Understanding the impacts of aerosol chemical composition and mixing state on
cloud condensation nuclei (CCN) activity in polluted areas is crucial for
accurately predicting CCN number concentrations (NCCN). In
this study, we predict NCCN under five assumed schemes of aerosol
chemical composition and mixing state based on field measurements in Beijing
during the winter of 2016. Our results show that the best closure is achieved
with the assumption of size dependent chemical composition for which
sulfate, nitrate, secondary organic aerosols, and aged black carbon are
internally mixed with each other but externally mixed with primary organic
aerosol and fresh black carbon (external–internal size-resolved, abbreviated
as EI–SR scheme). The resulting ratios of predicted-to-measured
NCCN (RCCN_p/m) were 0.90 – 0.98 under both clean and
polluted conditions. Assumption of an internal mixture and bulk chemical
composition (INT–BK scheme) shows good closure with RCCN_p/m
of 1.0 –1.16 under clean conditions, implying that it is adequate for CCN
prediction in continental clean regions. On polluted days, assuming the
aerosol is internally mixed and has a chemical composition that is size
dependent (INT–SR scheme) achieves better closure than the INT–BK scheme due
to the heterogeneity and variation in particle composition at different
sizes. The improved closure achieved using the EI–SR and INT–SR assumptions
highlight the importance of measuring size-resolved chemical composition for
CCN predictions in polluted regions. NCCN is significantly
underestimated (with RCCN_p/m of 0.66 – 0.75) when using the
schemes of external mixtures with bulk (EXT–BK scheme) or size-resolved
composition (EXT–SR scheme), implying that primary particles experience rapid
aging and physical mixing processes in urban Beijing. However, our results
show that the aerosol mixing state plays a minor role in CCN prediction when
the κorg exceeds 0.1.
Introduction
Atmospheric aerosol particles can serve as cloud condensation nuclei (CCN)
and, in turn, affect the optical and microphysical properties of clouds
(Twomey, 1977; Albrecht, 1989; Charlson et al., 1992). Additionally, an
increase in the aerosol number concentration may suppress precipitation in
shallow clouds and promote it in deep convective clouds (Rosenfeld et al.,
2008; Li et al., 2011). A key challenge to understanding indirect aerosol
effects is quantifying CCN spectra and their spatial and temporal variations.
The ability of particles to act as CCN mainly depends on their size,
chemical composition, and mixing state (McFiggans et al., 2006; Dusek et
al., 2006; Ma et al., 2013). The impacts of the size distribution and
chemical composition on CCN activity has been discussed in previous studies
(Dusek et al., 2006; Ervens et al., 2007; Broekhuizen et al., 2006; Yum et
al., 2005, 2007; Wiedensohler et al., 2009; Deng et al., 2013; Zhang et al.,
2014, 2016; Kawana et al., 2016). The effect of chemical composition can be
represented by a hygroscopicity parameter (κ) (Petters and
Kreidenweis, 2007) which is often used to predict NCCN (Moore et al.,
2012; Zhang et al., 2014). However, particle composition may vary from
single species to a mixture of multiple species for a given size. A
description of size-resolved chemical composition thus leads to a better
prediction of NCCN because it allows variation of κ with size
(Medina et al., 2007; Wang et al., 2010; Meng et al., 2014). Variations in
mixing state also impact NCCN prediction, with the effect dependent on
the hygroscopicity of the organic component (Wang et al., 2010). The
assumption of internal mixtures has been demonstrated to predict NCCN
well (Ervens et al., 2007; Chang et al., 2007; Andreae and Rosenfeld,
2008; Gunthe et al., 2009; Rose et al., 2008; Meng et al., 2014; Zhang et
al., 2014; Li et al., 2017). However, some studies have shown that detailed
information about the chemical composition and the mixing state is required
because of the complexity of the hygroscopicity of organics (Broekhuizen et
al., 2006; Bhattu and Tripathi, 2015) and the differences in the CCN
activity between fresh and aged aerosols (Gunthe et al., 2011). Therefore,
the impact of different assumptions concerning the mixing state and chemical
composition on accurately quantifying CCN concentrations needs further
investigation, especially in heavily polluted regions.
Beijing, a typical polluted city, frequently experiences severe haze
pollution episodes (Sun et al., 2013; Guo et al., 2014; Zheng et al., 2015),
particularly in winter. Several recent studies have focused on studying
particle hygroscopicity (Wu et al., 2016; Wang et al., 2017) and chemical
composition (Gunthe et al., 2011), and using bulk κ to predict CCN in
Beijing (e.g., Liu et al., 2014; Zhang et al., 2017). However, to our
knowledge, a comprehensive CCN closure test considering chemical composition
and mixing state is lacking for this polluted urban area. In particular, the
transformation of the particle mixing state may be very quick during severe
pollution conditions (Wu et al., 2016). During pollution events, the
hygroscopicity of organics and CCN activity are often enhanced rapidly
with the aging process (Gunthe et al., 2011; Kawana et al., 2016).
Therefore, the characterization and parameterization of CCN activation may
be more challenging in polluted regions due to the impacts of organics (Wang
et al., 2010; Meng et al., 2014; Che et al., 2016; Zhang et al., 2016).
