Introduction
The indirect influence of aerosol particles on the radiative balance of the
atmosphere through changes in cloud droplet number and the persistence of
clouds (Twomey, 1974; Albrecht, 1989) carries the largest uncertainty
amongst the presently known causes of radiative forcing (IPCC, 2013).
Thus, better understanding of aerosol particle formation, growth, and
activation is essential.
Field and laboratory experiments have been conducted with the aim of better
characterizing the particle physical and chemical parameters impacting on
cloud condensation nuclei (CCN) activation. Studies have addressed the
relative importance of the size distribution, particle composition, and
mixing state in determining CCN activation, but there are disagreements on
the relative importance of these parameters (e.g., Roberts et al., 2002;
Feingold, 2003; Ervens et al., 2005; Mircea et al., 2005; Dusek et al.,
2006a; Anttila and Kerminen, 2007; Hudson, 2007; Quinn et al., 2008; Zhang
et al., 2008; Deng et al., 2013, Ma et al., 2013). CCN closure studies are a
useful approach to test our knowledge of the controlling physical and
chemical factors and to help verify experimental results. The CCN number
concentration (NCCN) is usually predicted from measured aerosol
properties such as particle number size distribution and composition or
hygroscopicity based on Köhler theory. The closure between the measured
and estimated NCCN is often achieved under background atmospheric
conditions without heavy pollution (Chuang et al., 2000; Dusek et al.,
2003; VanReken et al., 2003; Rissler et al., 2004; Gasparini et al., 2006;
Stroud et al., 2007; Bougiatioti et al., 2009).
In urban and polluted areas, the particle size distribution is more complex (Lee
et al., 2003; Alfarra et al., 2004; Zhang et al., 2004; Salcedo et al.,
2006). Particle activation is affected by the composition and the mixing
state of aerosol particles. It has been demonstrated that particles are more
difficult to activate during biomass burning plume events (Mircea et al.,
2005; Lee et al., 2006; Clarke et al., 2007; Rose et al., 2010, 2011;
Paramonov et al., 2013; Lathem et al., 2013). Also, their activation ratios
are reduced by secondary organics formed from oxidation of common biogenic
emissions (VanReken et al., 2005; Varutbangkul et al., 2006; Mei et al.,
2013) and black carbon (Dusek et al., 2006b; Kuwata et al., 2007). Other
organic components (e.g., organic acids) have been shown to activate more
easily (Raymond and Pandis, 2002; Hartz et al., 2006; Bougiatioti et al.,
2011), but still much less than inorganic species. Therefore, testing the
understanding of parameters controlling CCN activation and growth is
challenging in heavily polluted areas. Furthermore, the main uncertainty in
predicting the magnitude of global aerosol indirect effects arises from
those regions under the influence of urban emissions (Sotiropoulou et al.,
2007). The study of aerosol–CCN closure and relationships within, and in the
outflow of, heavily polluted areas is thus important.
East Asia, especially the Jing (Beijing)–Jin (Tianjin)–Ji (Hebei) region, is
a fast developing and densely populated region including numerous
megacities, where anthropogenic aerosol emissions have increased
significantly over recent years (Streets et al., 2008) and where aerosol
loading is high and chemical composition is complex (Li et al., 2007a; Xin
et al., 2007). The high aerosol loading significantly influences
radiative properties, cloud microphysics, and precipitation (Xu, 2001; Li
et al., 2007b; Xia et al., 2007; Rosenfeld et al., 2007; Lau et al., 2008;
Li et al., 2011).
Field measurements of CCN have been made in East Asia, where megacities are
likely to be major sources of pollutants and CCN (Yum et al., 2007; Rose et
al., 2010, 2011; Gunthe et al., 2009; Yue et al., 2011; Liu et al., 2011;
Zhang et al., 2012; Deng et al., 2011; Leng et al., 2013). Despite the
significant accomplishments of these studies, limitations and
uncertainties exist. As a recent example over the region of interest, Deng
et al. (2011) over-predicted concentrations of CCN at a site in the North
China Plain by 19 % compared with direct measurements.
