ARTICLE IN PRESS

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D205-03, Resea

Atmospheric Environment 38 (2004) 5971–5981

www.elsevier.com/locate/atmosenv

Sources of polycyclic aromatic hydrocarbons to theHudson River Airshed

Jong Hoon Leea,1, Cari L. Gigliottia, John H. Offenberga,2, Steven J. Eisenreicha,b,Barbara J. Turpina,�

aDepartment of Environmental Sciences, Rutgers Cooperative Extension, Rutgers University, ENSR Building, 14 College Farm Road,

New Brunswick, NJ 08901, USAbJoint Research Centre, Institute for Environment and Sustainability: Inland and Marine Waters, Ispra, Varese I-20120, Italy

Received 15 December 2003; received in revised form 3 July 2004; accepted 3 July 2004

Abstract

Sources of polycyclic aromatic hydrocarbons (PAHs) to the Hudson River Estuary Airshed were investigated using

positive matrix factorization (PMF). A three-city dataset was used to obtain common factor profiles. The contributions

of each factor on each sampling day and site were then determined, and a sensitivity analysis was conducted. A stable

eight-factor solution was identified. PMF was able to identify a factor associated with air–surface exchange. This factor

contains low-molecular weight PAHs and was a dominant contributor to the measured PAHs concentrations. Factors

linked to motor vehicle use (diesel and gasoline vehicle emissions and evaporative/uncombusted petroleum) and natural

gas combustion were also major contributors. Motor vehicle combustion and oil combustion factors were the

predominant contributors to particle-phase PAHs, while natural gas combustion, air–surface exchange, and

evaporative/uncombusted petroleum factors made substantial contributions to gas-phase PAH concentrations. In

contrast to fine particulate matter (PM2.5), which is dominated by regional transport, spatial variations in PAH

concentrations suggest that PAH concentrations in the Hudson River Estuary Airshed are dominated by sources within

the New York–New Jersey urban–industrial complex.

r 2004 Elsevier Ltd. All rights reserved.

Keywords: PAHs; PMF; Source apportionment; The Hudson River Estuary; Air–surface exchange

1. Introduction

Atmospheric deposition is an important, and fre-

quently, the dominant source of polycyclic aromatic

e front matter r 2004 Elsevier Ltd. All rights reserve

mosenv.2004.07.004

ing author. Tel.: +1-732-932-9540; fax: +1-

ess: turpin@aesop.rutgers.edu (B.J. Turpin).

ress: Department of Chemical Engineering,

rsity, Potsdam, NY 13699-5707, USA.

ress: US Environmental Protection Agency,

rch Triangle Park, NC 27711, USA.

hydrocarbons (PAHs) to the North American Great

Lakes and the Chesapeake Bay. Recently, air deposition

has also been identified as a substantial source of PAHs

to the New York/New Jersey Hudson River Harbor

Estuary (Gigliotti et al., 2000). For example, the

atmospheric flux of benzo(a)pyrene, a well-known

product of fossil fuel combustion, has been reported as

21mgm�2 year�1 in a suburban area of New Jersey. This

is four times higher than the flux at Lake Michigan or

Chesapeake Bay (Eisenreich and Reinfelder, 2004).

Many urban and industrial centers are located on or

near coastal estuaries (e.g., Hudson River Estuary and

d.

ARTICLE IN PRESSJ.H. Lee et al. / Atmospheric Environment 38 (2004) 5971–59815972

NY Bight) and the Great Lakes. Emissions of pollutants

into the urban atmosphere can result in elevated local

and regional pollutant concentrations and localized

intense atmospheric deposition to the water surface

directly, contributing to the loading of toxics in urban

water bodies. In addition to environmental effects,

airborne PAHs contribute to human exposure directly

through inhalation and through ingestion of PAHs that

have bioaccumulated through the food web.

In order to effectively control air toxic deposition to

the Harbor Estuary, an understanding of the contribu-

tions of local sources and geographical source regions to

airshed concentrations is needed. The New Jersey

Atmospheric Deposition Network (NJADN) was estab-

lished to gain an understanding of the magnitude of

toxic chemical, trace metal, Hg and nutrient concentra-

tions and deposition throughout New Jersey, and to

assess local versus regional sources of air toxic deposi-

tion. PAHs are products of incomplete combustion.

