Atmospheric Environment 38 (2004) 3349–3362

ARTICLE IN PRESS

*Correspond

268-6654.

E-mail addr

1352-2310/$ - se

doi:10.1016/j.at

Improving source identification of Atlanta aerosolusing temperature resolved carbon fractions in

positive matrix factorization

Eugene Kima, Philip K. Hopkeb,*, Eric S. Edgertonc

a Department of Civil and Environmental Engineering, Clarkson University, Box 5708, Potsdam, NY 13699, USAb Department of Chemical Engineering, Clarkson University, Box 5708, Potsdam, NY 13699, USA

c Atmospheric Research and Analysis, Inc., 3500 Cottonwood Drive, Durham, NC 27707, USA

Abstract

Daily integrated PM2.5 (particulate matter p2.5 mm in aerodynamic diameter) composition data including eight

individual carbon fractions collected at the Jefferson Street monitoring site in Atlanta were analyzed with positive

matrix factorization (PMF). Particulate carbon was analyzed using the thermal optical reflectance method that divides

carbon into four organic carbon (OC), pyrolized organic carbon (OP), and three elemental carbon (EC) fractions. A

total of 529 samples and 28 variables were measured between August 1998 and August 2000. PMF identified 11 sources

in this study: sulfate-rich secondary aerosol I (50%), on-road diesel emissions (11%), nitrate-rich secondary aerosol

(9%), wood smoke (7%), gasoline vehicle (6%), sulfate-rich secondary aerosol II (6%), metal processing (3%), airborne

soil (3%), railroad traffic (3%), cement kiln/carbon-rich (2%), and bus maintenance facility/highway traffic (2%).

Differences from previous studies using only the traditional OC and EC data (J. Air Waste Manag. Assoc.

53(2003a)731; Atmos Environ. (2003b)) include four traffic-related combustion sources (gasoline vehicle, on-road

diesel, railroad, and bus maintenance facility) containing carbon fractions whose abundances were different between the

various sources. This study indicates that the temperature resolved fractional carbon data can be utilized to enhance

source apportionment study, especially with respect to the separation of diesel emissions from gasoline vehicle sources.

Conditional probability functions using surface wind data and identified source contributions aid the identifications of

local point sources.

r 2004 Elsevier Ltd. All rights reserved.

Keywords: Thermal optical method; Carbon fraction; Positive matrix factorization; Source apportionment; Conditional probability

function

1. Introduction

Positive matrix factorization (PMF) (Paatero, 1997)

has been shown to be a powerful alternative to

traditional receptor modeling of airborne particulate

matter (Huang et al., 1999; Willis, 2000; Qin et al.,

2002). PMF has been used to assess particle source

ing author. Tel.: +1-315-268-3861; fax: +1-315-

ess: hopkepk@clarkson.edu (P.K. Hopke).

e front matter r 2004 Elsevier Ltd. All rights reserve

mosenv.2004.03.012

contributions in many studies that utilized total carbon,

black carbon or two carbon fractions (organic carbon

and elemental carbon) measurements (Ramadan et al.,

2000; Polissar et al., 2001; Song et al., 2001; Kim et al.,

2003c). In the previous analysis of ambient PM2.5

(particulate matter p2.5mm in aerodynamic diameter)

compositional data including two carbon fractions (Kim

et al., 2003a), PMF could not clearly separate carbonac-

eous particle sources, especially traffic-related combus-

tion sources due to their similar chemical profiles and

emission patterns or these are averaged out over the 24 h

d.

ARTICLE IN PRESSE. Kim et al. / Atmospheric Environment 38 (2004) 3349–33623350

sampling interval. To separate combustion sources into

gasoline vehicles and diesel engines, detailed composi-

tional data are needed.

The thermal optical methods have been used to

analyze carbon mass in the ambient particles (Chow

et al., 1993; Birch and Cary, 1996). Using this method,

several individual carbon types including organic and

elemental carbon (OC and EC) fractions and the

pyrolized organic carbon (OP) are measured with

different temperature steps. Because the particles from

traffic-related combustion sources are mostly carbonac-

eous material (Watson et al., 1994; Lowenthal et al.,

1994), those sources might be separated and identified

by using temperature resolved fractional carbons in a

source apportionment study.

The objectives of this study are to examine the use of

carbon fractions to identify particulate matter sources

and estimate their contributions to the particle mass

concentrations. In the present study, PMF was applied

to an ambient PM2.5 compositional data set of daily

samples including eight individual carbon fractions

collected during a 2-year period at a monitoring site in

Atlanta, Georgia. The resolved PM2.5 particle sources

and their seasonal trends are discussed. The conditional

probability function was calculated to help identify the

likely locations of the PMF identified sources. The

results of this study were compared with the results of

previous PMF2 study (Kim et al., 2003a) and a

multilinear receptor model study (Kim et al., 2003b)

Fig. 1. Location of the Jefferson Stree

for the same PM2.5 compositional data including two

carbon fractions as well as the results of previous

chemical mass balance (CMB) approach using organic

compounds (Zheng et al., 2002).

