the air. This also suggests that the soil

source compounds are closely related to WIOCs.

Finally, multiple linear regression analysis (MLRA) was performed

using the aerosol concentrations as dependent variables and absolute

- 76 -

factor scores as independent variables to evaluate the source

contribution, following the method used by Larsen and Baker (2003)

and Yang et al. (2005). The mean percentage of contribution was

calculated as 37.7% for motor vehicle/sea salts, 27.2% for SOA, 20.1%

for the biogenic/meat cooking sources, 8.2% for the combustion source,

and 6.9% for soil source, respectively. Our results confirm the

dominance of motor vehicle/sea salt and SOA based on the measured

aerosol mass data.

2.4. Conclusions

We investigated the characteristics, sources, distributions, and

carbonaceous species of PM2.5 in Incheon, Korea. The average PM2.5

concentration was 41.9 ± 9.0 μg/m3. Ionic species (e.g., NO3-, SO4

2-,

and NH4+) and OC were the main contributors to PM2.5, which

accounted for 38.9 ± 8.8% and 18.9 ± 5.1% of the PM2.5 mass,

respectively. We also identified seasonal variation in PM2.5 and

secondary aerosol concentrations (e.g., NO3-, SO4

2). Among the ionic

species, there were sufficient NH4+ ions to fully neutralize SO4

2- and

NO3-. The high ratio of OC/EC (3.5–5.9) concentration in PM2.5 was

observed during the winter. An OC/EC ratio exceeding 2.0 indicated

that SOA was present throughout the entire study. The concentration of

WSOC ranged from 1.9 to 10.6 μg/m3 (mean 4.7 ± 0.8 μg/m3),

accounting for 25~89% (mean 58.9 ± 10.7%) of the measured OC

- 77 -

concentrations. WSOC concentration was moderately correlated with

NO3-, SO4

2-, and SOC (R2= 0.56, 0.67, and 0.65, respectively),

suggesting that the dominant components of WSOC were secondary

organic matter formed by the same pathways as NO3- and SO4

2-. PM2.5,

OC, and EC concentrations during smoggy periods were 1.5- 2 times

higher than those during non-event periods. There were significant

increases in the concentrations of secondary carbonaceous compounds

such as SOC and WSOC during smog periods and the winter season,

with average concentrations of 8.6 ± 4.0 and 7.3 ± 2.4 μg/m3 for smog

episodes and 7.3 ± 0.6 and 5.9 ± 0.1 μg/m3 in the winter, respectively (p

< 0.05).

Among the individual organic aerosols, n-alkanes, n-alkanoic acids,

levoglucosan, and phthalates were major components, whereas PAHs,

oxy-PAHs, hopanes, and cholestanes were minor components. Seasonal

variations in each organic aerosol in PM2.5 were also observed. The

concentrations of organic compounds during smoggy periods were

higher than those of PM2.5 during non-event periods. The n-alkane and

n-alkanoic acid species during smoggy periods were 10- 14 times

higher than during normal periods. Finally, we identified the source and

contribution of PM2.5 aerosols using PCA. The five main sources of

PM2.5 aerosols in this study were motor vehicle/sea salt, SOA,

combustion, biogenic/meat cooking, and soil sources with contribution

ratios of 37.7%, 27.2%, 20.1%, 8.2%, and 6.9%, respectively.

- 78 -

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Supplementary Materials

1. Discussion of analytical method QA/QC

In order to assess the recoveries and account for variability in the

method accurately, more detailed descriptions about QA/QC (i.e. MDL,

RSD (%), RPD (%), and recovery) were listed in Table S2-1. The

Recovery (%) for target analytes was summarized in Table S2-2.

Instrument conditions GC×GC-TOF/MS are reported in Table S2-3.

The average concentration of metal elements, WSOC, and SOC in

PM2.5 was also listed Table S2-4.

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Table S2-1. Method detection limits, RSD (%), and RPD (%) for target analytes

Analyte MDL RSD RPD g/m3 % %

OC 0.255 2.1 1.9 WSOC 0.027 0.3 0.6 WIOC 0.024 4.6 0.0 Na+ 0.055 1.0 2.0 NH4

+ 0.021 0.2 0.3 K+ 0.048 0.3 0.4 Cl- 0.021 0.3 0.3 NO3- 0.013 2.1 3.5 SO4

2- 0.047 0.4 0.5 Mg 0.002 2.1 3.6 Al 0.000 1.0 1.7 Si 0.002 2.2 4.2 P 0.003 2.7 3.6 Ca 0.004 2.4 4.2 Ti 0.001 1.0 1.8 V 0.001 1.2 2.0 Cr 0.001 1.0 1.8 Mn 0.004 0.6 1.0 Fe 0.002 1.7 3.0 Co 0.001 1.4 2.4 Ni 0.001 0.9 1.6 Cu 0.002 1.6 2.8 Zn 0.001 1.2 2.1 As 0.002 1.4 2.5 Se 0.002 1.7 2.9 Sr 0.001 1.6 2.7 Mo 0.003 1.8 3.1 Cd 0.001 1.0 1.8 Sn 0.001 1.1 1.9 Sb 0.002 1.4 2.4 Pb 0.003 0.8 1.3 1. The method detection limit (MDL) was calculated as three times the value of the standard deviation, obtained from seven consecutive analyses of low level samples. 2. The relative standard deviation (RSD, %) expresses the standard deviation as a percentage of the mean. 3. The RPD (relative percent difference, %) was estimated from two time measurement of sample.

- 88 -

Table S2-1. (continued)

Analyte MDL RSD RPD ng/m3 % %

Heptadecane 0.003 0.9 0.2 Octadecane 0.048 0.6 1.3 Nonadecane 0.011 0.7 1.2 Eicosane 0.006 0.6 1.0 Docosane 0.009 1.3 2.5 Tetracosane 0.005 1.3 2.5 Hexacosane 0.013 1.9 2.1 Heptacosane 0.013 1.9 2.1 Nonacosane 0.011 1.9 2.1 Dotriacontane 0.009 0.7 1.0 Triacontane 0.003 2.5 5.0 tetratriacontane 0.004 2.5 5.0 Hexanoic acid 0.069 2.7 4.8 Heptanoic acid 0.049 3.0 5.2 Nonanoic acid 0.050 1.5 2.6 Decanoic acid 0.040 0.8 0.1 Undecanoic acid 0.070 1.1 1.9 Dodecanoic acid 0.058 1.3 0.8 Tridecanoic acid 0.033 1.8 3.3 Tetradecanoic acid 0.044 0.7 1.3 Pentadecanoic acid 0.037 1.1 0.3 Hexadecanoic acid 0.030 2.6 4.6 Heptadecanoic acid 0.046 1.6 3.2 Octadecanoic acid 0.037 0.9 0.3 Nonadecanoic acid 0.046 1.3 1.8 Eicosanoic acid 0.037 2.4 4.1 Heneicosanoic acid 0.052 2.0 4.0 Tricosanoic acid 0.058 1.2 2.4 Tetracosanoic acid 0.064 2.6 5.2 Butanedioic acid 0.044 1.8 3.3 Pentanedioic acid 0.037 1.1 0.3 Hexanedioic acid 0.030 2.6 4.6 Nonanedioic acid 0.046 1.3 1.8 Naphthalene 0.015 1.6 1.0 Acenaphthene 0.023 0.8 1.0 Acenaphthylene 0.021 0.5 0.8 Fluorene 0.016 1.1 2.2 Phenanthrene 0.016 1.1 2.2 Anthracene 0.025 0.3 0.4 Fluoranthene 0.019 0.3 0.6

- 89 -

Table S2-1. (continued)

Analyte MDL RSD RPD ng/m3 % %

Pyrene 0.007 2.1 4.1 Benzo[a]fluoranthene 0.008 1.4 2.7 Benzo[b]fluoranthene 0.004 1.3 2.6 Benzo[k]fluoranthene 0.003 1.3 2.6 Benzo[a]pyrene 0.003 1.3 2.6 Benzo[e]pyrene 0.003 1.3 2.6 Benzo[b]triphenylene 0.008 2.9 5.7 Benzo[ghi]perylene 0.006 1.0 1.2 Chrysene 0.003 1.4 2.4 Indeno[1,2,3-cd]pyrene 0.005 1.0 1.0 17α(H),21β(H)-(22R)-Homohopane 0.029 1.5 0.6 17α(H),21β(H)-(22S)-Homohopane) 0.035 0.3 0.1 17α(H),21β(H)-30-Norhopane 0.031 2.4 4.6 17α(H),21β(H)-Hopane 0.036 1.0 1.4 17α(H)-22,29,30-Trisnorhopane 0.070 0.8 1.5 ααα 20R Cholestane 0.027 0.8 1.5 ααα(20R,24R)-24-Ethylcholestane 0.079 1.6 0.1 αββ 20R Cholestane 0.072 1.3 1.1 αββ(20R,24R)-24-Ethylcholestane 0.054 1.3 0.1 αββ(20R,24S)-24-Ethylcholestane 0.025 1.3 2.6 9,10-Anthracenedione 0.012 2.0 2.6 9H-Fluorenone 0.025 2.7 3.4 Benzofuran 0.006 0.3 0.0 11H-Benzo[a]fluorenone 0.005 1.3 2.5 7H-Benzo[c]fluorenone 0.005 1.3 2.5 naphtho[1,2-c]furan 0.005 1.3 2.5 Cholestol 0.014 1.2 1.9 Levoglucosan 0.005 1.3 2.5 Retene 0.005 1.3 2.5 Squalene 0.005 1.3 2.5 Dibutyl phthalate 0.005 1.3 2.5 Benzothiazole 0.005 1.3 2.5 Dehydroabetic acid 0.046 1.3 1.8 Phenanthrene-2methyl 0.015 1.6 1.0 Phenanthrene-3methyl 0.015 1.6 1.0 Phenanthrene-1methyl 0.015 1.6 1.0 Phenanthrene-1,7dimethyl 0.016 1.6 1.0 Pyrene-1methyl 0.007 2.1 4.1 Pyrene-4methyl 0.007 2.1 4.1 Chrysene-1methyl 0.003 1.4 2.4 1,2-Benzenecaboxylic acid 0.046 1.3 1.8

- 90 -

Table S2-2.Summary report on the Recovery (%) for target analytes

Analyte class Average recovery

(%)

Standard deviation

(%)

Ionic species 91 10

Metal compounds 98 7

Alkanes 80 13

Alkanoic acids 81 12

PAHs 83 15

※ Recovery efficiencies of ionic species and metallic elements were

determined by spiking a standard solution into a blank filter.

※ Recovery (%) of organic species was calculated from the

extraction recovery of the surrogate organic standards spiked.

- 91 -

Table S2-3. GC and MS Analysis Conditions

Parameter Configuration

Injection Splitless

Injection volume 2 μL

Temperature Injection Port : 250℃

Temperature program

First column oven Rate ℃/min) Target temp (℃) Duration

(min) Initial 60 5

5 300 20 Secondary column oven Rate (℃/min)

Target temp (℃) Duration (min)

Initial 70 5 5 315 20

He gas flow 1.2 mL/min

Column

First column : DB-5MS (cross-linked 5% phenyl methyl silicone 30m,ID;0.25mm, film thickness; 0.25μm)

Secondary column : DB-17MS (cross-linked 5% phenyl methyl silicone 1m,ID;0.18mm, film thickness; 0.18 μm)

Ionization energy EM volt (1800)

Temp Transfer line : 300℃, Ion source chamber : 230℃

Solvent Delay (min) 3 MS Data Collection Mode

Scan

MS Scan Range (amu) 35-600

- 92 -

Table S2-4. The average concentration of metal elements, WSOC, and SOC in PM2.5 samples (Unit : ng/m3 or ratio)

Class Species Spring Summer Autumn Winter Non event Smog-episode Yellow sand

Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD

Metal

Mg 226.3 96.6 132.5 10.5 126.4 39.9 189.8 38.3 168.8 64.4 241.8 108.7 279.8 117.0 Al 153.8 52.6 107.9 25.5 154.1 102.7 186.3 55.1 150.5 62.8 267.9 145.0 532.8 491.7 Si 322.9 93.3 274.0 86.1 233.4 64.3 331.7 65.4 290.5 78.6 365.1 136.8 613.3 510.8 P 61.2 8.6 74.1 6.3 70.3 4.8 65.5 5.5 67.8 7.5 84.3 17.9 68.1 22.0 Ca 495.2 29.4 189.1 26.7 203.5 58.0 396.5 66.5 321.1 141.6 323.0 34.9 442.7 230.3 Ti 17.0 3.4 12.7 1.9 14.6 7.0 18.9 3.3 15.8 4.4 23.0 8.4 29.5 17.2 V 69.8 12.7 87.0 44.9 47.1 2.8 48.9 0.9 63.2 26.3 51.4 2.5 111.9 120.6 Cr 324.2 44.0 236.2 5.0 193.8 3.9 296.9 68.2 262.8 63.6 216.2 23.7 262.9 83.5 Mn 61.8 46.1 22.6 0.6 36.0 10.7 55.7 1.9 44.0 26.0 76.5 23.3 46.9 22.2 Fe 673.8 202.5 413.1 7.2 519.6 155.7 1022.0 348.5 657.1 302.9 1006.9 305.2 1014.9 513.7 Ni 14.1 6.7 14.9 2.3 10.4 2.7 42.3 28.3 20.4 18.2 15.6 1.1 27.8 40.6 Cu 92.5 33.9 112.3 4.4 122.5 37.1 141.9 42.1 117.3 33.6 149.7 12.7 86.1 32.7 Zn 259.5 40.3 186.1 45.6 242.9 59.0 312.1 61.6 250.1 64.8 492.3 217.3 181.5 47.3 As 146.2 20.2 104.3 0.5 91.2 2.1 118.4 36.8 115.0 27.8 100.3 4.2 107.6 25.4 Se 12.5 4.2 12.4 1.3 17.8 13.6 9.4 1.2 13.0 6.9 12.2 2.2 12.7 5.6 Sr 4.6 0.7 2.0 0.7 2.3 0.9 3.9 1.0 3.2 1.3 3.8 1.5 4.7 2.2 Cd 1.4 0.9 0.4 0.2 1.7 1.0 2.9 1.1 1.6 1.2 8.8 10.1 1.6 1.6 Sn 12.2 2.9 12.4 4.2 16.0 12.0 11.5 1.8 13.0 5.9 12.5 5.9 8.9 2.6 Sb 4.6 1.3 8.2 8.6 3.2 1.5 8.1 4.1 6.0 4.7 6.9 4.1 3.8 2.0 Pb 58.6 15.1 26.5 5.4 34.5 11.3 97.2 39.8 54.2 34.4 109.2 55.7 32.4 7.2

WSOC, SOC

SOC 4415.2 927.7 2685.5 735.8 4017.1 803.1 7311.9 629.3 4607.4 1884.5 8580.2 4046.4 4934.4 1985.1 POC 2902.1 197.5 3037.6 309.0 3887.6 577.4 3559.1 188.1 3346.6 513.4 5245.7 1183.9 3099.8 1516.3 WSOC 4255.8 413.6 4261.1 753.4 4234.3 237.7 5920.5 93.0 4667.9 846.6 7284.6 2404.3 4104.0 1107.5 WIOC 85.2 11.2 148.7 8.9 129.7 7.1 105.6 12.2 117.3 26.5 137.2 63.5 183.1 131.3

Ratio

SOC/POC 1.6 0.2 0.9 0.3 5.8 8.1 2.1 0.1 2.6 4.0 1.6 0.7 1.8 0.9 OC/EC 4.8 0.6 3.5 0.6 3.9 0.2 5.9 0.2 4.5 1.0 5.1 1.3 5.4 1.8 SOC/OC 0.6 0.0 0.4 0.1 0.5 0.0 0.7 0.0 0.5 0.1 0.6 0.1 0.6 0.1 WSOC/OC 0.6 0.1 0.7 0.1 0.5 0.1 0.6 0.0 0.6 0.1 0.5 0.1 0.5 0.2 H+/OC 1.9 0.8 2.0 0.8 1.2 0.2 1.1 0.0 1.5 0.6 1.9 0.7 0.9 0.7

- 93 -

Table S2-5. Results of non-parametric mean comparison for PM2.5 species using Mann-Whitney t-test

Smog/Normal samples OC EC NH4+ NO3

- SO42- WSOC WIOC SOC POC Alkane Alkanoic acid DCA PAH Hopane

Mann-Whitney’s U 4.00 1.00 0.00 4.00 0.00 5.00 14.00 4.00 1.00 1.00 0.00 13.00 15.00 11.00

Wilcoxo’s W 82.00 79.00 78.00 82.00 78.00 83.00 92.00 82.00 79.00 79.00 78.00 91.00 93.00 89.00

Z -2.02 -2.45 -2.60 -2.02 -2.60 -1.88 -0.58 -2.02 -2.45 -2.45 -2.60 -0.72 -0.43 -1.01

P (both sides) 0.04 0.01 0.01 0.04 0.01 0.06 0.56 0.04 0.01 0.01 0.01 0.47 0.67 0.31

Yellow Sand(YS)/Non-YS OC EC NH4+ NO3

- SO42- WSOC WIOC SOC POC Alkane Alkanoic acid DCA PAH Hopane

Mann-Whitney’s U 29.00 17.00 16.00 14.00 26.00 19.00 20.00 28.00 17.00 25.00 0.00 23.00 8.00 5.00

Wilcoxo’s W 107.00 32.00 31.00 29.00 41.00 34.00 98.00 106.00 32.00 103.00 78.00 101.00 23.00 20.00

Z -0.11 -1.37 -1.48 -1.69 -0.42 -1.16 -1.05 -0.21 -1.37 -0.53 -3.16 -0.74 -2.32 -2.73

P (both sides) 0.92 0.17 0.14 0.09 0.67 0.25 0.29 0.83 0.17 0.60 0.00 0.46 0.02 0.01

Summer/Other seasons OC EC NH4+ NO3

- SO42- WSOC WIOC SOC POC Alkane Alkanoic acid DCA PAH Hopane

Mann-Whitney’s U 4.00 19.00 12.00 18.00 22.00 18.00 8.00 4.00 19.00 9.00 17.00 7.00 12.00 23.50

Wilcoxo’s W 10.00 25.00 18.00 24.00 28.00 24.00 161.00 10.00 25.00 15.00 23.00 160.00 18.00 176.50

Z -2.28 -0.69 -1.43 -0.79 -0.37 -0.79 -1.85 -2.28 -0.69 -1.75 -0.90 -1.96 -1.43 -0.22

P (both sides) 0.02 0.49 0.15 0.43 0.71 0.43 0.06 0.02 0.49 0.08 0.37 0.05 0.15 0.83

Winter/Other seasons OC EC NH4+ NO3

- SO42- WSOC WIOC SOC POC Alkane Alkanoic acid DCA PAH Hopane

Mann-Whitney’s U 9.00 20.00 9.00 6.00 20.00 6.00 13.00 7.00 20.00 21.00 6.00 5.00 3.00 20.00

Wilcoxo’s W 162.00 173.00 162.00 159.00 26.00 159.00 19.00 160.00 173.00 27.00 12.00 11.00 156.00 173.00

Z -1.75 -0.58 -1.75 -2.06 -0.58 -2.06 -1.32 -1.96 -0.58 -0.48 -2.06 -2.17 -2.38 -0.60

P (both sides) 0.08 0.56 0.08 0.04 0.56 0.04 0.19 0.05 0.56 0.63 0.04 0.03 0.02 0.55

- 94 -

2. Supplemental figures

In order to give some extra information on the study of the PM2.5

constituents, some information on the sampling site was provided with

Fig.S2-1. Comparison results of PM2.5 constituents during non-event,

smog episode, and yellow sand event are also showed in Fig. S2-2. In

addition, comparison of concentration and fraction of OC class’s

component in PM2.5 during non-event, smog episode, and yellow sand

events was found in Fig. S2-3.

Fig. S2-1. Location of the study sites in Incheon, Korea.

- 95 -

Con

cent

ration

(ug/

m3 )

0

10

20

30

40

50

60

70

80

90

100

110

120

OC EC Ammonia Nitrate Sulfate Other ion Total metal Unknown

Non-event Smog episode Yellowsand

Coc

netr

atio

n(ug

/m3 )

0

10

20

30

OC EC Ammonia Nitrate Sulfate Other ion Total metal Unknown

(N = 108) (N = 4) (N = 8)

Fig. S2-2. Comparison of PM2.5 constituents during non-event, smog episode, and yellow sand event.

- 96 -

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (u

g/m

3 )

0

2000

4000

6000

8000

10000

WSOC WIOC EC

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0

5000

10000

15000

20000 NH4+ NO3- SO42-

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0

1000

2000

3000

4000

Mg Al Si Ca Ti Mn Fe

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )0

500

1000

1500

2000

2500V Cr Ni Cu Zn As Se Cd Pb

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0

100

200

300

400

N_TETRACO N-PENTACO N-HEXACO N-OCTACO N_NONACO

Col 43 N_DOTRICO N_TETTRICO

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0

10

20

30

40

50

FLUORE PHENATHR ANTHRA FLUORA PYRENE B(A)F B(B)F B(K)F B(A)P BGHIPE CHRYSN INCDPY

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

rati

on (

ng/m

3 )

0

2

4

6

8

10

BA30NH AB_HOP Hopane Cholestane

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0

20

40

60

80

100

LOVOGUCOSAN

Fig. S2-3. Concentration of carbonaceous aerosol components and organic species of PM2.5 in Incheon by month.

- 97 -

∑ Alkane

∑ Alkanoic acid

∑ Aliphatic dicarboxylic acid

∑ PAHs

∑ Hopanes

∑ Cholestanes

∑ Other organic species

Org

anic

spe

ices

(ng/

m3 )

0

500

1000

1500

2000

2500

3000

Min

or s

peci

es: P

AH

s , H

opan

e, C

hole

stan

e (n

g/m

3 )

0

10

20

30

40

50

Spring Summer Autumn Winter Non-event Smog episode Yellow sand

Percent ratio of organic species

0 10 20 30 40 50 60 70 80 90 100

Cla

ss f

or s

easo

nm, e

vent

day

Spring

Summer

Autumn

Winter

Non-event

Smog episode

Yellow sand

∑ Alkane ∑ Alkanoic acid ∑ Aliphatic dicarboxylic acid ∑ PAHs ∑ Hopanes ∑ Cholestanes ∑ Other organic species

Fig. S2-4. Comparison of concentration and fraction of OC class’s component in PM2.5 during non-event, smog episode, and yellow sand event.

- 98 -

- 99 -

Chapter 3

Source Apportionment of PM2.5 at the

coastal area in Korea

Abstract

In this study, we analyzed the chemical composition of fine particulate

matter 2.5μm or less (PM2.5) collected at Incheon, the coastal area in

Seoul, Korea every third day from June 2009 to May 2010. Based on

the analyzed chemical species in the PM2.5 samples, the sources of

PM2.5 were identified using a positive matrix factorization (PMF). Nine

sources of PM2.5 were determined from PMF analysis. The major

sources of PM2.5 were secondary nitrate (25.4%), secondary sulfate

(19.0%), motor vehicle 1 (14.8%) with a lesser contribution from

industry (8.5%), motor vehicle 2 (8.2%), biomass burning (6.1%), soil

(6.1%), combustion and copper production emissions (6.1%), and sea

salt (5.9%). From a paired t-test, it was found that yellow sand samples

were characterized as having higher contribution from soil sources (p

<0.05). Furthermore, the likely source areas of PM2.5 emissions were

determined using the CPF and the PSCF. CPF analysis identified the

likely local sources of PM2.5 as motor vehicles and sea salt. PSCF

- 100 -

analysis indicated that the likely source areas for secondary particles

(sulfate and nitrate) were the major industrial areas in China. Finally,

using the source contribution of PM2.5 and associated organic

composition data, PCA was conducted to evaluate the accuracy of the

PM2.5 source apportionments by PMF. The PCA analysis confirmed

eight of the nine PM2.5 sources. Our result implies that the chemical

composition analysis of PM2.5 data and various modeling techniques

can effectively identify the potential contributing sources.

