Overall characteristics
Temporal variations in PM1 composition and chemical properties
The overall characteristics and temporal variations in winter PM1 in
Seoul, including mass and volume concentrations and size distributions, are
shown in Fig. 2, along with the time series of gaseous pollutants, e.g.,
CO, SO2, and Ox (Ox= O3 + NO2) and meteorological conditions (RH, temperature, wind
direction, wind speed). From 5 December 2015 to 21 January 2016, the average
concentration of PM1 (= NR-PM1+ BC) was
27.5 µgm-3, ranging from 2.6 to 90 µgm-3.
Assuming that PM1 represents approximately 80 % of PM2.5 mass
(Lim et al., 2012), we found that 29 % of the measurement days (i.e.,
14 days) violated the National Institute of Environmental Research (NIER)'s
daily PM2.5 standard (50 µgm-3) and 58 % of the
days (28 days) violated the WHO's daily standard (25 µgm-3).
Although severe haze with high PM1 concentration close to
90 µgm-3 was observed several times, the average mass
concentration of PM1 (27.5 µgm-3) was still moderate
because of the frequent occurrence of clean periods in winter. The average
concentration of PM1 measured in Korea during this study was similar to
or slightly lower than that measured in winter in several urban areas in
China, including Shanghai (Huang et al., 2012), Shenzhen (He et al., 2011),
Lanzhou (Xu et al., 2016), and Hong Kong (Li et al., 2015), but it was much
lower than in Beijing where the winter mass concentrations of PM1 were
found to be 7–10 times higher than in Seoul (Sun et al., 2014).
The large variations in PM1 mass concentrations
(2.6–90 µgm-3; Fig. 2e) and other pollutants (Fig. 2c),
such as CO (0.3–2.3 ppm), O3 (3–46 ppb), and
NO2 (8–98 ppb), reflected that air quality in Seoul is
influenced by dynamic changes in emission sources, atmospheric processes, and
meteorological conditions. In addition, new particle events were observed
during this study and they showed characteristics of a sharp increase in
ultrafine particle number concentration and subsequent growth of these
particles in size (Fig. 2j). This is the first finding of its kind in an
urban area of Korea, although frequent (∼ 7.4 % of the measurement
days) observations of new particle formation and growth events were reported
from the 3-year continuous measurements using SMPS at the Gosan station
(33.17∘ N, 126.10∘ E), which is in a pristine rural area
but is downwind of the Asian continent and faces the Yellow Sea (Kim et
al., 2013).
Based on the variations in PM1 concentrations and meteorological
conditions (Fig. 2), we divided the whole study into two typical periods:
(1) high-loading period (daily PM1>30 µgm-3) and
(2) low-loading period (daily PM1<14 µgm-3). Of the PM1
concentrations estimated based on the NIER and WHO daily and yearly
PM2.5 standards, 30 and
14 µgm-3 correspond to the middle values, respectively. As shown in Figs. 2 and 3, high and low-loading periods usually alternated during winter in Seoul. Comparisons
between the high and low-loading periods can indicate how different sources
and atmospheric processes influence air quality in this region. Details on
the periodic variations in air quality in Seoul are discussed in Sect. 3.4.
On average, OA was the largest component of the PM1, accounting for
44 % of the total mass, followed by nitrate (24 %), sulfate
(10 %), ammonium (12 %), BC (9 %) and chloride (1 %)
(Figs. 3a, 4a and Table 1). POA (primary organic aerosol, =HOA+COA+BBOA) and SOA (= SV-OOA + LV-OOA) accounted for 59 and
41 %, respectively, of OA mass (Sect. 3.3). Consequently,
∼ 36 % of PM1 consisted of primary materials
(POA+BC), with the remainder (64 %) being secondary
species (NO3- + SO42- + NH4+ + SOA).
This indicates that the aerosol pollution problem in Seoul during winter was
more strongly influenced by secondary aerosol formation processes. Details on
the sources and processes that led to severely bad air quality are discussed in
Sect. 3.4.
The molar-equivalent ratios of total inorganic anions to cation for
NR-PM1 (=(SO42-/48+NO3-/62+Cl-/35.5)/(NH4+/18)) were close to 1; thus, submicron aerosols were mostly
neutralized in the forms of ammonium salts such as NH4NO3,
(NH4)2SO4, and NH4Cl (Zhang et al., 2007b)
(Figs. 3d and 4b). Possible sources of ammonium in Seoul include on-road
vehicle emissions, the use of neutralizers in industry, and agricultural emissions
on the outskirts of Seoul. Note that particles appeared to have “excess”
NH4+ at high organic aerosol loadings (Fig. 4b), probably due to the
presence of carboxylate ions such as formate, acetate, and oxalate, which
were not counted in the calculation of ion balance (Ge et al., 2012b).
Further investigations into this issue might be necessary in the future.
(a) Average compositional pie chart of PM1 species
(nonrefractory PM1 plus black carbon (BC)) and each of the OA factors
over the whole campaign. The green outline indicates the fraction of total
OA and (b) shows a scatterplot that compares predicted NH4+ vs.
measured NH4+ concentrations. The predicted values were calculated
assuming full neutralization of the anions (e.g., sulfate, nitrate, and
chloride). The data points are colored by organic concentrations.
(c) Campaign-averaged size distributions for individual NR-PM1
species; (d) averaged mass fractional contributions of each
NR-PM1 species to the total NR-PM1 mass as a function of size.
(e) Overview of the average PM1 and OA compositions in Seoul
during winter; (f) average high-resolution mass spectrum of OA
colored by the different ion families. The average elemental ratios for the
OA fraction are described.
(a–e) Diurnal profiles, 1 h averaged, of mass-based
size distributions of each of the nonrefractory submicrometer particulate
matter (NR-PM1) species, colored by their mass concentration (left axis,
Dva, vacuum aerodynamic diameter) and average diurnal profiles of
each of the PM1 species (right axis); (f) average diurnal mass
fractional contribution of each PM1 species to the total PM1
diurnal mass and the total PM1 mass loading.
