ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-20-915-2020Contrasting size-resolved hygroscopicity of fine particles derived by HTDMA
and HR-ToF-AMS measurements between summer and winter in Beijing: the impacts of
aerosol aging and local emissionsImpacts of aging/sources on aerosols hygroscopicityFanXinxinLiuJieyaoZhangFangfang.zhang@bnu.edu.cnhttps://orcid.org/0000-0002-5395-601XChenLuCollinsDonXuWeiqiJinXiaoaiRenJingyeWangYuyinghttps://orcid.org/0000-0001-9762-8563WuHaoLiShangzeSunYelehttps://orcid.org/0000-0003-2354-0221LiZhanqinghttps://orcid.org/0000-0001-6737-382XCollege of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, ChinaDepartment of Chemical and Environmental Engineering, University of California Riverside, Riverside, California, 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, ChinaSchool of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaEarth System Science Interdisciplinary Center and Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
The effects of aerosols on visibility through scattering and absorption of
light and on climate through altering cloud droplet concentration are
closely associated with their hygroscopic properties. Here, based on field
campaigns in winter and summer in Beijing, we compare the size-resolved
hygroscopic parameter (κgf) of ambient fine particles derived by an HTDMA (hygroscopic tandem differential mobility analyzer) to that
(denoted as κchem) calculated by an HR-ToF-AMS
(high-resolution time-of-flight aerosol mass spectrometer) measurements
using a simple rule with the hypothesis of uniform internal mixing of
aerosol particles. We mainly focus on contrasting the disparity of κgf and κchem between summer and winter to reveal the impact
of atmospheric processes/emission sources on aerosol hygroscopicity and to
evaluate the uncertainty in estimating particle hygroscopicity with the
hypothesis. We show that, in summer, the κchem for 110, 150, and 200 nm particles was on average ∼10 %–12 % lower than
κgf, with the greatest difference between the values observed
around noontime when aerosols experience rapid photochemical aging. In
winter, no apparent disparity between κchem and κgf is observed for those >100 nm particles around
noontime, but the κchem is much higher than κgf
in the late afternoon when ambient aerosols are greatly influenced by local
traffic and cooking sources. By comparing with the observation from the other two sites (Xingtai, Hebei and Xinzhou, Shanxi) of north China, we verify
that atmospheric photochemical aging of aerosols enhances their
hygroscopicity and leads to 10 %–20 % underestimation in κchem if using the uniform internal mixing assumption. The effect is
found more significant for these >100 nm particles observed in
remote or clean regions. The lower κchem likely resulted
from multiple impacts of inappropriate application of the density and
hygroscopic parameter of organic aerosols in the calculation, as well as
influences from chemical interaction between organic and inorganic compounds
on the overall hygroscopicity of mixed particles. We also find that
local/regional primary emissions, which result in a large number of
externally mixed BC (black carbon) and POA (primary organic aerosol) in urban Beijing
during traffic rush hour time, cause a 20 %–40 % overestimation of the
hygroscopic parameter. This is largely due to an inappropriate use of
density of the BC particles that is closely associated with its morphology
or the degree of its aging. The results show that the calculation can be
improved by applying an effective density of fresh BC (0.25–0.45 g cm-3) in the mixing rule assumption. Our study suggests that it is critical to measure the effective density and morphology of ambient BC, in particular in those regions with influences of rapid secondary
conversion/aging processes and local sources, so as to accurately
parameterize the effect of BC aging on particle hygroscopicity.
Introduction
The effects of aerosols on visibility through scattering and absorption of
light and on climate through altering cloud droplet concentration are
influenced by their hygroscopic growth. Understanding and reducing the
uncertainty in prediction of the aerosol hygroscopic parameter (κ)
using chemical composition would improve model predictions of aerosol
effects on clouds and climate.
The hygroscopic properties of both the natural and anthropogenic aerosols,
in addition to being affected by its chemical composition (Gunthe et al.,
2009), are also affected by the particle mixing state and aging (Schill et
al., 2015; Peng et al., 2017a). For example, a recent laboratory study
showed that the coexisting hygroscopic species have a strong influence on
the phase state of particles, thus affecting chemical interactions between
inorganic and organic compounds as well as the overall hygroscopicity of
mixed particles (C. Peng et al., 2016). The field measurements also
demonstrated that the hydrophobic black carbon particles became hygroscopic
with atmospheric mixing and aging by organics (i.e., Peng et al., 2017a). In
a heavily polluted atmosphere with varied aerosol sources and sinks as well
as complex physical and chemical processes, the mixing state and its impact
on aerosol hygroscopicity is more complicated. The hygroscopicity of mixed
particles and mutual impacts between the components are still poorly
understood.
Previous studies have presented the difference between the κ obtained from HTDMA or CCNc (cloud condensation nuclei counter) measurements and that calculated based on the volume mixing
ratio of chemical components. Laboratory results from Cruz and Pandis (2000)
indicate that κgf of internally mixed ammonium sulfate and
organic matter is higher than κchem calculated for assumed
uniform internal mixing. But C. Peng et al. (2016) found that, for sodium
chloride and organic aerosol mixed particles, the measured growth factors
by HTDMA were significantly lower than calculations from the mixing rule
methods. In some field studies on aged aerosols, the κ was
underestimated by the calculation based on a uniform internal mixing
assumption, and they thus lead to an underestimation of CCN
concentration (Bougiatioti, et al., 2009; Chang et al., 2007; Kuwata et
al., 2008; Wang et al., 2010; Ren et al., 2018). However, during primary-emission-dominated periods, the κ value from calculations based on
bulk chemical composition was much higher than that measured by HTDMA
measurements (Zhang et al., 2017). The various results from previous studies
suggest distinct effects of aerosol mixing state on their hygroscopicity.
Overall, (i) to what extent the differences depend on the mixing state and
the extent of aging of the particles and (ii) how the different atmospheric processes and what kinds of mixing structure of the particles may result in
the disparity between the measured and calculated hygroscopic parameter have
not been clearly clarified by the previous studies. A comprehensive
investigation on the causes and magnitude of the effect is of great
significance to parameterize the effect of atmospheric processes/emissions
of aerosols on particle hygroscopicity in models.
In the atmosphere, the κ, which is related to the particle mixing
state diversity, varies largely across the size range of ambient fine
particles (Rose et al., 2010). However, previous studies just compared the
κ calculated from bulk chemical composition to that measured by HTDMA
(Zhang et al., 2017). Using size-resolved, not bulk chemical composition
measurements in different seasons is expected to provide a more comprehensive
understanding and insights into how the aerosol mixing state influences
their hygroscopicity, motivating our analysis that employs size-resolved
chemical composition measured by a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) in this study. The aim of
this paper is to study the hygroscopicity and mixing state characteristics
of fine particles in the Beijing urban area, as well as to reveal the impact of atmospheric processes/sources and mixing/aging on aerosol hygroscopicity
and elucidate the uncertainty in calculating the hygroscopic parameter using
simple mixing rule estimates based on size-resolved chemical composition.
