Introduction
Atmospheric PAHs are important contaminants in urban air because of their
carcinogenic and mutagenic properties (Li et al., 2006; Garrido et al.,
2014). They mainly result from incomplete combustion of carbon-containing
materials and can partition between the gas and the particulate phase
(Fernández et al., 2002; Hytönen et al., 2009; Shen et al., 2011).
This partitioning process strongly depends on particle sizes, PAH species and
temperature, and it affects PAH transport, deposition, and degradation as
well as health impacts. With regard to these,
particle sizes distributions of PAHs play a critical yet poorly understood
role. Of particular importance is the role played by high molecular mass PAHs
because most of them are carcinogenic and associated with fine aerosol
particles (Akyuz and Cabuk, 2009; Wu et al., 2014). Since inhalation
deposition depends on particle sizes, these fine particles loaded with PAHs
can travel deep into the human respiratory system and cause direct health
impact (Kawanaka et al., 2009; K. Zhang et al., 2012). Current knowledge on
PAH size distribution remains incomplete. Information is missing on
partitioning mechanisms and health effects of PAHs. To address these
concerns, further studies are necessary and important.
Over the past decade, numerous measurements of PAH size distribution have
been repeatedly carried out in various areas around the world, such as Seoul
(Korea; Lee et al., 2008); Saitama and Okinawa (Japan; Kawanaka et al., 2004;
Wang et al., 2009); Mumbai and Delhi (India; Venkataraman et al., 1999; Gupta
et al., 2011); Barcelona (Spain; Mesquita et al., 2014); Dresden (Germany;
Gnauk et al., 2011); Birmingham (England; Delgado-Saborit et al., 2013);
Lisbon (Portugal; Oliveira et al., 2011); Algiers (Algeria; Ladji et al.,
2014); Beauharnois (Canada; Sanderson and Farant, 2005); Los Angeles,
Massachusetts, Chicago, and Claremont (USA; Venkataraman and Friedlander,
1994; Allen et al., 1996; Offenberg and Baker, 1999; Miguel et al., 2004);
and Tianjing, Beijing, and Guangzhou (China; Wu et al., 2006; Zhou et al.,
2008; Yu and Yu, 2012). These studies, conducted in various countries and
cities showed that most PAHs existed on small particles and had a similar
modal distribution as isomers. PAH size distribution can vary with their
releasing sources and particle aging processes (Venkataraman et al., 1994).
In order to illustrate the partitioning mechanism of PAHs between particles,
Venkataraman et al. (1999) developed the equilibrium adsorption and
absorption theory, which explained the predominance of PAHs in nuclei and
accumulation mode particles, respectively, but failed to explain it in
coarse-mode particles. Allen et al. (1996) proposed that mass transfer by
vaporization and condensation helped estimate the particle size distribution
of PAHs. However, this theory did not account for particle deposition and
particles' influence on
residence time. Therefore, the mechanisms that govern PAH distribution in
different size particles have not yet been identified and require further clarification. The fine particles discussed
here can travel deep into the human respiratory system and, for the smallest
particles, potentially enter the bloodstream, thus exposing people to both
particles and the particle-bound compounds (Geiser et al., 2005). To solve
these problems, the first thing we should clarify is the releasing source of
size-specific PAHs as well as their transport characteristics in the human
respiratory system (Chen and Liao, 2006; Sheesley et al., 2009).
The present study aims to conduct an ambient measurement on particle size
distributions of PAHs associated with inhalation exposure at a megacity
Shanghai site during a 1-year period (2012–2013). The specific objectives
are as follows: (i) to investigate particle size distributions of PAHs;
(ii) to elaborate the mechanisms controlling PAH distribution among the
different size particles; and (iii) to estimate the inhalation exposure and
PAH source contribution.
Experimental setup and methods
Chemicals
All solvents were HPLC grade and bought from Tedia Company Inc., USA.
Standard mixtures of PAHs were purchased from Sigma-Aldrich, Shanghai, China.