The aim of this paper is to study aerosol hygroscopicity and CCN activity
under high aerosol loading conditions and to parameterize CCN number
concentrations by using CCN activation ratios (AR) as a proxy of the total
number of aerosol particles in the atmosphere. A cumulative Gaussian
distribution function (CDF) fit model is applied to aerosol data collected
under clean and polluted conditions to examine the influence of size
distribution, heterogeneity of chemical composition, and mixing state on CCN
activity. The hygroscopicity parameter (κ) is derived using
Köhler theory to study aerosol hygroscopicity on clean days and during
pollution events. In the CCN closure study, in addition to the parallel
observation (PO) closure test, we apply the CCN efficiency spectrum to
non-simultaneous condensation nuclei (CN) and bulk CCN observations, namely
the non-parallel observation (NPO) closure test, to estimate NCCN.
Finally, the relationship between bulk AR and aerosol physical and chemical
properties is examined.
Measurements and data
An intensive field campaign called the Aerosol-CCN-Cloud Closure Experiment
(AC3Exp) was conducted during June and July of 2013 at the Xianghe
Atmospheric Observatory (39.798∘ N, 116.958∘ E; 35 m a.s.l. (above sea
level)) located about 60 km southeast of the Beijing metropolitan area. This
site is surrounded by agricultural land, densely occupied residences, and
light industry. Set between two megacities (Beijing to the northwest and
Tianjin to the southeast) and less than 5 km west of the local town center
(with a population of 50 000), the site experiences frequent pollution
plumes. Depending on the wind direction, instruments at the Xianghe site
detect pollutants of urban, rural, or mixed origins, experiencing both fresh
biomass burning emissions and advected aged aerosols. Details about the
measurement location and meteorological conditions at the site can be found
in Li et al. (2007b, 2011).
Instruments and measurements
Bulk CCN activation was measured from 1 to 25 June 2013. Size-resolved
CCN were measured from 7 to 21 July 2013. Aerosol particle size
distributions (10–700 nm) were measured from 1 to 25 June 2013 and from
7 to 21 July 2013. During 1–25 June 2013, a scanning mobility particle
sizer (SMPS) was used independently for size distribution measurements. From
7 to 21 July 2013, it was combined with a Droplet Measurement
Technologies-Cloud Condensation Nuclei Counter (DMT-CCNc) (Lance et al.,
2006) and used for size-resolved CCN measurements. The CCN efficiency
spectrum was derived from size-resolved CCN observations made from 7 to
21 July 2013. The aerosol particle size distribution data independently
measured by the SMPS and bulk CCN measurements from 1 to 25 June 2013
combined with the derived CCN efficiency spectrum (Fig. 1) is used for the
NPO CCN closure test. Aerosol chemical composition was measured from 31 May
to 30 June 2013.
The aerosol inlet for the size distribution measurements was equipped with a
TSI Environmental Sampling System (Model 3031200), which consists of a
standard PM10 inlet, a sharp-cut PM1 cyclone, and a bundled Nafion dryer.
After drying, the sample flow with relative
humidity (RH) of < 30 % was drawn into the SMPS for the aerosol
size distribution measurements (10–700 nm). Meanwhile, the bulk NCCN at
different supersaturation (SS) was measured, using a continuous-flow CCN counter from the
DMT-CCNc. Each bulk CCN measurement cycle included three SS levels:
0.23, 0.51 and 0.80 %. The scanning times for those SS levels were
set at 7, 5, and 5 min, respectively.
The size-resolved CCN efficiency spectra were measured by coupling the same
DMT-CCNc used with the SMPS (Rose et al., 2008). In this setup the
particles are rapidly dried with RH < 30 % upon entering the
Differential Mobility Analyzer (DMA). Thus, size selection is effectively
performed under dry conditions, and the relative deviations in particle
diameter should be < 1 % except for potential kinetic limitations
(Mikhailov et al., 2009). The sample flow exiting the DMA was split into two
parts, with 0.3 L min-1 for the CPC and 0.5 L min-1 for the CCNC. The DMA,
controlled by the TSI-AIM software, scanned one size distribution every five
minutes. The DMT-CCNc was operated at a total flow rate of 0.5 L min-1 with a
sheath-to-aerosol flow ratio of 10. The inlet RH for DMT-CCNc was < 30 %. During the field campaign, the mean sample temperature and pressure
measured by the DMT-CCNc sensors were 23.5 ± 1.6 ∘C and
985.5 ± 3.6 hPa. The SS levels of DMT-CCNc were calibrated with
ammonium sulfate before and after the field campaign, following procedures
outlined in Rose et al. (2008). During each CCN measurement cycle,
calibrated effective SSs were 0.08, 0.11, 0.23, 0.42 and
0.80 %. The overall error (1σ) for the SS levels was estimated to
be < 3.5 %. The completion of a full measurement cycle took 60 min
(20 min for SS = 0.08 % and 10 min for the other SS levels).