In this study, we use size-resolved measurements of CCN activity and
size-resolved chemical composition information to predict NCCN using
field measurement data collected in Beijing during the winter of 2016. The
CCN closure study is carried out using five schemes with different
assumptions regarding particle mixing state and chemical composition. By
classifying the data into three different periods (nighttime, noontime, and
the evening rush hour), we also investigate the variations in aerosol mixing
state from fresh to relatively aged aerosols. The sensitivity of predicted
NCCN to the particle mixing state and organic volume fraction with the
aging of organic particles is also presented in the last section of the study.
Measurements and data
Data used here were measured from 15 November to 14 December 2016 during the
Air Pollution and Human Health (APHH) field campaign at the Institute of
Atmospheric Physics (IAP), Chinese Academy of Sciences (39.97∘ N,
116.37∘ E), which is a typical urban site with influences from traffic
and cooking emissions (Sun et al., 2015). The sampling instruments were
placed in a container at ground level.
The particle number size distribution (PNSD) was measured by a scanning
mobility particle sizer (SMPS; Wang et al., 2003). The SMPS consists of a
differential mobility analyzer (DMA; model 3081, TSI Inc.) and a
condensation particle counter (CPC; model 3772, TSI Inc.). Measurements of
size-resolved CCN efficiency spectra were made by an integrated system
combining the SMPS (Wang et al., 2003) and a Droplet Measurement
Technologies CCN counter (DMT-CCNc; Lance et al., 2006). The procedure to
couple the SMPS and the DMT-CCNc developed by Moore et al. (2010) was
followed. Atmospheric particles were sampled from an inlet located 1.5 m
above the roof of the container and were then passed through a silica gel
desiccant drying tube into the SMPS. The relative humidity of the sample
flow was below 30 %. The sample flow exiting the DMA was divided into 0.5 L min-1
for the CCNc and 0.5 L min-1 for the CPC. Before and after the field
campaign ammonium sulfate was used to calibrate the supersaturation (SS)
levels of the CCNc with longitudinal temperature differences of 2, 3, 5, 8,
10, 13, and 15 K as shown in Fig. S1 in the Supplement. Based on this calibration, the five
effective SS levels were 0.12, 0.14, 0.23, 0.40, and 0.76 %.
The PNSD spanned the size range of 10 – 550 nm with a measurement scan time
of 5 min. Total particle or condensation nuclei (CN) size distributions were
calculated with the multiple charge correction and transfer function used in
the TSI-AIM software. The CN number concentration (NCN) is the total
aerosol number concentration and is obtained by integrating the PNSD over
the size range of 10 – 550 nm. The full measurement cycle of the CCNc for the
five SS levels took one hour (20 min for 0.12 % and 10 min for each higher SS).
Size-resolved CCN efficiency data were inverted with a multiple charge
correction (Moore et al., 2010). The CCN number size distribution was
calculated by multiplying the CCN efficiency spectrum by the particle number
size distribution. The total CCN concentration was then calculated by
integrating the size-resolved NCCN. The bulk activation ratio (AR) was
calculated as NCCN/NCN. The results were stratified between
polluted and background conditions with an assumed threshold PM1 mass
concentration of 50 µg m-3.
An Aerodyne high-resolution time-of-flight aerosol mass spectrometer
(HR-ToF-AMS; DeCarlo et al., 2006) was housed in a sampling room on the
rooftop of a two-story building to measure size-resolved non-refractory
submicron aerosols, including organics, sulfate, nitrate, ammonium, and
chloride with a time resolution of ∼ 5 min. More details about
the HR-ToF-AMS and the measurement site have been described in previous
studies (Sun et al., 2010; Sun et al., 2016). The organics are classified
using positive matrix factorization (PMF) (Paatero and Tapper, 1994),
considering as being composed of two components: primary organic aerosol (POA)
representing non-hygroscopic particles and secondary organic aerosol (SOA)
representing hygroscopic particles. The first component consists
mainly of hydrocarbon-like organic aerosol (HOA), a surrogate of primary
organic aerosol (POA) from local combustion sources. The size
distribution of the primary organic aerosol (OA) was measured by the estimated
size-distribution of the C4H9+ fragment (Aiken et al., 2009;
Zhang et al., 2005). The size distribution of the SOA was calculated as the
difference between the total OA and POA.
The black carbon (BC) mass concentration was measured using a
seven-wavelength aethalometer (AE33, Magee Scientific Corp.). Zhao et al. (2017)
provides details about this instrument and the measurements it makes.
