ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-11739-2018Characterization of aerosol hygroscopicity, mixing state, and CCN activity
at a suburban site in the central North China PlainAerosol properties in the central North China PlainWangYuyinghttps://orcid.org/0000-0001-9762-8563LiZhanqingzli@atmos.umd.eduhttps://orcid.org/0000-0001-6737-382XZhangYingjieDuWeihttps://orcid.org/0000-0001-7890-3099ZhangFanghttps://orcid.org/0000-0002-5395-601XTanHaoboXuHanbingFanTianyihttps://orcid.org/0000-0002-1026-5067JinXiaoaiFanXinxinDongZipengWangQiuyanhttps://orcid.org/0000-0002-8425-4256SunYelehttps://orcid.org/0000-0003-2354-0221State Key Laboratory of Earth Surface Processes and Resource Ecology,
College of Global Change and Earth System Science, Beijing Normal
University, Beijing 100875, ChinaDepartment of Atmospheric and Oceanic Sciences and ESSIC, University
of Maryland, College Park, Maryland, USAState Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, ChinaCollege of Earth Sciences, University of Chinese Academy of Sciences,
Beijing 100049, ChinaKey Laboratory of Regional Numerical Weather Prediction, Institute of
Tropical and Marine Meteorology, China Meteorological Administration,
Guangzhou 510080, ChinaShared Experimental Education Center, Sun Yat-sen University,
Guangzhou 510275, ChinaCollaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Nanjing University of Information Science and
Technology, Nanjing 210044, ChinaZhanqing Li (zli@atmos.umd.edu)17August20181816117391175228November20178January20186June201811July2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/11739/2018/acp-18-11739-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/11739/2018/acp-18-11739-2018.pdf
This
study investigates aerosol hygroscopicity, mixing state, and cloud
condensation nucleation as part of the Atmosphere–Aerosol–Boundary
Layer–Cloud Interaction Joint Experiment performed in the summer of 2016 at
Xingtai (XT), a suburban site located in the center of the North China Plain
(NCP). In general, the probability density function (PDF) of the
hygroscopicity parameter (κ) for 40–200 nm particles had a unimodal
distribution, and mean κ-PDF patterns for different sizes were
similar, suggesting that the particles were highly aged and internally mixed
because of strong photochemical reactions. The κ calculated from the
hygroscopic growth factor in the daytime and at night suggests that
photochemical reactions largely enhanced the aerosol hygroscopicity. This
effect became weaker as the particle size increased. In addition, the aerosol
hygroscopicity was much larger at XT than at other sites in the NCP. This is
because new particle formation takes place much more frequently in the
central NCP, which is heavily polluted from industrial activities, than
elsewhere in the region. The evolution of the planetary boundary layer played
a dominant role in dictating aerosol mass concentration. Particle size was
the most important factor influencing the ability of aerosols to activate,
whereas the effect of chemical composition was secondary, especially when
supersaturation was high. Using a fixed value of κ=0.31 to calculate
the cloud condensation nuclei number concentration in this region suffices.
Introduction
Aerosols, defined as the mixture of solid and liquid particles
suspended in air, are ubiquitous in the atmosphere because of direct
emissions from biogenic and anthropogenic sources and the secondary
transformation from gas precursors. Aerosol particles play an important role
in climate change through direct and indirect effects (e.g., Ramanathan et
al., 2001; Rosenfeld et al., 2008; Li et al., 2016). However, the impact of
aerosols on climate change is difficult to simulate because of the highly
variable physical and chemical properties of aerosols and complex
aerosol–cloud interactions (IPCC, 2013; Lebo et al., 2017).
The hygroscopic growth and mixing state of aerosol particles are important
for estimating the direct radiative effect of aerosols on Earth's climate.
This is because the growth and mixing can change the particle size and
optical properties of aerosol particles, which influences the solar radiation
budget and atmospheric visibility. In addition, aerosol particles can be
activated as cloud condensation nuclei (CCN) under supersaturation (SS)
conditions. The variability in CCN number concentration (NCCN)
can modify both cloud microphysical properties (Twomey, 1974; Albrecht, 1989)
and morphology (Rosenfeld et al., 2008) and can lead to a broad impact on a
wide range of meteorological variables including severe weather events (Li et
al., 2017a).
Previous studies have addressed three main aerosol properties influencing the
CCN activation, namely, particle size, chemical composition, and mixing
state. However, their relative importance is different under different
environmental conditions (e.g., Dusek et al., 2006; Ervens et al., 2007;
Cubison et al., 2008; Deng et al., 2011; Zhang et al., 2014; Schmale et
al., 2018).
Ambient aerosols are composed of different species, including inorganic ions,
organic components, black carbon (BC), and mineral dust. Inorganics mainly
contain sulfate, nitrate, and ammonium, while organic aerosols (OAs) consist
of thousands of chemicals (Jacobson et al., 2000). The hygroscopicity and CCN
activity of a single component can be characterized according to laboratory
studies (e.g., Petters and Kreidenweis, 2007), but the properties of their
mixtures are hard to estimate because of the different chemical species and
mixing states of particles in the atmosphere. Therefore, aerosol
hygroscopicity and CCN activity are very different in different regions due
to different chemical compositions. Comprehensive field measurements of
aerosol properties in different regions are thus necessary to improve models.
China, especially the North China Plain (NCP), has been suffering from severe
air pollution over the last couple of decades due to rapid industrialization
and urbanization. Diverse sources and aging processes make aerosol properties
particularly diverse and complex in this part of the world. As such, the
region has drawn much attention regarding the aerosol mixing state,
hygroscopicity, and CCN activity (Deng et al., 2011; Liu et al., 2011; Zhang
et al., 2014; F. Zhang et al., 2016; S. L. Zhang et al., 2016; Wu et
al., 2016; Y. Wang et al., 2017). Liu et al. (2011) and Y. Wang et al. (2017)
have suggested that ambient particles are mostly an external mixture with
different hygroscopicities. Deng et al. (2011) have shown that the aerosol
number size distribution is critical in the prediction of NCCN
while Zhang et al. (2014, 2017) have highlighted the importance of chemical
composition in determining particle activation properties. However, these
studies were carried out using data from the northern part of the NCP. Few
studies have focused on the central region of the NCP. Compared to the
northern part of the NCP, the central part of the NCP is more affected by
industrial emissions because a dense cluster of China's heavy industries is
there (Fu et al., 2014). Measurements of aerosol properties in the central
part of the NCP are thus critically needed to investigate the impact of air
pollution on the environment and climate change.
Xingtai (XT), a city located in the center of the NCP, is considered one of
the most polluted cities in China. Local industrial and domestic sources of
pollution are the greatest contributors to severe haze events in that region
(Wang et al., 2014). A field experiment called the
Atmosphere–Aerosol–Boundary Layer–Cloud (A2BC) Interaction Joint
Experiment was performed at a suburban site in XT in the summer of 2016.
Differences in aerosol properties at this site and at sites in the northern
part of the NCP were found.
The paper is organized as follows. Sections 2 and 3 describe the measurement
method and data analysis theory. Section 4 presents and discusses the
measurement results, which include data time series, aerosol mixing state,
hygroscopicity, and CCN prediction and its sensitivity to chemical
composition. A summary and conclusions are given in Sect. 5.
