Particulate matter (PM) pollution in China is an emerging
environmental issue which policy makers and the public have increasingly paid
attention to. In order to investigate the characteristics, sources, and
chemical processes of PM pollution in Guangzhou, field measurements were
conducted from 20 November 2017 to 5 January 2018, with a time-of-flight
aerosol chemical speciation monitor (ToF-ACSM) and other collocated
instruments. Mass concentrations of non-refractory submicron particulate
matter (NR-PM1) measured by the ToF-ACSM correlated well with
those of PM2.5 or PM1.1 measured by filter-based methods. The
organic mass fraction increased from 45 % to 53 % when the air switched
from non-pollution periods to pollution episodes (EPs), indicating significant
roles of organic aerosols (OAs) during the whole study. Based on the mass
spectra measured by the ToF-ACSM, positive matrix factorization (PMF) with
the multilinear engine (ME-2) algorithm was performed to deconvolve OA into four
factors, including hydrocarbon-like OA (HOA, 12 %), cooking OA (COA,
18 %), semi-volatile oxygenated OA (SVOOA, 30 %), and low-volatility
oxygenated OA (LVOOA, 40 %). Furthermore, we found that SVOOA and nitrate
were significantly contributed from local traffic emissions while sulfate
and LVOOA were mostly attributed to regional pollutants. Comparisons between
this work and other previous studies in China show that secondary organic aerosol (SOA) fraction in
total OA increases spatially across China from the north to the south.
Two distinctly opposite trends for NR-PM1 formation were observed
during non-pollution periods and pollution EPs. The ratio of secondary PM
(SPM = SVOOA + LVOOA + sulfate + nitrate + ammonium) to primary PM
(PPM = HOA + COA + chloride), together with peroxy radicals RO2∗
and ozone, increased with increasing NR-PM1 concentration during
non-pollution periods, while an opposite trend of these three quantities was
observed during pollution EPs. Furthermore, oxidation degrees of both OA and
SOA were investigated using the f44/f43 space and the results show
that at least two OOA factors are needed to cover a large range of
f44 and f43 in Guangzhou. Comparisons between our results and other
laboratory studies imply that volatile organic compounds (VOCs) from traffic
emissions, in particular from diesel combustion and aromatic compounds, are the
most likely SOA precursors in Guangzhou. Peroxy radical RO2∗ was used as a tracer for SOA formed through gas-phase oxidation. For
non-pollution periods, SOA concentration was reasonably correlated with
RO2∗ concentration during both daytime and nighttime, suggesting that
gas-phase oxidation was primarily responsible for SOA formation. However,
there was no correlation between SOA and RO2∗ in pollution EPs,
suggesting a dramatically changed mechanism for SOA formation. This conclusion
can also be supported by different features of SOA in a van Krevelen diagram
between non-pollution periods and pollution EPs. Furthermore, for pollution
EPs, when NR-PM1 mass concentration was divided into six segments, in
each segment except for the lowest one SOA concentration was correlated
moderately with RO2∗ concentration, suggesting that gas-phase oxidation
still plays important roles in SOA formation. The intercepts of the above
linear regressions, which likely correspond to the extent of other
mechanisms (i.e., heterogeneous and multiphase reactions), increase with
increasing NR-PM1 mass concentration. Our results suggest that while
gas-phase oxidation contributes predominantly to SOA formation during
non-pollution periods, other mechanisms such as heterogeneous and multiphase
reactions play more important roles in SOA formation during pollution EPs
than gas-phase oxidation.
Introduction
With rapid development of human civilization, more attention is paid to air
quality by the public, government, and scientists, especially in developing
countries like China. In recent years, particulate matter (PM) pollution has
become one of the most concerning environmental issues because of its
significant effects on both climate change (IPCC, 2013) and human health
(Pope and Dockery, 2006). Atmospheric aerosols exert radiative forcing
directly through scattering or absorbing solar radiation or indirectly
through cloud formation. In addition, previous studies in the last decade
have shown that respiratory and cardiovascular diseases are highly related
to fine particles, revealing significant deleterious effects of ambient
aerosols on human health (Kreyling et al., 2006). Thus, knowledge of
chemical composition, formation mechanisms, and potential sources of fine
particles is essential for both the academic community and the public, since it
is still currently very limited even through decades of investigation. In
recent decades, most studies have focused on PM2.5 and made significant
progresses while less attention was paid to submicron particles (i.e.,
PM1). For instance, although the China National Ambient Air Quality
Standard (CNAAQS) for PM2.5 was established in 2012, the corresponding
national standard for submicron particles such as PM1 has not yet been
set up. In fact, it has been shown that PM1 particles may cause much
more damage to human health than PM2.5 due to their smaller sizes which
give them more easy access to human bodies (Ibald-Mulli et al., 2002;
Kreyling et al., 2006). Therefore, more extensive and in-depth studies
should be conducted for submicron particles besides PM2.5 to obtain a
comprehensive understanding on health and climate impacts of fine particles.
Field measurements of aerosol chemical composition mainly employ
filter-based offline and online mass spectrometric technologies. Although
traditional filter-based methods which are still widely used contribute
substantially to understanding of bulk aerosol chemical composition, its
obvious shortcomings, which include low time resolution from hours to days
and evaporative loss, limit the capacity of this technology in aerosol
measurements. In comparison, online mass spectrometric methods have higher
time resolutions varying from seconds to hours and are therefore proven to be an
efficient way to measure aerosol mass concentration and chemical
composition (Aiken et al., 2009; Zhang et al., 2011; Sun et al., 2013, 2014;
Crippa et al., 2013; Lee et al., 2015; Hu et al., 2017).
For example, the aerosol mass spectrometer (AMS) is one such instrument that is
widely employed in aerosol chemical composition measurements for its
reliable data quality and relatively high mass resolution. However, the full
version of an AMS tends to be costly and time-consuming in terms of its
operation and maintenance. As a simplified version of an AMS, the aerosol chemical
speciation monitor (ACSM) has been widely adopted in recent years among
research institutions and environmental monitoring stations for its
relatively simple operation, robustness, low cost, and sufficient time
resolution for field observations spanning months or longer (Allan et al.,
2010; Ng et al., 2011b; Sun et al., 2013, 2014; Y. Sun et al., 2016; Fröhlich et al., 2013). Certainly, this simplified design will
inevitably cause a few disadvantages for the ACSM. Compared with all types of
AMS, the ACSM gives up the ability of measuring particle size distribution,
which makes users lose a robust tool to characterize ambient particulate
matter and identify potential sources (Ge et al., 2012;
Lee et al., 2017). Besides, limited by relatively poorer resolution, mass
spectra collected by the ACSM cannot execute high-resolution peak fitting
(Timonen et al., 2016). This incapability prevents users from obtaining some
further information such as elemental ratio in particulate matter, which is
essential to our knowledge about climate effects or toxicity of aerosols
(Aiken et al., 2007; Chhabra et al., 2011; Canagaratna et al., 2015). The
simplification of fragmentation table for the ACSM, based on ambient AMS data,
also likely leads to some small deviations and makes this instrument
unsuitable for laboratory studies requiring high precision (Jimenez et al.,
2003; Allan et al., 2004; De Haan et al., 2009).
Numerous studies were conducted to investigate chemical composition, sources,
and secondary processes of PM through an AMS or ACSM. It has been
shown that organics generally account for a large proportion of PM (Zhang et al., 2007; Aiken et al., 2009; Allan et al., 2010; DeCarlo et
al., 2010; Sun et al., 2013; Lee et al., 2015; Bressi et al., 2016; H. Li et
al., 2017). In previous studies, mass spectral signals were input into the
positive matrix factorization (PMF) to explore source information of organic
aerosols (Zhang et al., 2011). Generally, OA can be deconvolved into primary
organic aerosols (POAs), which can be further classified according to
different markers of primary emissions (e.g., HOA, COA, and biomass burning aerosol – BBOA), and
oxygenated organic aerosols (OOA) which can be further resolved based on
oxidation degree (e.g., SVOOA and LVOOA). However, detailed source features
are diverse at different regions. For example, Aiken et al. (2009)
identified industry-induced local nitrogen-containing OA (LOA), which was
barely reported in other cities such as Beijing (Sun et al., 2018), London
(Allan et al., 2010), or Paris (Crippa et al., 2013). Similarly, biomass
burning OA (BBOA) which was clearly identified at some sites such as those
in Hebei (Huang et al., 2019), Mexico (Aiken et al., 2009), or Fresno (Ge et
al., 2012) was missing in other locations (during the same seasons) such as
Hong Kong (C. Sun et al., 2016) or Beijing (Sun et al., 2013). Although OA
sources have obvious spatial distinction, Jimenez et al. (2009) found a
common trend that the fraction of OOA (especially LVOOA) increases from
urban to rural areas.
Physical and chemical characteristics and secondary processes of PM were
investigated in previous studies (Liggio and Li, 2006; Shilling et al.,
2009; Marais et al., 2016). Photochemistry which highly depends on daytime
solar radiation is commonly believed to play a dominant role in the formation of
secondary PM (SPM = ammonium + nitrate + sulfate + OOA). However,
recent studies showed that the contribution from aqueous reactions or
heterogeneous reactions cannot be ignored. For instance, it has been found
that the heterogeneous hydrolysis of N2O5 on the surface of
deliquescent aerosols is a significant pathway for nitrate formation during
nighttime (Wang et al., 2018; Wen et al., 2018). Sun et al. (2013) proposed
that the mass concentration of sulfate substantially increased through fog
processes. In addition, numerous studies have suggested that the amount of SOA
from reactive uptake of water-soluble VOCs is comparable to that from gas-phase oxidation (Ervens et al., 2011; McNeill, 2015; Herrmann et al., 2015;
Marais et al., 2016).
Meanwhile, secondary organic aerosols, the most significant OA composition
in China, have not been sufficiently investigated, especially in field
measurements. In general, the odd oxygen (Ox=O3+NO2) was proven to be a robust indicator of photochemical intensity and
was therefore adopted in previous measurements to discuss SOA formation
(Herndon et al., 2008; Zhou et al., 2014). However, these studies employing
Ox only focused on daytime SOA formation while the nocturnal SOA
formation was generally ignored due to the limitation of Ox. Meanwhile,
interference from directly emitted NO2 also leads to some
uncertainties.
According to the traditional theory, oxidation of atmospheric VOCs, a
significant path for SOA growth, is initiated by important oxidants (e.g.,
OH, O3, or NO3) to form alkyl radicals (R⚫) which are
subsequently oxidized to form peroxy organic radicals (RO2⚫) and alkoxy radicals (RO⚫) (Kroll and Seinfeld, 2008; Ziemann and Atkinson, 2012). The simplest RO2∗ (RO2∗=ΣRO2⚫+HO2), HO2, is formed in the atmosphere via three pathways:
(1) from reactions of OH radicals with ozone or CO; (2) from oxidation of
VOCs; (3) from photolysis of formaldehyde (Levy, 1971; Ziemann and Atkinson, 2012; Sheehy et al., 2010; Stone et al., 2012; Griffith et al., 2013;
Wang et al., 2014). During daytime, RO2∗ radicals are mainly
photochemical products since their formation depends highly on solar
radiation or OH radical (Sheehy et al., 2010; Stone et al., 2012; Griffith
et al., 2013; Wang et al., 2014). During nighttime, however,
oxidation of VOCs initiated by ozone or nitrate radicals dominates formation
of RO2∗ radicals, especially in urban areas, which has been proven in
Volkamer et al. (2010) and Stone et al. (2014). Thus RO2∗
can serve as a tracer for photochemically induced SOA formation during daytime
and SOA formation induced by nocturnal gas-phase oxidation of VOCs during
nighttime.
Results from previous studies suggested that primary emissions, secondary
chemical processes, meteorological conditions, and regional transport are
possible major factors that determine local PM concentration but
significantly vary with seasons and locations (Sun et al., 2013; Y. Sun et al., 2016; Lee et al.,
2015; Bressi et al., 2016; Gani et al., 2019). Therefore,
it is essential to conduct continuous field measurements at various
locations to investigate the PM formation mechanisms, as well as the
temporal and spatial evolution. Guangzhou, a highly developed city in the
Pearl River Delta (PRD) region, is considered to be one of the most densely
populated cities in China. However, previous studies on local PM
characteristics in Guangzhou were mainly conducted by filter-based methods
with low time resolutions (Tan et al., 2009; Zhang et al., 2010; Tao et al.,
2012), and therefore knowledge of detailed PM characteristics is still
lacking. It is hence urged to perform field measurements with high time
resolutions in the city.