Averaged measured and fitted CCN efficiency spectra from
the 3-parameter CDF fits at SS = 0.08, 0.11, 0.23, 0.42,
and 0.80 % under polluted (POL) and background (BG) conditions during the
size-resolved CCN measurement period.
The measurement of non-refractory submicron aerosol species including
organics, sulfate, nitrate, ammonium, and chloride were made with an
Aerodyne Aerosol Chemical Speciation Monitor (ACSM) (Sun et al., 2012). The
ACSM uses the same aerosol sampling, vaporization and ionization modules as
the Aerosol Mass Spectrometer (AMS) (DeCarlo et al., 2006), but removes the
size components. During the field campaign, ambient aerosols were drawn
inside through a 1/2 in. (outer diameter) stainless steel tube at
a flow rate of ∼ 3 L min-1, of which ∼ 84 cc min-1 was sub-sampled into the ACSM. An URG cyclone (Model:
URG-2000-30ED) was also supplied in front of the sampling inlet to remove
coarse particles with a size cut-off of 2.5 mm. Before sampling into the
ACSM, aerosol particles are dried by silica gel desiccant. The residence
time in the sampling tube was ∼ 5 s. The ACSM was operated at a
time resolution of ∼ 15 min with a scan rate of the mass
spectrometer of 500 ms amu-1 from m/z 10 to 150. Regarding the
calibration of the ACSM, mono-dispersed, size-selected 300 nm ammonium
nitrate particles within a range of concentrations were sampled into both
the ACSM and a condensation particle counter (CPC). The ionization
efficiency (IE) was then determined by comparing the response factors of the
ACSM to the mass calculated with known particle size and number
concentrations from the CPC. Once the IE was determined, changes in the
internal standard naphthalene or air ions, e.g., m/z 28 (N2+) or
m/z 32 (O2+), were used to account for the degradation of the
detector. Other details including the instrument, aerosol sampling setup,
operations, and calibration are given in Sun et al. (2012) and Ng et al. (2011).
In addition to the ACSM, the black carbon (BC) in PM2.5 was simultaneously
measured at a time resolution of 5 min by a BC analyzer (Aethalometer, Model
AE22, Magee Scientific Corporation). The campaign averaged mass
concentrations of BC were ∼ 4.2 µg m-3, and the
averaged mass fraction was about 6 %, with maximum of 18 % and minimum of
2 %. During the experiment period, the campaign area was generally hot and
moist, with an average temperature of 23.6 ∘C and an average
ambient RH of 72.3 %.
Data
The raw CCN data for both bulk and size-resolved CCN measurements were first
filtered according to the instrument recorded parameters (e.g., temperature
and flow). For example, if the relative difference between the actual and
set sample flows was larger than 4 %, the data were flagged as invalid. If the “temperature stability” was recorded as “0”, the data was also
flagged as invalid data due to instrument fluctuations. These flagged data
were not used for further analysis. A multiple charge correction and transfer
function (Deng et al., 2011) was applied to each CN size distribution
spectrum as well as to the CCN efficiency spectrum. The CCN AR is the ratio
of NCCN / NCN. To examine CCN activity under different conditions,
the size-resolved CCN efficiency data were classified as polluted or as
background based on the aerosol loading as well as the synchronous surface
horizontal wind data. Polluted conditions were identified when the total CN
number concentration (NCN) was > 15 000 cm-3 and when
the airflow came from the southeast or east. Background cases were identified when
NCN was < 15 000 cm-3 and when winds were from the west or
northwest. NCN is the total aerosol number concentration with a
particle size range of 10–700 nm. Here, the background refers to a regional
background condition which represents a well-mixed atmosphere unaffected by
local emissions, like biomass burning. Bulk measurements of total CCN
number concentrations at SS levels of 0.23, 0.51 and 0.80 % could
lead to a considerable underestimation of NCCN under polluted
conditions (Deng et al., 2011) due to water depletion inside the column
(Lathem and Nenes, 2011). Therefore, in this study, data points with
NCN > 25 000 cm-3 were excluded. In the closure study,
CCN size distributions were calculated by multiplying the fitted
campaign-averaged CCN efficiency spectrum (a 3-parameter CDF fit) with the
aerosol particle number size distribution. The total NCCN was then
obtained by integrating the size-resolved NCCN over the whole size
range. Aerosol mass concentrations were processed using ACSM standard data
analysis software (version 1.5.1.1). Detailed procedures for the data
analysis have been described in Ng et al. (2011) and Sun et al. (2012).