(a) Map showing the location of the sampling site and
(b) the distribution of mean SO2 concentrations from May of
2012 to 2016.
MeasurementsSampling site and meteorology
The A2BC experiment was carried out at the National Meteorological Basic
Station located in XT (37.18∘ N, 114.37∘ E, 180 m above
sea level) from 1 May to 15 June 2016. This suburban site is situated ∼17 km northwest of the XT urban area in southern Heibei Province, which is
located in the central part of the NCP and to the east of the Taihang
Mountains (Fig. 1a). This region is heavily populated, urbanized, and
industrialized. Major industrial manufacturers include coal-based power
plants, steel and iron works, glassworks, and cement mills. Weak diffusion
conditions and heavy industrial emissions lead to exceptionally high
concentrations of particulate matter (PM) with diameters of less than 10 and
2.5 µm (PM2.5) as well as gas pollutants such as sulfur
dioxide (SO2), volatile organic compounds (VOCs), and nitrogen oxides
(NOx) during the frequently occurring haze episodes in this
region (Wang et al., 2014; Fu et al., 2014). Figure 1b shows the mean
distribution of SO2 concentrations from May 2012 to 2016, which
confirms that the measurement site is located in one of the pollution centers
in this region. A detailed analysis of gas precursors and aerosol chemical
species shows that this station is a representative site in this region
(Zhang et al., 2018).
Time series of meteorological variables measured at this meteorological
station are shown in Fig. S1 in the Supplement. This site is strongly
affected by mountain–valley winds. Southeasterly winds prevail during the day
and at night northwesterly winds prevail (Figs. S1 and S2). There was almost
no precipitation during the study period. The ambient temperature (T) and
relative humidity (RH) time series show opposing trends. Campaign-mean values
of T and RH are 21.9 ∘C and 51.6 %, respectively.
Instrumentation and operationAerosol hygroscopicity measurements
The custom-built hygroscopicity tandem differential mobility analyzer
(H-TDMA) used in this study has been described in detail by others (Tan et
al., 2013; Y. Wang et al., 2017). Briefly, ambient aerosols are first dried
and neutralized using a Nafion dryer and a soft X-ray charger. A differential
mobility analyzer (DMA1, model 3081L, TSI Inc.) is used to select
monodispersed particles of a certain diameter. The monodispersed particles
are then passed through a Nafion humidifier with a controlled higher RH and
are humidified. A second DMA (DMA2, same model as the DMA1) and a
water-based condensation particle counter (WCPC, model 3787, TSI Inc.) are
used to measure the number size distribution of the humidified particles. The
DMA1 and WCPC can also be connected directly to measure the 10–400 nm
particle number size distribution (PNSD). In this study, the dry diameters
selected by the DMA1 were 40, 80, 110, 150, and 200 nm, and the
humidified RH was set to 85 %. The RH calibration with ammonium sulfate
for the H-TDMA is shown in Fig. S3.
The hygroscopic growth factor (GF) is defined as the ratio of the humidified
diameter at a given RH to the dry diameter:
GF=Dp(RH)Dp0,
where Dp(RH) is the particle diameter at the given RH and
Dp0 is the dry diameter selected by the DMA1. The measured
distribution function versus GF can be calculated with WCPC data downstream
from the DMA1 and DMA2. The GF probability density function is then
retrieved using the TDMAfit algorithm (Stolzenburg and McMurry, 1988, 2008).
Aerosol chemical composition measurements
An aerosol chemical speciation monitor (ACSM) was used to measure
non-refractory submicron aerosol species (sulfate, nitrate, ammonium,
chloride, and organics) in real time. A PM2.5 URG Corporation cyclone (model
URG-2000-30ED) was installed in the front of the sampling inlet to remove
coarse particles (>2.5µm in diameter). Before sampling into
the ACSM, aerosol particles were dried (below 40 % RH) with a silica gel
diffusion dryer. The ACSM was calibrated routinely with pure ammonium nitrate
to determine its ionization efficiency. More detailed descriptions about the
ACSM are given by Ng et al. (2011) and Sun et al. (2012). A positive matrix
factor analysis was used to analyze the organic spectral matrices according
to Ulbrich et al. (2009). Three factors, i.e., hydrocarbon-like OA (HOA),
cooking OA (COA), and oxygenated OA (OOA), are chosen as the ACSM dataset.
HOA and COA are both primary organic aerosols (POAs) while OOA is the
secondary organic aerosol (SOA).
The ACSM does not detect refractory material such as BC, so a
seven-wavelength aethalometer (AE-33, Magee Scientific Corp.) with a cyclone with PM with
diameters less than 1 µm (PM1) was used to measure
the BC mass concentration of BC particles with diameters <1.0µm. Mineral dust and sea salt are the other refractory
species, but they typically exist in the coarse mode and contribute
negligibly to PM1 (Jurányi et al., 2010; Meng et al., 2014).
Aerosol size distribution and CCN measurements
The aerosol particle number size distribution (15–685 nm) was measured with
a scanning mobility particle sizer (SMPS) that was equipped with a long DMA
(model 3081L, TSI Inc.) and a condensation particle counter (model 3775, TSI
Inc.). A single-column continuous-flow thermal-gradient cloud condensation
nuclei counter (model CCNC-100, DMT Inc.) was used to measure the bulk CCN
number concentration. Five SS levels, i.e., 0.07 %, 0.1 %, 0.2 %,
0.4 %, and 0.8 %, were set in the CCNC and the running time was
10 min for each SS level. The SS levels in the CCNC were calibrated with
pure ammonium sulfate (Rose et al., 2008) before and after the measurement
campaign. The corrected SS levels were 0.11 %, 0.13 %, 0.22 %,
0.40 %, and 0.75 %.
The aerosol activation ratio (AR) at a certain SS is calculated as
NCCN divided by the total particle number concentration in the
15–685 nm range (N15–685nm), i.e., AR=NCCN/N15–685nm. The SMPS does not measure
particle number concentrations below 15 nm. Since the activation critical
diameter is always larger than 15 nm at these SS levels (Zhang et
al., 2014), this does not affect the calculated NCCN. Aerosol
particles with diameters greater than 685 nm are also not detected by the
SMPS. These larger particles will always act as CCN due to their larger dry
sizes. Note that the number concentration above 685 nm in the atmosphere is
always negligible (Jurányi et al., 2010).
Other measurements
In this study, a micro-pulse lidar (MPL-4B, Sigma Space Corp.) was used to
study the evolution of the planetary boundary layer (PBL), which plays a
crucial role in modulating surface air quality (Li et al., 2017b). The pulse
repetition rate of the MPL was 2.5 kHz at a visible wavelength of 532 nm.
The peak value of the optical energy of the laser beam was 8 µJ.
The pulse duration ranged from 10 to 100 ns, and the pulse interval was set
to 200 ns, corresponding to a spatial resolution of 30 m. The MPL-retrieved
PBL height is the altitude at which a sudden decrease in the scattering
coefficient occurs (Brooks, 2003; Quan et al., 2013). Trace-gas analyzers
(manufactured by Ecotech) were used to measure the gaseous species of ozone,
SO2, NOx, NO, and carbon monoxide. More detailed
descriptions of the analyzers are given by Zhu et al. (2016).
Two containers at ground level housed all sampling instruments. Two air
conditioners maintained the temperature at 20–25 ∘C inside the
containers. All stainless tube inlets were ∼1.5 m above the top of the
containers.
