Acta Oceanol. Sin., 2014, Vol. 33, No. 12, P. 32–39
DOI: 10.1007/s13131-014-0531-5
http://www.hyxb.org.cn
E-mail: [email protected]
Atmospheric concentration characteristics and gas/particle
partitioning of PCBs from the North Pacific to the Arctic Ocean
WANG Zhen1, NA Guangshui1, GAO Hui1, WANG Yanjie1, YAO Ziwei1*
1
National Marine Environmental Monitoring Center, Dalian 116023, China
Received 1 March 2014; accepted 8 July 2014
©The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2014
Abstract
Polychlorinated biphenyls (PCBs) were measured in atmospheric samples collected from the North Pacific
to the Arctic Ocean between July and September 2012 to study the atmospheric concentration characteristics of PCBs and their gas/particle partitioning. The mean concentration of 26 PCBs (vapor plus particulate
phase) (ƩPCBs) was 19.116 pg/m3 with a standard deviation of 13.833 pg/m3. Three most abundant congeners were CB-28, -52 and -77, accounting for 43.0% to ƩPCBs. The predominance of vapor PCBs (79.0% to
ƩPCBs) in the atmosphere was observed. ƩPCBs were negative correlated with the latitudes and inverse of
the absolute temperature (1/T). The significant correlation for most congeners was also observed between
the logarithm of gas/particle partition coefficient (logKP) and 1/T. Shallower slopes (from −0.15 to −0.46,
average −0.27) were measured from the regression of the logarithm of sub-cooled liquid vapor pressures
(logpºL) and logKP for all samples. The difference of the slopes and intercepts among samples was insignificant (p>0.1), implying adsorption and/or absorption processes and the aerosol composition did not differ
significantly among different samples. By comparing three models, the J-P adsorption model, the octanol/
air partition coefficient (KOA) based model and the soot-air model, the gas/particle partitioning of PCBs
in the Arctic atmosphere was simulated more precisely by the soot-air model, and the adsorption onto elemental carbon is more sensitive than the absorption into organic matters of aerosols, especially for lowchlorinated PCB congeners.
Key words: PCBs, gas/particle partitioning, Arctic Ocean, soot-air model, semi-volatile organic compounds
Citation: Wang Zhen, Na Guangshui, Gao Hui, Wang Yanjie, Yao Ziwei. 2014. Atmospheric concentration characteristics and gas/
particle partitioning of PCBs from the North Pacific to the Arctic Ocean. Acta Oceanologica Sinica, 33(12): 32–39, doi: 10.1007/
s13131-014-0531-5
1 Introduction
Polychlorinated biphenyls (PCBs) are a group of ubiquitous
semi-volatile organic compounds (SVOCs) characterized by
high lipophilicity, extreme toxicity and high persistence. Although PCBs have been banned for over 20 years, they are still
detected in all environmental media, even in the Arctic and
Antarctic (Helma and Bidleman, 2005; Galbán-Malagón et al.,
2013; Newton et al., 2013). Researchers considered that these
chemicals are still released into the atmosphere by primary
(e.g., vaporization or burning of products containing PCBs) and
secondary sources (e.g., air/sea and air/soil exchange) (Hung et
al., 2001; Breivik et al., 2002). The wide range of sub-cooled liquid vapor pressures (pºL) (from 6.6×10−2 Pa for CB-8 to 3.59×10−6
Pa for CB-205 at 20°C) makes different PCB congeners exist both
in vapor form and in association to particles in the atmosphere
(van Noort, 2009). Once emitted into the atmosphere, PCBs
would be partitioned between the two phases and approach to
a partitioning equilibrium according to their physic-chemical
properties and temperature dependencies. Under this condition, the distribution trend of PCBs between gas and particle
phases is considered as an important aspect regarding their
transport and transformation, such as the potential of their
long-range atmospheric transport (LRAT), wet/dry deposition
mechanisms and photolysis degradation (Montone et al., 2003;
Gambaro et al., 2005; Baek et al., 2011; Ma et al., 2011).
