Secondary organic aerosol (SOA) plays a significant role in atmospheric
chemistry. However, little is known about the vertical profiles of SOA in the
urban boundary layer (UBL). This knowledge gap constrains the SOA simulation
in chemical transport models. Here, the aerosol samples were synchronously
collected at 8, 120, and 260 m based on a 325 m meteorological
tower in Beijing from 15 August to 10 September 2015. Strict emission controls
were implemented during this period for the 2015 China Victory Day
parade. Here, we observed that the total concentration of biogenic SOA tracers
increased with height. The fraction of SOA from isoprene oxidation increased
with height, whereas the fractions of SOA from monoterpenes and sesquiterpenes
decreased, and 2,3-dihydroxy-4-oxopentanoic acid (DHOPA), a tracer of
anthropogenic SOA from toluene oxidation, also increased with height. The
complicated vertical profiles of SOA tracers highlighted the need to
characterize SOA within the UBL. The mass concentration of estimated secondary
organic carbon (SOC) ranged from 341 to 673 ngCm-3. The
increase in the estimated SOC fractions from isoprene and toluene with height
was found to be more related to regional transport, whereas the decrease in the
estimated SOC from monoterpenes and sesquiterpene with height was more subject
to local emissions. Emission controls during the parade reduced SOC by
4 %–35 %, with toluene SOC decreasing more than the other SOC. This
study demonstrates that vertical distributions of SOA within the UBL are
complex, and the vertical profiles of SOA concentrations and sources should be
considered in field and modeling studies in the future.
Introduction
In the middle of the 20th century, atmosphere pollution events began to be
frequently reported in different regions worldwide (White and Roberts, 1976;
Went, 1960; Barrie, 1986). Many studies on atmospheric aerosols have been
undertaken to understand the sources and evolution mechanisms of aerosols and
discuss their effects on climate and human health. It is well known that
atmospheric aerosols can impact radiative forcing, the hydrological cycle,
regional and global climate, and human health (Kanakidou et al., 2005; Su
et al., 2020), and these impacts were all shown in the Intergovernmental Panel
on Climate Change (IPCC) report (IPCC, 2014). Generally, 20 %–90 % of
the mass concentration of particulate matter (PM) is contributed by organic
aerosols (OAs) of which ca. 30 %–70 % is secondary organic aerosol
(SOA) (Huang et al., 2014; Ervens et al., 2011). SOA is generally formed
through the photooxidation of volatile organic compounds (VOCs), including
biogenic VOCs (BVOCs, e.g., isoprene, monoterpenes, sesquiterpenes, and
oxygenated hydrocarbons) from terrestrial vegetation and marine phytoplankton
and anthropogenic VOCs (AVOCs, e.g., toluene and naphthalene) from biomass
burning, coal combustion, vehicle exhausts, and solvent use.
Anthropogenic SOA (ASOA) and biogenic SOA (BSOA) are important contributors to
OA and air pollution in the atmosphere (Hodzic et al., 2016; Nault et al.,
2021; An et al., 2019). BSOA and ASOA fractions are potentially underestimated
in models according to previous studies (Volkamer et al., 2006;
Shrivastava et al., 2017). In recent years, a large number of studies based on
field observations suggest that the formation of BSOA can be enhanced by
anthropogenic precursors, an effect which is known as anthropogenic–biogenic
interactions (Zelenyuk et al., 2017; Goldstein et al., 2009; Shilling et al.,
2013). Simultaneously, SOA can be transported on a regional or global scale,
changing cloud condensation nuclei (CCN) size, influencing the climate, and
damaging human health (Russell and Brunekreef, 2009; Pöschl, 2005;
Shrivastava et al., 2017).
In the last decade, severe air pollution in China has attracted worldwide
attention (An et al., 2019; Huang et al., 2020a). The haze episodes in China
are suggested to result from a complex interplay of anthropogenic emissions,
atmospheric processes, regional transport, meteorological conditions, and
climatic conditions (An et al., 2019; Zheng et al., 2015; Sun et al., 2016; Du
et al., 2021; Huang et al., 2020b). The high contribution of secondary
aerosols to the PM pollution during haze events in China highlights the urgent
need to understand the compositions and processes of SOA formation in the
atmosphere (An et al., 2019; Huang et al., 2014). Previous studies have
reported the chemical characteristics of OA in many regions in China (Simoneit
et al., 1991; Wang et al., 2006; Xie et al., 2020; Li et al., 2018). However,
studies characterizing the vertical properties of SOA in the urban boundary
layer are lacking, which constrains research on the interactions of aerosols
and regional transport, local emissions, atmospheric processes, and
meteorological conditions in urban areas.
Vertical profiles of atmospheric dynamic structures, gaseous species, bulk
chemical compositions, and nitrogen isotopes in the urban boundary layer (UBL)
have been investigated over Beijing (Guinot et al., 2006; Sun et al., 2015;
Zhao et al., 2017; Chan et al., 2005; Wu et al., 2019). Several field studies
at the rainforest Amazon Tall Tower Observatory (ATTO) also measured the
vertical gradients of VOCs. (Andreae et al., 2015; Yáñez-Serrano
et al., 2018). However, vertical SOA profiles were still lacking. A previous
study reported that the loading of SOA is high above the surface layer during
the summer over the southeastern United States, which was potentially related
to the heterogeneous chemical reactions and gas-to-particle conversion of
BVOC oxidation products (Goldstein et al., 2009). This highlights the
pressing need to obtain the vertical SOA profiles in the cities, especially in
a Chinese megacity frequently enduring severe air pollution. It is meaningful
to learn the SOA properties and probe its behavior in the atmosphere. This
information also has regulatory implications for decision makers.