The experiment and theory in the study are introduced in Sect. 2. The
comparison between the hygroscopic parameter obtained from the HTDMA (hygroscopic tandem differential mobility analyzer) and
that calculated using size-resolved chemical composition is discussed in
Sect. 3. Conclusions from the study are given in Sect. 4.
Experiment and theorySite and instruments
Two field campaigns are conducted during winter 2016 and summer 2017 of
urban Beijing (Fig. 1, BJ: 39.97∘ N, 116.37∘ E) for
measurements of aerosol physical and chemical properties. The BJ site is
located at the Institute of Atmospheric Physics (IAP), Chinese Academy of
Sciences, which is between the north Third Ring Road and Fourth Ring Road in northern
Beijing. Local traffic and cooking emissions can be important at the site
(Sun et al., 2015). The sampling period in the cold season was from 16 November
to 10 December 2016, during the domestic heating period in Beijing. The
sampling period in the warm season was from 25 May to 18 June 2017.
The map location of the sites.
The particle number size distribution (PNSD) in the size range from 10 to 550 nm was measured with a scanning mobility particle sizer (SMPS; Wang and
Flagan, 1990; Collins et al., 2002), which consists of a long differential
mobility analyzer (DMA, model 3081L, TSI Inc.) to classify the particle and a
condensation particle counter (CPC, model 3772, TSI Inc.) to detect the size-classified particles. The sampled particles were dried to a relative humidity
<30 % before entering the DMA. The measurement time for each size
distribution was 5 min.
The HTDMA system used in this study has been described in detail in previous
publications (Tan et al., 2013; Wang et al., 2017; Zhang et al., 2017).
Here, only a brief description is given. A Nafion dryer dried the sampled
particles to a relative humidity <20 %, after which the steady-state charge distribution was reached in a bipolar neutralizer. The first
differential mobility analyzer (DMA1, model 3081L, TSI Inc.) selected
the quasi-monodisperse particles by applying a fixed voltage. The dry
diameters selected in this study were 40, 80, 110, 150, and 200 nm. The
quasi-monodisperse particles were humidified to a controlled RH (90 % in
this study) using a Nafion humidifier. A second DMA (DMA2, same model
as the DMA1) coupled with a water-based condensation particle counter
(WCPC, model 3787, TSI Inc.) measured the particle number size distributions
of the humidified aerosol. RH calibration with ammonium sulfate was carried
out regularly during the study.
The hygroscopic growth factor (Gf) is defined as the ratio of the mobility
diameter at a given RH to the dry diameter:
Gf=D(RH)D(dry).
The Gf probability density function (PDF) is retrieved based on the
TDMAinv algorithm developed by Gysel et al. (2009). Dry scans in which
the RH between the two DMAs was not increased were used to define the width
of the transfer function.
Size-resolved nonrefractory submicron aerosol composition was measured with
an Aerodyne high-resolution time-of-flight aerosol mass spectrometer
(HR-ToF-AMS; Xu et al., 2015). The particle mobility diameter was estimated
by dividing the vacuum aerodynamic diameter from the AMS measurements by
particle density. Because the uncertainty caused by the fixed density across
the size range is negligible (Wang et al., 2016), here, the particle density
is assumed to be 1600 kg m-3 (Zamora et al., 2019). AMS positive matrix
factorization (PMF) with the PMF2.exe (v4.2) method was performed to
identify various factors of organic aerosols. Xu et al. (2015) have
described the operation and calibration of the HR-ToF-AMS in detail. Black
carbon (BC) mass concentration was derived from measurements of light
absorption with a seven-wavelength aethalometer (AE33, Magee Scientific Corp.;
Zhao et al., 2017).
Data
The time series of the submicron particle mass concentration PM1, bulk
mass concentrations of the main species in PM1, mass fraction of the
chemical composition of PM1, and probability density function of growth
factor (Gf PDFs) for 40 and 150 nm particles during the campaign are
presented in Fig. 2. Quite distinct temporal variability of aerosol chemical
and physical properties was observed between winter and summer. The average
mass concentration of PM1 was 55.2 µgm-3 in the winter and
16.5 µgm-3 in the summer during our study periods. In this
study, we define the conditions when the mass concentration in winter period
was <20 and >80µgm-3 for clean and polluted conditions, respectively. Organic aerosol (OA),
consisting of secondary organic aerosol (SOA) and primary organic aerosol
(POA), was the major fraction during both the winter and summer sampling
periods. POA concentration was higher than that of SOA in the winter, which
reflects the influence of primary emissions such as coal combustion OA
(COOA) in Beijing (Hu et al., 2016; Sun et al., 2016). In contrast, SOA
usually dominated in the summer, which is evidence that secondary aerosol
formation played a key role in the source of PM1. Distinct hydrophobic
(with Gf of ∼1.0) and more hygroscopic (with Gf of
∼1.5) modes were observed from Gf PDFs of both small and
large particles. Sometimes the more hygroscopic mode particles were more
concentrated, and at other times the hydrophobic particles were. In general
though, the more hygroscopic mode dominated for larger particles (i.e., 150 nm), and the less hygroscopic mode did for the smallest particles (e.g., 40 nm). Occasionally, only the hydrophobic mode was evident for 150 nm
particles, which occurred when POA dominated the PM1. Only the
hygroscopic mode was discernable for 40 nm particles during new particle
formation (NPF) events that occurred more frequently in summer than winter
(Fig. 3).
Winter (left) and summer (right) time series of mass concentration
of PM1, bulk mass concentration of the main species in PM1, mass
fraction of the chemical composition of PM1, and Gf PDFs for 40 and 150 nm particles.
Theory and methodDerivation of the hygroscopic parameter, κ, from the growth
factor (Gf)
According to κ-Köhler theory (Petters and Kreidenweis, 2007), the
hygroscopicity parameter κ can be derived using the growth factor
measured by an HTDMA.
2κ=(Gf3-1)expADdGfRH-1,3A=4σs/aMwRTρw,
where Gf is hygroscopic growth factor measured by HTDMA, Dd is the
dry diameter of the particles, RH is the relative humidity in the HTDMA
(90 %, in our study), σs/a is the surface tension of the
solution/air (assumed here to be the surface tension of pure water, σs/a=0.0728 N m-2), Mw is the molecular weight of water, R is
the universal gas constant, T is the absolute temperature, and ρw
is the density of water.