Vehicular emissions from both gasoline- and diesel-

powered vehicles, oil and coal combustion, natural gas

consumption, municipal and industrial incinerators, and

wood/biomass burning are the largest sources of atmo-

spheric PAHs. Once PAHs are emitted, they redistribute

between the gas and particle phases and are removed by

oxidative and photolytic reactions and by dry and wet

deposition (Gigliotti et al., 2000). Deposited PAHs can

also be reemitted (i.e. through volatilization) when

temperatures increase.

In order to investigate the origin of PAHs deposited

into the Hudson River Harbor Estuary we conducted

factor analysis (i.e. positive matrix factorization (PMF))

on the gas plus particle-phase concentrations of 27

PAHs measured at an urban site located in the NY/NJ

Harbor Estuary (i.e. Jersey City, JC), a site down-wind

of the Harbor (i.e. Sandy Hook, SH), and the suburban

New Brunswick site (NB). PMF identifies factors of

covariant species, taking into consideration measure-

ment uncertainties (Juntto and Paatero, 1994). Note that

species can vary together because they come from the

same source type, because their emission rates covary or

because they are transported together. Factors were

named based on information about source profiles

compiled by Rogge (1993), Schauer (1998) and addi-

tional key references provided below, keeping in mind

(1) ways in which PAH profiles are likely to change with

residence time in the atmosphere and (2) other processes

that might introduce PAHs into the atmosphere.

2. Experimental

2.1. Measurement

Twenty-four-hour gas and particle samples were

collected on a quartz fiber filter (QFF) followed by a

polyurethane foam (PUF) adsorbent using a Hi-Volume

sampler in urban Jersey City (i.e. Liberty Science

Center), coastal Sandy Hook, and suburban New

Brunswick, New Jersey as part of NJADN. The Jersey

City site (40.711N/74.051W) is located within urban–in-

dustrial New Jersey across the Hudson River from New

York City. The site is about 0.5 km west of the Hudson

River and about 4 km east of Newark Bay and the

mouths of the Passaic and Hackensack Rivers. Details

about the New Brunswick site (40.481N/74.431W) and

the Sandy Hook site (40.461N/74.001W) are provided

elsewhere (Van Ry et al., 2000, 2002). Gas- and particle-

phase air samples used in this analysis were collected

every 3–12 days at the New Brunswick site, October

1997–January 2001 (N=161), at Jersey City, October

1998–January 2001 (N=76), and at Sandy Hook,

February 1998–January 2001 (N=94). The samples

were analyzed for PAHs of interest by a Hewlett-

Packard 6890 gas chromatograph (GC) coupled to a

Hewlett-Packard 5973 mass selective (MS) detector,

operated in selective ion monitoring mode. The sam-

pling, analytical and quality control procedures are

provided in detail by Gigliotti et al. (2000). Twenty-

seven PAHs whose molecular weight ranges from 166

(fluorene) to 300 (coronene) were used in this analysis

(Table 1).

2.2. The PMF analysis

PMF was run on a matrix of individual PAH

compound concentrations. Gas- and particle-phase

measurements were combined to prevent temperature-

driven gas–particle partitioning from influencing factor

profiles. As a receptor model, PMF solves a least

squares problem using ambient concentrations of air

pollutants and concentration uncertainties (Lee et al.,

2002). PMF defines the sample matrix as product of two

unknown factor matrices:

X ¼ GFþ E: (1)

The sample matrix (X) is composed of n observed

samples and m (m=27) chemical species. F is a matrix of

chemical profiles of p factors or sources. The G matrix

describes the contribution of each factor to any given

sample. E is the matrix of residuals. The PMF solution,

i.e. G and F matrices, are obtained through the PMF

algorithm by minimizing the objective function Q:

Q ¼Xn

i¼1

Xm

j¼1

ðeij=sijÞ2: (2)

Q is the sum of the squares of the difference (i.e. eij)

between the observations (X) and the model (GF),

weighted by the measurement uncertainties (sij). The

theoretical Q value is identical to the number of data

entries.

ARTICLE IN PRESS

Table 1

Mean polycyclic aromatic hydrocarbons in ngm�3 measured in the Hudson River Harbor Airshed

Compound Abbreviation MW New Brunswick (NB) Jersey City (JC) Sandy Hook (SH)

Mean7SD Bdl (Mis) Mean7SD Bdl (Mis) Mean7SD Bdl (Mis)