In this study, we employed a time-of-flight aerosol chemical speciation
monitor (ToF-ACSM) to measure chemical composition and mass concentrations
of submicron particles at an urban site in Guangzhou which is located at
the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences. The
following sections present measurement techniques for peroxy radicals,
followed by the methodology for data analyses. In the Results and discussion
section, temporal variations in chemical composition and mass concentration
for NR-PM1 are illustrated, followed by the source apportionment for OA
and the diurnal profiles for NR-PM1. Possible mechanisms for wintertime
SOA formation were explored through introducing RO2∗ as a proxy for
the intensity of VOC gas-phase oxidation during both daytime and nighttime.
ExperimentalMeasurement site and techniques
Chemical compositions of NR-PM1 consisting of Cl-,
SO42-, NO3-, NH4+, and organics were measured
from 20 November 2017 to 5 January 2018 by a ToF-ACSM at an urban air
quality monitoring site locating at the Guangzhou Institute of Geochemistry,
Chinese Academy of Sciences (GIG). Detailed descriptions of ToF-ACSM can be
found elsewhere (Fröhlich et al., 2013) and the methodology for data
analysis is presented in the next section. The site is surrounded by three
major traffic roads including Guangyuan road, Huanan road, and Keyun road.
Several university campuses are located to the north and west of the site while
commercial buildings, restaurants, and residential areas are next to its
east and south. In addition to aerosol measurements by the ToF-ACSM, ambient
gas species such as NOx and O3 were measured at the same site by
various gas analyzers (Thermo Scientific, USA) while filter samples were
collected by an Anderson nine-stage sampler. Meteorological data (wind speed
and direction, relative humidity, temperature, and pressure) and PM2.5
concentrations were measured about 2 and 4 km away from the sample site
by the South China Institute of Environmental Science and Guangzhou
Environmental Monitoring Center, respectively. The concentrations of the
total peroxy radicals were measured with a dual-channel PERCA (peroxy
radical chemical amplification) instrument (Yang et al., 2018,
2019). In this instrument, ambient mixing ratios of RO2∗ radicals were
converted to a larger amount of NO2 by reacting with NO and CO. The
amplified NO2 concentrations were then measured with a portable
broadband cavity-enhanced spectrometer (BBCES) with a precision of 40 pptv
(1σ, with 21 s data acquisition time) (Fang, et al., 2017). The
total uncertainty of the PERCA instrument was about 10 % with a precision
of about 0.4 pptv (1σ, 21 s).
ACSM data analysis
The final mass concentrations and mass spectra were processed from the raw
ToF-ACSM data by a standard ACSM data analysis software (Tofware, version 2.5.13) based on Igor Pro (version 6.37), with the widely applied procedures
described in Ng et al. (2011b) and Sun et al. (2012). Based on the on-site
calibrations, a relative ionization efficiency (RIE) value of 1.2 and 3.3
was obtained for sulfate and ammonium, respectively. RIE values of 1.1, 1.3,
and 1.4 were, respectively, adopted for nitrate, chloride, and organics
according to literature (Takegawa et al., 2005; Canagaratna et al., 2007).
Collection efficiency (CE), compensating for losses of the particles during
their collection, is considered to be another extremely important parameter
for quantification of the ACSM data. This quantity varies with acidity,
chemical composition, and water content of the particles (Matthew et al.,
2008). Here we consider the effects of acidity and water content to be
negligible based on the fact that the relative humidity (RH) in the sample line was kept below
30 % through a Nafion dryer and the aerosols were approximately
neutralized in Guangzhou (NH4+/NH4+predict=0.87). Thus, only chemical composition was considered to affect CE in this
work and we adopted a composition-dependent CE formulated by Middlebrook et
al. (2012) (i.e., CE=max (0.45, 0.0833+0.9167×ANMF),
where ANMF is the mass fraction of ammonium nitrate in NR-PM1) instead
of a widely used empirical value of 0.5. The results showed that only about
1 % of samples (78 of 6623) had CE values larger than 0.45 (others are
0.45), with the largest value being 0.578. Hence the influence induced by
fluctuation of the CE values is negligible and we chose a CE value of 0.45
for the ACSM measurements in this study.
Results and discussionMass concentrations and chemical composition
Figure 1 shows the time series of meteorological conditions (relative
humidity, temperature, pressure, wind speed, and wind direction), NR-PM1 and
PM2.5 mass concentrations, and NR-PM1 composition
(SO42-, NO3-, NH4+, Cl-, and organics)
from 20 November 2017 to 5 January 2018. Overall, the 10 min averaged
mass concentration of NR-PM1 ranged from 2.4 to 130 µg m-3.
The averaged concentration during the measurement period was 35.3±22.3µg m-3, among which about half (17.5 µg m-3) was
organics, followed by sulfate (7.0 µg m-3), nitrate (6.0 µg m-3), ammonium (4.6 µg m-3), and chloride (0.5 µg m-3). The NR-PM1 concentration (from the ToF-ACSM) was correlated
well with concentrations of both PM2.5 (Pearson correlation coefficient
(Rp) =0.83, from BAM-1020) and PM1.1 (Rp=0.86, from the
Anderson nine-stage sampler). Comparisons between ToF-ACSM and other
instruments are detailed in Fig. S1 in the Supplement. Overall, high NR-PM1 mass
concentration was observed after 22 December 2017 when the air was more
stagnant according to the wind speed (Fig. 1).
Temporal variation in (a) relative humidity, temperature, and pressure; (b) wind speed and direction (color-contoured); (c) total NR-PM1 and PM2.5; and (d) speciated concentrations of NR-PM1. EP1–EP5 are classified as pollution episodes
(EP1: 6–8 December; EP2: 9–13 December; EP3:
21–23 December; EP4: 25–30 December; EP5: 31 December–5 January); see text for details.
Moderate to severe pollution events were observed during the measurement
period, and we classified five pollution episodes (EP1–EP5)
alongside other periods defined as non-pollution based upon the
NR-PM1 mass concentrations in order to better understand the intrinsic
mechanisms in the PM evolution. Each pollution episode was defined as a
period that the NR-PM1 mass concentration continually increased from
less than 20 µg m-3 (10th percentile) to greater than 75 µg m-3 (90th percentile) and then fell below 20 µg m-3
again (Fig. 1d). For the entire study, organics and sulfate (Fig. 2)
dominated NR-PM1 mass concentration, consistent with previous studies
conducted in the PRD region during autumn and winter (He et al., 2011; Huang
et al., 2011; Qin et al., 2017). Fractions of 20 % and 49 %, respectively,
for sulfate and organics were similar to those in Panyu (25 % and 50 %
for sulfate and organics, respectively) and Shenzhen (28 % and 46 % for
sulfate and organics, respectively) but quite different from those in Kaiping
(both 36 %). This comparison reflects that the sulfate fraction increased
and the organic fraction simultaneously decreased from urban to rural areas
(Table S1), which could be likely attributed to different sources and
chemical aging processes between urban and rural areas in the PRD region.
The obvious facts are that heavy industry sections, such as power plants and
oil refineries which emit a tremendous amount of sulfur dioxide, the primary
precursor of sulfate, are mainly located in non-urban areas. Similar
urban-to-rural evolution of PM chemical composition was reported in previous
studies (Jimenez et al., 2009; Zhang et al., 2011; Y. J. Li et al., 2017),
providing further rationale for performing field measurements under
different source environments to investigate the chemical evolution of PM.
The average fraction of each chemical composition of (a) NR-PM1 (OA, nitrate, sulfate, ammonium, and chloride) and (b) organic
aerosols (LVOOA, SVOOA, HOA, and COA).
Summary of published NR-PM1 measurements in China.
LocationTimeAreaNR-PM1OASOA/OARef.(µg m-3)(%)(%)GuangzhouWinter, 2017Pearl River Delta region35.34970This studyPanyuWinter, 2014Pearl River Delta region55.450.561Qin et al. (2017)ShenzhenWinter, 2009Pearl River Delta region44.546.246.6He et al. (2011)KaipingWinter, 2008Pearl River Delta region33.136.375.4Huang et al. (2011)NanjingWinter, 2015Yangtze River Delta region32.53666Zhang et al. (2017)NanjingSummer, 2013Yangtze River Delta region36.84272Zhang et al. (2015)ShanghaiSummer, 2010Yangtze River Delta region273178Huang et al. (2012)Beijing∗WinterBeijing–Tianjin–Hebei region705243∗BeijingSummer, 2011Beijing–Tianjin–Hebei region803265Hu et al. (2016a)ShijiazhuangWinter, 2014Beijing–Tianjin–Hebei region1785022Huang et al. (2019)HandanWinter, 2015Beijing–Tianjin–Hebei region1784717H. Li et al. (2017)LanzhouWinter, 2014Northwest of China57.35537Xu et al. (2016)LanzhouSummer, 2012Northwest of China245359Xu et al. (2014)Hong KongWinterSouth of China20.745.569C. Sun et al. (2016)Hong KongSummer, 2011South of China15.62682Li et al. (2015)ZiyangWinter, 2012Southwest of China604071.2Hu et al. (2016b)
∗ The three quantities for winter Beijing are the averages of six studies (Y. Sun et al., 2013, 2014, 2016; Jiang et al., 2015; Wang et
al., 2015; Hu et al., 2016a).
Figure 3 shows a comparison of NR-PM1 characteristics among our work in
the PRD region and previous wintertime studies conducted in several other
Chinese megacities (Beijing, Nanjing, Shijiazhuang, Lanzhou, Hong Kong)
beyond the PRD region. Compared to other locations, the averaged mass
concentration of NR-PM1 in Guangzhou was much lower (35.5 µg m-3), indicating that the air of the PRD region was relatively clean in
terms of fine PM. The concentrations of NR-PM1 in the abovementioned
cities were all composed of a large fraction of OA, with a substantial
variation (37 %–58 %), indicating the significant
contribution of OA to fine particle mass loading over China. In addition, a
remarkable distinction on the fraction of secondary OA (SOA) in OA between
southern and northern China was found; that is, SOA dominated OA in southern
China (A SOA-to-OA ratio of 0.66 in Nanjing, 0.58 in Hong Kong, and 0.7 in
Guangzhou), compared to a lower ratio of SOA to OA in northern China (0.43
in Beijing, 0.37 in Lanzhou, and 0.22 in Shijiazhuang). The increasing ratio
of SOA to OA from the north to the south is probably due to more favorable
meteorological conditions such as solar radiation and temperature for
secondary chemical processes in southern China and more significant
contribution of coal combustion in northern China during wintertime (Sun et
al., 2013, 2014, 2018). Furthermore, Table 1 shows
that the SOA fraction is generally enhanced from winter to summer for a
specific site in China. In addition, Table 1 also revealed that SOA
formation is significantly influenced under different underlying surfaces
(urban, suburban, and country).
Comparison of submicron aerosols among several megacities in China
during winter including Guangzhou (this study) and five other cities. Red
bars represent the fraction of OA to NR-PM1 and blue bars represent the
fraction of SOA to OA. Green diamonds represent the average concentration of
NR-PM1 in each city.
OA apportionment
Positive matrix factorization with ME-2 engine algorithm was employed to
deconvolve OA into four factors, including two primary OA components (HOA
and COA) and two OOA components (SVOOA and LVOOA) which are usually treated
as SOA. The mass spectra and corresponding time series are depicted in Fig. 4.
The mass spectra and time series of the four OA components (HOA,
COA, SVOOA, and LVOOA).
Hydrocarbon-like OA (HOA)
A widely referred-to standard mass spectrum of HOA (Sun et al., 2013; C. Sun et al., 2016; Huang et al., 2019) derived by Ng et al. (2011c) was introduced as
an external constraint in this work, with an a value of 0.3 being chosen to
derive the final solution. The detailed selection of the α-value can be found in
the Supplement (method section). The final mass spectrum of HOA was
identified by the ion series representing CnH2n-1+ (m/z=27, 41, 55,
69, 83, 97, typical tracers of cycloalkanes or unsaturated hydrocarbon) and
CnH2n+1+ (m/z=29, 43, 57, 71, 85, 99, typical tracers of
alkanes). As shown in Fig. 4b, the concentration of HOA was well correlated
with that of NOx during the measurement period when both concentrations
were available, indicating considerable influences of traffic emissions on
the HOA mass loading. Diurnal variations in HOA concentration for both
pollution EPs and non-pollution periods are depicted in Fig. 5. The
variations for mass concentrations of both HOA and NOx were pronounced
for pollution EPs (much higher concentrations at night), which can be
attributed to human activities (e.g., traffic emission during rush hour).