2. Experimental

2.1. Sample collection and chemical analysis

The PM2.5 samples analyzed in this study were

collected at Jefferson Street monitoring site located

4 km northwest of downtown Atlanta as shown in Fig.

1. This monitoring site is located in an industrial and

commercial area. Daily integrated PM2.5 samples were

collected using the particulate composition monitor

(PCM, Atmospheric Research and Analysis, Inc.) that

permits simultaneous sampling on a three-stage filter

pack (Teflon, Nylon, and cellulose filter), a nylon

filter, and a quartz filter. Each sampling line has a

10 mm cyclone followed by a Well Impactor Ninety-Six

(WINS), which has a 2.5 mm-cutoff size in particle

aerodynamic diameter. The PCM includes carbo-

nate denuders and citric acid denuders upstream of

both the three-stage filter and the nylon filter. The

quartz filter includes an upstream carbon denuder to

remove gaseous organic materials. The Teflon filters of

the three-stage filter pack samples were measured for

mass concentrations and analyzed via energy dispersive

t monitoring site in Atlanta, GA.

ARTICLE IN PRESSE. Kim et al. / Atmospheric Environment 38 (2004) 3349–3362 3351

X-ray fluorescence (XRF) for chemical analysis (Chester

LabNet, Inc., Tigard, OR). The nitrate (NO3�) and

ammonium (NH4+) mass loss on the Teflon filter

was measured by the following nylon and cellulose

filters of the three-stage filter pack samples that were

analyzed via ion chromatography (IC). The nylon filters

of the independent sampling line were analyzed via

IC for sulfate ðSO2�4 Þ; NO3

�, and NH4+. The quartz

filter was analyzed via thermal optical reflectance/

Interagency Monitoring of Protected Visual Envi-

ronments (TOR/IMPROVE) protocol (Chow et al.,

1993) for eight temperature resolved carbon fractions

(Desert Research Institute, Reno, NV). This protocol

volatilizes OC by four temperature steps in a helium

atmosphere: OC1 at 120�C, OC2 at 250�C, OC3 at

450�C, and OC4 at 550�C. After OC4 response re-

turns to baseline or a constant value, the OP is oxidized

at 550�C in a mixture of 2% oxygen and 98% helium

atmosphere prior to the return of reflectance to its

original value. Then three EC fractions are measured

in oxidizing atmosphere: EC1 at 550�C, EC2 at 700�C,

and EC3 at 850�C. In addition, wind speed and

wind direction are measured hourly at the moni-

toring site.

In this study, 529 daily samples collected between

August 1998 and August 2000 and 28 species were

used. Daily samples in which all eight carbon fractions

were not available were excluded from this analysis.

XRF S and SO2�4 showed excellent correlations (slope =

3.1, r2=0.99), so it is reasonable to exclude XRF S

from the analysis. The analysis of the compositional

data revealed a mass closure problem. The measured

PM2.5 mass concentrations by three-stage filter were

compared with the sum of PM2.5 compositional data.

Approximately 34% of the measured PM2.5 mass

concentrations were smaller than the sum of species

concentrations in this comparison. This mass closure

problem (sum of species >PM2.5 mass) is thought to be

caused by the loss of semivolatile OC (Van Vaeck et al.,

1984) since the mass of the volatilized ammonium

nitrate has been already added to the measured mass

concentration. It also represents a problem for the

multilinear regression analysis that has generally been

used in the PMF analysis. Therefore, an alternative

approach described in next section was employed. The

EC1 concentration reported in TOR/IMPROVE pro-

tocol includes OP concentration. In this study, the

OP was subtracted from EC1 and utilized as an

independent variable in the PMF analysis. Summary

of PM2.5 speciation data used in this study is shown

in Table 1.

2.2. Data analysis

The general receptor modeling problem can be stated

in terms of the contribution from p-independent sources

to all chemical species in a given sample as follows

(Miller et al., 1972; Hopke, 1985).

xij ¼Xp

k¼1

gikfkj þ eij ; ð1Þ

where xij is the jth species concentration measured in the

ith sample, gik is the particulate mass concentration from

the kth source contributing to the ith sample, fkj is the

jth species mass fraction from the kth source, eij is

residual associated with the jth species concentration

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

independent sources. As pointed out by Henry (1987),

there are infinite number of possible solutions to the

factor analysis problem due to the free rotation of

matrices. To decrease rotational freedom, PMF uses

non-negativity constraints on the factors. The parameter

FPEAK and the matrix FKEY are used to control the

rotations (Paatero et al., 2002). PMF provides a solution

that minimizes an object function, Q(E), based upon

uncertainties for each observation (Paatero, 1997;

Polissar et al., 1998). This function is defined as

QðEÞ ¼Xn

i¼1

Xm

j¼1

xij �Pp

k¼1 gikfkj

uij

� �2

; ð2Þ

where uij is an uncertainty estimate in the jth element

measured in the ith sample.

