Introduction
Polybrominated diphenyl ethers (PBDEs) are a class of organobromine
compounds that are widely used as a flame retardant. They are applied to a
broad array of textiles and consumer products including plastics, polymers,
mattresses, upholstery, carpeting, building materials and electronic
equipment (de Wit, 2002; Alaee et al., 2003). Because the compounds are
additive rather than chemically bound to the products, they can be released
into the environment. They are persistent organic chemicals and can
bioaccumulate, therefore, they have become contaminants detectable in the
environment, in animals and in humans (Su et al., 2009; Besis and
Samara, 2012). Human uptake is thought to be through inhalation, dermal
absorption and consumption of contaminated food (Marklund et al., 2003;
Wensing et al., 2005). The primary source of exposure to humans is believed
to be consumption of contaminated fish, poultry, meat and dairy products
(Su et al., 2009; Besis and Samara, 2012). Occupational exposures may
occur in computer, electronic warehouses and formulation facilities
(Harrad et al., 2010). Inhalation exposure can take place through ambient
aerosol or dust containing PBDEs. Compared with dust PBDEs, the inhalation
of ambient aerosols may be a minor pathway for humans, but it has a
long-term bioaccumulation process in the human body. When PBDEs are suspended in
air, they can be present as particles. Since we can not say how long PBDEs
remain in the air, long-term exposure to PBDEs has a greater potential to
cause health effects than does short-term exposure to low levels because of
their tendency to build up in the human body over many years. Growing concerns
about the health impacts of PBDEs have led to a decline in their production
and finally, a ban of their use in the United States and Europe since 2004 (Kemmlein
et al., 2009). All technical mixtures of PBDEs were also totally phased out
in other regions, including China (Betts, 2008). However, it is likely
that long-term exposure will continue long after PBDE production has ended
through emissions from PBDE-containing products that are still being used.
Thus, it has become necessary to investigate the characteristics of particulate
PBDEs existing in urban ambient aerosols.
Over the past decade, measurements of atmospheric PBDEs have been carried out
in various areas around the world, such as Turkey (Cetin and Odabasi, 2008),
Japan (Kakimoto et al., 2014), Thailand (Muenhor et al., 2010) and China
(Yang et al., 2013) in Asia; the United States (Hale et al., 2003) and Canada (Wilford et
al., 2004) in North America; Greece (Besis et al., 2015), France
(Castro-Jimenez et al., 2011) and the Czech Republic (Okonski et al., 2014) in Europe; and
some places in the Arctic (Moller et al., 2011; Wang et al., 2005). In these
studies, particulate PBDEs were mainly investigated in individual
particle size fractions such as PM2.5 and PM10, and were rarely
related with size-resolved particles. Size distribution of particle-associated PBDEs
is crucial when evaluating human health risks since the size-resolved
particles dominate the deposition behavior of particles in the respiratory
tract. To the best of our knowledge, particle size distribution of PBDEs was
merely reported in Thessaloniki, Athens (Greece) (Mandalakis et al., 2009;
Besis et al., 2015), at e-waste recycling sites close to Guangzhou (China)
(B. Z. Zhang et al., 2012; Luo et al., 2014b) and at Brno (Czech Republic) (Okonski et
al., 2014). These studies showed that major lighter brominated congeners
existed on coarse particles, while most highly brominated congeners occurred
on fine particles. In association with the fact that fine particles can
easily penetrate/enter the alveolar region, fine-particle-bound highly
brominated congeners can travel deep into the lungs and cause serious health
problems for humans (Geiser et al., 2005). To clarify this issue we
first need to investigate the actual particle size distribution of PBDEs
through long-term observations.
This investigation was conducted in the urban site of Shanghai with the aim of
evaluating the size distribution of particle-associated PBDEs and their deposition in
the human respiratory tract. Besides this, the elucidation of the influence of some
factors such as the volatility, the chemical affinities and releasing source
on these distributions was also attempted.
Experimental and methods
Chemicals
Standard mixtures of PBDEs (BDE-17, 28, 47, 66, 71, 85, 99, 100, 138,
153, 154, 183, 190 and 209) were purchased from AccuStandard, Inc.
(United States). Internal standards (13C-BDE-28, 47, 99 and 153) were purchased
from Cambridge Isotope Laboratories (Andover, MA). PBDE congeners were divided
into five groups (e.g., tri-, tetra-, penta-, hexa- and hepta-BDE) based on the
number of bromine atoms in the molecule. The solvents used in this research
were high-performance liquid chromatography (HPLC)/spectro grade and bought from Tedia Company Inc. (United States).