(a) Scatterplot of the variations in fSO4 ratios
as a function of RH. (b) Diurnal profiles, 1 h averaged, of
SO2 and SO4, and (c) 1 h averaged diurnal
profiles of fSO4, RH, and solar radiation. The solar radiation
measurement site is located at 20 km away from the measurement site.
Polar plots of hourly averaged PM1 species concentrations (top
row), mass concentrations of the five OA factors identified from PMF analysis
(middle row), and the mixing ratios of various gas-phase species as a
function of WS and direction.
(a–e) Average diurnal profiles of the organic
matter-to-organic carbon (OM / OC), oxygen-to-carbon
(O / C), hydrogen-to-carbon (H / C),
nitrogen-to-carbon (N / C), and sulfur-to-carbon
(S / C) ratios of OA, where the O / C,
H / C, and OM / OC elemental ratios were
determined using the updated method (Canagaratna et al., 2015).
Overview of the results from PMF analysis, including the time series
of each of the OA factors and various tracer species.
Overview of the results from PMF analysis, including high-resolution
mass spectra of the (a) hydrocarbon-like organic aerosol (HOA),
(b) cooking OA (COA), (c) biomass burning OA (BBOA),
(d) semivolatile oxygenated OA (SV-OOA), and
(e) low-volatility oxygenated OA (LV-OOA) colored by different ion
families. (f–i) Average diurnal profiles of each of the OA factors
(the 90th and 10th percentiles are denoted by the whiskers above and below
the boxes. The 75th and 25th percentiles are denoted by the top and bottom of
the boxes. The median values are denoted by the horizontal line within the
box, and the mean values are denoted by the colored markers).
(k) Compositional pie chart of the average fractional contribution
of each of the OA factors to the total OA over the campaign;
(l) average diurnal mass fractional contribution of each of the OA
factors to the total OA diurnal mass and the total OA mass loading.
Diurnal patterns of PM1 composition
Diurnal patterns can provide insights into aerosol sources and formation
processes. In this study, the daily variations in concentrations of aerosol
species show distinctively different patterns, indicating that the sources
and formation processes of PM pollutants in Seoul were diverse and complex.
First, secondary inorganic species, such as sulfate, nitrate, and ammonium,
all displayed different diurnal profiles. In the case of nitrate (Fig. 5c),
the daily variations started to increase at ∼ 07:00, peaked at midday
(10:00–12:00), and then slowly decreased until 17:00. Previous studies
(Brown et al., 2006; Lurmann et al., 2006; Sun et al., 2012; Young et
al., 2016; Xu et al., 2014) attributed this type of daytime peak shortly
after sunrise to the mixing down of secondary aerosols formed at night in a
residual layer aloft. Later, the photochemical formation of nitrate from
NOx, emitted from rush hour traffic, contributed to an increase
in nitrate concentration during the day, a feature of regionally generated
secondary inorganic species. The decreasing trend in the afternoon is likely due to
the evaporative loss of semivolatile species at higher air temperatures as
well as the dilution effects due to the enhanced BL height in the afternoon.
A significant amount of nitrate can also be formed through nighttime chemistry,
as indicated by the high fractional contribution at night (Fig. 5f).
Given that somewhat high concentrations of O3
(∼ 12.0 ppb) and NO2 (∼ 41.7 ppb) were
observed at night (18:00–6:00) (Fig. S10), nitrate formation from
N2O5 hydrolysis possibly occurred.
Unlike nitrate, sulfate concentration was elevated at night, showing
a trend that started to increase from late afternoon (∼ 16:00), peaked
at around 10:00 on the following day, and then gradually decreased afterwards
to reach a minimum value at 16:00 (Figs. 5b, 6b). The overnight increase
starting in the late afternoon appeared to be associated with enhanced
gas-to-particle partitioning of SO2 and aqueous-phase processing
facilitated by the relatively low temperature and high RH at night (Fig. 6).
Indeed, fSO4 (= the ratio in sulfate (SO42-) to
SOx (SO42- + SO2) based on sulfur contents;
Kaneyasu et al., 1995) increased at night and showed relatively good
correlation with RH (R2=0.59, Fig. 6a). However, fSO4 started
to decrease at ∼ 6:00 (Fig. 6c) but sulfate concentration continued to
increase until 10:00 (Fig. 6b). This increase in sulfate concentration
appeared to be due to a similar reason as that of nitrate – mixing with the
higher concentration of sulfate in the upper residual layer formed at night.
The residual layer was also enriched with SO2 (Fig. 6b), for which
nighttime transport of air mass from industrial facilities located on the
western (Fig. S11) and southwestern outskirts of Seoul (Fig. 1b) might be
responsible. However, the bivariate polar plots (Fig. 7) indicate that high
SO2 concentration tended to occur under high-speed wind from the
south and southeast, which shifted relative to the locations of
the SO2 point sources (Fig. 1b). The reason might be geographical
since the position (north) of the Bukhan Mountain blocks the wind and
promotes the circulation of air masses. Similar trends were observed in a
previous study (Heo et al., 2009).
The decrease in both sulfate and SO2 concentrations between 10:00 and
17:00 (Fig. 6b) could be due to the dilution effect from rising BL height.
Since fSO4 showed only minor increases between 12:00 and 17:00 when
RH was low (Fig. 6c), gas-phase photochemical production of sulfate during
the day was unlikely to be an important process. This observation, together
with the nighttime increase of sulfate associated with high RH, suggests that
aqueous phase processing is an important driver for sulfate formation in
Seoul in winter. This conclusion is consistent with previous studies that
found that aqueous phase reactions were an important pathway for sulfate
formation in the atmosphere under humid conditions (Ervens et al., 2011; Ge
et al., 2012b).