The application of PMF depends on the estimated

uncertainties for each of the data values. The uncer-

tainty estimation provides a useful tool to decrease the

weight of missing and below detection limit data in

the solution.

The procedure of Polissar et al. (1998) was used to

assign measured data and the associated uncertainties as

the input data to the PMF. The concentration values

were used for the measured data, and the sum of the

analytical uncertainty and 13

of the detection limit value

was used as the overall uncertainty assigned to each

measured value. Values below the detection limit were

replaced by half of the detection limit values and their

overall uncertainties were set at 56

of the detection limit

values. Missing values were replaced by the geometric

mean of the measured values and their accompanying

uncertainties were set at four times of this geometric

mean value. In addition, the estimated uncertainties of

species that have scaled residuals larger than72 need to

be increased to reduce their weight in the solution

(Paatero, 2000; Hopke and Paatero, 2002). Large

uncertainties were assigned to several elements so that

their scaled residuals were smaller than72: EC3, As, Se,

Sn (two times of its accompanying uncertainties); Cl,

Mn, Pb, Sb, Ti, Al, Fe (3� ); Br (4� ). The uncertainty

must take into account both the measurement uncer-

tainty and the variability in the source profiles. It also

has to help to take into account the differences in scale

ARTICLE IN PRESS

Table 1

Summary of PM2.5 and 28 species mass concentrations used for PMF analysis

Species Concentration (ngm�3) Number of BDLb

values (%)

Number of missing

values (%)

Geometric

meanaArithmetic

mean

Minimum Maximum

PM2.5 15369 18264 1930 49265 0 (0.0) 0 (0.0)

OC1 415 569 42 4808 58 (11.0) 0 (0.0)

OC2 896 1015 210 4199 0 (0.0) 0 (0.0)

OC3 1152 1315 289 5282 0 (0.0) 0 (0.0)

OC4 738 879 206 4327 0 (0.0) 0 (0.0)

OP 491 653 1.7 2481 43 (8.1) 0 (0.0)

EC1 848 1240 9.6 7578 12 (2.3) 0 (0.0)

EC2 466 516 63 1275 2 (0.4) 1 (0.2)

EC3 43 35 1.0 195 487 (92.1) 0 (0.0)

SO2�4 4266 5318 526 18811 0 (0.0) 1 (0.2)

NH4+ 2349 2746 299 8683 0 (0.0) 3 (0.6)

NO3� 847 1103 127 6015 0 (0.0) 36 (6.8)

Cl 82 107 22 613 101 (19.1) 35 (6.6)

As 1.0 1.5 0.47 11 274 (51.8) 28 (5.3)

Br 2.7 3.7 0.26 35 27 (5.1) 27 (5.1)

Cu 1.8 3.5 0.61 42 197 (37.2) 26 (4.9)

Mn 1.2 1.7 0.37 10 155 (29.3) 26 (4.9)

Pb 3.5 6.4 1.2 78 173 (32.7) 26 (4.9)

Sb 2.1 3.4 1.9 19 233 (44.0) 27 (5.1)

Se 0.88 1.3 0.32 9.2 241 (45.6) 26 (4.9)

Sn 4.0 4.5 3.2 17 437 (82.6) 26 (4.9)

Ti 3.2 4.3 2.0 32 317 (59.9) 26 (4.9)

Zn 12 16 0.42 211 3 (0.6) 26 (4.9)

Al 10 19 5.7 719 344 (65.0) 26 (4.9)

Si 175 218 23 1989 0 (0.0) 26 (4.9)

K 61 73 7.7 350 0 (0.0) 30 (5.7)

Ca 61 76 4.6 599 1 (0.2) 26 (4.9)

Fe 90 109 13 580 0 (0.0) 26 (4.9)

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.

E. Kim et al. / Atmospheric Environment 38 (2004) 3349–33623352

between major species as compared with the lower

concentration species.

In northeastern US aerosol studies (Song et al., 2001),

PMF separated the sulfur into a high photochemistry

source and a low photochemistry source with seasonal

differences of the Se/S concentrations. In this study,

without adequate Se data, it was found necessary to

increase the estimated uncertainties of SO2�4 and NH4

+

by a factor of four to take the high photochemical

variability into account. Similarly, the estimated un-

certainties of OC1 were increased by a factor of four to

reduce the influence of the known positive artifact from

the adsorption of gaseous OC (Pankow and Mader,

2001) and the estimated uncertainties of EC1 were

increased by a factor of four for the additional

uncertainty from the subtraction of OP.