The 16 EPA priority PAHs were investigated, i.e., naphthalene (NAP,
two-ring), acenaphthylene (ANY, three-ring), acenaphthene (ANA, three-ring),
fluorene (FLU, three-ring), phenanthrene (PHE, three-ring), anthracene (ANT,
three-ring), fluoranthene (FLT, four-ring), pyrene (PYR, four-ring),
benz[a]anthracene (BaA, four-ring), chrysene (CHR, four-ring),
benzo[b]fluoranthene (BbF, five-ring), benzo[k]fluoranthene
(BkF, five-ring), benzo[a]pyrene (BaP, five-ring),
dibenz[a,h]anthracene (DBahA, five-ring),
indeno[1,2,3-cd]pyrene (IPY, six-ring), and
benzo[ghi]perylene (BghiP, six-ring). For the purpose of ease of
discussion, we divided these PAHs into four groups, i.e., three- to six-ring
PAHs based on their volatility and aromatic ring numbers (Allen et al., 1996;
Duan et al., 2005, 2007).
Sampling site
The measurements 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 city
(elevation about 4 m a.s.l.). This is a Fudan super monitoring station for
atmospheric chemistry, operational all year round. More information on this
site can be found in previous studies (X. Li, 2011; P. F. Li et al., 2011),
and hence only a brief introduction is given. The site is located in a
mixed-use neighborhood including many schools, supermarkets, and residences.
The site is also in close proximity to two major streets, i.e., Handan Road
(about 200 m south) and Guoding Road (about 300 m east). There is always
heavy traffic in this area due to the local and cross-border traffic. The
main releasing sources at this site include industries emissions, household
heating, road transport, and biomass burning.
Sample collection and pretreatment
An Anderson eight-stage air sampler (Tisch Environmental Inc., USA) was used
to collect aerosol samples with different size ranges, i.e., 10.0
(inlet)–9.0, 9.0–5.8, 5.8–4.7, 4.7–3.3, 3.3–2.1, 2.1–1.1, 1.1–0.7,
0.7–0.4, and < 0.4 µm (backup filter). The flow rate of
the sampler was controlled at 28.3 L min-1. The average collecting
time for each batch of samples was 120 h, and the air volume that passed
through the sampler was 203.8 m3. The sampling campaign was conducted
during the period December 2012–December 2013. A total of 189
size-segregated particle samples was obtained, including their corresponding
sampling information and meteorological conditions.
Quartz fiber membranes (Whatman QMA, ∅ 81 mm) were used to
collect aerosol particle samples. Before use, the membranes were baked at
450 ∘C for 4 h, equilibrated at 20 ∘C and 40 %
relative humidity for 24 h, and then weighed. After sampling, the membranes
were equilibrated at 20 ∘C in a desiccator for 24 h and weighed
again using the same procedure. Then, the membranes were stored in freezers
at -20 ∘C until they were extracted. Extraction was performed as
soon as possible to ensure minimal loss of volatile PAH species. The
procedure applied for PAHs pretreatment was Soxhlet extraction. Briefly, the
filter samples were put in a Soxhlet apparatus and extracted in a refluxing
dichloromethane–hexane mixture (1:1, ν / ν) for 36 h. The
temperature was controlled at 69 ∘C. After the extraction was
completed, the contents were filtered by a 0.45 µm PTFE membrane
to remove insoluble particles and then concentrated to exactly 2 mL by
rotary evaporator and under a gentle nitrogen stream. The final extracts were
stored in the refrigerator for further quantitative and qualitative analysis.
The detailed pretreatment procedure can be found elsewhere (Mai et al.,
2003).
Analytical procedure
All samples were quantified for 16 PAHs by an Agilent 7890A Series gas chromotograph (GC) coupled
to an Agilent 7000B Triple Quadrupole mass spectrometer (MS; GC–MS–MS, Agilent Technologies
Inc., USA) operated in electron ionization (EI) mode. The analysis was performed using the Multiple
reaction monitoring (MRM) procedure. The separation was achieved with a
HP-5MS capillary column
(30 m × 0.25 mm i.d. × 0.25 µm). The GC oven
temperature was programmed to rise from 70 ∘C (held for 2 min) to
280 ∘C at 15 ∘C min-1 and finally to 310 ∘C
at 5 ∘C min-1 with a hold of 1 min. The total program time
was 23 min. The temperatures of the injector, ion source, and transfer line
were controlled at 310, 300, and 310∘ C, respectively. Analyses were
carried out in a constant-flow mode. Ultra high-purity Helium (99.999 %)
was applied as carrier gas with a flow rate of 1.2 mL min-1. Nitrogen
was used as collision gas.