TheoryHygroscopicity parameter
To link hygroscopicity measurements below and above the water vapor
saturation, the Köhler theory (Köhler, 1936) is parameterized using
the hygroscopicity parameter κ (Petters and Kreidenweis, 2007). This
is known as the κ-Köhler theory. According to the theory, the
equilibrium equation for a solution droplet at a saturation ratio SD is
SD=D3-Dd3D3-Dd31-κexp4σs/aMwRTρwD,
where D and Dd are the wet and dry droplet diameters,
respectively, σs/a is the surface tension coefficient,
Mw is the mole mass of water, R is the universal gas constant,
T is the temperature, and ρw is the density of water.
Below the water vapor saturation, SD is RH, D is
Dp(RH), and Dd is Dp0 from
Eq. (1). The κ parameter is then calculated using H-TDMA data
according to Eqs. (1) and (2):
κgf=GF3-1⋅1RHexp4σs/aMwRTρwDdGF-1.
For a multicomponent particle, the Zdanovskii–Stokes–Robinson (ZSR) mixing
rule (Stokes and Robinson, 1966) can also estimate κ using chemical
composition data:
κchem=∑iεiκi,
where εi and κi are the volume fraction and κ
for the ith chemical component, respectively. The ACSM provides the mass
concentrations of inorganic ions and organics. A simplified ion-pairing
scheme such as that described by Gysel et al. (2007) is applied to convert
ion mass concentrations to mass concentrations of their corresponding
inorganic salts (see Table S1 in the Supplement). Table S1 also lists
κ and the gravimetric density of each individual component under
SS conditions. In the following discussions, κgf
and κchem denote the hygroscopicity parameters derived from
H-TDMA measurements and estimated using the ZSR mixing rule, respectively.
CCN estimation
The critical supersaturation (sc, sc=Sc-1) for the Dd of a particle with hygroscopicity
κ is calculated from the maximum of the κ-Köhler curve
(Eq. 2; Petters and Kreidenweis, 2007). Dd is also the
critical diameter corresponding to sc when κ is known.
The sc–Dd relationship can thus be established.
According to this relationship, the critical diameter
(D0,crit) can be calculated using the estimated
κchem (Eq. 4) at a given SS. All particles larger than
D0,crit will activate as CCN, assuming that aerosols are
internally mixed. Then the CCN number concentration can be estimated from the
integral of the aerosol size distribution provided by the SMPS from
D0,crit to the maximum measured size (Dmax):
NCCNSS=∫D0,crit(SS)DmaxdN(D)dlog(D)dlog(D).NCCNSS can then be compared to the number
of CCN at the same SS measured by the CCNC.
The time series of (a) the particle number size
distribution; (b) the aerosol number concentration in the 15–50 nm
range (N15–50nm) and the geometric mean diameter
(Dm); (c) the probability density function of
κgf for 40 nm and (d) 150 nm particles from 6 May
to 15 June 2016.
Results and discussionOverview
Figures 2 and 3 show time series of the main aerosol properties measured
during the field campaign. The PNSD changes dramatically (Fig. 2a) and the
aerosol number concentration in the 15–50 nm range
(N15–50nm) increases sharply in the morning almost
every day (Fig. 2b). The time series of the mean diameter (Dm) of
particles also shows that a growth process occurs after the sharp increase in
N15–50nm. All these phenomena suggest that new particle
formation (NPF) events frequently occurred at XT during the field experiment
(Kulmala et al., 2012; Y. Li et al., 2017). This is likely related to the
high concentration of gas precursors from mainly local emissions. High
emissions of SO2 and VOCs associated with the high oxidation capacity
in a polluted atmosphere make NPF events occur more frequently in northern
China (Z. Wang et al., 2017).
Figure 2c–d show time series of the probability density functions (PDFs) of
κgf (κ-PDF) for 40 and 150 nm particles,
respectively. In general, mono-modal κ-PDFs were observed. This is
different from κ-PDFs at other sites in China where bi- and trimodal
distributions dominate (Liu et al., 2011; Ye et al., 2013; Jiang et
al., 2016; S. L. Zhang et al., 2016; Y. Wang et al., 2017). Differences in
the aerosol mixing state explain this (see Sect. 4.2).
Figure 3a shows the bulk mass concentrations of organics, sulfate, nitrate,
ammonium, and chloride measured by the ACSM and the BC mass concentration
measured by the AE-33. Organics and sulfate were the dominant chemical
species with mass fractions in PM1 of 39.1 % and 24.7 %,
respectively. Figure 3b–c show the volume fractions of paired chemical
compositions and κchem, respectively. The average volume
fraction of inorganics ((NH4)2SO4+NH4HSO4+H2SO4+NH4NO4) was similar to that of organics
(POA + SOA), but their volume fractions changed diurnally. In general,
the volume fraction of inorganics increased during daytime while the volume
fraction of organics decreased. SOA was the dominant contributor to OA,
accounting for ∼69 % of the organics volume. This shows that
photochemical reactions were strong at XT during the field campaign (Huang et
al., 2014). The mean κchem in Fig. 3c was 0.31, with values
ranging from 0.20 to 0.40. The trend in κchem was similar to
that of the volume fraction of inorganics. This suggests that inorganics
played a key role in κchem. This is consistent with the
study by Wu et al. (2016).
Time series of (a) the bulk mass concentration of aerosol
species in PM1, (b) the volume fractions of POA, SOA, BC, and
inorganics with the simplified ion-pairing scheme, and (c) the
hygroscopicity parameter derived from the chemical composition
(κchem).
Mean probability density functions of κgf
(κ-PDFs) for different particle sizes and their standard deviations
(shaded areas) derived from H-TDMA data and measured at RH = 85 %.
Aerosol mixing state and hygroscopicity
Figure 4 shows mean κ-PDFs for different particle sizes derived from
H-TDMA data. For all particle sizes considered, κgf ranged
from 0 to 0.8, and the κ-PDF patterns were similar. This suggests that
hygroscopic compounds in different particle size modes were similar at XT. In
general, κ-PDF patterns show only one hydrophilic mode with a weak
hydrophobic mode occasionally appearing at night when photochemical reactions
are weak (Fig. S4). The κ-PDF patterns always show bi- or trimodal
distributions at other sites in China (Liu et al., 2011; Ye et al., 2013;
Jiang et al., 2016; Zhang et al., 2016; Y. Wang et al., 2017). Based on
previous studies (Liu et al., 2011; Y. Wang et al., 2017), ambient aerosols
can be classified into three groups according to their κgf
values:
nearly hydrophobic (NH): κgf<0.1,
less hygroscopic (LH): 0.1≤κgf<0.2,
more hygroscopic (MH): 0.2≤κgf.
Number fractions of different hygroscopic groups for different
particle sizes.
NH: nearly hydrophobic; LH: less hygroscopic; MH: more
hygroscopic.