Derivation of the hygroscopic parameter, κ, from chemical
composition data
For an assumed internal mixture, κ can also be calculated by a
simple mixing rule on the basis of chemical volume fractions (Petters and
Kreidenweis, 2007; Gunthe et al., 2009):
κchem=∑iεiκi,
where κi and εi are the hygroscopicity
parameter and volume fraction for the individual (dry) component in the
mixture, respectively. The AMS provides mass concentrations of organics and
of many inorganic ions. The inorganic components mainly consisted of
(NH4)2SO4 and NH4NO3 (Zhang et al., 2014). And
the values of κ are 0.48 for (NH4)2SO4 and 0.58 for
NH4NO3 (Petters and Kreidenweis, 2007). To estimate κorg, we used the following linear function derived by Mei et al. (2013): κorg=2.10×f44–0.11. We
derived the volume fraction of each species by dividing mass concentration
by its density. The densities are 1.77 g cm-3 for
(NH4)2SO4 and 1.72 g cm-3 for NH4NO3. The densities of organics are assumed to be 1.2 g cm-3 (Turpin and Lim, 2001). The κ and density of BC are assumed to be 0 and 1.7 g cm-3. In the following discussions, κgf and κchem denote the values derived from HTDMA measurements and
calculated using the Zdanovskii–Stokes–Robinson mixing rule (Stokes and Robinson, 1966), respectively.
In addition, we also compare the results from the field campaigns with those
from the other two sites, Xingtai (XT: 37.18∘ N–114.37∘ E)
and Xinzhou (XZ: 38.24∘ N–112.43∘ E), in the North China
Plain (Fig. 1). At the XZ site, we use the hygroscopic parameter (defined as
κCCNc) from size-resolved CCN measurements (Zhang et al., 2014,
2016) for comparison. More detailed descriptions of the method to retrieve
κCCNc can be found in Petters and Kreidenweis (2007). Both of
the κgf and κCCNc are derived based on κ-Köhler theory (Petters and Kreidenweis, 2007). But, different from the
κgf measured by the HTDMA system which is operated at a RH of
90 %, the κCCNc is derived by measuring aerosol CCN activity
under the condition of supersaturations with relative humidity of
>100 %. Previous studies from field measurements and
laboratory experiments showed that the κCCNc is generally
slightly larger or smaller than κgf, but they are basically
comparable and can well represent an overall aerosol hygroscopicity (e.g.,
Carrico et al., 2008; Wex et al., 2009; Good et al., 2010; Irwin et al.,
2010; Cerully et al., 2011; Wu et al., 2013; Zhang et al., 2017).
Results and discussionDiurnal variations of ambient-fine-particle physiochemical properties
and hygroscopic growth factor
The diurnal variations of the PNSD, mass concentration of PM1, mass
concentration and fraction of chemical components in PM1, and Gf PDFs
for 40 and 150 nm particles during the campaign are shown in Fig. 3. During
the summer an obvious peak value in the PNSD is observed around noontime due
to NPF events that typically started around 10:00 LT (local time). The
resulting sharp increase in number concentration of nucleation mode
particles was followed by decreased concentration and a rapid growth in
diameter of the particles along with increased mass concentration of SOA and
sulfate in PM1, indicating strong photochemical and secondary formation
processes during daytime in the summer (Peng et al., 2017b; shown by the red
box in Fig. 3). In contrast, NPF was not evident during the winter period,
which may in part be due to the much higher (∼3×) PM1 mass concentrations in the winter than in the summer. Note that peak
values in number concentration and in mass concentrations of PM1 and
POA occurred during the early evening (17:00–21:00 LT), indicating the
strong impact of local sources from traffic emissions and cooking (shown by
the black box in Fig. 3; Peng et al., 2014). In addition, the diurnal cycles of aerosol physical and chemical properties are also influenced by the diurnal
changes in the planetary boundary layer (PBL) that lead to the accumulation of
particles during nighttime when higher values of both number and mass
concentration were observed.
Owing to the continued local and primary emissions near the study site, the
Gf PDFs for 40 nm particles generally display a bimodal shape with more and
less hygroscopic modes (with Gf of ∼1.5 and ∼1.1 respectively) throughout the day in both winter and summer periods,
indicating an external mixing state for the 40 nm particles. Note that,
during nighttime and early morning in the winter, the more hygroscopic mode
dominated and was shifted to higher Gf than during the daytime. This is
thought to be due to heterogeneous/aqueous reactions on preexisting primary
small particles and/or coagulation/condensation processes that are enhanced
at night under lower ambient temperature and higher relative humidity, all
of which result in a more hygroscopic and more internally mixed aerosol (Liu
et al., 2011; Massling et al., 2005; Ye et al., 2013; Wu et al., 2016; Wang
et al., 2018a). Interestingly, in the summer period, the concentration of
the hydrophilic mode increased quickly around noontime and in the early
afternoon (12:00–16:00), with a corresponding decrease in the relative
concentration of the hydrophobic mode, which likely indicates a
transformation of the particles from an externally mixing state to an internally mixing state
as a result of the species condensation from the photochemical reaction (Wu
et al., 2016; Wang et al., 2017), resulting in an increase in particle
hygroscopicity. In addition, it is evident that 40 nm particles after 12:00
were dominated by NPF (Fig. 3). Therefore, the increase of hydrophobic mode
particles suggests that a large amount of hydrophilic particles are
generated from NPF. For 150 nm particles, the hygroscopic mode in the Gf PDF
is more dominant during daytime, in particular during the summer period when
the strong solar radiation promotes photochemical aging and growth, thus
producing a more internally mixed aerosol. The dominant hydrophobic mode at
around 18:00 was observed in both winter and summer and reflects abundant
traffic emissions and cooking sources (primarily with POA) during the early
evening period.
Campaign averaged diurnal variations in particle number size
distribution; mass concentration of PM1, bulk mass concentration of
main species in PM1, and mass fraction of chemical composition of PM1;
and Gf PDFs for 40 and 150 nm particles in winter (left panels) and summer
(right panels) measured in urban Beijing.
κgf dependence on Dp
The size dependence of particle hygroscopicity parameters for the winter and
summer periods is presented in Fig. 4. In the winter, the 40 nm particles
were the least hygroscopic, and the hygroscopicity of larger particles
(>80 nm) displayed an insignificant dependence on particle size.
The size independence for the larger particles is consistent with the
observed similarity in mass fractions of inorganic and organic species
across
the size range as shown in the pie charts in Fig. 4a. A similar dependence
of particle hygroscopicity on particle size was also observed in the urban
area of Beijing during the wintertime of 2014 (Wang et al., 2018b). In the
summer, hygroscopicity increased with increasing particle size, which is
expected based on the size-dependent patterns shown in the pie charts, with
the mass fraction of POA decreasing with the particles size and the mass
fraction of inorganics like sulfate and nitrate increasing with particle
size.
The dependence of κ on Dp at the urban Beijing site during
winter (a) and summer (b). The κ values are retrieved from the size-resolved
HTDMA measurements. The error bars represent ±1σ. The
size-resolved chemical mass fraction at the corresponding Dp is also
presented.
Closure of HTDMA and chemical-composition-derived κ
A closure study was conducted between κchem and κgf (Fig. 5) to
investigate the uncertainty of the two methods, and especially to further
illustrate whether particle hygroscopicity can be well predicted by
κchem calculated by assuming internal mixing. Since a
size-resolved BC mass concentration measurement was not
available during the campaign, we use the bulk mass fraction of BC particles
measured by the AE33 combining with size-resolved BC distribution measured
by a single particle soot photometer (SP2) in Beijing (Liu et al., 2019) to
estimate κchem. During the calculation, the BC core diameter
measured by SP2 has been converted to the diameter of coated BC particles by
multiplying factors of 1.4 and 2.6 under clean (with bulk BC mass
concentrations <2µgm-3) and polluted (with bulk BC
mass concentrations >2µgm-3) conditions
respectively (Liu et al., 2019).