Fluorene FL 166 2.5771.95 5.774.1 1.3 1.771.5

Phenanthrene PHE 178 9.0574.69 15.176.0 4.873.2

Anthracene ANT 178 0.3470.60 0.970.7 0.170.2

1-Methylfluorene 1-MF 1.4671.13 2.671.7 1.3 0.970.9

Dibenzothiophene DBT 0.8270.55 (3.7) 1.971.0 0.570.4

4,5-Methylphenanthrene 4,5-MP 0.6270.41 1.570.9 0.470.3

Methylphenanthrenes MPs(5) 8.2177.54 14.176.0 4.974.0

Methydibenzothiophenes MDBTs(3) 0.6770.67 (3.1) 1.570.9 0.570.4

Fluoranthene FLT 202 1.6370.81 3.471.9 1.070.8

Pyrene PYR 202 0.8670.48 2.271.2 0.570.4

3,6-Dimethylphenanthrene 3,6-DMP 0.3270.25 0.970.9 0.270.2

Benzo(a)fluorene BaF 216 0.1570.26 0.571.2 0.170.1

Benzo(b)fluorene BbF 216 0.0570.05 0.270.2 0.070.0

Retene RET 0.1070.08 0.270.2 5.3 0.170.1 9.2

Benzo(b)naphtho(2,1-d)thiophene BNT 0.0470.06 0.6 (3.7) 0.170.1 (1.3) 0.070.0

Cyclopenta(cd)pyrene CPP 226 0.0570.09 0.6 (3.7) 0.170.1 0.070.0 13

Benzo(a)anthracene BaA 228 0.1070.14 (5.0) 0.370.3 14 0.070.0 1.3

Chrysene/Triphenylene CHR+TRI 228 0.2370.23 0.570.6 0.170.1 1.3

Naphthacene NPT 228 0.0170.04 49 0.170.1 42 0.070.0 83

Benzo(b+k)fluoranthenes BFLTs 252 0.4170.49 0.771.0 0.270.2

Benzo(e)pyrene BeP 252 0.1770.17 0.471.1 0.170.1

Benzo(a)pyrene BaP 252 0.1070.13 0.6 0.270.2 0.070.0 2.6

Perylene PER 252 0.0270.04 2.5 0.170.1 2.6 0.070.0 13

Indeno(1,2,3-cd)perylene IP 276 0.2370.29 0.570.7 1.3 0.170.1 1.3

Benzo(g, h, i)perylene BghiP 276 0.2270.24 0.470.4 1.3 0.170.1

Dibenzo(a, h+a, c)anthracene DBA 278 0.0370.07 2.5 0.170.1 2.6 0.070.0 6.6

Coronene COR 300 0.2170.27 0.570.7 2.6 0.170.2 1.3

PAH abbreviations used in the text and figures, and PAH molecular weights (MW) are provided. SD identifies measurements within

one standard deviation of the mean. Bdl and Mis are percent of values below the DL and percent missing, respectively. Percent missing

is given in parentheses.

J.H. Lee et al. / Atmospheric Environment 38 (2004) 5971–5981 5973

Values of sij were calculated as follows:

sij ¼ U þ V maxðjX j; jY jÞ; (3)

where U is the matrix of species detection limits and V is

the measurement precision matrix. X is the

concentration matrix and the matrix Y is the array of

values fitted by G and F matrices. This calculation is

specified in the PMF initialization configuration by

setting the error model (EM) code to �14. This option is

recommended for environmental data analysis (Paatero,

2000).

Measurement detection limits were calculated as three

times the standard deviation of the field blanks for each

compound. Measurement precision for each compound

was calculated by propagation of error, taking into

consideration uncertainties in sampling, extraction, and

analysis. The analytical precision for each compound

(Vanaly) was calculated from replicate analyses of

samples and standards (pooled standard deviation/

average replicate mass; percent). Sampling uncertainties

were assumed to be dominated by uncertainties in

volume. In this study, the flow rate was measured before

and after sample collection using a calibrated orifice

meter and manometer. The maximum variability in flow

rate was calculated by applying the maximum and

minimum temperatures over the sampling period, and

the percent uncertainty in sample volume (Vvol) was

represented by the flow rate differential at minimum and

maximum temperatures divided by average flow rate.

Extraction efficiency was estimated for each sample

through the use of surrogate standards added immedi-

ately prior to Soxhlet extraction. Sample masses were

corrected for surrogate recoveries, so the uncertainty

introduced was represented by the variability in

surrogate recovery over time. Specifically, extraction

efficiency precision (Vext) was given by the square root of

the sum of the variances of the surrogate recovery of the

PUF and the QFF. The overall precision calculated by

ARTICLE IN PRESSJ.H. Lee et al. / Atmospheric Environment 38 (2004) 5971–59815974

propagation of error:

V ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðVanalyÞ

2þ ðVvolÞ

2þ ðV extÞ

2q

(4)

ranged from 13% to 28%.