Sample collection
The sampling campaign took place on the rooftop (20 m above the ground) of teaching building
No. 4 at Fudan University campus (121.50∘ E,
31.30∘ N), approximately 5 km northeast of downtown Shanghai
(elevation about 4 m a.s.l.). A Fudan super monitoring station for
atmospheric chemistry was running here all year round. The site is in
close proximity to shopping malls and residences, and the traffic around is busy
due to the close proximity to the sub-downtown. The main PBDE-releasing sources at this site
included industries emission, household heating and road transport. Details
regarding the sampling site are included in our previous work (P. F. Li et
al., 2011; X. Li et al., 2011). Particle samples were collected by drawing
air through a quartz fiber filter (Whatman QMA, ∅81 mm), using an
Anderson 8-stage air sampler (Tisch Environmental Inc., United States). The flow rate
was controlled at 28.3 L min-1. The cutoff aerodynamic diameters for
each stage were < 0.4, 0.4–0.7, 0.7–1.1, 1.1–2.1, 2.1–3.3,
3.3–4.7, 4.7–5.8, 5.8–9.0 and > 9.0 µm. The whole
measurement period ranged from December 2012 to November 2013. The sampling
time was 120 h for each sample batch. A total of 189 particle samples (21
sample batches containing 9 size fractions) were obtained at this site. Prior
to sampling, the filters that were wrapped in aluminum foil were baked at
450 ∘C for 12 h to remove organic materials. After sampling, loaded
filters, together with aluminum foils, were stored at -20 ∘C until
extraction. In addition, meteorological data during the measurement period
were obtained from the Fudan atmospheric monitoring station (Lv et al., 2015).
Sample extraction
The aerosol samples were extracted by a Soxhlet extractor with a mixture of
dichloromethane/hexane (1 : 1, ν/ν). The extraction time was 36 h
at a constant temperature of 69∘C. After extraction, the samples were
filtered through 0.45 µm PTFE syringe filters and concentrated
using a rotary evaporator (BÜCHI Rotavapor®, Switzerland) and a pure
N2 stream.
Instrumental analysis
Each compound was quantified by an Agilent 7890A Series gas chromatograph (GC) coupled to an
Agilent 7000B Triple Quadruple Mass Spectrometer (GC/MS/MS, Agilent Technologies Inc., United States)
operating at electron impact energy of 70 ev and using the multiple reaction
monitoring mode. Samples' separation was carried out by a DB-5ms
(15 m × 0.25 mm inner diameter with 0.25 µm film thickness)
capillary column (J&W Scientific, Folsom, CA). The column temperature
was initially at 150 ∘C, then proceeded to increase from 12 ∘C min-1
to 315 ∘C with 2 min hold. All samples were automatically injected
with 2 µL in pulse splitless mode. The injector temperature was set
to 330 ∘C, the transfer line to 310 ∘C and the ion trap to
300 ∘C. High-purity helium (99.999 %) was applied as a carrier gas
with a constant flow rate of 1.2 mL min-1 in the column. Nitrogen gas was
used as the collision gas in the MS. The identification and quantification of PBDEs
were done according to retention times, selected precursor ions, product ions
and the internal standard method relative to the closest eluting PBDE
surrogate. The calibration solutions were prepared at five concentrations and
contained uniform concentrations of the internal standards. For each analyte,
a relative response factor was determined for each calibration level using
the internal standard. The five response factors were then averaged to
produce a mean relative response factor for each species. Reported analyte
concentrations were corrected for internal standards recoveries. The
calibration curves showed a linear response in the range
0.1–5 µg L-1. The correlation coefficients of the
calibration curve for the different PBDEs were R2>0.99.
Quality control and assurance
Each batch of samples included
one procedural blank. In that case, only
BDE-71, 100, 154 and 190 were commonly detected at much lower levels
(< 5 %) in some samples. The mean values of blanks were then
subtracted from measured values of each sample. Method recoveries determined
by spiking the sampling process (five replicates) with a standard mixture of
PBDEs ranged from 75 to 175 %. In addition, isotopically labeled PBDEs
were added as an internal standard (added after extraction and clean-up, just
prior to GC-MS analysis) to check the instrument performance. Recoveries were
between 90 and 110 %. As a further quality control step, SRM 2585 (NIST,
Gaithersburg, MD, United States) was used as the reference material in this study.
Measured PBDE levels in SRM 2585 ranged from 75 to 120 % of certified
values. Repeatability was evaluated by performing four analyses of a standard
PBDEs solution containing the above-mentioned PBDEs and the surrogate
standards in the same conditions. The relative standard deviations of
the relative response factors were below 10 % for all PBDEs. The
method detection limits (DLs) and quantification limits were calculated as the
concentrations equivalent to 3 and 10 times the noise of the quantifier
ion for a blank sample (the DL ranged from 0.05 to 0.6 pg m-3). For the
purpose of statistical analysis, samples with concentrations under limit of detection (LOD) were
assigned concentrations equal to 0.5 LOD (Okonski et al., 2014).
Size-specific gas/particle partition
Two processes are commonly accepted for illustrating mechanisms of
particle–gas partition, i.e., adsorption and absorption process. In the case
of adsorption, it assumes that chemicals adsorb to active sites on the
surface of the particle. The gas–particle partitioning coefficients
(Kp-ads) during the adsorption process are described by
Pankow (1987):
Kp-ads=NsATSPTe(QL-Qv)/RT1600pL∘,
where Ns is the surface concentration of sorption sites (4×10-10 mol cm-2), ATSP is the specific surface area of
the particles, T is the ambient temperature (292 K), R is the ideal gas
constant (8.31 J mol-1 K-1), QL and Qv are the enthalpy of desorption from the surface and the enthalpy of
vaporization of the subcooled liquid (kJ mol-1), respectively and pL∘ is the vapor pressure of the subcooled liquid. In
contrast with PAHs, a similar situation was assumed for PBDEs that
QL-Qv≈1×104 J mol-1 (Aubin and
Abbatt, 2006). After logarithmic transformation on both sides in Eq. (1), we
obtain Eq. (2) as follows:
logKp-ads=-logpL∘+logATSP-8.35.