- 101 -

3.1 Introduction

Airborne particulates, particularly fine particulates, have serious

effects on visibility, climate, and human health. Fine particles can

penetrate the human respiratory tract and lungs. Several

epidemiological studies have reported a link between elevated particle

concentrations and increased mortality and morbidity (Charlson et al.,

1992; Laden et al., 2000; Ito et al., 2006). Aerosols also influence both

atmospheric visibility and climate through scattering and absorption of

solar radiation (Schwartz and Dockery, 1992; Schwartz, 1994).

Particulate matter (PM) is chemically and physically non-specific and

may originate from various emission sources, either natural or

anthropogenic (Russel and Allen, 2004).

Fine particles mainly consist of numerous inorganic compounds

including nitrate, sulfate, and various organic species (Heo et al., 2009).

Poor ambient air quality in the metropolitan areas has resulted from

dense populations, heavy traffic, and numerous industrial facilities and

has become a major environmental concern for the urban public. To

develop effective strategy to control fine particle pollution and improve

air quality in urban areas, it is important to identify aerosol sources and

estimate their influence on the concentration of fine particulate matter

2.5 μm or less (PM2.5).

- 102 -

PMF is a multivariate factor analysis technique developed by Paatero

and Tapper (1994) and has been widely applied in many studies for the

source apportionment using PM data (Polissar et al., 1998, 2001; Kim

et al., 2003; Larsen and Baker, 2003, 2008; Liu et al., 2003; Ogulei et

al., 2005, 2006; Lee and Hopke, 2006; Song et al., 2006; Brown et al.,

2007; Gildemeister et al., 2007; Kim et al., 2007; Shrivastava et al.,

2007; Subramanian et al., 2007). PMF analysis uses the realistic error

estimates to weight the data values and imposes non-negativity

constraints in the factor computational process (Liu et al., 2003). Some

studies have also coupled PMF results with surface wind direction and

air-mass back trajectories to obtain reasonable prediction of possible

source locations (Ashbaugh et al., 1985; Hopke et al., 1995; Ogulei et

al., 2005, 2006; Kim et al., 2006; Du and Rodenburg, 2007;

Gildemeister et al., 2007, Rizzo and Scheff, 2007). PM concentrations

have significantly decreased in metropolitan areas of Korea in recent

years but still exceed annual standards and remain at high levels

compared to concentrations in large cities worldwide. Located in a

coastal area close to Seoul, the capital city of South Korea, Incheon city

is often enveloped in sea fog and affected by long-range transport

(LRT) of pollutants from industrial complexes in China as well as

yellow sand dust formed through desertification (He et al., 2003; Han et

al., 2004; Kim et al., 2007; Heo et al., 2009, Zhang et al., 2012). The

combination of LRT pollutants and locally emitted pollutants can affect

the air quality of the coastal areas near Incheon city.

The objectives of this study are (1) to analyze the chemical

- 103 -

composition of PM2.5 collected at the coastal areas near Incheon city in

Korea, (2) to quantify the source contributions to PM2.5 at the coastal

area in Korea, using a PMF model, (3) to identify the actual local

sources and the likely locations of the regional sources by the CPF and

the PSCF, and (4) to determine the spatial and seasonal variations of

source contributions. Finally, each source contribution to measured

PM2.5 was compared with a dataset of individual organic species

measured.

3.2. Materials and methods

3.2.1 PM2.5 sampling

Ambient air particles were sampled in Incheon from the roof of the

Nam-Gu Council building (37.28N, 126.39W, 15 m elevation). This

location is a mixed residential and commercial area, including the

coastal area of the capital. The area is surrounded by two main

expressways that support much traffic as well as two industrial sites

(Nam-dong and Ga-jwa/Ju-an industrial complexes) which are located

in the southeast at distances of 10 and 12 km northeast of the sampling

site, respectively. Air quality at the sampling site was also affected by

pollutant emissions from the city’s seaport, which is located to the west,

and the international airport, both of which lie within a radius of 5~20

km from the sampling site (Fig. 3-1).

- 104 -

Fig. 3-1. Location of the study sites in the coastal area around the

capital of Korea.

The samples were collected every third day from June 2009 to May

2010. All sampling periods were approximately 24 h in duration. We

monitored not only the criteria air pollutants, but also meteorological

data on an hourly basis, including temperature, relative wind speed, and

wind direction.

The description of the sampling and measurement method has been

previously presented (Heo et al., 2009; Choi et al., 2012), and the

detailed methods were as follows; PM2.5 samples were collected using a

4-channel system that consists of two channel-annular denuder systems

(ADS) and two channel-filter packs (URG), similar to the EPA

Compendium Method IO-4.2 (1999). The 4-channel systems consisted

of size selective inlets, four cyclones (URG-2000-30EH, URG) to

- 105 -

provide a particle size cutoff based on the flow rates (16.7 L/min).

The collected samples were used for the analysis of PM2.5 gravimetric

concentration, water-soluble ionic species, carbonaceous species

(organic carbon and elemental carbon), and trace elements.

ADS system consisted of two annular denuder coated with sodium

carbonate and citric acid to collect acidic (SO2, HNO3) and basic (NH3)

gases followed by a Zeflour filter (47 mm Pall Life Sciences, 2 μm pore

size) located downstream of the denuder. These samples were used to

determine water soluble ionic species. The Zeflour filters were

followed by Nylasorb membrane filter (47 mm Pall Life Sciences, 2 μm

pore size) for the accurate measurement of volatilized nitrate and paper

filter (47 mm Whatman International Inc.) coated with citric acid, to

correct for NH4+. After sampling, reagent-grade deionized water was

used to extract the annular denuders and the filters. Extracted solutions

were analyzed using an ion chromatography (Dionex DX-120).

With one of filter pack systems, we collected PM2.5 samples on Teflon

filters (47 mm Pall Life Sciences, 2 μm pore size) to measure PM2.5

gravimetric mass and trace elements. PM2.5 mass was obtained by

weighing the Teflon filters before and after the sample collection using

the microbalance (Mettler-Toledo, precision: 10-6 g). The filters were

then used to determine trace elements using ICP/MS (Perkin Elmer).

The other filter holder was used to collect the quartz filter sample for

analysis of organic carbon, elemental carbon, and individual species of

organic aerosols. The quartz filters were prebaked 550oC for 10 h in a

furnace to remove a residual carbon species. OC/EC was analyzed

- 106 -

using NIOSH TOT (Thermal/Optical Transmittance) method (Chow et

al, 1993; Birch and Cary, 1996). To quantify WSOC, the residual

quartz filter samples were extracted with 20 mL of deionized water by

sonication for 60 min. The extracts were filtered with a syringe filter

(Millipore), and the filtrate subsequently analyzed for WSOC using a

TOC (Shimadzu) analyzer.

3.2.2 Quantification of organic compounds

The procedures of filter extraction and measurement to quantify

particle-phase organic compounds have been discussed in the following

references in details (Mazurek et al., 1987; Schauer et al., 2002;

Sheesley et al., 2004; Bae and Schauer., 2009, Choi et al., 2012), and

the description of procedures is as follows; in order to obtain sufficient

material for analysis, half of quartz filter samples were made into

composite samples on a monthly or episode sample basis. Pyrene-d10,

tetracosane-d50, and hexanoic acid-d6 were also added to each sample

as a surrogate standard before extraction. Filters were extracted with 50

mL of dichloromethane and sonication two times followed by 50 ml of

hexane extraction under the same condition. The extracted samples

were concentrated on the process of two-stages. First, the two extracts

were combined and reduced in volume to approximately 5 ml using

Turbovap II under the gentle stream of nitrogen. The samples were then

filtered into a graduated test tube throughout a PTFE syringe filter (0.2

um). Secondly, the volumes of the samples were reduced using a

- 107 -

Turbovap II under the nitrogen purging to a final volume of 1 ml. After

final extraction, each sample was spiked with a series of deuterated

internal standards containing tetracosane-d50 and 6-PAHs

(naphthalene-d8, acenaphthene-d10, phenanthrene-d10, chrysene-d12,

perylene-d12), respectively.

Half of the volume of the final extract was methylated using

diazomethane (1-methyl-3-nitro-1-nitrosoguanidine, MNNG). The

other half of the volume of the extract was reacted with silylation

reagent containing the mixtures of bis(trimethylsilyl)-

trifluoroacetamide (BSTFA), and 1% chlorotrimethylsilane to

derivatize COOH and OH groups to the corresponding trimethylsilyl

(TMS) esters and ethers, respectively. After the derivatization reaction,

the samples were concentrated on the pre-derivatized final volume.

In order to indentify various organic compounds in PM2.5 samples, the

extracted samples were analyzed by a LECO Pegasus 4D GCⅹGC-

TOFMS within 18 hours (Hamilton et al., 2004; Lee and Lane, 2010).

Detailed description about the operation condition of GC/MS was

summarized in the supplemental materials (Table S2-3). Pegasus II

software (LECO) was used for the data acquisition, and the US

National Institute of Standards and Technology (NIST) library was used

for the identification of species. Hundreds of certified standard

solutions have been prepared for the quantification of the organic

compounds (NIST 1494, 2266, 2277, 1649b; PAHs standards and some

organics are from Accustandard, ChemService, and Chiron Co.).

- 108 -

3.2.3 Quality assurance and control

Quality assurance and control (QA/QC) procedures were carried out

for data certification. More detailed QA/QC data was described in the

supplemental materials (Table S2-1, S2-2). For QA in the analysis of

the samples, blank filters were simultaneously examined using the

same methods as described above. Background contamination was

periodically monitored (every 20 samples) using field blanks that were

simultaneously processed with the field samples and filter blanks. For

all analytes, background contamination was less than 5% of the

associated samples. The relative percent difference (RPD, %) between

sampled concentrations was also used to evaluate the accuracy of

measurement for each pollutant and was typically within ±10% of the

standard value. The relative standard deviation (RSD, %) expresses the

standard deviation as a percentage of the mean. The RSDs of ionic

species, metallic elements, and individual organic species averaged

approximately 0.8, 1.4, and 1.4%, respectively. The method detection

limit (MDL) was calculated as three times the value of the standard

deviation, obtained from seven consecutive analyses of low level

samples. The MDL values of ionic species, metallic elements, and

individual organic species were estimated to be 0.01~0.05 g/m3,

0.0005~0.004 g/m3, and 0.003~0.079 ng/m3, respectively. Recovery

efficiencies of ionic species and metallic elements were determined by

spiking a standard solution into a blank filter once every 20 samples

and the recovery (%) of organic species was calculated from the

- 109 -

extraction recovery of the surrogate organic standards spiked. The

recoveries were estimated to be 91, 98, 80, 81, and 83% for ionic

species, metallic elements, alkanes, alkanoic acids, and polycyclic

aromatic hydrocarbons (PAH), respectively.

3.2.4 PMF model

PMF2 is a multivariate receptor model which estimates the source

profile and source contribution based on a least-squares approach

(Paatero, 1997). This PMF model weights the least-squares fit with the

known error estimates of the elements of the data matrix used to derive

the weighted values.

The bilinear factor analytic model denoted as PMF2 can be written as,

p

kijkjikij efgx

1

(1)

where xij is the jth species concentration measured in the ith samples

and. gik is the airborne mass concentration (μg/m3) from the kth source

contributing to the ith sample, fkj is the jth species fraction (μg/μg) from

the kth source, eij is the residual associated with the jth species

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

independent sources.

PMF provides a solution that minimizes an object function Q(E) based

on uncertainties for each species measured (Paatero, 1997).

- 110 -

n

i

m

j ij

p

kkjikij

s

fgxEQ

1 1

21 ][)( (2)

where sij is an uncertainty estimate for the jth species measured in the ith

sample.

The application of PMF depends on the estimation of uncertainty for

each observation. The procedures of Polissar et al. (1998) were applied

to assign the concentration values and their associated uncertainties as

inputs to the PMF analysis. Three criteria were used to determine

chemical species that were included in the PMF model: the signal-to

noise (S/N) ratio for each species, the percentage of measurements

below the detection limit (BDL) for each species, and the potential

source tracers Paatero and Hopke, 2003). The robust mode was also

selected to reduce the effects of extreme values on the PMF solution.

For this purpose, the value of 4.0 was chosen as the value for the

extreme threshold distance. The estimated uncertainties of extreme

values increased to weight these concentrations downward. The factor

profiles were transformed into profiles, with physically meaningful

units, by the regression of the total PM2.5 mass concentration measured

for each sample against the factor scores.

3.2.5 Conditional probability function (CPF)

To assess the likely location of local point sources, a CPF was used to

- 111 -

estimate the factor contribution by PMF analysis coupled with the time-

resolved wind directions and speed at the sampling site (Ogulei et al.,

2005, 2006; Kim et al., 2006; Gildemeister et al., 2007). To minimize

the averaging effect of diurnal wind measurements, the same daily

fractional contribution was assigned to each of the sampling days to

match the hourly wind data. CPF estimates the probability which will

exceed a predetermined threshold criterion at a given source

contribution from a given wind direction. If this occurs, the sources are

likely to be located in the directions that have high CPF values.

Specially, the CPF is defined as Eq. (3);

n

mCPF (3)

where m is the number of occurrences from wind sector that

exceed the thresholds, and n is the total number of data from .

In this study, 16 wind sectors ( =22.5o) were chosen to represent

the potential source directions, and calm wind conditions (≤1.0 m/s)

were excluded from the analysis because of the isotropic behavior of

wind direction at low wind speeds. The threshold criterion selected was

the upper 25th percentile value of the G-factor (source contribution

factor) for each source, which would clearly show the directionality of

various sources.

3.2.6 Potential source contribution function (PSCF)

To identify the likely location of the regional sources for long-range

transboundary aerosols, the PSCF was calculated using PMF resolved-

- 112 -

source contributions and 5-day air-mass back trajectories were

calculated using the Hybrid Single Particle Lagrangian Integrated

Trajectory (HYSPLIT) model with Global Data Assimilation System

(GDAS) 80-km grid meteorological data (Ashbaugh et al., 1985; Hopke

et al., 1995; Draxler and Rolph, 2007). PSCF is the conditional

probability that an air parcel that passed through the ijth cell had a high

concentration of a pollutant upon arrival at the monitoring site defined

as shown in Eq. (4);

ij

ijij n

mPSCF (4)

where nij is the total number of end points that fall in the ijth cell and

mij is the number of end points in the same cell associated with samples

that exceed the threshold criterion.

In this study, the upper 25th percentile contribution of each source was

used as the threshold criterion. Five-day air-mass back trajectories

starting every hour at heights of 500, 1000, and 1500 m above ground

level were computed using a vertical velocity model for every sample

day, resulting in the production of 120 hourly trajectory end points per

sample. These heights were adopted from the study of Hsu et al.(2003)

and Heo et al.(2009) in which trajectories at different heights should be

used to estimate PSCF to reduce the uncertainty induced from

variations in air parcel pathways.

The geophysical region covered by the trajectories was divided into

9,600 grid cells of 1º1º latitude and longitude to obtain an average of

- 113 -

133 trajectory end points per cell. The potential source areas were

likely to be located in an area with high PSCF values. To reduce the

effect of small values of nij, which result in high PSCF values with high

uncertainties, an arbitrary weighting function W(nij) (Eq. (5)) was

introduced to weight the PSCF values downward for any cell in which

the total number of endpoints was less than three times the average

number of the end points per cell (Hopke et al., 1995; Polissar et al.,

2001, Hwang and Hopke, 2007).

8020.0

1208040.0

40012070.0

)3(40000.1

ij

ij

ij

ijave

ij

n

n

n

nn

W (5)

- 114 -

3.3 Results and Discussion

3.3.1. PMF Results

Among the 35 chemical species analyzed in the PM2.5 samples, at

least 85% of the measurements were above the MDL and therefore

included in the PMF analysis. The samples with the parameters such as

Li+, F-, Be, Sb, and Tl were not detected nor near the detection limit. In

addition, the samples with the parameter such as Hg adopted different

methods which were not standardized in collecting samples. Therefore,

six parameters such as Li+, F-, Be, Sb, Hg, and Tl were excluded from

the PMF analysis. As a result, a total of 115 samples containing 29

different species were employed in the PMF analysis. Table 3-1

presented geometric means, arithmetic means, maximum and minimum

values, and the percentage of measurements below the detection limit

(BDL %) of the PM2.5 chemical species used for the PMF analyses.

The average PM2.5 concentration (42.6 ± 20.3 μg/m3) exceeded the

annual level set by the United States National Ambient Air Quality

Standards (15 μg/m3). The major fraction of PM2.5 consisted of ionic

species (accounting for 32.4 %), such as NO3-, SO4

2-, and NH4+, as well

as OC (accounting for 18.9 %) (Table 3-1). As described in section 2.4,

the statistics of Table 3-1 such as S/N ratio, missing value and

geometric mean data were used to determine chemical species and

assign the concentration values and their associated uncertainties.

To select modeling parameters and the number of factors, the

mathematical diagnostics and the apparent validity of the PMF

- 115 -

solutions were examined. The PMF diagnostics (e.g., model error, Q;

rotational ambiguity, rotmat) were based on those of Lee et al. (1999).

We investigated the Q-value for different numbers of factors and values

of the rotational parameter (FPEAK), as well as the variations in the

maximum individual column mean (IM), the maximum individual

column standard deviation (IS), and rotational freedom for the different

numbers of factors in PMF. The results are shown in Figs. S3-1a and

S3-1b. As the number of factors approached a critical value, IM and IS

clearly decreased. We also investigated the maximum rotmat, which

showed a significant increase from nine to ten factors (Fig. S3-1a).

Therefore, nine factors and a value of FPEAK = 0.0 presented the most

physically meaningful solution and the best agreement between a

calculated Q-value of 3,341 and a theoretical Q of approximately 3,335

(Fig. S3-1b).

- 116 -

Table 3-1. Summary statistics and mass concentrations of PM2.5 and 29 species measured for PMF analysis

Species Concentration (ng/m3) Missing

+ BDLb

(%)

S/Nc ratio Geometrica

Mean Arithmetic

Mean Minimum Maximum

PM2.5 38,086.3 42,563.0 118,57.9 99,434.0 0.0 - OC 7150.2 8037.2 1872.7 21,063.8 0.0 2.7 EC 1616.6 1787.1 15.1 3562.5 0.0 4.9 Na+ 892.5 983.6 237.0 2542.7 0.0 2.5 NH4

+ 3152.9 3756.2 564.4 10,755.1 0.0 3.4 K+ 467.0 529.5 75.2 1311.0 0.0 3.5 Cl- 1158.7 1587.7 66.3 7485.6 1.7 4.2 NO3

- 3213.8 4728.8 245.1 19,380.6 0.0 4.8 SO4

2- 4126.4 5284.9 114.3 18,841.5 0.9 4.6 Mg 132.6 154.3 26.9 910.5 0.9 1.9 Al 122.6 177.5 12.2 1318.6 0.0 4.8 Si 281.2 317.1 52.1 1483.0 0.9 1.9 P 66.9 69.7 30.3 129.4 0.0 3.4 Ca 258.8 287.6 101.8 743.2 0.0 1.8 Ti 15.5 17.9 2.5 57.2 2.6 2.3 V 62.6 73.9 36.2 906.1 0.0 4.0 Cr 228.7 236.2 149.7 596.2 0.0 3.0 Mn 24.8 35.8 4.1 462.5 13.0 3.3 Fe 600.9 710.7 124.7 3889.5 0.0 3.3 Co 0.6 0.8 0.2 6.7 36.5 1.5 Ni 13.0 18.8 3.8 277.3 13.9 2.6 Cu 53.8 83.5 2.7 619.0 9.6 4.4 Zn 213.9 248.7 58.6 1005.3 0.0 2.7 As 94.6 100.1 57.6 618.8 0.0 3.3 Se 15.9 18.0 5.5 200.6 0.0 2.3 Sr 3.0 3.4 0.9 10.3 0.0 4.0 Mo 2.1 2.4 0.4 8.6 9.6 1.6 Cd 1.4 2.1 0.2 19.5 7.8 3.4 Sn 10.7 12.5 4.2 161.2 0.0 2.3 Pb 37.8 57.6 5.3 485.1 0.0 4.3

a Data below the limit of detection were replaced by half of the reported detection limit values for the geometric mean calculations. b Below detection limit. c Signal-to-Noise ratio.

The resolution and its temporal variation were not changed, regardless

of the addition and elimination of some chemical species to the PMF

modeling.

- 117 -

The profiles of the nine PM2.5 sources were resolved from PMF

analysis and provided reasonable source profiles and an understanding

of the source contributions to the ambient mass concentrations.

Measured total PM2.5 concentrations were significantly correlated with

predicted concentrations as based on the PMF model (Fig. S3-2),

showing high correlation coefficients for the study site (r2 = 0.88).

The source profiles (F factor) of PM2.5 species and time-series plots of

source contribution (G factor) are shown in Figs. 3-2 and 3-3. The

major sources of PM2.5 were secondary nitrate, secondary sulfate,

motor vehicle1, with lesser contributions from motor vehicle 2,

industry, biomass burning, soil, combustion, and sea salt (Figs. 3-2 and

3-3). Next, Table 3-2 summarized the source contributions (%) of the

identified sources to PM2.5 mass concentrations. Figs. 3-3 and 3-4 also

showed the temporal and seasonal comparisons of source contributions

to the PM2.5 mass concentration.

Table 3-2. The source concentration (μg/m3) and contributions (%) of identified sources to PM2.5 mass concentrations

Source Concentration (μg/m3) Contribution (%)

mean standard deviation

total standard deviation

combustion (+ Cu) 2.6 1.5 6.1 3.5 soil 2.6 3.1 6.1 7.3 industry 3.6 2.4 8.5 5.6 motor vehicle 1 6.3 3.4 14.8 8.0 biomass burning 2.6 1.6 6.1 3.8 motor vehicle 2 3.5 3.7 8.2 8.7 secondary nitrate 10.8 10.1 25.4 23.7 sea salt 2.5 3.3 5.9 7.8 secondary sulfate 8.1 6.3 19.0 14.8

- 118 -

OC

EC

Na+

NH

4+K

+C

l-N

O3-

SO

42-

Mg

Al

Si

P Ca

Ti

V Cr

Mn

Fe

Co

Ni

Cu

Zn

As

Se

Sr

Mo

Cd

Sn

Pb

0.001

0.01

0.1

1

0.001

0.01

0.1

1

0.001

0.01

0.1

1

0.001

0.01

0.1

1

0.001

0.01

0.1

10.001

0.01

0.1

10.001

0.01

0.1

10.001

0.01

0.1

1

OC

EC

Na+

NH

4+ K+

Cl-

NO

3-S

O42

-M

g Al

Si P

Ca Ti V Cr

Mn Fe

Co Ni

Cu

Zn As

Se Sr Mo

Cd Sn Pb

0.001

0.01

0.1

1

Secondary Sulfate

Secondary Nitrate

Soil

Biomass Burning

Motor Vehicle 2

Industry (Sea port)

Combustion + Cu related

Sea Salt

Motor Vehicle 1

Con

cen

trat

ion

(ug/

ug)

Fig. 3-2. Source profiles obtained from PM2.5 samples (prediction ±

standard deviation) at the sampling site.

- 119 -

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10

02468

10

05

101520

05

101520

05

101520

02468

10

05

10152025

05

10152025300102030405060

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10 0

10203040

Motor vehicle 2

Soil

Biomass burning

Sea salt

Secondary nitrate

Secondary sulfate

Motor vehicle 1

Combustion + Cu related

Industry (Sea Port)

Mas

s co

nce

ntr

atio

n(u

g/m

3 )

Fig. 3-3. Time series plot for each source contribution of PM2.5 at the sampling site.

- 120 -

Secondary nitrate factor was characterized by high concentrations of

NH4+, NO3

-, and OC. In this study, secondary nitrate accounted for

25.4% of the PM2.5 mass concentration at the monitoring site (Fig. 3-2

and Table 3-2). Previous studies on the individual particles (Liu et al.,

2003; Hughes et al., 2000; Li et al., 2004) have found that the presence

of OC suggests the condensation of organic matter on the NH4NO3

particles, which is analogous to the process which leads to organic

carbon associated with sulfate particles (Song et al., 2001; Liu et al.,

2003). Secondary nitrate forms in the atmosphere from the oxidation of

NOx to HNO3 and the equilibrium with NH3 leading to NH4NO3

formation (Li et al., 2004). Also, many studies reported that the

formation of nitrate (NO3-) depends on the temperature, atmospheric

levels of NOX and NH3, and relative humidity (Seinfeld and Pandis,

1998; Song et al., 2001; Heo et al., 2009). As shown in Figs. 3-3 and 3-

4, secondary nitrate had a higher contribution in winter due to the low

temperatures and high humidity that facilitate the formation of

secondary nitrate particles (Ogulei et al., 2006; Lee and Hopke, 2006;

Kim and Hopke, 2008). This trend might be associated with the thermal

instability of ammonium nitrate and is consistent with previous

studies (Querol et al., 2004; Liu et al., 2003).