Table 1 gives the number fractions of each group for different particle
sizes. The MH group dominated all particle sizes. The number fractions of the
NH and LH groups were both less than 6.0 %. However, the volume fractions
of hydrophobic BC and low-hygroscopicity organics (where κBC is
approximately zero and κorganic is typically less than 0.1)
were ∼10.1 % and 47.4 %, respectively, according to chemical
composition measurements (Fig. 3b). This suggests that the particles were
highly aged and internally mixed at XT during the field campaign. The coating
of sulfates and secondary organics during the aging process changes the
structure of BC and makes these particles grow, which can significantly
enhance the hygroscopicities of particles (e.g., Zhang et al., 2008; Jimenez
et al., 2009; Tritscher et al., 2011; Guo et al., 2016). The observed
unimodal distribution of κ-PDF also suggests the internal mixing state
of the particles (Swietlicki et al., 2008).
Figure 5 shows the average size-resolved κgf derived from
H-TDMA data at XT and other sites in China. At XT, κgf values for
different particle sizes were larger in the daytime than at night, and the
difference between daytime and nighttime decreased with increasing particle
size. This suggests that the impact of photochemical reactions on aerosol
hygroscopicity is strong. The effect is weaker with increasing particle size
because most of the larger particles are always well aged.
Size-resolved aerosol hygroscopicity parameter derived from H-TDMA
data at XT and at other sites in China.
The magnitude of κgf was larger at XT than at other sites in
China. In particular, the magnitude of κgf was much larger
at XT than at sites in the northern part of the NCP, i.e., Beijing, Wuqing,
and Xianghe. The lower κgf in the Beijing urban area is
likely related to the more severe traffic emissions there (Ye et al., 2013;
Wu et al., 2016). Wuqing and Xianghe are located in the suburban area between
the two megacities of Beijing and Tianjin and are simultaneously affected by
traffic and industrial emissions. The magnitudes of κgf at
these two sites are higher than at Beijing but lower than at XT. Although
distant from these megacities, XT is situated in the industrial center of the
NCP, so particles there are more internally mixed and highly aged due to the
higher concentrations of precursors and strong photochemical reactions. This
is why κgf at XT is larger than at other sites. This
suggests that the hygroscopicities of particles from different emissions and
chemical processes differ in the NCP. The 40 nm particles were always more
hygroscopic than 80 nm particles at XT, especially in the daytime. This
differed from other sites likely because the coating effect of sulfates and
secondary organics is more significant for smaller particles (Tritscher et
al., 2011; Guo et al., 2016). Furthermore, since the field measurements took
place in a locality with heavy industrial activities, it is possible that
amine contributed significantly to the hygroscopicity of 40 nm particles.
Several studies have shown that amine compounds in the aerosol phase can be
hygroscopic, sometimes even at low RH (e.g., Qiu and Zhang, 2012; Chu et
al., 2015; Gomez-Hernandez et al., 2016).
Diurnal variations in (a) planetary boundary layer (PBL)
height retrieved from micro-pulse lidar data; (b) aerosol number and
mass concentrations in the 15–685 nm range (N15–685nm
and PM15–685nm, respectively) derived from the SMPS (an
aerosol density of 1.6 g cm-3 is assumed); (c) the
hygroscopicity parameter derived from the hygroscopic growth factor (κgf) and predicted from the bulk chemical composition (κchem); and (d) the mass fractions of different species.
Diurnal variations in aerosol propertiesDiurnal variations in aerosol number and mass concentrations
Figure 6a shows the diurnal variation in MPL-derived PBL height. The PBL
height is the altitude at which a sudden decrease in the MPL-measured scattering
coefficient occurs (Cohn and Angevine, 2000; Brooks, 2003). Note that the
retrieved PBL height is only valid from 07:00 to 19:00 local time (LT) (Quan
et al., 2013). The retrieved PBL height at night is not accurate because of
the likely influence of residual aerosols within the nocturnal PBL. The
evolution of PBL height from 07:00 to 19:00 LT is sufficient to analyze its
link with the change in aerosol number and mass concentrations during the
daytime. Figure 6b shows diurnal variations in aerosol number and mass
concentrations in the 15–685 nm range (N15–685nm and
PM15–685nm, respectively). Variations in
N15–685nm and PM15–685nm trended
opposite from each other. From 08:00 to 14:00 LT, the PBL height lifted from
∼0.5 to ∼0.6 km, while PM15–685nm
generally decreased from ∼24 to ∼19µgm-3. This
suggests the important effect of PBL evolution on
PM15–685nm. However, N15–685nm
sharply increased from ∼7600 cm-3 at 07:00 LT to ∼13000 cm-3 at 13:00 LT. This is related to the sudden burst of
small Aitken-mode particles (<50 nm) during NPF events. Newly formed fine
particles contribute little to PM15–685nm. In the
evening, PM15–685nm increased gradually while
N15–685nm decreased. The decline of the nocturnal PBL
and particle coagulation and growth explains this. In other words, the
evolution of the PBL influenced the aerosol mass concentration, while
particle formation and growth had a greater influence on the variation in
aerosol number concentration.
Diurnal variation in aerosol hygroscopicity
Figure 6c shows diurnal variations in κgf and
κchem. Values of κgf for different particle
sizes increased in the morning when the NPF event started. The increase was
sharpest for 40 nm particles. The increase in κgf in the
morning synchronized with the particle number concentration
(N15–685nm) but not with the PBL height, further
suggesting the impact of photochemical reactions on aerosol hygroscopicity.
The κgf for 40 nm particles increased from ∼0.32 at
07:00 LT to ∼0.44 at 15:00 LT and approached the κ value of
pure ammonium sulfate. This also suggests that a large number of hygroscopic
compounds were produced during NPF events. Figure S5 shows sharply increased
concentrations of SO2 and VOCs in the morning and enhanced
atmospheric oxidation capacity under high RH and low T conditions. The
production of sulfate and SOAs resulted. This is why aerosol hygroscopicity
and the occurrence of NPF events increased. Zhang et al. (2018) characterized
the aerosol chemistry during NPF events in this field campaign. The diurnal
pattern in κgf for 80–200 nm particles differs from that
of 40 nm particles. The differences in κgf for 80–200 nm
particles in the early morning were large but gradually decreased as the sun
rose. The κgf for 80–200 nm particles was similar but
lower than that for 40 nm particles after 11:00 LT. The condensation of
sulfates and secondary organics likely caused the enhanced hygroscopicity of
the 40–200 nm particles, especially of 40 nm particles (Fig. 6d).
Figure 6c also shows that the κchem for PM1 was lower
than the κgf for 40–200 nm particles and had a weaker
diurnal variation. This feature was stronger at noon when atmospheric
oxidation and the aging process were more rapid. The simple ZSR mixing rule
is responsible for this. During the daytime, the condensation of sulfuric
acid on organics or BC greatly enhances their hygroscopicities (Zhang et
al., 2008, 2017). The ZSR model cannot accurately represent this phenomenon.
Cruz and Pandis (2000) have shown that the measured κgf of
internally mixed (NH4)2SO4–OAs is larger
than the predicted κchem based on the ZSR model.
In summary, the ample supply of SO2 and VOCs provided sufficient
precursors for the strong photochemical reactions at XT during this field
campaign, and the production and condensation of sulfate and SOAs greatly
enhanced aerosol hygroscopicity, especially during the daytime. The oxidation
of precursors likely induced the observed frequent NPF events.
Diurnal variations in (a) CCN number concentration
(NCCN) and activation ratio (AR), and (b) the normalized
aerosol size distribution in the 15–685 nm particle size range.
