Incorporation of parametric factors into multilinear receptor model studies of Atlanta aerosol

Atmospheric Environment 37 (2003) 5009–5021

ARTICLE IN PRESS

AE International – North America

*Correspond

E-mail addr

hopkepk@clark

1352-2310/$ - se

doi:10.1016/j.at

Incorporation of parametric factors into multilinear receptormodel studies of Atlanta aerosol

Eugene Kima, Philip K. Hopkeb,*, Pentti Paateroc, Eric S. Edgertond

aDepartment of Civil and Environmental Engineering, Clarkson University, Box 5708, Potsdam, NY 13699, USAbDepartment of Chemical Engineering, Clarkson University, Box 5708, Potsdam, NY 13699, USA

cDepartment of Physical Sciences, University of Helsinki, Helsinki, FIN-00014, FinlanddAtmospheric Research and Analysis, Inc. 3500 Cottonwood Drive, Durham, NC 27707, USA

Received 11 February 2003; accepted 14 August 2003

Abstract

In prior work with simulated data, ancillary variables including time resolved wind data were utilized in a multilinear

model to successfully reduce rotational ambiguity and increase the number of resolved sources. In this study, time

resolved wind and other data were incorporated into a model for the analysis of real measurement data. Twenty-four

hour integrated PM2.5 (particulate matter p2.5mm in aerodynamic diameter) compositional data were measured inAtlanta, GA between August 1998 and August 2000 (662 samples). A two-stage model that utilized 22 elemental

species, two wind variables, and three time variables was used for this analysis. The model identified nine sources:

sulfate-rich secondary aerosol I (54%), gasoline exhaust (15%), diesel exhaust (11%), nitrate-rich secondary aerosol

(9%), metal processing (3%), wood smoke (3%), airborne soil (2%), sulfate-rich secondary aerosol II (2%), and the

mixture of a cement kiln with a carbon-rich source (0.9%). The results of this study indicate that utilizing time resolved

wind measurements aids to separate diesel exhaust from gasoline vehicle exhaust. For most of the sources, well-defined

directional profiles, seasonal trends, and weekend effects were obtained.

r 2003 Elsevier Ltd. All rights reserved.

Keywords: Source apportionment; Receptor modeling; Positive matrix factorization; Multilinear engine; PM2.5

1. Introduction

Advanced source apportionment methods for the

airborne particulate matter will be needed to assist in

State Implementation Plan as a data analysis tool for

identifying and apportioning airborne particulate matter

sources. Positive matrix factorization (PMF) (Paatero,

1997) and Unmix (Henry and Norris, 2002) have been

shown to be powerful alternatives to traditional receptor

modeling of airborne particulate matter (e.g. chemical

mass balance, conventional factor analysis) (Huang

et al., 1999; Willis, 2000; Qin et al., 2002; Maykut

ing author.

esses: [email protected] (E. Kim),

son.edu (P.K. Hopke).

e front matter r 2003 Elsevier Ltd. All rights reserve

mosenv.2003.08.035

et al., 2003). Bilinear PMF (PMF2) has been used to

assess particle source contributions in the Arctic (Xie

et al., 1999a), in Hong Kong (Lee et al., 1999), in

Phoenix (Ramadan et al., 2000), in Thailand (Chueinta

et al., 2000), in Vermont (Polissar et al., 2001), in three

northeastern US cities (Song et al., 2001), and in Atlanta

(Kim et al., 2003a). Unmix has been applied to several

aerosol data sets from Los Angeles (Kim and Henry,

2000b) and Phoenix (Lewis et al., 2003). Also, PMF2

and Unmix were compared in the northern Vermont

aerosol study (Poirot et al., 2001) and in the Seattle

particle size analysis (Kim et al., 2003b).

As pointed out by Henry (1987), there is rotational

ambiguity in the factor analysis problem that makes

infinite number of possible solutions. The incorporation

of additional information can be useful to improve the

d.

ARTICLE IN PRESS

Fig. 1. Location of the Jefferson Street monitoring site in

Atlanta, GA and major point sources contributing to the

monitoring site. (1): cement kiln, (2): rail yard, (3): metal

recycling, (4): bus station and (5): metal processing.