In PMF, species with missing or below detection limit

(DL) values are included by assigning large uncertain-

ties, and therefore a small weight, to these data. In this

study, random values between DL/3 and DL/2 were

assigned to represent the concentration and uncertainty

(i.e., 100% uncertainty) for values below the DL.

Missing values were represented by the geometric mean

concentration of the compound with an uncertainty

equal to four times the geometric mean.

PMF analyses were conducted on data from all three

sites together (331� 27) and each site separately (JC:

76� 27; NB: 161� 27; SH: 94� 27). For each data set

the number of factors was varied, each time running the

model 10 times starting from a different initial seed. This

was done to better understand the stability of the

solution. The model was run in the robust mode to keep

outliers from unduly influencing the results. The

‘‘optimal’’ solution (i.e. number of factors) was con-

sidered to have a Q value near the theoretical Q value

and a solution that did not depend on the initial seed.

Additionally, in the ‘‘optimal’’ solution, the addition of

one or two more factors resulted in no substantive

improvement in the ability to interpret factor profiles (F

matrix). When the ‘‘optimal’’ solution was identified, G

matrix values were regressed against the sum of

measured PAHs. Using the conversion factors obtained

from the regression (Lee et al., 2002), factor profiles and

factor contributions were calculated in pg ng�1 and

ngm�3, respectively.

It should be noted that the authors of this research

participated previously in a project in which three

groups independently analyzed a single data set using

PMF and UNMIX to identify the sources of fine

particles to Brigantine, NJ (Turpin et al., 2002). In

addition, independent PMF analyses of similar particle

species data sets all collected at Brigantine, NJ (over

different years) have been published by the authors of

the current research and others (Kim and Hopke, 2004;

Lee et al., 2002; Song et al., 2001). In both cases, a good

agreement was found. These intercomparisons add

confidence in the results of the current research.

3. Results and discussion

3.1. Identification of sources

Eight factors contributing to the PAH concentrations

measured at the three sites were identified. Six of these

have PAH profiles with strong similarities to particular

source types present in the area. A seventh factor

suggests the influence of a physical process, air–surface

exchange. The origin of the eighth factor is poorly

understood. It should be noted that two or more sources

could be represented by a single factor if their emissions

were strongly covariant in time, did not have distinctly

different PAH emission profiles, or were transported to

the sampling site together from the same source region.

The factor profiles for the three-city data set are

shown in Fig. 1. By analyzing data from the three cities

together, common factor profiles were obtained, and

factor contributions were obtained for each sampling

day at each location. The seasonal averages (summer:

May–October and winter: November–April) are shown

in Fig. 2. The optimal solution had a Q value of 8461,

slightly lower than the theoretical Q value (8937). The

eight-factor solution was chosen because an increase

from 6 to 7 to 8 factors improved the physical

interpretation of the factor profiles. A lower than

theoretical Q value could be obtained if the measure-

ment uncertainties were overestimated. Across the 10

runs with different initial seeds the Q value had a

coefficient of variation (CV) of 2% for the eight-factor

solution, and factor profiles varied little between runs

(with the exception of cyclopenta[cd] pyrene, CPP). This

suggests that a stable solution was obtained. The

modeled sum of PAHs overestimated the measured

sum of PAHs by 3% for the Jersey City data, and

underestimated the measured sum of PAHs by 11% and

5% for the New Brunswick and Sandy Hook data,

respectively.

Factor 1, explaining 25% (718.1) of the sum of

measured PAHs, was identified as air–surface exchange.

This factor contains predominantly low-molecular

weight, volatile PAHs and had higher concentrations

in warmer months, as expected by a temperature-driven

process (see Fig. 2). The ‘‘source’’ of PAHs described by

this process is the reservoir of PAHs in the soil and

water.

Factor 2 is consistent with the evaporation of

uncombusted petroleum during fuel handling and

refueling operations based on its high loading of

methylphenanthrenes (MPs; Kavouras et al., 2001;

Simcik et al., 1997) and lack of higher molecular weight

PAHs such as benzo[b+k] fluoranthenes (BFLTs),

benzo[g,h,i]perylene (BghiP), and coronene (COR;

Simcik et al., 1997). While Factor 2 is a good match

otherwise, phenanthrene (PHE) was not found in this

factor. This factor explains 17.5% (710.7) of the sum of

measured PAHs, and shows no seasonality.