Estimated versus measured cloud condensation nuclei (CCN) number
concentrations (NCCN) for ambient aerosols at four different
supersaturation (SS) levels. The NCCN is estimated based on
κ-Köhler theory using the real-time
κchem(a1–a4) and the mean
κchem(b1–b4). The slope and coefficient of
determination (R2) of the linear regression and the relative deviation
(RD) of estimated NCCN (RD =|NCCN_estimated-NCCN_measured|/NCCN_measured) are shown in each
panel. The regression line is overlaid on the measurements (solid line) and
the dashed line is the 1 : 1 line.
Sensitivity of NCCN estimates to κchem
as a function of time at (a) SS = 0.22% and
(b) SS = 0.75 %. The color scale indicates the relative
deviation (RD) of CCN estimates using the κchem value shown
on the ordinate. In each panel, open circles show the real-time
κchem. Note that RD is by definition zero at these points.
The black line is κ at RD = 10 % and the red line is the mean
value for κchem (0.31). Figure S8 shows the same plots but
for SS = 0.13 % and 0.40 %.
Diurnal variation in CCN number concentration and activation
ratio
Figure 7a shows the diurnal variations in NCCN and AR at
different SS levels. In the morning, NCCN first decreased then
increased,
while AR showed the opposite trend. This is related to the evolution of the
PBL and NPF events. At the initial stage of an NPF event, the newly formed
particles were less than 15 nm in size, which was below the detection limit
of the SMPS. As a result, N15–685nm decreased (Fig. 6b)
as the PBL lifted, and NCCN also decreased. However, the mixing
of aged particles within the PBL made the particle size (Fig. 7b) and AR
increase slightly. Condensation and the growth of new particles caused the
number of fine particles detected by the SMPS to increase rapidly. However,
because of their smaller sizes, some of these particles were not activated.
Therefore, NCCN increased, but AR decreased from 08:00 to
14:00 LT. In the afternoon and evening, NCCN and AR increased
slightly as particle sizes increased (Fig. 7b). These trends weakened as SS
decreased because the critical diameter is larger at low SS values and the influence
of aerosol size distribution on NCCN and AR is relatively weaker.
Particle size was the most important factor influencing aerosol activation
and CCN number concentrations, especially at larger SS values. Figure S6 shows the
results from a sensitivity test of particle size in a CCN closure study
similar to that performed by Dusek et al. (2006).
CCN estimation from chemical composition data
This section presents a CCN closure study and a discussion of the impact of
chemical composition on NCCN. It is reasonable to assume that
aerosols are internally mixed when estimating NCCN because H-TDMA
data showed that this was the case at XT. Figure 8a shows estimated
NCCN as a function of measured NCCN using real-time
κchem. The estimated NCCN correlates well with
measurements (R2≥0.85), but is generally overestimated. The slope of
each linearly fitted line is greater than 1.10 and increases with increasing
SS. The relative deviation (RD) increases from 16.2 % to 25.2 % as SS
increases from 0.13 % to 0.75 %, suggesting that estimates become
worse at larger SS values. The large measurement uncertainties of CCNC mainly cause
the overestimation of NCCN: (1) the temperature or high flow
rates in the CCNC may not allow enough time for particles to reach sizes
large enough to be counted by the optical particle counter at the exit of the
CCN chamber (Lance et al., 2006; Cubison et al., 2008) and (2) in high-particle-number-concentration environments, water depletion in the CCNC may
reduce the counting rate of the CCNC (Deng et al., 2011). These uncertainties
make measured NCCN lower than the actual NCCN. At
larger SS values, activated aerosols in the cloud chamber of the CCNC are greater in
number and smaller in size, so the impact of these uncertainties is greater.
Figure S7 shows results from the NCCN closure study for separated
NCCN. The CCN closure is reasonable when NCCN<5500 cm-3.
Figure 8b shows estimated NCCN using the mean value for κchem (κchem=0.31). Compared with results using
real-time values for κchem, the fit parameters and RD change
slightly, suggesting that the effect of chemical composition on
NCCN is weaker relative to the particle size. Figure 9 shows the
sensitivity of estimated NCCN to the variability in chemical
composition. The variability in the equipotential lines of RD suggests that
the sensitivity of NCCN is strongly time dependent. This is
attributed to the variability in the shape of the aerosol size distribution
(Jurányi et al., 2010), which further demonstrates the importance of
particle size to NCCN. The sensitivity of NCCN to
chemical composition (κchem) becomes weaker with increasing
SS, suggesting that chemical composition becomes less important in
NCCN estimates at larger SS. RD is always less than 10 % when
estimating NCCN using the mean value of κchem.
The value κ=0.31 is thus a good reference value to model
NCCN in this region.
In summary, the particle size is the most important factor influencing
aerosol activation at XT, especially at larger SS. The chemical composition
was not as important when estimating NCCN because particles were
highly aged and internally mixed at XT. Aerosol hygroscopicity was not
sensitive to estimates of NCCN.
Summary and conclusions
The Atmosphere–Aerosol–Boundary
Layer–Cloud (A2BC) Interaction Joint Experiment was carried out at a suburban
site (Xingtai, or XT) located in the central North China Plain (NCP) from
1 May to 15 June 2016. The study investigated aerosol hygroscopicity, the
mixing state, and CCN activity at XT.
In general, the probability density function (PDF) of the hygroscopicity
parameter κ (κ-PDF) for 40–200 nm particles was a unimodal
distribution, which is different from distributions at other sites in China.
Particles of all sizes covered a large range of κgf values (the
hygroscopicity parameter derived from H-TDMA measurements; mostly from 0 to
0.8) and showed similar κ-PDF patterns, suggesting that the
hygroscopic compounds in these particles from 40 to 200 nm were similar at
XT. The κ-PDF patterns also suggest that particles were highly aged
and internally mixed at XT during the field campaign. This is likely related
to strong photochemical reactions.
The mean κgf for different particle sizes was larger in the
daytime than at night. Daytime and nighttime κgf differences
decreased with increasing particle size. The impact of photochemical
reactions on aerosol hygroscopicity was strong, and the effect became weaker
as particle size increased. The coating of sulfates or secondary organics
likely enhanced the hygroscopicities of 40–200 nm particles. This effect
was more significant for 40 nm particles. Compared with other sites in
China, the aerosol hygroscopicity was much larger at XT because of the
sufficient number of precursors and strong atmospheric oxidation. The
comparison also shows that the hygroscopicities of particles from different
emissions and chemical processes differed greatly.
New particle formation events occurred frequently at XT during this field
campaign. The evolution of the planetary boundary layer influenced the
aerosol mass concentration, while particle formation and growth had a greater
influence on the variation in aerosol number concentration. Particle size was
the most important factor influencing aerosol activation and the CCN number
concentration (NCCN) at XT, especially at larger supersaturation
(SS). Although estimated NCCN correlated well with measurements
(R2≥0.85), NCCN was overestimated because of measurement
uncertainties. The effect of chemical composition on NCCN was
weaker relative to the particle size. Sensitivity tests show that the impact
of chemical composition on NCCN became weaker as SS increased,
suggesting that the effect of chemical composition on the estimation of
NCCN is less important at larger SS values. The value κ=0.31 is
a good proxy for NCCN in this region.