Due to an absence of size-resolved BC measurements, the BC size distribution
was calculated from the combination of an approximately lognormal
distribution measured by a single particle soot photometer (SP2, DMT) (Wu et
al., 2017) and the total BC mass concentration. Note that because the SP2
measures BC core diameter instead of the diameter of the BC-containing
particle, it would overestimate the BC mass concentration of smaller
particles but underestimate that of larger particles. Such overestimation
would likely lead to an underestimation of NCCN due to the increased
mass fraction of BC of total particles. The uncertainty of this effect is
evaluated in Sect. 4.3. The fresh and aged BC size distributions are
determined from the total BC size distribution measured by the SP2 (Wu et
al., 2017) and from the dependence of the fraction of internally mixed soot (Fin)
on particle diameter (Dp) observed in urban Beijing by Cheng
et al. (2012); the instruments produce different diameters. In this paper,
we have unified both the aerodynamic diameter from AMS and volume equivalent
diameter from SP2 to be mobility diameter. In addition, actual fresh BC
particles are not spheres and neither are some of the partially aged BC, but
because both the diameter measured from SP2 and the BC size distribution
from the literature assume these particles are spheres, the
fresh and aged BC in this study are therefore also assumed to be spherical.
TheoryCalculation of CCN concentration using κ-Köhler theory
In this study, we used the critical or cutoff particle diameter (Dcut)
and particle number size distribution to calculate NCCN. The method to
derive Dcut is based on κ-Köhler theory (Petters and
Kreidenweis, 2007), with the water vapor saturation ratio over the aqueous
solution droplet S given by the following:
S=D3-Dp3D3-Dp31-κexp4σwMwRTρwD,
where D is the droplet diameter, Dp is the dry diameter of the particle,
Mw is the molecular weight of water, σw is the surface
tension of pure water, ρw is the density of water, R is the gas
constant, and T is the absolute temperature. When κ> 0.1
it can be approximately expressed as
κ=4A327Dp3ln2Sc,A=4σwMwRTρw,
where Sc is the particle critical supersaturation. The other variables
in the equations are T= 298.15 K, R= 8.315 J K-1 mol-1,
ρw= 997.1 kg m-3, Mw= 0.018015 kg mol-1,
and σw= 0.072 J m-2 (Rose et al., 2008).
Schematic representation of the five different schemes: (a) INT–BK,
(b) EXT–BK, (c) EI–SR, (d) INT–SR, and (e) EXT–SR, in addition to (f) the BC size distribution used in this study. The fresh and
aged BC size distribution are retrieved from the total BC size distribution
measured by the SP2 (Wu et al., 2017) and the dependence of the fraction of
internally mixed soot (Fin) on particle diameter (Dp)
observed in urban Beijing (Cheng et al., 2012). The total BC size distribution
is used in the INT–SR and EXT–SR schemes, and the aged and fresh BC distributions
are used in the EI–SR scheme. In the EI–SR scheme, some BC particles are assumed
to already be aged and thus internally mixed with sulfate, nitrate, and SOA, and
some of them together with POA are freshly emitted and assumed not yet aged/coated
by other species (externally mixed).
For internally mixed particles, κ is calculated as follows (Petters
and Kreidenweis, 2007; Gunthe et al., 2009):
κchem=∑iεiκi,κorg=fPOA⋅κPOA+fSOA⋅κSOA,
where κi and εi are the hygroscopicity
parameter and volume fraction for the individual components in the mixture, respectively,
and fPOA and fSOA are the primary organic aerosol (POA) and
secondary organic aerosol (SOA) mass fractions in the mixture, respectively. The aerosol
mass spectrometer (AMS) mainly measured the particle mass size distributions
of SO42-, NO3-, NH4+, and organic compounds,
while the Zdanovskii–Stokes–Robinson relation requires the volume fractions
of the particle chemical composition (Stokes and Robinson, 1966; Zdanovskii,
1948). A simplified ion pairing scheme is used to calculate the mass
concentrations of the inorganic salts, which includes only NH4NO3
and (NH4)2SO4 as possible salts (Gysel et al., 2007). In
this study, we considered five components: NH4NO3,
(NH4)2SO4, SOA, POA, and BC. The κ(NH4NO3) is
equal to 0.67 and κ((NH4)2SO4) is equal to 0.61 (Petters and
Kreidenweis, 2007; Gunthe et al., 2009). The κorg is estimated
using the linear function derived by Mei et al. (2013a), namely,
κorg= 2.10f44- 0.11, where f44 is dependent upon
organics oxidation level. The mean κorg is 0.10 in our case.
The organics are classified into two factors: POA representing
non-hygroscopic particles (κ= 0) and SOA representing hygroscopic
species. In our study, the average contributions of POA and SOA to total
organics were 0.53 and 0.47, respectively. On the basis of Eq. (5),
κ(SOA) is assumed to be 0.2. κ(BC) is furthermore
assumed to be 0.
Assumptions about mixing state and chemical composition
To examine the influence of the mixing state and chemical composition on CCN
activation, five assumptions (Fig. 1) are used to predict NCCN.
Although the assumption of completely internal or external mixing for
ambient aerosols represents two extremely simplified schemes and may be
atmospherically unrealistic, it allows us to understand the importance of
the particle mixing state for predicting NCCN. In addition, size
independent and dependent compositions are derived from the mass
concentrations of different species measured by the AMS so that the impact
of chemical composition on CCN activity can be examined. A detailed
introduction of the five assumption schemes follows.