The averaged HOA concentration for pollution EPs ranged diurnally from 2.8
to 5.2 µg m-3 with the maximum value (about 16 % of total OA)
found around midnight and the minimum value (12 % of total OA) at noon
(13:00 LT, UTC+8). Meanwhile, the HOA concentration in pollution EPs increased rapidly
from 3.1 µg m-3 at ∼17:00 LT to 5.2 µg m-3
at around midnight and remained high afterward. Nocturnal rush hour
corresponded to HOA concentration peak around 20:00 LT but could not account
for the continuously high HOA concentration afterward. These consistently high
concentrations were likely attributed to emissions of heavy-duty vehicles
(HDVs) which are only allowed to drive through the city after 22:00 until
06:00 LT the next day according to the traffic regulation enforced in Guangzhou.
The pronounced impact of HDVs on the concentrations of both HOA and NOx
agrees with the previous study conducted in Panyu district, Guangzhou (Qin
et al., 2017). Other possible reasons for high nocturnal HOA mass loading
included lower boundary layer and a frequent thermal inversion layer formed at
night during winter. Besides, effects of emissions from heavy-duty vehicles
and the rapidly rising boundary layer after 07:00 LT would also account for the
insignificant peak of HOA during the morning rush hour. In comparison, the HOA
concentration showed almost no variations during non-pollution periods (even
during rush hour), which likely arose from its extremely low value
(<1µg m-3 which was close to an estimated detection
limit of 0.7 µg m-3 for OA with the ToF-ACSM).
Diurnal profiles of NR-PM1 species, trace gases, radicals,
and meteorological conditions. Dashed lines and solid lines represent the
averaged values during non-pollution periods and pollution EPs.
Cooking OA (COA)
Through factorization using PMF or the ME-2 engine, COA was frequently
deconvolved as a common OA component in urban areas (Sun et al., 2013; Y. Sun et al., 2016; Lee
et al., 2015; Qin et al., 2017). The mass spectrum of COA
deconvolved in this work was very similar to that of HOA except a higher
m/z 55-to-57 ratio of 2.1 which is very close to the range of 2.2–2.8 reported
from real cooking source measurements (Mohr et al., 2012). The concentration
of COA was correlated reasonably well (Rp=0.86) with m/z 55. As
shown in Fig. 5, the diurnal profile of COA for non-pollution periods showed
a typical bimodal pattern with a noon peak concentration of 1.6 µg m-3 (16 % of OA) at 13:00 LT during lunch time and a night peak
concentration of 4.3 µg m-3 (33 % of OA) at 19:00 LT during dinnertime. A similar bimodal pattern of COA diurnal profile for pollution EPs was
found, with a much higher ratio of night concentration peak (12.2 µg m-3) to noon concentration peak (2.5 µg m-3) than that for
non-pollution periods (5 vs. 2.7). In addition, compared to non-pollution
periods, the remarkably enhanced night concentration peak of COA for
pollution EPs was delayed from 19:00 to 21:00 LT. Here the much higher peak concentration at night than at noon for
COA are attributed to three causes: (1) more intensive cooking activities at night than at noon in the
Chinese cooking routine; (2) more adverse diffusion conditions caused by a
lower boundary layer and a frequent thermal inversion layer at night; (3) the lower temperature at night which facilitates semi-volatile compounds
from cooking emissions to partition into particles. Furthermore, pollution
EPs with remarkably enhanced and delayed night COA concentration peaks
corresponded to several important holidays such as the winter solstice festival
(EP3), Christmas (EP4), and New Year's Day (EP5), implying that
festival-induced emissions have significant impacts on local air pollution.
Oxygenated OA (OOA)
OOA, the generally accepted surrogate of SOA (Jimenez et al., 2009),
characterized by a high peak at m/z 44 (CO2+) in the mass spectra, was
almost exclusively contributed by electron ionization of ketones,
aldehydes, esters, and carboxylic acids. Furthermore, OOA could be further
deconvolved into two subcomponents, SVOOA and LVOOA, based on different
degrees of oxidation. SVOOA was distinguished from LVOOA by a higher ratio
of f43 to f44 (0.7 for SVOOA and 0.18 for LVOOA).
Correlations between OOA and SIA, along with correlation between
nitrate and sulfate, and the wind speed during pollution EPs and
non-pollution periods. (a) LVOOA vs. NO3-; (b) LVOOA vs. SO42-; (c)NO3- vs. SO42-; (d) SVOOA vs. NO3-; (e) SVOOA vs. SO42-. Blue circles represent
pollution EPs while gray crosses represent non-pollution periods. (f) Box
plot of wind speed in non-pollution periods and pollution EPs. Whiskers are the
10th and 90th percentiles; the top, median, and bottom lines of the box represent
75th, 50th, and 25th percentiles, respectively. Red dots are the averaged wind
speed for each scenario.
A high similarity between SVOOA and secondary inorganic aerosols (nitrate
and sulfate) in terms of time series was observed for the entire study (Fig. 4). It was found that
Pearson correlation coefficient (Rp) between SVOOA and nitrate (0.76)
was higher than that between SVOOA and sulfate (0.61), consistent with the
trend reported by previous studies which attributed it to analogous
semi-volatility and gas–particle partitioning between SVOOA and nitrate
(Aiken et al., 2009; DeCarlo et al., 2010; Zhang et al., 2011).
Interestingly, we found that the correlation between SVOOA and nitrate was
much better for pollution EPs than for non-pollution periods (Rp=0.64 vs. 0.34, Fig. 6d), which cannot be simply attributed to similar
volatility and photochemistry. Meanwhile, concentration of neither nitrate
nor SVOOA was obviously dependent on temperature in this study. Furthermore,
we found that the air during all pollution EPs was much more stagnant,
supported by much lower wind speed, than that during non-pollution periods
(Fig. 6f), implying that most nitrate and SVOOA were locally formed during
pollution EPs, while they likely originated from regional transport during
non-pollution periods. Since traffic emissions contribute largely to NOx
and VOCs, a higher correlation between SVOOA and nitrate during pollution
EPs than during non-pollution periods likely stemmed from shared sources of
precursors for the two aerosol components and implies that traffic emissions
could significantly influence SVOOA and nitrate mass loading under stagnant
meteorological conditions. These are further confirmed by the fact that in
general much higher correlations between SVOOA (or nitrate) and the traffic
trace species (CO, HOA, and NOx) were found during pollution EPs than
during non-pollution period (Table 2). As mentioned above, non-pollution
periods were always associated with high wind speeds which would induce strong
air advection and make local emissions hardly accumulate, leading to a large
fraction of PM1 contributed from regional transport. These regionally
transported nitrate and SVOOA were usually less correlated with traffic
tracers because of more complicated sources for SVOOA and nitrate outside
urban Guangzhou and different influences of secondary processes on these
species through transport. A previous laboratory study suggested that
photooxidation of traffic-related emissions leads to substantial amounts of
SOA (Weitkamp et al., 2007) which in our case are likely locally formed
SVOOA. The diurnal profiles of SVOOA for non-pollution periods and pollution
EPs showed explicit distinctions. During pollution EPs, the concentration of
SVOOA showed a pronounced diurnal variation which in general formed a flat
trough around noon and reached the maximum value around midnight. In
comparison, the diurnal profile of SVOOA concentration for non-pollution periods,
however, was much flatter, with two weak peaks likely reflecting
photochemical oxidation and local anthropogenic emissions.
These two different diurnal patterns of SVOOA concentration suggest
accumulation of local anthropogenic emissions under stagnant air conditions
during pollution EPs could largely influence mass loading of SVOOA.
Pearson correlations between traffic tracers (CO, HOA, and NOx) and
SVOOA (nitrate and sulfate) during different periods.
∗ Pearson correlations involving HOA for non-pollution periods may
have non-negligible uncertainty since HOA concentration in non-pollution
periods is near the method detection limit (MDL) of the ToF-ACSM for OA.
In contrast to SVOOA, the concentration of LVOOA showed a better correlation
with that of sulfate (Rp=0.85) than with nitrate (Rp=0.77), which is likely attributed to similarly low volatility of both LVOOA
and sulfate. Diurnal profile of LVOOA concentration showed roughly no
variation with slightly elevated values during the afternoon for both pollution
EPs and non-pollution periods, demonstrating that LVOOA was not significantly
influenced by local emissions but by aging processes, in particular
photochemistry. Prolonged aging of SOA leads to the high oxidation degree of
LVOOA, which should be considered as a kind of regional pollutant or
background aerosol (Li et al., 2013, 2015; C. Sun et al., 2016; Qin
et al., 2017), while freshly formed SOA from local emissions remains a lower
oxidation state which more readily becomes the SVOOA component as is
discussed in this section.
Diurnal profiles
In the previous section, we have discussed diurnal variations in the four
deconvolved OA components, and here important variations in other species and
meteorological conditions are detailed to better understand evolution of
NR-PM1 species and pollution characteristics in Guangzhou (Fig. 5).
Similar to SVOOA, remarkably different diurnal profiles of nitrate
concentration between pollution EPs and non-pollution periods were observed.
For non-pollution periods, diurnal nitrate concentration varied little with a
slight maximum occurring at 13:00 LT, implying the influence of photochemistry
on nitrate formation. For pollution EPs, however, nitrate concentration
showed a much more pronounced diurnal variation; that is, nitrate mass
loading stayed low around noon to afternoon and then increased steadily during
the night until it reached the maximum at 09:00 LT in the next day. Note that
nitrate concentration increased from dusk to early morning with similarly
increasing RH and oppositely decreasing temperature. Meanwhile, the
nocturnal NOx concentration was 67 % more than that during the daytime
(Table 3). Thus, higher RH, more abundant NOx, and lower temperature
during nighttime facilitate aqueous reactions and gas-to-particle
partitioning between gas-phase nitric acid and ammonium nitrate, which
played important roles in nocturnal nitrate formation during pollution EPs,
consistent with the previous study (Xue et al., 2014). In addition, the
morning nitrate peak at 09:00 LT can be attributed to a synergy of high NOx
emissions during rush hour and the most favorable conditions for ammonium
nitrate formation during 08:00–09:00 LT (i.e., low temperature,
high RH). Our results strongly demonstrate the importance of local anthropogenic
emissions and aqueous reactions in nitrate accumulation under stagnant air
conditions during pollution EPs.
Overview of meteorological conditions, trace gases, peroxy radical,
and NR-PM1 components during day and night.
Non-pollution Pollution Entire study DayNightDayNightDayNightGas & radical (ppb) O320.310.427.57.222.69.7NOx28.634.243.172.731.441.5RO2∗0.150.120.170.110.160.11Meteorological condition T (∘C)16.615.419.117.617.516.2RH (%)48.352.941.045.645.650.1WS (m s-1)3.13.21.61.42.52.5NR-PM1 (µg m-3) Org9.010.023.832.015.019.0SO42-4.64.99.910.26.87.1NO3-3.53.29.69.76.05.9NH4+3.13.16.56.64.54.5Cl-0.20.30.60.90.40.5HOA0.70.83.44.71.82.4COA1.32.02.07.21.64.1SVOOA2.53.07.39.64.45.7LVOOA4.54.310.710.07.16.6
The diurnal concentration of sulfate showed very slight variation for both
pollution EPs and non-pollution periods, with the averaged daytime
concentration being almost the same as that at night. This indicates that
the sulfate concentration was always less influenced by local anthropogenic
emissions due to the fact that power plants, the most important source of
sulfur dioxide (SO2), are rarely located in urban Guangzhou (Bian et
al., 2019). Thus, the sulfate concentration in this study should be largely
determined by regional transport during both pollution EPs and non-pollution
periods, which can also be supported by similar diurnal profiles of SO2
(Fig. S2). Interestingly, the diurnal variations in O3 and RO2∗
(ΣRO2•+HO2) for pollution EPs and
non-pollution periods showed a distinct daytime-to-nighttime pattern. The
daytime concentrations of the two species during pollution EPs were higher
than those during non-pollution periods while the nighttime concentrations
during pollution EPs were lower than those during non-pollution periods. This
variation pattern differed from all other NR-PM1 species which always
showed higher concentrations during pollution EPs than those during the
non-pollution period within a day. The lower nighttime concentrations of
O3 and RO2∗ during pollution EPs were probably attributed to the
enhanced consumption of O3 and RO2∗ due to elevated NOx and
VOCs concentrations at night. Our results suggest that O3 and RO2∗
were important nocturnal oxidants during pollution events.
