Overview of submicron aerosol characteristics
Temporal variations of PM1 composition and chemical
properties
The overall characteristics and temporal variations of PM1 at KIST
during KORUS-AQ are shown in Fig. 2, along with the time series of gaseous
pollutants, e.g., CO, SO2, O3, and Ox (Ox= O3 + NO2; Herndon, 2008), and meteorological
conditions (RH, temperature, wind direction, wind speed). From 14 April to
15 June 2016, the average concentration of PM1 (= NR-PM1+ BC)
was 22.1 µg m-3, ranging from 0.76 to
71 µg m-3. In addition to a severe haze episode with daily
PM1 concentration above 30 µg m-3 that continued for
6 days during 26–31 May, shorter haze episodes (daily PM1
> 30 µg m-3) occurred several times as well
(Fig. 2). In between high loading periods, aerosol concentration was
relatively low, with daily PM1 concentration typically lower than
14 µg m-3. The dramatic variations in PM1 mass
concentrations (0.76 to 71 µg m-3 for 2.5 min average;
Fig. 2f) and other pollutants (Fig. 2c, d), such as CO (0.2 to 1 ppm for
1 min average), O3 (3 to 82 ppb for a 1 min average), and
NO2 (6 to 76 ppb for a 1 min average) reflect the impacts of
dynamic changes in emission sources, atmospheric processes, and
meteorological conditions on air quality in SMA during spring.
The variations of individual PM1 components were substantial as well
(Fig. 2g, h). For instance, the mass concentration of organics ranged from
0.39 to 39 µg m-3 during this study, and on 20 May, it
rapidly increased from 7.6 to 24 µg m-3 over a period of
∼ 25 min and reached as high as 39 µg m-3 on 23 May
(Fig. 2h). The accumulation of OA during this episode appeared to be related
to a large enhancement of VOCs (e.g., isoprene, toluene) (Fig. 2e) coupled
with a high concentration of Ox (O3 + NO2),
strong solar radiation and stagnant conditions, which together promoted
intensive formation of secondary organic aerosol (SOA). The mass
concentration of sulfate also varied widely from 0.19 to 21 µg
m-3 during the entire period and increased from 1.2 to
20 µg m-3 from 24 to 26 May, likely due to favorable
meteorological conditions for sulfate formation and influences from the
regional and/or long-range transport. The variation of nitrate concentration
was substantial too, from 0.05 to 23.4 µg m-3, with low
concentrations generally occurring during daytime due to high temperature and
low humidity. Investigation of these different events (e.g., haze periods,
high organic) can provide insights into how different sources and atmospheric
processes influence air quality in this region. Detailed discussions on the
processes that led to high aerosol pollution events are presented in
Sect. 3.3 and 3.4.
Since the molar equivalent ratios of total inorganic anions to cations for
NR-PM1
(= (SO42- /48+ NO3- /62 + Cl- /35.5) / (NH4+ /18))
were close to 1 (Fig. S10), submicron aerosols appeared to be bulk balanced
in observed cations and anions and the ionic species were mainly present in
the forms of NH4NO3, (NH4)2SO4, and
NH4Cl. Possible sources of ammonia/ammonium in the SMA include
on-road vehicle emissions, neutralizer usage in industry, and agricultural
emissions at the outskirts of SMA.
One-hour averaged diurnal profiles for the various meteorological
parameters (a), gas-phase species (b), PM1 species
(c) and PM mass concentration, volume concentration, and number
concentration (d). Two of the figures (e, f) show the
fraction of PM1 mass and organic mass, respectively.
Overall, organics were an important aerosol component, on average accounting
for 44 % of PM1 mass. POA (= HOA + COA) and SOA
(= SV-OOA + LV-OOA) accounted for 59 and 41 %, respectively, of
the OA mass (detailed discussions on OA sources are provided in Sect. 3.3).
Secondary inorganic aerosol (SIA = sulfate + nitrate + ammonium)
on average accounted for 37 % of the total PM1 mass, with sulfate
contributing the most (20 %) (Fig. 1e). The non-refractory chloride
concentrations measured by the HR-ToF-AMS were mostly below the detection
limit during the present study. On average, ∼ 24 % of PM1 was
composed of primary materials (POA + BC), with the remainder (76 %)
being secondary species
(NO3- + SO42- + NH4+ + SOA)
(Fig. 1e), indicating that the aerosol pollution problem in SMA during spring
is mainly caused by secondary aerosol formation.