Assumption 1: internal mixture with bulk chemical composition (INT–BK)
In this scheme, submicron particles are assumed to be internally mixed with
bulk chemical composition, where the mass fraction of each component
(e.g., NH4NO3, (NH4)2SO4, SOA, POA, and BC) is uniform
throughout the full size range as shown in Fig. 1a. The overall κis
calculated from the bulk chemical composition measured by the AMS based on
the simple mixing rule (Eq. 4) to obtain the critical diameter at a
given SS. For calculating NCCN all (and only) particles with diameters
greater than Dcut are considered CCN-active. The total NCCN is
then calculated from the step-wise integration of the PNSD for Dp>Dcut.
The equations used in the calculations are as
follows:
CCNpre=∫DcutDendnlogDpdlogDp
Dcut=4A327∑iεiκiln2Sc3,
where Dcut is the critical diameter, Dend is the upper size
limit of the PNSD, n (logDp) is the function of the aerosol number size
distribution, i is the chemical component element, and the other parameters
are the same as those presented in Eqs. (2)–(4).
Assumption 2: internal mixture with size-resolved chemical composition (INT–SR)
For this scheme submicron particles are assumed to be internally mixed and
the chemical composition is size-dependent as shown in Fig. 1d. The
fractional contributions of the components at each size bin are derived from
mass size distributions of the five species considered, i.e.,
NH4NO3, (NH4)2SO4, SOA, POA, and BC.
For this assumption, the critical diameter is derived from the total
hygroscopic parameter, κ, at each size bin, j. For each size bin for
which Dp,j is greater than the calculated Dcut,j the
activated fraction was assumed to be 1.0 and for all others it was 0.0. The
NCCN is calculated as follows:
CCNpre=∫DbeginDendnlogDpdlogDpDcut,j=4A327∑iεijκijln2Sc3,
where Dbegin and Dend are the first and last diameters of the
PNSD, n (logDp) is the function of the aerosol number size
distribution, i is the chemical component element, j is the PNSD size bin, and
the other parameters are the same as those presented in Eqs. (2)–(4).
Assumption 3: external mixture with bulk chemical composition (EXT–BK)
For this scheme the submicron aerosol is treated as an external mixture.
This means that there are five types of particles, i.e., NH4NO3,
(NH4)2SO4, SOA, POA, and BC, and each particle consists of a
single component. The volume fraction of each component, which is derived
from bulk mass concentrations, does not vary with size (as shown in Fig. 1b).
At a given S, the critical diameter of each particle type is retrieved from
the κ of each component. The NCCN of each aerosol type is
calculated as the CCN-active particle number concentration multiplied by the
bulk volume fraction of the components as expressed in Eq. (10). The
NCCN of the five particle types are finally summed to obtain the total
NCCN. The specific equations are as follows:
CCNpre=∑i∫Dcut,iDendnlogDpdlogDp⋅ViDcut,i=4A327κiln2Sc3,
where Dcut,i is calculated for each component, i, at a given SS,
Vi is the volume fraction of each aerosol type, n (logDp) is the
function of the aerosol number size distribution, i is the chemical component
element, and the other parameters are the same as those presented in Eqs. (2)–(4).
Assumption 4: external mixture with size-resolved chemical composition (EXT–SR)
As with the EXT–BK scheme the same five particle types are considered and
their relative concentrations selected to match the measured composition.
But unlike with the EXT–BK scheme the relative concentrations of the five
particle types vary with particle size to capture the size-dependence of the
measured composition, as is depicted in Fig. 1e. The volume fraction of each
particle type at each size is first multiplied by the total particle number
size distribution (PNSD) to get the PNSDi of each aerosol type. The
NCCN of each particle type is then obtained from the step-wise
integration of the PNSDi for Dp>Dcut,i, and
summed to get the total NCCN, as described by Eq. (12). Similar
to EXT–BK, the critical diameter of each particle type is also derived from
the κ of each pure component at a given S.
CCNpre=∑i∫DbeginDendnlogDp⋅VijdlogDpDcut,i=4A327κiln2Sc3,
where Vi is the volume fraction of each particle type in a size bin,
n (logDp) is the function of the aerosol number size distribution, i is
the chemical component element, j is the particle size bin, and the other
parameters are the same as those presented in Eqs. (2)–(4).
Assumption 5: sulfate, nitrate, SOA, and aged BC internally mixed, and POA and fresh BC externally mixed, and all components with size-resolved chemical composition (EI–SR)
At each particle size sulfate, nitrate, and SOA with BC-aged are treated as
internally mixed, but POA and BC-fresh are present in separate particles and
are non-hygroscopic. As with INT–SR and EXT–SR the chemical composition is
size-dependent, as shown in Fig. 1c. The EI–SR scheme likely represents a
case that is most similar to that of actual atmospheric aerosols in
locations such as Beijing. The fresh and aged BC size distributions are
determined from the total BC size distribution measured by the SP2 (Wu et
al., 2017) and from the dependence of the fraction of internally mixed
soot (Fin) on particle diameter (Dp) observed in urban
Beijing by Cheng et al. (2012).
In this assumption the fresh BC and POA particles can serve as CCN only if
their diameter is larger than 200 nm – otherwise they are CCN-inactive. Thus,
the total NCCN of those externally mixed components
(NCCN_EXT) is calculated from the step-wise integration
of the product of the PNSD and the volume fraction of the fresh BC and POA
in each size bin larger than 200 nm.