Beijing, one of the super megacities of China, held the 2015 China Victory Day
parade in the late summer of 2015. The government had implemented strict
emission controls in Beijing and its seven surrounding provinces to improve
the air quality. This provided a unique chance to study atmospheric aerosols
under government interventions. Daily PM2.5 samples were
synchronously collected at three heights (8, 120, and
260 ma.g.l., respectively) based on
a 325 m meteorological tower in urban Beijing during the period
15 August to 10 September 2015. Observations at 8 m are more subject
to local emissions, whereas those at 120 and 260 m are more
representative of mixing and/or regional-scale influences (Sun et al., 2015;
Zhao et al., 2020). BSOA and ASOA tracers in PM2.5 were quantified
by gas chromatography and mass spectrometry (GC/MS); organic carbon (OC),
elemental carbon (EC), and water-soluble organic carbon (WSOC) in
PM2.5 were also determined. In addition, the tracer-based method
(Kleindienst et al., 2007) was used to estimate the contributions of biogenic
SOC (BSOC) and anthropogenic SOC (ASOC). The influences of emission controls
during the parade period on the characteristics of SOC were also
investigated. To the best of our knowledge, this was the first time that
vertical profiles of SOA tracers were measured at a molecular level in a
Chinese megacity. This campaign provided new insights into the formation
mechanisms of SOA in haze episodes and the influences of local emissions, regional transport, and mixing of heights on SOA over the North China
Plain (NCP). Furthermore, this study provided a scientific basis for China's
initiatives to guarantee good air quality in Beijing and contributed to
improving the simulations of SOA in the chemical transport models.
Materials and methodsSampling
Daily PM2.5 samples were collected at three heights: 8 m (at
the rooftop of a two-story building about 10 m away from the
325 m meteorological tower), 120, and 260 m (at the platforms
of the tower) in Beijing during the China Victory Day parade period (08:00–06:00 LT the next day; 15 August–10 September 2015). The sampling site is at the
Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences
(39∘58.53′ N, 116∘22.69′ E), which is in an
urban site (between the 3rd and 4th ring roads) of Beijing and surrounded by
a traffic road (∼50m), highway (∼300m), a public
park (∼500m to the southwest), restaurants (∼100m), residential housing, and a gas station (∼200m). The predominant vegetation types surrounding the sampling
site are deciduous broadleaf vegetation (acacia and Juglandaceae), shrub, and
lawn. The vegetation cover of the public park is more than
50 %. The predominant vegetation is also deciduous
broadleaf. Filter samples were collected onto pre-combusted
(450 ∘C combusted for 6 h) quartz fiber filters
(Pallflex, 20×25 cm) using high-volume air samplers (Tisch, USA) at a
flow rate of 1.1 m3min-1. The filter samples were enveloped in
aluminum foil and stored at -20 ∘C in darkness until
analysis. Meteorological parameters including wind speed (WS), wind direction
(WD), temperature (T), and relative humidity (RH) at the heights of 8, 120,
and 260 m were measured by the meteorological system on the
tower. Three periods are classified according to the phases of emission
controls by the government: before the parade (Before-P): 15–19 August;
during the parade (During-Parade): 20 August–3 September; and after the
parade (After-P): 4–10 September.
Carbonaceous component analyses
OC and EC in aerosols were directly analyzed by an OC/EC carbon aerosol
analyzer (Sunset Laboratory Inc., USA) following a NIOSH protocol (Mkoma
et al., 2013). A portion of each filter of 3.14 cm2 was extracted
with 15 mL ultrapure water under ultrasonication with ice water for
20 min. WSOC in this water extract was measured by a total organic
carbon (TOC) analyzer (model NPOC, Shimadzu, Japan). The concentrations of OC,
EC, and WSOC were calibrated with field blank filters.
Measurement of OA molecular compositions using GC/MS
A filter was extracted three times with dichloromethane/methanol (2:1,
v/v) under ultrasonication. The extracts were then filtered, concentrated by
a rotary evaporator, and blown to dryness. After that, the dried extracts
were reacted with 60 µL of
N,O-bis-(trimethylsilyl)trifluoroacetamide (BSTFA) with
1 % trimethylsilyl chloride and 10 µL of pyridine at
70 ∘C for 3 h. After sufficient reaction,
40 µL internal standard solvent (C13n-alkane,
1.43 ngµL-1) was added to the derivatives before GC/MS
analyses. Three field blank filters were treated as real samples and used for
quality calibration. GC/MS is performed on a Hewlett-Packard model Agilent
7890A GC coupled to a Hewlett-Packard model Agilent 5975C mass selective
detector (MSD). GC separation is equipped with a split/splitless injection and a
fused silica capillary column (DB-5MS, 30m×0.25mm
i.d., 0.25 µm film thickness). The GC oven temperature program was
set as follows: held at 50 ∘C 2 min, then increased to
120 ∘C at a rate of 15 ∘Cmin-1, heated up
to 300 ∘C at a rate of 5 ∘Cmin-1, and
finally held at 300 ∘C for 16 min. The mass
spectrometer was operated on the electron impact (IE) mode at 70 eV
and scanned from 50 to 650 Da. Organic marker measurements were
determined by comparing with references, a library, and authentic standards, and
were quantified with GC/MS response factors acquired using authentic standards
or surrogates (Fu et al., 2009). The data reported in this work were corrected
for the field blank but not for recoveries.