Closure of κchem calculated from size-resolved chemical
composition data and κgf retrieved from the hygroscopic growth factor by
HTDMA measurements in the winter (left panels) and summer (right panels) period.
The dots with different colors correspond to the observed time of a day during
the campaign as shown by the color bar. In each plot, the red dotted line is the 1:1
line, and the black solid line is the fitting line. The numbers in parentheses are
slopes of linear fits and correlation coefficients (R2).
Uncertainty in κ is due in part to measurement uncertainty of the HTDMA
system and uncertainty resulting from nonideality effects in the solution
droplets, surface tension reduction due to surface-active substances, and
the presence of slightly soluble substances that dissolve at a RH higher than
that maintained in the HTDMA (e.g., Wex et al., 2009; Good et al., 2010;
Irwin et al., 2010; Cerully et al., 2011; Wu et al., 2013). For example, the
HTDMA may overestimate the Dp of dry particles for the external mixed
BC particles, as BC-containing particles may shrink when humidified, leading
to an underestimate of the hygroscopic growth factor. However, our previous study
demonstrated that, for this region, estimates using HTDMA data are still
better representing the aerosol hygroscopicity than those using the simple
mixing rule based on chemical volume fractions for an assumed internal
mixture (Zhang et al., 2017). Therefore, here we focus on discussing and
exploring the uncertainty of κchem by taking κgf as the reference.
The results show that, although the slopes from linear fitting of κchem and κgf are close to 1.0, it shows correlations that are quite
poor (typically with correlation coefficients, R2, of
<0.3) between κchem and κgf of the
80, 110, 150, and 200 nm particles in both winter and summer. The poor
correlations reflect large uncertainty in one or both of the calculated
parameters that are likely due to the unreasonable assumption of particle
mixing state (e.g., Cruz and Pandis, 2000; Svenningsson et al., 2006; Sjogren
et al., 2007; Zardini et al., 2008), which varies with their aging and other
physiochemical processes in the atmosphere. Note that underestimation of
κchem for the summer occurred mostly in the afternoon (shown in blue dots in Fig. 5). This may be associated with photochemical processes
at around noontime. More specific investigations of the particle mixing and
aging impacts on κchem will be further addressed in the
following sections.
Diurnal variations of (a)κchem using size-resolved
chemical composition data and κgf in the winter and summer period
and (b) the ratio of κchem to κgf in the winter and summer
period. The shade regions denote the error bars (1σ).
Aerosols aging and source effects indicated by diurnal cycles of
κchem and κgf
The diurnal cycles of particle hygroscopicity in the summer and winter with
the use of the size-resolved chemical composition observations and the ratio
of κchem to κgf are shown in Fig. 6. In summer, at
09:00–15:00, the disparity between κchem and κgf
is insignificant for smaller particles (80 and 110 nm), and both of them show a slight decrease from 09:00 or 10:00 to 12:00–13:00 due to the frequent NPF
event that usually corresponds to a large fraction of organics (Fig. 3) in
urban Beijing. For larger particles (150 and 200 nm), the disparity between
κchem and κgf around noontime and in the early
afternoon is very significant, corresponding to >20 %
underestimation of particle hygroscopicity by κchem (with a
ratio of κchem to κgf of ∼0.8).
Similar patterns were also noted by Zhang et al. (2017) but only
based on a comparison between κchem derived from bulk chemical
composition and κgf. Our results based on size-resolved
measurements are consistent with that observed by Zhang et al. (2017), which again confirms the effect of the rapid photochemical aging of aerosol
particles on their hygroscopicity. In contrast, no significant differences between
κchem and κgf are observed during nighttime in
summer. Note that κchem is slightly higher than κgf during early evening traffic rush hour and cooking time, when
emissions of primary hydrophobic particles (e.g., BC and POA) are high (Fig. 3), thus resulting in a large percentage of externally mixed particles. Causes of the overestimation of κchem during the traffic rush
hour and cooking time will be discussed in the following paragraph. The
particles experience rapid conversion and mixing in urban Beijing due to
high precursor gases (Sun et al., 2015; Wu et al., 2016; Ren et al., 2018);
thus, the aged particles produced through photochemical processes in the
afternoon can mix and interact with the freshly emitted primary particles
from traffic and cooking sources (Wu et al., 2008). Therefore, during
nighttime (22:00–06:00 LT), the particles are more uniform and
internally mixed, which is reflective of the assumption for the calculation of
κchem; a much better consistency between κchem and
κgf is hence presented.
Diurnal cycles of κgf PDF for 80, 110, 150, and 200 nm particles in clean and polluted events in winter.
In winter, the disparity between κchem and κgf is
insignificant at 09:00–15:00 due to the weakening effect of photochemical
aging. From 15:00 to 21:00 LT, due to the strong vehicle and cooking sources
around the site, the particles are dominated by the hydrophobic mode with a
large concentration of externally mixed BC and POA particles (Fig. 3); the
calculated κchem is much higher than κgf, with a maximum ratio of κchem to κgf of 1.4; and the
greatest disparity is observed for small particles. The disparity is further
enhanced during clean periods when the hydrophobic mode is dominant (Figs. 7,
S1 in the Supplement). Note that during the nighttime, κchem is slight lower
than κgf, with a minimum ratio of κchem to
κgf of ∼0.8 for 80 nm particles and
∼0.9 for 110 and 150 nm particles at 02:00–04:00 LT (Fig. 6b), indicating an underestimation of particle hygroscopicity using
composition data. The disparity at nighttime is further increased during
heavily polluted events (Fig. S1), when the particles are more
internally mixed with only one hygroscopic mode (Fig. 7). We propose the
increased underestimation during polluted conditions is likely due to
enhanced condensation of secondary hygroscopic compounds (e.g., nitrate,
sulfate, SOA) on preexisting aerosols at lower temperature and or
hydrophilic SOA formation under higher relative humidity at nighttime (Wu et
al., 2008; Wang et al., 2016; An et al., 2019). However, such a condensation
effect during nighttime is less significant (indicated by the smaller
disparity between κchem and κgf) than the aging
effect caused by aerosol photochemical processes around noontime (J. F. Peng et
al., 2016).