The NCCN of the remaining components (sulfate, nitrate, and SOA with
BC-aged) that are treated as an internal mixture, denoted as
NCCN_INT, is predicted in the same way as for the INT–SR
scheme, with the only difference being that the PNSD is first multiplied by
the volume fraction of the mixed component particles for each size bin. The
total NCCN is thus calculated as the sum of NCCN_EXT
and NCCN_INT. The specific equations are as follows:
CCNpre=∫DbeginD200nlogDp⋅rjdlogDp+∫D200DendnlogDpdlogDpDcut,j=4A327∑iεijκijln2Sc3,
where Dbegin and Dend are the first and last diameters of the
PNSD, n (logDp) is the function of the aerosol number size
distribution, r is the volume fraction of the internal (hygroscopic) mixture
at each size, i is the chemical component element, j is the particle size bin,
and the other parameters are the same as those presented in Eqs. (2)–(4).
Diurnal variations in aerosol properties at the IAP site during the APHH field experiment, including the particle number size distribution measured
by the SMPS under (a) background (BG) and (b) polluted (POL)
conditions. (c) Mean particle number size distribution measured by the
SMPS during three periods (00:00–02:00, 12:00–14:00, and 17:00–20:00 LT)
under BG and POL conditions. Bulk chemical component mass concentrations (NO3,
POA, SOA, and BC) and f44 made under (d) BG and (e) POL conditions.
(a) Averaged fitted CCN efficiency spectra during the nighttime
period (00:00–02:00 LT, dashed lines), the noontime period (12:00–14:00 LT,
dotted lines), and the evening rush hour period (17:00–20:00 LT, solid lines)
for different diameters (60, 100, 150, and 200 nm). (b) The heterogeneity
of aerosol particles (σa/Da) derived from Eq. (17)
during the three selected periods.
Results and discussionDiurnal variations in aerosol properties
Diurnal variations in mean PNSD and bulk chemical composition under polluted
and background conditions are shown in Fig. 2. Significant diurnal
variations in PNSD are observed during the campaign. For both polluted and
background cases the abrupt increases in concentration of small particles
(Dp< 100 nm) from 17:00 to 20:00 LT (local time) are likely
related to fresh primary emissions from cooking and traffic sources (Wang et
al., 2017; Zhao et al., 2017), which is also evident in the significant
increase in mass concentration of non-hygroscopic POA (Fig. 2d and e). The
peak amplitude in the PNSD that occurs from about 08:00 to 12:00 LT is probably
associated with secondary formation processes, which is indicated by an
apparent increase of nitrate, SOA, and f44 (oxidation level of organics)
in the morning (08:00 LT) when photochemistry becomes significant. The effect
is more apparent on clean days. In addition, the PNSD amplitude and BC and
POA concentrations are high at nighttime, suggesting an influence from the
diurnal variation of the planetary boundary layer (PBL) height. In
particular, on polluted days the PBL plays a key role in regulating the
diurnal variation of primary components like POA and BC (e.g., Dzepina et
al., 2009; Cross et al., 2009). On clean days secondary formation and
primary sources play dominant roles in regulating diurnal variations. The
PNSD in clean cases has peaks at smaller Dp (∼ 30–40 nm,
Fig. 1c) compared to polluted cases (∼ 100 nm), which is
associated with particle growth accompanying atmospheric chemistry processes
during haze evolution (Guo et al., 2014; Wang et al., 2016).
Cumulative Gaussian distribution function fit and parameters derived from the CCN efficiency
The activated fractions measured at the five supersaturation levels were
fitted using the following two functions (Rose et al., 2008; Mei et al., 2013b):
Ra(S)=E2⋅1+erflnS-lnS*2σs2,fNCCN/NCN=a1+erfD-Daσa2,
where Ra(S) and f(NCCN/NCN) are the CCN activation fractions, the maximum
activation fraction (MAF) is equal to E or 2a, S* and Da are the midpoint
activation supersaturation and diameter, respectively, and
σs and σa are the cumulative distribution function (CDF)
standard deviations. During this field campaign, 2580 size-resolved CCN
efficiency spectra at five SS levels were measured. To illustrate the
characteristics of the activation spectra, the CDF fits are shown in Fig. 3
and in Tables S1 and S2 in the Supplement. A gradual increase in size-resolved AR with SS suggests
that particles had different hygroscopicities even at the same diameter. The
heterogeneity of particle chemical composition can be represented by the
ratio of σa and Da (i.e., σa/Da), where
σa is the standard deviation derived from the cumulative
Gaussian distribution function (Eq. 17) and Da is the activation
diameter (Rose et al., 2010). The ratio of σa/Da during
the three periods is shown in Fig. 3b.
(a) Retrieved mean critical activation diameters at SS = 0.12,
0.14, 0.23, 0.40, and 0.76 % under background (BG) and polluted (POL) conditions.
The box plots show mean critical activation diameters at the 25th, 50th, and
75th percentiles. (b) Difference in the mean critical activation
diameter between BG and POL cases.