Theory
As proposed by Petters and Kreidenweis (2007), κ can be used to
describe the ability of particles to absorb water vapor and act as CCN.
Based on Köhler theory (Köhler, 1936), κ relates the dry
diameter of aerosol particles to the critical water vapor SS. According to
measurements and thermodynamic models, κ is zero for insoluble
materials like soot or mineral dust. However, their hygroscopicity changes
due to the aging process, so the κ value then is > 0. The
magnitude of κ is ∼ 0.1 for secondary organic
aerosols, ∼ 0.6 for ammonium sulfate and nitrate, 0.95–1 for
sea salt (Niedermeier et al., 2008), and 1.28 for sodium chloride aerosols.
The effective hygroscopicity of mixed aerosols can be approximated by a
linear combination of the κ values of the individual chemical
components weighted by the volume or mass fractions (Kreidenweis et al.,
2008; Gunthe et al., 2009). In this study, we calculated κ based on both size-resolved CCN measurements and bulk chemical composition
observations made during the field campaign. The method to derive κ
is described below.
Derivation of the average hygroscopicity parameter
The magnitude of κ was derived from the measured size-resolved CCN
activated fraction using κ-Köhler theory (Petters and
Kreidenweis, 2007). In κ-Köhler theory, the water vapor
saturation ratio over an aqueous solution droplet, Sc, is given by:
Sc=D3-Dp3D3-Dp3(1-κ)exp4σwMwRTρwD,
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 is greater than
0.1, it can be conveniently expressed as:
κ=4A327Dp3Sc2,
where Sc is the particle critical supersaturation and is derived using
the approach described by Rose et al. (2008), and A is defined as:
A=4σwMwRTρwD.
The characteristic Sc of
the size selected CCN is represented by the SS level at which AR reaches
50 %. For the parameters listed above, 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.
Note that values derived from CCN measurement data through Köhler model
calculations assume that the surface tension of pure water must be regarded
as an “effective hygroscopicity parameter” accounting not only for the
reduction of water activity by the solute (“effective Raoult parameter”)
but also for surface tension effects (Petters and Kreidenweis, 2007). In
this study, a parameter called κa, which characterizes the
average hygroscopicity of CCN-active particles in the size range around
activated diameters (Da), is calculated from the data pairs of SS and
Da based on the κ-Köhler theory.
Derivation of the particle hygroscopicity
For a given internal mixture, κ can be predicted by a simple mixing
rule on the basis of chemical volume fractions, εi (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) components in the
mixture and i is the number of components in the mixture. We derive
εi from the particle chemical composition measured by
the ACSM. Measurements from the ACSM show that the composition of submicron
particles was dominated by organics, followed by nitrate, ammonium, and
sulfate. The contribution of chloride was negligible (with a volume fraction
of about < 2 %). The analysis of the anion and cation balance
suggests that anionic species (NO3-, SO42-) were
essentially neutralized by NH4+ over the relevant size range. For
refractory species, BC represented a negligible fraction of the total
submicron aerosol volume (less than 3 %). Sea salt and dust are usually
coarse mode particles with particle sizes > 1 µm (Whitby,
1978). The contribution of such types of aerosols is thus expected to be
negligible for sizes < 1000 nm. Therefore, the submicron particles
measured by the ACSM mainly consisted of organics, (NH4)2SO4,
and NH4NO3. The particle hygroscopicity is thus the volume average
of the three participating species:
κchem=κOrgεOrg+κ(NH4)2SO4ε(NH4)2SO4+κNH4NO3εNH4NO3.