In this study, as mentioned in the earlier section, nitrate, sulfate, and
chloride in PM1 appeared to be fully neutralized by ammonium, indicating
that the inorganic species were mainly present in the forms of
NH4NO3, (NH4)2SO4, and NH4Cl. Since
nitrate was more abundant compared to sulfate and chloride (Fig. 4a), the
ammonium diurnal pattern was similar to that of nitrate. However, a gradual
increase in ammonium was observed at night, likely due to the
enhancement of the sulfate concentration. Chloride accounted for a small
fraction of the PM1 mass and displayed a morning rush hour peak,
suggesting that the source of chloride is probably local, such as vehicle
emissions. Indeed, the polar plot of chloride in Fig. 7 does show the feature
of local source since high concentration occurred mostly at slow wind speed.
Furthermore, chloride showed a similar diurnal pattern as HOA (Sect. 3.3,
Fig. 10) and good correlation with HOA (r=0.8, Table 2).
Correlation coefficient (Pearson's r) for the linear regressions
between organic aerosol (OA) factors, including the sum of primary factors
(primary OA (POA) = hydrocarbon-like OA (HOA) + cooking OA
(COA) + biomass burning OA (BBOA)), as well as the sum of the oxidized
factors (oxidized OA (OOA) = semivolatile OOA (SV-OOA) + low-volatile
(LV-OOA)), and various particle- and gas-phase species and ions.
r
HOA
COA
BBOA
POA
SV-OOA
LV-OOA
OOA
(HOA + COA
(SV-OOA
+ BBOA)
+ LV-OOA)
Nitrate
0.32
0.30
0.41
0.43
0.87
0.63
0.87
Sulfate
0.25
0.17
0.09
0.23
0.71
0.80
0.88
Ammonium
0.41
0.30
0.37
0.45
0.87
0.69
0.90
Chloride
0.80
0.30
0.50
0.68
0.50
0.13
0.36
K (AMS)
0.59
0.60
0.63
0.77
0.61
0.22
0.47
Primary pollutants PAH
0.48
0.60
0.90
0.81
0.37
-0.11
0.14
BC
0.63
0.56
0.82
0.83
0.69
0.18
0.50
CO
0.52
0.54
0.62
0.74
0.61
0.29
0.51
NO2
0.45
0.64
0.61
0.72
0.57
0.20
0.44
AMS tracer ions (m/z value)
CO2+ (44)
0.32
0.38
0.41
0.47
0.84
0.75
0.92
C2H5N+ (43)
0.46
0.52
0.48
0.62
0.52
0.35
0.50
C2H4O2+ (60)
0.70
0.66
0.85
0.92
0.67
0.10
0.44
C3H5O2+ (73)
0.71
0.76
0.74
0.94
0.66
0.15
0.46
C3H3O+ (55)
0.55
0.86
0.64
0.88
0.66
0.24
0.51
C3H7+ (43)
0.91
0.69
0.63
0.96
0.46
0.02
0.27
C3H7N+ (57)
0.60
0.57
0.56
0.74
0.78
0.33
0.63
C4H7+ (55)
0.85
0.78
0.65
0.98
0.49
0.04
0.30
C4H9+ (43)
0.95
0.62
0.61
0.94
0.42
0.00
0.24
C5H11+ (57)
0.96
0.59
0.60
0.92
0.41
-0.01
0.23
C5H8O+ (84)
0.58
0.93
0.54
0.90
0.50
0.09
0.33
C6H10O+ (98)
0.57
0.95
0.51
0.89
0.41
0.02
0.24
C7H12O+ (112)
0.57
0.89
0.58
0.88
0.53
0.09
0.35
C9H7+ (115)
0.82
0.74
0.72
0.96
0.18
-0.05
0.10
CHN+ (27)
0.47
0.47
0.58
0.63
0.73
0.53
0.73
CN+ (26)
0.35
0.35
0.46
0.48
0.57
0.42
0.57
CH2SO2+ (77)
0.30
0.24
0.37
0.37
0.83
0.53
0.79
CH3SO2+ (78)
0.37
0.27
0.41
0.44
0.91
0.54
0.83
BC: black carbon, AMS: aerosol mass spectrometer, PAH: polycyclic
aromatic hydrocarbons. Values that are r> 0.7 are boldfaced.
In contrast to ammonium and nitrate, OA concentration tended to remain high
overnight and started to increase in the morning, with a maximum usually
occurring at 8:00 (Fig. 5a). This morning increase was likely the outcome of
a shallow BL coupled with enhanced primary emissions from rush-hour traffic.
Further discussions on this are given in Sect. 3.3.
Size distributions of the main components of PM1
The average mass-based size distributions of AMS species in terms of vacuum
aerodynamic diameter (Dva; DeCarlo et al., 2004) are shown in
Fig. 4c. Nitrate and sulfate had relatively different size distribution
profiles, with the mode of sulfate being about 100 nm larger than the mode of
nitrate. A possible reason for this difference was likely that nitrate was
mainly formed through photochemical reactions, whereas sulfate was more likely
to be formed by aqueous-phase reactions during this study. Another reason was
that sulfate was overall more aged than nitrate. As discussed in previous
sections, sulfate in Seoul was likely to be transported from regional sources
at night, whereas nitrate was apparently formed mostly locally. The
daily variations in the nitrate and sulfate size distributions
(Figs. 5b, c; S4) further support the different formation processes for these
two compounds. The size distributions of sulfate showed a prevalent droplet
accumulation mode (Dva=500 nm) that stayed fairly
constant compared to the one of nitrate (Fig. S4). However, the size
distributions of nitrate became broader between 9:00 and 15:00, with
significant changes in concentrations (Figs. 5c, S4a). In addition, nitrate
size distribution was quite broad (Fig. 4c, d), similar to observations in
various urban locations (e.g., Sun et al., 2009, 2011a; Drewnick et
al., 2004; Salcedo et al., 2006; Weimer et al., 2006; Young et al., 2016;
Zhang et al., 2005b).
The average size distribution of OA was wider than that of the inorganic
species, peaking at a Dva of ∼ 400 nm (Fig. 4c).