The partitioning of SVOCs between gas and particle phases
has been investigated thoroughly for several years (Pankow,
1987; Pankow and Bidleman, 1992; Wang, Na et al., 2013). In
general, it can be described with a gas/particle partition coefficient, KP (m3/μg)=(CP/TSP)/CA, where CP and CA are particleand gas-phase concentrations of SVOCs (ng/m3), and TSP is the
total suspended particle concentration in the atmosphere (μg/
m3). Previous studies have demonstrated that adsorption onto
the particulate surface and absorption into the organic matter
(OM) of aerosols are two primary mechanisms controlling the
gas/particle partitioning of SVOCs (Odabasi et al., 2006; Vardar
et al., 2008). Initially, Junge (1977) and Pankow (1987) proposed
a J-P adsorption model based on the Langmuir surface adsorption of SVOCs onto the particulates. Afterwards, an octanol/air
partition coefficient (KOA) based absorption model taking into
account the mechanism of the absorption into OM of aerosols
was introduced, and KOA was employed to describe the partitioning of SVOCs between air and OM of aerosols (Finzio et al.,
1997; Harner and Bidleman, 1998). Recently, researchers found
that both the adsorption and absorption processes play important roles on the gas/particle partitioning, thus, an improved
soot-air model was developed including KOA and a new sootair partition coefficient (KSA) by taking account of the absorp-
Foundation item: The Chinese Polar Environment Comprehensive Investigation and Assessment Programs under contract Nos 02-01, 03-04, 04-01
and 04-03; the National Natural Science Fundation of China under contract No. 21377032.
*Corresponding author, E-mail: [email protected]
33
WANG Zhen et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 12, P. 32–39
tion into OM as well as the adsorption onto the soot (Dachs and
Eisenreich, 2000).
The aims of this study were to investigate the concentrations
and the spatial distribution of 26 PCB congeners in the atmosphere from the North Pacific to the Arctic Ocean, estimate their
characteristics of gas/particle partitioning, and examine the reliability of the three models, the J-P adsorption model, the KOAbased absorption model and the soot-air model.
2 Materials and methods
2.1 Sampling
Air samples were collected between July and September
2012 onboard the ice-breaker R/V Xuelong (Snow Dragon). The
detailed sampling information is presented in Table 1. Total
19 air samples were collected with a high-volume air sampler
(HiVol) on the uppermost deck in the front of the ship. Glass
fiber filter (GFF) (250×200 mm, pore size: 0.7 μm) was used to
collect particles and polyurethane foam (PUF) plug (column,
diameter: 95 mm, high: 50 mm) was employed to capture vapor
PCBs. GFFs were baked at 400°C for 4 h followed by storage in
aluminum packages. PUF plugs were precleaned by Soxhlet extraction for 12 h using hexane. Air samples were collected with
an airflow rate of 1.0 m3/min and the total sampled air volume
averaged about 2 880 m3. To avoid sampling air coming from the
ship’s funnel, the HiVol was only operated under “good conditions”, i.e., with the air coming from the bow of the ship (Wang,
Na et al., 2013). After sampling, PUFs and GFFs were wrapped
in aluminum foil and stored in a freezer at −20°C. Five cleaned
GFFs and PUFs were exposed to the atmosphere during the
sampling period and considered as field blanks.
2.2. Extraction and analysis
CB-209 was added to samples as surrogate compounds before extraction. PUFs were soaked for 12 h with 500 mL hexane/
dichloromethane (DCM) (1:1, v:v), then were extracted with
ultrasonic bath 30 min. GFFs were soaked and extracted in ul-
trasonic bath with 50 mL hexane/DCM (1:1) for 30 min. The
extract solvents of PUFs and GFFs were condensed to about 5
mL with a rotary evaporator, and transferred to a pre-cleaned
column filled from the bottom with 1 cm of anhydrous Na2SO4
(pre-soaked in hexane), 2 g of activated silica gel, 4 g of neutral
alumina and 1 cm of anhydrous Na2SO4. Then samples were
eluted with 30 mL of hexane and 50 mL of hexane/DCM (1:1)
mixture successively. The second part of extracts were collected
and evaporated to about 5 mL with a rotary evaporator, then
concentrated to 200 μL under nitrogen gas stream. The internal
standard 2,4,5,6-TCMX was added into each sample prior to GC
analysis.
Chemical analysis was performed using Agilent 7890A gas
chromatograph with an electron capture detector (ECD) and a
DB-5 column (30 m×0.25 mm×0.25 μm). Nitrogen was used for
carrier gas at a flow rate of 1.5 mL/min with the constant column pressure of 1.18 MPa. The injection volume was 1.0 μL with
the splitless mode. The detector temperature was 300°C. The
temperature program was as follows: initial temperature was
80°C, increased to 180°C at a rate of 20°C/min, then increased
at a rate of 50°C/min to 250°C and held for 2 min. Finally, the
temperature elevated to 280°C at the rate of 30°C/min and held
for 5 min. In total, 26 PCB congeners were quantified (PCB congeners according to IUPAC numbering system): CB-8, -28, -52,
-66, -77, -81, -101, -105, -110, -114, -118, -123, -126, -128, -138,
-153, -155, -169, -170, -180, -187, -189, -194, -195, -200 and -205.