Among major sources, secondary sulfate accounted for 19.0% of total

PM2.5 mass concentration, and were represented by high loadings of

NH4+ and SO4

2- (Fig. 3-2 and Table 3-2). Secondary sulfate also

showed small seasonal variation (Figs. 3-3 and 3-4). The concentration

of secondary sulfate was higher in summer when photochemical

- 121 -

activity was at its highest at the sampling site. This source profile and

temporal variation agreed well with the previous studies (Ogulei et al.,

2005; Gildemeister et al., 2007; Kim et al., 2008).

Season

spring summer fall winter

Con

cen

trat

ion

(ug/

m3 )

0

5

10

15

20

25

30

Combustion (+ Cu)Soil Industry Motor vehicle 1 Biomass burning Motor vehicle 2 Secondary nitrateSea salt Secondary Sulfate

Fig. 3-4. Seasonal comparisons of source contributions to the PM2.5 mass concentration (mean ± standard deviation).

Two types of motor vehicles (termed motor vehicles 1 and 2) were

recorded at the sampling site. The source contributions to the PM2.5

mass concentration were 14.8% and 8.2% for motor vehicle categories

1 and 2, respectively (Fig. 3-2 and Table 3-2). Both motor vehicle

sources had their own source profiles with OC, EC, NH4+, Ca, Fe, Zn,

and Pb. Interpreted as a vehicular source ranging from vehicle exhaust

to traffic, these sources were clearly different from the other sources

due to the presence of trace elements linked to combustion of

lubricating oil (Ca, Zn) (Lough et al., 2005; Lee and Hopke, 2006;

- 122 -

Viana et al, 2008). Fe, Mn, and Cu, and to a lesser extent Pb are present

in brakewear particles and zinc is enriched in tyrewear particles

(Thorpe and Harrison, 2008; Gietl et al., 2010) However, since the

introduction of unleaded petrol, the concentrations of Pb are seen to be

reducing progressively (Stone et al., 2010), and hence, Pb is not used as

a stand-alone marker for vehicular emissions. When gasoline is entirely

lead-free, there are minor contributions to lead in air from brake pads

and road dust re-suspension. In particular, motor vehicle2 source in this

study contains a lot of lead decreased by reduction policy. Pb

compound could be found to be samples from road junctions and traffic

signals by the components of automotive catalytic converters,

brakewear, and some Pb related industries (Mathur et al., 2011;

Harrison et al, 2012). But, Looking at PSCF results on Fig.S3-5, most

of these particles were explained by the long range transport of mobile

source from china industrialized urban cities. Motor vehicle 2 category

had a higher OC/EC ratio of 9.7 compared with the ratio value of 3.1

for the motor vehicle 1 category. Motor vehicle 1 emissions had a

higher concentration in the winter season, but there was no temporal

variability for the motor vehicle 2 category (Fig. 3-3 and 3-4).

The fifth source was identified by high concentrations of Na+, Ca, Cr,

and Fe which are recognized as typical indicators of industrial

pollutants emitted from the sea port and nearby sites (Song et al., 2006;

Yoon et al., 2005). The source contribution of industry (seaport) to the

PM2.5 mass concentration was 8.5% (Fig. 3-2 and Table 3-2). Incheon

area has one of the largest ports in Korea where treated scrap iron, sea

- 123 -

sand (beach sand), grain, coal, and routine port emissions are

significant particle pollution sources (Fig. 3-1). In fact, the levels of air

pollutants at the seaport of Incheon, especially PM, are higher than in

other areas of the capital region. This result agrees with that of Yoon et

al. (2005), in which the higher concentrations at the seaport of Na+, Cl-,

Ca, and Fe among analyzed elements were obtained. Although these

constituents were present in low amounts, and made the small

contribution on total air pollution, these elements (Na+, Ca, Cr, and Fe)

had higher concentrations in summer than in winter (Figs. 3-3 and 3-4).

The sixth source was classified as biomass burning and contained high

concentrations of OC and K+ along with some Na+, NH4+, and Cl-.

Several studies suggested that the presence of OC and K+ might be

related to wood/biomass burning processes (Birch and Cary, 1996;

Chow et al., 1993; Kang et al., 2004; Park and Kim, 2005; Gildemeister

et al., 2007). Potassium (K) is used as a tracer of crustal dust in the

coarse range and soluble K for biomass burning in the fine range of

particulate matter (Harrison et al., 2012). Potassium and NH4 have also

been used as markers for wood burning and agricultural activities

(Khare and Baruah, 2010). Some studies have involved elevated

organic carbon, sulfur, and potassium (K) ion in the criteria to

distungish biomass burning factor (Gildemeister et al., 2007). This

source accounted for 6.1% of the total PM2.5 concentration (Fig. 3-2

and Table 3-2). As shown in Figs. 3-3 and 3-4, these particle sources

also had a distinct seasonal variation with the high contribution in

winter when biomass burning emissions often occurred from residential

- 124 -

fireplaces and commercial open burning.

The soil source was indicated by typical soil components such as Mg,

Al, Si, Ca, and Fe (Liu et al., 2003; Simoneit et al., 2004; Lee and

Hopke, 2006; Gildemeister et al., 2007; Heo et al., 2009) and its

contribution to the total PM2.5 mass was 6.1% (Fig. 3-2 and Table 3-2).

This combination was mostly interpreted as mineral/crustal matter

(local or regional re-suspension, city dust, crustal material, road dust,

etc.). A number of authors have used the assumption that soil dust

includes loadings of metals like Pb, Cr, Ni, Co etc. from various

sources due to deposition over time (Bandhu et al., 2000; Mouli et al.,

2006; Shrivastava et al., 2007; Chakrobarty and Gupta, 2010), while

other have segregated soil dust and road/re-suspended dust using the

absence/presence of crustal elements like Ca, Si, Al and metals Zn, Pb

and Cr in the source profile (Gupta et al., 2007; Chowdhury et al.,

2007; Kothai et al., 2008; Kulshrestha et al., 2009; Khare and Baruah,

2010). The contributions from this source to PM2.5 showed relative low

contributions in UK, Ireland, Germany or Finland (<5%). It is

suggested that airborne soil can be suspended in the air from road

traffic, construction sites, and wind-blown soil dust (Liu et al., 2003;

Lee and Hopke, 2006; Viana et al., 2008; Heo et al., 2009). The soil

source also showed higher contribution to PM2.5 mass during yellow

sand episodes than others (Fig. S3-3). This result implies that these dust

storms contain sand particles which can be transported from China to

much of Asia and are increasing in both frequency and their negative

effects (KMA-ADC., 2011, Heo et al., 2009). The soil contribution to

- 125 -

particulate matter is considered to be the result from a combination of

local soil and occasional episodes of intercontinental dust.

Combustion and copper production emissions were assigned to the

eighth profile, which accounted for 6.1% of the total PM2.5

concentration (Fig. 3-2 and Table 3-2). This source included Ni and V,

which are elements characteristic of residual oil combustion (Alexander

et al., 2001; Song et al., 2001; Ogulei et al., 2005, 2006), as well as Se

and As, which are elements associated with coal combustion

(Alexander et al., 2001; Ogulei et al., 2005, 2006). This particle source

was also highly correlated with a variation of Cu in samples, which

seems to be associated with copper production (non-ferrous metal) (Liu

et al., 2003; Lee and Hopke, 2006).

Most authors interpreted V, Ni, and SO42- as fuel-oil/petcoke

combustion or industrial emissions based on the characteristic V/Ni

signature of crude oil and its derivates (e.g., shipping emissions).

Ogulei et al. (2005) used Ni and V, Ogulei et al. (2005) used OC, EC,

Ni, and sulfate, Kim et al. (2008) carbons, S, V, and Ni as a marker for

oil-fired power plants or residual oil combustion. In international

studies, key markers for coal combustion include As, Se, Te and

SO42- and it has been found to contribute between 6 and 30 percent to

particulate matter in different studies (Khare and Baruah, 2010; Kumar

et al., 2001; Sharma et al., 2007; Srivastava and Jain, 2007). Selenium

(Se) has been reported to be a good marker for coal combustion (Hien

et al., 2001; Lee et al., 2008).

The final profile contained a higher contribution of Na+ and Cl-, and

- 126 -

was assigned to the sea salt source (Lee et al., 1999; Song et al., 2001;

Viana et al, 2008), accounting for 5.9% of the total PM2.5 concentration

(Fig. 3-2 and Table 3-2). This source also included a contribution from

road salt (Cl-, NO3-, and OC) (Rizzo and Scheff, 2007; Heo et al., 2009).

As shown in Figs. 3-3 and 3-4, this factor had a seasonal pattern with

high winter levels, probably due to the use of deicer on roads.

We also compared the difference in the contribution of PM2.5 sources

between normal samples (non-event) and episode samples (smog and

yellow sand events) (Fig.S3-3). Episode samples included both smog

episode samples (4 days, visibility below 100 m) and yellow sand

samples (8 days). From a paired t-test, it was found that, even though

the smog samples showed high concentrations of sea salt, motor vehicle

2, secondary nitrate, and secondary sulfate, statistically there was no

significant difference between normal samples and smog samples (p >

0.05). However, there was a significant difference in the source

contribution of PM2.5 between normal samples and most of the yellow

sand samples with contribution of soil source (p < 0.05). This result

implies that yellow sand samples can be characterized by high

contribution of soil source materials.

3.3.2 CPF results

CPF analysis was used to identify the location of local sources using

daily source contribution (estimated from the PMF model) coupled

with hourly wind direction data. CPF plots for seven local sources

(excluding secondary nitrate and sulfate) were in good agreement with

- 127 -

the actual location of local sources (Fig. 3-5).

CPF analysis for the combustion source identified residual oil

emissions from common fuels used for heating and industrial activities

(e.g., steel mill, oil refineries, small and large factories) located in the

northeast region, coal combustion emissions from a coal fired power

plant in the northwest region, and Cu production at a facility in the

northwest (Fig. 3-5).

CPF analysis for mobile sources indicated the northwest, northeast,

southwest, and east directions as the possible source regions, where two

highways and local roads are located (Fig. 3-5). The mobile source

likely originated mainly from motor vehicles operating on these

highways and the local roads close to the sample site. As previously

mentioned, the industrial source gave a higher CPF value in the west

direction where the seaport is located (Fig. 3-5).

CPF analysis also indicated that the contributions of sea salt increased

from the southwest direction, where the sea front is located together

with local roads and an open-air yard that receives snow removed from

the streets on winter days (Fig. 3-5). On the basis of the source profile

and seasonal patterns, coupled with the CPF results, we can infer that

fresh salt and road salts were main contributors to this particle source.

For soil sources, Asian dust storms were omitted to avoid the

influence of long-range transport in the CPF analysis. This approach

located minor sources of fine soil from the northeast, likely reflecting

the contribution of re-suspended soil particles from the local roads. The

CPF plot for biomass burning could identify the potential sources from

- 128 -

the southeast where commercial and residential areas were located (Fig.

3-5). This source was likely to be influenced by local residential wood

burning and commercial open burning.

0.0 0.2 0.4

0.00.20.4

030

60

90

120

150180

210

240

270

300

330

Combustion (+Cu)

0.0 0.2 0.40.0

0.2

0.4

030

60

90

120

150180

210

240

270

300

330

Soil

0.0 0.2 0.4

0.00.20.4

030

60

90

120

150180

210

240

270

300

330

Industry

0.0 0.2 0.4

030

60

90

120

150180

210

240

270

300

330

0.0 0.2 0.4

0.00.20.4

030

60

90

120

150180

210

240

270

300

330

Biomass burning

0.0 0.2 0.4

0.00.20.4

030

60

90

120

150180

210

240

270

300

330

0.0 0.2

0.00.2

030

60

90

120

150180

210

240

270

300

330

Secondary nitrate

0.0 0.2 0.4

0.00.20.4

030

60

90

120

150180

210

240

270

300

330

Sea salt

0.0 0.2

030

60

90

120

150180

210

240

270

300

330

Secondary sulfate

Motor vehicle 1 Motor vehicle 2

Fig. 3-5. CPF plots for the average source contributions deduced from PMF analysis.

- 129 -

3.3.3 PSCF results

PSCF was used to identify the likely locations of the regional sources

for long-range transboundary aerosols using source contributions and

5-day air-mass back trajectories. Assuming a mixing height of 300 m to

approximately 3 km, we calculated the PSCF using all the end points of

trajectories at three different starting heights (500, 1000, and 1500 m)

(Kim et al., 2007). Fig.S3-4 shows the total number of end points of 5-

day backward wind trajectories through the grid cell in the PSCF grid

domain and the dominance of westerlies. Fig. 3-6 presents the likely

source areas for regional aerosols (e.g., secondary nitrate, secondary

sulfate, biomass burning, and soil sources) in the sampling site over the

study period, using PSCF.

PSCF results for secondary nitrate in Fig. 3-6(a) show southeastern

areas of China (e.g., Shandong, Anhui, Jiangsu, Henan, and Hebei

provinces) as potential sources. In these areas, NH3 and NOx emissions,

which are both precursors of secondary aerosol, have continuously

increased owing to growing industrial, agricultural, livestock-farming,

and urban activities (Yamaji et al., 2004; Kim et al., 2006). NH3 is

emitted from areas where agricultural activity and livestock farming are

common (Yamaji et al., 2004; Kim et al., 2006; Zhang et al., 2010). In

contrast, NOx and SO2 are primarily emitted from power plants and

mobile sources in urban areas (e.g., Seoul, Tokyo, Beijing, and

Shanghai) (He et al., 2001; Song et al, 2006, Zhang et al., 2012).

Comparison of this map with the ammonia emissions inventory map

reported by Streets et al. (2003) revealed that most of the ‘hot spots’

- 130 -

identified by the PSCF map coincided with regions of high ammonia

emissions for this factor. In addition, PSCF results for secondary nitrate

aerosol in Seoul (Heo et al., 2009) also identified these areas as

potential sources of secondary nitrate.

The PSCF plot of secondary sulfate in Fig. 3-6(b) identifies the

eastern coastal industrial regions (e.g., Shanghai and Shandong) of

China as potential source areas of the pollution measured at the

monitoring site. In fact, the eastern coastal region of China has

experienced rapid economic development and an accompanying

increase in fossil-fuel consumption. Anthropogenic SO2 emissions in

China increased to 33.2 GT/year in 2006, which contributed

approximately one-fourth of global emissions (Lu et al., 2010). Chinese

SO2 emissions have accounted for more than 90% of East Asian

emissions since the 1990s. Undoubtedly, this has led to increased

ambient concentrations, deposition, transformation, and transport of

sulfur species (e.g., sulfur dioxide and sulfate) in both gaseous and

aerosol forms.

Biomass burning is prevalent in northwest Asia, and high PSCF

values were recorded along the Russian border, Mongolia, and China

(Fig. 3-6(c)). Siberia and eastern Russia are major forest fire areas

where huge amounts of carbon emissions and smoke plumes occur each

year (Jaffe et al., 2004). Therefore, particles originating from biomass

burning might be attributed to both local activities and long-range

transport. Finally, PSCF values for the soil source factor showed higher

values along the Mongolian desert (Gobi Desert) and Russian borders,

- 131 -

where desertification has been taking place alongside increased

industrialization (Fig.3-6(d)) (Wang et al., 2007; Heo et al., 2009;

KMA-ADC., 2011). Mongolian desert is one of the world’s largest

deserts and is assumed to be the main source of yellow sand transported

to Korea.

The PSCF analysis was also carried out to identify locations of the

sources such as combustion, industry, motor vehicle 1, motor vehicle 2,

and sea salt, respectively (Fig.S3-5). The results showed that sea salt,

motor vehicle 1, and industry were mainly influenced by local sources

rather than regional sources mediated by the long range transport. From

PSCF analysis, we were able to identify the source regions depending

on individual sources of PM2.5, and could estimate the contributions of

local and regional sources of PM2.5 by PMF model.

- 132 -

(a)

(b)

(c)

(d)

Fig. 3-6. PSCF map for (a) secondary nitrate, (b) secondary sulfate, (c) biomass burning, and (d) soil source resolved by PMF for at the sampling site, Korea during 2009 and 2010.

- 133 -

3.3.4 Comparison of PM2.5 source and its location

Source profiles and source contributions for nine individual sources of

PM2.5 by the PMF model were generated. From the results from CPF

and PSCF analysis, we could corroborate the identities assigned to the

PMF source factors. According to the PSCF analysis results, the

sources such as secondary nitrate, secondary sulfate, soil, and biomass

burning were classified as long-range transport sources. Not only actual

emissions data but also various PSCF outcomes described such a long-

range transport of the pollutants relatively well. In contrast, the sources

like combustion, motor vehicle 1, industry, sea salt could be explained

by local sources where was consistent with wind direction. Besides,

some sources such as soil, combustion, and motor vehicle 2 were

expected to be related to both regional and local pollution sources. In

other words, PSCF and CPF plot estimated the source regions only

when their contribution was high. Actually, both regional and local

sources have a lot of influence on PM2.5 and the consideration for these

two sources should be taken at the same time.

To corroborate the propriety of apportioning the source of PM2.5 by

the PMF model, monthly and episode period data with individual OC

constituents were also compared. In this analysis, both primary OC

(POC) and secondary OC (SOC) concentrations were estimated by the

data from OC and EC and included in the analysis. In addition, WSOC

constituent concentrations determined by a total OC analyzer were used

for the analysis. PCA was also performed using the contributional

concentration data for PM2.5 sources and individual organic matter data.

- 134 -

The results showed that eight out of the nine sources of PM2.5 indicated

by the PMF model could be sorted and substantiated through PCA

analysis (Fig.S3-8). The six main sources for PM2.5 were secondary

organic aerosol (SOA), POA, combustion and motor vehicle 1 sources,

soil, and biomass burning sources. Specifically, SOA was characterized

by high loading of secondary nitrate, secondary sulfate, WSOC, SOC,

NO3-, and SO4

2-. NO3- and SO4

2- are important markers for SOA and

contribute to the WSOC concentration. Kondo et al. (2007) also

suggested that the WSOC concentration could be used to infer SOA

formation owing to its polar characteristics and high water solubility.

POA was characterized by a high loading of sea salts and particle

emissions from motor vehicle 2. As a representative of the pollutant

component, this source also includes Na+, Cl-, non-soil metal, hopane,

cholestane, retene, and benzothiol. The more detailed information was

described in the supplementary materials (Fig.S3-8).

3.4. Conclusions

PM2.5 samples were collected at Incheon, a costal metropolitan area,

nearby Seoul, Korea, every third day from June 2009 to May 2010, and

their chemical composition was analyzed. Using the chemical analysis

results, we identified the sources of PM2.5 using PMF. Furthermore, the

likely source areas of PM2.5 emissions were determined from the CPF

and the PSCF.

The results showed that nine PM2.5 sources were identified by the

- 135 -

PMF analysis. The major sources of PM2.5 were secondary nitrate

(25.4%), secondary sulfate (19.0%), motor vehicle 1 (14.8%) with

lesser contributions from industry (8.5%), motor vehicle 2 (8.2%),

biomass burning (6.1%), soil (6.1%), combustion and copper

production emissions (6.1%), and sea salt (5.9%). From a paired t-test,

it was found that yellow sand samples were characterized as having

higher contribution from soil sources (p <0.05).

CPF results identified possible local source locations which included

motor vehicles, sea salt, combustion processes, and soil. The PSCF

results also indicated that likely regional pollution sources included the

southwest coast of industrialized China for secondary aerosol, northern

Asia for forest fire combustion particles, and Mongolia for yellow sand

particles from both desertification and industrial emissions. Finally,

using the source contribution of PM2.5 and organic species data, PCA

analysis was conducted to evaluate the accuracy of the PMF model in

apportioning these sources. Eight out of the nine sources of PM2.5

indicated by the PMF model were substantiated by the PCA analysis.

Our result indicates that the approach of the chemical composition

analysis for the PM2.5 data set conducted in this study can be used to

identify the contributing sources by receptor modeling and create better

PM pollution abatement strategies.

- 136 -

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Supplementary Materials

1. Complementary Graph for the PMF & PSCF Analysis

(a)

Number of factors

3 4 5 6 7 8 9 10 11 12 13

IM

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

IS

0

1

2

3

4

Rot

atio

nal

am

bu

gity

[m

ax(r

otm

at)]

0.0

0.2

0.4

0.6

0.8

1.0

IMISMax (rotmat)

(b)

Number of factors

3 4 5 6 7 8 9 10 11 12 13 14

Q-v

alu

e (c

han

ge

of f

acto

r)

2000

4000

6000

8000

10000

FPEAK

-1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

Q-v

alue

(ch

ange

of

FP

EA

K)

3400

3600

3800

4000Q-value (change of factor)Q-value (change of FPEAK)

Fig. S3-1 (a) IM (the maximum mean values of each species obtained from the scaled residuals), IS (the maximum standard deviation values of each species obtained from the scaled residuals), and rotational freedom as a function of the factors chosen in PMF, (b) Q-value for the different factor solutions and the change of “FPEAK” parameter.

- 147 -

Measured fine particle mass conc.(ug/m3)

0 20 40 60 80 100 120

Pre

dict

ed f

ine

part

icle

mas

s co

nc.(

ug/m

3 )

0

20

40

60

80

100

120

0.88R

1.57)(6.35 0.03)X(0.88Y2

Fig. S3-2. Correlation between predicted and observed mass concentrations using multiple linear regression analysis.

Sample type

Normal sample Smog Yellow sand

Con

cen

trat

ion

(ug/

m3 )

0

5

10

15

20

25

30

Combustion (+ Cu)Soil Industry Motor vehicle 1 Biomass burning Motor vehicle 2 Secondary nitrate Sea salt Secondary sulfate

Fig. S3-3. The source contributions of episode sample and normal

sample to the PM2.5 mass concentration (mean ± standard deviation).

- 148 -

Fig.S3-4. Total number of end points of 5-day backward wind trajectories started at four different altitudes (500, 1000, and 1500 m) during the sampling period.

- 149 -

( a ) ( b )

(c) ( d )

(e)

Fig. S3-5. PSCF map for PM2.5 five local sources such as (a) combustion, (b) industry, (c) motor vehicle 1, (d) motor vehicle 2, and (e) sea salt source resolved by PMF for at the sampling site, Korea.

- 150 -

OC

EC

Na+

NH

4+K

+C

l-N

O3-

SO42

-M

gA

lSi P C

aT

iV C

rM

nF

eC

oN

iC

uZ

nA

sSe Sr M

oC

dSn P

b

0.001

0.01

0.1

1

0.001

0.01

0.1

1

0.001

0.01

0.1

1

0.001

0.01

0.1

1

0.001

0.01

0.1

10.001

0.01

0.1

10.001

0.01

0.1

1

0.001

0.01

0.1

1

Secondary sulfate

Secondary nitrate

Soil

Motor vehicle 2

Sea salt

OC

EC

Na+

NH

4+ K+

Cl-

NO

3-S

O42

-M

g Al Si P

Ca Ti V Cr

Mn Fe

Co Ni

Cu

Zn As

Se Sr Mo

Cd Sn Pb

0.001

0.01

0.1

1

Biomass burning

Motor vehicle 1

0.001

0.01

0.1

1

Industry (sea port)

Combustion + Cu related

Con

cen

trat

ion

(ug/

ug)

Fig. S3-6. Comparison of source profile for 8 to 10 sources of PM2.5 in Incheon, Korea.

- 151 -

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10

02468

10

0

5

10

15

20

05

10152025

0

5

10

15

20

02468

10

0102030405060

05

10152025

05

1015202530

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10

02468

10

Residual oil combustion0

102030405060

Combustion + Cu related

Soil

Industry (sea port)

Motor vehicle 1

Biomass burning

Motor vehicle 2

Secondary nitrate

Sea salt

Secondary sulfate

Mas

s co

nce

ntr

atio

n(u

g/m

3 )

Fig. S3-7. Comparison of time series plot for 8 to 10 source contribution of PM2.5 in Incheon, Korea.