The OA size distribution varied as a function of the time of day (Fig. 5a),
with a broader profile extending to Dva<100 nm observed
during the morning rush hour and at night when primary emissions are
dominant. The wide size distribution of organics reflected the contribution
made by both primary and secondary aerosols, i.e., the fine mode from primary
aerosols and the accumulation mode from secondary formation. Similar
observations were reported from China (e.g., Huang et al., 2010; Sun et
al., 2010), and some urban areas in North America (Aiken et al., 2009;
Alfarra et al., 2004; Drewnick et al., 2004; Ge et al., 2012b; Zhang et
al., 2005b) and Europe (Allan et al., 2003; Dall'Osto et al., 2013).
OA characteristics and source apportionment
Bulk composition and elemental ratios of OA
Atmospheric OA are composed of complex materials that originate from
different sources and have undergone different atmospheric processes.
Understanding the chemical composition and sources of OA is important for
understanding the impacts of these aerosols.
Overall, OA from Seoul in winter was found to be composed of
approximately 71 % carbon, 18 % oxygen, 9 % hydrogen, and 2 %
nitrogen (Fig. 4e). The average carbon-normalized molecular formula of OA was
C1H1.8O0.37N0.022S0.0009, yielding an average organic
mass-to-carbon ratio (OM / OC) of 1.67. The largest
component of the OA mass spectral signal was found to be the
CxHy+ ion family (57 %, Fig. 4e), followed by
the CxHyO1+ (25 %) and
CxHyO2+ (11 %) ion families, with
smaller contributions from the CxHyNp+
(4 %), CxHyNpOz+ (2 %), and
HyO1+ (1 %) ion families. The largest peak in the
average OA spectrum was at m/z=43 (Fig. 4f), accounting for 8 % of
the total OA signal with a composition of C2H3O+ (49 %),
C3H7+ (49 %), CHON+ (1 %), and C2H5N+
(1 %). The second largest peak (6 % of the total OA signal) in the
average OA spectrum was at m/z=44, which was dominated by the
CO2+ ion (86.7 %), followed by C2H4O+ (7.8 %),
C2H6N+ (2.7 %), CH2NO+ (2.6 %), and C3H8+
(0.2 %). The peak at m/z=60 was composed almost entirely of
C2H4O2+ (94 %) and 88 % of the peak at m/z=73 was
composed of C3H5O2+, both of which are the tracers of wood burning
(Aiken et al., 2008; Alfarra et al., 2007). The peak at m/z=57, which is
used as a tracer for hydrocarbon-like organics from vehicle emissions (Zhang
et al., 2005a), accounted for 4 % of the total OA signal and was composed
predominantly of C4H9+ (75 %) and C3H5O+ (22 %)
in this study.
The time series of the H / C, O / C,
N / C, and S / C ratios of OA are shown
in Fig. 3e and f. The O / C ratio of an OA indicates its
average oxidation level, and more aged and oxidized organics tend to have
higher O / C ratios (Aiken et al., 2008; Jimenez et
al., 2009). The O / C varied substantially during this
study, ranging from 0.05 to 0.63, and the average O / C
ratio was 0.37±0.09. The average H / C ratio was
1.79±0.07 (1.60–2.29) and the average OM / OC was
1.67±0.12 (1.26–2.03). These values, which were calculated using the
updated elemental analysis method (Canagaratna et al., 2015), are within the
range of revised values observed at other urban locations (Canagaratna et
al., 2015 and references within). Upon examining the diurnal patterns, we
also found that the O / C and OM / OC
ratios started to increase in the morning and peaked in the afternoon
(Fig. 8a and e). The lowest value of both parameters occurred at around 8:00
due to enhanced vehicle emissions during morning rush hours coupled with low
BL height, which was also evident in the diurnal profile of the
H / C ratio (Fig. 8b). However, during the day
(8:00–16:00), the O / C ratio generally increased and the
H / C ratio decreased, suggesting that SOA production or
mixing with more aged aerosols from regional sources was important during the
day and outweighed the emissions POA. The decrease in the
OM / OC and O / C ratios as well as the
increase in H / C ratio in the late evening (19:00–20:00)
was consistent with an enhancement of POA emissions during the evening rush
hour and dinner time.
Although organic ions containing nitrogen and sulfur had relatively low
abundance (average N / C = 0.018; average
S / C = 0.001), both nitrogen-to-carbon
(N / C) and sulfur-to-carbon (S / C)
ratios showed distinct diurnal profiles where N / C was
enhanced during the day (8:00–16:00) and increased again in the late evening
(19:00). This suggests that particulate nitrogen-containing compounds in Seoul
were probably from both primary emissions and secondary formation. However,
S / C showed relatively strong enhancement during the day
(8:00–16:00), suggesting that sulfur-containing organics were mainly formed
by secondary processes during the day. This is further confirmed by
considering the correlation between different OA factors vs. AMS spectral
ions; nitrogen-containing ions had good correlation with both POA and SOA
factors, whereas sulfur-containing ions had good correlation only with OOAs
(Fig. S5; see Sect. 3.3.2).
Organic aerosol source apportionment and characteristics of OA
factors
Separation of distinct organic aerosol sources can be achieved through the
application of multivariate models such as PMF (Lanz et al., 2007; Ulbrich
et al., 2009; Zhang et al., 2011). In this study, five OA factors were
determined, consisting of three POA factors (HOA, COA, and BBOA) and two SOA
factors (LV-OOA and SV-OOA). The O / C ratios for the
factors were LV-OOA = 0.68, SV-OOA = 0.56, BBOA = 0.34,
COA = 0.14, and HOA = 0.06. The elemental ratios of the factors were
estimated using the updated method reported by Canagaratna et al. (2015). A
comparison of the O / C and H / C ratios
of each PMF factor, as determined by the methods of Aiken et al. (2008) and
Canagaratna et al. (2015), can be found in Table S1 in the Supplement. An
overview of the chemical composition of and temporal variations in the five
factors is shown in Figs. 9, 10, and S7. The five factors made similar
contributions to total OA mass, with LV-OOA (26 %) representing the
largest fraction of the OA mass and the smallest fraction accounted for by
SV-OOA (15 %). BBOA, COA, and HOA accounted for 23, 20, and 16 % of the
total OA mass, respectively. Together, primary components on average
accounted for 59 % of the total OA mass and SOA accounted for 41 %
(Fig. 10k). The chemical composition of and temporal variations in each factor
are discussed in detail below.