E. Kim et al. / Atmospheric Environment 37 (2003) 5009–50215010

solution. A more flexible multivariate method, the

multilinear engine (ME, Paatero, 1999), can fit any

model that can be expressed as a sum of products. It has

been used to analyze Arctic aerosol in a multiway model

(Xie et al., 1999b; Yli-Tuomi et al., 2003) and has been

used to fit the standard bilinear factor analysis model

(Ramadan et al., 2003). In a recent work, hourly

measured wind variables and other factors were utilized

in an expanded model to reduce rotational ambiguity.

This model was successfully tested using simulated data

developed by the US EPA (Paatero and Hopke, 2002)

and measured PM2.5 (particulate matter p2.5mm in

aerodynamic diameter) mass concentrations data

(Paatero et al., 2003).

The objective of this study is to examine the use of

such model with an actual particle composition data set.

The model incorporates time resolved wind measure-

ments into ME to enhance solution of multilinear

receptor model. In the present study, ME was applied

to PM2.5 compositional data of daily samples collected

during a two-year period at a monitoring site in Atlanta,

Georgia. The ME resolved PM2.5 sources are compared

with previous study for the same data set using PMF2

(Kim et al., 2003a). The directional profiles, hourly

patterns, seasonal trends, and weekend effects are

discussed.

2. Sample collection and chemical analysis

The PM2.5 compositional data analyzed in this study

consisted of measurements taken at Jefferson Street

monitoring site located 4 km northwest of downtown

Atlanta. This monitoring site is located in an industrial

and commercial area shown in Fig. 1. The prevailing

winds are from the east, southeast, southwest, and

northwest in summer. In winter, winds are mostly from

east and northwest. Daily integrated PM2.5 samples were

collected using the particulate composition monitor

(PCM, Atmospheric Research and Analysis, Inc.,

Durham, NC) that has three independent sampling lines

(air flow rate 16.7 l/min) 5m above ground. Each

sampling line has a 10 mm-cyclone (URG, Carrboro,NC) followed by WINS impactor (URG, Carrboro,

NC) which has a 2.5 mm-cutoff size (D50) in particleaerodynamic diameter (Vanderpool et al., 2001; Peters

et al., 2001). The PCM permits simultaneous sampling

on a 3-stage filter pack (Teflon, Nylon, and cellulose

filter; diameter 47mm), a Nylon filter (diameter 47mm),

and a quartz filter (diameter 37mm). The PCM includes

carbonate denuders and citric acid denuders upstream of

both the 3-stage filter and the Nylon filter. The quartz

filter includes an upstream carbon denuder (BYU,

Provo, UT) to remove gaseous organic materials

(Eatough et al., 1993). The Teflon filters of the 3-stage

filter pack samples were measured for mass concentra-

tions and analyzed via energy dispersive X-ray fluores-

cence (XRF) (Dzubay et al., 1988) by 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 3-stage

filter pack samples that were analyzed via ion chroma-

tography (IC) for NO3� and NH4

+ (Appel et al., 1981;

Pszenny et al., 1993). The measured mass concentrations

have been increased to account for the volatilized

ammonium nitrate. The Nylon filters of the independent

sampling line were analyzed via IC for sulfate (SO42�),

NO3�, and NH4

+. The quartz filter was analyzed via

thermal optical reflectance/Interagency Monitoring of

Protected Visual Environments (IMPROVE) protocol

(Chow et al., 1993) for organic carbon (OC) and element

carbon (EC) (Desert Research Institute, Reno, NV). In

addition, wind speed and wind direction are measured

hourly at the monitoring site: geometric mean of wind

speed was 1.37m/s.

In this study, 662 daily samples collected between

August 1998 and August 2000 and 22 species were used.

Daily samples in which wind speed or wind direction

was missing were excluded from this analysis. XRF S

and SO showed excellent correlations (slope=3.1,

r2 ¼ 0:99), therefore only IC SO42� was used in this

analysis. The analysis of the compositional data revealed

a mass closure problem. The measured PM2.5 mass

concentrations by 3-stage filter were compared with the

summations of PM2.5 particle species. Approximately

34% of the measured PM2.5 mass concentrations were

smaller than the summations 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

ARTICLE IN PRESSE. Kim et al. / Atmospheric Environment 37 (2003) 5009–5021 5011

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 ME analysis.