Matrix-matched calibration curves (5 to 1000 ng mL-1) were obtained
for all compounds on the GC–MS–MS instrument by plotting the compound
concentration vs. the peak area and determining the R2 using weighted
linear regression (1/x) with the quantitative analysis software for
GC–MS–MS. Limits of detection (LODs) and limits of quantification (LOQs)
were measured based on a signal-to-noise ratio of about 3 and 10,
respectively. The average blank value was subtracted from each signal above
the LOD. Recovery tests were used to estimate possible losses of PAHs during
the extraction process. The blank filters were spiked with the standard
mixture and went through the same procedures for analysis. The results
(n=3) showed that the mean recoveries ranged from 70 to 100 % for all
PAHs. All concentrations reported were corrected by their respective recovery
percentage.
Statistical analysis
Statistical analysis was carried out using partial least-squares regression
(PLS) procedure in the SIMCA-P software (Version 11.5, Umetrics Inc.,
Umeå, Sweden). The size-segregated particles and corresponding PAH
content were, respectively, used as Y variables and X variables in the
PLS model. All variables were centered and scaled to unit variance before the
analysis. In this way, all variables contributed with equal weight to the
model. An important parameter in PLS analysis is the cross-validation
correlation coefficient (O2), which is calculated from the predicted
residual sum of squares and can give an evaluation of the model's predictive
ability in SIMCA (Lindgren et al., 1995). A large O2 value
(> 0.5) means that the PLS model has a predictivity better than
chance. In addition, the observed vs. predicted plot can give more direct
displays for the values of the selected response. The correlation coefficient
(R2) between observed and predicted values can be utilized to evaluate
of the goodness of model fit. Generally, an R2 value higher than 0.8
indicates that the PLS model fits the data well.
PMF source apportionment
Source apportionment of the size-resolved PAHs was performed using Positive
Matrices Factorization (PMF). In the following, PMF will be outlined briefly
(Larsen and Baker, 2003; Ma et al., 2010). By analyzing measured
concentrations at receptor sites, the method can identify a set of factors
which can be taken to represent major emission sources (Paatero and Tapper,
1994). PMF models are expressed as follows:
xij=∑k=1pgikfkj+eij,
where X is a data matrix of i by the jth dimension, in which i is the
number of the size-segregated particle samples and j is the number of the
measured PAH species. fkj is the concentration of the jth PAH species
in the emissions from the kth source; gik is the contribution of the
kth source to the ith particle sample. eij is the portion of the
measured concentration that cannot be explained by the model.
By incorporating an uncertainty for each observation uij, the PMF
solution can minimize the objective function Q (Eq. 2),
Q=∑i=1n∑j=1mxij-∑k=1pgikfkjuij2.
The PMF model requires data on measured PAH concentrations for all samples,
together with information on the associated uncertainties. The confidence of
results can be maintained by adjusting the data uncertainties. This allows us
to lower the importance of these data through the least squares fit. The work
presented here is the US Environmental Protection Agency positive matric factorization (EPA PMF) version 3.0. Please find more information
about this on the US EPA website
(http://www.epa.gov/air-research/positive-matrix-factorization-model-environmental-data-analyses).
Human respiratory risk assessment
In order to evaluate the influence of the size-resolved PAHs on human
respiratory potential, we adopted an International Commission on Radiological
Protection (ICRP) model (ICRP, 1994) for these. Based on inhaled particles
sizes, the respiratory tract was divided into three main deposition regions:
head airway (HA), tracheobronchial region (TB), and alveolar region (AR). The
PAH concentrations were loaded into the ICRP model to calculate the
deposition efficiency and flux of inhaled PAHs.
Lifetime cancer risk (LCR) was applied to assess the cancer risk associated
with exposure to the size-resolved PAHs through inhalation of ambient
particles (Kawanaka et al., 2009; K. Zhang et al., 2012). The LCR was
calculated by the formula (US EPA, 1989)
LCR=EI×ED×CSF/(AT×BW),
where EI was the estimated inhalation rate (mg d-1), which was
calculated by deposition fluxes (mg h-1) and daily exposure time
(12 h d-1), ED was the exposure duration for an adult (30 years), CSF
was the inhalation cancer slope factor ((mg kg-1 d-1)-1), BW
was the body weight (∼60 kg), and AT was the average lifetime for
carcinogens (assuming 70 years for adults). LCR for exposure to PAHs in this
paper was based on the sum of BaP equivalent concentration
(BaPeq), which was calculated by multiplying each concentration
by its individual toxic equivalency factor (TEF) (Nisbet and Lagoy, 1992). As
suggested by the Office of Environmental Health Hazard Assessment (OEHHA), a value of 3.9 for BaP was usually applied as a
recommended value for the calculation of CSF in LCR formula (Liu et al.,
2007).