One-hour averaged diurnal profiles for sulfate, nitrate and various
parameters and proxies for formation pathways in winter
(5 December 2015–21 January 2016) and spring (14 April–15 June 2016);
temperature, relative humidity and KAN as the equilibrium
constant for gas-to-particle partitioning for ammonium nitrate in
(a) 2016 and (b) 2015. The 1 h averaged diurnal profiles
of SO2, SO42-, fSO4 and [SO2]
times solar radiation as a proxy for daytime H2SO4 formation in
(c) spring 2016 and (d) winter 2015; the 1 h averaged
diurnal profiles of NO2, NO3, and
[NO2][O3] as a proxy for nighttime formation of
HNO3 and subsequently particulate nitrate, and [NO2]
times solar radiation as a proxy for daytime HNO3 formation in
(e) spring 2016 and (f) winter 2015.
The average concentration and composition of PM1 measured in SMA during
this study were significantly different from those measured during
wintertime. For instance, compared to winter, the average PM1
concentration was lower during spring (22 vs. 27 µg m-3), the
mass fraction of sulfate was higher (20 vs. 10 %) but that of nitrate was
lower (17 vs. 24 %), and the total contribution of secondary species was
higher (76 vs. 64 %) (Kim et al., 2017). As discussed in the following
sections, these differences reflect the differences between the two seasons
in meteorological conditions and emissions and formation processes of air
pollutants.
Diurnal patterns of
PM1 composition and formation processes
As shown in Fig. 3, the diurnal cycles were vastly different among different
aerosol species. The daily variation of the average concentration of sulfate
was relatively flat and its mass-based size distribution shows a persistent
accumulation mode that peaks at 650 nm (Dva) (Fig. S11). These
observations together with a dispersed feature of the sulfate bivariate polar
plot (Fig. S13) indicate that particulate sulfate over SMA is mainly
associated with regional sources, such as the industrial facilities located
in the west and southwest of SMA (Fig. 1) (Kim et al., 2017). Indeed, the
polar plot of SO2 shows a strong association of high SO2
concentrations with westerly and southwesterly winds (Fig. S13). Figure 4c
shows the diurnal patterns of springtime sulfate, SO2, and the
molar ratio of sulfate (SO42-) to SOx (= SO42-+ SO2), i.e., fSO4, which is an indicator of the
extent of SO2 oxidation (Kaneyasu et al., 1995). fSO4
decreased from 0.24 to 0.21 between 06:00 and 10:00, during which
SO2 increased by ∼ 1 ppb (Fig. 4a). This change was likely
due to the breaking of the boundary layer which mixed down air masses more
enriched in SO2 from aloft. Also, fSO4 increased
gradually from 11:00 till 06:00 of the next day. For the daytime increase
from 11:00 to 18:00, the lifetime of SO2 was calculated to be
∼ 11 days, using the temperature-dependent daytime reaction rate
constant, k (8.75–9.24 × 10-13 cm3 s-1 ), for
SO2 + OH + M → H2SO4 (Burkholder et
al., 2015) and the corresponding OH concentration
(1.2 × 106 molecules cm-3) which is in the range of other
cities such as Tokyo (Miyazaki et al., 2006) and Beijing (Rao et al., 2016).
This suggests that the localized photochemical reaction of
SO2 + OH is not the major process for controlling
SO42- concentrations during this period. Nighttime increase in
fSO4 can be explained by the aqueous-phase oxidation of
SO2 facilitated by the high RH condition. Indeed, SO2
began to decrease at ∼ 19:00, when RH increased and T decreased
(Fig. 4a). Possible oxidants during night are NO2 and O3,
although SO2 oxidation by O3 and NO2 are both
pH-dependent and the reaction rates increase with pH (Seinfeld and Pandis,
2006). NH3 concentration in SMA is likely high since aerosol
particles in this region are bulk balanced in observed cations and anions
(Fig. S10), suggesting that oxidation by both O3 and NO2
could occur actively. Furthermore, NO2 has been investigated as an
important oxidant in aerosol water under hazy conditions (Cheng et al.,
2016). However, it is still open to other possible oxidation pathways since
any formation of H2SO4 will immediately suppress the pH,
although particles may begin as neutral. Thus the NO2 and
O3 pathways may potentially become self-limiting in particles
unless there is an abundance of gas-phase NH3 to compensate.