Therefore, an alternative approach described in next

section was employed instead. In this study, species for

which more than 90% of values were below the

detection limit were excluded. Summaries of PM2.5

speciation data used in this study are shown in Table 1.

3. Data analysis

The ordinary receptor modeling problem can be

stated in terms of the contribution from p independent

sources to all chemical species in a given sample (Miller

et al., 1972; Hopke, 1985, 1991) as follows:

X ¼ GFT þ E; ð1Þ

where X is a n � m data matrix with nmeasurements and

m number of elements. E is a n � m matrix of residuals

that are not fit by the model. G is a n � p source

contribution matrix with p sources, and F is a m � p

source profile matrix. For the consistent notation, F is

transposed in this study. The corresponding component

Table 1

Summary of PM2.5 and 21 species mass concentrations used for ME

Species Concentration (ng/m)3

Geometric meana Arithmetic mean Minimum

PM2.5 15871 18029 1930

SO42� 4488 5579 526

NH4+ 2485 2920 298

NO3� 877 1124 127

Cl 82 108 21

EC 1643 1982 227

OC 4024 4495 878

As 1.0 1.5 0.47

Br 2.9 4.2 0.26

Cu 2.0 3.8 0.59

Mn 1.4 2.0 0.37

Pb 3.7 6.6 1.1

Sb 2.0 3.5 1.9

Se 0.93 1.4 0.32

Sn 3.9 4.4 3.2

Ti 3.6 5.0 2.0

Zn 12 17 0.42

Al 11 29 5.7

Si 191 253 23

K 65 81 7.7

Ca 61 78 4.6

Fe 102 132 13

aData below the limit of detection were replaced by half of the repobBelow detection limit.

equation is

xij ¼Xp

k¼1

gikfjk þ eij ; ð2Þ

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.

In the expanded ME analysis, the bilinear model

shown in Eq. (2) is augmented by additional complex

equation that contains modeling information. The most

basic form of the this equation is

xij ¼XP

k¼1

Dðdi; kÞVðvi; kÞfjk þ e0ij ; ð3Þ

where D and V represent matrices, consisting of

unknown values to be estimated during the model fitting

process. The known index value di and ni indicate wind

direction and wind speed of the ith day for the kth

source, respectively. The indices are shown in parenth-

eses, not as subscripts for the typographic reasons. In

this study, the index value di is obtained from the

analysis

Number of BDLb

values (%)

Number of missing

values (%)

Maximum

49264 0 0

20851 0 5 (0.8)

10314 0 6 (0.9)

6014 0 44 (6.6)

613 115 (17.4) 44 (6.6)

10234 0 22 (3.3)

22089 0 21 (3.2)

11 337 (50.9) 35 (5.3)

204 29 (4.4) 34 (5.1)

42 214 (32.3) 34 (5.1)

13 166 (25.1) 34 (5.1)

78 195 (29.5) 34 (5.1)

105 321 (48.5) 34 (5.1)

10 284 (42.9) 34 (5.1)

17 551 (83.2) 34 (5.1)

55 363 (54.8) 34 (5.1)

211 3 (0.5) 34 (5.1)

1700 406 (61.3) 34 (5.1)

3966 0 34 (5.1)

996 0 34 (5.1)

702 1 (0.2) 34 (5.1)

1502 0 34 (5.1)

rted detection limit values for the geometric mean calculations.

ARTICLE IN PRESS

Table 2

Classification of auxiliary variables used for ME analysis

Index Auxiliary variable

Wind direction (degree) Wind speed(m/s) Calm wind (m/s) Time-of-day Time-of-year Weekend

1 0–35 1.0–1.5 0–1.0 3–6 Jan–Feb Weekend

2 35–73 1.5–2.1 7–9 Mar–Apr

3 73–92 2.1–2.8 10–13 May–Jun

4 92–112 >2.8 14–16 Jul–Aug

5 112–142 17–20 Sep–Oct

6 142–166 Nov–Dec

7 166–194

8 194–217

9 217–230

10 230–242

11 242–254

12 254–271

13 271–290

14 290–304

15 304–318

16 318–331

17 331–343

18 343–360


classification of the wind direction of the ith day into 18

indices as shown in Table 2. For example, if the wind

direction is 170� for the ith day, then di ¼ 7 for suchday. Four classifications were used for the value ni: Theclassifications were chosen to make each index interval

have approximately the same population.