Because of the mass closure problem noted above, the

measured particle mass concentration was included as

an independent variable in the PMF modeling to directly

obtain the mass apportionment without the usual

multilinear regression. The estimated uncertainties of

the PM2.5 mass concentrations were set at four times of

their values so that the large uncertainties decreased

their weight in the model fit. When the measured particle

mass concentration is used as a variable, the PMF

apportions a mass concentration for each source

according to its temporal variation. The results of

PMF modeling were then normalized by the appor-

tioned particle mass concentrations so that the quanti-

tative source contributions for each source were

obtained. Specifically,

xij ¼Xp

k¼1

ðckgikÞfkj

ck

� �; ð3Þ

where ck is directly apportioned mass concentration by

PMF for the kth source.

2.3. Conditional probability function

The conditional probability function (CPF) (Ash-

baugh et al., 1985; Kim et al., 2003a) was calculated to

ARTICLE IN PRESSE. Kim et al. / Atmospheric Environment 38 (2004) 3349–3362 3353

analyze point source impacts from various wind direc-

tions using source contribution estimates from PMF

coupled with wind direction values measured on site. To

minimize the effect of atmospheric dilution, daily

fractional mass contribution from each source relative

to the total of all sources was used rather than using the

absolute source contributions. The same daily fractional

contribution was assigned to each hour of a given day to

match to the hourly wind data. The CPF is defined as

CPF ¼mDy

nDy; ð4Þ

where mDy is the number of occurrence from wind sector

Dy that exceeded the threshold criterion, and nDy is the

total number of data from the same wind sector. In this

study, Dy was set to be 11.3. Calm winds (o1 ms�1)

were excluded from this analysis due to the isotropic

behavior of wind vane under calm winds. From the tests

with several different percentile of the fractional

contribution from each source, the threshold criterion

of upper 25% was decided to show clear directionality.

The sources are likely to be located to the direction that

have high conditional probability values.

3. Results

To find the number of sources, it is necessary to test

different numbers of sources and find the optimal one

Table 2

The comparison of average source contributions (%) to PM2.5 mass

fractions (Kim et al., 2003a), ME study with two carbon fractions (Ki

Average source c

PMF with two ca

fractionsa

Sulfate-rich secondary aerosol 55.7 (1.3)

Sulfate-rich secondary aerosol I

Motor vehicle 22.2 (0.7)

Diesel emissions

On-road diesel emissions

Nitrate-rich secondary aerosol 7.3 (0.2)

Wood smoke 10.8 (0.3)

Gasoline vehicle

Sulfate-rich secondary aerosol II

Metal processing

Metal recycling 0.5 (0.02)

Airborne soil 1.2 (0.07)

Railroad traffic

Cement kiln/carbon-rich 2.0 (0.06)

Bus maintenance facility/highway traffic

Bus station/metal processing 0.3 (0.02)

a Kim et al. (2003a).b Kim et al. (2003b).c This study.

with the most physically reasonable results. Also, since

rotational ambiguity exists in factor analysis modeling,

PMF was run many times with different FPEAK

values to determine the range within which the ob-

jective function QðEÞ value in Eq. (2) remains relatively

constant (Paatero et al., 2002). The optimal solution

should lie in this FPEAK range. In this way, subjective

bias was avoided to some extent. Also, certain species in

the source profiles can be pulled down to lower

concentrations using FKEY matrix to find the most

reasonable source profiles (Lee et al., 1999; Paatero,

2000). The final PMF solutions were determined by

experiments with different number of sources, different

FPEAK values, and different FKEY matrices with the

final choice based on the evaluation of the resulting

source profiles. The global optimum of the PMF

solutions were tested by using different seeds for the

pseudo-random initial values (Paatero, 2000).

In this study, the PMF identified eleven sources. A

value of FPEAK = 0 and a FKEY matrix provided the

most physically reasonable source profiles. For the

FKEY matrix, values of all elements were set to zero,

except: values of 2 and 3 for SO2�4 in wood smoke and

railroad traffic, respectively; values of 4, 5, 5, 5, and 6 for

NH4+ in gasoline vehicle, cement kiln/carbon-rich, metal

processing, bus maintenance facility/highway traffic,

and railroad traffic, respectively. The robust mode

was used to reduce the influence of extreme values on

the PMF solution. A data point was classified as an

concentrations among previous PMF study with two carbon

m et al., 2003b), and this PMF study with eight carbon fractions

ontribution (standard error)

rbon ME with two carbon

fractionsbPMF with eight carbon

fractionsc

54.3 (1.7) 49.7 (1.7)

11.2 (0.5)

10.5 (0.4)

8.9 (0.4) 8.5 (0.4)

2.9 (0.2) 6.5 (0.2)