Similar trends were observed during winter as well, although higher
SO2 and lower SO42- and fSO4 were observed
(Fig. 4d). Lower SO2 concentration during spring was likely due to
less coal combustion for heating and the higher SO42- and
fSO4 were due to more efficient conversion of SO2 to
SO42- during spring under stronger solar radiation or more
regional transport of SO42-. Previous study indicates that
nighttime aqueous-phase processing was an important driver for sulfate
formation during winter in SMA (Kim et al., 2017). However, gas-phase
photochemical oxidation of SO2 and regional transport appear to be
more important contributors to SO42- during spring. Indeed,
fSO4 correlated less well with RH during spring than during winter
(R2= 0.27 vs. 0.59) (Fig. S14).
Unlike sulfate, nitrate shows more dynamic diurnal cycles during both spring
and winter. Overall, nitrate concentration was lower in spring than in winter
despite faster photochemical production. This is due to higher temperature
(Fig. 4a, b), which drives the evaporation of ammonium nitrate, particularly
during spring daytime. Indeed, a depression of nitrate concentration occurred
during the daytime of spring, whereas a midday peak (between 09:00 and 15:00)
due to photochemical formation of nitrate was observed during winter. The
overnight increase in nitrate during springtime was likely driven by enhanced
gas-to-particle partitioning of ammonium nitrate associated with lower
temperature as well as nighttime formation of nitrate (e.g., through
N2O5 hydrolysis), which is consistent with the high concentrations
of O3 (∼ 20 ppb) and NO2 (∼ 42 ppb)
throughout the night (18:00–06:00). However, the peak nitrate concentration
(at ∼ 09:00) occurred 3 h later than the peaking of the ammonium
nitrate equilibrium constant (KAN) (∼ 06:00), which might
be due to the mixing down of a nocturnal residual layer (Prabhakar et al.,
2017). The equilibrium constant kAN can be calculated as
kAN=k298expa298T-1+b1+ln298T-298T
where T is the ambient temperature in Kelvin,
k(298) = 3.36 × 1016 (atm-2), a= 75.11, and b= -13.5 (Seinfeld and Pandis, 2006). Significant nitrate formation
through nighttime chemistry occurred during winter as well, due to lower
temperature and relatively high nighttime concentrations of NO2 and
O3. However, compared to springtime, the product of NO2
and O3 ([NO2][O3]) during winter was ∼ a
factor of 2 lower during night (Fig. 4a, b), indicating that nighttime
nitrate formation is more significant in spring.
[NO2][O3] is a proxy for nighttime formation rate of
particulate nitrate, since the reaction between NO2 and
O3 produces N2O5 and nitrate radical
(⚫NO3), which can react heterogeneously to form
HNO3 and subsequently particulate nitrate (Young et al., 2016).
Organics dominated PM1 composition throughout the day, with 1 h average
mass fractions varying from 40 to 48 % (Fig. 3). The average diurnal
profile of organics showed elevated concentration overnight and a clear
daytime peak from 13:00 to 18:00. The nighttime enhancement was consistent
with the accumulation of primary emissions from traffic and cooking due to
low boundary layer height and stagnant air conditions, whereas the daytime
enhancement was likely the outcome of photochemical formation of SOA.
Detailed discussions are given in Sect. 3.2.
(a) Diurnal variations of the size distribution of
NR-PM1 mass from the AMS (in vacuum aerodynamic diameter,
Dva); (b) volume from the SMPS (in mobility diameter,
Dm) and (c) number concentrations from the SMPS.
(a) Averaged mass fractional contributions of each
NR-PM1 species to the total NR-PM1 mass as a function of size;
(b) campaign-averaged size distributions for individual NR-PM1
species.