2.3 Quality control
The detailed quality assurance and control procedures were
described elsewhere (Wang, Na et al., 2013). Briefly, the recoveries of the surrogate compounds in PUFs and GFFs were 61.3%–
75.2% and 60.4%–79.3%, respectively, and PCB concentrations
were corrected using recoveries of surrogate compounds. Limited of detection (LOD) were derived as the mean plus three
times the standard deviation of the concentrations in five field
blanks (considering an average sampled air volume of 2 800 m3)
and the value of LOD was 0.05 pg/m3. PCB concentrations were
Table 1. Information of sampling on the ice-breaker R/V Xuelong (Snow Dragon) during the 5th Chinese National Arctic Research
Expedition from July 2nd to September 27th, 2012, and concentrations of 26 PCBs (pg/m3)
Sample
Sampling
Latitude
Longitude
Sampling
volume/m3
Temperature/°C
Relative
humidity/%
Vapor
Particulate phase
Total
phase concentration/
concentration/
concentration/
Percentage of
number
date
vapor PCBs/%
pg·m−3
pg·m−3
pg·m−3
101
Jul. 04
38°30′7″N
133°26′1″E
1 358
20.3
88
19.819
6.931
26.750
74.1
102
Jul. 05
43°37′53″N
138°33′21″E
1 373
15.7
79
24.240
5.962
30.202
80.3
103
Jul. 07
48°18′41″N
149°45′3″E
1 928
13.4
91
10.937
5.609
16.546
66.1
104
Jul. 09
50°2′22″N
157°43′6″E
2 826
10.3
87
6.968
3.901
10.869
64.1
105
Jul. 11
54°23′10″N
164°29′11″E
2 846
9.2
79
10.386
3.439
13.824
75.1
106
Jul. 13
57°24′8″N
175°7′17″E
2 841
9.3
90
12.370
3.963
16.333
75.7
107
Jul. 15
61°1′37″N
178°4′8″E
2 796
7.6
92
12.109
3.052
15.161
79.9
108
Jul. 18
64°33′40″N
168°38′50″E
2 849
7.3
83
13.307
3.578
16.885
78.8
109
Jul. 20
70°40′59″N
164°49′0″E
2 850
8.0
84
7.159
2.777
9.936
72.1
110
Aug. 21
68°42′57″N
14°47′29″W
2 864
9.6
87
8.008
4.017
12.025
66.6
111
Aug. 24
78°15′7″N
9°13′24″W
2 906
1.3
77
10.976
2.865
13.841
79.3
112
Aug. 26
82°10′17″N
78°35′5″E
2 916
0.3
78
15.535
1.651
17.185
90.4
113
Aug. 28
84°14′33″N
121°0′31″E
2 931
1.3
73
6.288
4.948
11.236
56.0
114
Aug. 30
87°11′25″N
121°56′51″E
2 922
0.7
74
10.189
4.433
14.622
69.7
115
Sep. 03
81°56′12″N
168°55′49″W
2 924
3.2
80
6.535
4.226
10.761
60.7
116
Sep. 05
71°16′12″N
164°33′19″W
2 834
12.0
84
4.797
3.176
7.973
60.2
117
Sep. 11
61°24′36″N
159°22′19″E
2 801
13.1
86
10.863
3.728
14.591
74.4
118
Sep. 13
51°44′16″N
159°22′19″E
2 766
23.0
89
32.209
4.604
36.813
87.5
119
Sep. 18
41°45′15″N
151°42′12″E
2 766
22.0
90
64.347
3.310
67.657
95.1
34
WANG Zhen et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 12, P. 32–39
7
6
4
φ=CP/(CP+CA)=KP(TSP)/[1+KP(TSP)]=cθ/(pºL+cθ),
(1)
where φ is the predicted fraction of a compound associated
with particles, θ is the particle surface area concentration (cm2
particles per cm3 air), and assumed to be 1.1×10−5 for urban air,
(1.0–3.5)×10−6 for rural air and 1.0×10−7 for remote air, and c is
a parameter that depends on the properties of the substance of
interest, is usually assumed to be 17.2 Pa·cm for PCBs (Lohmann and Lammel, 2004).