In Eq. (3), other information on the sources of

variation in the concentration that might aid the

separation of the sources can be incorporated. In this

study, wind direction, wind speed, time of day, time of

year, and weekend/weekday were used. For the wind

direction and wind speed, hourly averaged values were

used. The complete expanded model consists of the basic

bilinear equation and a multilinear equation specifying

the physical model

xij ¼XP

k¼1

gikfjk þ eij ; ð4Þ

xij ¼XP

k¼1

SðZi; kÞWðoi; kÞX24h¼1

Dðdih; kÞVðvih; kÞ

Rðeih; kÞTðlih; kÞfjk þ e0ij ; ð5Þ

where SðZi; kÞ is the element of matrix S with the indexvalues Zi corresponding to the time-of-year classification

of the ith day for the kth source. Time-of-year was

classified into six two-month periods (or season) as

shown in Table 2. For the value i ¼ 1–60 (January andFebruary), Zi ¼ 1: Wðoi; kÞ is the element of matrix Wwith the index values oi corresponding to weekend/

weekday factor of the ith day for the kth source. The

weekend effect matrix W has dimension 1 by p: In thisstudy, the weekday coefficients have been fixed at unity

so that only the weekend coefficients are variable. The

elements of matrix W specify the average strength of

each factor on weekend relative to the strength in

weekday. Dðdih; kÞ is the element of matrix D with theindex values dih for the wind direction during hour h of

the ith day for the kth source. Vðnih; kÞ is the element ofmatrix V with the index values nih for the wind speed

during hour h of the ith day for the kth source. Rðeih; kÞis the element of matrix R with the index values eih for

the calm wind (o1m/s) during hour h of the ith day for

the kth source. Because of isotropic wind direction, calm

wind was separated as an separate matrix R in this

analysis instead of being included in the wind speed

index matrix V. Also, the wind direction of calm wind

was not incorporated in the wind direction index matrix

D. Tðlih; kÞ is the element of matrix T with the indexvalues lih for the time-of-day during hour h of the ith

day for the kth source. The matrices, S,W, D, V, R, and

T contain unknown values to be estimated in the fitting

process. The specific factor elements used to fit a

particular data point are selected based on the hourly

or daily values of the corresponding auxiliary variables.

Therefore, these auxiliary variables are not fitted, but

served to determine the indices to the values to be fitted.

ME provides a solution that minimizes the value of Q;based upon uncertainty estimates for each observation

(Paatero, 1997, 2000) while the values of the unknown

matrices G, F, S,W, D, V, R, and T are to be determined

so that the model fit the data as well as possible. The Q


value is defined as

Q ¼Xn

i¼1

Xm

j¼1

eij

sij

� �2þXn

i¼1

Xm

j¼1

e0ij

s0ij

!2; ð6Þ

where sij is an uncertainty estimate for the bilinear

model and s0ij is an uncertainty estimate for the

multilinear model in Eq. (5) in the jth element measured

in the ith day.

The application of the ME 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 as well as account for the variability in

the source profiles. The effective error estimates should

be specified so that they include all uncertainties which

produce a deviation between the fit and the observed

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

to assign measured data and the associated uncertainties

as the input data to the ME. The sum of the analytical

uncertainty and 1/3 of the detection limit value was used

as the uncertainty assigned to each measured value.