Factor 3, accounting for 12.5% (710.3) of the sum of

measured PAHs, has strong similarities to natural gas

emissions. The combustion of natural gas leads to low

emissions of particulate matter (Rogge et al., 1993b).

This agrees well with the predominately gas-phase

nature of the Factor 3 source profile. Benzo[e]pyrene

(BeP) and BFLTs are prevalent in particle-phase natural

ARTICLE IN PRESS

0100200300400

Factor 2: Evaporative/uncombusted petroleum

050

100

400500

Factor 3: Natural gas combustion

100200300400

Factor 5: Heavier oil combustion

Con

cent

ratio

n (p

g/ng

)

0306090

120Factor 4: Diesel motor vehicles

0100

200

300

400

Factor 6: Oil combustion

0

80

160700

1400Factor 7:Gasoline motor vehicles

Compound

FLPHE

ANT1-

MF

DBT

4,5-

MP

MPs(

5)

MDBTs(

3) FLTPYR

3,6-

DMP

BaF BbFRET

BNTCPP

BaA

CHR+TRI

NPT

BFLTs

BeP BaPPER IP

BghiP

DBACOR

0100200300400

0

50

300600

Factor 1: Air-surface exchange

Factor 8: Other

Fig. 1. Source profiles (factor loadings) obtained from the PMF analysis of PAH concentrations from JC, NB, and SH (eight-factor

solution). PAH abbreviations are defined in Table 1. Darker bars highlight identified source tracers.

J.H. Lee et al. / Atmospheric Environment 38 (2004) 5971–5981 5975

gas home heating emissions (Rogge et al., 1993b). BeP

and BFLTs concentrations are low in Factor 3, but they

represent two of the largest predominately particle-

phase PAHs in the Factor 3 profile (Fig. 1). In natural

gas combustion chrysene (CHR) is expected to be

present with benzo[a]anthracene (BaA), fluoranthene

(FLT), and pyrene (PYR; Daisey et al., 1979), as seen in

this factor. Also, the natural gas combustion factor is

highest in the winter at all sites, consistent with a heating

source.

Factors 4 and 7 are consistent with diesel and gasoline

motor vehicle emissions, respectively, and contributed

7.0% (79.6) and 0.7% (75.3) to the sum of measured

PAHs. Thiophenic PAHs like dibenzothiophene (DBT)

and methyldibenzothiophenes (MDBTs(3)) are emitted

from petroleum-based sources such as gasoline and

diesel fuel combustion. While BghiP tends to be

enhanced in diesel vehicle emissions (see Factor 4),

DBT and MDBTs(3) are enhanced in gasoline vehicles

emissions (see Factor 7). In addition, Factor 7 shows a

small but significant COR peak as expected from

gasoline motor vehicle emissions. COR has been used

as an index PAH compound to differentiate between

gasoline and diesel vehicles because COR has not been

detected in diesel emissions (Rogge et al., 1993a).

Benzo[a]pyrene (BaP) and BaA are important tracers

in gasoline and diesel emissions (Harkov and Greenberg,

1985; Daisey et al., 1979). Other compounds identified

ARTICLE IN PRESS

WINTER SUMMER

0

10

20

30

0

7

14

21

28

35

0

7

14

21

28

35

Con

cent

ratio

n (n

g/m

3 )

0

1

2

3

4

JC

NBSH

Air-surface exchange Evaporative/uncombusted petroleum

Heavier oil combustion

Other

0

5

10

15

20Diesel motor vehicles

WINTER SUMMER

0.00

0.05

0.10

0.15

0.20Gasoline motor vehicles

0

5

10

15

20Natural gas combustion

Con

cent

ratio

n (n

g/m

3 )

0

1

2

3

4Oil combustion

Fig. 2. Average and standard deviation of factor concentrations (factor scores) for JC (black), NB (light gray), and SH (dark gray) for

each factor by season. Winter indicates November–April; summer refers to May–October.

J.H. Lee et al. / Atmospheric Environment 38 (2004) 5971–59815976

with diesel emissions are indeno[1,2,3-cd]perylene (IP; Li

and Kamens, 1993) and benzo[b]naphtho[2,1-d]thio-

phene (BNT; Harrison et al., 1996). Harrison et al.

(1996) state that BghiP and IP in diesel emissions and

gasoline emissions are similar, but diesel emissions are

enriched in B[b]- and B[k]FLT relative to gasoline

emissions. The concentration of BFLTs in Factor 4 is

five times higher than the concentration in Factor 7.