14.6 (0.4) 6.4 (0.3)

1.9 (0.1) 5.9 (0.2)

3.1 (0.2) 3.4 (0.2)

2.2 (0.1) 2.8 (0.1)

2.5 (0.06)

0.9 (0.04) 2.0 (0.09)

1.8 (0.1)

ARTICLE IN PRESS

Table 3

The comparison of average source contributions (%) to PM2.5 mass concentrations in April, July, and October 1999 and January 2000

between PMF with temperature resolved carbon fractions (this study) and CMB approach with composition resolved organic

compound data (Zheng et al., 2002)

PMF with carbon fractions CMB with organic compounds

Identified sources Average source

contribution (%)

Identified sources Average source

contribution (%)

Sulfate-rich secondary aerosol I and II 61.7 Secondary sulfate 28.2

Nitrate-rich secondary aerosol 7.6 Secondary nitrate 4.6

Secondary ammonium 9.3

Diesel emissionsa 12.0 Diesel exhaust 18.8

Gasoline vehicle 4.6 Gasoline exhaust 2.8

Airborne soil 2.3 Road dust 1.8

Wood smoke 5.4 Wood combustion 9.8

Meat cooking 3.1

Natural gas combustion 0.3

Vegetative detritus 1.1

Other organic matter 9.3

Metal processing 2.7

Cement kiln/carbon-rich 2.0

Other mass 1.5 Other mass 10.8

a Sum of contributions from on-road diesel emissions, railroad traffic, bus maintenance facility/highway traffic.

Fig. 2. Measured versus predicted PM2.5 mass concentration.

E. Kim et al. / Atmospheric Environment 38 (2004) 3349–33623354

extreme value if the model residual exceeds four

times the error estimate. The estimated uncertainties of

those extreme values were then increased so that the

weight of the extreme values in the solution were

decreased.

The average source contributions of each source to

the PM2.5 mass concentrations are compared in Table 2

among previous PMF study with two carbon fractions

(Kim et al., 2003a), a multilinear engine (ME) study with

two carbon fractions (Kim et al., 2003b), and this study.

Table 3 shows the comparison of average source

contributions to PM2.5 mass concentrations between

CMB approach utilizing organic compound composi-

tion data (Zheng et al., 2002) and this PMF study.

Zheng et al. (2002) applied CMB approach to PM2.5

organic compounds data collected at the same monitor-

ing site. The samples taken daily or every third day in

April, July, and October 1999 and January 2000 were

combined to form monthly composites and analyzed.

The average contributions from identified ten sources

shown in Table 3 were estimated from their Table 4 and

compared with average contributions from this study

corresponding to their sampling periods. Therefore, it

should be recognized that their limited number of

month-long organic compound composition may not

fully represent the 2 years of PM2.5 compositional data

used in this study.

In Fig. 2, a comparison of the daily reconstructed

PM2.5 mass contributions from all sources with mea-

sured PM2.5 mass concentrations shows that the

resolved sources effectively reproduce the measured

values and account for most of the variation in the

PM2.5 mass concentrations (slope=0.8870.01 and r2 =

0.91). This slope and r2 values show improvement in

model predictions when they are compared with those

from previous PMF analysis (slope=0.6870.01 and r2

= 0.83) and ME analysis (slope=0.8470.01 and r2 =

0.90) with two carbon fractions. Fig. 3 presents the

identified source profiles (value7standard deviation

resolved by PMF) and Fig. 4 shows time series plots

of estimated daily contributions to PM2.5 mass con-

centrations from each source. The PMF extracted

fractional carbon profiles are presented for the five

ARTICLE IN PRESS

Fig. 3. Source profiles resolved from PM2.5 samples (prediction7standard deviation).

E. Kim et al. / Atmospheric Environment 38 (2004) 3349–3362 3355

main combustion sources in Fig. 5. Conditional

probabilities of source directions for each source are

plotted in polar coordinates in Fig. 6. The point sources

show strong directionality that agree well with the

locations of known sources. The average source

contributions from sources to the PM2.5 mass contribu-

tions are compared between weekday and weekend in

Fig. 7. The seasonal average source contributions of

each source to the PM2.5 mass contributions are

presented in Fig. 8 (summer: April–September; winter:

October–March). The observed seasonal variations may

be due to variation in source strength or in transport

condition or in both.

4. Discussion

PMF extracted two different sulfate-rich secondary

aerosol sources in this study. Both sources have a high

concentration of SO2�4 and NH4

+. Sulfate-rich secondary

aerosol I has the highest source contribution to PM2.5

mass concentrations (50%). Sulfate-rich secondary aero-

sol II has higher carbon concentrations than sulfate-rich

secondary aerosol I accounting for 7% of the PM2.5 mass

concentration. Carbon and tracer elements typically

become associated with the secondary sulfate aerosol

(Liu et al., 2003) and is consistent with previous studies

(Ramadan et al., 2000; Song et al., 2001).