(a) Average high-resolution mass spectrum of OA colored by
the different ion families. The average elemental ratios for the OA fraction
are described; (b) average diurnal profiles of the organic matter to
organic carbon (OM / OC), oxygen to carbon (O / C), hydrogen to
carbon (H / C), and nitrogen to carbon (N / C), where the O / C,
H / C and OM / OC elemental ratios were determined using the updated
method (Canagaratna et al., 2015). The table shown is the overview of the OA
compositions in SMA during KORUS-AQ.
BC presented two peaks, one occurring during morning rush hour (07:00–10:00)
and the other in the afternoon between 14:00 and 15:00 (Fig. 3). Similar
trends were observed with HOA (Sect. 3.3) and particle number concentration
(Fig. 3), indicating that both peaks of BC were contributed by vehicle
emissions. The morning rush hour peak of primary air pollutants is commonly
observed in many other studies as well as during winter at the same site (Kim
et al., 2017); however, the enhancement of these species in the afternoon,
when elevated mixed layer height tends to dilute primary pollutants, is
unique. In addition, the afternoon increase in BC, HOA, and particle number
concentration began at ∼ 12:00 and reached a maximum around 16:00
(Figs. 3 and 8). This time period corresponded to the effective transport of
air masses from urban and industrial areas located in the south and southwest
of the KIST site (Fig. 1a) by a predominant southwesterly flow during
11:00–17:00 (Fig. S1). With an average wind speed of ∼ 2 m s-1,
the southwesterly wind would take ∼ 1–5 h to bring plumes from upwind
urban sites that are ∼ 7.2 km (e.g., Anyang) to 36 km (e.g., Incheon,
Siheung and Ansan) away from the KIST site (Fig. 1). Furthermore, the large
increase in particle number concentration (Fig. 3) and the apparent growth of
ultrafine particles (Fig. 5c) between ∼ 12:00 and 16:00 suggest that
new particle events might have happened in association with transport of
plumes from the southwest.
Size distributions of the main components of
PM1
Figure 5 shows the average mass-based size distributions of NR-PM1
species over the entire KORUS-AQ campaign and their daily evolution
behaviors. Sulfate, nitrate and ammonium in spring all show very similar size
distribution profiles, with a mode peaking at around 650 nm in vacuum
aerodynamic diameter (Dva; DeCarlo et al., 2004), suggesting that
SIA were internally mixed. The springtime size distribution profiles of SIA
at KIST are somewhat different than those observed during winter, which
peaked around 400–500 nm (Kim et al., 2017). The finding of bigger particle
sizes during spring than in winter could be due to faster particle growth
rates caused by higher photochemical activity during spring. Similarly, a
recent study in Beijing reported that the peak size of SIA during summer
(600 nm in Dva) was bigger than during winter (350 nm) (Hu et
al., 2016).
The average mass-based size distribution of organics was in general wider
than those of inorganic species, with a peak at ∼ 550 nm and a
shoulder peaking at ∼ 300 nm and extending down to ∼ 60 nm
(Fig. 6b). Similar observations were made in the winter at SMA and a number
of urban areas in China and North America (e.g., Kim et al., 2017, and
references therein). The wider size distribution of organics reflected the
contributions made by both primary and secondary aerosols, i.e., the
ultrafine mode dominated by primary aerosols and the accumulation mode
comprised mainly of secondary aerosols. The mode of the organics in spring
(500–600 nm) was bigger than in winter (400 nm), likely for the same
reason that the size mode of SIA was bigger during spring – enhanced
photochemical activity for secondary aerosol formation in spring than in
winter as well as fewer contributions of primary particles to fine-mode
particles from vehicular, cooking, and biomass burning sources.
The organic fraction was above 50 % across the whole size range and
almost 100 % in ultrafine-mode particles (especially in
Dva < 100 nm), whereas SIA dominated
(> 60 % of NR-PM1) in accumulation-mode particles, with
Dva > 500 nm in spring (Fig. 6).
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) semi-volatile oxygenated OA
(SV-OOA), and (d) low-volatility oxygenated OA (LV-OOA) colored by
different ion families; (e–h) 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)
with various tracer species.