Physiochemical properties reference dose (RfD) of the target PBDE congeners and
data from Yang et al. (2013).
Chemical
Molecule
MWa
RfD
logpL∘/(pa)b
logKOAc
Gas phasee
Particulate
(pgm-3)
phasee (pgm-3)
BDE-17
2,2′,4-tribromodiphenyl ether
406.9
1.0×105
-2.71d
9.31
15.64
0.93
BDE-28
2,4,4′-tribromodiphenyl ether
406.9
1.0×105
-2.95
9.4
30.04
1.17
BDE-71
2,3′,4′,6-tetrabromodiphenyl ether
485.8
1.0×105
-3.55d
10.2
–
–
BDE-47
2,2′,4,4′-tetrabromodiphenyl ether
485.8
1.0×105
-4.07
10.1
28.81
5.38
BDE-66
2,3′,4,4′-tetrabromodiphenyl ether
485.8
1.0×105
-4.27
10.25
6.11
0.38
BDE-100
2,2′,4,4′,6-pentabromodiphenyl ether
564.7
1.0×105
-4.91
10.82
3.38
1.52
BDE-99
2,2′,4,4′,5-pentabromodiphenyl ether
564.7
1.0×105
-5.14
10.96
8.02
5.06
BDE-85
2,2′,3,4,4′-pentabromodiphenyl ether
564.7
1.0×105
-5.40
11.03
2.58
2.42
BDE-154
2,2′,4,4′,5,6′-hexabromodiphenyl ether
643.6
2.0×105
-5.83
11.66
1.37
2.10
BDE-153
2,2′,4,4′,5,5′-hexabromodiphenyl ether
643.6
2.0×105
-6.08
11.77
1.27
2.39
BDE-138
2,2′,3,4,4′,5′-hexabromodiphenyl ether
643.6
2.0×105
-6.23
11.81
1.09
2.21
BDE-183
2,2′,3,4,4′,5′,6-heptabromodiphenyl ether
722.5
2.0×105
-6.75
12.52
1.67
10.01
BDE-190
2,3,3′,4,4′,5,6-heptabromodiphenyl ether
722.5
2.0×105
-7.00
12.71
–
–
a Molecular weight. b Subcooled liquid vapor
pressure in 292 K from Tittlemier et al. (2002). c Octanol–air
partition coefficient in 292 K from Harner and Shoeib (1998); d
data from Wang et al. (2008). e Data from Yang et al. (2013).
f Oral reference doses (pg kg-1 bw day-1) of
BDE-47, 99, 153 were suggested by the US Environmental Protection Agency's IRIS database (www.epa.gov/iris) and
RfDs of other tetra-, penta-, hexa-BDE congeners were assumed to be equivalent
reference doses of BDE congeners with the same bromine atoms. In addition,
RfDs of tri-BDE and hepta-BDE were assumed to be the same as those of BDE-47
and BDE-153, respectively.
Size-dependent ATSP adopted from the results of Yu and Yu (2012)
and the data are listed in Table 2. Based on three modes, we then obtained
Eq. (3) which is derived from Eq. (2).
logKp-ads=-logpL∘-6.64(Aitken mode:<0.4µm)-logpL∘-7.25(Accumulation mode: 0.4- 2.1µm)-logpL∘-7.06(Coarse mode: 2.1- 10µm)
The temperature-dependent pL∘ values of PBDE congeners were
calculated using the regression parameters by (logpL∘=A+B/T) (Tittlemier et al., 2002). In our study, the
average temperature of the sampling campaign was 292 K. The temperature-dependent pL∘ is listed in Table 1.
ATSP and fOM adopted from Yu and Yu (2012).
µm
< 0.4
0.4–0.7
0.7–1.1
1.1–2.1
2.1–3.3
3.3–4.7
4.7–5.8
5.8–9
9–10
ATSP (m2 g-1)
50
10
10
19
19
19
19
19
19
fOM
0.45
0.35
0.35
0.25
0.25
0.25
0.25
0.25
0.25
In the case of absorption, it assumes that atmospheric aerosols are coated
with an organic film and chemicals can absorb into this organic phase. The
gas–particle partitioning coefficients (Kp-abs) during the
absorption process are described by Finizio et al. (1997):
Kp-abs=10-9MoγoMOMγOMρOMfOMKOA,
where Mo and MOM are the mean molecular weights of
octanol and the organic matter phase (g mol-1), and γo and
γOM are the activity coefficients of the absorbing compound in
octanol and in the organic matter phase, respectively. fOM is the
fraction of organic matter phase on particles; KOA is the octanol–air
partition coefficient. ρOM is the density of octanol
(820 kg m-3 at 20∘).
With the assumption that MoMOM=1, γoγOM=1, Eq. (4) can be simplified to Eq. (5)
after logarithmic transformation on both sides:
logKp-abs=logKOA+logfOM-11.91.
Size-specific fOM was adopted from Yu and Yu (2012) and is listed in
Table 2. Through calculation, we can deduce Eq. (6) as follows:
logKp-abs=logKOA-12.26(Aitken mode:<0.4µm)logKOA-12.42(Accumulation mode: 0.4- 2.1µm)logKOA-12.51(Coarse mode: 2.1- 10µm).