- 152 -

2. Additional Comparison of Organic Species and PM2.5

Sources

To corroborate the propriety of apportioning the source of PM2.5 by

the PMF model, monthly and episode period data with individual OC

constituents were also compared. In this analysis, both primary OC

(POC) and secondary OC (SOC) concentrations were estimated by the

data from OC and EC and included in this analysis. In addition, WSOC

constituent concentrations determined by a total OC analyzer were used

for the analysis. PCA was also performed using the contribution data

for PM2.5 sources and individual organic matter data.

The six main sources for PM2.5 were SOA, POA, combustion and

motor vehicle 1 sources, soil, and biomass burning sources. SOA was

characterized by high loading of secondary nitrate, secondary sulfate,

WSOC, SOC, NO3-, and SO4

2-. NO3- and SO4

2- are important markers

for SOA and contribute to the WSOC concentration. Kondo (2007) also

suggested that the WSOC concentration could be used to infer SOA

formation owing to its polar characteristics and high water solubility.

POA was characterized by a high loading of sea salts and particle

emissions from motor vehicle 2, As a representative of the pollutants

component, this source also includes Na+, Cl-, non-soil metal, hopane,

cholestane, retene, and benzothiol. Hopane and cholestane are well

- 153 -

established molecular markers for motor vehicle exhaust. Na+ and Cl-

ions have also been considered as markers of a sea salt origin. The

combustion and motor vehicle 1 sources had factor profiles containing

EC, POC, cholestane, and a modest loading of SO42-. Biomass burning

emissions were characterized by high values of PAHs and levoglucosan,

which was probably derived from wood combustion for heating and

cooking (Simoneit et al., 2004). The soil source was characterized by

typical soil metals (Mg, Al, Si, Ca, Ti, Fe). The remaining source

profile was cooking emissions including cholesterol, but was not

determined as a PM2.5 source from the PMF model.

- 154 -

PM2.

5C

ombu

stio

n(+

Cu)

Soil

Indu

stry

Mot

orve

hicl

e1B

iom

ass

burn

ing

Mot

orve

hicl

e2Se

cond

ary

nitr

ate

Sea

salt

Seco

ndar

y su

lfate

OC

EC

WSO

CW

IOC

POC

SOC

Na+

Cl-

NO

3-SO

42-

Soil-

met

alN

on-s

oil M

Alk

ane

Alk

anoi

c ac

idD

ecar

boxy

lic a

cid

PAH

Hop

ane

Cho

lest

ane

Cho

lest

erol

Levo

gluc

osan

Ret

ene

Ben

zoth

iol

-0.50.00.51.01.5

-0.50.00.51.01.5

PM2.

5C

ombu

stio

n(+

Cu)

Soil

Indu

stry

Mot

orve

hicl

e1B

iom

ass

burn

ing

Mot

orve

hicl

e2Se

cond

ary

nitr

ate

Sea

salt

Seco

ndar

y su

lfate

OC

EC

WSO

CW

IOC

POC

SOC

Na+ C

l-N

O3-

SO42

-So

il-m

etal

Non

-soi

l MA

lkan

eA

lkan

oic

acid

Dec

arbo

xylic

aci

dPA

HH

opan

eC

hole

stan

eC

hole

ster

olLe

vogl

ucos

anR

eten

eB

enzo

thio

l

-1.0-0.50.00.51.01.5

-0.50.00.51.01.5

-0.50.00.51.01.5

-1.0-0.50.00.51.01.5

POA (Motor vehicle 2 and Sea salts)

SOA (Secondary nitrate and sulfate)

Combustion + Motor vehicle 1

Cooking emissions

Soil

Biomass burning

Fig. S3-8. Factor loadings from principal component analysis of PM2.5 source and major species (e.g., Organic species, WSOC, and SOC) after the varimax rotation.

- 155 -

Chapter 4

Molecular Marker Characterization of

Particulate Matter and Its Organic Aerosols

Using PMF

Abstract

In this study, one hundred and twenty particulate matter samples were

collected over a 1-year period at Incheon, Korea and were analyzed for

items such as organic carbon (OC), elemental carbon (EC), ionic

species, metal element, and particle-phase organic compound

(molecular markers) on the basis of 24-hour average concentration.

Data from more than 41 organic compounds along with measurements

of 22 inorganic compounds were analyzed using a factor analysis based

source apportionment model, PMF, which has been widely used in the

past with elemental data but not organic molecular markers. PMF

model was carried out and categorized by 3 different kinds of analysis

items, for example, first, 22 items such as OC, EC, Ionic compounds

and trace metals in TSP, second, 41 items in organic compounds, and

third, 63 items in both TSP(22) and organic compounds(41). In addition,

the likely source areas of particulate organic emissions were

- 156 -

determined from the CPF.

The nine sources were identified by the PMF analysis using 22 items.

The major sources of TSP were motor vehicle (17.4%), sea salt

(14.0%), secondary sulfate (13.7%), soil (12.8%), combustion (11.6%),

and industry (10.8%) with the lesser contributions from non-ferrous

industry (6.8%), secondary nitrate (5.4%), and road dust (3.6%).

Through the molecular marker-PMF analysis with the organic marker

compounds, the eight-sources were separated as follows: the resolved

source included combustion (low molecular weight (LMW)-PAHs,

biomass burning, vegetative detritus (n-alkane), benzo(a)pyrene,

secondary organic aerosol1(SOA1), SOA2, combustion ((high

molecular weight(HMW)-PAHs), and motor vehicle. We also tried to

find out individual organic marker species as source markers, and then

compared them with the resolved source only when used in

combination with inorganic pollutant data. The use of supplementary

organic concentrations may possibly identify additional source groups

such as biomass burning, vegetative detritus (n-Alkane), and SOA that

could not be differentiated when using traditional trace element data

alone.

The source contribution of PM and organic aerosol resolved by PMF

model showed different characteristics depending on the season. The

vegetative detritus and motor vehicle were increased during the

summer season by the increase in bogenic/photochmical activity.

Meanwhile, most of the other organic sources were prominent in the

winter season by the increase in air pollutant emissions and

- 157 -

atmospheric stability. In addition, CPF results identified possible local

source locations which included primary sources, biomass burning/soil,

motor vehicle/non-ferrous industry, vegetative detritus (n-Alkane),

benzo(a)pyrene, and combustion(PAHs). Through the comparison of

the modeling results with 63 items, PMF model results using only

molecular marker provide more detailed information on the organic

aerosols sources such as secondary organic aerosol, PAHs. The source

characterization of particulate organic aerosols will be a key foundation

to understand the importance of the issue and providing possible

solutions relevant to TSP reduction methods.

- 158 -

4.1. Introduction

Particlaue matters (PMs) are chemically and physically non-specific

and may originate from various emission sources, either natural or

anthropogenic (Russel and Allen, 2004). OC constitutes 60~ 80% of the

total particulate carbon in urban areas. The organic compound of

atmospheric aerosols in both rural and urban environments consist of a

complex mixture of hundreds of compounds, including many different

compound classes such as n-alkanes, n-alkanoic acid, PAHs, alcohols,

saccharides, and others (Rogge et al., 1991, 1993a, b, 1998; Schauer et

al., 1999a, b; Simoneit et al., 1999; Zheng et al., 2002; Park et al.,

2006). Among organic aerosols, PAHs in urban and industrial

atmospheres are some of the carcinogenic materials (IARC, 1984) and

are major byproducts of the incomplete combustion of all types of

organic matters.

Atmospheric aerosols play a key role in many environmental

processes at local and global scale affecting human health (respiratory

symptoms, bronchitis, heart attack and premature deaths), visibility, air

quality and the climate system. Fine particles can be penetrated into the

human respiratory tract and lungs. Several epidemiological studies have

reported a link between elevated particle concentrations and increased

mortality and morbidity (Charlson et al., 1992; Dockery et al., 1994;

Laden et al., 2000; Ito et al., 2006). Atmospheric aerosols originated

from a wide range of sources and atmospheric processes. Primary

- 159 -

organic aerosols refer to OA which is directly emitted from several

sources including anthropogenic sources (vehicular emissions, wood

burning, industrial processes, and cooking operations) and natural

sources (vegetative detritus) (Brown et al., 2002; Dutton et al., 2010;

Pindado and Perez., 2011). OA formed in the atmosphere through the

photochemical reaction of gas-phase precursors, known as SOA, which

is also known as a major contributor to carbonaceous particulate matter

in many areas (Robinson et al., 2007; Turpin and Huntzicker, 1995).

And OA has important influences on atmospheric physicochemical and

biochemical properties, including radioactive forcing, hygroscopicity,

and toxicity. SOA is an important contributor to air quality degradation,

visibility degradation, climate forcing and adverse impacts on human

health (Jacobson, 2002; Pope et al., 2004; Tabazadeh, 2005).

There has recently been a great interest in the development of methods

to apportion sources of primary and secondary organic aerosols (Turpin

and Huntzicker, 1995; Robinson et al., 2007; Zhang et al., 2009).

Apportioning organic aerosols into their sources and components

correctly is a critical step towards enabling efficient control strategies

and reducing harmful effects of particulate matter. The most common

receptor models can be categorized into univariate models such as

CMB and multivariate models such as PCA, PMF, and UNMIX.

However, few studies have performed a source apportionment

methodology incorporating organic molecular marker data due to the

time and effort required to collect enough time series of detailed

measurements and uncertainties associated.

- 160 -

CMB with MM has been successfully used in a number of locations in

the United States including the Los Angeles Basin (Schauer et al., 1996,

2002b), the San Joaquin Valley in California (Schauer and Cass, 2000),

and the southeastern United States (Zheng et al., 2002). A disadvantage

of the CMB model is to require the prior knowledge about source

profiles. Therefore, questions are always raised as to the accuracy of

the source profiles and the ability to quantify errors associated with

using source profiles that may not represent the sources impacting

receptor sites (Jaeckels et al., 2007). However, PMF does not require

source profiles as model inputs but does require knowledge about

source profiles to interpret the factors derived from the model as air

pollution sources (Jaeckels et al., 2007). PMF is especially applicable

to work with environmental data because it: (1) incorporates the

measurement uncertainties associated with environmental samples, and

(2) forces all of the values in the solution profiles and contributions to

be nonnegative, which is more realistic than solutions from other

methods like PCA (Reff et al., 2007; Wang et al., 2012). Historically,

MM data sets were generally not large enough for PMF analysis and

thus source apportionment was limited to CMB models. Recently, PMF

source apportionment has been successfully applied to one-year MM

data collected in St. Louis, Missouri (Jaeckels et al., 2007) and

Pittsburgh, Pennsylvania (Shrivastava et al., 2007) to investigate the

sources of OA mass. Some studies have also coupled PMF results with

surface wind direction and air-mass back trajectories to obtain

reasonable prediction of possible source locations (Ashbaugh et al.,

- 161 -

1985; Hopke et al., 1995; Ogulei et al., 2005, 2006; Kim et al., 2006;

Du and Rodenburg, 2007; Gildemeister et al., 2007, Rizzo and Scheff,

2007).

The objectives of this study are (1) to analyze the chemical

composition of TSP collected in Incheon city in Korea between June

2009 and May 2010, (2) to quantify the source contributions of PM and

organic aerosol using a PMF model coupled with MM, (3) to identify

the likely locations of the emission sources by the CPF, and (4) to

determine the seasonal variations of source contributions. In this study,

we presents PMF analysis results for ambient MM, ions, trace elements,

and EC/OC data to explore the role of specific MM and investigate the

sources of PM/OA in the industrialized urban city, Korea.

4.2. Materials and methods

4.2.1 PM sampling

Ambient air particles were sampled in Incheon from the roof of the

Nam-Gu Council building (37.28N, 126.39W, 15 m elevation). This

location is a mixed residential and commercial area, including the

coastal area of the capital. The area is surrounded by two main

expressways that support much traffic as well as two industrial sites

(Nam-dong and Ga-jwa/Ju-an industrial complexes) which are located

in the southeast at distances of 10 and 12 km northeast of the sampling

- 162 -

site, respectively. Air quality at the sampling site was also affected by

pollutant emissions from the city’s seaport, which is located to the west,

and the international airport, both of which lie within a radius of 5~20

km from the sampling site (Fig. 3-1).

The samples were collected every third day from June 2009 to May

2010. All sampling periods were approximately 24h in duration. We

monitored not only the criteria air pollutants, but also meteorological

data on an hourly basis, including temperature, relative wind speed, and

wind direction. The high volume sampler was used to collect ambient

particulate sample. It involves drawing large volumes of air (typically

in the range of 800~1000 m3) through a 20cmⅹ25cm quartz fiber filter

(20.3×25.4 cm, QMA, Whatman) substrate. The quartz filters were

prebaked 550 oC for 10 hour in a furnace to remove a residual carbon

species prior to air sampling. After collection, the samples were stored

in a freezer at -10 ℃ until analysis. The collected sample was used for

the determination of individual organic marker species, OC, EC, major

ionic species and trace elements.

Obtained from the sampling systems, the sample filters were used to

measure PM gravimetric mass that was obtained by weighing the

Teflon filters before and after the sample collection using the

microbalance (Mettler-Toledo, precision: 10-6 g) under the controlled

humidity (45 ± 5 %) and temperature (25 ± 3 ℃).

The analytical procedures followed for the analysis of chemical

species has been already described in the previous study (Choi et al.,

- 163 -

2012). Briefly, ionic components, such as SO42–, NO3

–, Cl–, Na+, K+,

NH4+ were extracted with water and analyzed by ion chromatography

(IC, Dionex DX-120) (anion column-IONPAC AS14A, eluent-8mM

Na2CO3/1mM NaHCO3 at 1mL/min; cation column-IONPAC CS12A-

20 mM; MSA eluent at 1mL/min). The filters were digested using a

laboratory microwave extraction system (Microwave 3000, Anton

Paar) to extract metals with a hydrochloric/nitric acid solution (USEPA

Methods IO 3.1). The digestate was filtered and then analyzed for 14

trace elements by Inductively Coupled Plasma/Mass Spectrometry

(ICP/MS, Perkin Elmer). In addition, OC/EC was analyzed based on

NIOSH TOT (Thermal/Optical Transmittance) method (Chow et al,

1993; Birch and Cary, 1996) using a sunset semi-continuous OC/EC

analyzer. To quantify WSOC, the residual quartz filter samples were

extracted with 20 mL of deionized water by sonication for 60 min. The

extracts were filtered with a syringe filter (Millipore), and the filtrate

subsequently analyzed for WSOC using a TOC (Shimadzu) analyzer.

4.2.2 Quantification of organic compounds

The procedures of filter extraction and measurement to quantify

particle-phase organic compounds have been discussed in the following

references in details (Mazurek et al., 1987; Schauer et al., 2002;

Sheesley et al., 2004; Bae and Schauer., 2009, Choi et al., 2012), and

the description of procedures is as follows; One-fourth of the quartz

filter samples is used for analysis of particulate organic species.

- 164 -

Pyrene-d10, tetracosane-d50, and hexanoic acid-d6 were also added to

each sample as a surrogate standard before extraction. Filters were

extracted with 50 mL of dichloromethane and sonication two times

followed by 50 ml of hexane extraction under the same condition. The

extracted samples were concentrated on the process of two-stages. First,

the two extracts were combined and reduced in volume to

approximately 5 ml using Turbovap II under the gentle stream of

nitrogen. The samples were then filtered into a graduated test tube

throughout a PTFE syringe filter (0.2 um). Secondly, the volumes of

the samples were reduced using a Turbovap II under the nitrogen

purging to a final volume of 1 ml. After final extraction, each sample

was spiked with a series of deuterated internal standards containing

tetracosane-d50 and 6-PAHs (naphthalene-d8, acenaphthene-d10,

phenanthrene-d10, chrysene-d12, perylene-d12), respectively. Half of

the volume of the final extract was methylated using diazomethane (1-

methyl-3-nitro-1-nitrosoguanidine, MNNG). The other half of the

volume of the extract was reacted with silylation reagent containing the

mixtures of bis(trimethylsilyl)-trifluoro-acetamide (BSTFA), and 1%

chlorotrimethylsilane to derivatize COOH and OH groups to the

corresponding trimethylsilyl (TMS) esters and ethers, respectively.

After the derivatization reaction, the samples were concentrated on the

pre-derivatized final volume.

In order to indentify various organic compounds in PM2.5 samples, the

extracted samples were analyzed by a LECO Pegasus 4D GCⅹGC-

TOFMS within 18 hours (Hamilton et al., 2004; Lee and Lane, 2010).

- 165 -

Detailed description about the operation condition of GC/MS was

summarized in the supplementary materials (TableS 1-3). Pegasus II

software (LECO) was used for the data acquisition, and the US

National Institute of Standards and Technology (NIST) library was used

for the identification of species. Hundreds of certified standard

solutions have been prepared for the quantification of the organic

compounds (NIST 1494, 2266, 2277, 1649b; PAHs standards and some

organics are from Accustandard, ChemService, and Chiron Co.).

Detailed quality assurance/quality control (QA/QC) procedures were

described previously, including recoveries, detection limits, and

analysis precision for organic compound (Choi et al., 2012).

4.3. Results and Discussion

4.3.1. Characterization of particle composition

In this study, we obtained a total of 120 particulate samples over a 1-

year period and analyzed the chemical species including OC, EC and

ionic and metallic compounds. In addition, more than 100 organic

compounds phase including n-alkanes, n-alkanoic acids, aliphatic

dicarboxylic acids, PAHs, oxidized PAHs (oxy-PAHs), and some

organic markers were identified and quantified. Table 4-1 summarizes

average concentrations, S/N ratio, missing value and geometric mean

data of chemical components of fine particles from a year–long study

included in the PMF model. These statistics were used to determine

- 166 -

chemical species and assign the concentration values and their

associated uncertainties.

TSP concentration is 121g/m3 (26 - 325g/m3), which is 2.4 times

higher than that of PM2.5 (41.9 μg/m3). The major fraction of PM

consisted of ionic species (accounting for 21.6 %), such as NO3-, SO4

2-,

and NH4+, as well OC (accounting for 10.5 %). These main compounds

have the lower ratios to TSP than that of PM2.5 samples (i.g ionic

species accounting for 38.9%, OC for 18.9%) described in Choi et al.

(2012). Among the individual organic aerosols measured, n-alkanes, n-

alkanoic acids, levoglucosan, and phthalates were major components,

whereas PAHs, oxy-PAHs, hopanes, and cholestanes were minor

components. The Σn–alkane (C26-C32) and Σ9-PAHs ranged from 1.49

to 65.38ng/m3 and 0.71 to 27.85ng/m3 respectively, being the higher

concentrations during cold seasons. n-Alkanes are emitted to the

atmosphere by a large variety of sources, such as fossil fuel vehicle

emissions, biomass combustion, and by resuspension of vegetative

detritus (Schauer et al., 2007; Drooge et al., 2012). The lower

molecular weight n-alkanes (n-C23 to n-C25) that were measured are

more related to traffic emissions, while the higher molecular weight n-

alkanes are almost entirely related to vegetation detritus as they are

present in large quantities in epicuticular wax from higher plants

(Aceves and Grimalt, 1993; Simoneit et al., 1991).

- 167 -

Table 4-1. Summary statistics and mass concentrations of TSP and organic species measured for PMF analysis

Species

Concentration (ng/m3) Missing + BDLb

(%)

S/Nc ratio

Min Max

TSP TSP 26,038.15 325,531.46 0.00 8.72

OC OC 2366.70 16,347.02 0.00 2.68

EC EC 587.84 3483.93 0.00 2.20

Na+ Na 260.66 2788.38 0.00 2.58

NH4+ NH4 569.86 13,908.00 0.00 4.37

K+ K 107.25 1475.98 0.00 2.43

Cl- Cl 317.21 7793.77 0.00 4.15

NO3- NO3 1351.57 41,784.78 0.00 5.19

SO42- SO4 1825.17 30,740.19 0.00 3.67

Mg Mg 231.97 2645.67 0.00 2.34

Al Al 382.60 15,956.21 0.00 2.16

P P 160.58 2570.14 0.00 2.44

Ca Ca 456.86 3985.85 0.00 2.10

Ti Ti 29.25 187.13 0.00 1.73

V V 27.33 159.52 0.00 1.92

Cr Cr 19.20 276.20 0.84 6.62

Fe Fe 527.95 4937.50 0.00 2.46

Mn Mn 36.93 314.77 0.00 1.71

Ni Ni 6.61 118.44 0.84 3.99

Cu Cu 86.00 1774.63 0.00 2.05

Zn Zn 108.00 1936.07 0.00 2.90

As As 17.62 189.28 0.00 2.80

Pb Pb 30.13 335.86 0.00 2.10

N-HEXACO Hexacosane (C26) 0.09 19.70 5.04 4.45

N-HEPTACO Heptacosane (C27) 0.87 41.76 0.84 2.18

N-OCTACO Octacosane (C28) 0.14 16.43 20.17 2.85

N_NONACO Nonacosane (C29) 0.18 20.48 0.00 2.73

N_TRICO Triacontane (C30) 0.16 10.65 5.04 2.54

N_DOTRICO Dotriacontane (C32) 0.06 3.72 33.61 2.51

ΣN-ALKANE(26-32) Σ n-alkane 1.49 65.38 - - FLUORA Fluoranthene 0.18 10.52 0.00 2.92 PYRENE Pyrene 0.07 2.87 0.00 2.45 B(A)F Benzo[a]fluoranthene 0.07 2.51 0.84 2.21 B(B)F Benzo[b]fluoranthene 0.11 6.12 5.88 3.01 B(K)F Benzo[k]fluoranthene 0.11 1.91 4.20 1.48 B(A)P Benzo[a]pyrene 0.00 0.93 0.00 4.63 BGHIPE Benzo[ghi]perylene 0.01 2.37 13.45 4.54

a Data below the limit of detection were replaced by half of the reported detection limit values for the geometric mean calculations. b Below detection limit. c Signal-to-Noise ratio.

- 168 -

Table 4-1. Summary statistics and mass concentrations of TSP and organic species measured for PMF analysis.

Species Concentration

(ng/m3) Missing + BDLb (%)

S/Nc ratio

Min Max CHRYSN Chrysene 0.07 3.79 7.56 3.19

INCDPY Indeno[1,2,3-cd]pyrene 0.06 2.51 15.97 2.44

Σ PAHs Σ PAHs 0.71 27.85 - -

BA30NH 17α(H),21β(H)-30Norhopane 0.04 0.85 13.45 1.43

AB_HOP 17α(H),21β(H)-Hopane 0.03 1.22 10.92 1.89

Σ HOPANE Σ Hopane 0.08 2.50 10.08 2.04

Σ CHOLESTANE Σ Cholestane 0.15 3.52 25.21 1.18

N_HEXDA Hexadecanoic acid( C16) 1.85 49.14 1.68 1.17

N_HEPDA Heptadecanoic acid (C17) 0.14 14.01 10.08 3.14

N_OCTDA Octadecanoic acid (C18) 1.33 15.42 1.68 1.20

N_NONDA Nonadecanoic acid (C19) 0.14 20.02 35.29 3.96

N_EICOA Eicosanoic acid (C20) 0.38 5.45 2.52 0.99

N_HENEICOA Heneicosanoic acid (C21) 0.29 38.24 19.33 2.97

N_TRICOSA Tricosanoic acid (C23) 0.17 1.67 16.81 0.95

N_TETRACOSA Tetracosanoic acid (C24) 0.27 3.44 2.52 1.24

Σ N-ALKANOIC A Σ n-alkanoic acid 4.58 77.71 - -

9H-FLUORENE 9H-Fluorenone 0.04 1.90 2.52 2.39

CHOLESTEROL Cholesterol 0.21 1.86 25.21 0.89

LOVOGUCOSAN Levoglucosan 0.05 121.20 0.84 4.96

RETENE Retene 0.02 1.06 0.00 2.88

SQULAENE Squalene 0.62 10.97 0.00 1.31

DB PHTHA Dibutyl Phthalate 1.64 28.84 0.00 1.47

BENZOTHIO Benzothiazole 1.33 18.20 0.00 1.17

NAPHTHFUR Naphtho[1,2-c]furan 0.01 0.26 0.84 1.54

BUTANDIOA Butanedioic acid 0.23 17.39 2.52 2.98

PENTADIOA Pentanedioic acid 0.01 0.43 0.00 2.70

NONANDIOA Nonanedioic acid 0.03 0.69 17.65 1.56

cis-PINOIC ACID cis-Pinonic acid 0.11 8.82 0.00 1.30

OLEIC ACID Oleic Acid 1.87 27.90 0.00 1.40

DEHYDROABIETIC A Dehydroabietic acid 0.07 3.89 0.00 2.66 a Data below the limit of detection were replaced by half of the reported detection limit values for the geometric mean calculations. b Below detection limit. c Signal-to-Noise ratio.