CCN activation curves and heterogeneity of chemical components
For larger particles with Dp> 100 nm, no significant
differences were observed in the CCN efficiency spectra (Fig. 3a),
suggesting a similar hygroscopicity during the three periods. For particles
with a Dp< 100 nm, the CCN efficiency spectrum observed during
the evening rush hour period showed a much more gradual increase (with
smaller slopes) in size-resolved AR than that derived for the other two
periods. This is attributed to the strong influence of POA emissions, which
consist of less hygroscopic and externally mixed smaller particles mainly
from cooking and traffic during the evening rush hour period (also indicated
by the increased σa/Da). Particles with a
Dp< 100 nm emitted during the evening rush hour period require a higher SS to
reach the same AR. However, for a Dp> 100 nm the slope of AR
with respect to SS was steep and near the instrumental limit obtained for a
pure ammonium sulfate aerosol. Che et al. (2016) have reported that
particles larger than about 150 nm have relatively uniform composition. This
suggests that particles become more internally mixed with growth from the
Aitken mode to the accumulation mode. This feature is also suggested by the
decreasing σa/Da with increasing particle diameter.
Mean critical activation diameter
The critical activation diameter at different SS levels under background and
polluted conditions is shown in Fig. 4. The differences in critical diameter
between polluted and background cases are calculated as
Dp_POL-Dp_BG. At lower SS levels
the critical diameters for polluted cases were slightly smaller than those
observed on clean days, suggesting larger particles are more CCN-active on
polluted days. This is expected based on a hygroscopic tandem differential mobility analyzer (HTDMA)
measurements that showed that particles in the accumulation mode on polluted days are more hygroscopic
than those on clean days in urban Beijing (Wang et al., 2017). At higher SS
the critical diameter on polluted days was a little higher than that
obtained under clean conditions, suggesting that particles with a Dp of
∼ 40 nm are less CCN active. This is likely because a high
concentration of small and hygroscopic particles in the Aitken mode arise
from the photochemistry-driven nucleation process on clean days. However, in
polluted cases, small particles are mostly composed of hydrophobic POA from
cooking and traffic sources. This was also observed by Wang et al. (2017)
who showed that 40 nm particles are less hygroscopic on polluted days.
However, the differences in critical diameter between polluted and
background cases are small, reflecting a relatively minor influence of
hygroscopicity on CCN activity.
Maximum activation fraction (MAF)
As shown in Fig. 5, the maximum activated fractions on clean and polluted
days during the campaign are less than 1, which suggests that at least some
sampled aerosols were externally mixed (Gunthe et al., 2011). For example,
the MAF for particles with a Dp of ∼ 180 nm was around 0.78
at SS = 0.12 % under background conditions, indicating that
∼ 22 % of the particles are non-hygroscopic. The higher MAFs
under polluted conditions suggest a more internally mixed aerosol (Wu et
al., 2016; Wang et al., 2017). The MAF during the 12:00–14:00 LT period was
highest, which is likely due to strong photochemical aging processes that
lead to more internal mixing of the aerosol.
Mean maximum active fractions (MAFs) of CCN activation spectra under
polluted (POL) and background (BG) conditions during the three periods of interest
(00:00–02:00, 12:00–14:00, and 17:00–20:00 LT). The MAF of pure (NH4)2SO4
particles at the different SS levels (magenta line) is also plotted.
Predicted NCCN as a function of measured NCCN
using the five assumptions (colored symbols – scheme associated with each symbol listed in detail at the end of this caption) at three supersaturation levels (0.23, 0.40, and 0.76 %) under polluted (POL) and background (BG) conditions.
The numbers in parentheses are the slope (first number) and the correlation
coefficient (second number). + INT–BK (IB) internal mixture, bulk composition;
∘ INT–SR (IS) internal mixture, size-resolved composition;
* EXT–BK (EB)
external mixture, bulk composition; □ EXT–SR (ES) external mixture, size-resolved
composition; ▿ EI–SR (EIS) external mixture, sulfate, nitrate, SOA, and aged BC
internally mixed, and POA and fresh BC externally mixed, size-resolved composition.
CCN closure study and the sensitivity of predicted NCCN to assumed aerosol mixing state and chemical composition
Figure 6 shows the comparisons between predicted and measured NCCN at
different SS levels under background and polluted conditions. The ratios of
predicted-to-measured NCCN (RCCN_p/m) ranged
from 0.66 to 1.16, suggesting significant influences of the different
assumptions on CCN prediction. The EI–SR assumption scheme predicts
NCCN very well, with RCCN_p/m of 0.90–0.98
(corresponding to a slight underestimation of 2–10 %). For the EI–SR
scheme, hydrophobic POA and a portion of the BC are assumed to be externally
mixed while the other species (sulfate, nitrate, SOA, and aged BC) are
assumed to be internal mixtures. The assumption is physically sound, and the
result implies that the EI–SR represents the actual mixing state
and compositions of the particles well. The slight underestimation may be due to an
overestimation of fresh BC caused by the method (see Sect. 2)
used to retrieve it. Also, the slightly larger underestimation of NCCN
for the BG case in the EI–SR scheme shown in Fig. 6 may suggest that aerosols
during clean periods are mostly aged and internally mixed.