Moreover, CPP, which is enriched in Factor 7, is a

gasoline motor vehicle tracer, although the peak is

abnormally high (The size of the CPP peak varied

considerably with seed run). While Factors 4 and 7 have

been labeled diesel and gasoline, respectively, it must be

noted that considerable overlap exists between emissions

profiles for diesel- and gasoline-powered engines. Some

vehicles of each type are better characterized by the

other profile due to engine size, age, maintenance and

operation. For this reason, these two factors have been

combined in the pie charts of Fig. 3.

Factors 5 and 6 are enriched in oil combustion tracers.

Oil combustion emissions are enhanced in volatile PAHs

(PHE, PYR, FLT) with smaller but significant con-

tributions of high-molecular weight PAHs like COR, IP

and BghiP (Kavouras et al., 2001), as seen in Factor 6.

In addition, BFLTs, chrysene/triphenylene (CHR/TRI)

and BaA are frequently observed in oil combustion

emissions (Rogge et al., 1997). Factor 6 peaks in the

winter, as shown in Fig. 2. In comparison to Factor 6,

Factor 5 has higher loadings of high-molecular weight

PAHs (COR, BghiP, and IP) and lower loadings of low-

molecular weight PAHs. For this reason, we have

labeled Factor 5 ‘‘heavier oil combustion,’’ and suspect

that combustion of jet and aviation fuel oil, residual fuel

oil, and ship fuel oil contribute to this factor. The Jersey

City site is surrounded by major international airports

(Newark International, The John F. Kennedy Interna-

tional, and LaGuardia International), ship ports and

marine terminals, including the Port of Newark and Port

ARTICLE IN PRESS

Fig. 3. Study-wide average mass contribution (%) of each

factor to the observed total PAH mass concentration (sum of

measured species) for JC (a), NB (b), and SH (c). ‘‘Motor

vehicles’’ is the sum of the factors called gasoline motor

vehicles, diesel motor vehicles, and evaporative/uncombusted

petroleum.

J.H. Lee et al. / Atmospheric Environment 38 (2004) 5971–5981 5977

of Elizabeth. Fuel oil is frequently used instead of diesel

or gasoline to power both small and large ships (Bacha

et al., 1998). Factors 5 and 6 contributed 1.7% (72.8)

and 1.7% (75.7), respectively, to the sum of measured

PAHs.

The last factor, Factor 8, accounting for 27.6%

(716.2) of the sum of measured PAHs, was labeled

‘‘Other.’’ This factor could have local and/or regional

source contributions. This factor was higher during the

low-temperature months (November–April; Fig. 2). This

factor is characterized by loadings of low-molecular

weight PAHs. The origin of this factor is not well

understood, but is unlikely to be derived from

combustion.

3.2. Source apportionment

The contribution of each factor to the total PAH

concentration at each site is shown in Fig. 3; the

contributions of individual factors to the concentration

of each individual PAH were examined but are not

shown. With a couple of exceptions, factor scores were

highest in Jersey City, lower in New Brunswick, and

lowest at Sandy Hook in both seasons. Factor concen-

trations at the Jersey City site were roughly twice those

in New Brunswick. Differences between Jersey City and

New Brunswick are less pronounced for mobile source

factors than they are for oil and natural gas combustion

and evaporated petroleum factors. In fact, New Bruns-

wick had the highest summertime concentration of the

gasoline motor vehicle factor. As expected, factor

concentrations were higher in the winter than in the

summer for the combustion sources. ‘‘Evaporative/

uncombusted petroleum’’ did not show a strong

seasonality, consistent with the continuous need for

refueling, and the factor labeled ‘‘air–surface exchange’’

exhibited much higher concentrations in the warmer

periods, as expected (see Fig. 2).

The average factor contributions in Fig. 3 suggest that

oil and natural gas combustion were responsible for a

larger percentage of the total atmospheric loading of

PAHs in Jersey City than at the other sites, whereas a

larger percentage of the PAH loading in Sandy Hook

was attributed to motor vehicles and surface-to-air

volatilization. In this figure, gasoline and diesel motor

vehicle and evaporative/uncombusted petroleum

(i.e. refueling) factors were combined and labeled

‘‘motor vehicles.’’ The percent contribution of motor

vehicles was comparable at the Jersey City and New

Brunswick sites.