Ambient concentrations of Σn–alkanoic acids (n-C16 to n-C24)

ranged from 4.58 to 77.71 ng/m3. The highest values for acids were

reached during summer, while showed lowest values during winter.

These seasonal variations in the alkanoic acids suggest an influence

- 169 -

from the biological sources of aerosols such as plant wax particles,

fungi, bacteria, spore, pollen and algae. Levoglucosan, a marker of

wood combustion, was also detected to the concentration of 1,21.2

ng/m3 during the winter season.

4.3.2 PMF Results

Due to the analytical limitations of the organic pollutants, PM2.5

samples were analyzed only on a monthly basis in the previous study

(Choi et al., 2012). For this reason, the source evaluation of PM2.5,

particularly in its organic material, has not been done adequately.

However because most of particulate organic materials exist in fine

particle, source apportionment of organic species in TSP can provide

the indirect information on the source of organic compounds in PM2.5.

PMF model was performed for source assessment and categorized by

3different kinds of analysis items, for example, first, 22 bulk items such

as OC, EC, ionic compounds, and trace metals in TSP, second, 41 items

in organic compounds, and third, 63 items in both bulk items (22) and

organic compounds (41). PM/OC sources were characterized by

modeling results utilizing various components variables. Finally, their

temporal and seasonal contributions were also evaluated from the

source apportionment. The local pollution sources were also estimated

using the CPF model. Because the PMF model generates factors and

source contributions based on data from an entire sampling period, it is

important to select species that are stable and conserved in the

- 170 -

atmosphere and have at least half of the data above detection limits. To

determine which species to select, we calculated the method detection

limit (MDL) for each organics species, and then investigated other

literatures where the substances of any item is used as input variable

(Shrivastava et al., 2007; Zhang et al., 2009; Stone et al., 2010; Pindado

and Perez., 2011; Drooge et al., 2012; Wang et al., 2012). After

reviewing, we performed PMF model on 63 measured variables

comprising concentrations (ng/m3) of different molecular marker

species: levoglucosan as a specific marker for biomass burning, EC and

hopanes (i.e., 17(H),21(H)-norhopane, 17(H),21(H)-hopane and sum of

hopane, cholestane) as key markers for vehicular emissions (Table 4-1).

In addition, a series of C26-C32 n-alkanes were selected since these

range alkanes demonstrate high odd-carbon preference that is specific

to biogenic sources. In order to apportion industrial emissions, nine

PAHs (fluoranthene, pyrene, benzo[a]fluoranthene, benzo[b]fluor-

anthene, benzo[k]fluoranthene, benzo[a]pyrene, benzo[ghi]perylene)

(Table 4-1) were included as fitting species in the PMF. In addition, a

set of OC, EC, 14 trace metals (Mg, Al, P, Ca, Ti, V, Cr, Fe, Mn, Ni,

Cu, Zn, As and Pb), and 6 ionic species (Na+, NH4+, K+, Cl-, NO3

-,

SO42-) was considered in the model.

To select modeling parameters and the number of factors, the

mathematical diagnostics and the apparent validity of the PMF

solutions were examined. The PMF diagnostics (e.g., model error, Q;

rotational ambiguity, rotmat) were based on those of Lee et al. (1999).

We investigated the Q-value for different numbers of factors and values

- 171 -

of the rotational parameter (FPEAK), as well as the variations in the

maximum individual column mean (IM), the maximum individual

column standard deviation (IS), and rotational freedom for the different

numbers of factors in PMF. The results are shown in Figs.S4-2, S4-6

and S4-10. As the number of factors approached a critical value, IM

and IS clearly decreased. We also investigated the maximum rotmat,

which showed a significant increase from seven to ten factors.

4.3.2.1 The PMF Results for TSP

At first, to find the pollution sources of TSP, PMF model was

performed using the concentration data for the ingredients of TSP such

as carbon, ion, and heavy metals. In results, nine factors and a value of

FPEAK = 0.2 presented the most physically meaningful solution and

the best agreement between a calculated Q-value of 2,619 and a

theoretical Q of approximately 2,618. The source profiles and source

contributions for nine individual sources of TSP were generated by the

PMF model (Fig.4-1). The source profiles (F factor) of TSP and time-

series plots of source contribution (G factor) are shown in Figs.4-1 and

4-2. Next, Fig. 4-3 summarized the source contributions (%) of the

identified sources to TSP mass concentrations. Fig. 4-4 also showed the

temporal and seasonal comparisons of source contributions to the TSP

mass concentration. The resolved nine sources for TSP were motor

vehicle, sea salt, secondary sulfate, soil, combustion, and industry (sea

port) with the lesser contributions from non-ferrous industry, secondary

- 172 -

nitrate, and road dust (Fig. 4-1).

First, the sea salt source was characterized by high contribution of Na+

and Cl-, accounting for 14.0 % of the total TSP concentration (Figs. 4-1

and 4-3). The main component of sea salt is sodium chloride (NaCl)

with traces of magnesium (Mg) and sulfate (SO42-) (Lee et al., 1999;

Song et al., 2001; Viana et al, 2008). This factor exhibits a seasonal

pattern with high spring and winter levels (Figs. 4-2 and 4-4). This

specific source could also include some aerosolized road salt in winter

when the deicer was used on roads (Song et al., 2001).

Secondary sulfate were represented by high loadings of NH4+ and

SO42-. This source accounted for 13.7% of total TSP mass

concentration (Figs. 4-1 and 4-3). As with PM2.5 findings (Choi et al.,

2013), secondary sulfate also showed seasonal variation being high

contribution in the summer when the photochemical activity is highest

at the monitoring site (Figs. 4-2 and 4-4). The industry source had

factor profiles containing high concentrations of Na+, Ca, Cr, and Fe

which are recognized as typical indicators of industrial pollutants

emitted from the sea port and nearby sites (Song et al., 2006; Yoon et

al., 2005). The source contribution of industry (sea port) to the TSP

mass concentration was 10.8% (Figs. 4-1 and 4-3). These results were

comparable to the previous study of Yoon et al. (2005), in which the

higher concentrations at the seaport of Na+, Cl-, Ca, and Fe among

analyzed elements were obtained.

- 173 -

OC

EC

Na

NH

4

K Cl

NO

3

SO

4

Mg

Al

P Ca

Ti

V Cr

Fe

Mn

Ni

Cu

Zn

As

Pb

0.001

0.01

0.1

1

0.00.20.40.60.81.0

0.001

0.01

0.1

1

0.00.20.40.60.81.0

0.001

0.01

0.1

0.00.20.40.60.81.0

0.001

0.01

0.1

1

0.00.20.40.60.81.0

0.001

0.01

0.1

1

0.00.20.40.60.81.00.001

0.01

0.1

0.00.20.40.60.81.0

0.001

0.01

0.1

1

0.00.20.40.60.81.0

0.001

0.01

0.1

1

0.00.20.40.60.81.0

OC

EC Na

NH

4 K Cl

NO

3

SO

4

Mg Al P

Ca Ti V Cr

Fe

Mn Ni

Cu

Zn As

Pb

0.0001

0.001

0.01

0.1

1

0.00.20.40.60.81.0

Secondary sulfate

Secondary nitrate

Soil

Non-ferrous industry

Sea salt

Coal Combustion

Motor vehicle

Industry (sea port)

Road dust

Con

cen

trat

ion

(ug/

ug)

Ex p

lain

ed v

aria

tion

Fig. 4-1. Source profiles of obtained from TSP samples(prediction ± standard deviation) at the sampling site.

- 174 -

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10

020406080

100

020406080

100120

0

20

40

60

80

010

203040

0

20

40

60

80

0

20

40

60

010203040

0

20

40

60

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10 0

20

406080

Mas

s co

nce

ntr

atio

n(u

g/m

3 )Secondary sulfate

Secondary nitrate

Soil

Non-ferrous industry

Sea salt

Coal Combustion

Motor vehicle

Industry (sea port)

Road dust

Fig. 4-2. Time series plot for each source contribution obtained from TSP samples at the sampling site.

The fourth source was identified by high concentrations of NH4+,

NO3-, and OC which are recognized as typical indicators of secondary

nitrate (Song et al., 2006; Yoon et al., 2005). This factor explains for

5.4% of total TSP mass concentration, which is somewhat lower

- 175 -

contribution rate than that of PM2.5 (Figs. 4-1 and 4-3). Similarly from

the results of the PM2.5, the presence of OC in this fractionation

indicated that the organic matter could be condensed on the NH4NO3

particles (Song et al., 2001; Liu et al., 2003). The soil source was

characterized as the fifth source by typical soil metals (Mg, Al, Si, Ca,

Ti, Fe) (Simoneit et al., 2004; Lee and Hopke, 2006; Gildemeister et al.,

2007; Heo et al., 2009). The soil contribution to the total TSP mass was

12.8% (Figs. 4-1 and 4-3), showing two times higher than that of PM2.5.

The soil source also showed higher contribution to PM mass during the

spring than others season (Figs. 4-2 and 4-4).

Secondary nitrate

(5.4%)

Secondary sulfate(13.7%)

Motor vehicle(17.4%)

Industry (Sea port)(10.8%)

Combustion(11.6%)

Sea salt(14.0%)

Non-ferrous industry(6.8%)

Soil(12.8%)

Road dust(3.6%)

Fig. 4-3.The source contributions (%) of identified sources to TSP mass concentrations.

Interpreted as a motor vehicle source ranging from vehicle exhaust to

traffic, these sources were clearly different from the other sources due

to the presence of trace elements linked to road dust (Fe, Mg) and

combustion of lubricating oil (Ca, Zn) (Lough et al., 2005; Lee and

- 176 -

Hopke, 2006; Viana et al, 2008). In this source, EC and organic matter

(OM) were also reported, separately. The source contributions to the

TSP mass concentration were 17.4% for motor vehicle categories with

peak contribution in the summer (Figs. 4-1 and 4-3).

The seventh source was classified as coal combustion and contained

high concentrations of Cr and As along with some OC and Cl-. Several

studies suggested that the four most critical trace elements for coal

combustion were Hg, As, Cr, and Se. In thus, the presence of Cr and As

on this particle might be related to coal combustion processes (Harrison

et al., 1996; Alexander et al., 2001). This source accounted for 11.6%

of the total TSP concentration (Figs. 4-1 and 4-3). As shown in Figs. 4-

2 and 4-4, these particle sources appeared to peak contribution during

winter when coal combustion emissions were common for heating.

The remaining two sources were road dust and non-ferrous industry

emissions, not determined as a PM2.5 source from the PMF model. The

road dust source is largely dominated by Zn, Pb, Al, Mn and some

inorganic compounds, and contributes about 3.9% of the observed mass

(Figs. 4-1 and 4-3). Wind-driven suspension of surface soils gives rise

to airborne particles whose composition has much in common with

road dusts. Besides that, trace elements such as Ba and Cu (brake

pads) and Zn (tyres) are enriched in traffic-generated particles relative

to natural soils and dusts (Viana et al., 2008; Pant and Harrison, 2012).

The non-ferrous industry factor was characterized by high

concentrations of EC, Ni, Cu, and mild emissions of Zn, Pb, and trace

elements. This source contributed to 6.8% of the PM2.5 mass

- 177 -

concentration. It is difficult to distinguish the pollution source of heavy

metals, because they are released from a wide variety of emissions

source. Looking at the results of conventional sources classified, non-

ferrous industry factors have source profiles including the trace metals

Cu, Mn, and Zn (Lee and Hopke, 2006).

Season

spring summer fall winter

Con

cen

trat

ion

(ug/

m3 )

0

10

20

30

40

50

60

70

Sea saltSecondary SulfateIndustry Secondary nitrate Soil Motor vehicle Coal combustionRoad dustNon-ferrous industry

Fig. 4-4. Seasonal comparisons of source contributions to TSP mass concentration (mean ± standard deviation).

Kar et al. (2010) has used Zn, Cu, and Ni as marker for

electroplating/galvanizing units. Shridhar et al. (2010) distinguished

between industrial emissions (Ni, Cr, Mn, Cu, and Zn) and emissions

from battery units (Pb). There are northeast port, copper-nickel smelter,

and some industrial facilities in the northwest within 10km from the

sampling site. CPF analysis for the non-ferrous industry source

identified the non-ferrous industrial facility (e.g., steel mill, oil

refineries, small and large factories) located in the northeast region,

(Fig. 3-5). Therefore, local non-ferrous related industrial activities are

considered to be the likely source of PM at the sampling site.

- 178 -

Table 4-2. Pearson correlation coefficient for organic compounds in TSP and PM2.5

TSP PM2.5

OC EC SOC POC WSOC WIOC OC EC SOC POC WSOC WIOC

TSP

OC 1.000 0.445** 0.980** 0.451** 0.827** 0.878** 0.792** 0.653** 0.702** 0.667** 0.601** 0.036

EC 1.000 0.261** 0.999** 0.244** 0.497** 0.146 0.444** 0.050 0.453** 0.009 0.042

SOC 1.000 0.267** 0.839** 0.837** 0.826** 0.608** 0.758** 0.628** 0.648** 0.029

POC 1.000 0.249** 0.502** 0.150 0.447** 0.050 0.453** 0.009 0.042

WSOC 1.000 0.458** 0.793** 0.571** 0.733** 0.587** 0.743** 0.115

WIOC 1.000 0.589** 0.554** 0.491** 0.558** 0.308** -0.042

PM2.5

OC 1.000 0.686** 0.953** 0.726** 0.804** -0.192

EC 1.000 0.482** 1.000** 0.447** -0.083

SOC 1.000 0.482** 0.797** -0.196

POC 1.000 0.466** -0.086

WSOC 1.000 -0.153

WIOC 1.000

- 179 -

4.3.2.2 PMF results for OC using molecular markers

A study on the composition and the sources of particulate organic

matters almost had not been done in our country. Thus, there is little

information on the organic components of particulate matter.

Therefore, it is very important to understand the sources and

composition of organic carbon. For this assessment, 41 items were

analyzed for the composition of the organic carbon. PMF analysis was

also carried out to estimate the sources of organic aerosol using the

composition of organic carbon. As shown in Table 4-2, the

composition of organic carbon included in the TSP and PM2.5 very

closely related to each other. Thus, sources of organic carbon in TSP

are available for the indirect assessment of PM2.5. The 7~9 factor

solutions were also examined to assess the consistency of the solution

with the current understanding the particulate aerosol sources. The

final selection of a factor PMF-2 model was ultimately made based on

a combination of model diagnostics and factor interpretability. The

non-rotated solutions (FPEAK = 0) were judged most interpretable

with corresponding Q value of 4,886. As a result, each of eight factors

has a distinctive grouping of species that can be associated with a

specific source class. The source profiles (F factor) of TSP from

41pecies and their time-series plots of source contribution (G factor)

are shown in Figs. 4-5 and 4-6. Next, Fig.4-7 summarized the source

contributions (%) of the identified sources to OC mass concentrations.

Figs.4-6 and 4.8 also showed the temporal and seasonal comparisons

of source contributions to the OC mass concentration.

- 180 -

EC

N-H

EX

AC

O

N-H

EP

TA

CO

N-O

CT

AC

ON

_NO

NA

CO

N_T

RIC

ON

_DO

TR

ICO

FL

UO

RA

TH

EN

EP

YR

EN

EB

(A)F

B(B

)FB

(K)F

B(A

)PB

GH

IPE

CH

RY

SN

INC

DP

YB

A30

NH

AB

_HO

PH

OPA

NE

CH

OL

ES

TA

NE

N_H

EX

DA

N_H

EP

DA

N_O

CT

DA

N_N

ON

DA

N_E

ICO

AN

_HE

NE

ICO

AN

_TR

ICO

SA

N_T

ET

RA

CO

SA9H

-FL

UO

RE

NE

CH

OL

ES

TE

RO

LL

OV

OG

UC

OSA

NR

ET

EN

ES

QU

AL

EN

ED

B P

HT

HA

BE

NZ

OT

HIO

NA

PHT

HF

UR

BU

TA

ND

IOA

PE

NT

AD

IOA

NO

NA

ND

IOA

cis-

PIN

OIC

AC

IDO

lLE

IC A

CID

DE

HY

DR

OA

BIE

A

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

0.00.20.40.60.81.0

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

0.00.20.40.60.81.0

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

0.00.20.40.60.81.0

EC

N-H

EX

AC

O

N-H

EP

TA

CO

N-O

CT

AC

ON

_NO

NA

CO

N_T

RIC

ON

_DO

TR

ICO

FLU

OR

AT

HE

NE

PY

RE

NE

B(A

)FB

(B)F

B(K

)FB

(A)P

BG

HIP

EC

HR

YSN

INC

DPY

BA

30N

HA

B_H

OP

HO

PAN

EC

HO

LE

STA

NE

N_H

EX

DA

N_H

EPD

AN

_OC

TD

AN

_NO

ND

AN

_EIC

OA

N_H

EN

EIC

OA

N_T

RIC

OSA

N_T

ET

RA

CO

SA9H

-FL

UO

RE

NE

CH

OL

ES

TE

RO

LL

OV

OG

UC

OSA

NR

ET

EN

ES

QU

AL

EN

ED

B P

HT

HA

BE

NZ

OT

HIO

NA

PHT

HF

UR

BU

TA

ND

IOA

PEN

TA

DIO

AN

ON

AN

DIO

Aci

s-P

INO

IC A

CID

OlL

EIC

AC

IDD

EH

YD

RO

AB

IE A

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

0.00.20.40.60.81.0

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

0.00.20.40.60.81.01e-5

1e-4

1e-3

1e-2

1e-1

1e+0

0.00.20.40.60.81.0

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

0.00.20.40.60.81.0

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

0.00.20.40.60.81.0

Motor vehicle

Vegetative detritus (n-Alkane)

Biomass burning

Combustion (LMW-PAHs)

Benzo(a)pyrene

Combustion (HMW-PAHs)

SOA2

SOA1

Con

cen

trat

ion(

ng/n

g)

Exp

lain

ed v

aria

tion

Fig. 4-5. Source profiles obtained from organic data (prediction ± standard deviation) using 41organic marker species in Incheon, Korea.

- 181 -

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10

0

100

200

300

050

100150200

020406080

100

050

100150200

0200400600800

0200400600800

1000

0200400600800

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10 0

200400600800

Mas

s co

nce

ntr

atio

n(n

g/m

3 )

Combustion (LMW-PAHs)

Biomass burning

Vegetative detritus (n-Alkane)

Benzo(a)pyrene

SOA1

SOA2

Combustion (HMW-PAHs)

Motor vehicle

Fig. 4-6. The source contributions (%) of identified sources to OC mass

concentrations calculated from PMF model using 41organic marker species. The resolved eight-source included combustion(LMW-PAHs),

biomass burning, vegetative detritus(n-Alkane), benzo(a)pyrene,

SOA1, SOA2, combustion (HMW -PAHs), and motor vehicle (Fig. 4-

5). Two combustion (PAHs) factors were characterized by high levels

- 182 -

of PAHs. As distinguished from other studies, these organic markers

are defined as ‘‘combustion sources (PAHs)’’ to distinguish them

from the biomass combustion or mobile exhaust factors (Pindado and

Perez., 2011; Zhang et al., 2009). PAHs are the products of incomplete

combustion from various sources, such as fossil fuel combustion and

biomass burning. These factors were slightly associated with NOX, CO,

and SO2, which were related with combustion processes (TableS 4-1).

One of these two factors, combustion (LMW-PAHs) was characterized

by high loading of middle molecular PAHs (i.e., those containing four-

rings; fluoranthene and pyrene) along with some higher molecular

PAHs (i.e., those containing five to seven rings; benzo[a]fluoranthene,

benzo-[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, benzo

[ghi]perylene, chrysene, and indeno[1,2,3-cd]pyrene). On the other

hand, combustion (HMW-PAHs) was explained by the dominance of

higher molecular PAHs. Combustion (LMW-PAHs) and combustion

(HMW-PAHs) sources accounted for 8.3 % and 18.4 % of the total OC

concentration, respectively (Figs. 4-5 and 4-7). They were associated

with each other and showed a similar pattern in seasonal contribution

to organic aerosol. This separation can be explained that the size

distribution of non-volatile PAHs, such as HMW-PAHs basically

reflects the gas-to-particle transformation after their emission, whereas

semi-volatile PAH species, LMW-PAHs could result from their

volatilization, condensation, and adsorption to pre-existing particles

(Venkataraman and Friedlander, 1994; Kavouras and Stephanou,

2002). In addition, the difference in the contribution of PAHs source

- 183 -

along molecular weight could be the reasons that the smaller

molecular weight PAHs, relatively high volatility, more readily

distributed in the gas phase than in the particulate distribution.

Many researches have been conducted to estimated the sources of

PAHs, generated by a variety of pollutants, in more detail (Harrison et

al., 1996; Yunker et al., 2002; Larsen and Baker, 2003; Jaeckels et al.,

2007; Lee and Kim, 2007; Liu et al., 2007). The differences in emission

sources can be qualitatively identified on the basis of PAH composition

profiles represented by diagnostic ratios (Harrison et al., 1996; Yunker

et al., 2002; Liu et al., 2007). FigS. 4-9 illustrates such diagnostics as a

plot of FLA/(FLA + PYR) against IcdP/(IcdP + BghiP) for particulate

PAHs. By rule of thumb, both FLA/(FLA + PYR) and

IcdP/(IcdP+BghiP) are greater than 0.5 in the case of coal/biofuel

emissions, but less than 0.5 in the case of petroleum petroleum

combustion. Both Combustion (PAHs) are characterized by mixed

source with high FLA/(FLA + PYR), but mid IcdP/(IcdP + BghiP)

values. The similar to those from this investigation are those from other

northeastern Asian cities, where the major PAH source was also mixed

sources combustion (Harrison et al., 1996; Yunker et al., 2002; Liu et

al., 2007). The relative abundance of BbF+BkF to FLA may help to

distinguish between the coal and biofuel combustion sources to a

certain extent (Wang et al., 2007). We also calculated the relative

abundance and found the dominance of BbF+BkF levels in aerosols,

confirming a stronger influence of coal burning are higher than those of

the other species (FigS.4-9). The seasonal and temporal variations of

- 184 -

PAHs are shown in Figs. 4-6 and 4-8. The factor contribution shows

strong seasonality with a wintertime maximum, consistent with fuel

combustion for domestic heating in the winter. The increase in

particulate PAH concentration during the winter time has been reported

in previous studies (Rogge et al., 1993c; Gigliotti et al., 2000; Park et

al., 2000; Schauer et al., 2003; Liu et al., 2007).

Combustion(LMW-PAHs)

(8.3%)

SOA1(16.5%)

Benzo(a)pyrene(1.3%)Vegetative detritus (n-Alkane)(1.3%)

Motor vehicle(29.7%)

Biomass burning(2.3%)

SOA2(22.2%)

Combustion (HMW-PAHs)

(18.4%)

Fig. 4-7. The source contributions (%) of identified sources to organic carbon mass concentrations calculated from PMF model using organic markers-41species.

Biomass burning was characterized by the contribution of high values

of PAHs and levoglucosan, which was probably derived from wood

combustion for heating and cooking (Simoneit et al., 1999, 2004;

Jaeckels et al., 2007; Drooge et al., 2012). This factor explained 99% of

the apportioned levoglucosan, and 2.3% of OC mass concentration

- 185 -

(Figs. 4-5 and 4-7). Several studies classified biomass burning factor on

the basis of the contribution of levoglucosan (Shrivastava et al., 2007;

Bi et al., 2008; Drogg et al., 2012; Wang et al., 2012). Levoglucosan is

a major pyrolysis product of cellulose and hemi cellulose, and has

recently been highlighted as a useful molecular marker of biomass

burning aerosols (Simoneit et al., 1999, 2004). Biomass burning has

become a global concern and occurs in urban and rural areas for heating,

cooking, waste disposal and aesthetic reasons (Simoneit, 2002, Bi et al.,

2008). As expected (Hemann et al, 2009; Heo et al., 2013), the high

contribution of this factor were observed in the winter season (Fig. 4-6),

when wood burning for domestic heating and biomass waste burning in

fields was practiced in the vicinity of the sampling sites. The seasonal

variation in biomass burning is similar to that observed in previous

studies (Shrivastava et al., 2007; Stone et al., 2010).