Harner and Bidleman (1998) proposed a KOA-based absorption model to predict KP with octanol/air partition coefficient
(KOA) of target chemicals and the mass fraction of organic matter on aerosol (fOM):
K P = f OM
ζ OCT
MWOCT
K OA ,
ζ OM ρ OCT MWOM 1012
(2)
where ζOCT and ζOM are activity coefficients of the absorbing
compound in octanol and OM, ρOCT is the density of octanol
(0.82 kg/L at 20°C), MWOCT and MWOM are the mean molecular weight of octanol and OM. With the assumptions of ζOCT/
ζOM=MWOCT/MWOM=1. Equation (2) can be transformed in:
logKP=logKOA+logfOM–11.91.
(3)
Many previous studies showed that, besides absorption,
adsorption partitioning could also be an important sorption
mechanism during the gas/particle partitioning, therefore, an
overall gas/particle partition coefficient that considers both absorption and adsorption mechanisms in the soot is introduced
by the following equation (the soot-air model) (Dachs and
Eisenreich, 2000):
KP
f OM
ζ OCT
MWOCT
α EC
K OA + f EC
KSA ,
ζ OM ρOCT MWOM 1012
α AC1012
(4)
where fEC is the fraction of elemental carbon in the aerosol,
αEC and αAC are specific surface areas of elemental carbon and
activated carbon, respectively, and KSA is the soot-air partition
coefficient. It was assumed that elemental carbon accounted
for soot carbon and there was no difference between soot and
elemental carbon, thus the following assumption can be adopted: αEC/αAC=ζOCT/ζOM=MWOCT/MWOM=1 (Dachs and Eisenreich, 2000), and the value of αEC was taken as 100 m2/g based
on Bucheli and Gustafsson (2000). van Noort (2003) introduced
the equation to estimate the value of KSA for different PCB congeners:
logKSA=−0.85 logpºL+8.94–log(998/αEC).
(5)
3
2
1
0
CB-8
CB-28
CB-52
CB-66
CB-77
CB-81
CB-101
CB-105
CB-110
CB-114
CB-118
CB-123
CB-126
CB-128
CB-138
CB-153
CB-155
CB-169
CB-170
CB-180
CB-187
CB-189
CB-194
CB-195
CB-200
CB-205
2.4 Three models describing the gas/particle partitioning of
SVOCs
There are three representative models which describe the
gas/particle partitioning of SVOCs. The most widely used model is the adsorption model (the J-P adsorption model) based on
the linear Langmuir isotherm (Pankow, 1987):
PCBs concentrations/pg∙m−3
blank corrected (including method blanks and field blanks).
Fig.1. Average concentrations and the standard deviations of the 26 PCBs (vapor plus particulate) for all the
19 air samples.
3 Results and discussion
3.1 Atmospheric PCB concentrations
The atmospheric concentrations of 26 PCBs in the 19 samples are summarized in Table 1, along with data on average air
temperature, relative humidity, sampling volume, vapor plus
particulate phase concentrations of PCBs. The average value
of the total concentration (vapor plus particulate phase) of the
26 PCBs (ƩPCBs) was 19.116 pg/m3 with a standard deviation
of 13.833 pg/m3. The highest concentration (67.657 pg/m3) was
measured near Hokkaido of Japan, and the lowest value (7.973
pg/m3) occurred at the Chukchi Sea, indicating the atmospheric concentrations of PCBs were influenced significantly
by potential human activities. The profile of PCB congeners was
dominated by low-chlorinated congeners (Fig. 1), including di-,
tri- and tetra-chlorinated congeners, and the most abundant
congeners were CB-28 (3.022 pg/m3), -52 (2.056 pg/m3) and
-77 (3.136 pg/m3), accounting for 15.8%, 10.8% and 16.4% to
ƩPCBs, respectively.
For all 19 samples, the predominance of vapor PCBs in the
atmosphere was observed and a small portion was presented
in the particle phase. The average percent of the vapor phase
compounds to ƩPCBs was 79.0%, and the proportions of vapor PCBs for different chlorinated PCBs were: di- (90.9%), tri(84.6%), tetra- (82.5%), penta- (69.6%), hexa- (76.5%), hepta(68.0%) and octa- (51.7%). Obviously, the proportions of vapor
PCBs for low-chlorinated congeners were higher than those for
high-chlorinated counterparts. The higher levels of vapor phase
PCBs in the atmosphere have been pointed out in many other
studies (Mandalakis et al., 2007; Bogdal et al., 2012). This phenomenon can be explained by their wide range of pºL for different chlorinated PCBs. As stated above, pºL is an important
factor influencing the distribution of PCBs in gas and particle
phases. PCBs with higher pºL values are prone to exist in the
vapor phase, such as di- and tri-chlorinated PCBs, whereas the
high-chlorinated congeners are subject to bind to particulates
due to their lower pºL (Wang, Ren et al., 2013).
