Atmospheric aerosol composition and source apportionments to aerosol in southern Taiwan

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Atmospheric Environment 40 (2006) 4751–4763

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Atmospheric aerosol composition and source apportionments toaerosol in southern Taiwan

Ying I. Tsaia,�, Chien-Lung Chenb

aDepartment of Environmental Engineering and Science, Chia Nan University of Pharmacy and Science, 60, Sec. 1,

Erh-Jen Rd., Jen-Te, Tainan 717, Taiwan, ROCbDepartment of Finance, Fortune Institute of Technology, No. 1-10, Nwongchang Rd., Daliao, Kaohsiung 831, Taiwan, ROC

Received 5 January 2006; accepted 7 April 2006

Abstract

In this study, the chemical characteristics of winter aerosol at four sites in southern Taiwan were determined and the

Gaussian Trajectory transfer coefficient model (GTx) was then used to identify the major air pollutant sources affecting the

study sites. Aerosols were found to be acidic at all four sites. The most important constituents of the particulate matter

(PM) by mass were SO42�, organic carbon (OC), NO3

�, elemental carbon (EC) and NH4+, with SO4

2�, NO3�, and NH4

+

together constituting 86.0–87.9% of the total PM2.5 soluble inorganic salts and 68.9–78.3% of the total PM2.5–10 soluble

inorganic salts, showing that secondary photochemical solution components such as these were the major contributors to

the aerosol water-soluble ions. The coastal site, Linyuan (LY), had the highest PM mass percentage of sea salts, higher in

the coarse fraction, and higher sea salts during daytime than during nighttime, indicating that the prevailing daytime sea

breeze brought with it more sea-salt aerosol. Other than sea salts, crustal matter, and EC in PM2.5 at Jenwu (JW) and in

PM2.5–10 at LY, all aerosol components were higher during nighttime, due to relatively low nighttime mixing heights

limiting vertical and horizontal dispersion. At JW, a site with heavy traffic loadings, the OC/EC ratio in the nighttime fine

and coarse fractions of approximately 2.2 was higher than during daytime, indicating that in addition to primary organic

aerosol (POA), secondary organic aerosol (SOA) also contributed to the nighttime PM2.5. This was also true of the

nighttime coarse fraction at LY. The GTx produced correlation coefficients (r) for simulated and observed daily

concentrations of PM10 at the four sites (receptors) in the range 0.45–0.59 and biases from �6% to �20%. Source

apportionment indicated that point sources were the largest PM10 source at JW, LY and Daliao (DL), while at Meinung

(MN), a suburban site with less local PM10, SOx and NOx emissions, upwind boundary concentration was the major PM10

source, followed by point sources and top boundary concentration.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: PM2.5; PM2.5–10; Spatial variations; Chemical composition; Source–receptor relationship

e front matter r 2006 Elsevier Ltd. All rights reserved

mosenv.2006.04.029

ing author. Tel.: +886 6 266 0208;

9090.

ess: [email protected] (Y.I. Tsai).

1. Introduction

The composition of atmospheric aerosols is animportant factor in component deposition, cloudalbedo, and cloud condensation nuclei (Charlsonet al., 1987; Lukac, 1994; Facchini et al., 1999;Horvath, 2003; Nho-Kim et al., 2004; Kocifaj and

.

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ARTICLE IN PRESSY.I. Tsai, C.-L. Chen / Atmospheric Environment 40 (2006) 4751–47634752

Horvath, 2005), in the impact of aerosols on humanhealth (Chan and Lippmann, 1980; Braunfahrlan-der et al., 1992; Dockery and Pope, 1994; Popeet al., 1995; Berico et al., 1997; Horvath, 1996), andin visibility (Horvath et al., 1997; Tsai et al., 2003).Understanding the physical and chemical character-istics of atmospheric aerosols is therefore crucial.

The most influential components of atmosphericaerosols are sulfate and nitrate, which are involvedin most atmospheric reactions and react withcations such as NH4

+ and Na+. Another importantcomponent is the set of carbonaceous materials,which may contribute 20–40% of the PM2.5 mass ina highly polluted atmosphere (Rogge et al., 1993).Nunes and Pio (1993) pointed out that fine particlesin suburban areas may be as much as 76%carbonaceous materials.