Vegetative detritus (n-alkane) was also determined by n-alkane, some

alkanoic acid, hopane, and cholestane (Fig. 4-5). More than 68% of n-

alkane between n-C26 and C33 was apportioned to this factor (Fig. 4-7).

The slight presence of hopane & cholestane is also showes the potential

impact of vehicle emission. The contribution of vegetative detritus (n-

alkane) to the TSP concentration in the atmosphere is 1.3% and

increased during the summer season, because of increased metabolic

activity in plants. n-alkanes are emitted to the atmosphere by a large

variety of sources, such as fossil fuel vehicle emissions, biomass

combustion, and by resuspension of vegetative detritus (Schauer et al.,

2007, Drogge et al., 2012; Pindado, and Perez, 2011). The lower

- 186 -

molecular weight n-alkanes (n-C23 to n-C25) that were measured are

more related to traffic emissions, while the higher molecular weight n-

alkanes, and particularly the odd carbon numbered n-alkanes: n-C27, n-

C29 and n-C31, are almost entirely related to vegetation detritus as they

are present in large quantities in epicuticular wax from higher plants

(Simoneit and Mazurek, 1982; Rogge et al., 1993c).

The carbon preference index of n-alkanes (CPI), defined as the ratio of

the mass of odd-to-even alkanes, have been used as indicators of

biogenic and anthropogenic contributions to organic aerosol (Simoneit,

1989; Simoneit and Mazurek, 1982; Simoneit et al., 1999; Rogge et al.,

1993c; Zheng et al., 1997). Biogenic sources emit odd carbon number

alkanes in greater concentrations than even carbon number alkanes, and

therefore have CPIs > 1; anthropogenic emissions evidence no carbon

preference and have CPIs of about 1. The CPI was around 3.2 in this

source, indicating modest contributions of vegetation detritus. The

preferential presence of even n-alkanoic acids (CPI value, 0.4) is also

consistent with primary biogenic emissions (Simoneit and Mazurek,

1982; Rogge et al., 1993c). Urban environments, with large

contributions from anthropogenic emissions, generally have CPIs

ranging between 1.1 and 2.0, while rural environments with more

biogenic influence generally have CPIs above 2.0 (Simoneit, 1989;

Gogou et al., 1996; Zheng et al., 2000; Brown et al, 2002). Alkanes

contained in the TSP are determined to be an impact biogenic origin,

while alkanes in PM 2.5 are judged to be greater as an artificial source.

These results are in agreement with the other studies which reported

- 187 -

that natural n-alkanes were preferentially associated with the coarse

particles and the anthropogenic compounds in the smaller particles

(Sicre et al., 1987; Alves et al., 2000; Kavouras and Stephanou, 2002;

Bi et al., 2008).

Season

spring summer fall winter

Con

cen

trat

ion

(ng/

m3 )

0

100

200

300

400

500

600

Combustion (LMW-PAHs)Biomass burningVegetative detritus (n-Alkane)Benzo(a)pyreneSOA1SOA2Combustion (HMW-PAHs)Motor vehicle

Fig. 4-8. Seasonal comparisons of source contributions to organic carbon mass concentrations calculated from PMF model using organic markers-41species.

Two types of SOA (termed secondary organic aerosol) were recorded

at the sampling site. The SOA was identified by the presence of

secondary organic aerosol (some decarboxylic acid, such as butandioic

acid and pentanedioic acid). These two source contributions to the OC

mass concentration were 16.5% and 22.2% for SOA1 and SOA2,

respectively (Figs. 4-5 and 4-7). Although some dicarboxylic acids also

can be emitted directly from primary sources, such as motor vehicle

exhausts, meat cooking, and wood burning (Grosjean et al., 1978;

Kawamura and Kaplan, 1987; Rogge et al., 1993c), in most locations,

secondary reactions are primarily responsible for their ambient

- 188 -

concentrations (Heald et al., 2010; Paulot et al., 2011). These

compounds also have a role in the production of cloud condensation

nuclei due to their tendency to increase the hygroscopicity of organic

aerosol (Kerminen et al., 2000). Both factors exhibited contributions

year-round with an increase in the spring and the summer (Figs. 4-6

and 4-8). This pattern indicates higher rates of secondary reactions and

dicarboxylic acids emissions in the summer, which is consistent with

previous findings (Zhang and Tao, 2009; Pindado and Perez, 2011; Heo

et al., 2013). This fact is in agreement with the known trend of SOA to

be mainly formed during warm days, when there are higher

temperatures that encourage atmospheric reactions (Pindado and Perez,

2011).

Even though the contribution to particulate matter is small (4.2%),

benzo(a)pyrene source was assigned to the seventh profile. This source

included benzo(a)pyrene and some PAHs species. Benzo(a)pyrene is a

PAH that is formed mainly as a result of incomplete combustion of

organic materials during industrial and other human activities. As with

PAHs, these activities include processing of coal and crude oil,

combustion of natural gas, combustion of refuse, vehicle traffic,

cooking and tobacco smoking, as well as natural processes such as

forest fires (Yassaa et al., 2001; Zhang and Tao, 2009). Compared with

other PAHs, benzo(a)pyrene may be easily broken down by light

(photolysis) or may react with ozone or nitrogen dioxide (Larsen and

Baker, 2003; Park et al., 2006). For this reason, it will create the

difference in source composition of PAHs. This fact was confirmed by

- 189 -

the relevance between SOA1 factor and benzo(a)pyrene factor (TableS

4-1). In addition, other PAHs sources such as commercial/residential

burning and traffic related sources may also be the cause of the

separation of benzo(a)pyrene factor.

Motor vehicle factor accounted for 29.7% of total OC mass

concentration, and was represented by the presence of EC, n-Alkanoic

acid, 17β(H),21α(H)-30-norhopane, 17α(H),21β (H)-hopane, hopanes,

and cholestanes (Fig. 4-5). This motor vehicle factor contributed

around 56% of the hopanes mass included in the model, and 66% of

cholestanes, respectively. EC is commonly associated with motor

vehicle emissions, specifically diesel emissions, and attributed 48% of

the EC mass conentration to this factor. Hopanes and cholestane,

constituents of lubricating oils, are present in refined petroleum

products and have been identified as unique source markers for motor

vehicle exhaust (Simoneit, 1999; Schauer et al., 2002; Stone et al.,

2010). They are also present are present in emissions from fossil fuel

combustion and related sources (Simoneit, 1986): coal combustion

(Oros and Simoneit, 2000) and fuel oil combustion (Rogge et al., 1997).

4.3.2.3 PMF results for PM using bulk and organic items

Next, the characteristics of resolved sources were also investigated

when the entire data set (63 species) are used to estimate the source

profiles and contribution. This analysis could provide information

about how different sources were evaluated, if individual components

- 190 -

of organic carbon are added to the existing bulk items as input variables.

The 7-9 factor solutions were also examined to assess the consistency

of the solution with the current understanding the particulate aerosol

sources. A seven-factor model did not separate the two important

source classes such as vegetative detritus, secondary nitrate. The final

selection of a factor PMF model was ultimately made based on a

combination of model diagnostics and factor interpretability. The non-

rotated solutions (FPEAK = 0) were judged most interpretable with

corresponding Q value of 7,568. As a result, each of eight factors has a

distinctive grouping of species that can be associated with a specific

source class. The source profiles (F factor) of OC from 41 species and

their time-series plots of source contribution (G factor) are shown in

Figs. 4-9 and 4-10. Next, Fig.4-11 summarized the source contributions

(%) of the identified sources to TSP mass concentrations.

Each profile obtained by PMF using 63 items was compared with

source profiles resolved from the organic data (Figs. 4-9 and 4-10).

Model results revealed that the major sources contributing to the TSP at

sampling sites are primary source, motor vehicle/non-ferrous industry,

and SOA2/secondary nitrate. Small amounts of TSP are contributed by

combustion (PAHs), biomass burning/soil, SOA1, benzo(a)pyrene, and

vegetative detritus (n-Alkane).

Primary factors are characterized with some primary source specific

compounds, low contribution of SOA tracers (Fig. 4-9). The urban

primary factor is characterized with large contribution of inorganic

species (Na+, Cl-, and some metal elements) associated with a

- 191 -

combination of urban primary sources such as sea salts, road dust, and

some industry (metal processing) sources. They are consistent with

some resolved sources from PMF analysis using 22 bulk items (Lee et

al., 1999; Song et al., 2001; Yoon et al., 2005; Song et al., 2006; Viana

et al, 2008). This source accounted for 35.9 % of the total TSP mass

concentration (Fig. 4-11). This factor contribution was no distinctive

temporal pattern, but elevated in the fall time when atmospheric

stability increased (Fig. 4-10).

Biomass burning/soil factors were characterized by the contribution of

Mg, Al, Fe, Mn, PAHs, and levoglucosan (Fig. 4-9). This factor

contributed 7.5% to the ambient TSP mass concentration at the

sampling site. This source included levoglucosan, which was probably

derived from wood combustion for heating and cooking (Simoneit et al.,

1999, 2004; Jaeckels et al., 2007; Drooge et al., 2012), as well as soil

metals (Mg, Al, Si, Ca, Ti, Fe), which are element associated with soil

source (Liu et al., 2003; Simoneit et al., 2004; Lee and Hopke, 2006;

Gildemeister et al., 2007; Heo et al., 2009). The high contribution of

the pollutant was in the winter and the spring. This variation could be

explained by the impact of the origins of pollutants from the biomass

buring in winter and the soil factor in spring. The seasonal variation in

biomass burning/soil is similar to that observed in previous studies (Lee

and Hopke, 2006; Gildemeister et al., 2007; Shrivastava et al., 2007;

Heo et al., 2009; Stone et al., 2010).

- 192 -

OC

EC

Na

NH

4K C

lN

O3

SO

4M

gA

lP C

aT

iV C

rF

eM

nN

iC

uZ

nA

sP

bN

-HE

XA

CO

N

-HE

PTA

CO

N-O

CT

AC

ON

_NO

NA

CO

N_T

RIC

ON

_DO

TR

ICO

Flu

oran

then

eP

yren

eB

(A)F

B(B

)FB

(K)F

B(A

)PB

GH

IPE

CH

RY

SN

INC

DPY

BA

30N

HA

B_H

OP

HO

PAN

EC

HO

LE

STA

NE

N_H

EX

DA

N_H

EP

DA

N_O

CT

DA

N_N

ON

DA

N_E

ICO

AN

_HE

NE

ICO

AN

_TR

ICO

SAN

_TE

TR

AC

OSA

9H-F

LU

OR

EN

EC

HO

LE

STE

RO

LL

OV

OG

UC

OS

AN

RE

TE

NE

SQ

UA

LE

NE

DB

PH

TH

AB

EN

ZO

TH

ION

AP

HT

HF

UR

BU

TA

ND

IOA

PE

NT

AD

IOA

NO

NA

ND

IOA

cis-

PIN

OIC

AC

IDO

lLE

IC A

CID

DE

HY

DR

OA

BIE

A

1e-61e-51e-41e-31e-21e-11e+0

0.00.20.40.60.81.0

1e-61e-51e-41e-31e-21e-11e+0

0.00.20.40.60.81.0

1e-61e-51e-41e-31e-21e-11e+0

0.00.20.40.60.81.0

OC

EC Na

NH

4 K Cl

NO

3SO

4M

g Al P

Ca Ti V Cr

Fe

Mn Ni

Cu

Zn

As

PbN

-HE

XA

CO

N

-HE

PT

AC

ON

-OC

TA

CO

N_N

ON

AC

ON

_TR

ICO

N_D

OT

RIC

OF

luor

anth

ene

Pyr

ene

B(A

)FB

(B)F

B(K

)FB

(A)P

BG

HIP

EC

HR

YSN

INC

DPY

BA

30N

HA

B_H

OP

HO

PAN

EC

HO

LE

ST

AN

EN

_HE

XD

AN

_HE

PDA

N_O

CT

DA

N_N

ON

DA

N_E

ICO

AN

_HE

NE

ICO

AN

_TR

ICO

SAN

_TE

TR

AC

OSA

9H-F

LU

OR

EN

EC

HO

LE

STE

RO

LL

OV

OG

UC

OSA

NR

ET

EN

ES

QU

AL

EN

ED

B P

HT

HA

BE

NZ

OT

HIO

NA

PH

TH

FU

RB

UT

AN

DIO

AP

EN

TA

DIO

AN

ON

AN

DIO

Aci

s-PI

NO

IC A

CID

OlL

EIC

AC

IDD

EH

YD

RO

AB

IE A

1e-61e-51e-41e-31e-21e-11e+0

0.00.20.40.60.81.0

1e-61e-51e-41e-31e-21e-11e+0

0.00.20.40.60.81.0

1e-61e-51e-41e-31e-21e-11e+0

0.00.20.40.60.81.0

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

0.00.20.40.60.81.0

1e-61e-51e-41e-31e-21e-11e+0

0.00.20.40.60.81.0

Motor vehicle/Non-ferrous industry

Vegetative detritus (n-Alkane)

SOA1

Biomass burning/Soil

SOA2/Secondary nitrate

Primary source(Sea salts/Industry/Road dust)

Combustion (PAHs)

Benzo(a)pyreneCon

cen

trat

ion

(ng/

ng)

Exp

lain

ed v

aria

tion

Fig. 4-9. Source profiles obtained from TSP samples (prediction ± standard deviation) using 63species in Incheon, Korea.

- 193 -

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10

0300006000090000

120000150000180000

0100002000030000400005000060000

020000400006000080000

100000120000

05000

10000150002000025000

010000200003000040000500006000070000

0

10000

20000

30000

40000

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10 0

300006000090000

120000150000

01000020000300004000050000

Mas

s co

nce

ntr

atio

n(n

g/m

3 )

Primary source(Sea salts/Industry/Road dust)

Biomass burning/Soil

Motor vehicle/Non-ferrous industry

Vegetative detritus (n-Alkane)

SOA1

Benzo(a)pyrene

Combustion (PAHs)

SOA2/Secondary nitrate

Fig. 4-10. Timeseries plot for each source contribution of TSP using 63species in Incheon, Korea.

Motor vehicle/non-ferrous industry accounted for 17.9% of total TSP

mass concentration, and was represented by the presence of EC, Ni, Cu,

Fe, Ca, n-Alkanoic acid, 17β(H),21α(H)-30-norhopane, 17α(H),21β

(H)-hopane, hopanes, and cholestanes (Fig. 4-9). Hopanes and

cholestane, constituents of lubricating oils, are present in refined

- 194 -

petroleum products and have been identified as unique source markers

for motor vehicle exhaust (Simoneit, 1999; Schauer et al., 2002; Stone

et al., 2010). Some metallic elements including Ni, Cu, and Pb were

also found in this factor. These metallic elements were also confirmed

and separated as the non-ferrous industry factor, which was resolved

from PMF analysis using traditional bulk items.

Vegetative detritus (n-alkane) was also determined by n-alkane,

alkanoic acid, hopane, and cholestane (Fig. 4-9). More than 60% of n-

alkane between n-C26 and n-C33 was apportioned to this factor. The

contribution of vegetative detritus (n-alkane) to the TSP concentration

is 3.5% and increased during the summer season because of increased

metabolic activity in plants. The slight presence of hopane &

cholestane also showed the potential impact of vehicle emission. The

CPI value is as high as 4.1, which indicates a higher contribution of

biogenic process in this sample.

Two types of SOA (termed secondary organic aerosol and secondary

nitrate) were seperated at the sampling site. The SOA was identified by

the presence of secondary inorganic aerosol (ammonium ion, nitrate

ion) or secondary organic aerosol (some decarboxylic acid such as

butandioic acid and pentanedioic acid). SOA1 showed a high

distribution in butandioic acid, while secondary nitrate/SOA2 presented

high composition of pentanedioic acid and secondary ions, respectively.

These two source contributions to the TSP mass concentration were

6.4% and 17.0% for SOA1 and SOA2/secondary nitrate, respectively

(Figs. 4-9 and 4-11). Finally, PAHs sources were separated into

- 195 -

combustion (PAHs) and benzo(a)pyrene, which were high levels in

winter. Combustion (PAHs) and benzo(a)pyrene accounted for 7.6%

and 4.2%, of the total TSP concentration (Figs. 4-5 and 4-7).

Motor vehicle/Non-ferrous industry

(17.9%)

Combustion (PAHs)(7.6%)

Biomass burning/Soil

(7.5%)

Vegetative detritus(n-Alkane)

(3.5%)

Primary Source(35.9%)

SOA1(6.4%)

Benzo(a)pyrene(4.2%)

SOA2/Secondary nitrate(17.0%)

Fig. 4-11. The source contributions (%) of identified sources to TSP mass concentrations calculated from PMF model using 63species.

4.3.4 CPF results

CPF analysis was used to identify the location of local sources using

daily source contribution (estimated from the PMF model) coupled

with hourly wind direction data. The CPF plots for seven sources

(excluding secondary nitrate and sulfate) suggest the impact of local

sources (Figs. 4-12 and 4-13). The CPF plot for two combustion

(LMW-PAHs and HMW-PAHs) sources shown in Figs. 4-12 and 4-13

suggested that this source tended to impact the sampling site when

- 196 -

transport was from the northwest, consistent with the location of

heating combustion areas and industrial complexes just northwest of

sampling sites. The CPF plot for biomass burning factor pointed

northeasterly where commercial and residential areas are located. This

factor was likely influenced by local residential biomass burning and

commercial open burning. The vegetative detritus (n-Alkane) source

showed high contributions coming from the northeast where the forest

is located. For benzo(a)pyrene source, this approach has the dominant

contribution in the northeastern direction in which residential or

commercial burning occurred. CPF analysis for motor vehicle sources

indicated the northwest directions as the possible source regions, where

local roads are located (Figs. 4-12 and 4-13). This mobile source likely

originated mainly from motor vehicles operating on the local roads

close to the sample site. CPF analysis for the primary aerosol identified

local soil sources from the northwestern to the southwestern direction,

where air borne particles could be resuspended from local emissions

such as road traffic, parking areas, unpaved roads, construction sites,

and wind-blown soil dust could also produce airborne soil particles

(Figs. 4-12 and 4-13).

- 197 -

0.0 0.2 0.4 0.6

0.00.20.40.6

030

60

90

120

150180

210

240

270

300

330

Combustion (LMW-PAHs)

0.0 0.2 0.4

030

60

90

120

150180

210

240

270

300

330

Biomass burning

0.0 0.2

030

60

90

120

150180

210

240

270

300

330

Vegetative detritus (n-Alkane)

0.0 0.2 0.4

030

60

90

120

150180

210

240

270

300

330

Benzo(a)pyrene

0.0 0.2

030

60

90

120

150180

210

240

270

300

330

SOA1

0.0 0.2

030

60

90

120

150180

210

240

270

300

330

SOA2

0.0 0.2 0.4

030

60

90

120

150180

210

240

270

300

330

Combustion (HMW-PAHs)

0.0 0.2

030

60

90

120

150180

210

240

270

300

330

Motor vehicle

Fig. 4-12. CPF plots for the average source contributions deduced from organic carbon based on molecular marker PMF analysis.

- 198 -

0.0 0.2 0.4

0.00.20.4

030

60

90

120

150180

210

240

270

300

330

Primary Aerosol

0.0 0.2 0.4

030

60

90

120

150180

210

240

270

300

330

Biomass burning/Soil

0.0 0.2 0.4

030

60

90

120

150180

210

240

270

300

330

Motor vehicle/Non-ferrous industry

0.0 0.2

030

60

90

120

150180

210

240

270

300

330

Vegetative detritus (n-Alkane)

0.0 0.2 0.4

030

60

90

120

150180

210

240

270

300

330

SOA1

0.0 0.2 0.4

030

60

90

120

150180

210

240

270

300

330

Benzo(a)pyrene

0.0 0.2 0.4

030

60

90

120

150180

210

240

270

300

330

Combustion (PAHs)

0.0 0.2

030

60

90

120

150180

210

240

270

300

330

SOA2/Secondary nitrate

Fig. 4-13. CPF plots for the average source contributions deduced

from TPS-molecular marker PMF analysis.

4.4 Conclusions

In this study, one hundred and twenty particulate matter samples were

collected over a 1-year period at Incheon, Korea and were analyzed for

items such as organic carbon (OC), elemental carbon (EC), ionic

- 199 -

species, metal element, and particle-phase organic compound

(molecular markers) on the basis of 24-hour average concentration.

Data from more than 41 organic compounds along with measurements

of 22 inorganic compounds were analyzed using a factor analysis based

on source apportionment model, PMF, which has been widely used in

the past with elemental data but not organic molecular markers. PMF

model was carried out and categorized by 3 different kinds of analysis

items, for example, first, 22 items such as OC, EC, Ionic compounds

and trace metals in TSP, second, 41 items in organic compounds, and

third, 63 items in both TSP (22) and organic compounds (41). In

addition, the likely source areas of particulate organic emissions were

determined from the CPF.

The nine sources were identified by the PMF analysis using 22 items.

The major sources of TSP were motor vehicle (17.4%), sea salt

(14.0%), secondary sulfate (13.7%), soil (12.8%), combustion (11.6%),

and industry (10.8%) with the lesser contributions from non-ferrous

industry (6.8%), secondary nitrate (5.4%), and road dust (3.6%).

Through the molecular marker-PMF analysis with the organic marker

compounds, the eight-sources were separated as follows: the resolved

source included combustion (LMW-PAHs), biomass burning,

vegetative detritus (n-alkane), benzo(a)pyrene, SOA1, SOA2,

combustion (HMW-PAHs), and motor vehicle. We also tried to find out

individual organic marker species as source markers, and then

compared them with the resolved source only when used in

combination with inorganic pollutant data. The use of supplementary

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organic concentrations may possibly identify additional source groups

such as biomass burning, vegetative detritus (n-Alkane), and SOA that

could not be differentiated when using traditional trace element data

alone.

The source contribution of PM and organic aerosol resolved by PMF

model showed different characteristics depending on the season. The

vegetative detritus and motor vehicle were increased during the

summer season by the increase in bogenic/photochmical activity.

Meanwhile, most of the other organic sources were prominent in the

winter season by the increase in air pollutant emissions and

atmospheric stability. In addition, CPF results identified possible local

source locations which included primary sources, biomass burning/soil,

motor vehicle/non-ferrous industry, vegetative detritus (n-Alkane),

benzo(a)pyrene, and combustion(PAHs). Through the comparison of

the modeling results with 63 items, PMF model results using only

molecular marker provide more detailed information on the organic

aerosols sources such as secondary organic aerosol, PAHs. The source

characterization of particulate organic aerosols will be a key foundation

to understand the importance of the issue and providing possible

solutions relevant to TSP reduction methods.