XT is located in the most polluted region in China. The multitude of
factories in the region generate strong emissions. The plenitude of gas
precursors and strong photochemical reactions at XT make aerosol properties
there unique. More field measurements on gas–particle transformation and
aerosol properties in this region are needed for studying haze formation
mechanisms and climate effects.
Data used in the study are available from the first author
upon request (wang.yuying@mail.bnu.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-11739-2018-supplement.
ZL and YW designed the experiment; YW, YZ, and WD
carried it out and analyzed the data; other co-authors participated in
science discussions and suggested analyses. YW prepared the paper with
contributions from all co-authors.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Regional transport
and transformation of air pollution in eastern China”. It is not associated
with a conference.
Acknowledgements
This work was funded by National Key R&D Program of China
(2017YFC1501702), National
Natural Science Foundation of China (NSFC) research projects (grant no.
91544217, 41675141, 41705125), the National Basic Research Program of China
“973” (grant no. 2013CB955801), and the China Scholarship Council (award
no. 201706040194). We thank all participants in the field campaign for their
tireless work and cooperation.
Edited by: Yuan Wang
Reviewed by: two anonymous referees
ReferencesAlbrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness,
Science, 245, 1227–1230, 10.1126/science.245.4923.1227, 1989.Brooks, I. M.: Finding boundary layer top: application of a wavelet
covariance transform to lidar backscatter profiles, J. Atmos. Ocean. Tech.,
20, 1092–1105,
10.1175/1520-0426(2003)020<1092:FBLTAO>2.0.CO;2,
2003.Chu, Y., Sauerwein, M., and Chan, C. K.: Hygroscopic and phase transition
properties of alkyl aminium sulfates at low relative humidities, Phys. Chem.
Chem. Phys., 17, 19789–19796, 10.1039/c5cp02404h, 2015.Cohn, S. A. and Angevine. W. M.: Boundary layer height and entrainment zone
thickness measured by lidars and wind-profiling radars, J. Appl. Meteorol.,
39, 1233–1247,
10.1175/1520-0450(2000)039<1233:BLHAEZ>2.0.CO;2,
2000.Cruz, C. N. and Pandis, S. N.: Deliquescence and hygroscopic growth of mixed
inorganic-organic atmospheric aerosol, Environ. Sci. Technol., 34,
4313–4319, 10.1021/es9907109, 2000.Cubison, M. J., Ervens, B., Feingold, G., Docherty, K. S., Ulbrich, I. M.,
Shields, L., Prather, K., Hering, S., and Jimenez, J. L.: The influence of
chemical composition and mixing state of Los Angeles urban aerosol on CCN
number and cloud properties, Atmos. Chem. Phys., 8, 5649–5667,
10.5194/acp-8-5649-2008, 2008.Deng, Z. Z., Zhao, C. S., Ma, N., Liu, P. F., Ran, L., Xu, W. Y., Chen, J.,
Liang, Z., Liang, S., Huang, M. Y., Ma, X. C., Zhang, Q., Quan, J. N., Yan,
P., Henning, S., Mildenberger, K., Sommerhage, E., Schäfer, M.,
Stratmann, F., and Wiedensohler, A.: Size-resolved and bulk activation
properties of aerosols in the North China Plain, Atmos. Chem. Phys., 11,
3835–3846, 10.5194/acp-11-3835-2011, 2011.Dusek, U., Frank, G. P., Hildebrandt, L., Curtius, J., Schneider, J., Walter,
S., Chand, D., Drewnick, F., Hings, S., and Jung D.: Size matters more than
chemistry for cloud-nucleating ability of aerosol particles, Science, 312,
1375–1378, 10.1126/science.1125261, 2006.Eichler, H., Cheng, Y. F., Birmili, W., Nowak, A., Wiedensohler, A.,
Brüggemann, E., Gnauk, T., Herrmann, H., Althausen, D., Ansmann, A.,
Engelmann, R., Tesche, M., Wendisch, M., Zhang, Y. H., Hu, M., Liu, S., and
Zeng, L. M.: Hygroscopic properties and extinction of aerosol particles at
ambient relative humidity in South-Eastern China, Atmos Environ, 42,
6321–6334, 10.1016/j.atmosenv.2008.05.007, 2008.Ervens, B., Cubison, M., Andrews, E., Feingold, G., Ogren, J. A., Jimenez, J.
L., DeCarlo, P., and Nenes, A.: Prediction of cloud condensation nucleus
number concentration using measurements of aerosol size distributions and
composition and light scattering enhancement due to humidity, J. Geophys.
Res., 112, D10S32, 10.1029/2006JD007426, 2007.Fu, G. Q., Xu, W. Y., Yang, R. F., Li, J. B., and Zhao, C. S.: The
distribution and trends of fog and haze in the North China Plain over the
past 30 years, Atmos. Chem. Phys., 14, 11949–11958,
10.5194/acp-14-11949-2014, 2014.Gomez-Hernandez, M., McKeown, M., Secrest, J., Marrero-Ortiz, W., Lavi, A.,
Rudich, Y., Collins, D. R., and Zhang, R.: Hygroscopic characteristics of
alkylaminium carboxylate aerosols, Environ. Sci. Technol., 50, 2292–2300,
10.1021/acs.est.5b04691, 2016.Guo, S., Hu, M., Lin, Y., Gomez-Hernandez, M., Zamora, M. L., Peng, J.,
Collins, D. R., and Zhang, R.: OH-Initiated oxidation of m-xylene on black
carbon aging, Environ. Sci. Technol., 50, 8605–8612,
10.1021/acs.est.6b01272, 2016.Gysel, M., Crosier, J., Topping, D. O., Whitehead, J. D., Bower, K. N.,
Cubison, M. J., Williams, P. I., Flynn, M. J., McFiggans, G. B., and Coe, H.:
Closure study between chemical composition and hygroscopic growth of aerosol
particles during TORCH2, Atmos. Chem. Phys., 7, 6131–6144,
10.5194/acp-7-6131-2007, 2007.Huang, R., Zhang, Y., Bozzetti, C., Ho, K., Cao, J., Han, Y., Daellenbach, K.
R., Slowik, J. G., Platt, S. M., Canonaco, F., Zotter, P., Wolf, R., Pieber,
S. M., Bruns, E. A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski,
M., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., An, Z., Szidat, S.,
Baltensperger, U., Haddad, I. E., and Prévôt, A. S. H.: High
secondary aerosol contribution to particulate pollution during haze events in
China, Nature, 514, 218–222, 10.1038/nature13774, 2014.
IPCC: Climate change 2013: Scientific basis, Fifth assessment of the
Inter-governmental Panel on Climate Change, Cambridge University Press,
2013.Jacobson, M. C., Hansson, H. C., Noone, K. J., and Charlson, R. J.: Organic
atmospheric aerosols: review and state of the science, Rev. Geophys., 38,
267–294, 10.1029/1998RG000045, 2000.Jiang, R. X., Tan, H. B., Tang, L. L., Cai, M. F., Yin, Y., Li, F., Liu, L.,
Xu, H. B., Chan, P. W., Deng, X. J., and Wu, D.: Comparison of aerosol
hygroscopicity and mixing state between winter and summer seasons in Pearl
River Delta region, China, Atmos. Res., 169, 160–170,
10.1016/j.atmosres.2015.09.031, 2016.Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., et al.: Evolution of
organic aerosols in the atmosphere, Science, 326, 1525–1529,
10.1126/science.1180353, 2009.Jurányi, Z., Gysel, M., Weingartner, E., DeCarlo, P. F., Kammermann, L.,
and Baltensperger, U.: Measured and modelled cloud condensation nuclei number
concentration at the high alpine site Jungfraujoch, Atmos. Chem. Phys., 10,
7891–7906, 10.5194/acp-10-7891-2010, 2010.Köhler, H.: The nucleus in and the growth of hygroscopic droplets,
T. Faraday Soc., 32, 1152–1161, 10.1039/TF9363201152, 1936.Kulmala, M., Petäjä, T., Nieminen, T., Sipilä, M., Manninen, H.