Hydrocarbon-like OA (HOA)
Alkyl fragments (CnH2n+1+ and
CnH2n-1+) made a substantial contribution to the
HOA factor, with major peaks at m/z 41, 43, 55, and 57, which were mostly
composed of C3H5+, C3H7+, C4H7+, and
C4H9+ ions, respectively (Fig. 10a). These major peaks and the
overall picket-fence fragmentation pattern resulting from the
CnH2n+1+ ions are typical features of the HOA
spectra reported in other studies and are due to the association of these
aerosols with fossil fuel combustion (e.g., Alfarra et al., 2007; Lanz et
al., 2008; Sun et al., 2011b; Zhang et al., 2005a; Huang et al., 2010; Morgan
et al., 2010; Ng et al., 2011). In addition, strong correlations were
observed between the time series of HOA and the
CnH2n+1+ and CnH2n-1+
ions, e.g., C3H7+ (r=0.91), C4H7+ (r=0.85),
C4H9+ (r=0.95), and C5H11+ (r=0.96) (Fig. 9a
and Table 2). Due to the dominance of chemically reduced hydrocarbon species,
the O / C ratio of the HOA in this study was low (0.06),
whereas the H / C ratio was high (2.21). The
O / C ratio of HOA in this study was similar to the updated
values of HOA (0.05–0.25) from other studies (Canagaratna et al., 2015).
The regular enhancement of HOA around 7:00–9:00, as shown in its diurnal
profile (Fig. 10f), was consistent with the occurrence of morning rush-hour
traffic in Seoul and the association of HOA with vehicle emissions. HOA
concentration decreased rapidly from 8:00 to 12:00 and remained low in
the afternoon, mainly due to dilution associated with rising BL height. A
slow increase in HOA concentration began at ∼ 16:00 and persisted until
the next morning, suggesting that the shallow BL enhanced the gradual
accumulation of the pollutants from vehicle emissions. However, the
correlations of the time series of HOA with gaseous tracers of primary
emissions (i.e., BC, NO2, and CO) were only moderate (Fig. 9a and
Table 2), mainly because these pollutants are emitted not only from vehicular
sources but also from other combustion sources, e.g., biomass burning.
Indeed, the correlations are much stronger between these pollutants and total
POA (=HOA+COA+BBOA) (r=0.7-0.81, Fig. 11
and Table 2).
In this study, major differences were observed between weekdays and weekends
for HOA, including other primary species, e.g., BC and POA factors (Fig. S10).
For example, the diurnal patterns of HOA and BC changed significantly between
the weekdays and weekends and a general decrease in the morning rush-hour peak
over the weekend was observed, likely due to a decrease in commuting
activities because people were more likely to be at home. This weekend effect
is a typical urban feature, which was observed in Fresno (Young et
al., 2016) and the Northeastern US (Zhou et al., 2016b), as well.
Cooking OA (COA)
COA, as resolved by AMS OA spectra, has been widely reported in urban areas
with high population densities (He et al., 2010; Huang et al., 2010; Mohr et
al., 2012; Sun et al., 2011b; Young et al., 2016; Ge et al., 2012a; Wang et
al., 2016; Xu et al., 2014); however no results have yet been reported from
Seoul. In this study, COA was found to account for 20 % of the total OA
mass, which is higher than HOA (Fig. 10k). The diurnal pattern of COA displayed a
large evening peak, with a maximum concentration occurring at 19:00, i.e.,
dinner time. Elevated COA concentration and larger fractional contribution to
OA mass were observed throughout the night (Fig. 10g, l).
Scatterplot between (a) SOA and sum of inorganic
(NO3+SO4+NH4), (b) POA and BC,
(c) POA and NO2, and (d) POA and CO.
Similar to HOA, the mass spectrum of COA in this study also contained many
alkyl fragments, but to a lesser extent (75.8 % of the total signal in
the COA spectrum compared to 87.9 % of the total signal in the HOA
spectrum) (Fig. 10b). However, COA contains significantly larger amounts of
oxygen-containing ions than HOA (e.g.,
CxHyO1+ = 15.4 % vs. 7.6 % and
CxHyO2+ = 5.1 % vs. 2.3 %)
(Fig. 10a), and thus has a higher O / C ratio (0.14) and a
lower H / C ratio (1.89). The OM / OC
ratio was 1.3 and the H / C ratio was 1.78 for COA. The
observed O / C ratio (0.14) of COA in Seoul was at the
lower end of the range (0.14–0.27) of the revised, measured
O / C ratio of COA in other studies, e.g., Barcelona (0.27)
(Mohr et al., 2012), New York City (NYC) (0.23) (Sun et al., 2011a), and
Fresno (0.14 in 2010; Ge et al., 2012b; and 0.19 in 2013; Young et
al., 2016). Previous studies suggest that C3H3O+ (m/z 55) and
C3H5O+ (m/z 57) were major fragments of aliphatic acids (e.g.,
linoleic acid and palmitic acid) in cooking oils or animal fat and therefore
used these ions as key tracers for identifying the presence of aerosols from
cooking-related activities (He et al., 2004; Adhikary et al., 2010; Mohr et
al., 2009; Zhao et al., 2007). In addition, C5H8O+ (m/z 84) and
C6H10O+ (m/z 98) have been proposed as AMS tracers for COA as
well (Ge et al., 2012a; Sun et al., 2011b). In this study, the time series of
COA correlated well with these ions, e.g., C3H3O+ (r=0.86),
C5H8O+ (r=0.93), C7H12O+ (r=0.89), and
C6H10O+ (r=0.95) (Fig. 9b and Table 2), and COA was a major
contributor to the signals of C5H8O+, C6H10O+, and
C7H12O+, accounting for 57, 69, and 52 %, respectively, of
their signals (Fig. S6). To show the chemical difference between COA and
other OA factors, Mohr et al. (2012) used the relationships between the
fractions of OA signals at m/z 55 and m/z 57 (i.e., f55 and
f57) or between those of C3H3O+ and C3H5O+ (i.e.,
fC3H3O+ and fC3H5O+) after subtracting the
contributions from the oxygenated OA factors and found that the ratios
between m/z 55 (C4H7+ + C3H3O+) and m/z 57
(C4H9+ + C3H5O+) in COA were much higher (2.2–2.8)
than the ratios (0.9–1.1) in other POAs (e.g., HOA and BBOA). The COA
resolved in Seoul in this study had an m/z 55-to-m/z 57 ratio of 2.2,
which is within the range of the values for COA reported in Mohr et al.