- 201 -

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Supplementary materials Table S4-1. Factor loadings from principal component analysis of organic aerosol in PM after varimax rotation

F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 Combustion (LMW-PAHs) 0.880

Biomass burning -0.330 0.354 Vegatative detritus 0.891 Benzo(a)pyrene 0.469 0.653 SOA1 0.780 SOA2 0.322 0.819 Combustion (HMW-PAHs) 0.859 0.355

Motor vehicle 0.808 OC 0.653 0.443 0.300 EC -0.364 0.319 0.528 SOC 0.362 0.660 0.433 POC -0.362 0.322 0.536 WSOC 0.425 0.493 0.559 WIOC 0.618 Na -0.487 0.324 0.415 NH4 0.495 0.767 K 0.359 0.711 Cl 0.517 0.566 NO3 0.512 0.710 SO4 0.743 Mg 0.824 Al 0.392 0.333 0.498 -0.326 P 0.377 -0.352 0.398 Ca 0.823 Ti 0.844 V 0.711 Cr 0.330 0.703 Fe 0.856 Mn 0.317 0.752 Ni -0.369 0.665 Cu 0.494 Zn 0.497 0.311 0.357 As 0.817 Pb 0.325 0.433 0.369 N-HEXD 0.656 N-HEPD 0.501 0.344 N-OCTD 0.772 0.457 N-NONAD 0.871 N-EICO 0.778 N-HENEI 0.865 N-DOCO 0.721 0.360 0.382 N-TRICO 0.625 0.314 N-TETRACO 0.613 0.570 N-PENTACO 0.619 0.544 N-HEXACO 0.896 N-HEPTACO 0.632 0.535 N-OCTACO 0.858 N_NONACO 0.348 N_TRICO 0.865 N_DOTRICO 0.866

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Table S4-1. Factor loadings from principal component analysis of organic aerosol in PM after varimax rotation

F1 F2 F3 F4 F5 F6 F7 F8 F9 F10

FLU 0.919

PYR 0.917

B(A)F 0.851 0.369

B(B)F 0.842 0.351

B(K)F 0.653 0.359

B(A)P 0.463 0.650

BGHIPE 0.807 0.461

CHRYSN 0.888 0.331

INCDPY 0.835 0.403

BA30NH 0.557 0.381

AB_HOP 0.691

HOPANE 0.678 0.350

CHOLESTANE 0.574

N_HEXDA 0.428 0.688

N_HEPDA 0.350 0.569

N_OCTDA 0.301

N_NONDA 0.525

N_EICOA 0.381 0.803

N_HENEICOA 0.643

N_TRICOSA 0.791

N_TETRACOSA 0.353 0.815

9H-FLUORENE 0.878

CHOLESTEROL 0.359 0.318

LOVOGUCOSAN -0.328 0.358

RETENE 0.909

SQUALENE 0.560 -0.312 0.323

DB PHTHA 0.777

BENZOTHIO 0.408 0.313 0.493

NAPHTHFUR 0.604 0.560

BUTANDIOA 0.819

PENTADIOA 0.803

NONANDIOA

cis-PINOIC ACID 0.358

OlLEIC ACID 0.702

DEHYDROABIEA 0.774 0.420

CO 0.440 0.771

SO2 0.442 0.553

O3 -0.450 -.592

NO 0.396 0.822

NO2 0.792

WS -0.315 -.505 -0.33

TEMP -0.746 0.408

HUM

RAD -0.349 0.364 -.338

- 216 -

Table S4-2. Factor loadings from principal component analysis of PM after varimax rotation

F1 F2 F3 F4 F5 F6 F7 F8 F9 F10

Primary Aerosol -0.539 0.416 0.322 -0.397

Biomass burning/Soil -0.339 -0.363 0.347 0.312

Motor Vehicles -0.327 0.871 Vegatative detritus 0.873 0.301

SOA1 0.814

Benzo(a)pyrene 0.464 0.645 PAHs 0.924

SOA2/ Secondary Nitrate

0.941

OC 0.301 0.552 0.538 0.331 EC -0.334 0.343 0.560

SOC 0.396 0.550 0.539

POC -0.331 0.346 0.568 WSOC 0.452 0.643 0.333

WIOC 0.330 0.572

Na -0.541 0.348 0.382 NH4 0.870 0.307

K 0.777

Cl 0.520 -0.312 0.491 NO3 0.832 0.329

SO4 0.785

Mg 0.829 Al 0.343 0.350 0.521 -0.308

P 0.364 -0.414 0.377

Ca 0.821 Ti 0.843

V 0.729

Cr 0.321 -0.392 0.688 Fe 0.860

Mn 0.310 0.751 Ni -0.341 0.715

Cu 0.560

Zn 0.312 0.444 0.363 As 0.808

Pb 0.512 0.339

N-HEXD 0.602 0.395 N-HEPD 0.308 0.413 0.451

N-OCTD 0.787 0.404

N-NONAD 0.883 N-EICO 0.790

N-HENEI 0.878

N-DOCO 0.729 0.354 0.419 N-TRICO 0.632 0.309 0.316

N-TETRACO 0.615 0.613

N-PENTACO 0.621 0.588 N-HEXACO 0.891

N-HEPTACO 0.632 0.581

N-OCTACO 0.848 N_NONACO 0.373

N_TRICO 0.862

N_DOTRICO 0.864

- 217 -

Table S4-2. Factor loadings from principal component analysis of PM after varimax rotation

F1 F2 F3 F4 F5 F6 F7 F8 F9 F10

FLU 0.913

PYR 0.909

B(A)F 0.862 0.301

B(B)F 0.848

B(K)F 0.652

B(A)P 0.459 0.670

BGHIPE 0.813 0.368

CHRYSN 0.893

INCDPY 0.843 0.303

BA30NH 0.544 0.384

AB_HOP 0.660

HOPANE 0.648 0.352

CHOLESTANE 0.532

N_HEXDA 0.405 0.710

N_HEPDA 0.628

N_OCTDA -0.306 0.345

N_NONDA 0.525

N_EICOA 0.825

N_HENEICOA 0.686

N_TRICOSA -0.305 0.772

N_TETRACOSA 0.339 0.812

9H-FLUORENE 0.870

CHOLESTEROL 0.368 0.321

LOVOGUCOSAN -0.340 -0.351 0.333 0.331

RETENE 0.917

SQUALENE 0.571 -0.301 0.332

DB PHTHA 0.767

BENZOTHIO 0.356 0.553 0.309

NAPHTHFUR 0.491 0.662

BUTANDIOA 0.748

PENTADIOA 0.814

NONANDIOA

cis-PINOIC ACID 0.422

OlLEIC ACID 0.337 0.650

DEHYDROABIEA 0.789 0.385

CO 0.462 0.401 0.666

SO2 0.466 0.379 0.458

O3 -0.465 0.319 -0.552

NO 0.429 0.781

NO2 0.387 0.718

WS -0.465 -0.357

TEMP -0.721 0.452

HUM

RAD -0.351 0.424

- 218 -

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0

2000

4000

6000

8000

10000

12000

14000

WSOC WIOC EC

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0

5000

10000

15000

20000

25000

30000

35000

NH4 NO3 SO4

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0

2000

4000

6000

8000

10000

12000

14000Mg Al Ca Ti Fe Mn

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )0

500

1000

1500

2000

2500V Cr Ni Cu Zn As Pb

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0

10

20

30

40N-HEXACO N-HEPTACO N-OCTACO N_NONACO N_TRICO N_DOTRICO

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0

5

10

15

20

25

30

Fluoranthene Pyrene B(A)F B(B)F B(K)F B(A)P BGHIPE CHRYSN INCDPY

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

BA30NH AB_HOP Hopane Cholestane

Month

1 2 3 4 5 6 7 8 9 10 11 12

Con

cent

ratio

n (n

g/m

3 )

0

10

20

30

40

LOVOGUCOSAN

Fig. S4-1. Concentrations of carbonaceous aerosol components and organic species of TSP in Incheon by month

- 219 -

- PMF analysis for TSP using 22 compounds

(a)

Number of factors

2 3 4 5 6 7 8 9 10 11 12 13 14

Q-v

alue

(chan

ge o

f fa

ctor

)

0100020003000400050006000700080009000

100001100012000130001400015000

FPEAK

-1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

Q-v

alue

(chan

ge o

f F

PE

AK

)

2600

2700

2800

2900

3000

3100

Q-value (change of factor)Q-value (change of FPEAK)

(

b)

Number of factors

3 4 5 6 7 8 9 10 11 12 13 14

IM

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

IS

0

1

2

3

4

5

6

7

Rot

atio

nal

am

bu

gity

[m

ax(r

otm

at)]

0.00

0.05

0.10

0.15

0.20

0.25

IMISMax (rotmat)

Fig. S4-2. The diagonistic factor of PMF model using traditional

21items (a) IM, IS, and rotational freedom as a function of the factors chosen in PMF, (b) Q-value for the different factor solutions and the change of “FPEAK” parameter.

- 220 -

Measured fine particle mass conc.(ug/m3)

0 50 100 150 200 250 300

Pre

dict

ed f

ine

part

icle

mas

s co

nc.(

ug/m

3 )

0

50

100

150

200

250

300

0.93R

)19.4(1.44 0.03)X(1.07Y2

Fig. S4-3. Correlation between predicted and observed mass concentrations using multiple linear regression analysis. This results was obtained from PMF analysis (22 items)

- 221 -

OC

EC

Na

NH

4

K Cl

NO

3

SO4

Mg

Al

P Ca

Ti

V Cr

Fe

Mn

Ni

Cu

Zn

As

Pb

0.001

0.01

0.1

1

0.001

0.01

0.1

1

0.001

0.01

0.1

0.001

0.01

0.1

1

0.001

0.01

0.1

10.001

0.01

0.1

10.001

0.01

0.1

1

10

0.001

0.01

0.1

1

OC

EC Na

NH

4 K Cl

NO

3

SO4

Mg Al P

Ca Ti V Cr

Fe

Mn Ni

Cu

Zn

As

Pb

0.001

0.01

0.1

1

0.001

0.01

0.1

1

Secondary sulfate

Secondary nitrate

Soil

Non-ferrous industry

Sea salt

Motor vehicle

Road dust

Industry (sea port)

Coal CombustionCon

cen

trat

ion

(ug/

ug)

Fig. S4-4. Comparison of source profile for 8 to 10 sources of TSP using 22items in Incheon, Korea.

- 222 -

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10

020406080

100

020406080

100120

010203040506070

01020304050

020406080

100

01020304050

0102030405060

010203040506070

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10 05

101520253035

Residual oil combustion0

20406080

100

Secondary sulfate

Secondary nitrate

Soil

Non-ferrous industry

Sea salt

Motor vehicle

Road dust

Industry (sea port)

Coal Combustion

Mas

s co

nce

ntr

atio

n(u

g/m

3 )

Fig. S4-5. Comparison of timeseries plot for 8 to 10 source contribution of TSP using 22items in Incheon, Korea.

- 223 -

- PMF analysis for Organic Carbon using 41 molecular markers

(a)

Number of factors

3 4 5 6 7 8 9 10 11 12 13 14

Q-v

alu

e (c

han

ge o

f fa

ctor

)

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

FPEAK

-1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

Q-v

alu

e (c

han

ge o

f F

PE

AK

)

48004900500051005200530054005500560057005800590060006100620063006400

Q-value (change of factor)Q-value (change of FPEAK)

(

b)

Number of factors

3 4 5 6 7 8 9 10 11 12 13 14

IM

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

IS

2

3

4

5

6

Rot

atio

nal

am

bu

gity

[m

ax(r

otm

at)]

0.0

0.1

0.2

0.3

0.4

IMISMax (rotmat)

Fig. S4-6. The diagonistic factor of PMF model using 41 molecular

markers only. (a) IM, IS, and rotational freedom as a function of the factors chosen in PMF, (b) Q-value for the different factor solutions and the change of “FPEAK” parameter.

- 224 -

Fig. S4-7. Comparison of source profile for 7 to 9 sources of organic carbon using 41 organic marker species in Incheon, Korea

EC

N-H

EX

AC

O

N-H

EPT

AC

ON

-OC

TA

CO

N_N

ON

AC

ON

_TR

ICO

N_D

OT

RIC

OFL

UO

RA

TH

EN

EPY

RE

NE

B(A

)FB

(B)F

B(K

)FB

(A)P

BG

HIP

EC

HR

YS

NIN

CD

PYB

A30

NH

AB

_HO

PH

OP

AN

EC

HO

LE

STA

NE

N_H

EX

DA

N_H

EPD

AN

_OC

TD

AN

_NO

ND

AN

_EIC

OA

N_H

EN

EIC

OA

N_T

RIC

OSA

N_T

ET

RA

CO

SA9H

-FL

UO

RE

NE

CH

OL

EST

ER

OL

LO

VO

GU

CO

SAN

RE

TE

NE

SQU

AL

EN

ED

B P

HT

HA

BE

NZ

OT

HIO

NA

PH

TH

FUR

BU

TA

ND

IOA

PEN

TA

DIO

AN

ON

AN

DIO

Aci

s-PI

NO

IC A

CID

OlL

EIC

AC

IDD

EH

YD

RO

AB

IE A

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

1e-5

1e-4

1e-3

1e-2

1e-1

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

1e-5

1e-4

1e-3

1e-2

1e-1

1e+01e-5

1e-4

1e-3

1e-2

1e-1

1e+01e-5

1e-4

1e-3

1e-2

1e-1

1e+0

EC

N-H

EX

AC

O

N-H

EPT

AC

ON

-OC

TA

CO

N_N

ON

AC

ON

_TR

ICO

N_D

OT

RIC

OF

LU

OR

AT

HE

NE

PYR

EN

EB

(A)F

B(B

)FB

(K)F

B(A

)PB

GH

IPE

CH

RY

SNIN

CD

PYB

A30

NH

AB

_HO

PH

OPA

NE

CH

OL

EST

AN

EN

_HE

XD

AN

_HE

PD

AN

_OC

TD

AN

_NO

ND

AN

_EIC

OA

N_H

EN

EIC

OA

N_T

RIC

OSA

N_T

ET

RA

CO

SA9H

-FL

UO

RE

NE

CH

OL

EST

ER

OL

LO

VO

GU

CO

SA

NR

ET

EN

ESQ

UA

LE

NE

DB

PH

TH

AB

EN

ZO

TH

ION

APH

TH

FUR

BU

TA

ND

IOA

PE

NT

AD

IOA

NO

NA

ND

IOA

cis-

PIN

OIC

AC

IDO

lLE

IC A

CID

DE

HY

DR

OA

BIE

A

1e-5

1e-4

1e-3

1e-2

1e-1

1e+0

1e-51e-41e-31e-21e-11e+01e+1

Combustion (LMW-PAHs)

Biomass burning

Vegetative detritus (n-Alkane)

Benzo(a)pyrene

SOA1

SOA2

Combustion (HMW-PAHs)

Motor vehicle

Meat cooking

Con

cen

trat

ion

(ng/

ng)

- 225 -

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10

0100020003000400050006000

0500

1000150020002500

0200400600800

10001200

0200400600800

1000120014001600

0100020003000400050006000

02000400060008000

1000012000

02000400060008000

1000012000

02000400060008000

100001200014000

Residual oil combustion

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10 0

2000

4000

6000

8000

Combustion (LMW-PAHs)

Biomass burning

Vegetative detritus (n-Alkane)

Benzo(a)pyrene

SOA1

SOA2

Combustion (HMW-PAHs)

Motor vehicle

Meat cooking

Mas

s co

nce

ntr

atio

n(n

g/m

3 )

Fig. S4-8. Comparison of timeseries plot for 7 to 9 source contribution of organic carbon using 41organic marker species in Incheon, Korea.

- 226 -

FLA/(FLA + PYR)

0.3 0.4 0.5 0.6 0.7 0.8 0.9

IcdP

/(Ic

dP +

Bgh

iP)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

PetroleumCombustion

Coal/BiomassCombustion

PetroleumCombustion

Coal/BiomassCombustion

Fig. S4-9. Plot of FLA/(FLA + PYR) against IcdP/(IcdP + BghiP) for PAH source diagnostics. Two dash lines represent the thresholds for petroleum combustion and coal/biofuel burning.

- 227 -

- PMF analysis for TSP using 63 compounds

(traditional 22items + 41 molecular markers)

(a)

Number of factors

3 4 5 6 7 8 9 10 11 12 13 14

Q-v

alu

e (c

han

ge o

f fa

ctor

)

0100020003000400050006000700080009000

1000011000120001300014000

FPEAK

-1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

Q-v

alu

e (c

han

ge o

f F

PE

AK

)

7400750076007700780079008000810082008300840085008600870088008900900091009200

Q-value (change of factor)Q-value (change of FPEAK)

(b)

Number of factors

3 4 5 6 7 8 9 10 11 12 13 14

IM

0.0

0.5

1.0

1.5

2.0

2.5

3.0

IS

2

3

4

5

6

7

Rot

atio

nal

am

bu

gity

[m

ax(r

otm

at)]

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

IMISMax (rotmat)

Fig. S4-10. The diagonistic factor of PMF model using traditional 21items couple with 41 molecular markers. (a) IM, IS, and rotational freedom as a function of the factors chosen in PMF, (b) Q-value for the different factor solutions and the change of “FPEAK” parameter.

- 228 -

OC

EC

Na

NH

4K C

lN

O3

SO

4M

gA

lP C

aT

iV C

rFe M

nN

iC

uZ

nA

sPb N

-HE

XA

CO

N

-HE

PT

AC

ON

-OC

TA

CO

N_N

ON

AC

ON

_TR

ICO

N_D

OT

RIC

OFl

uora

nthe

nePy

rene

B(A

)FB

(B)F

B(K

)FB

(A)P

BG

HIP

EC

HR

YS

NIN

CD

PY

BA

30N

HA

B_H

OP

HO

PA

NE

CH

OL

ES

TA

NE

N_H

EX

DA

N_H

EP

DA

N_O

CT

DA

N_N

ON

DA

N_E

ICO

AN

_HE

NE

ICO

AN

_TR

ICO

SA

N_T

ET

RA

CO

SA

9H-F

LU

OR

EN

EC

HO

LE

ST

ER

OL

LO

VO

GU

CO

SAN

RE

TE

NE

SQ

UA

LE

NE

DB

PH

TH

AB

EN

ZO

TH

ION

AP

HT

HF

UR

BU

TA

ND

IOA

PE

NT

AD

IOA

NO

NA

ND

IOA

cis-

PIN

OIC

AC

IDO

lLE

IC A

CID

DE

HY

DR

OA

BIE

A

1e-61e-51e-41e-31e-21e-11e+0

1e-61e-51e-41e-31e-21e-11e+0

1e-61e-51e-41e-31e-21e-11e+0

1e-61e-51e-41e-31e-21e-11e+0

1e-61e-51e-41e-31e-21e-11e+01e-61e-51e-41e-31e-21e-11e+01e-61e-51e-41e-31e-21e-11e+0

OC

EC Na

NH

4 K Cl

NO

3S

O4

Mg Al P

Ca Ti V Cr

Fe

Mn Ni

Cu

Zn

As

PbN

-HE

XA

CO

N

-HE

PT

AC

ON

-OC

TA

CO

N_N

ON

AC

ON

_TR

ICO

N_D

OT

RIC

OFl

uora

nthe

neP

yren

eB

(A)F

B(B

)FB

(K)F

B(A

)PB

GH

IPE

CH

RY

SN

INC

DP

YB

A30

NH

AB

_HO

PH

OP

AN

EC

HO

LE

ST

AN

EN

_HE

XD

AN

_HE

PD

AN

_OC

TD

AN

_NO

ND

AN

_EIC

OA

N_H

EN

EIC

OA

N_T

RIC

OS

AN

_TE

TR

AC

OS

A9H

-FL

UO

RE

NE

CH

OL

ES

TE

RO

LL

OV

OG

UC

OS

AN

RE

TE

NE

SQ

UA

LE

NE

DB

PH

TH

AB

EN

ZO

TH

ION

AP

HT

HF

UR

BU

TA

ND

IOA

PE

NT

AD

IOA

NO

NA

ND

IOA

cis-

PIN

OIC

AC

IDO

lLE

IC A

CID

DE

HY

DR

OA

BIE

A

1e-61e-51e-41e-31e-21e-11e+0

1e-61e-51e-41e-31e-21e-11e+0

Primary source(Sea salts/Industry/Road dust)

Biomass burning/Soil

Motor vehicle/Non-ferrous industry

Vegetative detritus (n-Alkane)

SOA1

Benzo(a)pyrene

Combustion (LMW-PAHs)

SOA2/Secondary nitrate

Combustion (HMW-PAHs)

Con

cen

trat

ion

(ng/

ng)

Fig. S4-11. Comparison of source profile for 7 to 9 sources of TSP

using 63items in Incheon, Korea.

- 229 -

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10

0

50000

100000

150000

200000

0

20000

40000

60000

80000

020000400006000080000

100000120000

0

20000

40000

60000

80000

0

20000

40000

60000

80000

020000400006000080000

1000000

10000

20000

30000

40000

0300006000090000

120000150000180000

6/1/09 7/1/09 8/1/09 9/1/09 10/1/09 11/1/09 12/1/09 1/1/10 2/1/10 3/1/10 4/1/10 5/1/10 6/1/10 0

20000400006000080000

100000

Primary source(Sea salts/Industry/Road dust)

Biomass burning/Soil

Motor vehicle/Non-ferrous industry

Vegetative detritus (n-Alkane)

SOA1

Benzo(a)pyrene

Combustion (LMW-PAHs)

SOA2/Secondary nitrate

Combustion (HMW-PAHs)

Mas

s co

nce

ntr

atio

n(n

g/m

3 )

Fig. S4-12. Comparison of time series plot for 7 to 9 source contribution of TSP using 63items in Incheon, Korea.

- 230 -

- 231 -

Appendix

Association of Fine Particulate Matter from

Different Sources with Daily Motality

Epidemiological studies have provided evidence for an association

between exposure to ambient particulate matter (PM) and increased

mortality and morbidity (Dockery et al., 1993; Pope et al., 1995; Laden

et al., 2000; Ito et al., 2011; Cao et al., 2012; Son et al., 2012). PM2.5 is

known to vary in chemical composition with source. Thus, PM toxicity

may well depending on its source and chemical composition. However,

the specific sources and constituents responsible for the adverse effects

of PM2.5 have not been investigated, although a few studies have

considered PM metals in relation to oxidative stress (Bae et al. 2010) or

to lung function (Hong et al. 2007). Therefore, understanding and

characterizing the health effects of PM components and sources is

crucial for effective regulatory control of the particulate matter

pollution.

In the past, studies on health impact assessment of PM2.5 were carried

out only for a few items such as heavy metals, organic carbon, and ion

component. With the development of analytical techniques for the

analysis of organic ingredients, the study of the association health

effects depending on the individual components of organic carbon

becomes available and is believed to be necessary.

- 232 -

The purpose of this study is to estimate the cause-specific mortality

effects of PM2.5 sources and its constituents in Incheon, Korea, for

August 2009 through October 2010. We applied a Poisson generalized

linear model, adjusting for time, temperature, and relative humidity to

investigate the association between risk of mortality and PM2.5 sources

and its constituents.

Methods

Motality data

Daily motality data from 2009 to 2010 employed in our analysis were

obtained from the Korea National Statistical Office. All mortality data

provide non-confidential information on individuals including state of

death, county of death, age, gender, date of death, and primary cause of

death. For this study, we examined the motality data and classified into

death causes using the International Classification of Disease 10th

Revision (ICD10). The particular causes examined in this study were

non-accidental causes (10th revision ICD codes A00-R99), respiratory

disease (ICD10 J00–J98), and cardiovascular disease (ICD10 I01–I99).

Statistical analysis

We previously identified relative contribution of each sources to total

PM2.5 mass using PMF model. Daily counts of deaths and PM pollution

levels were linked by date and analyzed with time–series analyses (Bell

- 233 -

et al. 2004). To estimate the relationship between daily mortality and

PM2.5 sources and chemical constituents, we applied a Poisson

generalized linear model with natural cubic splines for time and

meteorology.

ln[E(Yt)]= βj0 + βjΧj

t + ajDOWt + ns(timet) + ns(temperaturet) +

ns(humidityt)

where E(Yt) is the expected number of deaths on day t; βj0 is the

model intercept for exposure j (i.e., PM2.5 total mass or a particular

chemical component); aj is the vector of regression coefficients for day

of the week for model of exposure j; DOWt is the categorical variable

for day of the week; ns(timet) is the natural cubic spline of a variable

representing time to adjust for long-term trends and seasonality, with 6

degrees of freedom (df) per year; ns (temperaturet) is the natural cubic

spline of current-day temperature on day t, with 3 df; and ns(humidityt)

is the natural cubic spline of current-day humidity on day t, with 3 df.

The variable Χjt represents the level of exposure j on day t, where the

exposure is PM2.5 total mass or a specific component. The variable βj

denotes the relationship between exposure j and mortality risk.

To examine the temporal relationship of PM2.5 constituents with

mortality, we considered PM2.5 source contribution with lag structures

of exposure on the same day (lag 0) and up to 3 days before (lag 0, lag

1, lag 2, and lag 3). Analyses were conducted with SAS (version 9.2;

SAS Institute Inc., Cary, NC, USA). To compare relative risk impact of

- 234 -

PM2.5 sources, Results are expressed as the percentage change in

mortality with 95% confidence interval (CI) per interquartile range

(IQR) increase of PM2.5 source contribution and each PM2.5 chemical

constituent. Statistical significance was defined as p < 0.05.