Values below the detection limit were replaced by half of

the detection limit values and their uncertainties were set

at 5/6 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 measured species that had

scaled residuals larger than72 were increased to reducetheir residuals (Paatero, 2000; Hopke and Paatero,

2003). Large uncertainties were assigned to several

elements so that their scaled residuals were smaller than

72: PM2.5 mass concentration, As, Se, Ti, Si, K, and Ca

(two times of its accompanying uncertainties); OC, EC,

Cl, Mn, Sb, Sn, and Al (3� ); SO42�, NH, Br and Fe

(4� ); Cu (5� ).In Eq. (5), the multilinear equation is one of multiple

possible models depending on the understanding of the

system under study while the mass balance in the

bilinear equation should be much more applicable.

Because the variability of the index factors is restricted

by the model, Eq. (5) will produce a significantly poorer

fit to the data than the bilinear equation (Eq. (4)).

Therefore, the uncertainty estimates corresponding to

the multilinear equation must be larger than those

corresponding to the bilinear equation to decrease the

weight of the multilinear equation in the solution. In this

study, from the experiments with different uncertainties,

estimated uncertainties of multilinear equation were set

at nine times of estimated uncertainties of bilinear

equation.

Generally, the results of ME are normalized by a

scaling constant that is determined by regressing total

PM mass concentrations against estimated source

contribution values, so that the quantitative source

contributions as well as profiles for each source are

obtained (Kim et al., 2003c). In this study, because of

the mass closure problem noted above, the measured

PM2.5 mass concentrations were included as an inde-

pendent variable in the ME analysis to directly obtain

the mass apportionment without the usual multilinear

regression. When the measured PM2.5 mass concentra-

tion is used as a variable, the ME apportions a mass

concentration for each source according to its time

variation. The results of ME analysis were then normal-

ized by the apportioned particle mass concentrations so

that the quantitative source contributions for each

source were obtained. Specifically

xij ¼XP

k¼1

ðckgikÞfjk

Ck

� �; ð7Þ

where ck is directly apportioned mass concentration by

ME for the kth source.

To reduce the influence of extreme values on the ME

solution, the Robust mode was used for this study. A

data point was classified as an extreme value if the model

residual exceeds four times the estimated uncertainty.

The estimated uncertainty values of those extreme

values were then increased so that the influence of the

outliers were reduced.

The six independent variables in the multilinear model

are correlated. Also, this analysis is based on daily

integrated speciation data and hourly measured meteo-

rological data. In order to avoid spurious results and

obtain unique smooth solution, it is necessary to

regularize the model at the expense of possibly losing

some detail. In this study, the wind speed index matrix

V, time-of-day index matrix T, and weekend effect

matrixW were pulled towards unity as a regularization,

such as

Vðr; kÞ ¼ 1 ðr ¼ 1;y; 5; k ¼ 1;y; pÞ; ð8Þ

where Vðr; kÞ is the element of matrix V with the rth

index value for the kth source. In addition, smooth

equation was used for wind directional index matrix D

Dðr; kÞ ¼ Dðr þ 1; kÞ ðr ¼ 1;y; 17; k ¼ 1;y; pÞ: ð9Þ

The uncertainty estimates corresponding to these

auxiliary equations were specified so that the contribu-

tions to the value of Q from these equations were

relatively small to the contributions from the bilinear

equation (Eq. (4)). The regularization removed spurious

variations from factors that were not essential for

achieving a good model fit.

4. Results and discussion

To determine the number of sources, it is reasonable

to test different numbers of sources and use the one that

both adequately fits the data and provides the most

ARTICLE IN PRESSE. Kim et al. / Atmospheric Environment 37 (2003) 5009–50215014

physically meaningful results. As pointed out by Henry

(1987), rotational ambiguity exists in factor analysis

modeling. Therefore, ME was run hundreds times to

determine the optimal solution in the range within which

the objective function QðEÞ values in Eq. (6) remainsrelatively constant. The selected final ME solutions were

determined by trial and error with different number of

sources as well as different uncertainty estimates, and

based on the evaluation of the resulting factor profiles.

Nine sources were obtained from fitting the expanded

model. Within the range of relatively constant QðEÞvalues, the factor profiles were relatively stable. In the

eight-source model, gasoline and diesel exhaust profiles

were combined to make a motor vehicle profile. In ten-

source model, an additional source profile that had zero

mass concentration was deduced indicating that nine-

source model was appropriate. The sensitivity analyses

were conducted by excluding each index matrix from the

multilinear equation (Eq. (5)). Diesel exhaust was not

separated from gasoline exhaust without one of six index

matrices: Diesel and gasoline exhausts were shown as a

motor vehicle source or two OC high sources in the

results.