It must be noted that the environmental fate and

effects of PAHs vary considerably from compound to

compound, and by phase (i.e. gas or particle; Bidleman,

1988). In fact, different physical/chemical processes

control the deposition of particles and gases into the

Hudson River Estuary watershed. Since most of the

PAH mass is found in the gas phase, Figs. 2 and 3 focus

attention on gas-phase PAHs. To explore the results

further, the factor contributions to individual PAH

concentrations were examined (not shown). Natural gas

combustion contributed mostly to low-molecular weight

PAHs (ANT, 1-MF, 4,5-MP, MPs(5), 3,6-DMP) which

are found almost entirely in the gas phase in the

ARTICLE IN PRESSJ.H. Lee et al. / Atmospheric Environment 38 (2004) 5971–59815978

atmosphere. In addition, air–surface exchange and

evaporative/uncombusted petroleum factors made

significant contributions to low-molecular weight PAHs.

Four- and five-ring PAHs (FLT, PYR, BbF, CHR,

TRI, NPT, BFLTs, BeP, BaP, PER, and DBA),

which partition between the gas and particle

phases, had substantial contributions from oil combus-

tion and gasoline and diesel motor vehicle factors.

BNT, BaA, CHR+TRI, BFLTs, BeP, BaP, and

perylene (PER) concentrations were dominated by the

diesel vehicles factor, while CPP and thiophenic PAHs

such as DBT and MDBTs(3) were dominated by the

gasoline vehicle factor. The oil combustion factor

contributed both to low- and high-molecular weight

PAHs. The highest molecular weight PAHs (i.e. with six

or seven rings) were dominated by the heavier oil

combustion factor. This examination suggests that

motor vehicle-related emissions and oil combustion are

the predominant sources of particle-phase PAHs,

including benzo[a]pyrene, in the Hudson River Estuary

air basin.

3.3. Sensitivity analysis

To examine the sensitivity of the results to reasonable

perturbations in approach, PMF analyses run with the

Jersey City data alone were compared with results

obtained for Jersey City when PMF was conducted on

the entire three-city dataset. PMF was conducted 10

times with different initial seed values for each dataset.

0

2

4

6

8

10

12

14

16

Motor vehicles Natural gascombustion

Air-exc

Co

nce

ntr

atio

n (

ng

/m3 )

Fig. 4. Sensitivity analysis: study-wide average factor concentration

analysis initialized using two different seeds. R2s1 and R2s10 result f

seeds. ‘‘Motor vehicles’’ is the sum of the factors called gasoline moto

petroleum.

The variation in Q value across the 10 runs was o5%

(coefficient of variation) and the resulting factor profiles

showed only modest variations from run to run,

suggesting that a stable solution was obtained for both

datasets. Fig. 4 illustrates the sensitivity of the results to

initial seed and data selection. Jersey City factor

concentrations obtained for two randomly selected

three-city runs (R1 seed 3, 6) and two randomly selected

Jersey City runs (R2 seed 1, 10) are shown. Evaporative/

uncombusted petroleum, gasoline motor vehicle and

diesel motor vehicle factors are combined and labeled

‘‘motor vehicle.’’ No significant difference was found

between the twenty runs for motor vehicles, air–surface

exchange, and heavier oil factors at the 95% confidence

level (a ¼ 0:5) according to one-way analysis of variance

(ANOVA; SAS/STATs software version 8.2, SAS

Institute Inc., 1990), suggesting stable model results for

those factors without regard to seed number or data

selection (i.e. three-city run or one-city run). One three-

city oil combustion factor run was significantly different

from the others (R1S3, shown), but despite this,

uncertainties in this factor’s contribution are still

modest. In contrast, the natural gas factor concentra-

tions show two-fold differences between the three-city

and one-city results (differences with seed number are

not significant).

The sensitivity analysis suggests that the uncertainties

in the factor concentrations are on the order of 5–10%,

with the exception of the natural gas combustion factor,

which is more uncertain. The difference in the one-city

surfacehange

Oil combustion Heavier oilcombustion

R1s3

R2s6

R2s1

R2s10

s for JC. R1s3 and R1s6 are results from the three-city PMF

rom analysis of the JC data only, initialized with two different

r vehicles, diesel motor vehicles, and evaporative/uncombusted

ARTICLE IN PRESSJ.H. Lee et al. / Atmospheric Environment 38 (2004) 5971–5981 5979

and three-city natural gas results could be due to

between-city differences in the composition of natural

gas emissions near the three sites (e.g., different

contributions from home heating and industrial com-

bustion), or differences in the degree of atmospheric

processing between emission and sampling at the three

sites. It should also be noted that any additional sources

of PAHs that have not been identified in the solution

(e.g., wood burning) contribute to one or more of the

eight factors in an unknown way. Between-city differ-

ences in unidentified sources could also pose a challenge

in the analysis of a multiple-site dataset. For these

reasons, we expect the one-city results to be more

accurate. However, analysis of all three sites together

provides a common definition for each factor, which is

necessary for comparisons to be made across the three

cities. This sensitivity analysis, then, demonstrates the

validity of the three-city solution for all but one factor.