Research undertaken in north-east Asia includesOhta and Okita’s 1990 study in Sapporo in Japanwhich revealed that the major chemical componentsof aerosol fine particles include EC, OC, SO4

2�,NO3�, NH4

+, Cl�, Na+, crustal matter and water. Inthe Seoul region of South Korea winter aerosolSO4

2�, NH4+ and NO3

� concentrations are higherthan in other seasons and constitute 26–38% of thePM2.5 mass (Lee et al., 1999). Yao et al. (2002)found that the major species in PM2.5 in the Beijingand Shanghai regions of China are SO4

2�, NH4+ and

NO3�: in Beijing, soluble ions were found to contain

44% SO42�, 25% NH4

+ and 16% NO3� and in

Shanghai, 46%, 18% and 17%, respectively. InHong Kong, NH4

+ and SO42� constituted 31% and

42%, respectively, of the PM2.5 water-solubleinorganic salts (Pathak et al., 2003). SO4

2�, NO3�,

NH4+ and carbonaceous materials combined repre-

sent 40–60% of the fine particle mass of highlypolluted aerosols in central Taiwan’s coastal regions(Tsai and Cheng, 1999, 2004). Lin (2002) assessedthe separate contributions of these components tothe mass of atmospheric aerosols in Kaohsiung insouthern Taiwan and reported that SO4

2�, NO3� and

NH4+ represent up to 90% of the soluble ions and

42.4% of the PM2.5 mass while total carbon (TC),that is EC plus OC, represents about 20.8% of thePM2.5 mass. The EC/TC ratio of atmosphericaerosol and source emissions may be regarded asthe index for secondary organic aerosol (SOA)formation, but in the long term it is carbon inprimary aerosol that contributes to the fine fraction(Turpin and Huntzicker, 1995).

In recent years, trajectory and receptor modelshave been used to evaluate the relationship between

pollution sources and receptor (e.g. Chen et al.,2002; Tsuang et al., 2002; Watson et al., 2002; Qinand Oduyemi, 2003), with a view to applying properand effective strategies for improving air quality(Streit and Guzman, 1996; Kaharabata et al., 2000).The circuit trajectory transfer-coefficient model(CTx) (Tsuang et al., 2002; Chen et al., 2002),based on a circuit-type parameterization (Tsuangand Chao, 1997, 1999), is able to quantify source–receptor relationships for primary pollutants andsecondary sulfate and nitrate from ground sources.Using the CTx, Tsuang et al. (2002) found that incentral Taiwan during PM10 episodes in 1998 linesources contributed 62% of the CO, 65% of theNOx and 21% of the nitrate aerosol, point sourcescontributed 46% of the SO2, 57% of the sulfateaerosol and 27% of the PM2.5, and area sourcescontributed 18% of the PM2.5 and 60% of thePM2.5–10. Tsuang (2003) modified this CTx forelevated pollutant levels using a new Gaussianplume formula, to create the Gaussian trajectorytransfer-coefficient model (GTx), with the advan-tage that source–receptor relationships can bedetermined in a single run.

Poor air quality has become a chronic problem inthe urban and industrial regions of southernTaiwan. Despite the status of PM2.5 and PM2.5–10

aerosols as major pollutants and the composition ofan aerosol being largely dependent on its sourcing,few studies of this region have addressed aerosolsource apportionment. In this study, therefore, theGTx (Tsuang et al., 2002, 2003a, b) was used toidentify the major air pollutant sources affectingsouthern Taiwan in winter, when pollution levelsare typically at their highest (Tsai et al., 2003; Tsaiand Kuo, 2005).

2. Materials and methods

2.1. Study region and sampling sites

The sampling sites (receptors), Meinung (MN),Jenwu (JW), Daliao (DL), and Linyuan (LY), areall located in Kaohsiung County in southernTaiwan, a mix of industrial and agricultural areasthat borders the largest city in the south, KaohsiungCity (Fig. 1). The site in MN, a small town, is in aless polluted area, with low industrial and vehicleemissions relative to the other sites. The other threesites are near industrial complexes with petrochem-ical and metal manufacturing as the major indus-tries. Additionally, the site in JW is located in the

ARTICLE IN PRESS

Fig. 1. Location of meteorological stations and air quality monitoring stations (triangles), receptors (triangle shown with adjacent

numbered circle: 1 ¼Meinung, 2 ¼ Jenwu, 3 ¼ Daliao, 4 ¼ Linyuan), background monitoring station (Henchung) and distribution of

PM10 emission rate (shaded area) in southern Taiwan. (a) MN, (b) JW, (c) DL, (d) LY.

Y.I. Tsai, C.-L. Chen / Atmospheric Environment 40 (2006) 4751–4763 4753

vicinity of a major intersection, between HighwayNo.1 and Highway No.10, and hence is subject torelatively high vehicle emissions. In DL, the site isnear an industrial complex of printing, pulp andpaper, chemical non-metal and metal manufactur-ing industries, while in LY it is located south of apetrochemical industrial complex and 100m fromthe coast. Fig. 1 also shows the location ofmeteorological and air quality monitoring stationsand annual PM10 emissions.