Results and discussion
Occurrence and size distribution of PAHs
The sampling time series of PAH concentration (ng m-3),
size-segregated particles (µgm-3), temperature (∘C),
visibility (km), and relative humidity (%).
Figure 1 presents the time variation of the total PAHs, size-segregated
particles, visibility, and relative humidity (RH) during the sampling period.
Results show that high PAHs episodes coincide with high PM levels, along with
the low RH and low visibility. Average total PAH concentrations adsorbed on
particles range from 41.6 to 66.6 ng m-3 (average:
48.7 ng m-3). The concentration of total particles during the
observation period varies from 54.8 to 209.6 µg m-3 (average:
122.8 µg m-3). Among them, the daily PM2.5 concentration
is 61.8 µg m-3, which is obviously higher than the annual
(daily) national air quality standard of 10 (25) µg m-3 set
by the World Health Organization (WHO, 2006). Most particle masses are found in the accumulation mode
size ranges (0.4–2.1 µm). Fine particles are typically higher
than coarse particles in Shanghai air. This finding is consistent with
previous research on particle size distribution in Shanghai (Wang et al.,
2014). The PM2.5 / PM10 ratio of 50(± 8) % suggests
that the anthropogenic component of particle matter as represented by the
PM1 fraction is significant in the studied area (Theodosi et al., 2011).
Seasonal variation of three- to six-ring PAHs.
Particle size distribution of PAHs 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
aerodynamic diameter (Dp).
For the investigation of seasonal trends, the PAH data are divided into four
seasonal groups, i.e., spring (March to May), summer (June to August), autumn
(September to November), and winter (December to February). Figure 2 shows
seasonal variation of PAH average concentration in aerosol particles. Results
indicate that the mean concentration of particle-bound PAHs undergoes
distinct seasonal variation, i.e., the highest levels are found in cooler
seasons, while lowest or those below the detection limit are found during
warmer seasons. The most abundant PAH species in winter are five- and
four-ring PAHs (16 and 13 ng m-3), followed by six- and three-ring PAHs (7.5 and
6.5 ng m-3). Given these data, it can be noted that the season
variation and particle size influence the concentration of PAHs. Shanghai is
situated in the subtropics along the east coast of China. The seasonal
variation of weather in Shanghai is closely related to and controlled by the
northern subtropical monsoon system. In winter, the popular northwest wind
can drive the air pollutants from the north Chinese mainland to Shanghai,
while in summer, the popular southeast wind can bring clean oceanic air mass
from the Pacific Ocean to Shanghai. In cold seasons (winter and autumn),
elevated winter- and fall-PAH concentrations, particularly at urban sites,
are most likely due to the higher level of fresh emissions from primary
sources (such as wood smoke and vehicular emissions). Moreover, cold ignition
of gasoline-powered vehicles during cold seasons may lead to an increase in
the level of high molecular weight PAHs such as four- to six-ring PAHs (Arhami et
al., 2010). The atmospheric conditions in winter, such as low temperatures,
low intensity of solar radiation, and decreased PAH photodegradation, also
favor the condensation and adsorption of PAHs on suspended particles that are
present in urban air. On the other hand, in warm seasons (summer and spring),
the concentrations of PAHs are reduced, possibly due to the high
temperatures, higher mixed layer height, and heavy rainfall that may
effectively remove particle-bound PAHs from the atmosphere. Additionally,
high temperature and solar radiation favor the photochemical oxidation of
PAHs. This seasonal pattern has been reported in many urban atmospheres
(Teixeira et al., 2012; van Drooge and Ballesta, 2009; Ma et al., 2010). More
details will be included in the following mode discussion and source
attribution of PAHs.