Chemical evolution
Figure 7 depicts the dependence of mass concentrations and fractions of
NR-PM1 components on NR-PM1 mass loading. The mass concentrations
of all the NR-PM1 components increased almost linearly with increase
in the NR-PM1 mass concentration. However, various trends were detected
for the fractions of different NR-PM1 components. For example, organics
were the dominant component of NR-PM1 with an increasing fraction from
44 % to 57 % as the NR-PM1 mass concentration increased up to
>90µg m-3 (Fig. 7b). The fractions of HOA and COA,
considered primary OA, varied in a similar way; that is, both fractions
increased up to 11 % for HOA and 15 % for COA as NR-PM1 mass
concentration increased from ∼35 to
>90µg m-3. The variations in the fractions of SOA
species and secondary inorganic aerosol (SIA) species with NR-PM1 mass loading were much more
complicated. To get an overall insight into chemical evolution of the
aerosols, we compare dependences of the ratio of secondary particulate
matter (SPM = SVOOA + LVOOA + sulfate + nitrate + ammonium) to
primary particulate matter (PPM = HOA + COA + chloride) and the
concentration of RO2∗ and O3 on NR-PM1 mass concentration
(Fig. 8). For the whole period, all three quantities followed consistent
and clear trends that they all initially increased with increase in
NR-PM1 mass concentration until all reached peak values at a
NR-PM1 concentration of ∼35µg m-3, and
subsequently decreased with increase in the NR-PM1 mass loading.
Interestingly, the trends for the three quantities during non-pollution
periods were consistent with the initial monotonic increase when NR-PM1
mass concentration was below 35 µg m-3 (Fig. 8b), a concentration
value that was rarely exceeded during this period. Meanwhile only if pollution
EPs were considered did the trends monotonically decrease with NR-PM1
mass concentration up to ∼95µg m-3 (Fig. 8c).
During non-pollution periods when strong advection induced by high wind speed
facilitated dilution and diffusion of local primary pollutants, the SPM/PPM
ratio increased with increasing concentrations of photochemical products
(i.e., O3 and RO2∗) and NR-PM1, strongly suggesting that
secondary processes, especially photochemistry, were the main drivers for
NR-PM1 accumulation during this period. Under this circumstance,
interestingly, the overall increase in SPM fraction (or SPM/PPM) with
increase in NR-PM1 mass concentration was consistent with increasing
mass fractions of LVOOA and nitrate, yet opposite from decrease in SVOOA
fraction (Figs. 7b and S3). The decrease in SVOOA fraction with increase
in NR-PM1 mass concentration corresponded to increase in LVOOA
fraction, implying progressive conversion of SVOOA to LVOOA during the
aerosol aging processes. During pollution EPs when stagnant air conditions
supported by low wind speeds facilitated accumulation of local primary
pollutants, the SPM/PPM ratio together with concentration of O3 and
RO2∗ were observed to drop with increasing mass concentration of
NR-PM1, indicating that production of NR-PM1 was more driven by
primary emissions rather than secondary processes under this circumstance.
Thus, our results indicate totally different intrinsic mechanisms
responsible for NR-PM1 accumulation between pollution EPs and
non-pollution periods.
Dependences of (a) mass concentration and (b) mass fraction of NR-PM1 components on NR-PM1 mass loading. The data are plotted in an interval of 10 µg m-3 of NR-PM1. The error bars are standard deviations of each NR-PM1 species.
Dependences of SPM/PPM ratio, concentrations of the atmospheric oxidants (O3 and RO2∗) on NR-PM1 mass loading for (a) entire study, (b) non-pollution period, and (c) pollution EPs. The binned data are
also presented as solid circles with an interval of 10 µg m-3
NR-PM1. The error bars are standard deviations.
Oxidation degree and peroxy radical tracerOxidation degree
Oxidation degree represents the extent to which aerosols (OA and SOA) are
oxidized. Figure 9 depicts locations of both OA and SOA from this study in
f44/f43 space. The triangle area (enclosed by two black dashed lines
and the f43 axis) for ambient OOA (SOA) was defined by Ng et al. (2010),
and results from several laboratory studies with or without aerosol seeds
(Bahreini et al., 2005; Liggio et al., 2005; Liggio and Li, 2006; Weitkamp
et al., 2007) are also shown for comparison. The f44 and f43 for
each SOA point were calculated through an algorithm reported in Canonaco et
al. (2015). Most of OA samples in this study were located inside the
triangle area, indicating that OA is mainly composed of SOA, which was
consistent with the finding from ME-2 analyses; that is, SOA contributes
about 70 % to total OA. Interestingly, SOA points in f44/f43 space
showed a strong linear trend that f44 increased with decreasing
f43, leading to a large span of f44 and f43 in SOA which then
requires more than one SOA factor to explain such a large variation. This
result quite differs from some other studies in northern China where usually
only one SOA factor was deconvolved (Sun et al., 2012, 2013;
Huang et al., 2019). The substantial differences in SOA factorization
between Guangzhou and other cities in northern China can be likely
attributed to a much higher oxidative atmosphere in Guangzhou. In fact, a
previous study has shown that the second highest OH concentration was
observed in the PRD region around the world (Rohrer et al., 2014). In
addition, the majority of SOA points were well distributed around the
connection line of SVOOA and LVOOA (unimodal residual), indicating that SOA
points were well captured by the ME-2 engine in our study (Canonaco et al.,
2015). Thus, the deconvolution of SVOOA and LVOOA in this study well
represented the observed large variation in f44 and f43 for SOA in
Guangzhou.
Plot of f44 vs. f43 for this study, along with several
laboratory studies (a Bahreini et al., 2005; b Liggio et al., 2005; c Weitkamp et al., 2007). SOA is color-coded by OA mass concentration, and the size of a circle is proportional to the corresponding
NR-PM1 mass concentration. The triangle area (enclosed by two black
dashed lines and the f43 axis) for ambient OOA (SOA) was defined by Ng et
al. (2010).
Most SOA points were located close to the right side of the triangle,
which was quite different from the OA points. Interestingly, the shape of
SOA points in f44/f43 space formed roughly a tilted triangle, which
means both average values and variation ranges of f43 remarkably
decreased with increasing f44.The triangle suggests that aging processes
result in substantially similar OOA components regardless of their original
precursors (Ng et al., 2010). The f44 values of SOA in this study were
much higher than those of SOA generated in a laboratory, which should be
attributed to limited residence time in the chamber or much higher mass loadings
of laboratory-produced aerosols which facilitate gas-to-particle
partitioning of semi-volatile organic compounds (SVOCs) and heterogeneous
reactions (Liggio and Li, 2006; Shilling et al., 2009; Ng et al., 2010;
Ziemann and Atkinson, 2012). Figure 9 also shows that the f44 and f43
values of SOA generated from laboratory m-xylene oxidation and from aged
diesel exhaust were closest to those of the SOA and modeled SVOOA from this
study, implying that traffic emissions (in particular from diesel
combustion) and aromatic compounds are likely precursors for SOA in
Guangzhou. This result also agrees with numerous studies conducted in urban
areas (Lee et al., 2015; C. Sun et al., 2016; Qin et al., 2017).
Peroxy radical tracer
Here we introduce the total peroxy radicals RO2∗ as a reference tracer
to discuss both daytime and nocturnal SOA formation (Fig. 10).
Figure 10 shows the variations in SOA concentration with RO2∗
concentration under different scenarios (non-pollution daytime period,
pollution daytime EPs, non-pollution nighttime period, and pollution
nighttime EPs). The SOA concentration increased with the RO2∗
concentration for both non-pollution daytime and nighttime periods with both
moderate correlation coefficients (R2=0.41), indicating that
formation and growth of SOA could be attributed to photochemical oxidation
during non-pollution daytime while nocturnal gas-phase oxidation of VOCs led
to SOA accumulation during non-pollution nighttime. Thus, it can be
concluded that gas-phase oxidation was responsible for SOA formation during
non-pollution periods. Meanwhile, SOA oxidation degree, represented by
f44 in SOA, increased in general with increasing SOA concentration
during non-pollution periods, implying that a higher oxidative condition
simultaneously led to generation of SOA and conversion of SVOOA to LVOOA
(Fig. 7b). In contrast to the good correlations between SOA and RO2∗
concentrations during non-pollution periods, such correlations were not seen
during the pollution EPs for both daytime and nighttime scenarios (Fig. 10);
instead, an opposite trend that f44 in SOA decreased with an increase in
SOA concentration was observed (Fig. 10c–d). These results imply that other
mechanisms besides gas-phase oxidation were responsible for the formation of
SOA that is less oxidized during pollution EPs. In addition, correlations
between SVOOA (LVOOA) and RO2 were explored by plotting dependence of
SVOOA (LVOOA) concentrations on RO2∗ concentration during non-pollution
periods and pollution periods (Fig. S15). The results show better
correlations and larger slope for LVOOA vs. RO2∗ than for SVOOA vs. RO2∗ during non-pollution periods. In contrast, neither LVOOA nor SVOOA
was correlated to RO2∗ during pollution EPs.
SOA concentration as a function of RO2∗ concentration during different scenarios (non-pollution daytime period, pollution daytime EPs, non-pollution nighttime period, and pollution nighttime EPs). SOA concentrations are color-coded by f44 in SOA. All the regressions are orthogonally linear.
Plot of estimated H/C ratio as a function of estimated O/C ratio for SOA in the van Krevelen diagram: (a) non-pollution vs. pollution EPs; (b) non-pollution vs. pollution EPs with an overlapped NOx concentration
range during the two periods. The area enclosed by the two red boundary
lines was defined for ambient OOA components in Ng et al. (2011a).
We further investigate the distinctly different mechanisms for SOA formation
between non-pollution and pollution EPs by plotting the estimated H/C ratio as a
function of O/C ratio in the van Krevelen diagram (Fig. 11). The O/C and H/C
ratios were estimated from f43 and f44, which were proposed by Aiken
et al. (2008) and Ng et al. (2011a), respectively. Similar diagrams based on
ACSM unit mass resolution data were reported in previous studies (Brito et
al., 2014; Reece et al., 2017; Saha et al., 2018). The area enclosed by the
two red boundary lines was defined for ambient OOA components in Ng et al. (2011a). Our data points were slightly outside of this area and were further
shifted to the upper right corner of the plot. Similar differences from
other ambient or laboratory measurements were also reported in previous
studies (Budisulistiorini et al., 2018; Saha et al., 2018), which were
attributed to different precursors emitted or aging processes. As shown in
Fig. 11a, the H/C ratio is linearly correlated with the O/C ratio and is
confined into a narrow belt during non-pollution periods, indicating the
precursors, mechanism, and chemical components of SOA are likely similar (Ng
et al., 2010, 2011a). Although the H/C ratio follows similar trends with O/C
ratio during pollution periods, the shape is much broader with respect to
the H/C ratio, especially in the middle portion of the O/C ratio, strongly
indicating that more diverse components in SOA are present surrounding the
measurement site in Guangzhou. A wider range of H/C ratio during pollution
EPs implies more diverse precursor sources and (or) different mechanisms which
lead to formation of SOA components with highly variable H/C ratios (Ng et
al., 2010, 2011a). Although we cannot totally rule out the possibility of
dramatic changes in the emission sources, it is unlikely for those changes
to occur within a month of the measurement period. Hence it is more likely
that the formation mechanisms during non-pollution and pollution periods are
distinctly different. Here we propose the two most possible different mechanisms
of SOA formation during pollution EPs: (1) gas-phase oxidation under
enhanced NOx concentration which leads to dramatically different SOA
components from these under lower NOx concentration (Ziemann and Atkinson,
2012); (2) other mechanisms such as additional heterogeneous/multiphase
reactions from dramatic increases in PM mass loading and hence more
available particle surfaces or volumes for reactions. We replot estimated
H/C ratio vs. estimated O/C ratio in the van Krevelen diagram with an
overlapped NOx concentration range during the two periods to remove the
enhanced NOx effects (Fig. 11b). Similar patterns were obtained,
highlighting the possibility of significant heterogeneous/multiphase
reactions in the pollution EPs.