Pollutant emissions in the study region, home to60% of Taiwan’s petrochemical plants and over5000 factories, amounted to 39.6 ktons of TSP,17.4 ktons of PM10, 37.7 ktons of SOx, 48.9 ktons ofNOx, 55.0 ktons of CO, 4.5 ktons of methane, and72.7 ktons of NMHC (non-methane hydrocarbon)in 1997 (CTCI Corporation, 1999).

2.2. Sampling program

Aerosol samples were collected using one PM2.5

and one PM10 miniature personal environmentalmonitor (MSP) at each site, placed 3m apart on the

roof of a building about 10–15m above ground, setat an air flow rate of 10.070.1 Lpm and fitted with37mm diameter quartz (Pallflex 2500 QAT-UP)filter paper. The sampling period covered December2002–January 2003 when typical winter prevails inTaiwan. Simultaneous intensive sampling was un-dertaken for 5 continuous days in December 2002and again in January 2003 at all four sites, with asingle sample being collected either over the 11 hperiod 7:00 am–6:00 pm (‘‘daytime’’) or the 13 hperiod 6:00 pm–7:00 am (‘‘nighttime’’), producing atotal of 160 samples.

2.3. Chemical analysis and quality assurance

At the end of each sample period the filterpaper was weighed on an analytical balance(Mettler, Teldo AT261) and then cut into threepieces in preparation for chemical analyses of PM2.5

particles. A half of the filter paper was used forcationic and anionic analyses, a quarter for TCdetermination, and another quarter for EC deter-mination.


In preparation for cation and anion (SO42�, NO3

�,Cl�, NH4

+, Na+, K+, Mg2+ and Ca2+) analyses,the filter paper was placed in a PE bottle, 5.0mL ofpure water was added, and the contents were shakenin an ultrasonic vibrator (Branson 5210) for 90min.The liquid was then filtered through a 0.2 mm etheracetate filter and the filtrate was injected into aDionex DX-120 Ion Chromatograph (IC). Based onan average 7.2m3 air sample over 12 h, methoddetection limits (MDLs) were 0.021mgm�3 forSO4

2�, 0.035mgm�3 for NO3�, 0.030mgm�3 for Cl�,

0.045mgm�3 for NH4+, 0.035mgm�3 for Na+,

0.042mgm�3 for K+, 0.034mgm�3 for Mg2+ and0.025mgm�3 for Ca2+.

One of the quarters of filter paper allotted tocarbon analyses was heated in an oven at340–345 1C for 20min to remove OC (Tsai andCheng, 1999). Subsequently this and the otherquarter were analyzed for their carbon contentusing a Heraeus CHN-O-Rapid elemental analyzer(EA) with a TC detector to detect the quantity ofCO2 generated from the carbon. Analyses wereperformed with a 1.1min heating time, 950 1Coxidation tube temperature, and 600 1C reducingtube temperature. Acetanilide (N ¼ 10.36%,C ¼ 71.09%, H ¼ 6.71%, O ¼ 11.80%) was usedas the standard to prepare the calibration curve. Thequantity of TC, EC and OC was determined bycomparing carbon content results from the twopieces of filter paper. Again based on an average7.2m3 air sample over 12 h, MDLs for TC, EC andOC were 0.16, 0.11, and 0.19 mgm�3, respectively.

2.4. Emissions and meteorological data

Table 1 shows emissions in kilotons of NOx, SO2

and PM10 in southern Taiwan in 1997 (CTCICorporation, 1999). The data reveal that PM10

Table 1

Emission inventory in 1997 for southern Taiwan (Tainan City, Tainan C

(CTCI Corporation, 1999)

Emission inventory

(kton yr�1)

Sources

Point Line

Gasoline vehicle Diesel ve

NOx 88.271 (60%) 28.778 (20%) 25.106 (1

SO2 94.544 (96%) 0.383 (o1%) 0.693 (oPM10 18.812 (26%) 4.159 (6%) 4.374 (6

emissions were derived mostly from ground sources( ¼ line+area sources), which emitted 74% ofPM10, whereas NOx and SO2 emissions were derivedmostly from elevated sources ( ¼ point sources)(60% and 96%, respectively).

At the four sites selected for this study, hourlyPM10 concentrations were obtained from theTaiwan Environmental Protection Administration(EPA/Taiwan) data, while hourly surface wind datawere obtained from either the Taiwan CentralWeather Bureau or the EPA.