We suppose that the higher/lower κchem should first be
closely associated with temporal changes in actual effective density of BC
with the particles aging/diurnal variations of local emissions. It has been
demonstrated that rapid aging of BC can occur over a few hours in the
polluted urban area (J. F. Peng et al., 2016). The externally mixed BC particles have a fractal structure and chain-like aggregates and have been reported with an effective density of 0.25–0.45 g cm-3 (McMurry et al., 2002), while the BC particles in the κchem calculation are assumed to be void-free with an effective density of 1.7 g cm-3. This leads to a lower BC volume fraction than it actually is and thus a greater κchem during
the traffic rush hour and cooking time when BC particles are mostly freshly
emitted with uncompacted structure. In addition, the significant increase in
volume fraction of POA during the late afternoon would result in changes in
composition of organic aerosols, and thereby a density much closer to that of
POA than the assumed one (1.2 g cm-3) in the calculation should be
applied. A sensitivity test has been done to examine the effect of density
of BC and organics on the calculated κchem (Fig. 8). The result
shows that the κchem value can be reduced by 16 %–33 % by
decreasing the BC effective density from 1.7 to 0.25–0.45 g cm-3. This basically explains the disparity between κchem
and κgf during the traffic rush hour when a large amount of BC
is freshly emitted. The changes in κchem are within ±4 % by varying the organic density from 1.2 (mixture of SOA and POA) to
1.0 (typically for POA) or 1.4 g cm-3 (typically for SOA) (Zamora et
al., 2019), showing a much lower impact of variations of organic density on
κchem. In conclusion, the result demonstrated that the
disparity between κchem and κgf during the late afternoon in winter is largely due to the inappropriate use of the BC
particle density that is closely associated with its morphology or the degree
of its aging. Our study suggests that, to accurately parameterize the effect
of BC aging on particle hygroscopicity, it is critical to measure the
effective density and morphology of ambient BC, in particular in those
regions with complex influences of rapid secondary conversion/aging
processes and local sources.
Sensitivity of κchem to variations in the density of BC
(a) and organics (b).
In that way, the lower κchem value derived around noontime in
summer, when BC aerosols may be more compact through strong photochemical
aging, is probably due to application of a lower BC density in the
calculation. However, the sensitivity test indicates that, to fill the gap
between κchem and κgf observed at noontime in
summer, the effective density of BC should be extremely high due to
decreased sensitivity of κchem to BC density with its aging. In
this case, the density of BC has been assumed to be 1.7 g cm-3, which
reflects a very compacted and void-free structure of the BC particles. This
currently applied value represents an upper limit for the effective density
of ambient BC particles according to previous observations near or in
Beijing (Zhang et al., 2015), which suggested the aged BC generally has
an effective density of 1.2–1.4 g cm-3. Using these ambient observed
values would lead to further underestimation of κchem. In
addition, the photochemical aging can change the overall effective density
of organic aerosols by changing their chemical composition. However,
the effective density of the photochemical oxidized organic particles (e.g.,
SOA) does not change much on the timescale of several hours and was
observed ranging between 1.2 and 1.3 g cm-3 (Bahreini et al., 2005). It
can only explain ∼4 % at most of the underestimation in
κchem around noontime in summer by applying a density value of
1.4 g cm-3 (typically for SOA). Therefore, the application of higher
densities of BC and organics in the calculation cannot fully explain the
disparity between κchem and κgf during the early afternoon in summer when strong photochemical processes are expected.
The uncertainty in the calculation of κchem may be also related to
the uncertainty caused by the hygroscopic parameter of organics that vary widely
over a range of diverse constituents of SOA (Suda et al., 2012). The lower
κchem indicates that the κ of secondary organic aerosols
formed through the strong photochemical oxidation processes in summer of
urban Beijing is likely underestimated. In this study, the mean κ value
of organics derived from the f44 parameterized equation is 0.20±0.02, ranging from 0.17 to 0.23 during 09:00–17:00. While the organic
aerosols, especially for particles in accumulated mode, may be more
hygrophilic with a much larger κ, i.e., >0.2 due to large
formation of highly oxidized OA. One can easily understand that increasing the κ of organic aerosols from 0.2 to 0.3 can explain about 11 %–13 % of the underestimation of κchem, but this represents an upper limit of the impact of hygroscopicity of organic aerosols on the calculation. This is
because the κ value of 0.3 corresponds to the maximum possible
for ambient organic aerosols. Additionally, the f44 parameterized
equation tends to overestimate the κ according to Fröhlich et al. (2015), which should yield a larger κchem. Finally, the
coexisting hygroscopic and hydrophobic species may have a strong influence
on the phase state of particles, also likely affecting chemical interactions
between inorganic and organic compounds as well as the overall
hygroscopicity of mixed particles (C. Peng et al., 2016). Overall, the lower
κchem caused by the photochemical aging effect likely
resulted from multiple impacts of inappropriate application of the density and
hygroscopic parameter of organic aerosols in the calculation, as well as the
influences from chemical interaction between organic and inorganic compounds
on the overall hygroscopicity of mixed particles. This topic warrants
further investigations.
Diurnal variations in (a)κchem and κgf
for 150 and 200 nm particles at the BJ site; (b)κchem and
κgf for 40, 80, 110, 150, and 200 nm particles at the XT site; (c)κchem and mean κCCNc for particles at the XZ site; and
(d) ratio of mean κchem to κgf at the three sites.
Observation from other stations
The aging process in the summer period is related to photochemical
processing in strong solar radiation conditions. The photochemical reactions
produce sulfate and secondary organic aerosol, condensing on the surface of
slightly hygroscopic or nonhygroscopic primary aerosols (such as BC) (Zhang et al.,
2008). To confirm such a photochemical aging effect on particle
hygroscopicity, we further examine the diurnal variations of κchem and κgf or κCCNc (only at the XZ site)
based on observations in summer at two other sites in north China (Fig. 1).
The XT site is located in the suburbs of XT city, which is about 400 km south of Beijing, with high levels of industrialization and urbanization. Due to industrial emissions and typically weak ventilating winds, concentrations of
PM2.5, black carbon, and gaseous precursors are usually high at the
site (Fu et al., 2014). Xinzhou is located in the north of Taiyuan and about 360 km southwest of Beijing and is surrounded by mountains on three sides.
Local emissions from motor vehicles and industrial activities have
relatively little influence on the sampled aerosol (Zhang et al., 2016).
Because of its location and elevation, the aerosol at the XZ site is usually
aged and transported from other areas. The sampling period was from 22 July to 26 August 2014 and from 17 May to 14 June 2016 at the XZ and XT site
respectively.
We find that the case at the XT site is very similar to that observed in BJ
(Fig. 9a), with a lower κchem than κgf around noon
time. But, because of much fewer influences from the local sources at XT
compared to that at BJ, such underestimation by κchem continued
until night at XT (Fig. 9b). Interestingly, a noontime lower κchem was not observed in the diurnal cycles at the XZ site, where
κchem and κCCNc had similar diurnal patterns
(Fig. 9c) with a roughly constant ratio of κchem to κCCNc of ∼0.8–0.9 (Fig. 9d). This is probably because
the XZ site is usually the recipient of aerosols transported from other
areas that are already aged and well-mixed, with minimal impact of further
aging (Zhang et al., 2017). Also, the rate of oxidation and condensation may
be slow in the relatively remote area where the gas precursors and oxidants
are not as high as they are closer to source regions. But at XT, which is
located in the heavily polluted area in the North China Plain (Fu et al.,
2014), aerosol emissions and processing are more similar to that in urban
Beijing. These observations from other sites further confirm the
photochemical aging effect that will largely underestimate the particle hygroscopicity using a simple mixing rule based on chemical composition.