Air mass backward trajectory
To investigate the influences of air mass on urban aerosols of Beijing,
3 d backward trajectories starting at 300 ma.g.l. every
6 h were calculated for each sample using the HYSPLIT4 model
(http://ready.arl.noaa.gov/HYSPLIT.php, last access: 28 August 2021). Cluster analyses were applied to estimate the influence of air
mass. As shown in Fig. S1 in the Supplement, seven clusters were
determined. Air mass from south, southeast, and northeast of Beijing accounted
for >70%. Especially, for pollution days, retroplumes of air
masses were calculated by the FLEXPART (FLEXible PARTicle dispersion) model
(Fig. S2 in the Supplement). Detailed information about the model was
described in a previous study (Wei et al., 2018). The model was set with a
height of 300 ma.g.l. and 3 d backward trajectories.
Ancillary parameters
The ground surface concentrations of PM2.5, CO, SO2,
NO2, and O3 were obtained from the monitor station of the
Olympic Center (39.98∘ N, 116.40∘ E) about 3 km away
from our sampling site, which is available on national urban air quality
and real-time publishing platforms
(http://106.37.208.233:20035/, last access: 28 August 2021). The hourly levels of these parameters are shown in Fig. S4 in the Supplement.
Results and discussion
Meteorological parameters (wind speed, wind direction, temperature, and
relative humidity) at the sampling site during the observation period are
shown in Fig. 1. These meteorological parameters have been reported in a
previous study (Zhao et al., 2017). The prevailing winds at 8 m were
either easterly or westerly, while at 120 and 260 m the wind
directions were dominated by northerlies. Wind at the ground surface
(8 m) was weaker than that at upper layers, which was likely related
to the influences of surrounding buildings near the sampling site. Some high
buildings are several hundred meters away from the sampling site. Vertical
differences in wind speeds and directions suggest that samples collected at
8 m are more related to local source emissions, whereas samples
collected at upper layers are more influenced by the regional scale. Air
temperature decreased slightly with the height, while relative humidity (RH)
increased. This feature possibly plays a role in the vertical profile of the
gas-to-particle partitioning of organic aerosols (Sun et al., 2015).
Temporal series of vertical meteorological parameters including
(a) relative humidity (RH) and temperature (T), (b) wind direction (WD) and
wind speed (WS), and (c) wind roses. Three pollution events (including E1 to
E3) are indicated by grey shading (E1: 16 to 19 August; E2:
29 August; and E3: 7 to 8 September). The
light grey shading during E1 is a short rain event that reduced the loading
of OA in aerosols. N represents the northern wind.
Three pollution episodes (marked as E1, E2, and E3) were recorded during the
sampling period. The pollution episodes were defined according to a previous
study (Zhao et al., 2017) and the air quality index (AQI) from the Chinese
national environmental monitoring center
(http://www.cnemc.cn, last access: 28 August 2021). The
prevailing winds during these pollution episodes varied with height between 8
and 260 m (Fig. S3 in the Supplement). The wind in the upper layers (120 and
260 m) was mainly from the south, whereas at the ground surface layer
(8 m) it was from the north. Similar to the air mass footprints
(Fig. S2), these results suggest that the air masses from the southern region
significantly contributed to the haze pollution in Beijing (Zheng et al.,
2015; Tian et al., 2019).
The concentrations of WSOC and OC were 2.73±1.31 and 5.03±2.28µgCm-3 at 260 m,
2.69±1.55 and 5.32±2.88µgm-3 at 120 m, and 2.03±0.99 and
4.37±1.69µgm-3 at 8 m, respectively (Fig. S4
and Table S1 in the Supplement). There were no significant differences between
the average concentrations of WSOC and OC at the three layers (Table S2 in the
Supplement). However, the fractions of WSOC to OC at the upper layers (120 and
260 m: 51.1 % and 54.0 %, respectively) were
higher than that at 8 m (46.9 %) (Table S1). The
correlation coefficient values (R2) between WSOC and OC were also higher
at upper layers: 0.96, 0.93, and 0.47 at 260, 120, and 8 m,
respectively (Fig. S5 in the Supplement). These results reveal a predominant
contribution of secondary sources to OA at the upper layers, indicating that
organic aerosols in the upper layers were more oxidized than in the ground
surface layer. This highlights the importance of investigating the vertical
profiles of SOA in the UBL. In addition, primary sources from local dust and
soil resuspension, such as primary biological aerosols which contain a high
abundance of water-insoluble organic compounds (Wang et al., 2019),
potentially caused the lower fractions of WSOC to OC at the ground surface
than at the upper layers.