KOA has been reported as a function of temperature (logKOA=A+B/T) (Harner and Shoeib, 2002). In this study, the average
temperature of the sampling campaign was 292 K. The temperature-dependent logKOA is listed in Table 1, along with other physiochemical properties
of the target PBDE congeners.
Pankow (1994a) proposed a definition of the measured particle–gas partition
coefficient (Kp-measured) to characterize the partitioning
behavior of semi-volatile organic compounds (SVOCs) between the gas and
particulate phases:
Kp-measured=P/TSP/G,
where P and G are PBDEs in particulate- and gas-phase concentration,
respectively, and TSP is the total suspended particulate
(µg m-3). After linear regression between logKp-measured∼logpL∘ and logKp-measured∼logKOA, we can obtain the relationship
between logKp-measured∼logpL∘ and
Kp-measured∼logKOA.
In association with Eqs. (4), (6) and (7), we can investigate the sorption
mechanisms governing particle size distribution of PBDEs by comparing
theoretical Kp-ads and Kp-abs with measured
Kp-measured.
Seasonal concentrations of size-resolved particulate PBDEs in the urban site of
Shanghai (pg m-3).
Compound
Spring
Summer
Autumn
Winter
Mean
Range
Mean
Range
Mean
Range
Mean
Range
BDE-17
nd
nd–0.37
0.08
nd–0.27
0.11
nd–0.52
0.10
nd–0.46
BDE-28
nd
nd–0.18
nd
nd–0.13
nd
nd–0.18
0.11
nd–0.68
BDE-71
nd
nd–0.17
nd
nd–0.18
nd
nd–0.19
nd
nd–0.93
BDE-47
0.48
0.24–0.82
0.57
0.35–1.12
0.44
0.24–0.80
0.38
0.11–1.16
BDE-66
nd
nd–0.16
nd
nd–0.19
nd
nd
0.16
nd–1.46
BDE-100
nd
nd
nd
nd
nd
nd
nd
nd–0.23
BDE-99
3.28
1.27–6.73
1.74
1.01–7.10
4.14
0.63–8.29
4.74
1.61–12.4
BDE-85
2.38
0.78–7.15
1.12
0.31–3.06
2.31
0.45–4.50
3.54
0.81–12.2
BDE-154
nd
nd–2.85
nd
nd–0.42
nd
nd
nd
nd–2.85
BDE-153
1.08
nd–3.07
0.44
nd–2.40
0.42
nd–2.15
1.29
nd–5.43
BDE-138
1.05
0.30–2.85
0.46
nd–2.19
1.20
0.34–2.15
1.18
0.29–2.89
BDE-183
0.65
nd–0.78
nd
nd–0.67
0.72
nd–1.12
0.81
nd–1.25
BDE-190
nd
nd–0.91
nd
nd
nd
nd–0.76
nd
nd–0.96
Σ13PBDE
90.3
76.3–106
52.6
30.6–82.2
93.9
90.7–100
117
104–141
nd denotes values that are not detected.
Human respiratory risk assessment
Because the size-resolved particles played a key role in health risk
assessment through inhalation (Luo et al., 2014a), we adopted a so-called
International Commission on Radiological Protection (ICRP) model (1995) to
evaluate the deposition efficiencies and fluxes of inhaled PBDEs in the human
respiratory tract. The human respiratory tract can be divided into three
regions: head airway (HA), tracheobronchial region (TB), and alveoli region
(AR). The particle deposition efficiency (DE) in HA, TB and AR is estimated
by the followed simplified Eqs. (8)–(10):
DEHA=IF×11+e6.84+1.183lnDp+11+e0.924-1.885lnDp
DETB=3.52×10-3Dp×e-0.234(lnDp+3.40)2+63.9×e-0.819(lnDp-1.61)2DEAR=0.0155Dp×e-0.416(lnDp+2.84)2+19.11×e-0.482lnDp-1.3622,
where Dp is diameter of the particle, and IF is the inhalable fraction
of all particles, IF =1-1-11+7.6Dp2.8×10-4/2.
The deposition flux (DF, pg h-1) of inhaled particulate PBDEs in the
respiratory tract is estimated by
DF=∑(DEi×Ci)×V,
where DEi is the particle deposition efficiency in each region for
Dpi (the average diameter of each particle size fraction);
Ci is the PBDEs' concentration in particle Dpi (pg m-3);
and V is the breathing rate. The lower and upper limit diameters of
particles in this research were assumed to be 0.1 and 30 µm,
respectively. The respiration rate under normal conditions was considered to
be 0.45 m3 h-1 (K. Zhang et al., 2012).
In addition, we applied hazard quotient (HQ) values to assess the non-cancer
risk of size-resolved PBDEs through inhalation. The formula is as follows:
HQ=DI/(BW×RfD),
where DI is daily intake (pg day-1) and calculated by multiplying
deposition flux (DF: pg h-1) with average exposure time
(ET: h day-1), BW is the mean body weight of an adult (60 kg) and RfD is
the reported oral reference dose for PBDEs (pg kg-1 bw day-1).
In order to understand the impact of risk and uncertainty in size-resolved
particles, we used Monte Carlo simulations to produce probability
distributions of hazard levels with 5000 trials. Moreover, we used
the SPSS version 22.0 (IBM company, Chicago, IL, United States) to perform Pearson
correlation analysis for all data and considered p values of smaller than
0.01 or 0.05 statistically significant.