The INT–SR and INT–BK schemes which assume that aerosol is internally mixed
also predict NCCN reasonably well at lower SS. The prediction is better
on background days, reflecting the more homogenous aerosol composition in
clean conditions. With increasing SS this overestimation became more
pronounced, which is likely due to limitations of the AMS measurements. The
AMS distributions show that the mass concentration was most impacted by
particles with diameters near ∼ 100–400 nm. Because particles
in that size range tended to be more hygroscopic than those with
diameters < 100 nm, this leads to an overestimation of κ
(underestimation of the critical diameter) and a resulting overestimation of
NCCN at high SS. With decreasing SS the critical diameter increased
and the deviation using the INT–BK and INT–SR schemes decreased. Detailed
explanations about this effect have been given by Wang et al. (2010) and
Zhang et al. (2017). Overall, the INT–BK and INT–SR schemes achieve CCN
closure within, what is deemed here, an acceptable overprediction of 0–16 %.
The EXT–BK and EXT–SR schemes underestimated NCCN, with
RCCN_p/m of 0.66 – 0.75.
Overall, the internal-mixing schemes achieve much better closure than
those assuming external mixtures. Our results suggest that freshly emitted
particles in urban Beijing may experience a quick conversion and mixing with
pre-existing secondary particles, e.g., converting from externally mixed to
internally mixed (or from hydrophobic to hydrophilic, along with a decrease
in the volume of POA and BC) as previously reported (Riemer et al., 2004;
Aggarwal and Kawamura, 2009; Jimenez et al., 2009; Wu et al., 2016; Peng et
al., 2016). In summary, under background conditions, the INT–BK scheme
achieved the best CCN closure, implying that the INT–BK assumption is likely
sufficient to predict CCN in clean continental regions. However, in polluted
regions, the EI–SR and INT–SR schemes may achieve better closure.
As mentioned in Sect. 2, because the SP2 measures BC core diameter and
not the diameter of the BC-containing particle, the method would
overestimate the BC mass concentration of smaller particles but
underestimate that of larger particles. This effect adds uncertainty to the
CCN prediction when using the EXT–SR scheme and is evaluated here (Fig. 7).
For the evaluation, we predict NCCN with the retrieved fresh BC size
distribution only in the EXT–SR scheme, which represents an upper limit of
the overestimation of the fresh BC size distribution due to the SP2
measurement. Therefore, the result represents the largest underestimation of
NCCN caused by the BC-containing particle effect. Our result shows that
the underestimation of NCCN is reduced from 28 to 25 % by
changing the total BC size distribution to that of just the fresh BC. That
means that the overestimation of fresh BC due to the BC-containing particle
effect in the SP2 measurements would lead to a maximum underestimation of
3 % of NCCN. The minimal uncertainty contributed by uncertainty in
the BC size distribution could be explained by the small fractional
contribution of BC to the total particle concentration. In conclusion, such
an effect is quite small or negligible compared to the overall large
underestimation of NCCN with the EXT–SR assumption.
Predicted NCCN as a function of measured NCCN
using the EXT–SR (ES) assumption (colored symbols) at S= 0.76 %. The pink
and blue circles denote the results predicted using total and fresh BC size
distributions, respectively. The numbers in parentheses are the slope (first
number) and the correlation coefficient (second number).
Diurnal variations in the ratio of predicted-to-measured NCCN
at a supersaturation level of 0.23 % under background (BG) and polluted (POL)
conditions. The following symbols are used to define the schemes displayed: + INT–BK internal mixture, bulk composition; ∘ INT–SR
internal mixture, size-resolved composition; * EXT–BK external mixture,
bulk composition; □ EXT–SR external mixture, size-resolved composition;
▿ EI–SR external mixture, sulfate, nitrate, SOA, and aged BC
internally mixed, and POA and fresh BC externally mixed, size-resolved composition.
Performance of the five schemes at different times of the day
To investigate the performance of the five schemes at different times of the
day, the diurnal variations in the RCCN_p/m (SS = 0.23 %)
derived by the schemes are shown in Fig. 8. In general, the
INT–BK, INT–SR, and EI–SR schemes can predict NCCN well during all
periods of the day under polluted or background conditions.
RCCN_p/m values are within the acceptable
±20 % uncertainty range (Wang et al., 2010; Zhang et al., 2017). Compared
with other periods, the predicted NCCN during the morning and evening
rush hour periods showed the most sensitivity to the different assumption
schemes, especially on clean days (Fig. 8b). For example, the
RCCN_p/m derived using the INT–SR schemes reaches
values up to > 1.2, and the RCCN_p/m
obtained using the EXT–BK scheme decreased to a minimum value of
∼ 0.5. The INT–SR, INT–BK, and EI–SR assumptions overestimate
NCCN for the evening rush hour period by up to ∼ 20 %.
This may be because most freshly emitted POA and BC particles during evening
traffic hours are hydrophobic and do not contribute to the NCCN. In
addition, for EI–SR
assumption, a portion of BC is assumed aged and
internally mixed with sulfate, nitrate, and SOA, which may reduce the actual
fraction of fresh BC during rush hour period and thereby lead to an
overestimation of NCCN.