Fig. 2. Source profiles.

Fig. 2 presents the identified source profiles and Fig. 3

shows time series plots of estimated daily contributions

to PM2.5 mass concentrations from each source. A

comparison of the daily reconstructed PM2.5 mass

contributions from all sources with measured PM2.5

mass concentrations is shown in Fig. 4. When the

uncertainties associated with this data set are consid-

ered, the squared correlation coefficient of 0.90 indicates

that the resolved sources effectively account for most of

the variation in the particle mass concentration. Also,

this squared correlation coefficient 0.90, linear regres-

sion coefficient 0.84, and intercept 1.84 show improve-

ment in model predictions when they are compared with

those from PMF2 analysis (0.83, 0.68, and 3.54,

respectively). The average source contributions of each

source to PM2.5 mass concentration are compared

between PMF2 and ME in Table 3.

The sulfate-rich secondary aerosols I and II are

characterized by high concentrations of SO42� and

NH4+. The sulfate-rich secondary aerosol I has the

highest source contribution to PM2.5 mass concentra-

tions (54%). The sulfate-rich secondary aerosol II that

has higher OC concentration than sulfate-rich secondary

Fig. 3. Time series plot of source contributions.


aerosol I accounts for 2% of the PM2.5 mass concentra-

tion. OC typically becomes associated with the second-

ary sulfate aerosol. For the sulfate-rich secondary

aerosol I (NH4)2SO4 accounts for 46% of the PM2.5

mass concentration and remaining 8% is accounted by

mostly OC. This OC association is consistent with

previous Phoenix (Ramadan et al., 2000) and north-

eastern US (Song et al., 2001) aerosol studies.

In studies of northeastern US particle source, PMF2

separated the sulfur into a high photochemistry source

and a low photochemistry source with seasonal differ-

ences of the Se/S concentrations (Polissar et al., 2001).

In previous study for the same Atlanta data set (Kim

et al., 2003a), PMF2 could not separate to two sulfate

sources due to the poor correlation between S and Se. In

contrast to this previous study, the expanded model

Table 3

Comparison of average source contributions (%) to PM2.5 mass conc

Average source contribution (

PMF2a

Sulfate-rich secondary aerosol I 55.7 (1.3)

Motor vehicle exhaust 22.2 (0.7)

Gasoline exhaust

Diesel exhaust

Nitrate-rich secondary aerosol 7.3 (0.2)

Metal processing

Metal recycling 0.5 (0.02)

Wood smoke 10.8 (0.3)

Airborne soil 1.2 (0.07)

Sulfate-rich secondary aerosol II

Cement Kiln/Carbon-rich 2.0 (0.06)

Bus/Metal processing 0.3 (0.02)

aKim et al. (2003a).bThis study.

Fig. 4. Measured versus predicted PM2.5 mass concentration.

extracted two different sulfate-rich secondary aerosol

sources. The deduced profiles of the sulfate-rich

secondary aerosol I are very similar to those of previous

study (Kim et al., 2003a). The sum of these mass

contributions (56%) is consistent with 56% of previous

study using PMF2 and with the study of three north-

eastern US cities which identified secondary sulfate

source contributions of 47%, 55%, and 51% to the

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

The next profiles are gasoline and diesel exhaust that

were not separated in previous PMF2 analysis (Kim

et al., 2003a). By using the expanded model, they were

separated because of the directionality, seasonal and

time-of-day variation, and weekday/weekend difference.

They are represented by high OC and EC whose

abundances differ between the sources (Lowenthal et al.,

1994; Watson et al., 1994, 2001; Watson and Chow,

2001). Gasoline and diesel exhaust account for 15% and

11% of the PM2.5 mass concentration, respectively. The

sum of both mass contributions (26%) is consistent with

22% of previous PMF2 analysis (Kim et al., 2003a). The

ratio of OC/EC is 3.27 for gasoline vehicle exhaust and

0.88 for diesel exhaust show reasonable agreement with

typical 2.05 in fresh gasoline exhaust and 0.72 in diesel

exhaust for PM10 (Cadle et al., 1999).