Results and discussion
PBDEs occurrence and seasonal variation
Most PBDE congeners were detected in the vast majority of samples (Fig. 1 and
Table 3). BDE-71, 100, 154 and 190 were sometimes present close to the
detection limits of the method. Due to the erratic concentration of BDE 209,
this compound has been removed from further analysis. The box plot in Fig. 1
summarizes the concentrations measured throughout the year and allows for
easy visualization of PBDE congener groups (e.g., tri-, tetra-, penta-, hexa-
and hepta-BDEs). The box contains the middle 50 % of the data, whereas
the top and bottom end of the box represent the 75th and 25th percentiles of
the data set, respectively. The extensions (whiskers) at either end of
the box indicate the 95th and 5th percentile, and the solid spheres represent the
maximum and minimum values. The median concentrations are indicated by the
solid vertical lines, whereas the mean concentrations are depicted by the
horizontal line. In general, the size of the box and the length of the
whiskers are an indicator of the variability in concentrations at a given
site for a given compound containing the same number of bromine atoms. A
small box shows that the distribution is uniform over the entire sampling
period and vice versa. In these groups, penta-BDEs (49.5±21.5 pg m-3) were the dominant congeners detected in all samples,
followed by hexa-BDE (16.7±7.8 pg m-3), hepta-BDE (11.2±3.1 pg m-3), tetra-BDE (5.9±1.3 pg m-3) and tri-BDE
(1.3±0.3 pg m-3). Among individual PBDEs, PBDE-47, 99 and 85
were detected in 100 % of the ambient aerosol samples, with BDE-99 and
85 being the most dominant congeners. This may be due to the fact that less
brominated BDEs have longer half-lives (years) and could be formed through
debromination of more brominated congeners (Bezares-Cruz et al., 2004). The
observed average concentrations of particulate Σ13 PBDEs ranged
from 30.6 to 141.2 pg m-3, with a mean value of 86.3 pg m-3
(Table 3). This result was consistent with the results of the previous
measurements in Shanghai, 108–367 pg m-3 (Yang et al., 2013), and
104±54 pg m-3 (Yu et al., 2011), but was much lower than in
Beijing, 760 pg m-3 (Yang et al., 2013), and in waste recycling zones of
Qingyuan close to Guangzhou, 3260 pg m-3 (Tian et al., 2011). In
addition, these results were compared with those reported in aerosol samples
from Ontario (Larsen and Baker, 2003), Chicago (Hoh and Hites, 2005) and
three different stations in western Europe (Lee et al., 2004), as well as
other Asian cities such as Osaka (9.9–22.3 pg m-3) (Kakimoto et al.,
2014), Busan (5.3–16 pg m-3; Rudich et al., 2007) and Singapore
(7.5 pg m-3; Shen et al., 2013). These direct comparison of PBDEs
concentrations between various urban environments should be done with
caution. Because of the differences existing within any urban environment,
PBDE levels could be significantly affected by the location of the sampling
site and its proximity to emission sources. Moreover, sampling methodology is
a critical parameter affecting the comparison between the observed
concentrations of PBDEs in different sites. In most published studies,
collection of particulate PBDEs has been performed by using different
sampling, pretreatment and instrumental analysis system devices and in some
cases, underestimation of PBDE concentrations might have occurred because more
volatile species were mainly in the gas phase and easily lost during
membrane sampling or the storage period.
Concentration profiles of PBDE homologue groups in the urban
atmosphere. Solid vertical lines and the horizontal lines represent median
and mean values, respectively. Box plots represent 25th–75th percentiles; whiskers indicate 5th and 95th percentile. The solid
circles represent maximum and minimum values.
Pearson correlation between particulate PBDEs and weather
parameters: visibility, temperature, wind speed and PM2.5.
Mean normalized size distribution of particle-associated PBDEs for
all samples. dC is the concentration on each filter, C is the sum
concentration on all filters and dlogDp is the logarithmic size interval
for each impactor stage in particle diameter (Dp).
Seasonal variations were distinct at these urban sites, with significantly
higher concentrations measured during winter (104–141 pg m-3) and
lower concentrations measured during summer (30.6–82.2 pg m-3)
(Table 3). Higher concentrations in winter were at least in part due to
increased emissions and due to the distinctive meteorological conditions,
including reduced mixing heights and lower precipitation depth for favoring
the pollutants' accumulation in the atmosphere (Volckens and Leith, 2003). In
addition, the adsorption of gaseous PBDEs on particles was likely to increase
during winter since the partition coefficient, Kp, was inversely
correlated with temperature (r=-0.867, p<0.01) (see Fig. 2).
Lower concentrations in summer may have been caused by wet scavenging since
some summer sampling days experienced precipitation at this site. Seasonal
variations in PBDEs can also be explained by the Asian monsoon patterns.
Shanghai sites are situated in a transitional zone of the northern
subtropical monsoon system, where the northwesterly winter monsoon transports
polluted air masses from mainland China, while the southeasterly summer
monsoon transports cleaner oceanic aerosols from the oceans (Western
Pacific) (Shi and Cui, 2012). Moreover, higher wind speeds appeared to be
typically associated with lower concentrations of PBDEs (r=-0.583, p<0.01) (Fig. 2). Higher concentrations of PBDEs were associated
with a higher PM2.5 level (r=0.629, p<0.01) and lower visibility
(r=-0.686, p<0.01). This seasonal pattern was consistent
with those measured in Huaniao Island (Li et al., 2015), Qingyuan (Tian et
al., 2011) and Dongguan (Zhang et al., 2009).