(2012) (Fig. S9). In addition, the ratios between f55 and f57 for
OA in Seoul increased proportionally as the fractional contribution of COA to
total OA increased (Fig. S9b), with a V shape indicated by the two edges
defined by the COA and the HOA factors from several urban AMS data sets (Mohr
et al., 2012). These observations together confirm the identification of COA
at Seoul.
Biomass burning OA (BBOA)
Wood combustion was found to be another important POA source (23 %,
Fig. 10k) in Seoul during winter, in addition to vehicle and cooking
emissions. BBOA is typically prevalent during winter in locations where wood
is used for residential heating (Ge et al., 2012a; Crippa et al., 2013; Zhang
et al., 2015; Young et al., 2016). The mass spectrum of BBOA showed strong
signals of oxygenated ions (CxHyO1+: 27.1 % of total
BBOA signal and CxHyO2+: 10.6 % of total BBOA signal)
and was more oxidized than HOA and COA (Fig. 10). Among the three POA
factors, the O / C ratio of BBOA was the highest (0.34) and
the H / C ratio was the lowest (1.74), similar to the
results reported in several previous studies (e.g., Aiken et al., 2009; Ge et
al., 2012a; Mohr et al., 2012). An analysis of the OA spectra (Fig. 10c)
revealed the typical features of BBOA, with dominant peaks at m/z 60
(100 % being C2H4O2+) and m/z 73 (95 % being
C3H5O2+), which are known fragments of levoglucosan and related
species (e.g., mannosan and galactosan) (Cubison et al., 2011). Scatterplots
of f44 vs. f60 indicate a higher f60 and lower f44 (i.e.,
toward the center of the triangle area of the biomass burning plumes) as the
relative importance of BBOA to the total OA increased (Fig. S9). The f44
and f60 of BBOA (0.05 vs. 0.016) in this study were also within the
range of values found for the other ambient BBOA factors or biomass burning
aerosols from chamber studies. HOA and COA, in contrast, had much lower
f60 values (< 0.01). The time series of BBOA correlated well with
C2H4O2+ (r=0.85) and C3H5O2+ (r=0.74) (Fig. 9d
and Table 2) as well as other biomass burning tracer species, including
potassium (r=0.63) and BC (r=0.82). BBOA also correlated well with
nitrogen-containing species, particularly C3H4N+ (r=0.75),
C2H4N+ (r=0.70), and CHN+ (r=0.58), which was
consistent with the emissions of nitriles from biomass burning activities
(Simoneit et al., 2003). There was also a strong correlation between the
concentration of polycyclic aromatic hydrocarbons (PAHs; r=0.90) and
BBOA, indicating that biomass burning was a main source of PAH in Seoul in
winter. Similarly, in winter in Fresno, California, it was found that BBOA
correlates well with N-containing ions and PAHs (Ge et al., 2012a; Young et
al., 2016). Given that PAHs are byproducts of incomplete combustion, many of
which are mutagenic and carcinogenic (Dzepina et al., 2007; Hannigan et
al., 1998; Marr et al., 2006), our findings suggest that adverse health
effects associated with biomass burning emissions should be of concern during
winter in the Seoul region.
BBOA was the most abundant primary OA in Seoul during this study, accounting
for 39 % of the POA mass and 23 % of the total OA mass. However,
biomass burning is not the main fuel for residential heating in Seoul, and
thus the BBOA observed in this study must have originated from other
wood-burning activities, either locally or regionally. The fact that the
observed BBOA was relatively oxidized (O / C =0.34),
with a large contribution from m/z 44 (f44 = 5.1 %), was
consistent with the observations of more aged BBOA instead of primary
wood-stove emissions (Crippa et al., 2013; Zhang et al., 2015; Zhou et
al., 2016a). The diurnal profile of BBOA showed an enhancement at around 9:00
and a background BBOA concentration of ∼ 1 µgm-3.
Given that the polar plot of BBOA showed high concentrations at both low and
high wind speeds (Fig. 7), the sources of BBOA in Seoul likely include both
local and regional wood-burning activities. Local wood-burning activities
were possibly for the purposes of heating open and public areas (e.g.,
construction areas, market), disposing of leaves and woody trash in the city,
and heating some residences. Regional sources of BBOA are possibly from open
biomass burning in agricultural areas near Seoul (Heo et al., 2009) and
transport from North Korea or from Russia (Jung et al., 2016).
Semivolatile and low-volatile oxygenated OA (SV-OOA and LV-OOA)
In addition to the three POA factors, two OOA factors were identified and
were found to account for an average of 41 % of the OA mass (Fig. 10k).
OOA is ubiquitous and dominant in the atmosphere (Jimenez et al., 2009; Zhang
et al., 2007a) but usually accounts for less than half of the OA mass
observed during winter in urban locations such as NYC, Tokyo, Fresno, and
Manchester (Zhang et al., 2007b).