Concentrations of identified secondary organic compounds are shown in
Table S1, including BSOA tracers (isoprene, monoterpene, and sesquiterpene
oxidation products), ASOA tracers (2,3-dihydroxy-4-oxopentanoic acid (DHOPA)
and phthalic acid for toluene and naphthalene oxidation products,
respectively), polyacids, and aromatic acids in the aerosols at three
heights. Most of these molecular tracers showed a higher abundance at high
layers (≥120m) than at 8 m, except for pinic acid (PA),
pinonic acid (PNA), 3-acetyladipic acid, and β-caryophyllinic acid. Table S2
shows significant differences in the average concentrations of these SOA
tracers with height, except for monoterpene SOA tracers. Many factors can
regulate the vertical profiles of SOA: (1) lower temperature and higher RH at
the upper layers than the ground surface layer are potentially favorable to the
condensation of semi-volatile organic compounds onto particles (Carlton et
al., 2009; Hallquist et al., 2009); (2) local emission, regional transport,
and vertical mixing can influence the relative loading and fraction of SOA in
aerosols (Brown et al., 2013); and (3) atmosphere oxidation capacity can also play
a role in the formation of SOA (H. Wang et al., 2018). Thus, vertical
distributions of SOA can be useful for investigating the atmospheric behavior
of aerosols in the UBL.
Vertical characteristics of SOA tracersEmissions of BVOCs
Vegetation species, plant growth stage, and environmental conditions can
impact the release of BVOCs (Wang et al., 2003; Benjamin et al., 1997), which
contribute to the vertical profiles of BSOA tracers. Northwest China is mainly
grasslands or barren lands, while other areas of China, especially the south
of China, are rich in terrestrial plants (Ran et al., 2012). The emission
inventory showed that in summer a large amount of BVOCs were mainly emitted
from the northeast, north, and southeast regions with only a small amount from
southwest China (Yan et al., 2005). Isoprene is one of the most abundant
non-methane VOCs, mostly emitted by broadleaf plants (deciduous or evergreen
trees) and marine phytoplankton (Sharkey et al., 2008). Back trajectories
showed that 70 % of the air masses originated from the south or
northeast regions of Beijing (Fig. S1), suggesting isoprene oxidation products
were potentially influenced by the regional-scale emissions of BVOCs from
these regions. Monoterpenes are mainly emitted from needle leaf trees
(e.g., coniferous plants), and the emissions from soil and litter in local
places may be larger than those from vegetation (Faiola et al.,
2014). Sesquiterpenes are mainly emitted from plants and trees, which are
controlled by many factors, such as temperature and stage of plant growth
(Duhl et al., 2008; Faiola et al., 2019). The different contributions from
various BVOC emissions are one possible factor that influences the observed
vertical profiles of BSOA tracers. Terrestrial vegetation can emit a broad
spectrum of BVOCs. Ambient temperature, solar radiation, soil moisture, and
pollution situation can also affect their formation processes and
concentrations in the atmosphere. In addition, oxidation processes (such as
reaction rates and lifetime) simultaneously control the properties of BSOA in
the atmosphere (Jaoui et al., 2007; Tarvainen et al., 2005).
Vertical distribution of BSOA tracers
The total concentrations of BSOA tracers were 31.5±16.8, 36.4±26.1, and 50.2±27.0ngm-3 at 8, 120, and 260 m, respectively
(Table S1). The vertical distribution properties of BSOA tracers are related
to complicated factors, such as regional transport and ambient temperatures
influencing different BSOA species (Goldstein et al., 2009). The total
concentrations of isoprene SOA tracers were 19.7±12.0, 27.1±22.4, and 38.7±24.1ngm-3 at 8, 120, and 260 m, respectively, among
which C5-alkene triols (the sum of
cis-2-methyl-1,3,4-trihydroxy-1-butene, 3-methyl-2,3,4-trihydroxy-1-butene,
and trans-2-methyl-1,3,4-trihydroxy-1-butene) were the most abundant
compounds, followed by 2-methylerythritol (2-MTeryth),
2-methylthreitol (2-MTthrei), and 2-methylglyceric acid (2-MGA)
(Table S1). The total concentrations of monoterpene SOA tracers were 10.5±5.18ngm-3 (8 m), 8.45±3.68ngm-3
(120 m), and 10.5±3.86ngm-3 (260 m).
Pinonic acid was the most abundant species at 8 m, whereas
3-methyl-1,2,3-butanetricarboxylic acid (MBTCA) was the dominant compound at
120 and 260 m. The concentrations of the sesquiterpene SOA tracer (β-caryophyllinic acid) were 1.32±0.63, 0.89±0.89, and 1.02±0.69ngm-3 at 8, 120,
and 260 m, respectively. The abundance of isoprene SOA tracers
increased with height, while there was no significant variation in the
concentrations of the monoterpene and sesquiterpene SOA tracers.
Vertical and temporal variations in BSOA tracers from (a)
isoprene, (b) monoterpenes, and (c) sesquiterpene. Measurement heights were
at 8 m (solid circles), 120 m (grey circles), and 260 m (open circles).
Relative mass fractions are shown in (d).