The nitrate-rich secondary aerosol is identified by its

high concentration of NO3�. This source accounts for

9% of the PM2.5 mass concentration. Similarly, PMF2

deduced 7% contributions from this source. The metal

processing is characterized by Zn, Si, Fe, and Cl (Small

et al., 1981; US EPA, 2002) accounting for 3% of the

PM2.5 mass concentration. 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

entrations between PMF2 and ME

standard error) Pearson correlation coefficient ðrÞ

MEb

54.3 (1.7) 0.97

14.6 (0.4)

11.2 (0.5)

8.9 (0.4) 0.95

3.1 (0.2)

2.9 (0.2) 0.56

2.2 (0.1) 0.97

1.9 (0.1)

0.9 (0.04) 0.96


concentration may be from lubrication oil used for the

grinding and shredding although there could be adsorp-

tion of low vapor pressure organic compounds on the

primary particles. Previously, PMF2 apportioned 0.5%

from metal processing and 0.3% from the mixed source

of bus and metal processing.

Wood smoke containing OC, EC, and K (Watson

et al., 2001) contribute 3% to the PM2.5 mass

concentration in contrast to the 11% contribution

estimated from PMF2. Also, Table 3 shows poor

correlation (r ¼ 0:56) between PMF2 estimated andME estimated wood smoke contributions. The 8%

difference is explained by increased contributions mostly

from gasoline vehicle/diesel exhaust (3.5% increase),

metal processing (2.3% increase), nitrate-rich secondary

aerosol (1.7% increase), and sulfate-rich secondary

aerosol I/II (0.5% increase). This difference shows that

the expanded model separates sources differently when

more information about sources such as directional,

hourly, and seasonal variation is incorporated. July 4

Fig. 5. Wind directional indice

fireworks in 1999 and 2000 that produce high K

concentration appear as wood burning contributions in

Fig. 3. A similar effects was seen in the Underhill, VT

results (Polissar et al., 2001).

Airborne soil consists of Si, Fe, Ca, Al, and K

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

particles of these crustal elements could be produced by

unpaved roads, construction sites, and wind-blown soil

dust. This source contributes 2% to the PM2.5 mass

concentration consisting with 1% estimated by PMF2.

The cement kiln/carbon-rich source is characterized by

OC, EC, and Ca (US EPA, 2002). It is likely to include

contributions from a cement kiln located about 7 km

northwest of the site and an unknown carbon-rich

source located in the same direction. This source

contributes 0.9% to the PM2.5 mass concentration.

Previously, PMF2 deduced 2% contribution from this

source.

Fig. 5 presents the wind directional factor values in

which the matrix D values are plotted on polar

s for each of the source.


coordinates. The point sources show strong direction-

ality that agree well with the locations of known sources.

There is clear directionality for all of identified sources

except for Sulfate-rich secondary aerosol I. The sulfate-

rich secondary aerosol II that has high OC concentra-

tion points to the southeast in the direction of the

junction of highways I-75 and I-20 and downtown

Atlanta. The polar plot of gasoline vehicle exhaust

factor also points to the southeast toward the highway

junction and downtown Atlanta.

The diesel exhaust appear to have contributions from

all directions and weakly points to the northeast where

highways I-75 intersects I-85. There is railroad traffic

around the monitoring site and rail yard is located about

2 km northwest of the site. Also, a bus station is located

only about 200m southeast of the site. These diesel

vehicle exhaust seem to be mixed with other diesel

exhaust. The nitrate-rich secondary aerosol also points

Fig. 6. Wind speed indices f

to the northeast highway junction. For the metal

processing, there are indications of higher contributions

from the direction of east and southeast. Those may

show the contributions from a metal recycling facility

located east of the site and several metal processing

facilities located southeast of the site. The plot for wood

smoke points to the southeast and southwest where a

residential area is located. Airborne soil has contribu-

tions from the southwest and northwest. The direction

of the cement kiln situated about 7 km northwest of the

monitoring site is clearly shown in Fig. 5.