Size distribution and process mechanism of particle-associated
PBDEs
Among the PBDE congeners measured, we chose BDE-47, 85, 99, 138, 153 and
183 for the study of size distribution due to detection frequencies higher
than other congeners. Figure 3 plots the average size distributions of these
PBDEs in the continuous smoothed curves inverted from the sample data. The
results showed that particulate PBDEs exhibited a bimodal distribution with a
mode peak in the accumulation particle size range and the second mode peak in
the coarse particle size ranges. As the number of bromine atoms in the
molecule increased, accumulation-mode peak intensity increased, while coarse-mode peak intensity decreased, which indicated that the lighter brominated
congeners BDE 47 and 85 were mainly associated with particles larger than
2.1 mm, whereas the highly brominated congeners were mainly sorbed to the
fine particles. The similar bimodal distribution of PBDEs also occurred in
Heraklion (Mandalakis et al., 2009), Brno and Telnice (Okonski et al., 2014)
and Guangzhou (B. Z. Zhang et al., 2012). Differences in the particle size
distribution of individual PBDEs could reflect differences in their emission
sources but there was no credible scientific evidence in support of this
claim. Although published data on size distribution of particle-associated
PBDEs are not available for comparison, analogous trends have also been
observed for other classes of organic contaminants such as PAHs. Previous
field measurements by cascade impactors demonstrated that PAHs with more rings were
sorbed to the fine aerosol fraction, while more volatile or species with a low number of
rings were associated with larger particles (Wang et al., 2015; Kavouras et al.,
1999; Kawanaka et al., 2004; Bi et al., 2005). The reason for this is
the different volatility of PAHs, since more volatile species are absorbed to
fine aerosol and are distributed in coarse particles by rapid volatilization and
condensation. In comparison, for the PAHs with more rings, due to the lower
vapor pressures, the time required for this repartitioning process is much
longer (Bi et al., 2005); therefore, they tend to remain in fine particles
that are initially emitted (Duan et al., 2007). This hypothesis can explain the
relatively higher abundance of more volatile PAHs in the coarse particle
mode. Similarly, it can be applied to the size distribution of
particle-associated PBDEs. To further confirm this hypothesis, the geometric
mass diameter (GMD) for particulate PBDEs was calculated and correlated with
logarithmic subcooled liquid vapor pressures (logPL) (Fig. 4).
The mean GMD values for all PBDE congeners ranged from 1.9 to
2.9 µm in Shanghai, which was higher than those in Greece
(0.14–0.63 µm) (Mandalakis et al., 2009) and Guangzhou
(0.98–1.98 µm) (Luo et al., 2014b). Moreover, there is a positive
moderate correlation between GMD and logPL (r=0.69, p<0.01), indicating that the GMD increases as the volatile of PBDE
congeners increases. This phenomenon becomes more apparent in the coarse size
fraction with an increased positive correlation (r=0.75, p<0.01) (right panel in Fig. 4). This result suggests that most coarse-particle-bound PBDEs contain higher volatile species such as tri- and
tetra-BDEs. They are derived from the secondary distribution process, i.e.,
revolatilize from fine particles and recondense onto coarse ones (Wang et
al., 2008; La Guardia et al., 2006).
Pearson correlation between GMD and logpL∘ of all
particulate PBDEs (left) as well as between mass fractions of PBDE congeners in coarse-size particles (MFCP) and logpL∘
(right) at 292 K.
Moreover, chemical affinities also played an important role in PBDEs'
distribution process. Theoretically, highly brominated congeners have a strong
hydrophobicity and prefer to bound with small particles because they have
large surface areas (Venkataraman et al., 1999). Such an explanation,
however, cannot adequately account for the PBDEs' distribution patterns
observed in the present study. Perhaps, in fact, other factors, e.g., emission
sources, sampling sites and weather conditions (temperature and/or relative
humidity) might also influence their distributions (Zielinska et al., 2004).
Although there were still difficulties in totally clarifying the size
distributing mechanism of PBDEs or other SVOCs in the current study, it is important to
consider integrating all factors in future studies.
Preliminary study on PBDEs' partitioning mechanisms
Usually, two major mechanisms, i.e., adsorption and absorption, play an
important role in PBDEs' partitioning to multimodal urban aerosols (Lohmann
and Lammel, 2004). To clarify these processes, the theoretical
Kp-ads and Kp-abs were respectively calculated based on
Eqs. (3) and (6) because they involved the size-specific parameters, usually
including organic matter fractions and the available adsorptive sites, on
aerosol particles (Pandis et al., 1992; Pankow, 1994b). The obtained
theoretical Kp-ads and Kp-abs values were estimated and
compared with measured Kp-measured (from Eq. 7). Since we had no
gas PBDEs' concentrations, the measured Kp-measured values were based on a
recent study in Shanghai by Yang et al. (2013). In their studies, both gas
and particulate PBDEs' concentration were reported at an urban site about
∼50 km away from our site (Table 1). A range of
10–70 µg m-3 was assumed for size-specific particle
concentration to calculate Kp-measured. The measurement periods
ranged from September 2008 to August 2009. The average temperature was
18.4 ∘C, similar to the temperatures in our study (19 ∘C).