Conclusions
Using measurements of aerosol composition and hygroscopicity made in Beijing
(BJ) during a winter period of 2016 and a summer period of 2017, this paper
analyzes the daily variation and seasonal differences of size-resolved
aerosol hygroscopicity in urban Beijing. We mainly focus on studying the
disparity of κgf and κchembetween summer and
winter to reveal the impact of atmospheric processes and mixing state of the
particles on its hygroscopicity. The uncertainty in calculating κ by
using chemical composition with a uniform internal mixing hypothesis is
elucidated from the diurnal variations of the difference between the
calculated values: in summer, a lower κchem is obtained around
noontime, with a ratio of κchem to κgf of about
0.8–0.9 for large particles (i.e., 150 and 200 nm), showing an
underestimation of particle hygroscopicity by using a simple mixing rule
based on chemical composition. Combining with the observation from XT and
XZ, we attribute the underestimation to the rapid noontime photochemical
aging processes in summer, which induces the aging effect that will lead to
a lower κ if assuming a uniform mixing of the particles. The lower
κchem likely resulted from multiple impacts of inappropriate
application of the density and hygroscopic parameter of organic aerosols in the
calculation, as well as the unknown influences from chemical interaction
between organic and inorganic compounds on the overall hygroscopicity of
mixed particles.
In winter, a larger κchem than κgf for >100 nm particles is derived around noontime and in the early afternoon, with
a maximum ratio of κchem to κgf of 1.2–1.4 when
the particles are dominated by the hydrophobic mode with a large number of
externally mixed POA particles from strong vehicle and cooking sources. We
attribute this large disparity between κchem and κgf to changes in BC morphology that can be indicated by the effective
density of BC. The sensitivity test shows that it can well explain the
disparity during the traffic rush hour by applying a BC effective density of
0.25–0.45 g cm-3. However, we suggest that, to accurately parameterize
or account for the effect of BC density on particle hygroscopicity, future
investigations need to measure the effective density of ambient BC, in
particular in those regions with complex local sources. Our results
highlight the impacts of atmospheric processes and sources on aerosol mixing
state and hygroscopicity, which should be quantified and considered in
models for different atmospheric conditions.
Data availability
All data used in the study are available on http://www.geodoi.ac.cn/WebEn/doi.aspx?Id=1356
(10.3974/geodb.2019.06.11.V1, Fan et al., 2019) or from the corresponding author upon
request (fang.zhang@bnu.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-915-2020-supplement.
Author contributions
FZ and JL conceived the conceptual development of the paper. XF
directed and performed the experiments with LC, XJ, YW, and FZ.
FZ, JL, and XF conducted the data analysis and wrote the draft of the
paper, and all authors edited and commented on the various sections of
the paper. JL and XF contributed equally to this work.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We thank all participants of
the field campaign for their tireless work and cooperation. We also would
like to thank the two anonymous reviewers for their insightful and
constructive comments.
Financial support
This research has been supported by the National Natural Science Foundation of China (NSFC) research projects (grant nos. 41975174 and 41675141) and the National Key R&D Program of China (grant no. 2017YFC1501702).
Review statement
This paper was edited by James Allan and reviewed by two anonymous referees.
ReferencesAn, Z., Huang, R. J., Zhang, R., Tie, X., Li, G., Cao, J., Zhou, W., Shi,
Z., Han, Y., Gu, Z., and Ji, Y.: Severe haze in Northern China: A synergy of
anthropogenic emissions and atmospheric processes, P.
Natl. Acad. Sci. USA, 116, 8657–8666,
10.1073/pnas.1900125116, 2019.Bahreini, R., Keywood, M. D., Ng, N. L., Varutbangkul, V., 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, 2005.Bougiatioti, A., Fountoukis, C., Kalivitis, N., Pandis, S. N., Nenes, A., and Mihalopoulos, N.: Cloud condensation nuclei measurements in the marine boundary layer of the Eastern Mediterranean: CCN closure and droplet growth kinetics, Atmos. Chem. Phys., 9, 7053–7066, 10.5194/acp-9-7053-2009, 2009.Carrico, C. M., Petters, M. D., Kreidenweis, S. M., Collett Jr., J. L.,
Engling, G., and Malm, W. C.: Aerosol hygroscopicity and cloud droplet activation
of extracts of filters from biomass burning experiments, J. Geophys. Res.,
113, D08206, 10.1029/2007JD009274, 2008.Cerully, K. M., Raatikainen, T., Lance, S., Tkacik, D., Tiitta, P., Petäjä, T., Ehn, M., Kulmala, M., Worsnop, D. R., Laaksonen, A., Smith, J. N., and Nenes, A.: Aerosol hygroscopicity and CCN activation kinetics in a boreal forest environment during the 2007 EUCAARI campaign, Atmos. Chem. Phys., 11, 12369–12386, 10.5194/acp-11-12369-2011, 2011.Chang, R.-W., Liu, P., Leaitch, W., and Abbatt, J.: Comparison between
measured and predicted CCN concentrations at Egbert, Ontario: Focus on the
organic aerosol fraction at a semi-rural site, Atmos. Environ., 41,
8172–8182, 2007.Collins, D. R., Flagan, R. C., and Seinfeld, J. H.: Improved inversion of