(a) Scatter plots of SOA and RO2∗ during pollution EPs at different NR-PM1 mass concentration segments, and (b) correlation coefficients, slopes, and intercepts of linear regressions between SOA and RO2∗ for NR-PM1 mass concentration segments ranging from 30–40 to
>70µg m-3. The regressions are orthogonally linear,
and all correlations of the concentration segments were statistically
significant (p value <0.01; see detailed statistical information in
Table S3).
To further explore mechanisms that can explain SOA formation during
pollution EPs, a plot of SOA concentration as a function of RO2∗
concentration for segmental NR-PM1 mass concentrations is shown in Fig. 12. A total of six segments of concentrations were set, with concentrations
smaller than 30 and larger than 70 up to about 110 µg m-3 being
the lowest and highest segments, respectively and with an interval of 10 µg m-3 between 30 and 70 µg m-3 (Fig. 12a). For
comparison, dependence of SOA concentration on RO2∗ concentration
during non-pollution periods is also included in Fig. 12. As discussed
above, the overall correlation between SOA and RO2∗ concentrations was
poor without a clear trend between the two quantities (Fig. 10c–d).
However, they were reasonably correlated and showed the reasonable trend that
SOA concentration increased with increasing RO2∗ concentration in all
segments except for the lowest one, suggesting that gas-phase oxidation
still played an important role in SOA formation. Poor correlation between SOA
and RO2∗ concentrations in the lowest segment might be due in part to
substantial scattering of the data. Under the same RO2∗ concentration,
interestingly, more SOA was formed with increasing NR-PM1
concentration. Here, for simplicity, we define that the slope and intercept
of linear regression between SOA and RO2∗ concentrations in each
segment when NR-PM1 ranged from 30–40 to >70µg m-3 represented, respectively, SOA formed due to RO2∗ and SOA
contributed from other pathways. It is found that substantial intercept
values were obtained from the linear regressions during pollution EPs, while
they approached almost zero during non-pollution periods. Meanwhile, these
intercepts increased from 7.5 to 16.2 µg m-3 when NR-PM1 mass loading increased from 30–40 to >70µg m-3.
Thus, the exact pathways implied by these significant intercepts
could not be identified from this study due to obviously insufficient
measurement data. They are most likely attributed to heterogeneous and/or
multiphase reactions on particle surfaces or inside particles.
Conclusions
A field campaign employing a ToF-ACSM for measurements of NR-PM1 chemical composition was conducted from 20 November 2017 to 5 January 2018
at an urban site in Guangzhou, China. The reliability of the ToF-ACSM was
confirmed by the good correlation between mass concentrations of NR-PM1
measured by this instrument and those of PM2.5 and PM1.1 measured
from other filter-based methods. Chemical composition of NR-PM1 at
this site was dominated by organics, followed by sulfate, nitrate, and
ammonium, and only an insignificant fraction (1 %–2 %) of chlorine was
measured. We classified five pollution episodes (EPs) according to mass
concentration of NR-PM1. Mass fraction of organics was higher during
pollution EPs than during non-pollution periods, corresponding to a decrease
and an increase in sulfate fraction, respectively, for the two periods. Our
results together with other previous studies show increasing SOA/OA mass
concentration ratio across China from the north to the south.
Positive matrix factorization (PMF) with the multilinear engine (ME-2) algorithm
was employed to deconvolve OA into four factors including hydrocarbon-like
OA (HOA, 12 %), cooking OA (COA, 18 %), semi-volatile oxygenated OA
(SVOOA, 30 %), and low-volatility oxygenated OA (LVOOA, 40 %) according
to the mass spectra acquired by the ToF-ACSM during the study. One primary
OA, HOA, was found to originate from heavy-duty vehicle (HDV) emissions
after midnight when those vehicles are allowed to enter urban
Guangzhou according to the traffic regulation. Another primary OA, COA, was
found to be significantly contributed by nocturnal cooking activities during
pollution EPs. Those activities were extended during festival celebrations,
leading to an obviously delayed peak concentration for COA. Both cooking and
traffic emissions contributed significantly to nocturnal PM accumulation
because they emitted not only primary aerosols (COA and HOA) but also
substantial amounts of precursors for SOA or nitrate. Concentrations of
SVOOA and nitrate were correlated well with traffic tracers (i.e., CO, HOA,
and NOx) during the pollution EPs, suggesting that SVOOA and nitrate
were significantly contributed from local traffic emissions under stagnant
meteorological conditions and that they originated from shared precursor
sources.
Concentrations of HOA, COA, nitrate, and SVOOA showed much more pronounced
diurnal variation during pollution EPs since all four species were largely
influenced by local anthropogenic emission under stagnant conditions. For
comparison, both sulfate and LVOOA showed little diurnal variations during
both non-pollution periods and pollution EPs as they were contributed by
regional pollutants and were barely influenced by local anthropogenic
emissions. In addition, nocturnal concentrations of both O3 and
RO2∗ during pollution EPs were lower than those during non-pollution
periods, implying that both O3 and RO2∗ could serve as important
nocturnal oxidants during pollution EPs. Chemical evolution of NR-PM1
suggests that different intrinsic mechanisms were responsible for
NR-PM1 accumulation between pollution EPs and non-pollution periods.
During non-pollution periods, the SPM/PPM ratio increased with increasing
concentrations of photochemical products (i.e., O3 and RO2∗) and
NR-PM1 concentration, strongly suggesting that secondary processes,
especially photochemistry, were the main mechanism for NR-PM1
accumulation. During pollution EPs, the SPM/PPM ratio, together with
concentration of O3 and RO2∗, was observed to drop with increasing
NR-PM1 mass concentration, indicating that production of NR-PM1
was more driven by primary emissions rather than secondary processes. The
f44/f43 space, proposed by Ng et al. (2010), was employed to
investigate oxidation degree of OA and SOA, and the results suggested that
two OOA factors were needed to cover a wide range of f44 and f43 in
SOA in Guangzhou. Furthermore, we conclude that traffic-emitted VOCs and
aromatic compounds were most likely SOA precursors in Guangzhou by
comparing SOA from our measurements with those from other laboratory
studies.
Peroxy radicals RO2∗ were measured in this study and were used as a
tracer for gas-phase oxidation to explore SOA formation during both daytime
and nighttime, an advantage over Ox which can only be used as an
indicator of daytime photochemistry in addition to significant interference
by directly emitted NO2, especially in urban areas. During non-pollution
periods, SOA concentration increased with RO2∗ concentration for both
daytime and nighttime and we conclude that formation and growth of SOA could
be attributed to photochemical oxidation during daytime while nocturnal gas-phase oxidation of VOCs led to SOA accumulation during nighttime. We also
found that SOA oxidation degree increased in general with increasing SOA
concentrations, implying that a higher oxidation condition simultaneously
led to generation of SOA and conversion of SVOOA to LVOOA. During pollution
EPs, however, an overall opposite trend was observed; that is, f44 in
SOA decreased with an increase in SOA concentration. In addition, the overall
correlations between SOA and RO2∗ concentrations were poor for both
daytime and nighttime. The reasons for the above poor correlations are
attributed to other mechanisms besides gas-phase oxidation which are
responsible for SOA formation during pollution EPs. Possible mechanisms were
explored by dividing PM1 mass loadings into six segments, and a linear
regression in each segment was made between SOA and RO2∗
concentrations. The results showed that individual correlations were
reasonably good except for the lowest segment due to data scattering,
indicating that gas-phase oxidation still plays an important role. Values of
slopes from linear regressions of those reasonable correlations were then
found to increase with increasing NR-PM1 mass loading, suggesting that
more gas-phase oxidation products of VOCs were partitioned into particles
under high PM mass loading. Furthermore, substantial intercept values were
found from the above linear regressions in contrast to almost zero intercept
for the non-pollution period, strongly suggesting that other mechanisms besides
gas-phase oxidation contributed significantly to SOA formation. We speculate
that those other mechanisms are most likely heterogeneous and/or multiphase
reactions.
Data availability
All the data presented are available from the
corresponding author upon reasonable request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-7595-2020-supplement.
Author contributions
SZ and XuW designed this study. SZ, JG, MC, WZ, CY, XX,
and WS conducted the experiments. JG, SZ, and JZ wrote the paper. YS, WH, YH,
ZZ, PC, QF, JH, SF, and XiW were involved in the data analysis and scientific
discussions of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Multiphase chemistry of secondary aerosol formation under severe haze”. It is not associated with a conference.
Acknowledgements
This work was funded by the National Key Research and Development Program of
China (grant nos. 2016YFC0202205, 2017YFC0210104), the National Natural Science
Foundation of China (grant nos. 41875152, 91644225, 21577177), Science and Technology
Program of Guangdong Province (Science and Technology Innovation Platform
Category, grant no. 2019B121201002), and the National Natural Science Foundation
as a key project (grant nos. 41530641, 41630422). Jun Zhao acknowledges funding support
from the “111 plan” project of China (grant no. B17049), Scientific and
Technological Innovation Team Project of Guangzhou Joint Research Center of
Atmospheric Sciences, China Meteorological Administration (grant no. 201704). The authors thank Xinhui Bi and Jingjing Feng for their
support in the field study.
Financial support
This research has been supported by the National Key Research and Development Program of China (grant nos. 2016YFC0202205, 2017YFC0210104), the National Natural Science Foundation of China (grant nos. 41875152, 91644225, 21577177), the Science and Technology
Program of Guangdong Province (Science and Technology Innovation Platform
Category, no. 2019B121201002), the National Science Fund for Distinguished Young Scholars (grant no. 4142502), the National Natural Science Foundation as a key project (grant nos. 41530641, 41630422), the “111 plan” project of China (grant no. B17049), and the Scientific and Technological Innovation Team Project of Guangzhou Joint Research Center of Atmospheric Sciences, China Meteorological Administration (grant no. 201704).
Review statement
This paper was edited by Jian Wang and reviewed by two anonymous referees.
ReferencesAiken, A. C., DeCarlo, P. F., and Jimenez, J. L.: Elemental Analysis of
Organic Species with Electron Ionization High-Resolution Mass Spectrometry,
Anal. Chem., 79, 8350–8358, 10.1021/ac071150w, 2007.Aiken, A. C., DeCarlo, P. F., Kroll, J. H., Worsnop, D. R., Huffman, J. A.,
Docherty, K. S., Ulbrich, I. M., Mohr, C., Kimmel, J. R., Sueper, D., Sun,
Y., Zhang, Q., Trimborn, A., Northway, M., Ziemann, P. J., Canagaratna, M.
R., Onasch, T. B., Alfarra, M. R., Prevot, A. S. H., Dommen, J., Duplissy,
J., Metzger, A., Baltensperger, U., and Jimenez, J. L.: O/C and OM/OC ratios
of primary, secondary, and ambient organic aerosols with high-resolution
time-of-flight aerosol mass spectrometry, Environ. Sci. Technol., 42,
4478–4485, 2008.Aiken, A. C., Salcedo, D., Cubison, M. J., Huffman, J. A., DeCarlo, P. F., Ulbrich, I. M., Docherty, K. S., Sueper, D., Kimmel, J. R., Worsnop, D. R., Trimborn, A., Northway, M., Stone, E. A., Schauer, J. J., Volkamer, R. M., Fortner, E., de Foy, B., Wang, J., Laskin, A., Shutthanandan, V., Zheng, J., Zhang, R., Gaffney, J., Marley, N. A., Paredes-Miranda, G., Arnott, W. P., Molina, L. T., Sosa, G., and Jimenez, J. L.: Mexico City aerosol analysis during MILAGRO using high resolution aerosol mass spectrometry at the urban supersite (T0) – Part 1: Fine particle composition and organic source apportionment, Atmos. Chem. Phys., 9, 6633–6653, 10.5194/acp-9-6633-2009, 2009.Allan, J. D., Delia, A. E., Coe, H., Bower, K. N., Alfarra, M. R., Jimenez,
J. L., Middlebrook, A. M., Drewnick, F., Onasch, T. B., Canagaratna, M. R.,
Jayne, J. T., and Worsnop, D. R.: A generalised method for the extraction of
chemically resolved mass spectra from Aerodyne aerosol mass spectrometer
data, J. Aerosol. Sci., 35, 909–922, 10.1016/j.jaerosci.2004.02.007, 2004.Allan, J. D., Williams, P. I., Morgan, W. T., Martin, C. L., Flynn, M. J., Lee, J., Nemitz, E., Phillips, G. J., Gallagher, M. W., and Coe, H.: Contributions from transport, solid fuel burning and cooking to primary organic aerosols in two UK cities, Atmos. Chem. Phys., 10, 647–668, 10.5194/acp-10-647-2010, 2010.Bahreini, R., Keywood, M. D., Ng, N. L., Varutbangkul, V., Gao, S., Flagan,
R. C., Seinfeld, J. H., Worsnop, D. R., and Jimenez, J. L.: Measurements of
Secondary Organic Aerosol from Oxidation of Cycloalkenes, Terpenes, and
m-Xylene Using an Aerodyne Aerosol Mass Spectrometer, Environ. Sci.