Fig. 6 shows the wind speed values. The general trend

of the nitrate-rich secondary aerosol is that the values

decrease with increasing wind speed indicating a dilution

effect: the emitted mass is diluted with increasing wind

speed so that the concentration decreases. The nitrate-

rich secondary aerosols were trapped near the ground

before being diluted by higher wind speeds. For other

or each of the source.

ARTICLE IN PRESS

Fig. 7. Time-of-day pattern for each of the source.


eight sources, the wind speed factors do not show strong

or clear trend indicating that their contributions to the

monitoring site were not much affected by wind speed.

Fig. 7 shows the time-of-day factor values. The

nitrate-rich secondary aerosol is high in the evening

through morning and decreases in the afternoon. This is

likely due to lower temperatures and higher relative

humidity (Mangelson et al., 1997). The other sources do

not show strong diurnal variations indicating that the

variations of their hourly contributions were weak. It is

thought to be caused by high source strengths in the

daytime and high concentrations at night due to the

temperature inversion.

Fig. 8 shows the seasonal factor values. The sulfate-

rich secondary aerosol I shows strong seasonal variation

with high concentrations in summer time when the

photochemical activity is highest (Polissar et al., 2001;

Song et al., 2001). In contrast, the sulfate-rich secondary

aerosol II that has high OC concentration shows slightly

higher concentrations in winter when the vapor pressure

of OC is lower and therefore the secondary organic

aerosol is more easily formed. The 13 variation observed

in the time-of-year indices may be due to variation in

source strength or in transport condition or in both. The

gasoline exhaust has the lowest contributions between

September and October. The diesel exhaust contributed

the lowest in summer and the highest between Septem-

ber and October. It is thought to be caused by seasonal

change of prevailing wind directions: decreased wind

frequency from the southeast where the highway

junction and downtown are located made low contribu-

tions of gasoline exhaust and increased wind frequency

from the northwest where the rail yard is located made

high contribution of diesel exhaust in September and

October. The nitrate-rich secondary aerosol has seasonal

variation with maxima in the winter time. This indicates

that lower temperatures and higher relative humidity

help the formation of nitrate aerosols in Atlanta. This is

ARTICLE IN PRESS

Fig. 8. Seasonal profile for each of the source.

Fig. 9. Weekend effect for each of the source.

E. Kim et al. / Atmospheric Environment 37 (2003) 5009–5021 5019

consistent with results from three northeastern US sites

(Song et al., 2001).

The metal processing source has a winter-high

seasonal pattern. The wood smoke source has seasonal

trend with high in winter time indicating residential

wood burning. The airborne soil shows strong seasonal

variation with high concentrations in the drier season,

especially the highest concentration in spring agricultur-

al tilling time. The mixture of cement kiln and carbon-

rich source has seasonal trend with high in summer.

These seasonal patterns are consistent with previous

study using PMF2 (Kim et al., 2003a).

Weekend effects are presented in Fig. 9. The factor

values are the average strength of each source on

weekend relative to the strength on weekday. There were

reduced contributions from the diesel exhaust and the

mixture of cement kiln and carbon-rich source on

weekends. The strong weekday high variations of the


diesel exhaust demonstrate that the main sources of

these are likely to be commuter buses, railroad traffic,

delivery trucks or closely located bus station.

5. Conclusion

The ordinary bilinear factor analysis model augmen-

ted by an expanded multilinear model that contains time

resolved meteorological measurements reduced the

rotational ambiguity in the solution and aided in

identifying the sources. A total of 662 samples, 22

elemental species, two wind variables, and three time

variables were used for this analysis. Nine sources were

identified and directional profiles, wind speed depen-

dencies, hourly patterns, seasonal trends, and weekend

effects were obtained. As different from previous study

for the same data set using PMF2 (Kim et al., 2003a),

two sulfate-rich secondary aerosol sources were ex-

tracted and the diesel exhaust was separated from

gasoline exhaust by using the expanded model. This

study demonstrated that the time resolved wind and

other data were successfully incorporated into ME for

the analysis of real measurement data and could

significantly improve source apportionment study.

Acknowledgements

This study was supported by the Southern Company.

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