A linear relationship was observed for the 19 samples between ƩPCBs and the latitudes (y=−0.52x+51.62, r2=0.32,
p<0.05). Although the value of r2 was low (0.32), the correlation
was considered statistically significant due to the significant lev-
35
WANG Zhen et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 12, P. 32–39
el p=0.011<0.05. The slope (−0.52) was negative, indicating that
the concentrations of PCBs in the ambient air decreased with the
increase of latitudes. A significant correlation between ƩPCBs
and 1/T (K−1) was also observed: ƩPCBs=−107 127×(1/T)+397.84
(r2=0.45, p<0.05). Due to high proportions of vapor PCBs to
ƩPCBs, the concentrations of vapor PCBs and 1/T displayed
a significant correlation as expected (y=−100 751x+371.29,
r2=0.40, p<0.05). The results were consistent with the findings
of polycyclic aromatic hydrocarbons in the Arctic atmosphere
reported by Wang, Na et al. (2013).
3.2 Influence of temperature
According to Yamasaki et al. (1982) and Kaupp and McLachlan (1999), assuming the aerosol surface (θ, cm2/cm3) is proportional to the concentration of TSP, the measured KP of PCBs will
be related to the ambient temperature. This behavior can be described in terms of the Langmuir isotherm equation:
logKP=A/T+B,
(6)
where A (slope) and B (intercept) are constants specific to individual congeners and T is the absolute temperature (K). Regression results for all the 26 PCB congeners are presented in
Table 2. For most congeners (except for CB-52, -77, -170, -189
and -205), the temperature dependence of logKP was statistically significant (p<0.05). As expected, the slopes (A) were positive, which confirmed that logKP increased with a decrease in
temperature as a result of a decrease of vapor fraction of PCBs
at lower temperature.
In general, a steep slope regressed from Eq. (6) indicates that
the target compounds in ambient atmosphere are controlled by
evaporation from the local surroundings of the sampling sites,
while a shallow slope indicates that they are input from LRAT
(Wania et al., 1998). The relatively strong temperature dependence and the shallowness of the slopes observed in this study
revealed that the LRAT of PCBs played a quite important role in
the Arctic atmosphere.
3.3 Gas/particle partitioning of PCBs
A linear relationship between logKP and logpºL was usually
used to describe the relation of the physic-chemical properties
(e.g., pºL) and the gas/particle partitioning of SVOCs:
logKP=mrlogpºL+br,
(7)
where mr and br are empirical constants. Based on Pankow
(1994) and Gaga and Ari (2011), meaningful information about
the partition of SVOCs can be extracted from the slope mr and
the intercept br of the regression line.
In this study, logpºL (Pa) of PCB congeners under the sampling temperature was calculated employing the method proposed by van Noort (2009). The data points where the congeners
were below the detection limit were replaced with the values of
LOD in the calculations. The plot of logpºL versus logKP for all
the 19 samples is presented in Fig. 2, and the regression parameters of mr, br, r2 and p for each sample are listed in Table 3.
For all samples, the correlation of logpºL–logKP was statistically significant (r2=0.23–0.49, p<0.01), and the slopes mr were
in the range of −0.15 and −0.46 with an average value of −0.27.
The two-tailed t-test results indicated that the difference of the
slopes and intercepts among samples was insignificant (p > 0.1),
which implied adsorption and/or absorption processes and the
Table 2. Regression results of 1/T versus logKP for all the 26 PCB congeners in the atmosphere for Eq. (6)
PCBs
CB-8
CB-28
CB-52
CB-66
CB-77
CB-81
CB-101
CB-105
CB-110
CB-114
CB-118
CB-123
CB-126
CB-128
CB-138
CB-153
CB-155
CB-169
CB-170
CB-180
CB-187
CB-189
CB-194
CB-195
CB-200
CB-205
A
2 607
2 480
3 268
5 924
2 165
6 665
3 781
6 089
2 744
4 953
4 516
4 970
4 248
2 561
2 562
6 139
3 499
4 607
675
2 629
2 492
2 067
2 648
2 721
4 870
348
B
−11.41
−10.55
−13.76
−22.77
−8.87
−25.23
−14.97
−22.43
−11.35
−19.20
−17.49
−18.99
−16.03
−10.09
−10.64
−23.01
−13.47
−17.39
−3.82
−10.35
−10.09
−8.30
−10.42
−10.70
−18.26
−1.93
r2
0.34
0.39
0.19
0.39
0.16
0.52
0.36
0.63
0.24
0.51
0.45
0.46
0.65
0.40
0.32
0.74
0.52
0.47
0.02
0.47
0.23
0.16
0.45
0.45
0.46
0.00
p
<0.01
<0.01
=0.064
<0.01
=0.085
<0.01
<0.01
<0.01
<0.05
<0.01
<0.01
<0.01
<0.01
<0.01
<0.05
<0.01
<0.01
<0.01
=0.545
<0.01
<0.05
=0.091
<0.01
<0.01
<0.01
=0.859
WANG Zhen et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 12, P. 32–39
3.4 Comparisons of KP according to different models
According to Eqs (1)–(5) mentioned above, the predicted φ
values for the three models, the J-P adsorption model, the KOAbased absorption model and the soot-air model, can be obtained. To compare the difference between the measured φ values (φmeas) and the predicted φ values (φpred), the ratios of φmeas/
φpred of each PCB congeners and their standard deviations (sd)
for the three models were listed in Table 4. The values of logKOA
for PCBs at different temperatures were calculated based on the
fragment constant method proposed by Li et al. (2006).