Technol., 39, 5674–5688, 10.1021/es048061a, 2005.Bian, Y., Huang, Z., Ou, J., Zhong, Z., Xu, Y., Zhang, Z., Xiao, X., Ye, X., Wu, Y., Yin, X., Li, C., Chen, L., Shao, M., and Zheng, J.: Evolution of anthropogenic air pollutant emissions in Guangdong Province, China, from 2006 to 2015, Atmos. Chem. Phys., 19, 11701–11719, 10.5194/acp-19-11701-2019, 2019.Bressi, M., Cavalli, F., Belis, C. A., Putaud, J.-P., Fröhlich, R., Martins dos Santos, S., Petralia, E., Prévôt, A. S. H., Berico, M., Malaguti, A., and Canonaco, F.: Variations in the chemical composition of the submicron aerosol and in the sources of the organic fraction at a regional background site of the Po Valley (Italy), Atmos. Chem. Phys., 16, 12875–12896, 10.5194/acp-16-12875-2016, 2016.Brito, J., Rizzo, L. V., Morgan, W. T., Coe, H., Johnson, B., Haywood, J., Longo, K., Freitas, S., Andreae, M. O., and Artaxo, P.: Ground-based aerosol characterization during the South American Biomass Burning Analysis (SAMBBA) field experiment, Atmos. Chem. Phys., 14, 12069–12083, 10.5194/acp-14-12069-2014, 2014.Budisulistiorini, S. H., Riva, M., Williams, M., Miyakawa, T., Chen, J., Itoh, M., Surratt, J. D., and Kuwata, M.: Dominant contribution of oxygenated organic aerosol to haze particles from real-time observation in Singapore during an Indonesian wildfire event in 2015, Atmos. Chem. Phys., 18, 16481–16498, 10.5194/acp-18-16481-2018, 2018.Canagaratna, M. R., Jayne, J. T., Jimenez, J. L., Allan, J. D., Alfarra, M.
R., Zhang, Q., Onasch, T. B., Drewnick, F., Coe, H., Middlebrook, A., Delia,
A., Williams, L. R., Trimborn, A. M., Northway, M. J., DeCarlo, P. F., Kolb,
C. E., Davidovits, P., and Worsnop, D. R.: Chemical and microphysical
characterization of ambient aerosols with the aerodyne aerosol mass
spectrometer, Mass Spectrom. Rev., 26, 185–222, 10.1002/mas.20115, 2007.Canagaratna, M. R., Jimenez, J. L., Kroll, J. H., Chen, Q., Kessler, S. H., Massoli, P., Hildebrandt Ruiz, L., Fortner, E., Williams, L. R., Wilson, K. R., Surratt, J. D., Donahue, N. M., Jayne, J. T., and Worsnop, D. R.: Elemental ratio measurements of organic compounds using aerosol mass spectrometry: characterization, improved calibration, and implications, Atmos. Chem. Phys., 15, 253–272, 10.5194/acp-15-253-2015, 2015.Canonaco, F., Slowik, J. G., Baltensperger, U., and Prévôt, A. S. H.: Seasonal differences in oxygenated organic aerosol composition: implications for emissions sources and factor analysis, Atmos. Chem. Phys., 15, 6993–7002, 10.5194/acp-15-6993-2015, 2015.Chhabra, P. S., Ng, N. L., Canagaratna, M. R., Corrigan, A. L., Russell, L. M., Worsnop, D. R., Flagan, R. C., and Seinfeld, J. H.: Elemental composition and oxidation of chamber organic aerosol, Atmos. Chem. Phys., 11, 8827–8845, 10.5194/acp-11-8827-2011, 2011.Crippa, M., DeCarlo, P. F., Slowik, J. G., Mohr, C., Heringa, M. F., Chirico, R., Poulain, L., Freutel, F., Sciare, J., Cozic, J., Di Marco, C. F., Elsasser, M., Nicolas, J. B., Marchand, N., Abidi, E., Wiedensohler, A., Drewnick, F., Schneider, J., Borrmann, S., Nemitz, E., Zimmermann, R., Jaffrezo, J.-L., Prévôt, A. S. H., and Baltensperger, U.: Wintertime aerosol chemical composition and source apportionment of the organic fraction in the metropolitan area of Paris, Atmos. Chem. Phys., 13, 961–981, 10.5194/acp-13-961-2013, 2013.DeCarlo, P. F., Ulbrich, I. M., Crounse, J., de Foy, B., Dunlea, E. J., Aiken, A. C., Knapp, D., Weinheimer, A. J., Campos, T., Wennberg, P. O., and Jimenez, J. L.: Investigation of the sources and processing of organic aerosol over the Central Mexican Plateau from aircraft measurements during MILAGRO, Atmos. Chem. Phys., 10, 5257–5280, 10.5194/acp-10-5257-2010, 2010.
De Haan, D. O., Corrigan, A. L., Tolbert, M. A., Jimenez, J. L., Wood, S.
E., and Turley, J. J.: Secondary organic aerosol formation by self-reactions
of methylglyoxal and glyoxal in evaporating droplets, Environ. Sci.
Technol., 43, 8184–8190, 2009.Ervens, B., Turpin, B. J., and Weber, R. J.: Secondary organic aerosol formation in cloud droplets and aqueous particles (aqSOA): a review of laboratory, field and model studies, Atmos. Chem. Phys., 11, 11069–11102, 10.5194/acp-11-11069-2011, 2011.Fang, B., Zhao, W. X., Xu, X. Z., Zhou, J. C., Ma, X., Wang, S., Zhang, W. J.,
Venables, D. S., and Chen, W. D.: Portable broadband cavity-enhanced
spectrometer utilizing Kalman filtering: application to real-time, in situ
monitoring of glyoxal and nitrogen dioxide, Opt. Express, 25,
26910–26922, 10.1364/OE.25.026910, 2017.Fröhlich, R., Cubison, M. J., Slowik, J. G., Bukowiecki, N., Prévôt, A. S. H., Baltensperger, U., Schneider, J., Kimmel, J. R., Gonin, M., Rohner, U., Worsnop, D. R., and Jayne, J. T.: The ToF-ACSM: a portable aerosol chemical speciation monitor with TOFMS detection, Atmos. Meas. Tech., 6, 3225–3241, 10.5194/amt-6-3225-2013, 2013.Gani, S., Bhandari, S., Seraj, S., Wang, D. S., Patel, K., Soni, P., Arub, Z., Habib, G., Hildebrandt Ruiz, L., and Apte, J. S.: Submicron aerosol composition in the world's most polluted megacity: the Delhi Aerosol Supersite study, Atmos. Chem. Phys., 19, 6843–6859, 10.5194/acp-19-6843-2019, 2019.Ge, X., Setyan, A., Sun, Y., and Zhang, Q.: Primary and secondary organic
aerosols in Fresno, California during wintertime: Results from high
resolution aerosol mass spectrometry, J. Geophys. Res., 117, D19301, 10.1029/2012jd018026, 2012.Griffith, S. M., Hansen, R. F., Dusanter, S., Stevens, P. S., Alaghmand, M., Bertman, S. B., Carroll, M. A., Erickson, M., Galloway, M., Grossberg, N., Hottle, J., Hou, J., Jobson, B. T., Kammrath, A., Keutsch, F. N., Lefer, B. L., Mielke, L. H., O'Brien, A., Shepson, P. B., Thurlow, M., Wallace, W., Zhang, N., and Zhou, X. L.: OH and HO2 radical chemistry during PROPHET 2008 and CABINEX 2009 – Part 1: Measurements and model comparison, Atmos. Chem. Phys., 13, 5403–5423, 10.5194/acp-13-5403-2013, 2013.He, L. Y., Huang, X. F., Xue, L., Hu, M., Lin, Y., Zheng, J., Zhang, R., and
Zhang, Y. H.: Submicron aerosol analysis and organic source apportionment in
an urban atmosphere in Pearl River Delta of China using high-resolution
aerosol mass spectrometry, J. Geophys. Res., 116, D12304, 10.1029/2010jd014566,
2011.Herndon, S. C., Onasch, T. B., Wood, E. C., Kroll, J. H., Canagaratna, M.
R., Jayne, J. T., Zavala, M. A., Knighton, W. B., Mazzoleni, C., Dubey, M.
K., Ulbrich, I. M., Jimenez, J. L., Seila, R., de Gouw, J. A., de Foy, B.,
Fast, J., Molina, L. T., Kolb, C. E., and Worsnop, D. R.: Correlation of
secondary organic aerosol with odd oxygen in Mexico City, Geophys. Res.
Lett., 35, L15804, 10.1029/2008gl034058, 2008.Herrmann, H., Schaefer, T., Tilgner, A., Styler, S. A., Weller, C., Teich,
M., and Otto, T.: Tropospheric aqueous-phase chemistry: kinetics,
mechanisms, and its coupling to a changing gas phase, Chem. Rev., 115,
4259–4334, 10.1021/cr500447k, 2015.Hu, W., Hu, M., Hu, W., Jimenez, J. L., Yuan, B., Chen, W., Wang, M., Wu,
Y., Chen, C., Wang, Z., Peng, J., Zeng, L., and Shao, M.: Chemical
composition, sources and aging process of submicron aerosols in Beijing:
contrast between summer and winter, J. Geophys. Res., 121, 1955–1977, 10.1002/2015JD024020, 2016a.Hu, W., Hu, M., Hu, W.-W., Niu, H., Zheng, J., Wu, Y., Chen, W., Chen, C., Li, L., Shao, M., Xie, S., and Zhang, Y.: Characterization of submicron aerosols influenced by biomass burning at a site in the Sichuan Basin, southwestern China, Atmos. Chem. Phys., 16, 13213–13230, 10.5194/acp-16-13213-2016, 2016b.Hu, W., Campuzano-Jost, P., Day, D. A., Croteau, P., Canagaratna, M. R.,
Jayne, J. T., Worsnop, D. R., and Jimenez, J. L.: Evaluation of the new
capture vaporizer for aerosol mass spectrometers (AMS) through field studies
of inorganic species, Aerosol Sci. Tech., 51, 735–754, 10.1080/02786826.2017.1296104, 2017.Huang, R.-J., Wang, Y., Cao, J., Lin, C., Duan, J., Chen, Q., Li, Y., Gu, Y., Yan, J., Xu, W., Fröhlich, R., Canonaco, F., Bozzetti, C., Ovadnevaite, J., Ceburnis, D., Canagaratna, M. R., Jayne, J., Worsnop, D. R., El-Haddad, I., Prévôt, A. S. H., and O'Dowd, C. D.: Primary emissions versus secondary formation of fine particulate matter in the most polluted city (Shijiazhuang) in North China, Atmos. Chem. Phys., 19, 2283–2298, 10.5194/acp-19-2283-2019, 2019.Huang, X.-F., He, L.-Y., Hu, M., Canagaratna, M. R., Kroll, J. H., Ng, N. L., Zhang, Y.-H., Lin, Y., Xue, L., Sun, T.-L., Liu, X.-G., Shao, M., Jayne, J. T., and Worsnop, D. R.: Characterization of submicron aerosols at a rural site in Pearl River Delta of China using an Aerodyne High-Resolution Aerosol Mass Spectrometer, Atmos. Chem. Phys., 11, 1865–1877, 10.5194/acp-11-1865-2011, 2011.Huang, X.-F., He, L.-Y., Xue, L., Sun, T.-L., Zeng, L.-W., Gong, Z.-H., Hu, M., and Zhu, T.: Highly time-resolved chemical characterization of atmospheric fine particles during 2010 Shanghai World Expo, Atmos. Chem. Phys., 12, 4897–4907, 10.5194/acp-12-4897-2012, 2012.