2.5. Estimation of source apportionments to aerosol

The GTx model (Tsuang, 2003), though not ascomplete as some aerosol Eulerian models, is able tosimulate the daily variation of PM concentrations ata receptor, allowing source–receptor relationshipsof primary pollutants and secondary aerosols to beestimated. The model uses the Gaussian plumeequation to account for the formation of secondarysulfate aerosol and nitrate aerosol from the oxida-tions of gaseous SO2 and NOx emissions. For asteady-state Gaussian plume, the hourly contribu-ted concentration Cc (mgm

�3) at a travel time t (s)and at a crosswind distance y (m) from an elevatedpoint source with an emission rate of qc (mg s

�1) of aprimary pollutant c, such as primary aerosol, SO2 orNOx, is given as (EPA/US, 1995):

Ccðt; y; zÞ ¼qcF cðtÞ

2pussyðtÞszðtÞ

� exp �0:5y

syðtÞ

� �2" #

V ðt; zÞ, ð1Þ

where us is the effective stack height wind speed(m s�1), sy and sz are horizontal and verticaldispersion parameters (m), respectively, Fc is the mass

ounty, Kaohsiung City, Kaohsiung County and Pintung County)

Area

hicle Paved road Unpaved road Other area

sources

7%) 0 0 3.821 (3%)

1%) 0 0 4.270 (4%)

%) 18.033 (25%) 2.147 (3%) 24.741 (34%)

ARTICLE IN PRESSY.I. Tsai, C.-L. Chen / Atmospheric Environment 40 (2006) 4751–4763 4755

fraction of material c remaining in the plume after atravel time of t, and V(t, z) is the vertical distributionfraction with respect to the plume center line.

The mechanisms of dry deposition, scavenging,background pollutant transport and subsidencefrom the top planetary boundary layer wereconsidered when estimating source apportionmentsin this study.

3. Results and discussion

3.1. Chemical species and compositions of fine and

coarse particulate matter

Fig. 2 shows the average mass and chemicalcompositions of PM2.5 (fine fraction) and PM2.5–10

SulfateNitrate

Sea Salt

AmmoniumCrustal

Elemental COrganic C

Remainder0

10

20

30

PM2.5 mass conc.= 49.6+/-12.6 µg m-3

PM2.5-10 mass conc.= 31.0+/-7.8 µg m-3

Con

cent

ratio

n (µ

g m

-3)

0

10

20

30 PM2.5 mass conc.= 72.5+/-16.5µg m-3

PM2.5-10 mass conc.= 44.3+/-9.1µg m-3

Con

cent

ratio

n (µ

g m

-3)

Chemical species in aerosol

SulfateNitrate

Sea Salt

AmmoniumCrustal


Remainder


(a) (b

(c) (d

Fig. 2. Mean chemical composition of PM2.5 and PM2.5–10 aerosols a

(a) MN, (b) JW, (c) DL, (d) LY.

(coarse fraction) at the four sites, while Table 2 listschemical species’ average percentages of the PMmass and the ratio of chemical species in the finefraction to that in the coarse fraction. The averagePM2.5 mass was highest at LY, followed by DL, JWand MN, while the average PM2.5–10 mass washighest at DL, followed by LY, JW and MN,indicating that the air quality at LY and DL isaffected by PM from emissions from the neighbor-ing industrial complexes, and that MN, located inan area with less industrial and municipal trafficemissions, has relatively better air quality. Theneutralization ratio (NR) (Tsai and Cheng, 1999),shown in Table 2, was o1.0 at all four receptors,indicating that aerosols were at all times acidicwhether in fine or coarse fraction. At JW and DL

0

10

20

30

PM2.5 mass conc.= 62.1+/-14.5 µg m-3

PM2.5-10 mass conc.= 40.1+/-10.8 µg m-3

Con

cent

ratio

n (

g m

-3)

0

10

20

30 PM2.5 mass conc.= 74.5+/-14.6 µg m-3

PM2.5-10 mass conc.= 43.7+/-7.1 µg m-3

Con

cent

ratio

n (

g m

-3)

SulfateNitrate

Sea Salt

AmmoniumCrustal


Remainder


SulfateNitrate

Sea Salt

AmmoniumCrustal


Remainder


)

)

µµ

t four sites in southern Taiwan, December 2002–January 2003.

ARTICLE IN PRESS

Table

2

Averagecontributionsto

thePM

mass

atfoursitesduringtheperiodDecem

ber

2002–January

2003

Species

MN

JWDL

LY

PM

2.5

(N¼

20)a

PM

2.5–10

(N¼

20)

PM

2:5=P

M2:5�10b

PM

2.5

(N¼

20)

PM

2.5�10

(N¼

20)

PM

2:5=P

M2:5�10

PM

2.5

(N¼

20)

PM

2.5�10

(N¼

20)

PM

2:5=P

M2:5�10

PM

2.5

(N¼

20)

PM

2.5�10

(N¼

20)

PM

2:5=P

M2:5�10

PM

ma

ssco

nce

ntr

ati

on

(mg

m�

3)

49.6712.6

31.077.8

1.60

62.1714.5

40.1710.8

1.55

72.5716.5

44.379.1

1.64

74.5714.6

43.777.1

1.70

Per

cen

tag

eco

ntr

ibu

tio

no

fP

Mm

ass

(%

)