Relative deviations between NCCN predicted under the
assumptions of internal (INT–BK) and external (EXT–BK) mixtures
[(NCCN⋅ INT–BK -NCCN⋅ EXT–BK) (NCCN⋅ EXT–BK)-1]
as a function of κorg with organic volume fractions of
< 50 (a), 50–60 (b), > 60 % (c) using all
observed data points (d). The different colors represent different supersaturation levels
(see inset in (a).
Use of the EXT–BK or EXT–SR assumption for the polluted case resulted in a
predicted NCCN that was underestimated by ∼ 30–40 % at
night (00:00–06:00 LT). Expectedly, the prediction using the two schemes
improved during the daytime and evening rush hours, e.g., the
RCCN_p/m changed from about 0.6 to 0.8. This is
likely associated with heavy urban traffic emissions/residential cooking
sources during the daytime that lead to more externally mixed particles
under polluted conditions; while, at night, the particles are less
influenced by those local primary sources (Zhao et al., 2017). Wang et al. (2017)
showed that the probability density function of κ during rush
hour has a bimodal distribution and a hydrophobic mode from locally emitted
particles. This also leads to reasonably accurate estimates of NCCN
during nighttime with larger error during the daytime when using the
internal mixing assumptions (INT–BK, INT–SR, and EI–SR) for polluted cases (Fig. 8).
Impact of mixing state and organic volume fraction on predicted NCCN and their variation with aerosol aging
To further examine the sensitivity of predicted NCCN to the particle
mixing state and organic volume fraction with the aging of organic
particles, the relative deviation between NCCN predicted assuming
internal and external mixtures as a function of κorg was
calculated, with the results shown in Fig. 9. The schemes that assume
internal and external mixtures use bulk composition of organics, sulfate,
and nitrate, which simplifies the analysis and interpretation of the
results. For the data collected throughout the field campaign, the organic
volume fraction is categorized as < 50, 50–60, and
> 60 %. The deviation between the concentrations predicted
assuming internal and external mixtures is calculated as [(NCCN⋅ INT–BK -NCCN⋅ EXT–BK)
(NCCN⋅ EXT–BK)-1]. The result shows that the
relative deviation increased as the organic volume fraction increased. For
organic volume fractions less than 50 % the maximum difference can only
reach up to 20 % (SS = 0.76 %). This is consistent with previous studies
that reported differences less than 20 % when xorg< 30 %
(Sotiropoulou et al., 2006; Wang et al., 2010). The maximum deviation
approaches 100 % for xorg> 60 % at SS = 0.76 %.
Overall, the deviation is largest when the organics are less or
non-hygroscopic, i.e., when κorg< 0.05. The
deviation decreased rapidly as κorg increased to 0.05 in all
cases. For κorg of 0.1 the differences were less than 20 %,
even with high organic fractions. Moreover, differences were 10 % or less
for κorg of 0.15, suggesting that the mixing state plays a
minor role when κorg exceeds 0.1.
Conclusions
In this study, we have investigated the importance of aerosol chemical
composition and mixing state on CCN activity based on measurements made
during a field campaign carried out in Beijing in the winter of 2016. The
NCCN was predicted by applying κ-Köhler theory and using
five schemes that assume different mixing state and chemical composition combinations.
We show that there is a significant impact of the different assumptions on
CCN prediction, with RCCN_p/m ranging from 0.66 to 1.16.
The best estimates of NCCN under both background and polluted
conditions were obtained when using the EI–SR scheme, with a resulting
RCCN_p/m of 0.90 – 0.98. Under background conditions,
the INT–BK scheme also provided reasonable estimates, with
RCCN_p/m ranging from 1.00 to 1.16. This suggests that
the INT–BK assumption is likely sufficient to predict CCN in clean
continental regions. On polluted days, the EI–SR and INT–SR schemes are
believed to achieve better closure than the INT–BK scheme due to the
heterogeneity in particle composition across different sizes. The improved
closure obtained using the EI–SR and INT–SR assumptions highlights the
importance of knowing the size-resolved chemical composition for CCN
prediction in polluted regions. The EXT–SR and EXT–BK schemes markedly
underestimate NCCN on both polluted and clean days, with an
RCCN_p/m of 0.66 – 0.75. The diurnal variations in
RCCN_p/m show that the predicted NCCN during
the evening rush hour period is most sensitive to the mixing state
assumptions. The RCCN_p/m ranged from ∼ 0.5 to ∼ 1.2, reflecting the impact from
evening traffic and cooking sources (both with large amounts of hydrophobic
POA). We also find however, that the particle mixing state plays a minor role
when κorg exceeds 0.1, even with a high organic fraction.
The data in the study are available from the authors upon
request (fang.zhang@bnu.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-6907-2018-supplement.
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 does not belong to a conference.
Acknowledgements
This work was funded by the NSFC research project (41675141 and 91544217), the
National Basic Research Program of China (2017YFC1501702), the Fundamental Research
Funds for the Central Universities, and the Natural Science Foundation (NSF)
(AGS1534670). We thank all participants of the field campaign for their tireless
work and cooperation.
Edited by: Renyi Zhang
Reviewed by: three anonymous referees
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