As can be seen from Table 4, φpred obtained from the soot-air
model were obviously closer to the field measured values (φmeas)
(φmeas/φpred more approaches to 1) in comparison to the values
predicted by the J-P model and the KOA-based model. Thus,
2
logKp = (−0.31 0.02)logp°L − (2.84
1
r2 = 0.36, p < 0.01
0
−1
−2
−3
−4
−5−
−8
−7
−6
−5
−4
logp°L
−3
−2
−1
0
Fig.2. Plot of logpºL versus logKP for all the 19 samples.
0.0
logKp at the intersection point
aerosol composition did not differ significantly among different
samples. The slope values measured in this study were considerably lower than the expected −1, which were consistent with
results of previous studies. Mandalakis and Stephanou (2007)
reported the slopes in the range of −0.16 and −0.59 with a mean
of −0.40 for atmospheric PCBs in a rural area of eastern Germany. A shallower slope of −0.33 was reported in Greek Coastal
sites (Mandalakis and Stephanou, 2002). Based on Mandalakis
and Stephanou (2007), slopes deviating from −1 are more frequently observed for PCBs regardless of the sampling location,
and slopes are shallower at rural and coastal sites in comparison to urban sites. Previous studies indicated that some factors,
such as temperature fluctuation during sampling, sampling
limitation, non-exchangeability, can cause slope deviation from
−1, and shallower slopes can be observed even at equilibrium
(Pankow, 1994; Simcik et al., 1998). A possible reason for shallow slopes in this study is the nonexchangeable fraction of PCBs
that can increase as the aerosol ages, and is expected to be larger
for samples from remote sites than those from urban areas. Under the condition of low levels of TSP, even few nonexchangeable compounds can lead to considerable deviations of slopes
(Pankow and Bidleman, 1992). Furthermore, the mr values were
observed to be negatively correlated with the temperature (°C)
(mr=−0.005 7t−0.21, r2=0.30, p<0.05), which indicated that mr
would approach to −1 with the increase of ambient temperature. Considering less PCBs will be in particle phase at higher
temperature, the influence of nonexhangeable PCB congeners
in particle phase on KP is expected to be less at higher temperatures in comparison to lower temperatures.
Factors that influence the slope mr will also change br, and
they are expected to exhibit some degree of interrelation whether the gas-particle partitioning is based on thermodynamic or
nonthermodynamic mechanism. A linear regression between
mr and br (br=msmr+bs) was conducted and the significant correlation was observed: br=4.80mr−1.24 (r2=0.61, p<0.05). Under
the condition of significant linear correlation of mr and br, all
the regressions of logpºL versus logKP would show a trend to intersect at a point (logpºL, logKP) defined by (−ms, bs) (Sitaras et
al., 2004; Callén et al., 2008). To verify this hypothesis, logKP was
calculated for all the 19 samples with logpºL=−ms=−4.80. Obviously, the values of logKP lie near the line of −1.24 (Fig. 3), and
the one-sample Kolmogorove-Smirnov test revealed that they
were normal distributed (p=0.469 > 0.05, mean=−1.24). The defining of the intersection point, in essence, provides a chance to
predict the partitioning constant of a compound, such as PCBs
and PAHs, between logKP for a given value of logpºL.
logKp
36
−0.5
−1.0
−1.5
−1.24
−2.0
−2.5
Fig.3. Plot of logKP at the intersection point for the 19
samples. Value of bs (−1.24) was marked with a line.
the gas/particle partitioning of PCBs in the Arctic atmosphere
is more precisely simulated by the soot-air model than the J-P
adsorption model and the KOA-based model. A similar conclusion was deduced from a previous study of PAHs in the Arctic
atmosphere by Wang, Na et al. (2013).