Results and Discussion

We identified 11,690 deaths that occured between 1 June 2009 and 31

May 2010 in our study population. On average, 31.9 nonaccidental

deaths occurred per day, including 8.3 from cardiovascular diseases and

2.7 from respiratory diseases. The mean daily average temperature and

humidity in Incheon were 12.1°C and 67.9%, respectively. Fig. A1

summarizes the quantative risk effect for all-cause, cardiovascular, and

respiratory mortality using single-day lags 0–3 of PM2.5 sourecs. For

the eight source factors of PM2.5 identified in sampling area, we found

some associations between PM2.5 sources and specific-cause mortality.

Nonaccident-cause mortality was strongly associated with industry (sea

port), motor vehicle1, 2 and secondary sufate (p < 0.05). The effect

estimates of PM2.5 sources varied by lag structures and mortality out-

comes. The Estimated percent increases in relative risk (ER) for

nonaccident-cause mortality was 7.25% [95% confidence interval (CI),

3.12 – 11.37%] at 1 lag day and 7.51% [95% confidence interval (CI),

3.34 – 11.68%] at 2 lag day for an interquartile range (IQR) increase in

industry source(sea port) factor. An IQR increase in motor vehicle1

- 235 -

(3.76 μg/m3) was associated with a 5.38% increase (95% confidence

interval (CI), 2.76 – 7.99%) in nonaccident-cause mortality on the same

day. An IQR increase in motor vehicle 2 (3.00 μg/m3) factor also

exhibited the moderate association for nonaccident-cause mortality

(%ER = 3.98%; 95% CI, 1.68 – 6.28) at lag 0 day and the strong

association (%ER = 4.57%; 95% CI, 2.31 – 6.82%) at lag 3 day,

respectively. Nonaccident-cause mortality increased by 6.36% (CI, 3.94

– 8.78%), with an IQR increase of the secondary sulfate source factor.

For cardiovascular mortality, we found the effect estimates of PM2.5

sources (soil, motor vehicle 2, and secondary sulfate) that were

significantly associated with at least one outcome and lag period. An

IQR increment in the 1-day lagged soil factor (2.07 μg/m3) was

associated with 8.91% increase (95% CI, 0.57 – 17.26%) in

cardiovascular mortality. There was a stronger relationship with

cardiovascular deaths, with an adjusted OR of 10.55% (95% CI, 6.02 –

15.08%) for IQR increase in motor vehicle 2 at lag 3. Secondary

products of fuel combustion (SO42- and NH4

+) also exhibited the

stronger associations with cardiovascular mortality than did other

sources. An IQR increment in the concentrations of secondary sulfate

(6.33 μg/m3) corresponded to a 12.70% (95% CI, 8.01 – 17.40%) at lag

0 day, 13.94% (95% CI, 9.30 – 18.57%) at lag 2 day, and 8.53% (95%

CI, 3.84 – 13.24%) at lag 3 day increase of cardio vascular mortality,

respectively.

For respiratory mortality, only industry factor showed associations

with respiratory mortality at the p < 0.10 level. Excess risk for

- 236 -

respiratory mortality was higher than the other mortality, but was not

statistically significant. The more detail descriptions for the risk of

PM2.5 sources identified in this study are expected to be conducted in

future.

- 237 -

0 1 2 3-10

-5

0

5

10

0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3

Non-accidential death

Respiratory death

Cardiovascular death0 1 2 3 0 1 2 3 0 1 2 3

0 1 2 3-20-15-10

-505

101520

0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3

Combustion

0 1 2 3-30-20-10

0102030

Soil0 1 2 3

Industry

0 1 2 3

Motor veh

icle1

0 1 2 3

Secondary

nitrate

0 1 2 3 0 1 2 3

Biomass0 1 2 3 0 1 2 3

Sea sal

t0 1 2 3

Motor veh

icle2

Secondary

sulfa

te

****

**

****

*Per

cen

t E

xces

s R

isk

per

IQ

R

** ** * *** **

*

Fig. A1. Estimated percent increases [mean (95% CI)] in total, cardiovascular, and respiratory mortality per IQR increase in source contribution on the current day (lag 0) or the previous 1–3 days (lags 1, 2, and 3), adjusted for temporal trend, temperature, and relative humidity.

- 238 -

References

Bae S., Pan X.C., Kim S.Y., Park K., Kim Y.H., Kim H., 2010.

Exposures to particulate matter and polycyclic aromatic hydrocarbons and oxidative stress in schoolchildren. Environ mental Health Perspectives 118, 579-583.

Bell M.L., Samet J.M., Dominici F., 2004. Timeseries studies of

particulate matter. Annual Review Public Health 25, 247-280. Cao J., Xu H., Xu Q., Chen B., Kan H., 2012. Fine Particulate Matter

Constituents and Cardiopulmonary Mortality in a Heavily Polluted Chinese City. Environmental Health Perspectives 120, 374-378.

Dockery D.W., Pope C.A., Xu X., Spengler J.D., Ware J.H., Fay M.E.,

Ferris B.G., Speizer F.E., 1993. An association between air pollution and mortality in six U.S. cities. New England Journal of Medicine 329, 1753-1759.

Hong Y.C., Hwang S.S., Kim J.H., Lee K.H., Lee H.J., Lee K.H., 2007.

Metals in particulate pollutants affect peak expiratory flow of schoolchildren. Environmental Health Perspectives 115, 430-434.

Ito K., Robert Mathes R., Ross Z., Nadas A., Thurston G., Matte T.,

2011. Fine Particulate Matter Constituents Associated with Cardiovascular Hospitalizations and Mortality in New York City, Environmental Health Perspectives 119, 467-473.

Laden F., Neas L.M., Dockery D.W., Schwartz J., 2000. Association of

Fine Particulate Matter from Different Sources with Daily Mortality in Six U.S. Cities. Environmental Health Perspectives 108, 941-947.

Pope C.A, Thun M.J., Namboodiri M.M., Dockery D.W., Evans J.S.,

Speizer F.E., Heath C.W., 1995. Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. American Journal of Respiratory and Critical Care Medicine 151, 669- 674.

Son J.Y., Lee J.T., Kim K.H., Jung K., Bell M.L., 2012.

Characterization of Fine Particulate Matter and Associations between Particulate Chemical Constituents and Mortality in Seoul, Korea Environmental Health Perspectives 120, 872-878.

- 239 -

Chapter 5

Conclusions

Airborne particulate matter (PM) has adverse effects on human

morbidity and mortality, visibility, climate change, and materials. Even

though the main composition of fine particles has been reported in

several studies, only 10~20% of the organic compounds has been

quantified as individual organic species. In order to develop effective

strategy for reducing fine particle pollution, it is very important to

analyze the components and evaluate the source of particulate matter.

The purpose of this study is to evaluate the characteristics of particulate

matter and determine sources using molecular markers (MM).

To find out the characteristics of PM in Incheon, PM samples were

collected for 1 year and analyzed for its composition. One hundred and

twenty samples for fine particle (PM2.5) and TSP were collected in

Incheon area from 2009 to 2010. The collected samples were analyzed

for the main ingredients such as OC, EC, ions, heavy metals and other

major components. In addition, water-soluble organic carbon (WSOC)

and the main ingredients of organic aerosol (OA) were analyzed. The

results by this analysis were used as input data of the source

apportionment model, positive matrix factorization (PMF), which has

been widely used as a basic model, unlike MM.

- 240 -

The first study was performed to elucidate the characteristics, sources,

and distribution of PM2.5 and carbonaceous species in Incheon, Korea.

To do this, we analyzed the major components of PM2.5 such as OC, EC,

ionic, and metallic species in individual samples. Furthermore, organic

species and WSOC were evaluated to characterize the influence of

individual PM2.5 components. The average PM2.5 concentration (41.9 ±

9.0 μg/m3) exceeded the annual level set by the United States’ National

Ambient Air Quality Standards (15 μg/m3). The major fraction of PM2.5

consisted of ionic species (accounting for 38.9 ± 8.8%), such as NO3-,

SO42-, and NH4

+, as well as organic carbon (OC) (accounting for 18.9 ±

5.1%). We also analyzed the seasonal variation in PM2.5 and secondary

aerosols such as NO3- and SO4

2- in PM2.5. As an important aerosol

indicator, WSOC (mean 4.7 ± 0.8 μg/m3, 58.9 ± 10.7% of total OC)

showed a strong relationships with NO3-, SO4

2-, and SOC (R2 = 0.56,

0.67, and 0.65, respectively), which could represent favorable

conditions for SOC formation during the sampling period. Among the

individual organic aerosols measured, n-alkanes, n-alkanoic acids,

levoglucosan, and phthalates were major components, whereas

polycyclic aromatic hydrocarbons (PAHs), oxy-PAHs, hopanes, and

cholestanes were minors. The concentration of organic compounds

during smoggy periods was higher than that of non-event periods. The

concentration of n-alkane and n-alkanoic acid species during the

smoggy periods was 10-14 times higher than that of the normal period.

Using principal component analysis coupled with multiple linear

regression analysis, we identified motor vehicle/sea salt, secondary

organic aerosols, combustion, biogenic/meat cooking, and soil sources

- 241 -

as primary sources of PM2.5

In the second study, on the basis of the analyzed chemical species in

the PM2.5 samples, the sources of PM2.5 were identified using a positive

matrix factorization (PMF) model. And finally nine sources of PM2.5

were determined. The major sources of PM2.5 were secondary nitrate

(25.4%), secondary sulfate (19.0%), motor vehicle 1 (14.8%) with a

lesser contribution from industry (8.5%), motor vehicle 2 (8.2%),

biomass burning (6.1%), soil (6.1%), combustion and copper

production emissions (6.1%), and sea salt (5.9%) respectively. From a

paired t-test, it was found that the samples during the yellow sand

periods were characterized by higher contribution from soil sources (p

< 0.05). Furthermore, the possible source areas of PM2.5 emissions were

determined by using the conditional probability function (CPF) and the

potential source contribution function (PSCF). CPF analysis identified

the motor vehicles and sea salt as possible local sources of PM2.5. PSCF

analysis indicated that the possible sources for secondary particles

(sulfate and nitrate) were related to the major industrial complexes in

China.

In the final study, MM-PMF was preformed to evaluate the sources of

PM and organic carbons. PMF model was carried out and three

different analysis items were categorized. For example, first, 22 items

such as OC, EC, ionic compounds and trace metals in TSP, second, 41

items in organic compounds, and third, 63 items in both TSP (22) and

organic compounds (41). The nine sources of TSP were identified by

the PMF analysis using 22 items. The major sources of TSP were motor

- 242 -

vehicle (17.4%), sea salt (14.0%), secondary sulfate (13.7%), soil

(12.8%), combustion (11.6%), and industry (10.8%) with the lesser

contributions from non-ferrous industry (6.8%), secondary nitrate

(5.4%), and road dust (3.6%). From the molecular marker-PMF

analysis including only organic marker compounds (41species), the

eight-sources were separated as follows: The resolved eight sources

included combustion (LMW-PAHs), biomass burning, vegetative

detritus (n-Alkane), benzo(a)pyrene, SOA1, SOA2, combustion

(HMW-PAHs), and motor vehicle. Among them, secondary organic

aerosol, PAHs, and motor vehicle were evaluated as three major

sources of organic carbon sources. The source contribution of organic

aerosol resolved by PMF model showed different characteristics

depending on the season. The vegetative detritus and motor vehicle

were increased during the summer season by the increase in

biogenic/photochemical activity. However, most of the other organic

sources were prominent in the winter season by the increase in the level

of air pollution emission and atmospheric stability. In addition, CPF

results identified possible locations for local source, which included

primary sources, biomass burning/soil, motor vehicle/non-ferrous

industry, vegetative detritus (n-alkane), benzo(a)pyrene, and

combustion (PAHs).

Through this study, various sources of PM were evaluated by using

MM-PMF analysis. Sea port and combustion sources were found as the

additional PM2.5 sources that did not appear in other areas. The

contribution of sea salt and soil pollutants in the coarse particle was

- 243 -

two times as high as those in the fine particle. However, secondary

organic and inorganic species generated by the oxidation reaction of

primary pollutants occupied a very large portion of fine particulate

matters. As a result, secondary oxidation reaction was considered as a

primary cause of PM2.5. Therefore, it is very important to explain the

process for finding out the sources of these secondary pollutants and to

evaluate those sources in detail. Even though local sources existed,

PSCF analysis indicated that a certain part of pollutants such as

secondary aerosol, soil, and biomass burning have been associated with

long-range transport. Another important fact was that SOA, motor

vehicle, and combustion (PAHs) were identified as a major source of

organic carbon. Finally, there was an increase by more than 10 times in

particulate organic pollutants in the fine dust during the smog period.

This study has some important implications; first, it is first attempt to

analyze and evaluate the organic constituents of particulate matter in

Korea. Second, we found the chemical composition of particulate

matter for more than one hundred organic and inorganic species. And

finally, we could identify the contribution and major sources for

particulate compounds through receptor model. This kind of

characterization process for particulate organic aerosols will be a key

foundation to understand the importance of the issue and be helpful to

provide possible solutions which are relevant to PM reduction measures

in the future.

- 244 -

Future Research

The research will be continued to investigate the composition of the

particulate organic carbon using GCⅹGC TOF/MS. Future Research

will focus on finding the risks of exposure to PM2.5 and chemical

species on mortality in Incheon, Korea. Another research is still in

progress on the particle size distribution of specific pollutants such as

organic and inorganic constituents. In addition, the source of organic

carbon in ultra-fine particles attempted to evaluate using molecular

markers-CMB model (MM-CMB), one of the receptor models. We

have already operated on the real-time ion mointoring system for ionic

species as well as semi-continuous OC/EC field instrument for

particulated carbon measurement. From this measurement, we can

obtain more important information on the characteristics of fine particle.

- 245 -

국문초록

본 연구는 대기오염물질 중에 있는 다양한 입자상 오염

물질과 오염원의 특성을 평가하고자, 2009-2010 년간 인천

지역의 초미세입자(PM2.5)와 TSP 에 대한 조사를 하였다. 채취

된 시료는 OC, EC, 이온성분, 중금속 등 주요 구성 성분을

조사하였으며, 유기에어로졸(OA) 주요성분과 이차유기에어

로졸(SOA) 의 주요성분으로 알려져 있는 WSOC 를 분석을 함

으로써 유기에어로졸의 성분특성 및 SOA 의 생성과 관련된

항목들에 대하여 살펴보았다. 또한 수용모델을 이용하여

미세먼지와 총먼지의 오염원을 평가하였고, 오염원 특성을

비교 분석하였다. 또한, 입자상물질의 유기탄소의 주요성분을

이용하여 유기탄소의 주요 오염원을 평가하였다.

첫 번째 연구에서는 PM2.5 성분분석 결과를 중심으로 성분

특성에 대한 분석을 하였다. 조사결과, PM2.5 농도는 연평균

농도로 약 41.9ug/m3 수준이었으며,

성분별 평균농도는 OC, EC,

이온성분, 중금속의 농도가 각각 7.9ug/m3, 1.8ug/m

3, 18 ug/m

3,

2.6ug/m3

으로, 유기탄소와 이온성분이 주요 구성성분으로

조사되었다. PM2.5 농도는 계절별로는 겨울철에 가장 높았으며,

봄, 가을, 여름 순의 농도를 보였다. 이온성분은 계절적으로

겨울철에는 NOX, NH4+ 등 오염물질 농도 증가와 낮은 혼합고,

온도 등에 의해 NO3- 성분(6.7ug/m

3, 연평균 농도 4.6ug/m

3)이,

봄철에는 광화학반응의 영향으로 SO42-가 높은 농도(5.5ug/m

3,

연평균 농도 5.1ug/m3)를 보였다. 이는 국내에서 조사된 연구

- 246 -

결과와 비슷한 경향을 보인 것으로 분석되었다. 또한, 수용성

탄소성분인 WSOC 의 평균농도는 4.7ug/m3였고, 유기탄소 중

평균 58.9%를 차지하는 것으로 조사되었다. WSOC 는

OC/EC 비를 이용하여 계산된 SOC 및 NO3- 등 이차오염물질의

지표성분간에 비교적 높은 연관성 (R2

계수, 0.4-0.6)을

나타내어 유기탄소 중 WSOC 성분이 이차유기 오염물질의

주요성분임을 보여주었다. 유기 에어로졸의 개별성분을

분석하기 위하여, 채취량이 미량인 PM2.5 시료(quater filter)는

월단위로 합한 다음 전 처리와 농축과정을 거쳐 GC×GC-

TOF/MS 를 이용하여 alkanes, alkanoic acids, alkenoic acids,

PAHs, hopanes, cholestanes, carboxylic acids 등 organic

aerosol 의 주요성분에 대하여 정량-정성분석을 하였다. Species

그룹별로는 alkanoic acid, alkane 계열 성분이 각각 77,

119ng/m3

으로 전체의 65%을 차지하는 주요성분이었으며,

PAHs(3.4%), carboxylic acid(2.6%), hopane, cholestane (1%

이하) 등은 미량성분으로 분석되었다. 계절별 로는 alkanoic

acid, alkane 계열은 가을철에 높았으며, PAHs, hopane,

cholestane 등 주로 연소 및 이동 오염원과 관련된 물질은

겨울철, carboxylic acid 계열의 물질은 SOA 와 관련된 물질로

여름철에 상대적으로 높은 편 이었다.

상기 분석된 자료를 토대로 평상시와 스모그(4 일), 황사

(8 일) 등 미세먼지오염도의 변화가 있는 기간동안 오염물질의

변화를 분석하였다. 분석결과, 스모그시 미세먼지 등 주요

성분은 약 1.7-2 배 가량 증가하였으나, 황사시에는 평상시와

별다른 차이를 보이지 않았다. 특징적인 점은 Organic

- 247 -

species 가 스모그 발생시 평상시 보다 10 배 이상 증가하였고,

스모그시 유기에어로졸 성분 (특히, alkane, alkanoic acid)이

뚜렷이 증가하는 것으로 분석으며 통계적으로 유의한 수준

이었다 (p < 0.05). 황사시에는 중금속의 농도가 평상시와

스모그 발생시 보다도 높게 조사되었다.

두번째 연구에서는 PM2.5 성분분석 결과를 바탕으로 오염원

평가를 위해 가장 보편적을 이용되고 있는 PMF 모델을

수행하였다. 분석결과, PM2.5의 주요 오염원은 secondary

nitrate (25.4%), secondary sulfate (19.0%), motor vehicle 1

(14.8%) 등 이었고, 그 이외에 industry (8.5%), motor vehicle 2

(8.2%), biomass burning (6.1%), soil (6.1%), combustion and

copper production emissions (6.1%), and sea salt (5.9%)등의

오염원을 확인하였다. 이러한 오염원의 각각의 계절별

시간대별 오염원의 기여도 패턴의 변화와 주변예상오염원의

예측결과를 비교하여 비교적 평가하였으며, 황사시 토양기원

중금속의 증가를 확인할 수 있었다 (p < 0.05). 또한 CPF

모델을 이용하여 motor vehicles, sea salt, combustion 등

지역적 오염원을 확인 할 수 있었다. 또한 PSCF 분석을

통해서는 이차오염물질인 secondary particles (sulfate and

nitrate), soil source, biomass burning 등 오염원의 장거리

이동에 의한 영향을 받고 있음을 확인 하였다.

세번째 연구에서는 입자상 물질인 TSP시료를 채취하여, 이온,

중금속, 탄소성분과 유기오염물질의 개별성분을 분석 하였으며,

이중에서 63개 molecular marker 성분을 수용모델에 적용하여

입자상물질과 유기탄소의 오염원을 각각 분석하였다. 우선,

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중금속, 이온, 탄소성분 등 22개성분을 이용하여 입자상물질

중 TSP의 8개의 오염원을 확인하였다. 분리된 주요오염원은

motor vehicle (17.4%), sea salt (14.0%), secondary sulfate

(13.7%), soil (12.8%), combustion (11.6%), and industry (10.8%)

이었고, non-ferrous industry (6.8%), secondary nitrate (5.4%),

and road dust (3.6%) 등의 기타오염원으로 확인되었다. 이와

더불어, 유기탄소의 오염원을 평가하기 위해, 유기탄소의

개별성분인 molecular marker 41개 항목을 이용하여 molecular

marker-PMF (MM-PMF) 분석을 수행 하였으며, combustion

(LMW-PAHs), biomass burning, vegetative detritus (n-alkane),

benzo(a)pyrene, SOA1, SOA2, combustion (HMW-PAHs), motor

vehicle 등 8개의 오염원을 확인하였다. 이들 유기탄소의

오염원은 주로 SOA, PAHs, motor vehicle 등이 주요

오염원으로 평가되었다. 특히, 유기탄소 41개 성분을 이용하여

유기탄소의 오염원을 평가를 함으로써 biomass burning,

vegetative detritus (n-alkane), SOA 등 세가지 오염원을

추가적으로 분리하였고, 이에 대한 평가를 할 수 있었다. 또한,

이들 오염원은 계절별로 특성을 보이고 있었다. vegetative

detritus (n-alkane)와 SOA1, SOA2는 광화학적 반응과

생물대사 활동이 활발한 봄철과 여름철 높은 특징을 보였고,

나머지 PAHs와 관련된 오염원은 난방, 차량 등 각종 연소로

인한 배출량이 큰 겨울철에 높은 특징을 보였다. 이

연구에서는 수 백가지의 성분으로 구성되어 있으며, 생성기전

의 복잡한 것으로 알려져 있는 유기탄소의 오염원을 분리하고

설명하는 중요한 성과를 거두었다.

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이 연구결과를 정리하면, 국내 다른연구에서는 확인되지

않았던 항만(sea port), 북서지역과 남동 지역에 석탄/유류

연소오염원이 미세먼지의 오염원으로 나타났다. 또한,

조대먼지의 경우 토양과 해염오염원이 미세먼지 보다

상대적으로 높은 특징을 보였으며, 주변 도로먼지 및 북서

방향에 위치한 비철금속산업이 특징적인 오염원으로 평가

되었다. 특히, 미세먼지와 같은 입자상 물질에서는 유기탄소

및 이온성분이 2차적인 산화반응에 의해 생성되는 이차오염

물질이 매우 큰 비중을 차지하였다. 이러한 이차 오염물질의

생성과정 및 발생원에 대한 평가는 앞으로 매우 중요한

과제라 생각된다. 또한 지역적인 오염원, 뿐만 아니라

이차오염물질과 토양, 생체연소 오염원은 일정부분 장거리

이동에 의한 영향도 본 연구에서 확인되었으며, 향후 이를

정량화하여 평가하는 것에 대한 연구가 필요하다 하겠다.

그리고, 유기탄소의 오염원을 확인한 결과, SOA, motor

vehicle, and combustion (PAHs) 오염원이 매우 큰 비중을

차지하고 있는 것으로 나타나 이에 대한 좀 더 세부적인

연구가 필요한 것으로 나타났다. 채취시기 중에서는 스모그

기간의 미세먼지 중 알칸, 유기산 등 일부 유기탄소 성분이

미세먼지 농도 증가수준보다 매우 높게 나타나고 있어,

스모그시 이러한 미세먼지 중 유기탄소의 성분변화에 대한

평가 및 지속적인 모니터링 통한 관리가 매우 중요하다

하겠다.

본 연구결과는 입자상 물질 중 국내에서 연구성과가 거의

없는 유기성분 등 다양한 성분에 대한 성분특성을 평가하였고,

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오염원을 평가하였으며 특히, 초미세먼지 중 다양한 성분의

특성과 오염원을 해석하였다. 이러한 연구는 초미세먼지의

거동과 관련된 자세한 정보를 제공함으로써 대기질 정책

수립에 중요한 연구기반이 될 것으로 기대된다. 향후 분석된

성분과 오염원의 평가 기반을 바탕으로 건강영향 평가에 접목

하므로서 인체위해도를 관련한 연구도 진행 한다면 환경

보건학적으로 의미있는 결과를 제시 할 수 있을 것이다.

주요어: PM2.5, positive matrix factorization (PMF), molecular

markers (MM), GC×GC-TOFMS, conditional probability

function (CPF), potential source contribution function

(PSCF).

학 번: 2002-30844