In many cases, OOA can be further separated into a
low-volatile–more-oxygenated OOA (LV-OOA–MO-OOA) and a
semivolatile–less-oxygenated OOA (SV-OOA–LO-OOA), which represent different
degrees of aging and oxidation (Jimenez et al., 2009; Ng et al., 2010, and
reference therein; Setyan et al., 2012; Xu et al., 2015). In this study, two
OOA factors, SV-OOA and LV-OOA, were observed to account for 15 and 26 %
of the total OA mass, respectively (Fig. 10k). As shown in the triangle plots
in Fig. S9, SV-OOA (O / C = 0.56,
H / C = 1.90) resides within the region representing
fresher SOA, with a low f44, and LV-OOA
(O / C = 0.68, H / C = 1.61) was
similar to aged and highly-oxidized OA, with a high f44. It has been
observed that fresh OOA becomes increasingly more oxidized and less volatile
through aging processes in the atmosphere resulting in LV-OOA. Furthermore,
the evolution of SOA is regarded as a continuum of oxidation. The mass
spectra of both LV-OOA and SV-OOA were very similar to the spectra of OOA
factors reported in other cities (e.g., Hayes et al., 2013; Mohr et
al., 2012; Zhang et al., 2014).
Comparisons between the time series of SV-OOA and LV-OOA with gaseous
species, aerosol species, and meteorological parameters further confirmed
their secondary nature. As shown in Table 2, SV-OOA and LV-OOA strongly
correlated with nitrate (r=0.87 and 0.63, respectively) and sulfate (r=0.71 and 0.80, respectively), whereas the correlations between POA factors
and the inorganic aerosol species were low (r=0.09-0.41). The Pearson's
correlation coefficient between total OOA (= SV-OOA + LV-OOA) and the
sum of secondary inorganic aerosols
(NO3- + SO42- + NH4+) was as high as
0.91 (Fig. 11a). These results confirm the association of SV-OOA and LV-OOA
with SOA.
As discussed above, sulfate in Seoul is mainly associated with regional
sources, while nitrate is often formed more locally due to the intense urban
emissions of NOx. The better correlations between SV-OOA and
nitrate and between LV-OOA and sulfate (Table 2) suggest that SV-OOA likely
had more local sources whereas LV-OOA likely had more regional sources.
Furthermore, SV-OOA correlated more strongly with methanesulfonic acid (MSA),
an SOA species that tends to be semivolatile. As shown in Table 2, the
correlations of SV-OOA and LV-OOA with AMS spectral ions for MSA (Ge et
al., 2012a), i.e., CH3SO2+ (r=0.90 and 0.53, respectively), and
CH2SO2+ (r=0.83 and 0.53, respectively) corroborated the
different natures of SV-OOA (fresher, more local) and LV-OOA (aged, more
regional). The diurnal profiles of SV-OOA and LV-OOA also reflected the
features of local versus regional sources (Fig. 10i, j). SV-OOA
concentration had a clear peak during midmorning to afternoon (10:00–11:00,
Fig. 10i); however, LV-OOA concentration was relatively constant throughout
the day, suggesting regional sources of this aerosol component (Fig. 10j).
Similar observations were also reported in other areas such as North America
(e.g., Budisulistiorini et al., 2015; Sun et al., 2011b; Woody et al., 2016;
Zhou et al., 2016b; Zhang et al., 2005b), Europe (e.g., Mohr et al., 2012;
Young et al., 2015), and Asia (e.g., Huang et al., 2010; Jiang et al., 2015;
Wang et al., 2016). Finally, the polar plots of both OOAs showed more
dispersed features compared to the POA factors, especially HOA and COA, but
SV-OOA appeared to have a stronger association with local processes since its
high concentrations tended to be associated with lower wind speed, compared
to LV-OOA (Fig. 7).
Relative importance of local and regional influences on air quality
in Seoul during winter
In an effort to improve ambient air quality, the Korean government enacted
the Special Act on Seoul Metropolitan Air Quality Improvement to regulate the
concentrations of key pollutants such as SO2, CO, NO2,
O3, PM, and Pb (lead) in 2005. However, Seoul is still
facing poor air quality problems, especially in terms of high concentrations
of PM2.5 and O3. PM2.5 has been one of the primary concerns
due to its detrimental impacts on human health as well as on visibility.
O3 is an important air pollutant itself and can contribute to the
secondary formation of PM2.5. Since the development of effective air
pollution control policies must rely on knowledge about the sources, it is
important to investigate the major formation processes and emission sources
that contribute to the high-PM loadings. Therefore, in this section, we
examine how both primary emissions and secondary formation affect PM loadings
in Seoul during winter.
Comparisons of averaged properties measured during high particulate
matter (PM), loading, and low-PM loading periods; (a, b) wind rose
plots, colored by wind speed for each different period; (c, d)
fractional contributions of each species to the total PM1 (nonrefractory
PM1 plus BC) mass; (e) ratios of absolute concentrations of
each PM1 and gas species during high-loading and low-loading periods;
(f) comparisons of averaged absolute concentrations of PM1
species and gaseous pollutants for each different period.
Comparison of aerosol properties and meteorological parameters
between the high-loading and low-loading periods.
High-PM loading
Low-PM loading
Average nonrefractory submicrometer particulate matter (NR-PM1)
mass concentration (µgm-3) (average ± 1σ)
43.6±2.4
12.6±7.1
O / C (H / C) ratio*
0.36 (1.82)
0.41 (1.75)
Trace gas conc. (CO (ppm) and NO2/O3/SO2 (ppb))
1.2/56/61/7.5
0.5/30/18.4/6.4
Temperature (∘C) (average ± 1σ)
2.5±3.4
-2.8 ± 4.4
RH (%) (average ± 1σ)
71±15
50±12
* Calculated using the improved Canagaratna ambient method
(Canagaratna et al., 2015). PM: particulate matter, NR-PM1:
nonrefractory submicrometer particulate matter, RH: relative humidity.