Vertical profiles in the concentrations of SOA tracers from (a)
isoprene, (b) monoterpenes, (c) sesquiterpene, and (d) DHOPA in daily samples
collected at three heights. The samples collected during E1, E2, and E3
periods are marked with bold blue, black, and red lines, respectively. The
sampling date during the pollution days is also marked.
Vertical distributions of the average concentrations of SOA
tracers from (a–d) isoprene, (e–i) monoterpenes, (m)β-caryophyllinic acid, (n) dihydroxy-4-oxopentanoic acid (DHOPA), and (o)
phthalic acid in PM2.5. Panels (a–m) and (n–o) are tracers of BSOA and
ASOA, respectively (HDCCA is the abbreviation of
3-(2-hydroxyethyl)-2,2-dimethyl-cyclobutane carboxylic acid).
The time series of the concentrations of BSOA from isoprene, monoterpenes, and
sesquiterpene at the three layers and their relative contributions are shown
in Fig. 2 and Fig. S6 in the Supplement. Their vertical patterns are also shown
in Figs. 3 and 4. Generally, isoprene SOA tracers increased with height, while
the other two kinds of SOA tracers varied slightly with height. From 8 to
260 m, the contributions of total BSOA tracers by isoprene SOA tracers
increased from 63 % to 77 %, while the fractions from
monoterpene SOA tracers and the sesquiterpene SOA tracer decreased from
33 % to 21 % and 4 % to 2 %,
respectively (Figs. 2d and S7 in the Supplement). It indicates that oxidized
products from isoprene are more important contributors to SOA in Beijing over
the late summer than other BVOC products. It suggests that regional transport
potentially contributes more to isoprene SOA, while SOA from monoterpenes and
sesquiterpene is likely more influenced by local sources. In addition, some
other factors (such as transformation and condensation processes) can also
lead to these patterns. Our results are in agreement with the field
observations over the United States and the modeled vertical distributions of
isoprene-derived SOA, that is, high loadings of SOA from isoprene oxidation
occurred above the surface layer (Zhang et al., 2007; Goldstein et al., 2009).
In particular, each kind of BSOA tracer displayed different temporal and
vertical distributions (Figs. S6 and 4). These features are potentially
influenced by many causes. The predominant reason is likely related to local
emissions and regional transport (Du et al., 2017). Secondly, the mixing of
heights (Q. Wang et al., 2018) and meteorological conditions of the atmosphere
(Ding et al., 2011) is potentially another important factor. Moreover,
oxidation processes (Claeys et al., 2004; Szmigielski et al., 2007) and
emissions (Wang et al., 2008; Faiola et al., 2014) of BVOCs can also cause
this complex vertical profiles of SOA.
Vertical variations in the photooxidation of BSOA tracers
Isoprene SOA tracers are the photooxidation products of isoprene with
atmospheric oxidants (e.g., OH, O3, and NOx). The isoprene
oxidation mechanisms are dependent on atmospheric conditions (Bates and Jacob,
2019; Wennberg et al., 2018). These processes are influenced by many factors,
such as atmospheric conditions (humidity, temperature, and solar radiation)
and the acidity of aerosols (Claeys et al., 2004; Kleindienst et al., 2009;
Nguyen et al., 2015; Surratt et al., 2010). Specifically, 2-MGA is mainly
formed under a high NOx level, while 2-MTs (the sum of
2-MTeryth and 2-MTthrei) are formed under a low
NOx level. The ratio of 2-MTs to 2-MGA can reflect the impacts of
NOx loading on the isoprene oxidation processes (Surratt et al.,
2010). In this study, the average ratio of 2-MTs to 2-MGA was 5.20±2.24
at 8 m, higher than that at 120 m (3.80±1.95) and
260 m (3.15±1.83) (Fig. 5a). The lower ratio aloft suggested
aerosols transported from other polluted regions with higher NOx levels
contributed to the isoprene oxidation products in the upper-layer aerosols of
Beijing. The impacts of other factors (e.g., relative humidity, temperature,
and oxidizing capacity) on the heterogeneous oxidations of isoprene cannot be
ignored (H. Wang et al., 2018).
Temporal variations in the mass concentration ratios among
different biogenic SOA tracers in PM2.5: (a) 2-MTs/2-MGA; (b) 2-MTs/C5-alkene triols, and (c) MBTCA/(PA + PNA).
The average ratios of 2-MTs to C5-alkene triols were 0.97±1.17,
1.33±1.24, and 3.97±3.08 at 8, 120, and 260 m, respectively
(Fig. 5b). Both C5-alkene triols and 2-MTs can be formed from
epoxydiol (IEPOX) derivatives of isoprene (Wang et al., 2005). Some studies
also suggested that the loading of 2-MTs increased with the enhancement of
aerosol acidity (Surratt et al., 2007), and the relative humidity can affect
the ratio of 2-MTs to C5-alkene triols (Surratt et al.,
2010). Recent studies suggested the ratio of 2-MTs to C5-alkene
triols decreased with aerosol acidity (Yee et al., 2020), and
C5-alkene triols were likely formed from thermal degradation of
2-methyltetrol sulfates for GC/MS artifacts (Cui et al., 2018). Hence, it is
difficult to explain the different ratios of 2-MTs to C5-alkene
triols at three heights. It also suggests that more field observations on the ratios of SOA tracers are needed.