E., Lehtipalo, K., Dal Maso, M., Aalto, P. P., Junninen, H., Paasonen, P.,
Riipinen, I., Lehtinen, K. E. J., Laaksonen, A., and Kerminen, V.-M.:
Measurement of the nucleation of atmospheric aerosol particles, Nat. Protoc.,
7, 1651–1667, 10.1038/nprot.2012.091, 2012.Lance, S., Nenes, A., Medina, J., and Smith, J. N.: Mapping the operation of
the DMT continuous flow CCN counter, Aerosol Sci. Tech., 40, 242–254,
10.1080/02786820500543290, 2006.Lebo, Z. J., Shipway, B. J., Fan, J., Geresdi, I., Hill, A., Miltenberger,
A., Morrison, H., Rosenberg, P., Varble, A., and Xue, L.: Challenges for
cloud modeling in the context of aerosol-cloud-precipitation interactions,
B. Am. Meteorol. Soc., 98, 1749–1755, 10.1175/BAMS-D-16-0291.1, 2017.Li, Y., Zhang, F., Li, Z., Sun, L., Wang, Z., Li, P., Sun, Y., Ren, J., Wang,
Y., Cribb, M., and Yuan, C.: Influences of aerosol physiochemical properties
and new particle formation on CCN activity from observation at a suburban
site of China, Atmos. Res., 188, 80–89,
10.1016/j.atmosres.2017.01.009, 2017.Li, Z., Lau, W. K.-M., Ramanathan, V., Wu, G., Ding, Y., Manoj, M. G., Liu,
J., Qian, Y., Li, J., Zhou T., Fan, J., Rosenfeld, D., Ming, Y., Wang, Y.,
Huang, J., Wang, B., Xu, X., Lee, S.-S., Cribb, M., Zhang, F., Yang, X.,
Zhao, C., Takemura, T., Wang, K., Xia, X., Yin, Y., Zhang, H., Guo, J., Zhai,
P. M., Sugimoto, N., Babu, S. S., and Brasseur, G. P.: Aerosol and monsoon
climate interactions over Asia, Rev. Geophys., 54, 866–929,
10.1002/2015RG000500, 2016.Li, Z., Daniel, R., and Fan, J. W.: Aerosols and their impact on radiation,
clouds, precipitation, and severe weather events, Oxford Research
Encyclopedias: Environmental Science, Interactive Factory,
10.1093/acrefore/9780199389414.013.126, 2017a.Li, Z., Guo, J., Ding, A., Liao, H., Liu, J., Sun, Y., Wang, T., Xue, H.,
Zhang, H., and Zhu, B.: Aerosols and boundary-layer interactions and impact
on air quality, Natl. Sci. Rev., 4, 810–833, 10.1093/nsr/nwx117,
2017b.Liu, P. F., Zhao, C. S., Göbel, T., Hallbauer, E., Nowak, A., Ran, L.,
Xu, W. Y., Deng, Z. Z., Ma, N., Mildenberger, K., Henning, S., Stratmann, F.,
and Wiedensohler, A.: Hygroscopic properties of aerosol particles at high
relative humidity and their diurnal variations in the North China Plain,
Atmos. Chem. Phys., 11, 3479–3494, 10.5194/acp-11-3479-2011, 2011.Lopez-Yglesias, X. F., Yeung, M. C., Dey, S. E., Brechtel, F. J., and Chan,
C. K.: Performance evaluation of the Brechtel Mfg. Humidified Tandem
Differential Mobility Analyzer (BMI HTDMA) for studying hygroscopic
properties of aerosol particles, Aerosol Sci. Tech., 48, 969–980,
10.1080/02786826.2014.952366, 2014.Meng, J. W., Yeung, M. C., Li, Y. J., Lee, B. Y. L., and Chan, C. K.:
Size-resolved cloud condensation nuclei (CCN) activity and closure analysis
at the HKUST Supersite in Hong Kong, Atmos. Chem. Phys., 14, 10267–10282,
10.5194/acp-14-10267-2014, 2014.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, 2011.Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of
hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem.
Phys., 7, 1961–1971, 10.5194/acp-7-1961-2007, 2007.Qiu, C. and Zhang, R.: Physiochemical properties of alkylaminium sulfates:
hygroscopicity, thermostability, and density, Environ. Sci. Technol., 46,
4474–4480, 10.1021/es3004377, 2012.Quan, J., Gao, Y., Zhang, Q., Tie, X., Cao, J., Han, S., Meng, J., Chen, P.,
and Zhao, D.: Evolution of planetary boundary layer under different weather
conditions, and its impact on aerosol concentrations, Particuology, 11,
34–40, 10.1016/j.partic.2012.04.005, 2013.Ramanathan, V., Crutzen, P. J., Kiehl, J. T., and Rosenfeld, D.: Aerosols,
climate, and the hydrological cycle, Science, 294, 2119–2124,
10.1126/science.1064034, 2001.Rose, D., Gunthe, S. S., Mikhailov, E., Frank, G. P., Dusek, U., Andreae, M.
O., and Pöschl, U.: Calibration and measurement uncertainties of a
continuous-flow cloud condensation nuclei counter (DMT-CCNC): CCN activation
of ammonium sulfate and sodium chloride aerosol particles in theory and
experiment, Atmos. Chem. Phys., 8, 1153–1179, 10.5194/acp-8-1153-2008,
2008.Rosenfeld, D., Lohmann, U., Raga, G. B., O'Dowd, C. D., Kulmala, M., Fuzzi,
S., Reissell, A., and Andreae, M. O.: Flood or drought: How do aerosols
affect precipitation?, Science, 321, 1309—1313,
10.1126/science.1160606, 2008.Schmale, J., Henning, S., Decesari, S., Henzing, B., Keskinen, H., Sellegri,
K., Ovadnevaite, J., Pöhlker, M. L., Brito, J., Bougiatioti, A.,
Kristensson, A., Kalivitis, N., Stavroulas, I., Carbone, S., Jefferson, A.,
Park, M., Schlag, P., Iwamoto, Y., Aalto, P., Äijälä, M.,
Bukowiecki, N., Ehn, M., Frank, G., Fröhlich, R., Frumau, A., Herrmann,
E., Herrmann, H., Holzinger, R., Kos, G., Kulmala, M., Mihalopoulos, N.,
Nenes, A., O'Dowd, C., Petäjä, T., Picard, D., Pöhlker, C.,
Pöschl, U., Poulain, L., Prévôt, A. S. H., Swietlicki, E.,
Andreae, M. O., Artaxo, P., Wiedensohler, A., Ogren, J., Matsuki, A., Yum, S.