Characteristics and source apportionment of organic aerosol
Overall, on a mass basis, OA from SMA during spring was composed of
approximately 66 % carbon, 24 % oxygen, 8 % hydrogen, and 2 %
nitrogen (Fig. 7). The average carbon-normalized molecular formula of OA was
C1H1.67O0.49N0.02S0.002, yielding an average organic
mass-to-carbon ratio (OM / OC) of 1.82. The average elemental ratios,
which were calculated using the updated elemental analysis method
(Canagaratna et al., 2015), are within the range of the revised values
observed at other urban locations (Canagaratna et al., 2015; Young et al.,
2016, and references therein). Upon examining the diurnal patterns of the
atomic ratios among elements in OA, we found that O / C and OM/OC ratios
had similar patterns but that the pattern of H / C was different, due to
variations in the relative contributions of POA and SOA. Also,
nitrogen-to-carbon (N / C) ratios showed a distinct diurnal profile with
a bimodal feature peaking at 10:00 and 16:00, similar to the O / C
diurnal profile.
In this study, four distinct OA factors were determined, including two types
of POA (HOA and COA) and two types of OOA (LV-OOA and SV-OOA). The O / C
ratios for LV-OOA, SV-OOA, COA, and HOA were 0.91, 0.44, 0.19, and 0.15,
respectively. An overview of the chemical composition and temporal and
diurnal variations of the four OA factors are shown in Figs. 2i and 8.
LV-OOA (34 %) represents the largest fraction of the OA mass, followed by
SV-OOA (27 %), COA (22 %) and HOA (17 %) (Fig. S17).
Briefly, HOA showed the typical picket fence fragmentation pattern as
commonly seen in freshly emitted vehicle POA, with major peaks at m/z's 41,
43, 55, and 57, which are mostly composed of C3H5+,
C3H7+, C4H7+, and C4H9+ ions,
respectively (Fig. 8a). HOA also showed strong correlations with tracer ions,
C3H7+ (r = 0.87), C4H7+
(r = 0.81), C4H9+ (r = 0.95), and
C5H11+ (r = 0.96) (Fig. S18 and Table S3). The average
ratio of HOA/BC was 1.03, which is lower than the ratio for light-duty
vehicles (1.4) and higher than that for diesel trucks (0.5) (Ban-Weiss et
al., 2008), reflecting the fact that SMA traffic comprises both gasoline and
diesel vehicles. Similar HOA/BC values were observed in other large urban
areas, such as Pittsburgh (1.41 ± 0.22; Zhang et al., 2005b), New York
City (1.29) (Sun et al., 2011), Mexico City (1.25) (Aiken et al., 2009), and
Xianghe, China (0.91) (Sun et al., 2016). A lower HOA/BC ratio was observed
in winter in Korea (0.58), probably due to the impacts of biomass burning
(Kim et al., 2017).
Scatter plots of (a) OOA; (b) LV-OOA; and
(c) SV-OOA vs. Ox during daytime (10:00–16:00) in spring 2015.
Note that the fittings for the organic dominant period (20 May,
17:00–24 May, 00:00) are colored in red, and for the rest of the periods
they are colored in black.
As widely reported in highly populated cities, a COA factor was resolved in
SMA as well. The COA showed good correlations in time series with the key
tracer ions commonly used to justify the presence of aerosols from
cooking-related activities (He et al., 2004; Adhikary et al., 2010; Mohr et
al., 2009, 2012; Zhao et al., 2007; Ge et al., 2012; Sun et al., 2011, 2013;
Young et al., 2016; Allan et al., 2010; Huang et al., 2010; Hayes et al.,
2013; Dall'Osto et al., 2013), such as C3H3O+ (m/z 55; r= 0.75), C3H5O+
(m/z 57; r = 0.61), C5H8O+ (m/z 84; r = 0.89),
C6H10O+ (m/z 98; r = 0.99) and C7H12O+
(m/z 112; r = 0.70) (Fig. S18 and Table S2). It was also a major
contributor to the signals of C5H8O+, C6H10O+, and
C7H12O+, accounting for 62, 94, and 67 %, respectively
(Fig. S16). The mass spectrum of the COA determined in this study was almost
identical to the COA spectrum determined in winter 2015–2016 at the same
site (Figs. 8b and S15) (Kim et al., 2017). In addition, as shown in
Fig. S19b, the ratios between f55 and f57 for OA in Seoul increased
proportionally as the fractional contribution of COA to total OA increased,
forming a “V” shape with two edges defined by the COA and the HOA factors