Eight monoterpene SOA tracers have been identified here, with pinonic acid
(PNA), pinic acid (PA), and MBTCA being the dominant compounds (Table S1).
The different temporal and vertical patterns of these tracers are displayed in
Figs. S6 and 4. MBTCA can be produced by further oxidations of PNA and PA by
OH radical (Szmigielski et al., 2007; Ding et al., 2016). Thus, the ratio of
MBTCA to (PNA+PA) can represent the aging extent of monoterpene-derived
SOA. The ratio of MBTCA to (PNA + PA) at 8 m (0.24±0.10) was
lower than those at 120 m (0.84±0.44) and 280 m (1.49±0.77) (Figs. 5d and S7), indicating that SOA from monoterpenes was much
fresher at the surface than the upper layers. These results suggested that the
lower height (8 m) was more relevant for local fresh aerosols, whereas
the higher layer (260 m) was more subject to regional aged aerosols,
and the middle layer (120) was likely the mixed influence of local and
regional aerosols. This conclusion can also be supported by the more
significant correlation between PNA and MBTCA at 8 m than those at 120
and 260 m (Fig. S8 in the Supplement).
β-Caryophyllinic acid is produced by the oxidation of β-caryophyllene emitted from trees and plants (Jaoui et al., 2007). The
average concentration of β-caryophyllinic acid decreased and then
increased slightly with height (Fig. 4). This could be associated with
relatively high ambient temperature (Duhl et al., 2008) or β-caryophyllene released from the soil or litter around the ground surface
(Zhu et al., 2016). It is noteworthy that the correlations (r) of β-caryophyllinic acid with other SOA tracers (polyacids, aromatic acids,
2-MGA, C5-alkene triols, and 3-hydroxyglutaric acid) were stronger
at 120 and 260 m than those at 8 m (Fig. S7), implying that
these tracers had the same origins and were potentially associated with
regional transport of aerosols at upper layers.
Vertical profiles of BSOA tracers during pollution events
The winds during the pollution episodes were mostly from the south of Beijing
(Fig. S2), which contributed to the formation of air pollution in the city. It
was also found that the variations in RH were different for E1 and E3. The
southwest winds, which potentially carried high RH and pollutant air masses to urban Beijing (Q. Wang et al., 2018), likely leading to the vertical
variation in RH during E1. These results suggest that E1 is likely related to
regional transport. However, minor vertical variation in RH during E3 suggests
complex pollution. The concentrations of EC and the ratio of EC/OC (Fig. S4)
showed extremely low values and varied vertically during E2 when compared with
other pollution events, suggesting that E2 is largely influenced by
regional transport. In addition, the increasing levels of pollution parameters
(such as O3, SO2, and NO2) also contributed to
the pollution episodes.
Total concentrations of BSOA tracers increased with height during the 17 and
19 August episodes (E1) and the 29 August episode (E2), and complex vertical
distributions were recorded in other pollution days. The lower concentration
of BSOA tracers (13.2 ngm-3) at 120 m on 18 August (E1)
than average values (27.1 ngm-3) during the whole sampling period
was likely related to the removal by a short-lived rain event. High abundance
and increasing fractions of isoprene SOA tracers with height were recorded on
17 and 19 August (E1 and E2; Fig. 6), likely associated with the regional
transport from southern areas of Beijing (Figs. S2 and S3). The lower
abundance of isoprene oxidation products aloft than at the surface layer on
16 August (E1) was likely influenced by the air masses from the northwest. The
same difference on 7 to 8 September (E3) was likely influenced by the air
masses from the northeast. Monoterpene SOA tracers during the pollution events
showed vertical patterns similar to the average values, that is, the
concentrations and fractions recorded at the ground surface layer were higher than at
the upper layers due to local emissions. However, their concentrations
increased with height on 19 August (E1) (Fig. 3b), likely influenced by
regional transport. The sesquiterpene SOA tracer showed unusually vertical
distribution patterns during the episodes, that is, higher concentrations were
recorded at the upper layers than at the ground surface layer (Fig. 3c), which
was also associated with the regional transport.
Relative mass contributions of three kinds of BSOA tracers during
the pollution days at three heights. The sum concentrations of BSOA tracers
(ngm-3) are shown in the center of each pie.
The vertical patterns of BSOA tracers during the pollution events highlighted
the significant roles of air mass origins, regional transport, local
emissions, and oxidation processes in urban aerosols of Beijing. More field
measurements are needed to address the interactions between SOA formation and
the urban boundary layer. In addition, it is important to investigate the
vertical profiles of ASOA and its interactions with BSOA. ASOA is a larger
contributor to the loading of SOA and the formation of air pollution in urban
areas (Fan et al., 2020; An et al., 2019).
Vertical profiles of DHOPA
DHOPA is an anthropogenic secondary organic compound which is often used as a
tracer for toluene-derived (aromatic hydrocarbon) SOA and can only be detected
in the particulate phase (Kleindienst et al., 2007; Al-Naiema and Stone,
2017). DHOPA concentrations were 0.90±0.53,
1.50±1.09, and 2.03±1.69ngm-3 at
8, 120, and 260 m, respectively. The average concentrations at 8 and
260 m differed significantly. This vertical pattern was more obvious
during pollution episodes, except for 16 August and 7 September when air
masses were from the northwest and northeast (Fig. S2). Thus, the increasing
abundance of DHOPA at the upper layers during the pollution episodes was most
likely related to the pollutants from the southern region of Beijing.