Figure 12 shows comparisons of the average concentrations of PM1
components as well as other air pollutants under high- and low-PM loading
conditions depicted in Figs. 2 and 3. The average concentrations of all
aerosol components and OA sources were 1.7–8.6 times higher during the
high-loading periods compared to the low-loading periods (Table 3;
Fig. 12e, f). A main reason appeared to be meteorological conditions. For
example, high-loading periods were generally stagnant with low wind speed
(0.99±0.7 ms-1) (Fig. 12a, b), leading to the accumulation
of pollutants, especially those mainly from local sources. Indeed, among all
species, SV-OOA, HOA, nitrate, and COA showed the highest increases during
high-loading periods and their average enhancement in concentrations was 8.6,
5.2, 4.7, and 4.5 times, respectively, the values during the low-loading
periods (Fig. 12e). In addition to accumulating primary pollutants, stable
meteorological conditions can also lead to longer atmospheric residence time,
which facilitates the local formation of secondary species such as SV-OOA and
nitrate. Furthermore, the relatively high RH during the high-loading periods
(71 % ±15; Table 3, Fig. 12a) likely also enhanced the formation of
secondary species such as sulfate and LV-OOA through aqueous-phase
processing. Further evidence for enhanced aqueous-phase processing of
secondary aerosol species during high-PM loading periods is shown in Fig. 13;
the size distributions of all secondary inorganic species (nitrate, sulfate,
and ammonium) were significantly larger during the more polluted periods,
peaking at 500–600 nm in Dva, compared to the cleaner
periods (peaking at 300–400 nm). A previous study in a US city in
winter also observed that high RH conditions, and thus enhanced aqueous-phase
processing, led to increases in the size modes of sulfate, nitrate, and
ammonium (Ge et al., 2012b).
Averaged mass-based size distributions of (a) sulfate,
(b) nitrate, and (c) ammonium during the entire (broken
grey curve) high-PM (red curve) and low-PM (blue curve) periods as marked
in Figs. 2 and 3.
Averaged compositional bar graph of PM1 species
(nonrefractory PM1 plus black carbon, BC) and each of the OA factors in
different clusters from the four-cluster solution. The trajectories were
released at half of the mixing height at the KIST (latitude:
37.60∘ N, longitude: 127.05∘ E) and the average arrival
height for the back trajectories for this study was approximately
191 m.
The enhancement ratios of the other primary pollutants, CO,
SO2, NO2, BC, and BBOA, were in the range of 1.2–2.5,
significantly lower than those of HOA and COA (Fig. 12e). This reflects the
fact that they all had bigger contributions from regional sources compared
to HOA and COA (Fig. 7). Conversely, average O3 concentration
showed a substantial decrease (by ∼ 70 %) during high aerosol
loading periods (Fig. 12e and f). In addition to enhanced titration reactions
by NOx, another possible reason for O3 decrease was
reduced photochemical reactions due to inhibition of light caused by high
concentration of PM (He et al., 2014).
The high-loading periods corresponded closely to air masses that are
classified as cluster 4 (Fig. 14), which had the shortest trajectories, i.e.,
slowest wind speeds, as well as the lowest travel height compared to the
other three clusters. The air mass in cluster 4 thus likely held larger
amounts of pollutants and precursors from the ground. In addition, since air
masses in each cluster were expected to pass over regions indicated by
the corresponding trajectories, investigating the composition and masses of
aerosol in each cluster could shed light on how various upwind areas influence
air quality at the measurement site. For example, PM in this type of air
mass (cluster 4) could also be more oxidized, containing a larger fraction
of secondary pollutants due to longer residence time in the atmosphere.
Therefore, it would be was composed of a higher fraction of nitrate.
The average aerosol composition during the high-loading periods (Fig. 12) was
similar to that for the whole period (Fig. 4), consistent with frequent
occurrence of high aerosol pollution episodes, which indicates that these
events determined the overall characteristics of PM1 in Seoul during
winter. Given that local emissions were mostly responsible for these
pollution events, controlling the emissions of both primary aerosol particles
and precursors for secondary species from local sources might be an effective
way to manage air quality in Seoul in winter.
Low-PM loading periods (average±1σ=12.6±7.1 µgm-3 for PM1) were commonly associated with high
WS (1.8±1.1 ms-1), low RH (50 %), and long-distance
transport of air masses from Russia, Northern China (Inner Mongolia),
Mongolia, or North Korea (i.e., Clusters 1, 2, and 3 from back-trajectory
analysis; Fig. 14). All three clusters appeared to originate from Russia;
however, there were some differences among clusters. For example, Cluster 1
passed over Mongolia and North Korea, whereas Clusters 2 and 3 passed over
China. Furthermore, Cluster 3 was composed of the longest trajectories.
Aerosol composition was somewhat different between the high-loading and low-
loading periods. Since strong wind could inhibit the accumulation of local
primary and secondary species while bringing in pollutants from upwind
sources, the mass fractions of species influenced more strongly by local
sources, such as nitrate (27 vs. 20 %), SV-OOA (8 vs. 3 %), HOA (7
vs. 4 %), and COA (8 vs. 7 %), were lower during low-loading periods
compared to more polluted periods. Conversely, those of regional sources,
such as sulfate (10 vs. 12 %), LV-OOA (10 vs. 20 %), and BBOA (9 vs.
12 %), were enhanced (Fig. 12). Although Clusters 1, 2, and 3 all
represented regional transport conditions, PM mass concentrations and
compositions were somewhat different because of different origin of air
masses. Specifically, Clusters 1 and 2 were almost directly to the north,
whereas Cluster 3 was more towards the west (Fig. 14). In comparison,
Clusters 1 and 2 had higher fractions of BBOA but lower fractions of LV-OOA
and sulfate. A possible explanation for this observation is that the
northwestern area might have more anthropogenic sources than the northern
area does.
As shown in Fig. 2, PM concentration often changed abruptly with the
appearance and dissipation of a high-PM1 event occurring rather quickly
(within several hours). The changes were commonly associated with changes in
meteorological conditions, especially wind direction and speed. Similar
trends of sudden changes in air quality have also been observed in other
studies in China and have been attributed to meteorology (Zhang et
al., 2015). For these reasons, it appears that regional meteorology played an
important role in causing high-PM pollution conditions in Seoul.