Ibald-Mulli, A., Wichmann, H. E., Kreyling, W., and Peters, A.:
Epidemiological evidence on health effects of ultrafine particles, J.
Aerosol Med., 15, 189–201, 2002.
IPCC: Climate Change 2013 – The Physical Science Basis. Working Group I
Contribution to the Fifth Assessment Report of the IPCC, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.
K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge
University Press, Cambridge, UK, 2013.Jiang, Q., Sun, Y. L., Wang, Z., and Yin, Y.: Aerosol composition and sources during the Chinese Spring Festival: fireworks, secondary aerosol, and holiday effects, Atmos. Chem. Phys., 15, 6023–6034, 10.5194/acp-15-6023-2015, 2015.Jimenez, J. L., Jayne, J. T., Shi, Q., Kolb, C. E., Worsnop, D. R.,
Yourshaw, I., Seinfeld, J. H., Flagan, R. C., Zhang, X., and Smith, K. A.:
Ambient aerosol sampling using the aerodyne aerosol mass spectrometer, J.
Geophys. Res.-Atmos., 108, 8425, 10.1029/2001JD001213, 2003.Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., Prevot, A. S. H., Zhang,
Q., Kroll, J. H., DeCarlo, P. F., Allan, J. D., Coe, H., Ng, N. L., Aiken,
A. C., Docherty, K. S., Ulbrich, I. M., Grieshop, A. P., Robinson, A. L.,
Duplissy, J., Smith, J. D., Wilson, K. R., Lanz, V. A., Hueglin, C., Sun, Y.
L., Tian, J., Laaksonen, A., Raatikainen, T., Rautiainen, J., Vaattovaara,
P., Ehn, M., Kulmala, M., Tomlinson, J. M., Collins, D. R., Cubison, M. J.,
Dunlea, J., Huffman, J. A., Onasch, T. B., Alfarra, M. R., Williams, P. I.,
Bower, K., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S.,
Demerjian, K., Salcedo, D., Cottrell, L., Griffin, R., Takami, A., Miyoshi,
T., Hatakeyama, S., Shimono, A., Sun, J. Y., Zhang, Y. M., Dzepina, K.,
Kimmel, J. R., Sueper, D., Jayne, J. T., Herndon, S. C., Trimborn, A. M.,
Williams, L. R., Wood, E. C., Middlebrook, A. M., Kolb, C. E.,
Baltensperger, U., and Worsnop, D. R.: Evolution of Organic Aerosols in the
Atmosphere, Science, 326, 1525, 10.1126/science.1180353, 2009.
Kreyling, W. G., Semmler-Behnke, M., and Möller, W.: Ultrafine
particle–lung interactions: does size matter?, J. Aerosol Med., 19, 74–83,
2006.Kroll, J. H. and Seinfeld, J. H.: Chemistry of secondary organic aerosol:
Formation and evolution of low-volatility organics in the atmosphere, Atmos.
Environ., 42, 3593–3624, 10.1016/j.atmosenv.2008.01.003, 2008.Lee, B. P., Li, Y. J., Yu, J. Z., Louie, P. K. K., and Chan, C. K.:
Characteristics of submicron particulate matter at the urban roadside in
downtown Hong Kong-Overview of 4 months of continuous high-resolution
aerosol mass spectrometer measurements, J. Geophys. Res., 120, 7040–7058, 10.1002/2015jd023311, 2015.Lee, B. P., Wang, H., and Chan, C. K.: Diurnal and day-to-day characteristics of ambient particle mass size distributions from HR-ToF-AMS measurements at an urban site and a suburban site in Hong Kong, Atmos. Chem. Phys., 17, 13605–13624, 10.5194/acp-17-13605-2017, 2017.Levy, H.: Normal Atmosphere: Large Radical and Formaldehyde Concentrations
Predicted, Science, 173, 141–143, 10.1126/science.173.3992.141, 1971.Li, H., Zhang, Q., Zhang, Q., Chen, C., Wang, L., Wei, Z., Zhou, S., Parworth, C., Zheng, B., Canonaco, F., Prévôt, A. S. H., Chen, P., Zhang, H., Wallington, T. J., and He, K.: Wintertime aerosol chemistry and haze evolution in an extremely polluted city of the North China Plain: significant contribution from coal and biomass combustion, Atmos. Chem. Phys., 17, 4751–4768, 10.5194/acp-17-4751-2017, 2017.Li, Y. J., Lee, B. Y. L., Yu, J. Z., Ng, N. L., and Chan, C. K.: Evaluating the degree of oxygenation of organic aerosol during foggy and hazy days in Hong Kong using high-resolution time-of-flight aerosol mass spectrometry (HR-ToF-AMS), Atmos. Chem. Phys., 13, 8739–8753, 10.5194/acp-13-8739-2013, 2013.Li, Y. J., Lee, B. P., Su, L., Fung, J. C. H., and Chan, C. K.: Seasonal characteristics of fine particulate matter (PM) based on high-resolution time-of-flight aerosol mass spectrometric (HR-ToF-AMS) measurements at the HKUST Supersite in Hong Kong, Atmos. Chem. Phys., 15, 37–53, 10.5194/acp-15-37-2015, 2015.Li, Y. J., Sun, Y., Zhang, Q., Li, X., Li, M., Zhou, Z., and Chan, C. K.:
Real-time chemical characterization of atmospheric particulate matter in
China: A review, Atmos. Environ., 158, 270–304, 10.1016/j.atmosenv.2017.02.027, 2017.Liggio, J. and Li, S.-M.: Organosulfate formation during the uptake of
pinonaldehyde on acidic sulfate aerosols, Geophys. Res. Lett., 33, L13808, 10.1029/2006gl026079, 2006.Liggio, J., Li, S.-M., and McLaren, R.: Heterogeneous Reactions of Glyoxal
on Particulate Matter:? Identification of Acetals and Sulfate Esters,
Environ. Sci. Technol., 39, 1532–1541, 10.1021/es048375y, 2005.Marais, E. A., Jacob, D. J., Jimenez, J. L., Campuzano-Jost, P., Day, D. A., Hu, W., Krechmer, J., Zhu, L., Kim, P. S., Miller, C. C., Fisher, J. A., Travis, K., Yu, K., Hanisco, T. F., Wolfe, G. M., Arkinson, H. L., Pye, H. O. T., Froyd, K. D., Liao, J., and McNeill, V. F.: Aqueous-phase mechanism for secondary organic aerosol formation from isoprene: application to the southeast United States and co-benefit of SO2 emission controls, Atmos. Chem. Phys., 16, 1603–1618, 10.5194/acp-16-1603-2016, 2016.Matthew, B. M., Middlebrook, A. M., and Onasch, T. B.: Collection
Efficiencies in an Aerodyne Aerosol Mass Spectrometer as a Function of
Particle Phase for Laboratory Generated Aerosols, Aerosol Sci. Tech., 42,
884–898, 10.1080/02786820802356797, 2008.McNeill, V. F.: Aqueous organic chemistry in the atmosphere: sources and
chemical processing of organic aerosols, Environ. Sci. Technol, 49,
1237–1244, 10.1021/es5043707, 2015.Middlebrook, A. M., Bahreini, R., Jimenez, J. L., and Canagaratna, M. R.:
Evaluation of Composition-Dependent Collection Efficiencies for the Aerodyne
Aerosol Mass Spectrometer using Field Data, Aerosol Sci. Tech., 46,
258–271, 10.1080/02786826.2011.620041, 2012.Mohr, C., DeCarlo, P. F., Heringa, M. F., Chirico, R., Slowik, J. G., Richter, R., Reche, C., Alastuey, A., Querol, X., Seco, R., Peñuelas, J., Jiménez, J. L., Crippa, M., Zimmermann, R., Baltensperger, U., and Prévôt, A. S. H.: Identification and quantification of organic aerosol from cooking and other sources in Barcelona using aerosol mass spectrometer data, Atmos. Chem. Phys., 12, 1649–1665, 10.5194/acp-12-1649-2012, 2012.Ng, N. L., Canagaratna, M. R., Zhang, Q., Jimenez, J. L., Tian, J., Ulbrich, I. M., Kroll, J. H., Docherty, K. S., Chhabra, P. S., Bahreini, R., Murphy, S. M., Seinfeld, J. H., Hildebrandt, L., Donahue, N. M., DeCarlo, P. F., Lanz, V. A., Prévôt, A. S. H., Dinar, E., Rudich, Y., and Worsnop, D. R.: Organic aerosol components observed in Northern Hemispheric datasets from Aerosol Mass Spectrometry, Atmos. Chem. Phys., 10, 4625–4641, 10.5194/acp-10-4625-2010, 2010.Ng, N. L., Canagaratna, M. R., Jimenez, J. L., Chhabra, P. S., Seinfeld, J. H., and Worsnop, D. R.: Changes in organic aerosol composition with aging inferred from aerosol mass spectra, Atmos. Chem. Phys., 11, 6465–6474, 10.5194/acp-11-6465-2011, 2011a.Ng, N. L., Herndon, S. C., Trimborn, A., Canagaratna, M. R., Croteau, P. L.,
Onasch, T. B., Sueper, D., Worsnop, D. R., Zhang, Q., Sun, Y. L., and Jayne,
J. T.: An Aerosol Chemical Speciation Monitor (ACSM) for Routine Monitoring
of the Composition and Mass Concentrations of Ambient Aerosol, Aerosol Sci.
Tech., 45, 780–794, 10.1080/02786826.2011.560211, 2011b.Ng, N. L., Canagaratna, M. R., Jimenez, J. L., Zhang, Q., Ulbrich, I. M., and Worsnop, D. R.: Real-Time Methods for Estimating Organic Component Mass Concentrations from Aerosol Mass Spectrometer Data, Environ. Sci. Technol., 45, 910–916, 10.1021/es102951k, 2011c.