scanning DMA data, Aerosol Sci. Tech., 36, 1–9, 2002.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.Fan, X., Liu, J., Zhang, F., Chen, L., Collins, D., Xu, W., Jin, X., Ren, J., Wang, Y., Wu, H., Li, S., Sun, Y., and Li, Z.: HTDMA and HR-ToF-AMS Measured in situ Dataset in Winter of 2016 and Summer of 2017 at the Beijing Observation Station[DB/OL], Global Change Research Data Publishing & Repository, 10.3974/geodb.2019.06.11.V1, 2019.Fröhlich, R., Crenn, V., Setyan, A., Belis, C. A., Canonaco, F., Favez, O., Riffault, V., Slowik, J. G., Aas, W., Aijälä, M., Alastuey, A., Artiñano, B., Bonnaire, N., Bozzetti, C., Bressi, M., Carbone, C., Coz, E., Croteau, P. L., Cubison, M. J., Esser-Gietl, J. K., Green, D. C., Gros, V., Heikkinen, L., Herrmann, H., Jayne, J. T., Lunder, C. R., Minguillón, M. C., Močnik, G., O'Dowd, C. D., Ovadnevaite, J., Petralia, E., Poulain, L., Priestman, M., Ripoll, A., Sarda-Estève, R., Wiedensohler, A., Baltensperger, U., Sciare, J., and Prévôt, A. S. H.: ACTRIS ACSM intercomparison – Part 2: Intercomparison of ME-2 organic source apportionment results from 15 individual, co-located aerosol mass spectrometers, Atmos. Meas. Tech., 8, 2555–2576, 10.5194/amt-8-2555-2015, 2015.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.Good, N., Topping, D. O., Allan, J. D., Flynn, M., Fuentes, E., Irwin, M., Williams, P. I., Coe, H., and McFiggans, G.: Consistency between parameterisations of aerosol hygroscopicity and CCN activity during the RHaMBLe discovery cruise, Atmos. Chem. Phys., 10, 3189–3203, 10.5194/acp-10-3189-2010, 2010.Gunthe, S. S., King, S. M., Rose, D., Chen, Q., Roldin, P., Farmer, D. K., Jimenez, J. L., Artaxo, P., Andreae, M. O., Martin, S. T., and Pöschl, U.: Cloud condensation nuclei in pristine tropical rainforest air of Amazonia: size-resolved measurements and modeling of atmospheric aerosol composition and CCN activity, Atmos. Chem. Phys., 9, 7551–7575, 10.5194/acp-9-7551-2009, 2009.Gysel, M., McFiggans, G. B., and Coe, H.: Inversion of tandem differential
mobility analyser (TDMA) measurements, J. Aerosol Sci., 40, 134–151, 10.1016/j.jaerosci.2008.07.013, 2009.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, 2016.Irwin, M., Good, N., Crosier, J., Choularton, T. W., and McFiggans, G.: Reconciliation of measurements of hygroscopic growth and critical supersaturation of aerosol particles in central Germany, Atmos. Chem. Phys., 10, 11737–11752, 10.5194/acp-10-11737-2010, 2010.Kuwata, M., Kondo, Y., Miyazaki, Y., Komazaki, Y., Kim, J. H., Yum, S. S., Tanimoto, H., and Matsueda, H.: Cloud condensation nuclei activity at Jeju Island, Korea in spring 2005, Atmos. Chem. Phys., 8, 2933–2948, 10.5194/acp-8-2933-2008, 2008.Liu, D., Joshi, R., Wang, J., Yu, C., Allan, J. D., Coe, H., Flynn, M. J., Xie, C., Lee, J., Squires, F., Kotthaus, S., Grimmond, S., Ge, X., Sun, Y., and Fu, P.: Contrasting physical properties of black carbon in urban Beijing between winter and summer, Atmos. Chem. Phys., 19, 6749–6769, 10.5194/acp-19-6749-2019, 2019.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.Massling, A., Stock, M., and Wiedensohler, A.: Diurnal, weekly, and seasonal
variation of hygroscopic properties of submicrometer urban aerosol
particles, Atmos. Environ., 39, 3911–3922, 10.1016/j.atmosenv.2005.03.020, 2005.McMurry, P. H., Wang, X., Park, K., and Ehara, K.: The Relationship between Mass
and Mobility for Atmospheric Particles, Aerosol Sci. Tech., 36, 227–238,
2002.Mei, F., Hayes, P. L., Ortega, A. M., Taylor, J. W., Allan, J. D., Gilman,
J. B., Kuster, W. C., de Gouw, J. A., Jimenez, J. L., and Wang, J.: Droplet
activation properties of organic aerosols observed at an urban site during
CalNex-LA, J. Geophys. Res., 118, 2903–2917, 10.1002/jgrd.50285, 2013.Peng, C., Jing, B., Guo, Y. C., Zhang, Y. H., and Ge, M. F.: Hygroscopic
behavior of multicomponent aerosols involving nacl and dicarboxylic
acids, J. Phys. Chem. A, 120, 1029–1038, 2016.Peng, J., Hu, M., Guo, S., Du, Z., Shang, D., Zheng, J., Zheng, J., Zeng, L., Shao, M., Wu, Y., Collins, D., and Zhang, R.: Ageing and hygroscopicity variation of black carbon particles in Beijing measured by a quasi-atmospheric aerosol evolution study (QUALITY) chamber, Atmos. Chem. Phys., 17, 10333–10348, 10.5194/acp-17-10333-2017, 2017a.Peng, J., Hu, M., Du, Z., Wang, Y., Zheng, J., Zhang, W., Yang, Y., Qin, Y., Zheng, R., Xiao, Y., Wu, Y., Lu, S., Wu, Z., Guo, S., Mao, H., and Shuai, S.: Gasoline aromatics: a critical determinant of urban secondary organic aerosol formation, Atmos. Chem. Phys., 17, 10743–10752, 10.5194/acp-17-10743-2017, 2017b.Peng, J. F., Hu, M., Wang, Z. B., Huang, X. F., Kumar, P., Wu, Z. J., Guo, S., Yue, D. L., Shang, D. J., Zheng, Z., and He, L. Y.: Submicron aerosols at thirteen diversified sites in China: size distribution, new particle formation and corresponding contribution to cloud condensation nuclei production, Atmos. Chem. Phys., 14, 10249–10265, 10.5194/acp-14-10249-2014, 2014.Peng, J. F., Hu, M., Guo, S., Du, Z. F., Zheng, J., Shang, D. J., Zamora, M.
L., Zeng, L. M., Shao, M., Wu, Y. S., Zheng, J., Wang, Y., Glen, C. R.,
Collins, D. R., Molina, M. J., and Zhang, R. Y.: Markedly enhanced
absorption and direct radiative forcing of black carbon under polluted urban
environments, P. Natl. Acad. Sci. USA, 113, 4266–4271, 10.1073/pnas.1602310113,
2016.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.Ren, J., Zhang, F., Wang, Y., Collins, D., Fan, X., Jin, X., Xu, W., Sun, Y., Cribb, M., and Li, Z.: Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban Beijing, Atmos. Chem. Phys., 18, 6907–6921, 10.5194/acp-18-6907-2018, 2018.Rose, D., Nowak, A., Achtert, P., Wiedensohler, A., Hu, M., Shao, M., Zhang, Y., Andreae, M. O., and Pöschl, U.: Cloud condensation nuclei in polluted air and biomass burning smoke near the mega-city Guangzhou, China – Part 1: Size-resolved measurements and implications for the modeling of aerosol particle hygroscopicity and CCN activity, Atmos. Chem. Phys., 10, 3365–3383, 10.5194/acp-10-3365-2010, 2010.Schill, S. R., Collins, D. B., Lee, C., Morris, H. S., Novak, G. A., and
Prather, K. A.: The impact of aerosol particle mixing state on the
hygroscopicity of sea spray aerosol, ACS Central Science, 1, 132–141,
2015.Sjogren, S., Gysel, M., Weingartner, E., Baltensperger, U., Cubison, M. J.,
Coe, H., Zardini, A. A., Marcolli, C., Krieger, U. K., and Peter, T.:
Hygroscopic growth and water uptake kinetics of two-phase aerosol particles
consisting of ammonium sulfate, adipic and humic acid mixtures, J. Aerosol
Sci., 38, 157–171, 10.1016/j.jaerosci.2006.11.005, 2007.