To better describe PAH distribution, the particle fractions are divided into
three modes: Aitken (Dp < 0.4 µm),
accumulation (0.4 < Dp < 2.1 µm),
and coarse (Dp > 2.1 µm) mode. The Aitken
and accumulation modes together constitute “fine” particles. We plot a
log–log chart, i.e., dC/dlogDp against Dp
(particle diameter) on the log scale, in which dC is the PAH concentrations
in each particle size bin and dlogDp is the size width of each
impactor channel (Kawanaka et al., 2004; Venkataraman and Friedlander, 1994;
Venkataraman et al., 1999). Figure 3 clearly shows that most PAHs have a
bimodal particle size distribution which contains one mode peak in the
accumulation size range (0.4–2.1 µm) and another mode peak in
coarse size range (3.3–9.0 µm). As the number of PAHs' aromatic
rings increases, the intensities of two peaks vary a lot, i.e., the
accumulation mode peak increases, while the coarse-mode peak decreases and
even disappears in five- and six-ring PAHs. This is due to the fact that less
volatile PAH species preferentially condense on fine particles and more
volatile ones are inhibited on smaller particles because of the Kelvin effect
(Hien et al., 2007; Keshtkar and Ashbaugh, 2007). This kind of mode
distribution that appears in Shanghai is similar to those found in Mumbai,
India (Venkataraman et al., 1999), but different from those in Boston, MA
(Allen et al., 1996). From the results of PAH distribution, we see an
important implication regarding health hazards from inhalation exposure.
Since the majority of high molecular weigh PAHs has mutagenic and/or
carcinogenic properties and almost exclusively exists on fine particles,
these PAHs can travel deep into the human respiratory system and hence can
pose a serious health risk through exposing a person to both particles and
the loaded carcinogenic PAHs (Kameda et al., 2005).
Atmospheric processing and partitioning mechanisms
Previous studies on atmospheric process regarding PAHs have mainly focused on
gas–particle partitioning (R. Zhang et al., 2012; McWhinney et al., 2013),
but few studies are associated with the particle size distribution of PAHs.
Thus, we use the size-resolved PAH data to assess the PAH aging and partitioning process among
different size particles. Empirical evidence suggests that mass ratios of PAH
to particulate matter (PAH / PM) can provide some valuable indications of
PAH atmospheric processes. When PAH compounds and particles that are produced
from incomplete combustion of organic material are released into the air,
they can be expected to be involved in the particle aging process because
some PAHs could be photooxidized to form SOA (secondary organic aerosol) and
others might adsorb or absorb on preexisting particles via either
self-nucleation or gas–particle partitioning. This would lead to an increase
in atmospheric fine particulate matter (Kavouras et al., 1999; Kamens et al.,
1999; Yu et al., 1999; Kamens and Jaoui, 2001; Chan et al., 2009). That is to
say that the aging process can decrease the value of total-PAH / PM (Duan
et al., 2005; Bi et al., 2005). Figure 4 shows the variation of total
PAH / PM values across particle sizes. In general, PAH / PM ratios
decrease gradually with the increase in particle size. This indicates that
the different values of PAH / PM across particle size can be the result
of different aging processes. In order to further verify the particle aging
process, we use BaA / CHR as another indicator of particle aging. BaA is
expected to be degraded more easily than BaA isomers during transport because of their higher
reactivity. Using the ratios of a more reactive PAH compound to a less
reactive one, such as BaA / CHR, An / Phe, and BaP / Bep, a
higher ratio indicates relatively little photochemical processing of the air
mass. On the other hand, a lower ratio is reflective of more aged PAHs.
Therefore, it can be used to determine whether the air masses collected are
fresh or aged (Ding et al., 2007). Figure 4 shows the decrease in
BaA / CHR with the increase in particle sizes, showing the same trend as
PAH / PM. Generally, relatively higher ratios occur in small-particle
size ranges, and lower ratios exist in large-particle size ranges, suggesting
smaller particles sampled at urban sites are relatively fresh, while bigger
particles are relatively aged. Because particulate-phase PAHs are susceptible
to photodegradation, the decrease in BaA / CHR with the increase in
particle sizes shows that photodegradation plays an important role in the
particle aging process, especially for the relatively larger urban aerosol
particles. It should be noted that the explanation of particle aging in the
present study still contains some uncertainties because of the scarcity of
“aging time scale” data; therefore, further studies (e.g., theoretical
models and chamber simulation experiment) are needed. Although the present
results do not take the partitioning process into account directly, this study has taken advantage of
the size-resolved PAH data to examine the governing mechanisms for particle
size distribution.