Pope III, C. A. and Dockery, D. W.: Health effects of fine particulate air
pollution: lines that connect, J. Air Waste Manage., 56, 709–742, 2006.Qin, Y. M., Tan, H. B., Li, Y. J., Schurman, M. I., Li, F., Canonaco, F., Prévôt, A. S. H., and Chan, C. K.: Impacts of traffic emissions on atmospheric particulate nitrate and organics at a downwind site on the periphery of Guangzhou, China, Atmos. Chem. Phys., 17, 10245–10258, 10.5194/acp-17-10245-2017, 2017.Reece, S. M., Sinha, A., and Grieshop, A. P.: Primary and Photochemically Aged Aerosol Emissions from Biomass Cookstoves: Chemical and Physical Characterization, Environ. Sci. Technol., 51, 9379–9390, 10.1021/acs.est.7b01881, 2017.Rohrer, F., Lu, K., Hofzumahaus, A., Bohn, B., Brauers, T., Chang, C.-C.,
Fuchs, H., Häseler, R., Holland, F., Hu, M., Kita, K., Kondo, Y., Li,
X., Lou, S., Oebel, A., Shao, M., Zeng, L., Zhu, T., Zhang, Y., and Wahner,
A.: Maximum efficiency in the hydroxyl-radical-based self-cleansing of the
troposphere, Nat. Geosci., 7, 559–563, 10.1038/ngeo2199, 2014.Saha, P. K., Reece, S. M., and Grieshop, A. P.: Seasonally Varying Secondary Organic Aerosol Formation From In-Situ Oxidation of Near-Highway Air, Environ. Sci. Technol., 52, 7192–7202, 10.1021/acs.est.8b01134, 2018.Sheehy, P. M., Volkamer, R., Molina, L. T., and Molina, M. J.: Oxidative capacity of the Mexico City atmosphere – Part 2: A ROx radical cycling perspective, Atmos. Chem. Phys., 10, 6993–7008, 10.5194/acp-10-6993-2010, 2010.Shilling, J. E., Chen, Q., King, S. M., Rosenoern, T., Kroll, J. H., Worsnop, D. R., DeCarlo, P. F., Aiken, A. C., Sueper, D., Jimenez, J. L., and Martin, S. T.: Loading-dependent elemental composition of α-pinene SOA particles, Atmos. Chem. Phys., 9, 771–782, 10.5194/acp-9-771-2009, 2009.Stone, D., Whalley, L. K., and Heard, D. E.: Tropospheric OH and HO2 radicals: field measurements and model comparisons, Chem. Soc. Rev., 41,
6348–6404, 10.1039/c2cs35140d, 2012.Stone, D., Evans, M. J., Walker, H., Ingham, T., Vaughan, S., Ouyang, B., Kennedy, O. J., McLeod, M. W., Jones, R. L., Hopkins, J., Punjabi, S., Lidster, R., Hamilton, J. F., Lee, J. D., Lewis, A. C., Carpenter, L. J., Forster, G., Oram, D. E., Reeves, C. E., Bauguitte, S., Morgan, W., Coe, H., Aruffo, E., Dari-Salisburgo, C., Giammaria, F., Di Carlo, P., and Heard, D. E.: Radical chemistry at night: comparisons between observed and modelled HOx, NO3 and N2O5 during the RONOCO project, Atmos. Chem. Phys., 14, 1299–1321, 10.5194/acp-14-1299-2014, 2014.Sun, C., Lee, B. P., Huang, D., Jie Li, Y., Schurman, M. I., Louie, P. K. K., Luk, C., and Chan, C. K.: Continuous measurements at the urban roadside in an Asian megacity by Aerosol Chemical Speciation Monitor (ACSM): particulate matter characteristics during fall and winter seasons in Hong Kong, Atmos. Chem. Phys., 16, 1713–1728, 10.5194/acp-16-1713-2016, 2016.Sun, Y., Wang, Z., Dong, H., Yang, T., Li, J., Pan, X., Chen, P., and Jayne,
J. T.: Characterization of summer organic and inorganic aerosols in Beijing,
China with an Aerosol Chemical Speciation Monitor, Atmos. Environ., 51,
250–259, 10.1016/j.atmosenv.2012.01.013, 2012.Sun, Y. L., Wang, Z. F., Fu, P. Q., Yang, T., Jiang, Q., Dong, H. B., Li, J., and Jia, J. J.: Aerosol composition, sources and processes during wintertime in Beijing, China, Atmos. Chem. Phys., 13, 4577–4592, 10.5194/acp-13-4577-2013, 2013.Sun, Y. L., Jiang, Q., Wang, Z., Fu, P., Li, J., Yang, T., and Yin, Y.:
Investigation of the sources and evolution processes of severe haze
pollution in Beijing in January 2013, J. Geophys. Res., 119, 4380–4398, 10.1002/2014JD021641, 2014.Sun, Y., Du, W., Fu, P., Wang, Q., Li, J., Ge, X., Zhang, Q., Zhu, C., Ren, L., Xu, W., Zhao, J., Han, T., Worsnop, D. R., and Wang, Z.: Primary and secondary aerosols in Beijing in winter: sources, variations and processes, Atmos. Chem. Phys., 16, 8309–8329, 10.5194/acp-16-8309-2016, 2016.Sun, Y., Xu, W., Zhang, Q., Jiang, Q., Canonaco, F., Prévôt, A. S. H., Fu, P., Li, J., Jayne, J., Worsnop, D. R., and Wang, Z.: Source apportionment of organic aerosol from 2-year highly time-resolved measurements by an aerosol chemical speciation monitor in Beijing, China, Atmos. Chem. Phys., 18, 8469–8489, 10.5194/acp-18-8469-2018, 2018.Takegawa, N., Miyazaki, Y., Kondo, Y., Komazaki, Y., Miyakawa, T., Jimenez,
J. L., Jayne, J. T., Worsnop, D. R., Allan, J. D., and Weber, R. J.:
Characterization of an Aerodyne Aerosol Mass Spectrometer (AMS):
Intercomparison with Other Aerosol Instruments, Aerosol Sci. Tech., 39,
760–770, 10.1080/02786820500243404, 2005.Tan, J. H., Duan, J. C., Chen, D. H., Wang, X. H., Guo, S. J., Bi, X. H.,
Sheng, G. Y., He, K. B., and Fu, J. M.: Chemical characteristics of haze
during summer and winter in Guangzhou, Atmos. Res., 94, 238–245, 10.1016/j.atmosres.2009.05.016, 2009.Tao, J., Shen, Z., Zhu, C., Yue, J., Cao, J., Liu, S., Zhu, L., and Zhang,
R.: Seasonal variations and chemical characteristics of sub-micrometer
particles (PM1) in Guangzhou, China, Atmos. Res., 118, 222–231, 10.1016/j.atmosres.2012.06.025, 2012.Timonen, H., Cubison, M., Aurela, M., Brus, D., Lihavainen, H., Hillamo, R., Canagaratna, M., Nekat, B., Weller, R., Worsnop, D., and Saarikoski, S.: Applications and limitations of constrained high-resolution peak fitting on low resolving power mass spectra from the ToF-ACSM, Atmos. Meas. Tech., 9, 3263–3281, 10.5194/amt-9-3263-2016, 2016.Volkamer, R., Sheehy, P., Molina, L. T., and Molina, M. J.: Oxidative capacity of the Mexico City atmosphere – Part 1: A radical source perspective, Atmos. Chem. Phys., 10, 6969–6991, 10.5194/acp-10-6969-2010, 2010.Wang, F., An, J., Li, Y., Tang, Y., Lin, J., Qu, Y., Chen, Y., Zhang, B.,
and Zhai, J.: Impacts of uncertainty in AVOC emissions on the summer ROx
budget and ozone production rate in the three most rapidly-developing
economic growth regions of China, Adv. Atmos. Sci., 31,
1331–1342, 10.1007/s00376-014-3251-z, 2014Wang, H., Lu, K., Chen, X., Zhu, Q., Wu, Z., Wu, Y., and Sun, K.: Fast particulate nitrate formation via N2O5 uptake aloft in winter in Beijing, Atmos. Chem. Phys., 18, 10483–10495, 10.5194/acp-18-10483-2018, 2018.
Wang, Q., Sun, Y., Jiang, Q., Du, W., Sun, C., Fu, P., and Wang, Z.: Chemical
composition of aerosol particles and light extinction apportionment before
and during the heating season in Beijing, China, J. Geophys. Res.-Atmos.,
120, 12708–12722, 2015.Weitkamp, E. A., Sage, A. M., Pierce, J. R., Donahue, N. M., and Robinson,
A. L.: Organic Aerosol Formation from Photochemical Oxidation of Diesel
Exhaust in a Smog Chamber, Environ. Sci. Technol., 41, 6969–6975, 10.1021/es070193r, 2007.Wen, L., Xue, L., Wang, X., Xu, C., Chen, T., Yang, L., Wang, T., Zhang, Q., and Wang, W.: Summertime fine particulate nitrate pollution in the North China Plain: increasing trends, formation mechanisms and implications for control policy, Atmos. Chem. Phys., 18, 11261–11275, 10.5194/acp-18-11261-2018, 2018.Xu, J., Zhang, Q., Chen, M., Ge, X., Ren, J., and Qin, D.: Chemical composition, sources, and processes of urban aerosols during summertime in northwest China: insights from high-resolution aerosol mass spectrometry, Atmos. Chem. Phys., 14, 12593–12611, 10.5194/acp-14-12593-2014, 2014.Xu, J., Shi, J., Zhang, Q., Ge, X., Canonaco, F., Prévôt, A. S. H., Vonwiller, M., Szidat, S., Ge, J., Ma, J., An, Y., Kang, S., and Qin, D.: Wintertime organic and inorganic aerosols in Lanzhou, China: sources, processes, and comparison with the results during summer, Atmos. Chem. Phys., 16, 14937–14957, 10.5194/acp-16-14937-2016, 2016.Xue, J., Yuan, Z., Lau, A. K. H., and Yu, J. Z.: Insights into factors
affecting nitrate in PM2.5 in a polluted high NOx environment through
hourly observations and size distribution measurements, J. Geophys. Res.,
119, 4888–4902, 10.1002/2013JD021108, 2014.Yang, C., Zhao, W., Fang, B., Xu, X., Zhang, Y., Gai, Y., Zhang, W.,
Venables, D. S., and Chen, W.: Removing Water Vapor Interference in Peroxy
Radical Chemical Amplification with a Large Diameter Nafion Dryer, Anal.
Chem., 90, 3307–3312, 10.1021/acs.analchem.7b04830, 2018.Yang, C., Zhao, W., Fang, B., Yu, H., Xu, X., Zhang, Y., Gai, Y., Zhang, W.,
Chen, W., and Fittschen, C.: Improved Chemical Amplification Instrument by
Using a Nafion Dryer as an Amplification Reactor for Quantifying Atmospheric
Peroxy Radicals under Ambient Conditions, Anal. Chem., 91, 776–779, 10.1021/acs.analchem.8b04907, 2019.Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Allan, J. D., Coe, H.,
Ulbrich, I., Alfarra, M. R., Takami, A., Middlebrook, A. M., Sun, Y. L.,
Dzepina, K., Dunlea, E., Docherty, K., DeCarlo, P. F., Salcedo, D., Onasch,
T., Jayne, J. T., Miyoshi, T., Shimono, A., Hatakeyama, S., Takegawa, N.,
Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian,
K., Williams, P., Bower, K., Bahreini, R., Cottrell, L., Griffin, R. J.,
Rautiainen, J., Sun, J. Y., Zhang, Y. M., and Worsnop, D. R.: Ubiquity and
dominance of oxygenated species in organic aerosols in
anthropogenically-influenced Northern Hemisphere midlatitudes, Geophys. Res.
Lett., 34, L13801, 10.1029/2007gl029979, 2007.
Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Ulbrich, I. M., Ng, N. L.,
Worsnop, D. R., and Sun, Y.: Understanding atmospheric organic aerosols via
factor analysis of aerosol mass spectrometry: a review, Anal. Bioanal.
Chem., 401, 3045–3067, 10.1007/s00216-011-5355-y, 2011.Zhang, Y. J., Tang, L. L., Wang, Z., Yu, H. X., Sun, Y. L., Liu, D., Qin, W., Canonaco, F., Prévôt, A. S. H., Zhang, H. L., and Zhou, H. C.: Insights into characteristics, sources, and evolution of submicron aerosols during harvest seasons in the Yangtze River delta region, China, Atmos. Chem. Phys., 15, 1331–1349, 10.5194/acp-15-1331-2015, 2015.Zhang, Y., Tang, L., Croteau, P. L., Favez, O., Sun, Y., Canagaratna, M. R., Wang, Z., Couvidat, F., Albinet, A., Zhang, H., Sciare, J., Prévôt, A. S. H., Jayne, J. T., and Worsnop, D. R.: Field characterization of the PM2.5 Aerosol Chemical Speciation Monitor: insights into the composition, sources, and processes of fine particles in eastern China, Atmos. Chem. Phys., 17, 14501–14517, 10.5194/acp-17-14501-2017, 2017.Zhang, Z., Engling, G., Lin, C.-Y., Chou, C. C. K., Lung, S.-C. C., Chang,
S.-Y., Fan, S., Chan, C.-Y., and Zhang, Y.-H.: Chemical speciation,
transport and contribution of biomass burning smoke to ambient aerosol in
Guangzhou, a mega city of China, Atmos. Environ., 44, 3187–3195, 10.1016/j.atmosenv.2010.05.024, 2010.Zhou, S. Z., Wang, T., Wang, Z., Li, W., Xu, Z., Wang, X., Yuan, C., Poon, C.
N., Louie, P. K. K., Luk, C. W. Y., and Wang, W.: Photochemical evolution of
organic aerosols observed in urban plumes from Hong Kong and the Pearl River
Delta of China, Atmos. Environ., 88, 219–229, 10.1016/j.atmosenv.2014.01.032, 2014.Ziemann, P. J. and Atkinson, R.: Kinetics, products, and mechanisms of
secondary organic aerosol formation, Chem. Soc. Rev., 41, 6582–6605, 10.1039/c2cs35122f, 2012.