SO

42�

18.08

13.60

2.13

16.42

13.99

1.82

17.64

12.59

2.29

18.84

11.72

2.74

NO

3�13.39

12.41

1.73

17.74

14.59

1.88

13.52

13.85

1.60

15.31

12.15

2.15

Sea

saltc

2.44

4.84

0.81

3.46

8.53

0.63

3.35

8.66

0.63

4.35

9.27

0.80

NH

4+8.81

7.64

1.85

10.11

6.24

2.51

8.73

6.61

2.16

9.14

7.37

2.11

Crustald

3.10

5.06

0.98

2.85

3.99

1.11

2.55

3.97

1.05

2.68

4.85

0.94

EC

12.32

13.31

1.48

11.66

11.03

1.64

11.89

11.17

1.74

10.52

10.37

1.73

OC

18.33

17.41

1.68

18.60

13.67

2.11

16.02

16.36

1.60

15.22

14.49

1.79

Rem

ainder

e23.53

25.73

1.46

19.16

27.96

1.06

26.29

26.78

1.61

23.94

29.79

1.37

NR

ratiof

0.83

0.89

0.90

0.68

0.84

0.78

0.80

0.97

aNumber

ofsamples.

bRatioofchem

icalspeciesin

PM

2.5–PM

2.5–10.

cContents¼

sum

ofNa++

Cl�.

dContents¼

sum

ofCa2++

Mg2++

K+.

eUnidentified

amountin

PM

mass.

f Neutralizationratiofrom

TsaiandCheng(1999).

Y.I. Tsai, C.-L. Chen / Atmospheric Environment 40 (2006) 4751–47634756


there was less NH4+ and a lower NR ratio in PM2.5–10

than in PM2.5, indicating that the coarse particleshave more acidic properties at these two sites.

At LY, DL and MN, SO42� and OC were the

major PM2.5 components in both fine and coursefractions, whereas at JW, located near the intersec-tion of two highways, OC and NO3

� were the majorcomponents. Sulfate levels in the PM2.5 wereparticularly high at LY and DL, due to theirproximity to petrochemical industrial complexes.The lowest SO4

2� content in PM2.5 was recorded atMN, although as a percentage of the total mass itwas higher than at JW and DL, at 18.1%, becauseMN is relatively less affected by emissions andtherefore produced a total mass that was less than atthe other receptors. For the same reason, OC as apercentage of the mass was higher at MN than atthe other three sites.

The secondary photochemical water-soluble in-organic aerosols, sulfate, nitrate and ammonium,together constituted 40.3% of the PM2.5 mass and87.9% of the total weight of all water-soluble ions atMN, 44.3% and 87.5% at JW, 39.9% and 87.1% atDL, and 43.3% and 86.0% at LY. In PM2.5–10, theyconstituted 33.7% of the mass and 78.3% of thetotal weight of all water-soluble ions at MN, 34.8%and 73.6% at JW, 33.1% and 72.4% at DL, and31.2% and 68.9% at LY. These figures representextremely high ratios of the total quantity of allwater-soluble ions in the PM, especially in PM2.5,9.6–17.1% higher than in PM2.5–10, indicating thatthese water-soluble ions originate mostly fromsecondary photochemical water-soluble aerosol.

The highest actual and percentage sea salt in PMwas recorded at LY, on the coast, where coarsefraction sea salt constituted 9.3% of the PM2.5–10

mass and 4.4% of the PM2.5 mass. Conversely, thelowest actual and percentage sea salt in PM wasrecorded at MN, furthest from the coast. At all fourreceptors, sea salt was extant in higher ratios in thecoarse fraction than in the fine fraction.

There were similar levels of crustal matter in bothcoarse and fine fractions at all four receptors, butwith a higher contribution ratio in the coarsefraction, indicating that crustal matter as repre-sented by Ca2+, Mg2+ and K+ is distributedmainly in coarse particles. The undeterminedremainder was also at a higher ratio in the coarsefraction, showing that this is non-soluble crustalmatter. Moreover, previous studies have shown thatcrustal elements originating from natural weath-ering of the earth’s crust exist mainly in coarse

particles (Braaten and Cahill, 1986; Mori et al.,2002, 2003; Guo et al., 2004; Ma et al., 2005).

Ratios of chemical species in PM2.5 to those inPM2.5–10 (Table 2) show that 450% of the sea saltand crustal matter was in the coarse fractionwhereas 450% of SO4

2�, NO3�, NH4

+, EC and OCwas in the fine fraction. Indeed, ratios 42.0 forSO4

2� at LY, DL and MN, for NH4+ at LY, DL and

JW, for NO3� at LY, and for OC at JW, indicate

that 467% of these chemical species were con-tained in fine fractions. This demonstrates thataerosols from man-made pollution are mainlypresent in fine fractions, though they nonethelessalso exist in large quantities in coarse fractions.