ARTICLE IN PRESS

Fig. 4. Time series plot of source contributions.

E. Kim et al. / Atmospheric Environment 38 (2004) 3349–33623356

In Fig. 8, the sulfate-rich secondary aerosol I shows

strong seasonal variation with higher concentrations in

summer when the photochemical activity is highest

(Polissar et al., 2001; Song et al., 2001). Sulfate-rich

secondary aerosol II shows slightly higher contributions

in summer. In the previous PMF and ME studies (Kim

et al., 2003a,b) for the same compositional data

including two carbon fractions, PMF identified one

sulfate-rich secondary aerosol source and ME identified

two sulfate-rich secondary aerosol sources.

The contribution of 56% from previous PMF

identified sulfate-rich secondary aerosol source to the

PM2.5 mass concentration is consistent with the sum of

two sulfate-rich secondary aerosol sources identified by

ME (56%) and identified by PMF with eight carbon

fractions in this study (56%). Also, this is consistent

with the study of three northeastern US cities which

identified its contributions of 47%, 55%, and 51% to

the PM2.5 mass concentration (Song et al., 2001).

However, two sulfate-rich secondary aerosols extracted

by ME show different contributions and seasonal trends

from those extracted by PMF with the eight carbon

fractions. Especially, as shown in Table 2 and Fig. 8,

PMF estimated that sulfate-rich secondary aerosol II

ARTICLE IN PRESS

Fig. 5. Fractional carbon profiles for combustion sources (prediction7standard deviation).

E. Kim et al. / Atmospheric Environment 38 (2004) 3349–3362 3357

contributes 6% and shows a summer-high seasonal

trend, whereas ME identified sulfate-rich secondary

aerosol II contributes 2% and has winter-high seasonal

trend. These differences indicate that ME utilizing time

resolved wind data and PMF utilizing temperature

resolved carbon data deduce sources differently when

meteorological information such as wind profile or

composition resolved data are incorporated to aid the

source separation.

Four different traffic-related combustion factors were

identified in this study: gasoline vehicles accounting for

6% of the PM2.5 mass concentration, on-road diesel

emissions accounting for 11%, railroad traffic account-

ing for 3%, and bus maintenance facility/highway traffic

accounting for 2%. They are represented by high carbon

fractions whose abundances differ among the sources.

As shown in Fig. 5, gasoline vehicle emissions have high

concentrations of OC fractions. In contrast, three

different types of diesel engine sources were tentatively

identified containing high concentrations of EC (Low-

enthal et al., 1994; Watson et al., 1994, 2001; Watson

and Chow, 2001).

The gasoline vehicle source has large amounts of OC3

and OC4. The CPF plot of this source points to the

southeast which is the direction of downtown Atlanta

and the junction of highways I-75 and I-20. Also, the

sharp spike pointing to the northeast is likely to indicate

the junction of highways I-75 and I-85. The source

identified as on-road diesel emissions contains high

concentrations of EC1 and OC2. This source appears to

have contributions from south and northeast where

highway I-20 and the highway junction, respectively, are

located. The ‘‘railroad’’ profile source is represented by

high EC2 concentration. There is railroad traffic around

the monitoring site. In CPF plot of this source, the

contributions from northwest of the site indicates that

most of the influence of this source is from the rail yard

located about 2 km northwest of the site. This assign-

ment is quite tentative as there are no profiles of railroad

diesel emission profiles to which these results can be

compared.

The bus maintenance facility/highway traffic source

has high concentrations of EC1 and OC3. The CPF plot

suggests that this source includes contributions from the

southeasterly direction which include a bus maintenance

facility located only about 200 m southeast of the site.

Also, other contributions from northeast highway

junction indicate that bus emissions are mixed with

some highway traffic emissions that have similar

compositions to the emission from bus maintenance

facility. The strong weekday-high variations of bus

maintenance facility/highway traffic source shown in

Fig. 7 demonstrate that the bus maintenance facility

only operates on weekdays. The weekday-high varia-

tions of diesel engine contributions were shown in

Phoenix aerosol study (Lewis et al., 2003). The other

ARTICLE IN PRESS

Fig. 6. CPF plots for the highest 25% of the mass contributions.

E. Kim et al. / Atmospheric Environment 38 (2004) 3349–33623358

traffic-related combustion sources do not show the same

strong weekday/weekend variation. The ratio of the

average contributions of diesel emissions relative to

gasoline vehicle of 2.3 (=2.46 mgm�3 sum of three diesel

emissions/1.07 mgm�3 gasoline vehicle source) is reason-

ably close to model derived ratios of 3.2 in Pasadena and

3.0 in West Los Angeles (Schauer et al., 1996). The sum

of mass contributions from four traffic-related combus-

tion sources is 21% of the PM2.5 mass concentration.