S., Stratmann, F., Baltensperger, U., and Gysel, M.: Long-term cloud
condensation nuclei number concentration, particle number size distribution
and chemical composition measurements at regionally representative
observatories, Atmos. Chem. Phys., 18, 2853–2881,
10.5194/acp-18-2853-2018, 2018.Stokes, R. H. and Robinson, R. A.: Interactions in aqueous nonelectrolyte
solutions. I. Solute-solvent equilibria, J. Phys. Chem., 70, 2126–2131,
10.1021/j100879a010, 1966.
Stolzenburg, M. R. and McMurry, P. H.: TDMAFIT user's manual, Department of
Mechanical Engineering, Particle Technology Laboratory, University of
Minnesota, Minneapolis, 1–61, 1988.Stolzenburg, M. R. and McMurry, P. H.: Equations governing single and tandem
DMA configurations and a new lognormal approximation to the transfer
function, Aerosol Sci. Tech., 42, 421–432, 10.1080/02786820802157823,
2008.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.Swietlicki, E., Hansson, H. C., Hämeri, K., Svenningsson, B., Massling,
A., McFiggans, G., McMurry, P. H., Petäjä, T., Tunved, P., Gysel, M.,
Topping, D., Weingartner, E., Baltensperger, U., Rissler, J., Wiedensohler,
A., and Kulmala, M.: Hygroscopic properties of submicrometer atmospheric
aerosol particles measured with H-TDMA instruments in various environments –
a review, Tellus B, 60, 432–469, 10.1111/j.1600-0889.2008.00350.x,
2008.Tan, H., Xu, H., Wan, Q., Li, F., Deng, X., Chan, P. W., Xia, D., and Yin,
Y.: Design and application of an unattended multifunctional H-TDMA system, J.
Atmos. Ocean. Tech., 30, 1136–1148, 10.1175/JTECH-D-12-00129.1, 2013.Tritscher, T., Jurányi, Z., Martin, M., Chirico, R., Gysel, M., Heringa,
M. F., DeCarlo, P. F., Sierau, B., Prévôt, A. S. H., Weingartner, E.,
and Baltensperger, U.: Changes of hygroscopicity and morphology during ageing
of diesel soot, Environ. Res. Lett., 6, 034026,
10.1088/1748-9326/6/3/034026, 2011.Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8,
1251–1256, 10.1016/0004-6981(74)90004-3, 1974.Ulbrich, I. M., Canagaratna, M. R., Zhang, Q., Worsnop, D. R., and Jimenez,
J. L.: Interpretation of organic components from Positive Matrix
Factorization of aerosol mass spectrometric data, Atmos. Chem. Phys., 9,
2891–2918, 10.5194/acp-9-2891-2009, 2009.Wang, L. T., Wei, Z., Yang, J., Zhang, Y., Zhang, F. F., Su, J., Meng, C. C.,
and Zhang, Q.: The 2013 severe haze over southern Hebei, China: model
evaluation, source apportionment, and policy implications, Atmos. Chem.
Phys., 14, 3151–3173, 10.5194/acp-14-3151-2014, 2014.Wang, Y., Zhang, F., Li, Z., Tan, H., Xu, H., Ren, J., Zhao, J., Du, W., and
Sun, Y.: Enhanced hydrophobicity and volatility of submicron aerosols under
severe emission control conditions in Beijing, Atmos. Chem. Phys., 17,
5239–5251, 10.5194/acp-17-5239-2017, 2017.Wang, Z., Wu, Z., Yue, D., Shang, D., Guo, S., Sun, J., Ding, A., Wang, L.,
Jiang, J., Guo, H., Cheung, H. C., Morawska, L., Keywoodm, M., and Hu, M.:
New particle formation in China: current knowledge and further directions,
Sci. Total Environ., 577, 258–266, 10.1016/j.scitotenv.2016.10.177,
2017.Wu, Z. J., Zheng, J., Shang, D. J., Du, Z. F., Wu, Y. S., Zeng, L. M.,
Wiedensohler, A., and Hu, M.: Particle hygroscopicity and its link to
chemical composition in the urban atmosphere of Beijing, China, during
summertime, Atmos. Chem. Phys., 16, 1123-138, 10.5194/acp-16-1123-2016,
2016.Ye, X., Tang, C., Yin, Z., Chen, J., Ma, Z., Kong, L., Yang, X., Gao, W., and
Geng, F.: Hygroscopic growth of urban aerosol particles during the 2009
Mirage-Shanghai Campaign, Atmos. Environ., 64, 263–269,
10.1016/j.atmosenv.2012.09.064, 2013.Zhang, F., Li, Y., Li, Z., Sun, L., Li, R., Zhao, C., Wang, P., Sun, Y., Liu,
X., Li, J., Li, P., Ren, G., and Fan, T.: Aerosol hygroscopicity and cloud
condensation nuclei activity during the AC3Exp campaign: implications for
cloud condensation nuclei parameterization, Atmos. Chem. Phys., 14,
13423–13437, 10.5194/acp-14-13423-2014, 2014.Zhang, F., Li, Z., Li, Y., Sun, Y., Wang, Z., Li, P., Sun, L., Wang, P.,
Cribb, M., Zhao, C., Fan, T., Yang, X., and Wang, Q.: Impacts of organic
aerosols and its oxidation level on CCN activity from measurement at a
suburban site in China, Atmos. Chem. Phys., 16, 5413–5425,
10.5194/acp-16-5413-2016, 2016.Zhang, F., Wang, Y., Peng, J., Ren, J., Collins, D., Zhang, R., Sun, Y.,
Yang, X., and Li, Z.: Uncertainty in predicting CCN activity of aged and
primary aerosols, J. Geophys. Res.-Atmos., 122, 11723–11736,
10.1002/2017JD027058, 2017.Zhang, R., Khalizov, A. F., Pagels, J., Zhang, D., Xue, H., and McMurry, P.
H.: Variability in morphology, hygroscopicity, and optical properties of soot
aerosols during atmospheric processing, P. Natl. Acad. Sci. USA, 105,
10291–10296, 10.1073/pnas.0804860105, 2008.Zhang, S. L., Ma, N., Kecorius, S., Wang, P. C., Hu, M., Wang, Z. B.,
Größ, J., Wu, Z. J., and Wiedensohler, A.: Mixing state of
atmospheric particles over the North China Plain, Atmos. Environ., 125,
Part A, 152–164, 10.1016/j.atmosenv.2015.10.053, 2016.
Zhang, Y., Du, W., Wang, Y., Wang, Q., Wang, H., Zheng, H., Zhang, F., Shi,
H., Bian, Y., Han, Y., Fu, P., Canonaco, F., Prévôt, A. S. H., Zhu,
T., Wang, P., Li, Z., and Sun, Y.: Aerosol chemistry and particle growth
events at an urban downwind site in the North China Plain, Atmos. Chem. Phys.
Discuss., 10.5194/acp-2017-889, in review, 2018.Zhu, Y., Zhang, J., Wang, J., Chen, W., Han, Y., Ye, C., Li, Y., Liu, J.,
Zeng, L., Wu, Y., Wang, X., Wang, W., Chen, J., and Zhu, T.: Distribution and
sources of air pollutants in the North China Plain based on on-road mobile
measurements, Atmos. Chem. Phys., 16, 12551–12565,
10.5194/acp-16-12551-2016, 2016.