from several urban AMS datasets (Mohr et al., 2012). Furthermore, the diurnal
profile of the COA displayed a large enhancement at evening starting between
∼ 18:00 and 19:00, corresponding to dinner time, and a small noon-time
peak at ∼ 12:00, corresponding to lunch time, although the features do
not reflect fully the expected cooking activities. Nevertheless, these
observations generally corroborate the association of the COA factor with
cooking activities. The relatively weak noon-time enhancement of the COA was
probably due to strong dilution of primary emissions caused by convection and
elevated boundary layer height. It is also possibly related to Korean cooking
habits. Previous AMS studies reported COA factors with less pronounced
patterns at noon or evening as well (Hayes et al., 2013). In addition, the
COA of this study on average accounted for ∼ 18 % of the total OA
mass during lunch time and ∼ 37 % during dinner time, which are
within the values observed for COA in Beijing (Sun et al., 2016, and
references therein). Nevertheless, it is important to caution that the
noon-time and nighttime peaks could be influenced by photochemical formation
of SOA and evening POA emissions coupled with reduced boundary layer height,
respectively. In addition, while the COA tracer ions mentioned above tend to
represent fatty acids which are usually a significant component of cooking
aerosols (He et al., 2004; Mohr et al., 2009; To et al., 2000; Zheng et al.,
1997), they can also be contributed by other sources such as plant wax,
fossil fuel, soil particles, and SOA (Wang et al., 2006; Zhao et al., 2014).
For these reasons, it is possible that the COA factor identified in this
study might contain significant contributions from sources other than cooking
activities, similar to situations reported in several other studies
(Dall'Osto et al., 2013; Mohr et al., 2009, 2012; Hayes et al., 2013).
Furthermore, a recent study reported that the relative ionization efficiency
(RIE) of cooking OA tends to be higher than the typical RIE of 1.4 used for
organic aerosol quantification (Reyes-Villegas et al., 2018), suggesting that
cooking aerosol concentrations were likely overestimated in this study.
Besides the two POA factors, two OOA factors were identified, and both showed
major ion fragments representative of oxidized organics, e.g., CO2+
(m/z 44) and C2H3O+ (m/z 43). SV-OOA
(O / C = 0.56; H / C = 1.90) resides within the region
representing fresher SOA in the triangle plots in Fig. S19, whereas the
LV-OOA factor is characterized by a high O / C ratio (= 0.91),
indicating aged and highly oxidized OA, respectively. It was found to account
for an average of 61 % of the OA mass (Fig. S17a), with LV-OOA and SV-OOA
being 34 and 27 %, respectively. Both SV-OOA and LV-OOA correlated
positively with Ox during the afternoon (r = 0.53, 0.6,
respectively), and the correlation between total SOA
(= LV-OOA + SV-OOA) and Ox was even higher (r = 0.65;
Fig. 9a), indicating that afternoon SOA formation was strongly impacted by
photochemistry. This observation is consistent with Herndon et al. (2008),
who observed a strong correlation between OOA and Ox in photochemically
processed urban plumes from Mexico City. The average OOA / Ox ratio
observed in the present study (0.13 µg m-3 ppbv-1) is
within the range of values from Mexico City and other megacities, including
Tokyo, Los Angeles and Paris (0.13–0.18) (Zhang et al., 2015).
(a) Average compositional pie chart of PM1 species
(non-refractory PM1 plus black carbon (BC)) and each of the OA factors
over an organic-dominant period (20 May, 17:00–24 May, 00:00). The green
outline indicates the fraction of total OA; and (b) the average
high-resolution mass spectrum of OA colored by the different ion families.
The average elemental ratios for the OA fraction are described (a);
(c) two clusters of back-trajectories of air masses arriving at KIST
during the organic-dominant period (20 May, 17:00–24 May, 00:00).