Therefore, their data could serve as a useful reference for us to compare with
the theoretical Kp-ads and Kp-abs derived from adsorption
and absorption in our study. Note that we took < 0.4, 0.4–2.1 and
> 2.1 µm for Aitken, accumulation and coarse mode,
respectively.
Comparison of theoretical Kp based on adsorption (a) and
absorption (b) in three modes with measured Kp values by Yang et
al. (2013) in Shanghai from 2008 to 2009.
The plots of measured log Kp-measured vs. logpL∘
and log KOA were presented in Fig. 5, along with two sets of
theoretical Kp-ads and Kp-abs based on adsorption and
absorption in three modes. As presented, significant linear correlations were
found between measured logKp-measured and logpL∘
(R2=0.76) as well as measured log Kp-measured and logKOA (R2=0.77). For the same class compounds under equilibrium
conditions by either adsorption or absorption, the slope of log–log plots of
Kp and pL∘ was expected to be close to -1 (Pankow and
Bidleman, 1992) and the slope of log–log plots of Kp and
KOA should be close to 1 (Finizio et al., 1997). However, more
gentle regression lines (slopes: -0.53, 0.68) were detected (Fig. 5),
similar to slopes reported in previous studies (Cetin and Odabasi, 2008; Yang
et al., 2012). The deviations were possibly caused by kinetic limitations
(nonequilibrium partition), thermodynamic limitations (lack of constancy in
desorption) and additional sorption (Harner and Bidleman, 1998; Cousins and
Mackay, 2001; Lohmann et al., 2007).
Pearson correlation matrix for the concentrations of PBDE
congeners.
BDE-17
BDE-28
BDE-71
BDE-47
BDE-66
BDE-100
BDE-99
BDE-85
BDE-154
BDE-153
BDE-138
BDE-183
BDE-190
BDE-17
1
0.75b
0.45a
0.18
-0.11
0.69b
0.18
0.38
-0.11
-0.16
0.11
0.39
0.16
BDE-28
1
0.65b
-0.05
0.39
0.69b
0.45a
0.69b
-0.16
0.05
0.04
0.28
0.15
BDE-71
1
0.16
0.27
0.82b
0.60b
0.70b
-0.06
0.34
0.31
0.18
0.48a
BDE-47
1
0.01
0.36
-0.22
-0.19
0.08
-0.27
-0.08
0.24
0.71b
BDE-66
1
0.27
0.30
0.51a
0.26
0.18
-0.10
-0.06
0.25
BDE-100
1
0.54a
0.67b
0.11
0.13
0.22
0.19
0.57b
BDE-99
1
0.83b
0.14
0.37
0.68b
0.30
0.41
BDE-85
1
0.18
0.37
0.44a
0.21
0.37
BDE-154
1
0.35
0.34
0.20
0.19
BDE-153
1
0.56b
-0.07
0.09
BDE-138
1
0.47a
0.37
BDE-183
1
0.28
BDE-190
1
Significant values are marked in bold.
a Correlation is significant at 0.05 level (two-tailed).
b Correlation is significant at 0.01 level (two-tailed).
The three mode data sets of theoretical logKp-ads and logKp-abs in Fig. 5a and b were calculated using Eqs. (3) and (6),
considering only the adsorption mechanism or the absorption mechanism, respectively.
As expected, the slopes for them were all -1. Both logKp-ads
considering only adsorption and logKp-abs considering only
absorption were compared with measured logKp-meaused in Fig. 5.
The results showed that the Kp-measured values of highly brominated
congeners (e.g., BDE-85, 99, 100, 138, 153, 154 and 183) in three
modes fell into the regression line of the theoretical Kp-ads.
(Fig. 5a), while the measured Kp-measured values of lighter
brominated congeners (e.g., BDE-17, 28, 47 and 66) in three modes fell
into the regression line of the theoretical Kp-abs (Fig. 5b). These
facts revealed that adsorption on surfaces of particles appeared to be
responsible for the bimodal distribution of highly brominated congeners, while
absorption into organic matter seemed to play an important role for lighter
brominated congeners. In addition, we found that the measured
Kp-measured lines are both close to the theoretical
Kp-ads line in Aitken regression lines (Fig. 5a) and the
theoretical Kp-abs lines in the accumulation and coarse regression
lines (Fig. 5b). This meant that the mechanisms controlling the particle size
distribution of PBDEs included adsorption to Aitken-mode particles and
absorption to accumulation- and coarse-mode particles. Adsorption is dependent
on available aerosol surface area (ATSP) and absorption on
available aerosol organic mass (fOM). Although ATSP and
fOM could not be measured and empirical data of ATSP or
fOM were adopted from references in this paper, we did provide a way
to investigate the mechanisms for size distribution of SVOCs from the view of
gas–particle partition.