Ratios of total PAHs / PM (ng µg-1) and BaA / CHR (ng ng-1) across particle sizes.
Currently, the reliable mechanisms for controlling PAH distribution between
different size particles include adsorption to nucleus particles, adsorption
and absorption to accumulation particles, and multilayer adsorption on coarse
particles (Venkataraman et al., 1999). Adsorption and absorption depend,
respectively, on available particle surface area and organic mass. If PAHs
are firstly associated with the particle surface, the PAH / PM mass ratio
will show a 1/Dp dependence (assuming particles are spherical)
and then will generate a straight line of slope -1 on a log vs. log axis
(Venkataraman et al., 2002). Figure 5 shows that all slope values from the
plots of log(PAH / PM) against log(Dp) are above -1,
suggesting that multiple mechanisms, i.e., adsorption and absorption, control
the PAHs' distribution among different size particles. Moreover, the slope
values decrease with the increase in ring number of PAHs, which means
adsorption plays a much stronger role in the distribution process of five-
and six-ring PAHs than three- and four-ring PAHs. The reason is the
relatively lower volatility of five- and six-ring PAHs, which makes them
adjust to multiple adsorptive equilibria more slowly. Moreover, chemical
affinities may also play an important role in the adsorption process. Most
five- and six-ring PAHs have a strong hydrophobicity and tend to affiliate
with small particles because they can provide large surface areas
(Venkataraman et al., 1999). Such an explanation, however, can not adequately
account for PAHs' equilibrium mechanisms observed in the present study.
Perhaps in fact five- and six-ring PAHs do not attain equilibrium due to the
slow mass transfer, but they reach a steady state between the gaseous and
particulate phases (Yu and Yu, 2012).
Plots of lg(TPAHs / PM)-lg(Dp) for PAHs
with different ring numbers.
Statistical analysis
In an attempt to understand how particle size affects PAH species, we built a
statistical model using PLS regression based on PAH concentration and
particle size data. After calculating, five components are adopted because
they can give the most stable results and easily interpretable factors. The
number of components in PLS is also consistent with the results of the PMF
followed, as discussed in the next section. By plotting the observed
(measured) particle sizes versus the predicted particle sizes, we obtain a
goodness of fit with R2=0.87, a goodness of prediction with Q2=0.80, and a goodness of root mean square error (RMSE) with a value of 0.87.
Figure 6 shows the observed vs. predicted plot from the model. The plot
performs well in predicting the size-resolved PAHs over the size range
between 0.4 and 10 µm. There is no systematic underestimation (or
overestimation), and most points fall close to the 45∘ line. The
results achieve the desired separation without overlap among nine particle
size ranges. The model can explain 91 % of X and 87 % of Y and
predict 80 % of Y. These predictions are not de novo predictions, since
all the data are part of the observed set. Nevertheless, these predicted
results do validate the model effectiveness and the measured data
reliability.
Measured and predicted total PAHs in all particles with sizes ranges
from < 0.4 to 10 µm. The dashed line represents the
45∘ line.
Similarities between PAH profiles in the two adjacent sizes can be further
identified by coefficient of divergence (CD), which is a self-normalizing
parameter used to evaluate the divergence degree of two sets of data (Kong et
al., 2012). CD is determined as follows:
CDjk=1p∑i=1pxij-xikxij+xik2,
where j and k stand for the two adjacent particle fractions, p is the
number of investigated PAHs, and xij and xik represent the
concentrations of PAHs species i for size j and k (Kong et al., 2011).
CD ranges from 0 to 1. A low CD value (< 0.2) indicates a high level
of homogeneity in PAH distribution between two adjacent sizes, while CD
values larger than 0.2 indicate heterogeneous PAH spatial distribution
(Wilson et al., 2005). Figure 7 shows the PAHs' CD diagrams, which are
distinguished by different colors. For the
comparison between the adjacent sizes, most CDjk values are less than
0.2, except CD0.4,0.4∼0.7 (0.26) and CD1.1∼2.1,2.1∼3.3
(0.31), indicating that PAHs among PM0.4, PM0.4-2.1, and
PM2.1-10 show a high spatial heterogeneity in source factor
contributions.