(a) Time series of ambient air temperature (T), relative
humidity (RH), and precipitation (Precip.); (b) time series of total
particulate matter (PM1), scanning mobility particle sizer (SMPS) volume
concentrations, and the 24 h averaged PM1+ BC with bars;
(c) time series of the organic (Org.), nitrate (NO3-),
sulfate (SO42-), ammonium (NH4+) and BC aerosols;
(d) time series of the mass fractional contribution of organic
aerosols (Org.), nitrate (NO3-), sulfate (SO42-),
ammonium (NH4+), and BC to total PM1 concentration;
(e) time series of each factor derived from the positive matrix
factorization (PMF) analysis; (f) time series of mass fractional
contribution of OA factors to OA; (g–j) wind rose plots, colored by
wind speed; and (k–n) fractional contributions of each species to
the total PM1 (non-refractory-PM1 plus BC) mass for each stage in
haze life.
Impacts of intense SOA formation on haze
PM1 concentration jumped from 11 to 55 µg m-3 between
17:00 and 17:45 on 20 May, during which concentrations of all PM1
species (except for COA), SO2, NO2, and biogenic and
anthropogenic VOCs (e.g., isoprene and toluene) increased sharply (Fig. 2h).
As shown in Fig. S20, the onset of this pollution episode was associated with
a change in wind direction from southeast to northwest, indicating that it
was mainly caused by transport of polluted air masses. Wind speed was low and
wind direction alternated between north and east during the next 3 days, and
the concentrations of most air pollutants rose and fell in correlation with
the wind shifts. However, LV-OOA remained elevated after the initial sharp
rise from 5.6 to 16 µg m-3 and increased to a maximum
concentration of ∼ 25 µg m-3 on 23 May. SOA
(= SV-OOA + LV-OOA) was a dominant aerosol component throughout the
entire episode (20 May 17:00 to 24 May 0:00) and on average accounted for
∼ 60 % of the PM1 mass (Fig. 10a). Since this episode was
characterized by high daytime O3 concentration, air temperature,
and solar radiation and elevated VOC concentrations (Fig. 2), SOA production
was likely fast. In addition, the meteorological conditions were generally
stagnant (e.g., slow wind speed and low mixing height) during this period
(Rapid Science Synthesis Report, 2017), facilitating the accumulation of
pollutants. Overall, intense photochemical reactions, high concentrations of
gaseous precursors, and stagnant atmospheric conditions were likely
responsible for the intense formation and accumulation of SOA during this
episode. For example, as shown in Fig. 9, the correlation between SV-OOA and
Ox during this period was tight and showed a slope (i.e.,
SV-OOA / Ox ratio) twice as high as the rest of the study
(0.11 µg m-3 ppb-1 vs.
0.053 µg m-3 ppb-1). This is an indication that SOA was
formed more efficiently during this high SOA episode. However, the
correlations of LV-OOA and total OOA = (LV-OOA + SV-OOA) vs. Ox
were both poor during this high OA episode, suggesting that in addition to
photochemical reactions, other factors such as aging processes which occurred
under the stagnant air flow condition likely contributed to the high
concentration of SOA as well.
On the other hand, formation of secondary inorganic aerosol species was
limited during this event. One of the reasons was that air masses that
arrived at the KIST site during this period (20 May, 17:00–24 May, 00:00)
mainly originated from the east (Fig. 9c), where SO2 emission
sources are sparse, thus containing low sulfate concentrations. Another
reason was that temperature was high (24 ± 3 ∘C) and RH was
low (36 ± 11 %) during this period, unfavorable for particulate
nitrate formation. These results indicate that SOA formation could be a
leading cause of haze episodes in SMA during springtime.
Regional and local influences on haze events
Haze episodes occur often in East Asia, including Seoul, Korea (e.g., Kim et
al., 2017, and references therein). Many investigations were conducted in
China and suggest that the formation of severe haze pollution is a combined
result of stagnant meteorological conditions associated with intense
secondary aerosol formation, regional transport and primary emissions (Huang
et al., 2014; Sun et al., 2010, 2014; Herndon et al., 2008; Wang et al.,
2016a, b; Zheng et al., 2015). Our investigation of the occurrence of haze
episodes in Seoul during winter 2015–2016 suggested that accumulation of
primary pollutants and enhanced formation of secondary pollutants on a local
scale were the main causes of wintertime haze episodes (Kim et al., 2017).
However, the characteristics and the causes of haze episodes in the other
seasons have not yet been investigated, although this information is required
to better design reduction strategies for PM in SMA. To address this
knowledge gap, in this section, the lifecycle of a major springtime haze
episode in SMA is discussed.