Stokes, R. H. and Robinson, R. A.: Interactions in aqueous nonelectrolyte solutions: I. Solute-solvent equilibria, J. Phys. Chem., 70, 2126–2130, 1966.Suda, S. R., Petters, M. D., Matsunaga, A., Sullivan, R. C., Ziemann, P. J.,
and Kreidenweis, S. M.: Hygroscopicity frequency distributions of secondary
organic aerosols, J. Geophys. Res., 11, D04207, 10.1029/2011JD016823, 2012.Sun, Y. L., Wang, Z. F., Du, W., Zhang, Q., Wang, Q. Q., Fu, P. Q., Pan, X. L., Li, J., Jayne, J., and Worsnop, D. R.: Long-term real-time measurements of aerosol particle composition in Beijing, China: seasonal variations, meteorological effects, and source analysis, Atmos. Chem. Phys., 15, 10149–10165, 10.5194/acp-15-10149-2015, 2015.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.Svenningsson, B., Rissler, J., Swietlicki, E., Mircea, M., Bilde, M., Facchini, M. C., Decesari, S., Fuzzi, S., Zhou, J., Mønster, J., and Rosenørn, T.: Hygroscopic growth and critical supersaturations for mixed aerosol particles of inorganic and organic compounds of atmospheric relevance, Atmos. Chem. Phys., 6, 1937–1952, 10.5194/acp-6-1937-2006, 2006.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.Wang, J., Cubison, M. J., Aiken, A. C., Jimenez, J. L., and Collins, D. R.: The importance of aerosol mixing state and size-resolved composition on CCN concentration and the variation of the importance with atmospheric aging of aerosols, Atmos. Chem. Phys., 10, 7267–7283, 10.5194/acp-10-7267-2010, 2010.Wang, J., Zhang, Q., Chen, M.-D., Collier, S., Zhou, S., Ge, X., Xu, J.,
Shi, J., Xie, C., Hu, J., Ge, S., Sun, Y., and Coe, H.: First chemical
characterization of refractory black carbon aerosols and associated coatings
over the Tibetan Plateau (4730 m a.s.l), Environ. Sci. Technol., 51, 14072,
10.1021/acs.est.7b03973, 2017.Wang, Q., Zhao, J., Du, W., Ana, G., Wang, Z., Sun, L., Wang, Y., Zhang, F.,
Li, Z., Ye, X., and Sun, Y.: Characterization of submicron aerosols at a
suburban site in central China, Atmos. Environ., 131, 115–123,
10.1016/j.atmosenv.2016.01.054, 2016.Wang, S. C. and Flagan, R. C.: Scanning Electrical Mobility Spectrometer,
Aerosol Sci. Tech., 13, 230–240, 1990.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, Y., Li, Z., Zhang, Y., Du, W., Zhang, F., Tan, H., Xu, H., Fan, T., Jin, X., Fan, X., Dong, Z., Wang, Q., and Sun, Y.: Characterization of aerosol hygroscopicity, mixing state, and CCN activity at a suburban site in the central North China Plain, Atmos. Chem. Phys., 18, 11739–11752, 10.5194/acp-18-11739-2018, 2018a.Wang, Y., Wu, Z. Ma, N., Wu, Y., Zeng, L., Zhao, C., and Wiedensohler, A.:
Statistical analysis and parameterization of the hygroscopic growth of the
sub-micrometer urban background aerosol in Beijing, Atmos. Environ., 175,
184–191, 10.1016/j.atmosenv.2017.12.003, 2018b.Wex, H., Petters, M. D., Carrico, C. M., Hallbauer, E., Massling, A., McMeeking, G. R., Poulain, L., Wu, Z., Kreidenweis, S. M., and Stratmann, F.: Towards closing the gap between hygroscopic growth and activation for secondary organic aerosol: Part 1 – Evidence from measurements, Atmos. Chem. Phys., 9, 3987–3997, 10.5194/acp-9-3987-2009, 2009.Wu, Z., Hu, M., Lin, P., Liu, S., Wehner, B., and Wiedensohler, A.: Particle
number size distribution in the urban atmosphere of Beijing, China, Atmos.
Environ., 42, 7967–7980, 10.1016/j.atmosenv.2008.06.022, 2008.Wu, Z. J., Poulain, L., Henning, S., Dieckmann, K., Birmili, W., Merkel, M., van Pinxteren, D., Spindler, G., Müller, K., Stratmann, F., Herrmann, H., and Wiedensohler, A.: Relating particle hygroscopicity and CCN activity to chemical composition during the HCCT-2010 field campaign, Atmos. Chem. Phys., 13, 7983–7996, 10.5194/acp-13-7983-2013, 2013.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, 1123–1138, 10.5194/acp-16-1123-2016, 2016.Xu, W. Q., Sun, Y. L., Chen, C., Du, W., Han, T. T., Wang, Q. Q., Fu, P. Q., Wang, Z. F., Zhao, X. J., Zhou, L. B., Ji, D. S., Wang, P. C., and Worsnop, D. R.: Aerosol composition, oxidation properties, and sources in Beijing: results from the 2014 Asia-Pacific Economic Cooperation summit study, Atmos. Chem. Phys., 15, 13681–13698, 10.5194/acp-15-13681-2015, 2015.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.Zamora, M. L., Peng, J., Hu, M., Guo, S., Marrero-Ortiz, W., Shang, D., Zheng, J., Du, Z., Wu, Z., and Zhang, R.: Wintertime aerosol properties in Beijing, Atmos. Chem. Phys., 19, 14329–14338, 10.5194/acp-19-14329-2019, 2019.Zardini, A. A., Sjogren, S., Marcolli, C., Krieger, U. K., Gysel, M., Weingartner, E., Baltensperger, U., and Peter, T.: A combined particle trap/HTDMA hygroscopicity study of mixed inorganic/organic aerosol particles, Atmos. Chem. Phys., 8, 5589–5601, 10.5194/acp-8-5589-2008, 2008.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., Zhang, R., Sun, Y., Collin, D.,
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 op-tical properties of
soot aerosols during atmospheric processing, P. Natl. Acad. Sci. USA, 105, 10291–10296,
10.1073/pnas.0804860105, 2008.Zhang, R., Wang, G., Guo, S. Zamora, M., and Wang, Y.: Formation of urban
fine particulate matter, Chem. Rev., 115, 3803–3855, 2015.Zhang, Y., Zhang, Q., Cheng, Y., Su, H., Kecorius, S., Wang, Z., Wu, Z., Hu, M., Zhu, T., Wiedensohler, A., and He, K.: Measuring the morphology and density of internally mixed black carbon with SP2 and VTDMA: new insight into the absorption enhancement of black carbon in the atmosphere, Atmos. Meas. Tech., 9, 1833–1843, 10.5194/amt-9-1833-2016, 2016.
Zhao, J., Du, W., Zhang, Y., Wang, Q., Chen, C., Xu, W., Han, T., Wang, Y., Fu, P., Wang, Z., Li, Z., and Sun, Y.: Insights into aerosol chemistry during the 2015 China Victory Day parade: results from simultaneous measurements at ground level and 260 m in Beijing, Atmos. Chem. Phys., 17, 3215–3232, 10.5194/acp-17-3215-2017, 2017.