The concentration of the natural gas combustion factor

in the three-city solution appears to be low by about a

Fig. 5. Annual average PAH concentrations at two locations upw

Washington Crossing, NJ) and at JC, NB, and SH, NJ. Shown are: (a)

PAH, and (b) benzo[a]pyrene, a high-molecular weight predominantl

factor of two. This is consistent with the one-city New

Brunswick solution (not shown), in which 24%, rather

than 12% (three-city), of the sum of PAHs are

attributed to the natural gas factor. Other one-city and

three-city New Brunswick results are in reasonable

agreement (oil+heavier oil 4.7% vs. 3.4%; air–surface

exchange 19% vs. 24%; motor vehicle 30% vs. 22% for

one-city and three-city solutions).

3.4. Local and regional contributions

Differentiating the contributions of local, regional,

and upwind PAH sources to the airshed feeding the

Hudson River Estuary is of great interest to those

developing strategies to reduce loadings of toxic

compounds in the Estuary. Some insights into the

importance of local PAH sources can be gained by

comparison with New Jersey fine particulate matter

(PM2.5) concentrations. Variations in PM2.5 concentra-

tions across the state of New Jersey are highly

ind of the NY–NJ urban–industrial complex (Chester and

phenanthrene, a low-molecular weight predominantly gas-phase

y particle-phase PAH.

ARTICLE IN PRESS

Fig. 5. (Continued)

J.H. Lee et al. / Atmospheric Environment 38 (2004) 5971–59815980

synchronized, as expected for a species that is dominated

by regional sources and/or transport from upwind

(Chuersuwan, 2001; Chuersuwan et al., 2000). In

contrast, peaks in PAH concentrations and PAH factor

scores at Jersey City, New Brunswick and Sandy Hook

were poorly synchronized in time, with the exception of

the air–surface exchange factor. Variations in air–sur-

face exchange are driven by variations in air tempera-

ture, which occur regionally.

Chuersuwan et al. (2000) and Chuersuwan (2001)

concluded that annual average PM2.5 concentrations at

the Brigantine National Wildlife Refuge in southern

New Jersey provided a reasonable estimate of the Mid-

Atlantic States ‘‘regional background’’ for PM2.5 (i.e.

continental background, transport and regional

sources). At most, one-third of this is continental

(natural) background. This ‘‘regional background’’

accounts for 70% of the annual average PM2.5

concentration in Newark, NJ and 75% in Elizabeth

and Camden. Therefore, they concluded that, on an

annual basis, no more than 25–30% of PM2.5 in urban

New Jersey is local.

Fig. 5 shows a different picture for PAHs. The annual

average sum of PAHs at Chester and Washington

Crossing, locations generally upwind of the NY–NJ

urban–industrial complex, are only 20–30% of those at

Jersey City (not shown). This suggests that roughly 75%

of PAHs in the Hudson River Estuary airshed originate

within the NY–NJ urban–industrial area (i.e. locally) on

an annual basis. This appears to be true both for high-

and low-molecular weight PAHs, as shown in Fig. 5 for

phenanthrene (low molecular weight) and benzo[a]pyr-

ene (high molecular weight). Thus, local sources of

PAHs are substantial contributors to the Hudson River

Estuary airshed.

Acknowledgements

This project is funded by the United States Environ-

mental Protection Agency and the New Jersey Depart-

ment of Environmental Protection (project manager,

Alexander V. Polissar) under Grant #SR-01-020. The

concentration data used in this research are a result of

ARTICLE IN PRESSJ.H. Lee et al. / Atmospheric Environment 38 (2004) 5971–5981 5981

previous research funded in part by the Hudson River

Foundation (project officer, Dennis Suszkowski) under

Grant #004/99A and the New Jersey Department of

Environmental Protection, Division of Science and

Research (project manager, Michael Aucott), and the

New Jersey Agricultural Experiment Station (NJAES).

The PMF model was used under a licensing agreement

with Dr. Pentti Paatero of the University of Helsinki,

Finland. We gratefully acknowledge the tremendous

effort of the NJADN field and analytical teams that

produced the quality-assured PAH dataset.

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