Shorter haze episodes with daily average PM1 concentrations higher than
30 µg m-3 occurred several times during this study (Fig. 2).
In addition, a severe haze episode lasted for 6 days from 26 to 31 May.
Figure 11 presents a case study of the full cycle of this haze episode, which
is classified into four stages: Stage 1 (S1, 24 May, 07:30–11:30)
representing a clean period (precipitation) before the haze, Stage 2 (S2,
24 May, 11:30–26 May, 18:00) representing the formation stage of the haze,
Stage 3 (S3, 26 May, 18:00–31 May, 24:00) representing the haze period with
high concentrations of PM, and Stage 4 (S4, 1 June, 00:00–2 June, 24:00)
representing the cleaning of haze. This classification was done
mainly based on changes in atmospheric conditions, i.e., precipitation, wind
direction and speed.
On 24 May, there was a short clean period (07:30 to 11:30; Period S1) when
the average PM1 concentration was only 9 µg m-3 due to
precipitation. PM concentration started to increase substantially after the
rain stopped, and the increase was accompanied by a change in aerosol
composition. During both Periods S1 and S2 (24 May, 11:30–26 May, 18:00),
the predominant wind direction was southwest (Fig. 11b). Analyses of the
MODIS images (Fig. S21), back-trajectories (Fig. S22) and meteorological
conditions (Rapid Science Synthesis Report, 2017) all indicated direct
transports of air masses from the northwest, where large SO2
emission sources are located. The change in PM1 composition during
Period S2 reflected the influence from such regional transport processes. For
example, the mass fractions of species associated with regional sources, such
as sulfate (28 % during S2 vs. 20 % during the entire period) and
LV-OOA (18 vs. 15 %), increased (Fig. 1l, Table S4), whereas the
fractions of local pollutants such as SV-OOA (5 vs. 12 %), HOA (5 vs.
10 %), COA (5 vs. 7 %) and BC (4 vs. 7 %) decreased compared to
the averaged PM1 composition during the entire period. In addition, the
mass fraction of nitrate, one of the local secondary species, was also
enhanced (20 vs. 17 %), and this was mainly due to the gas-particle
partitioning of HNO3 and nighttime heterogeneous reactions in the
nitrate formation facilitated by high RH (78 %) and low temperature
(18 ∘C) (Table S4). A good correlation (r2= 0.48) between
nitrate and RH corroborates the role of aqueous processes (Fig. S23).
During Period S3 (26 May, 18:00–31 May, 24:00), wind speed was reduced
(Fig. 11, Table S4) and a more stagnant condition developed over the SMA.
High mass loadings of submicron aerosol species persisted due to a lack of
ventilation. In addition, similar to observations during a winter haze study
at SMA (Kim et al., 2017), stagnant condition facilitated the accumulation of
primary and secondary pollutants from local sources while limiting the
transport of regional species. For example, the mass fractions of all the
local pollutants in PM1 were enhanced during S3 compared to S2, e.g., BC
(6 % during S3 vs. 4 % during S2), HOA (8 % vs. 5 %), COA
(6 % vs. 5 %) and nitrate (22 % vs. 20 %), whereas the
fractions of regional species decreased, e.g., sulfate (25 % vs.
28 %) and LV-OOA (9 % vs. 18 %).
From 1 to 2 June (Period S4), wind direction suddenly changed from westerly
to north/northeasterly and average wind speed increased to 1.7 m s-1
(Table S4). This process cleaned out the atmosphere and reduced PM1
concentration to an average value of 14 µg m-3. OA was a
major chemical species during this period, followed by sulfate and nitrate.
During this time (S4), RH was low (∼ 48 %), which was less
favorable for nighttime formation of nitrate. Furthermore, wind was
predominantly from the north, whereas the main sources of SO2 and
sulfate were located in the west, resulting in a low concentration of sulfate
in SMA.
Overall, unlike the haze episodes observed in winter 2015 (Kim et al., 2017),
which were mainly due to local influences under stagnant conditions, the
spring haze events observed in this study occurred due to a combination of
regional and local effects. A thorough understanding of the various haze
scenarios and their underlying causes is required to better design air
quality improvement strategies.