Similarity comparisons of PAHs
profiles for the adjacent particles fractions.
Profiles of the five factors resolved by the PMF model from all PAH
data sets.
Emission source of size-resolved PAHs
The different PAH distributions between fine and coarse particles may be
attributed to different emission sources. By applying the PMF model, the
optimal five main factors have been chosen after comparing three to seven factors. Five identified sources are, respectively, associated with
vehicular emission, biomass burning, coal combustion, petroleum residue, and
air–surface exchange. Figure 8 shows the profiles for all factors. Factor 1
presents a profile with high factor loadings for five- and six-ring PAHs,
i.e., B(b+k)F, BaP, IPY, DBahA, and BghiP. These high molecular weight PAHs
are reported as dominant in vehicle emissions (Bostrom et al., 2002; Ravindra
et al., 2008). BbF and BkF are attributed to diesel motor vehicle emissions,
while BaP and BaA are attributed to gasoline and diesel markers (Harrison et
al., 1996; Sofowote et al., 2008). Thus, this factor is designated vehicular
emissions without distinguishing between diesel and gasoline releasing.
Factor 2 is dominated by high loadings of PHE, FLU, and BbF and moderate
loadings of CHR, BkF, BaA, IPY, and BghiP. This factor profile mainly comes
from biomass burning that has been described in a previous study (Poulain et
al., 2011). As the occurrence of biomass burning in Shanghai city is normally
low, this source is most likely long-range transport, rather than local
emission. Factor 3 is characterized by B(b+k)F, CHR, BaA, and BghiP. These
compounds have been reported by different authors as coal combustion source
markers (Yang et al., 2002; Lin et al., 2011). Although in Shanghai, natural
gas is one of the main fuels used for domestic heating, there are still
central heating systems using coal and petrol-derived fuels. Moreover, the
influence of power plant, steel, and iron industries using coal as fuel may
be also reflected in this factor. Factor 4 is mainly defined by four- and
five-ring PAHs. High levels of these compounds, especially for PHE, are
associated with crude-oil or refined-petroleum emissions and their
degradation products (Zakaria et al., 2002). Thus, this factor is likely to
represent petroleum residue or the derivatives of oil spill, leakage from
vehicles, and discharge from municipal and industrial wastewater, etc. Factor
5 is more influenced by two- and three-ring PAHs. These PAHs are favored in
air–surface exchange (Gigliotti et al., 2002). The “exchange” here means
that the aged PAHs are probably released into the atmosphere again from
contaminated soil or wastewater and then adsorbed later by the particles.
Moreover, they also arrive here through long-range transport and finally
deposition on particle surfaces. Thus, factor 5 is ascribed to air–surface
exchange.
Factor contributions to size-segregated particles by the PMF model
from full PAH data sets.
Figure 9 summarize the results of PAHs' source apportionment associated with
factor contributions. As expected, the results are quite different for the
different particle sizes. Coal combustion and biomass burning, respectively,
accounted for 29 and 29 % of accumulation mode PAHs as well as 12 and
13 % of coarse-mode PAHs. Their contribution for particulate PAHs
significantly decreases with the increase in particle size because large
particles have large deposition velocities from the air. Air–surface
exchange and petroleum residue account, respectively, for 9 and 10 % of
accumulation mode PAHs as well as 30 and 27 % in coarse-mode
PAHs. Note that the contribution of vehicle-derived PAHs (vehicular
emission) are almost constant over all the year, i.e., they contribute
22 % of accumulation mode PAHs and 18 % of coarse-mode PAHs. In
combination with PAH mode distribution, we know that high levels of PAHs
occur in accumulation mode particles. Together with Aitken mode particles, we
can obtain 80 % of PAHs from the contribution of fine particles (Aitken
and accumulation mode particles). It is apparent that these PAHs came from vehicle exhaust, coal combustion, and biomass
burning.