Correlation analysis of PBDEs
Table 4 presents a Pearson correlation matrix among PBDE congeners based on
concentrations. Significant correlation was found among the tri-BDEs (BDE-17
and 28, r=0.75, p<0.05), as well as penta-BDEs (BDE-100, 99
and 85, r=0.67–0.83, p<0.05). These high-correlation values
suggested that tri-BDEs and/or penta-BDEs shared a common source and/or exhibited
a similar distribution behavior in environment. BDE-28 significantly
correlated with BDE-71 (r=0.65, p<0.05), and both of them
also significantly correlated with penta-BDEs 100, 99 and 85. As we know,
penta-BDEs were frequently detected in ambient particles around solid waste
incineration plants (Dong et al., 2015). In this observation, we also found
that penta-BDEs appeared in high concentrations (mean: 49.5 pg m-3)
compared with other congeners. Thus, we concluded that these penta-BDEs and
correlated congeners probably came from the same source regions because there
are numerous solid waste incineration plants located in surrounding
places of Shanghai. Hepta-BDE (BDE-183 and 190) correlated poorly with the
other congeners, with only two exceptions, which were statistically
significant at the r=0.47, p<0.01 level for BDE-183 and 138,
as well as r=0.71, p<0.05 level for BDE-190 and 47,
respectively. This suggested that hepta-BDEs might not originate from the same
sources. Other studies showed the possibility of the decomposition of
higher brominated PBDEs to form lower brominated PBDEs in the atmosphere
(Eriksson et al., 2004; Söderström et al., 2004; Kajiwara et al.,
2008). Here we did not measure higher brominated PBDEs (octa- and deca-BDEs)
and no correlation analysis was performed on them. However, as seen in Fig. 1,
the hexa- versus hepta-BDEs concentrations (mean: 16.7 and
11.2 pg m-3) were relatively high, and thus indicative of multiple
releasing sources in this area; i.e., local emissions, higher brominated PBDEs
breakdown and long-range atmospheric transport may have potentially
contributed to hexa- and hepta-BDEs' contaminations.
Implications for health
In this section, we calculated the regional deposition flux in the human
respiratory tract based on Eqs. (8), (9) and (10). Figure 6 showed the
deposition fluxes of size-resolved PBDEs for adult men. The total deposition
fluxes of Σ13 PBDEs were calculated at 26.8 pg h-1. Among these
compounds, penta-BDEs were the major congeners and contributed a mean value of
58 % (range: 31–70 %) to the total deposition fluxes. The percent
contribution of Σ13 PBDEs to the respiratory tract was 84.4 %
(22.64 ng h-1) in the head airway, 4.6 % (1.24 ng h-1) in
the tracheobronchial and 11.0 % (2.93 pg h-1) in the alveoli
regions, respectively. Moreover, we also found that coarse particles
contributed major PBDEs in the head and tracheobronchial regions, while fine
particles (accumulation- plus Aitken-mode particles) contributed many PBDEs
in the alveoli region. As we know, the size distribution of
particle-associated PBDEs has a decisive influence on their potential health
effects. Considering that fine particles can penetrate deeper into the
respiratory system compared to coarse particles, fine-particle-bound PBDEs
are expected to accumulate in the lower parts of the lungs and pose a greater
risk to human health.
Deposition fluxes of size-resolved particulate PBDEs in the human
respiratory tract.
Hazard quotient (HQ) for particulate PBDEs in the atmosphere of Shanghai.
PBDEs are the sum of BDE-17, 28, 71, 47, 66, 100, 99, 85, 154, 153, 138, 183,
and 190.
Probability distributions of hazard quotient of PBDEs in Monte
Carlo simulations with 5000 trials.
We further evaluated the human health risk that is caused by PBDEs by using a HQ
approach based on data on inhaled PBDEs. Fig. 7 showed that the HQ values of the
individual congeners ranged from 4.0×10-7 to 6.8×10-5, with a total value of 1.6×10-4 for Σ13 PBDEs.
During the assessment process, we found that the HQ values were highly
dependent on the variable daily intake (DI, see Eq. 12), which was
inconstant and would result in uncertainty in the risk evaluation. Taking
these situations into consideration, we utilized Monte Carlo (MC) simulation
to evaluate the influences of uncertainty on this exposure model and to
examine whether a difference exists between the model and the calculated HQ.
In the simulation, the MC procedure was repeated 5000 times with different
calculated HQ values. The results of the simulation are depicted in Fig. 8, which
exhibits a wide gamma distribution. The 95 % percentile values of HQ
were in the range 9.15–8.36×10-5, with a mean value of
8.76×10-5. In comparison with the corresponding experimental HQ
data (1.6×10-4), excellent agreement could be observed between
the data, indicating the accuracy of our simulation. By comparison, these HQ
values in the present study were much lower than the risk guideline value
(1.0) recommended by the US Environmental Protection Agency. Even under heavy exercise conditions
(assuming a high breathing rate of 3 m3 h-1), the estimated HQ of
Σ13 PBDEs was only (1.17±0.42)×10-3, far less than 1.
Thus, particulate PBDEs in the atmosphere of the urban site of Shanghai posed low non-cancer risk
through inhalation. However, it was noteworthy here that only particle-phase
PBDEs were included in the assessment and we were not sure whether the risk
posed by atmospheric PBDEs (gas plus particles) exceeded the threshold.
Specifically, BDE-47 and BDE-99 mainly existed in the gas phase, and were probably
more toxic and bioaccumulative than other congeners (Darnerud, 2003).
Furthermore, we only measured 13 PBDE congeners, neglecting risk caused by
substantial octa-BDE and deca-BDE in particles, and other types of exposure,
like ingestion or dermal contact, were not considered either. Thus, it can not
be suggested that the occurrence of particulate PBDEs in Shanghai is not an
issue; rather, we advocate further studies that measure more PBDE
congeners in not only particle phase, but also gas phase, in relation to health risk
assessment.