3.2. Ratios of chemical species in PM during daytime

and nighttime

The ratios of nighttime to daytime concentrationsof PM2.5 and PM2.5–10 species, shown in Fig. 3,reveal that at LY and JW the nighttime increase inmass is greater in the PM2.5–10 fraction and, exceptfor sea salt, crustal matter, and EC in PM2.5 at JWand in PM2.5–10 at LY, all aerosol componentsrecorded higher nighttime concentrations. At night,the atmospheric average mixing height and averagewind velocity are both lower than during the day(2267174m versus 5537244m and 2.0m s�1 versus2.3m s�1, respectively), limiting the vertical and tosome extent the horizontal dispersion of atmo-spheric aerosol, leading to higher aerosol concen-tration at nighttime. At the coastal site, LY,daytime sea salt is higher than nighttime salt, shownby a nighttime/daytime ratio for sea salt in thecoarse fraction of 0.82, and this is due to theprevailing daytime onshore sea breeze. DaytimeNO3� is also higher in the coarse fraction at LY

(ratio also o1.0), unlike at the other three sites, dueto the influence of coarse NaNO3 aerosols in seabreeze (Tang and Munkelwitz, 1994; Hoffman et al.,2004). At MN, furthest away from the ocean, thecontents of daytime and nighttime sea salt in bothfine and coarse fractions are similar; their concen-trations are not affected by sea breeze.

Nighttime/daytime ratios ofo1.0 show that at allfour sites the crustal matter in both PM2.5 andPM2.5–10 aerosol is higher during daytime. At JWthe crustal matter ratio in PM2.5–10 aerosol is just0.81 whereas the nitrate ratio in PM2.5 aerosol is41.2, indicating the presence of daytime dust re-entrainment from heavy traffic loadings associatedwith the nearby highway intersection (Claiborn

ARTICLE IN PRESS

PM masSulfa

teNitra

teSea salt

AmmoniumCrusta

l

Elemental C

Organic C

RemainderPMmas

SulfateNitrate

Sea salt

AmmoniumCrustal


Remainder

PM masSulfate

NitrateSea salt

AmmoniumCrustal


RemainderPM mas

SulfateNitrate

Sea salt

AmmoniumCrustal

Elemental C

Organic C

Remainder

0

1

2

3

Rat

io o

f ni

ghtti

me/

dayt

ime

PMco

mpo

nent

con

cent

ratio

ns

Chemical species in aerosol Chemical species in aerosol

Chemical species in aerosol Chemical species in aerosol

PM2.5 PM2.5-10

0

1

2

3

PM2.5 PM2.5-10

0

1

2

3

Rat

io o

f ni

ghtti

me/

dayt

ime

PMco

mpo

nent

con

cent

ratio

ns

0

1

2

3R

atio

of

nigh

ttim

e/da

ytim

e PM

com

pone

nt c

once

ntra

tions

Rat

io o

f ni

ghtti

me/

dayt

ime

PMco

mpo

nent

con

cent

ratio

ns

PM2.5

PM2.5-10

PM2.5 PM2.5-10

(a) (b)

(c) (d)

Fig. 3. Ratio of nighttime/daytime PM component concentrations in the period December 2002–January 2003. (a) MN, (b) JW, (c) DL,

(d) LY.

Y.I. Tsai, C.-L. Chen / Atmospheric Environment 40 (2006) 4751–47634758

et al., 1995; Venkatram et al., 1999; Kuhns et al.,2001) and nighttime accumulation, due to the loweraverage atmospheric mixing height, of NO3

� photo-chemical products from NOx emitted during the dayby traffic. At MN, a countryside location with moreunpaved roads than other sites, the crustal matterratio in PM2.5–10 aerosol is also low, at 0.86, againindicating the presence of daytime dust re-entrain-ment, whereas the nitrate ratio in PM2.5 aerosol ishigher, at 42.0. This is the highest ratio ofnighttime/daytime nitrate in PM2.5 aerosol amongall four sites and is again a result of lower nighttimeaverage atmospheric mixing height.

3.3. Ratios of OC/EC in fine and coarse particles

EC, known as black carbon or graphitic carbon,is a byproduct of incomplete combustion of organic

carbon (OC) (Turpin and Huntzicker, 1995; Low-enthal et al., 1997; Castro et al., 1999). The OC/ECratio of both coarse and fine fractions (Fig. 4) was atall times 41.2 and peaked at 2.5, indicating thatOC constituted 55–71% of TC. At MN, DL andLY, the daytime OC/EC ratio in the fine fractionwas higher than at nighttime, demonstrating thatdaytime direct emissions of primary organic aerosol(POA) are greater than emissions of carbonaceousmaterial (EC aerosol), whereas it was higher at JW(approximately 2.2) during nighttime, revealingthat at this site, which has heavy traffic loadings,SOA formed in the atmosphere through chemicalreactions from reactive organic gases and subse-quent gas-to-particle partitioning processes contrib-uted to the nighttime aerosol in addition tothe contribution from POA (Turpin et al., 1991,2000).