In addition, DHOPA correlated well (r>0.7) with aromatic acids and
polyacids at all of the three heights, suggesting that they had similar
origins, such as anthropogenic aromatic VOCs (Ding et al., 2017; Al-Naiema and
Stone, 2017). DHOPA also showed moderate correlations (r>0.5) with 2-MGA,
C5-alkene triols, 3-hydroxyglutaric acid (3-HGA), and β-caryophyllene acid
(Fig. S7). Previous studies have reported that urban pollution can enhance the
formation of natural aerosols (Shrivastava et al., 2019); the existence of
aromatic compounds can lead to high loading of α-pinene-derived SOA
(Shilling et al., 2013; Zelenyuk et al., 2017). These moderate correlations
also suggest that the anthropogenic sources are related to biogenic sources,
and their interaction mechanisms still need more investigation.
SOC estimation by the tracer-based method
The tracer-based method is used to estimate the contributions of different
sources to SOC along vertical gradients. The fraction factors for SOC from
isoprene, monoterpenes, and sesquiterpene (Iso_SOC, Mon_SOC, and Sesq_SOC)
are set as 0.155±0.039, 0.231±0.111, and 0.0230±0.0046,
respectively, and those for toluene SOC (DHOPA as a tracer) and naphthalene
SOC (phthalic acid as a surrogate) are 0.0079±0.0026 and 0.0199,
respectively (Kleindienst et al., 2007, 2012). It should
be noted that estimations of fraction factors in chamber processes deviate
from the real atmospheric environment (Ding et al., 2014). Quantitative
uncertainties, system errors, volatility of BSOA tracers, and other factors
could also increase the challenge in getting a more accurate estimation of
SOC.
Temporal variations in the estimated SOC and their percentages of OC at the
three heights are shown in Fig. 7 and Table S3 in the Supplement. The total
concentrations of these estimated SOCs were 341±150ngCm-3
(average percentages in OC: 8.05±3.17 %), 444±283ngCm-3 (8.60±3.66 %), and 673±385ngCm-3 (13.4±4.81 %) at 8, 120, and
260 m, respectively. Toluene SOC was the dominant contributor to SOC
(32 %, 41 %, and 35 % at 8, 120, and
260 m, respectively), followed by naphthalene SOC and BSOC. The sum of
ASOC (toluene and naphthalene SOC) contributed more than 50 % of
these SOCs at the three heights, and their concentrations and fractions increased with height (Fig. 7c), suggesting a significant impact of anthropogenic
sources from regional transport on urban aerosols of Beijing. The average
concentrations of BSOC ranged from 157 to 272 ngCm-3 and
accounted for 3.80±1.46 % (8 m), 3.09±0.97 %
(120 m), and 5.63±2.32 % (260 m) of OC (Table S3).
Temporal variations in the estimated SOC and other OC at three
heights: (a) the concentrations of estimated SOC (right axis) and other OC
(left axis) and (b) the fraction of estimated SOC and other OC in OC. Relative
mass fractions of OC and estimated SOC are shown in (c) and (d). Other OC is
not captured by the source apportionment. Iso_SOC,
Mon_SOC, and Sesq_SOC represent BSOC estimated
from isoprene, monoterpenes, and sesquiterpene, respectively. Toluene SOC
and naphthalene SOC represent anthropogenic SOCs (ASOC) that were estimated
by DHOPA and phthalic acid, respectively.
BSOC showed different fractions at the three layers. Iso_SOC fractions at the
upper layers were higher than those at the ground surface, while Mon_SOC and
Sesq_SOC fractions at the ground surface were the highest (Fig. S8). These
features illustrate the large contribution of regional transport to
isoprene-derived SOC above the surface layer, while monoterpenes and
sesquiterpene were likely influenced by local emissions. Consequently, the
fractions of toluene SOC and Iso_SOC increased with height, Mon_SOC and
Sesq_SOC fractions decreased with height and naphthalene SOC fractions were
similar at the three heights, suggesting that the regional transport is rich
in toluene SOC and Iso_SOC. In addition to the influence of local emissions and regional transport, meteorological conditions, atmosphere turbulence, and
UBL structure also cannot be ignored.
Temporal variation in mass fractions of estimated SOC in
PM2.5 and its relative contributions during three periods (Before-P
means before parade, During-P means during parade, and After-P means after
parade) at three heights. The values in the center of the pies represent the
average concentrations of estimated SOC, and the sizes of the pies are related
to the concentrations.
Impacts of emission controls on estimated SOC loadings
The average concentrations of estimated SOC before, during, and after the
parade (marked as Before-P, During-Parade, and After-P, respectively) are
shown in Fig. 8. The estimated SOC concentrations during the parade (320±111, 370±163, and 594±264ngCm-3 at 8, 120, and 260 m, respectively)
decreased by ∼12% (364±199ngCm-3) and
10 % (356±177ngCm-3) at 8 m, decreased by 35 % (571±419ngCm-3) and 16 %
(441±279ngCm-3) at 120 m, and decreased by
31 % (864±585ngCm-3) and increased by
4 % (570±229ngCm-3) at 260 m when
compared to the Before-P and After-P, respectively. The SOC at the upper
layers decreased more than at the ground surface layer, suggesting the
efficient mitigation of SOC on a regional scale. The previous studies during
the same period (Zhao et al., 2017; Wu et al., 2019) showed a high frequency
of southerly winds before the parade and north winds during the parade at the
high layers. It suggests that the north winds were also an important reason
for the reduction in SOC during the parade.