Moreover, three models tend to underestimate the values
of φ of low-chlorinated PCBs (φmeas/φpred > 1), while the sootair model overestimates φpred of the heavier congeners, such as
CB−169, −189 and −205. The divergence of the field measured
values from the predicted data was more obvious for the lowchlorinated congeners. Unexpectedly relatively high particle
phase concentrations of low-chlorinated congeners lead to
considerable deviations from predicted values, which implied
that KP of low-chlorinated congeners was affected more significantly by particulates (soot) than their higher counterparts. The
results were in consistent to the previous finding that soot has a
critical influence on the gas/particle partitioning of SVOCs, especially for high volatility species (Lohmann and Lammel, 2004;
He and Balasubramanian, 2009; Gaga and Ari, 2011; Wang, Na
et al., 2013).
Gas/particle partitioning of PCBs is expected to be affected
by organic and elemental carbon contents of aerosols because
they depend on local factors and particle characteristics. In the
present study, the sensitivity of organic matter fraction (fOM)
and elemental carbon fraction (fEC) in aerosols to the change
of φ was explored employing various fOM and fEC values. Based
on the report of He and Balasubramanian (2009), the variation
37
WANG Zhen et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 12, P. 32–39
Table 3. Regression parameters of mr, br, r2 and p for Eq. (7)
Samples
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
mr
−0.31
−0.35
−0.24
−0.15
−0.26
−0.28
−0.28
−0.27
−0.20
−0.21
−0.21
−0.27
−0.28
−0.27
−0.27
−0.18
−0.25
−0.46
−0.38
br
−2.96
−2.94
−2.20
−2.16
−2.91
−2.33
−2.93
−2.40
−2.21
−2.20
−2.22
−2.07
−2.15
−2.23
−2.50
−2.03
−2.89
−3.28
−3.48
r2
0.42
0.33
0.31
0.23
0.29
0.42
0.49
0.30
0.32
0.31
0.33
0.25
0.48
0.37
0.34
0.28
0.40
0.43
0.28
p
0.000
0.001
0.002
0.005
0.002
0.000
0.000
0.002
0.001
0.004
0.001
0.009
0.002
0.001
0.002
0.007
0.000
0.000
0.008
Table 4. Summarized data of φmeas/φpred for the three models: the J-P adsorption model, the KOA-based absorption model and the
soot-air model (fOM=0.10, fEC=0.02)
PCBs
CB-8
CB-28
CB-44
CB-52
CB-66
CB-77
CB-81
CB-87
CB-101
CB-105
CB-110
CB-114
CB-118
CB-123
CB-126
CB-128
CB-138
CB-153
CB-155
CB-169
CB-170
CB-180
CB-187
CB-189
CB-194
CB-195
CB-200
CB-205
The J-P model
φmeas/φpred
sd
1 145.2
1 043.7
392.3
438.1
189.5
307.3
149.0
189.7
109.3
279.9
32.7
33.9
12.6
10.1
110.9
124.6
55.3
92.7
20.7
13.1
54.2
100.7
7.7
6.6
15.4
22.9
14.3
13.2
5.7
5.5
17.0
19.6
7.8
8.4
8.4
7.3
253.0
248.1
1.5
1.7
2.2
3.5
2.9
2.9
8.0
10.2
1.1
0.9
0.8
0.5
2.0
2.0
6.3
8.4
1.2
0.9
The KOA model
φmeas/φpred
sd
674.5
397.7
235.2
127.5
137.4
185.1
65.0
65.5
77.0
134.5
35.7
31.0
18.6
18.1
56.5
35.9
28.5
30.4
20.9
14.7
22.6
16.9
7.3
4.9
10.2
6.8
12.4
10.1
6.8
4.4
9.5
5.8
4.1
3.2
6.5
4.5
75.7
51.7
1.5
1.0
1.1
0.8
1.9
0.9
3.4
2.5
1.1
0.5
0.7
0.2
1.1
0.4
2.3
1.5
1.0
0.5
range of fOM and fEC were 0.10–0.30 and 0.02–0.05, respectively,
for most aerosols. Thus, the φmeas/φpred ratios were calculated
under the condition of fOM=0.10 (and 0.30) and fEC=0.02 (and
0.05) (Table 5).