ARTICLE IN PRESS

0

1

2

3

4

Rat

io o

f O

C/E

C in

the

fine

fra

ctio

n

MN

0

1

2

3

4

Day

Night

Day

Night

Rat

io o

f O

C/E

C in

the

coar

se f

ract

ion

LYDLJWMN

JW DL LY

Fig. 4. Ratio of OC/EC in fine and coarse aerosols at four sites in the period December 2002–January 2003.


At LY and JW the OC/EC ratio in the coarsefraction at night was about or above 2.2, indicatingthe existence of more SOC in this fraction. Duringthe daytime, the OC/EC ratios were around 1.4,signifying that the contribution by coarse and fineparticle OC and EC to the PM2.5–10 mass duringdaytime is stable and consistent.

3.4. Source apportionments to aerosol in southern

Taiwan

The simulated concentrations of PM10 andsource apportionment are shown in Fig. 5. Somedata are not displayed due to malfunction of PM10

monitoring instruments in the period. The bias is


defined as

bias ¼ðcp � coÞ

co, (2)

where cp is the calculated mean concentration andco is the observed mean concentration.

Fig. 5 shows that the biases of the PM10

concentrations for the four receptors are from�6% to �20%, and that the correlation coefficients(r) are in the range 0.45–0.59. The simulated PM10

data agrees well with observed data at the fourreceptors.

The PM10 emission inventory (CTCI Corpora-tion, 1999) for an area of 20� 20 km2 centered onreceptor sites MN, JW, DL and LY and the PM10

source apportionments to the four receptors areshown in Table 3, and the total NOx and SOx

emission rates for the same areas are 2.0, 33.9, 48.2and 42.8 ktons yr�1 and 0.2, 13.6, 46.5 and45.3 ktons yr�1, respectively. At DL and LY point

0

250

PM10

(µg

m-3

)

n = 59r = 0.45bias = -19%

n = 49r = 0.57bias = -6%

point line area

200

150

100

50

0

250

PM10

(µg

m-3

)

200

150

100

50

Meinung

200212/1 12/11 12/21

date12/31 1/10 1/20 1/30

2003

200212/1 12/11 12/21

date12/31 1/10 1/20 1/30

2003

Daliao

(a) (

(c) (

Fig. 5. The observed daily concentrations and the calculated source

December 2002 to 31 January 2003, where ‘‘n’’ is the number of days, ‘‘r

the positive and negative deviation from the observed value as a perc

sources, ‘‘line’’ represents line sources, ‘‘area’’ represents area so

‘‘subsidence’’ represents top boundary concentration, and ‘‘obs’’ is the

sources were the largest emission sources whereasthey were only the third largest emission source atMN and JW. Table 3 shows that the major sourcesof PM10 at JW were point sources (27%), whichemitted 23% of PM10 within the 20� 20 km2 area,paved road dust (14%), which emitted 30% of PM10

within the 20� 20 km2 area, and upwind boundaryconcentration (14%). The major sources of PM10 atDL were point sources (32%), which emitted 46%of PM10 within the 20� 20 km2 area, upwindboundary concentration (14%), diesel vehicles(12%) and gas vehicles (12%). Similarly, the majorsources of PM10 at LY were also point sources(37%), which emitted 65% of PM10 within the20� 20 km2 area, upwind boundary concentration(14%), diesel vehicles (12%) and gas vehicles (11%).The data shows that point sources contributed thelargest fractions of PM10 for many of the primaryPM10 emissions as well as NOx/SOx-convertedaerosol. On the other hand, the major sources of

Jenwun = 62r = 0.51

n = 62r = 0.59bias = -10%

upwind subsidance obs

0

250

PM10

(µg

m-3

)

200

150

100

50

0

250

PM10

(µg

m-3

)

200

150

100

50

200212/1 12/11 12/21

date12/31 1/10 1/20 1/30

2003

200212/1 12/11 12/21

date12/31 1/10 1/20 1/30

2003

Linyuan

bias = -18%

b)

d)

apportionment at (a) MN, (b) JW, (c) DL and (d) LY, from 1

’’ is the correlation coefficient, ‘‘bias’’ is determined by estimating

entage of the observed concentrations, ‘‘point’’ represents point

urces, ‘‘upwind’’ represents upwind boundary concentration,

observed PM10 concentration.