We found that the fractions of ASOC decreased and Iso_SOC increased as a
response to the emission controls. The ASOC fractions at 8 m were
59±8% (Before-P), 47±5% (During-Parade), and
57±8% (After-P), and Iso_SOC fractions were 18±5%,
18±2%, and 12±2%, respectively. The ASOC
fractions at 120 m were 64±5% (Before-P), 61±10% (During-Parade), and 65±8% (After-P), and
Iso_SOC fractions were 17±5%, 23±6%, and 16±6%, respectively. The ASOC fractions at 260 m were 63±10 % (Before-P), 53±9 % (During-Parade), and 64±9 %
(After-P), and Iso_SOC fractions were 24±8 %, 34±9 %, and 21±9 %, respectively. The decreased contributions of ASOC during the control
period indicated the emission controls were effective in mitigating
anthropogenic sources, with the control on toluene SOC being particularly
effective. However, emission mitigation was not so efficient to control BSOC,
especially for Iso_SOC, implying that SOA from isoprene oxidation was
potentially a more stable contributor than other VOCs in Beijing during the
late summer.
Consequently, these results indicate that regional emission controls changed
the aerosol SOC composition. Moreover, meteorological conditions and other
factors (e.g., atmospheric oxidation state) could also impact the variations
in SOC during different sampling periods, such as the wind shift before and
after the parade and the complex vertical distributions of particulate nitrate
(Zhao et al., 2017; H. Wang et al., 2018; Wu et al., 2019).
Conclusions
The vertical properties of SOA tracers in aerosols were investigated over the
late summer in Beijing. The sum of BSOA tracers were 31.5±16.8ngm-3 (8 m), 36.4±26.1ngm-3
(120 m), and 50.2±27.0ngm-3 (260 m). BSOA
tracers from isoprene were the dominant compound, followed by monoterpenes and
sesquiterpene. The fractions of isoprene SOA tracers showed an
increasing vertical pattern aloft, whereas monoterpene and sesquiterpene SOA tracers
showed opposite variations. These vertical characteristics of BSOA tracers
were influenced by multiple factors, such as their photooxidation processes,
local sources, and regional transport of their precursors. The isoprene
oxidation products were largely influenced by air masses from regional
transport, while monoterpene oxidation products were mainly influenced by
local emission sources. The specific vertical distributions of BSOA tracers
during pollution episodes suggest a significant contribution of regional
transport of aerosols from the southern regions of Beijing. The average
concentrations of the toluene tracer (DHOPA) were 0.90±0.53ngm-3 (8 m), 1.50±1.09ngm-3
(120 m), and 2.03±1.69ngm-3 (260 m). DHOPA
showed an increasing pattern aloft with larger variations during the episodes,
also suggesting the regional transport from the southern regions.
Estimated by the tracer-based method, the sum concentrations of estimated SOC
were 341±150ngCm-3 (8 m), 444±283ngCm-3 (120 m), and 673±385ngCm-3 (260 m), with toluene SOC being the
dominant compound, followed by naphthalene SOC, Iso_SOC, and other SOC. The
increasing SOC aloft suggests a contribution from the regional transport. The
increase in toluene SOC and Iso_SOC fraction with height indicates that
the air masses subject to the regional transport were potentially rich in
toluene- and isoprene-derived SOC. The implementation of joint regional
prevention and control by the government can significantly reduce the amount
of SOC. However, they are likely more efficient in reducing toluene SOC but
not isoprene-derived SOC. Our study demonstrates the variability in SOA within
the urban boundary layer and highlights that vertical profiles of SOA are
critical to improving the simulation of SOA in chemical transport models.
Data availability
The atmospheric particulate matter data
used for analysis are available in the Supplement, and the data
are also available upon request from the corresponding author Pingqing Fu
(fupingqing@tju.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-12949-2021-supplement.
Author contributions
HR, LiaW SY, JZ, LL, and WZ conducted
the laboratory analysis. WH, LibW, SY, LR, MK, QX, SS, XP, ZW, YS, and KK
reviewed and commented on the paper. PF designed the research. HR and PF
wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The vertical meteorological data were
obtained from the Institute of Atmospheric Physics (IAP), the Chinese
Academy of Sciences (CAS). The tower samples were collected with the help of
the staff of IAP. Detailed tables and figures about the data in this
paper are present in the supporting information. The language of this
paper has been edited by International Science Editing
(http://www.internationalscienceediting.com, last access: 28 August 2021).
Financial support
This research has been supported by the National Key Research and Development Program of China (grant no. 2017YFC0212700), the National Natural Science Foundation of China (grant nos. 41625014, 41475117, and 41571130024), and China Postdoctoral Science Foundation (grant no. 390/0401130003).
Review statement
This paper was edited by Barbara Ervens and reviewed by two anonymous referees.
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