This total is consistent with 22% from motor vehicle

source estimated by previous PMF study with two

carbon fractions and 26% from the sum of gasoline

vehicle and diesel emission sources estimated by ME.

However, different contributions from gasoline vehi-

cle and diesel emissions are deduced in this study. The

previous ME study estimated 14.6% contribution from

gasoline vehicle source and 11.2% contribution from

diesel emissions, whereas this study estimated 6.4%

from gasoline vehicle source and 14.8% from sum of

three diesel emissions. The different contributions from

gasoline vehicle source as well as the separations of three

diesel emissions are thought to be caused by specific

ARTICLE IN PRESS

Fig. 7. The comparisons of model resolved contributions between weekday and weekend (mean795% confidence interval).

Fig. 8. The seasonal comparison of source contributions to

PM2.5 mass concentration (mean795% confidence interval).

E. Kim et al. / Atmospheric Environment 38 (2004) 3349–3362 3359

apportionment of eight carbon fractions. The gasoline

vehicle, on-road diesel emissions, and bus maintenance

facility/highway traffic sources contributed more to the

PM2.5 mass in the winter. In contrast, railroad traffic

contributed more in the summer.

The nitrate-rich secondary aerosol is identified by its

high concentration of NO3�. This source includes NH4

+

that becomes associated with the secondary nitrate

aerosol. It has seasonal variation with maxima in winter.

These peaks in winter indicate that low-temperature and

high-relative humidity help the formation of nitrate

aerosols in Atlanta. This is consistent with previous

study for the aerosol data from three northeastern US

sites (Song et al., 2001). The CPF plot shows the

contributions from northwest as well as northeast

highway junction. This source accounts for 9% of the

PM2.5 mass concentration. This is consistent with

previous 7% contribution and 9% contribution esti-

ARTICLE IN PRESSE. Kim et al. / Atmospheric Environment 38 (2004) 3349–33623360

mated by PMF and ME, respectively, from same data

including only two carbon fractions.

The wood smoke source is characterized by OC and K

(Watson et al., 2001) contributing 7% to the PM2.5 mass

concentration. Previous PMF study showed 11% con-

tribution and ME study showed only 3% contribution

to the PM2.5 mass concentration. This source profile has

large amount of lower temperature carbon fractions

(OC1–OC3) as shown in Fig. 5. This source has seasonal

trend with higher in winter and short-term peaks in

spring and summer. The winter peaks suggests residen-

tial wood burning with the spring and summer events

being due to forest fires. There are also prescribed

burnings to the south of Atlanta in winter. The CPF plot

for wood smoke points to the south where the prescribed

burnings and a residential area are located.

For the next source, a metal processing is suggested

because of the profile characterized by its high mass

fraction of Zn, Si, Fe, and Cl (Small et al., 1981; US

EPA, 2002). A metal recycling facility located about

0.7 km east of the site is mainly used for storage,

grinding, shredding, and loading onto railcars. Also,

several metal processing facilities are located about 6 km

southeast of the site. The high OC fractions are likely to

be from lubrication oil used for the grinding and

shredding although there could be adsorption of low

vapor pressure organic compounds on the primary

particles. This source has a winter-high seasonal pattern.

In Fig. 6, there are indications of higher contributions

from the direction of east and southeast. Those may

show the contributions from a metal recycling facility

and several metal processing facilities. This source

accounts for 3% of the PM2.5 mass concentration which

is consistent with 3% estimated by ME. In contrast,

previous PMF study with two carbon fractions identi-

fied the metal recycling facility accounting for 1%

contribution and combination of bus station and metal

processing facilities accounting for 0.3%.

The airborne soil is represented by Si, Fe, Al, K, and

Ca (Watson and Chow, 2001; Watson et al., 2001)

contributing 3% to the PM2.5 mass concentration. The

previous PMF study (Kim et al., 2003a) showed 1%

contribution and ME study showed 2% contribution to

the PM2.5 mass concentration. The crustal particles

could be contributed by unpaved roads, construction

sites, and wind-blown soil dust. This source has

contributions mainly from southwest. The airborne soil

shows strong seasonal variation with higher concentra-

tions in the dry summer season. As shown in Fig. 7,

there were reduced contributions from the airborne soil

on weekends. This results suggests that the airborne soil

is mainly crustal particles resuspended by traffic on the

roads.

The cement kiln/carbon-rich source is characterized

by Ca (US EPA, 2002), OC3, and EC2. It is likely to

include contributions from a cement kiln located about

7 km northwest of the site and an unknown carbon-rich

source. Stationary cement kilns typically combust fossil

fuels with high efficiency. The high carbon concentration

of this source indicates that the cement kiln and a

carbon-rich source are co-located and daily emission

patterns are similar. This source contributes 2% to the

PM2.5 mass concentration. This value is consistent with

previous 2% contribution deduced by PMF (Kim et al.,

2003a). ME deduced 1% contribution from this source.