ARTICLE IN PRESS

Table 3

PM10 emission inventory in 1997 (CTCI Corporation, 1999) in an area of 20� 20 km2, centered on Meinung, Jenwu, Daliao and Linyuan

stations, and source apportionment for the four receptors from December 2002 to January 2003

Sources Point Line Area Upwind

boundary

conc.a (%)

Top boundary

conc.b (%)Diesel

vehicle

Gasoline

vehicle

Paved road Unpaved

road

Other area

sources

PM10 emission inventory in the region of 20� 20 km2 (kton yr�1)

Meinung 0.529 (22%) 0.172 (7%) 0.139 (6%) 0.634 (26%) 0.055 (2%) 0.900 (37%)

Jenwu 3.454 (23%) 1.126 (7%) 1.093 (7%) 4.511 (30%) 0.075

(o1%)

5.010 (33%)

Daliao 6.110 (46%) 0.630 (5%) 0.608 (5%) 2.599 (20%) 0.067

(o1%)

3.205 (24%)

Linyuan 5.903 (65%) 0.344 (4%) 0.273 (3%) 1.388 (15%) 0.037

(o1%)

1.128 (13%)

Source apportionment

Meinung 17% 13% 12% 11% 3% 9% 19 16

Jenwu 27% 10% 12% 14% 2% 11% 14 10

Daliao 32% 12% 12% 10% 2% 9% 14 9

Linyuan 37% 11% 12% 8% 2% 8% 14 8

aConcentrations just outside the boundary of Fig. 1, where the concentrations are interpolated from nearby stations.bObserved at a background monitoring station, Henchung, Pintung, Taiwan.


PM10 at MN were upwind boundary concen-tration (19%), point sources (17%), which emitted22% of PM10 within the 20� 20 km2 area, and topboundary concentration (16%). As the only sitelocated in a suburban area, MN experienced lessPM10, SOx and NOx emissions than the other threereceptors, making the effects of boundary concen-trations and long distance transportation signifi-cant.

4. Conclusion

All winter aerosols collected had NRs of o1.0,indicating that all aerosols, whether in fine or coarsefraction, were acidic. The total mass of sulfate,nitrate and ammonium constituted 39.9–44.3% ofthe PM2.5 mass and 86.0–87.9% of the total water-soluble inorganic salts in PM2.5, demonstrating thatthe secondary photochemical water-soluble speciesas represented by sulfate, nitrate and ammoniumare the major contributors to aerosol water-solubleions. At all four sites there was more sea salt in thecoarse fraction than in the fine, and this wasespecially true at LY, the coastal site. Furthermore,daytime sea salt in the coarse fraction exceedednighttime, due to the prevailing daytime onshore seabreeze. MN is the furthest away from the coast andhad the lowest percentage of sea salt in PM mass.

Additionally, sea salt and crustal matter distributedmainly to the coarse fraction. At JW frequentdaytime traffic causes dust re-entrainment to con-tribute to a higher nighttime OC/EC ratio (approxi-mately 2.2) in the fine fraction, thus at this sitecontribution to the nighttime aerosol comes notonly from POA but also from the remaining SOA inthe atmosphere.

The GTx model was used to simulate the dailyvariation in PM concentration at a receptor in thisstudy. The source–receptor relationship can beeasily quantified and applied to traditional sour-ce–receptor analysis. Source apportionment for theperiod December 2002 to January 2003 showedthat, for receptors DL and LY, point sources,upwind boundary concentration, and gasoline anddiesel vehicle emissions contributed a large fractionof PM10 (70% and 74%, respectively) and for JW,point sources, paved road emissions and upwindboundary concentration were the major PM10

sources. For MN, however, located in a suburbanarea with less pollutant emissions in the vicinity,upwind boundary concentration, point sources andtop boundary concentration were the importantsources. Furthermore, the above results couldprovide a view to applying proper and effectivestrategies for improving air quality in southernTaiwan.


Acknowledgements

Authors gratefully thank National Science Coun-cil, Taiwan (Grant # NSC 91-2211-E-041-006, NSC91-2212-E-268-002 and NSC 92-2211-E-268-002)and Environmental Protection Bureau of Kaoh-siung County, Taiwan for financial support of thisresearch. We would like to thank Mr. Tzu-HsiangWeng and Mr. Yi-Hsuan Lin in the Department ofEnvironmental Engineering and Science, Chia NanUniversity of Pharmacy and Science, for prelimin-ary contribution with data collection and analysis.Additionally, we thank Director Mao-Song Wangand 2nd Section Chief Fu-Chen Shieh, Environ-mental Protection Bureau of Kaohsiung County,Taiwan for helpful discussions.

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Atmospheric aerosol composition and source apportionments to aerosol in southern Taiwan