Respiratory exposure to PAHs
In order to assess deposition efficiency and flux of size-resolved PAHs in
the human respiratory tract, we applied an International Commission on
Radiological Protection (ICRP) model (1994). More details on calculating from
the model are included elsewhere (K. Zhang et al., 2012; Kawanaka et al.,
2009). The breath rate of inactive people is considered to be
0.45 m3 h-1. Figure 10 shows the deposition fluxes of
size-resolved PAHs and their relative contributions in the head,
tracheobronchial region, and alveolar region. We can find a flux peak value
in accumulation mode particles (1.1–2.1 µm), similar to the
particle size distribution of PAHs as described previously (see Sect. 3.1).
The total PAH deposition flux is 8.8 ± 2.0 ng h-1, which is
higher than that in indoor air of the urban community of Guangzhou, China
(3.7 ng h-1) (K. Zhang et al., 2012), but it is lower than that to
which traffice police in Beijing are exposed (280 ng h-1 at the respiratory rate of 0.83 m3 h-1) (Liu
et al., 2007). Moreover, we find that the relative PAHs abundance varies a
lot with the particle size. When particle size increases, the relative PAHs
abundance increases in the head region, remains unchanged in tracheobronchial
region, but decreases in the alveolar region. These results indicate that
coarse particles contribute many PAHs in the head region, while fine
particles contribute most PAHs in the alveolar region. These fine or
ultrafine particles can also pass rapidly from the human lung into the
circulatory system, which may cause systematic exposure to PAHs (Nemmar et
al., 2002).
Deposition fluxes (estimated by the ICRP model) and relative
abundance of the size-segregated PAHs in the head airway, tracheobronchial
region, and alveolar region in the human respiratory tract.
Evaluating respiratory exposure needs to incorporate a consideration of the
deposition efficiency of size-resolved PAHs. Deposition efficiency
represents the deposition effectiveness of atmospheric PAHs in the human
respiratory tract. The efficiency can then be calculated by the formula of the ICRP model. Figure 11 shows the regional deposition efficiency of PAHs across
particle sizes. Generally, the deposition efficiency of PAHs increases with
the particles size increase except for the alveolar region, in which the
PAH deposition efficiency increases with particle size decrease. This
suggests that smaller particles can easily pass the respiratory tract and
be deposited in the alveolar region. This, combined with the fact that most five- and
six-ring PAHs tend to adsorb on smaller particles, makes them more important
for potential health damage.
Deposition efficiencies (estimated by the ICRP model) of the
size-segregated PAHs in the head airway, tracheobronchial region, and
alveolar region.
We can utilize the LCR to estimate the exposure of PAHs through the
inhalation of ambient particles. Figure 12 shows the LCR variations of
inactive (breath rate: 0.45 m3 h-1) and active people (breath
rate: 0.83 m3 h-1) during haze and non-haze periods. The curve of
LCR displays a unimodal distribution with only one distinct peak located at
1.1–2.1 µm. Accumulation mode PAHs contribute about 54 % of
LCR, suggesting that accumulation particles are major carcinogenic-PAH
carriers. After calculation, we can obtain an LCR value of
6.3(± 0.8) × 10-7 in a normal respiratory condition
(0.45 m3 h-1) during the Shanghai haze period, which approaches
the cancer risk guideline value (10-6) (US EPA, 2005). As we know, the
value of LCR depends strongly on the respiratory rate. If we apply an average
respiratory rate of 0.83 m3 h-1 (for people who exercise outside)
(Liu et al., 2007), the LCR value will arrive at
1.2(± 0.2) × 10-6, which exceeds the cancer risk guideline
value; especially on severe haze days the value can reach up to
1.5 × 10-6. Note that this value is only for the
size-resolved particulate PAHs, which are responsible for some of the
respiratory risks posed by atmospheric PAHs. If the gaseous PAHs are also
taken into account, the cancer risk will probably be much higher. In
combination with previous PMF source analysis, we find that the sources of
these PAHs are mainly biomass burning (24 %), coal combustion (25 %),
and vehicular emission (27 %). This is consistent with the previous
epidemiological studies that smaller particles can lead to a greater risk of
cardiovascular toxicity through breathing (Pope et al., 2009). Thus, it
appears to be important to carry out stricter control of smaller-particle
emissions, particularly aiming at the reduction of their releasing sources.
Panel (a): lifetime cancer risk (LCR) due to exposure to
the size-segregated PAHs through inhalation for inactive and active people
during haze and non-haze period. Panel (b): source contribution to
accumulation mode PAHs during haze period by PMF analysis.