The direction of the cement kiln situated northwest of

the site is clearly shown in Fig. 6. This source did not

show a strong seasonal trend. However, there were

reduced contributions from this source on weekend.

The PMF could not extract meat cooking, natural gas

combustion, and vegetative detritus sources without the

resolved organic compounds data as compared with

CMB results in Table 3. In contrast, the CMB study

with organic compounds could not separated two sulfate

secondary aerosols, three diesel emissions, metal proces-

sing and cement kiln due to the limited source profiles

required for the CMB approach.

The PMF extracted source profiles include several

associated species. The sulfate-rich secondary aerosol

includes ammonium and carbon fractions. The sum of

contributions from the sulfate- and nitrate-rich second-

ary aerosols identified by PMF accounts 69% of PM2.5

mass concentration. The sum of contributions from the

secondary sulfate, nitrate, and ammonium identified by

CMB accounts 42% of PM2.5 mass concentration. This

difference is caused by organic compounds that becomes

associated with the sulfate and nitrate aerosol in the

atmosphere. In CMB analysis, these organic compounds

are likely to be split to other combustion sources such as

meat cooking, natural gas combustion, and unidentified

organic matters. The gasoline vehicle contribution

estimated by PMF is higher than CMB estimated

contributions. In contrast, diesel emissions and wood

smoke estimated by PMF is lower than CMB estimated

contributions. The contribution of 22% from diesel

exhaust and gasoline exhaust estimated by CMB is

consistent with the sum of contributions from gasoline

vehicle and three diesel emissions estimated by PMF

(21%). PMF deduced 2% contribution from airborne

soil which is consistent with 2% deduced by CMB.

The PMF derived source profiles of the particulate

carbon fractions can be compared with the limited

available source data. Based on chassis dynamometer

tests of light-duty gasoline vehicles and heavy-duty

diesel vehicles, Watson et al. (1994) reported the

temperature resolved carbon fractions measured in

Phoenix by IMPROVE/TOR protocol in 1989. In their

results, gasoline vehicles have high concentrations of OC

fractions. In contrast, diesel vehicle contains high

concentrations of EC fractions. Lowenthal et al. (1994)

reported similar profiles for the diesel trucks and buses

based on the measurements in 1992. As shown in Fig. 5,

ARTICLE IN PRESSE. Kim et al. / Atmospheric Environment 38 (2004) 3349–3362 3361

the gasoline vehicle source includes the lower-tempera-

ture carbon fractions (OC1–OC4). The on-road diesel

emissions, railroad traffic, and bus maintenance facility/

highway traffic contain large amounts of the elemental

carbon fractions (EC1–EC3). Specifically, the gasoline

vehicle source has large amount of OC3 and OC4. The

on-road diesel emissions contain higher concentrations

of EC1 and OC2. Railroad diesels are represented by a

high EC2 concentration. Bus maintenance facility/high-

way traffic source has higher concentrations of EC1

and OC3.

Wood smoke has large amounts of lower-temperature

carbon fractions (OC1–OC3). Comparison of the PMF-

derived carbon fraction profiles with measured source

test profiles reveals interesting differences: measured

gasoline and diesel particles contain larger amount of

OC1 fraction than those of PMF estimations. This

difference may be due to the source sampling artifact

that is caused by the adsorption of fresh semi-volatile

organic compounds by quartz filters (Pankow and

Mader, 2001). Other possible explanation is the atmo-

spheric chemical process of lower molecular weight

organic compounds between source and sampling sites.

5. Conclusion

Daily collected PM2.5 compositional data at a

monitoring site in Atlanta were analyzed through

PMF. Including temperature resolved eight carbon

fractions, the PMF effectively resolved 11 sources for

PM2.5. Sulfate-rich secondary aerosol had the largest

contribution to the PM2.5 mass in Atlanta. The impacts

from the sources were visualized using plots of condi-

tional probability functions. Those plots clearly showed

the direction of sources such as highway junctions,

residential area, downtown and other point sources. In

contrast to the previous studies for the same composi-

tional data including only the traditional two carbon

fractions (Kim et al., 2003a,b), three diesel emissions

sources were separated from gasoline vehicle by utilizing

eight carbon fractions. The on-road diesel emissions,

railroad traffic, and bus maintenance facility/highway

traffic contain large amount of the higher-temperature

carbon fractions (EC1–EC3). The gasoline vehicle

source has the lower-temperature carbon fractions

(OC1–OC4). This study demonstrated that the tempera-

ture resolved carbon fractions aided separation of

traffic-related combustion sources and significantly

improved source apportionment study.

Acknowledgements

This study was supported by the Southern Company.

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