The soot-air model
φmeas/φpred
sd
125.0
80.1
50.0
29.5
34.1
43.4
18.1
18.9
19.3
37.1
8.0
7.0
4.1
3.9
18.1
12.5
8.8
8.6
6.0
3.8
7.3
6.1
2.1
1.4
3.0
2.1
3.7
3.1
2.0
1.1
3.7
2.3
1.6
1.2
2.4
1.5
31.9
20.2
0.8
0.4
0.7
0.5
1.0
0.4
1.8
1.4
0.7
0.3
0.6
0.1
0.8
0.2
1.4
0.8
0.8
0.4
For low-chlorinated PCB congeners, the changes of φmeas/
φpred ratios were not considerable between fOM=0.10 and
fOM=0.30. However, the ratios varied significantly between
fEC=0.02 and fEC=0.05. For most high-chlorinated PCBs, such as
38
WANG Zhen et al. Acta Oceanol. Sin., 2014, Vol. 33, No. 12, P. 32–39
Table 5. Comparison of φmeas/φpred ratios under different values of fOM and fEC
PCBs
CB-8
CB-28
CB-44
CB-52
CB-66
CB-77
CB-81
CB-87
CB-101
CB-105
CB-110
CB-114
CB-118
CB-123
CB-126
CB-128
CB-138
CB-153
CB-155
CB-169
CB-170
CB-180
CB-187
CB-189
CB-194
CB-195
CB-200
CB-205
fEC=0.02
fEC=0.05
fEC=0.02
fEC=0.05
fOM=0.10
125.0
50.0
34.1
18.1
19.3
8.0
4.1
18.1
8.8
6.0
7.3
2.1
3.0
3.7
2.0
3.7
1.6
2.4
31.9
0.8
0.7
1.0
1.8
0.7
0.6
0.8
1.4
0.8
fOM=0.10
56.3
23.0
16.2
8.8
9.2
3.9
2.0
9.3
4.5
3.2
3.7
1.1
1.6
2.0
1.2
2.1
1.0
1.4
17.6
0.7
0.5
0.8
1.1
0.6
0.6
0.7
1.0
0.7
fOM=0.30
91.2
35.1
22.8
11.7
12.9
5.7
3.0
11.3
5.5
4.1
4.6
1.5
2.1
2.5
1.5
2.4
1.1
1.6
17.4
0.7
0.5
0.8
1.1
0.6
0.5
0.6
0.9
0.7
fOM=0.30
48.2
19.3
13.1
6.9
7.5
3.3
1.7
7.1
3.5
2.6
2.9
1.0
1.4
1.6
1.1
1.7
0.8
1.2
12.0
0.6
0.4
0.7
0.9
0.6
0.5
0.6
0.8
0.7
CB-169, -189 and -205, the variations of ratios were not notable.
The comparison results under different fOM and fEC revealed that
the adsorption onto elemental carbon is more sensitive than
the absorption into OM of aerosols, especially for low-chlorinated congeners. Although the elemental carbon contents are
lower than OM in aerosols (Dachs and Eisenreich, 2000), the
results indicate that PCB congeners adsorption onto the soot
is significant, and PCBs have a higher affinity for soot particle
(elemental carbon) compared to OM. Therefore, adsorption of
PCBs onto the soot fraction of atmospheric aerosols is an important mechanism affecting their gas/particle partitioning,
especially for low-chlorinated PCB congeners.
4 Conclusions
The average concentration (vapor plus particulate phase) of
the 26 PCB congeners in the atmosphere from the North Pacific
to the Arctic Ocean was 19.116 pg/m3. CB-28, -52 and -77 were
three most abundant compounds, which accounted for 15.8%,
10.8% and 16.4% to ƩPCBs, respectively. Vapor PCBs were dominated in all samples (79.0% to ƩPCBs). Negative correlation was
observed between ƩPCBs and the latitudes. For all samples,
significant correlation was also observed between logpºL and
logKP, and shallow slopes (from −0.15 to −0.46, mean −0.27)
were measured. The comparison results of the three models indicate that the soot-air model is more suitable for describing
the gas/particle partitioning of PCBs in the Arctic atmosphere,
and the adsorption onto elemental carbon is more sensitive
than the absorption into organic matters of aerosols, especially
for low-chlorinated PCB congeners.
Acknowledgements
Authors would like to thank the Chinese Arctic and Antarctic
Administration, all members of the 5th Chinese National Arctic
Research Expedition and the captain and crew of R/V Xuelong
(Snow Dragon).
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