The value of κ(NH4)2SO4 is 0.67 and κNH4NO3 is 0.61, derived from previous laboratory
experiments (Petters and Kreidenweis, 2007). The following linear function
derived by Mei et al. (2013) was used to estimate κOrg in our study: κOrg=2.10×f44-0.11. The
mean value of κOrg during the field campaign wasps
0.115 ± 0.019. Species volume fractions were derived from mass
concentrations and densities of participating species. The densities of
(NH4)2SO4 and NH4NO3 are 1770 kg m-3 and 1720 kg m-3, respectively. The density of organics is 1200 kg m-3
(Turpin et al., 2001).
Results and discussion
Cumulative Gaussian distribution function fit and parameters derived from the cloud condensation nuclei efficiency
The spectra of measured CCN efficiency under both polluted and background
conditions were fitted with a CDF (Rose et al., 2008):
fNCCN/NCCN=a1+erfD-Daσa2,
where the maximum activated fraction (MAF) is equal to 2a; Da is the
midpoint activation diameter; and σa is the CDF standard
deviation. These parameters were determined for each spectrum. If MAF = 1 by
changing the parameter “a” to 0.5, the spectrum is characteristic for
internally mixed aerosols with homogeneous composition and hygroscopicity.
The 3-parameter fit results represent the average activation properties of
the aerosol particle fraction. During the field campaign, about 1200
size-resolved CCN efficiency spectra for atmospheric aerosols at SS levels
of 0.08 to 0.80 % were measured. Figure 1 shows campaign-averaged
spectra of both measured and fitted CCN efficiency at SS levels of 0.08,
0.11, 0.23, 0.42, and 0.80 % for background and polluted
conditions. The slope of AR with respect to diameters near Da in Fig. 1
provides information about the heterogeneity of the composition for the
size-resolved particles. For an ideal case when all CCN-active particles
have the same composition and size, a steep change of AR from 0 to MAF would
be observed as D reaches Da. A gradual increase in size-resolved AR with
particle diameter suggests that aerosol particles consisted of different
hygroscopicities. The gentler slopes of AR around Da during pollution events show that the particle composition was more heterogeneous than the
particle composition under background conditions. Significant differences in
size-resolved CCN efficiency spectra under polluted and clean conditions at
lower SS levels have been derived. The different shapes suggest that aerosol
hygroscopicity and CCN activity would be affected by local emission sources,
e.g., biomass burning.
Activation diameter
The three parameters (MAF, Da, and σ), describing the CCN efficiency
spectra derived from the 3-parameter CDF fits, and κa
under polluted and clean conditions, are summarized in Table 1. Activation
diameters under polluted and clean conditions are denoted as
Da_POL and Da_BG in Table 1, respectively. As expected, Da
decreases with increasing SS under both background and polluted conditions. At a given SS,
Da_POL is greater than Da_BG, suggesting that particles under polluted
conditions would be activated at a larger diameter. As SS increases, the
difference between Da_POL and Da_BG decreases.
Parameters describing the CCN efficiency spectra and hygroscopicity
for polluted (_POL) and background (_BG)
cases: the activation diameter (Da), the maximum activated
fraction (MAF), the CDF standard deviation (σ), the
heterogeneity parameter (σ/Da), and the hygroscopicity
parameter (κa) values shown are arithmetic mean values with
one standard deviation averaged over the entire measurement period.
SS
Da_POL
MAF_POL
σ_POL
σ/Da_POL
κa_POL
Da_BG
MAF_BG
σ_BG
σ/Da_BG
κa_BG
0.08 %
190.43 ± 6.11
0.98 ± 0.01
33.34 ± 4.49
0.17 ± 0.02
0.32 ± 0.03
178.68 ± 4.22
0.98 ± 0.01
32.73 ± 2.07
0.18 ± 0.01
0.38 ± 0.02
0.11 %
161.80 ± 15.10
0.98 ± 0.01
38.61 ± 7.62
0.22 ± 0.03
0.26 ± 0.05
151.03 ± 2.90
0.97 ± 0.01
28.56 ± 1.97
0.19 ± 0.01
0.33 ± 0.02
0.23 %
94.05 ± 8.47
0.96 ± 0.01
27.87 ± 6.30
0.26 ± 0.04
0.31 ± 0.05
91.75 ± 2.48
0.96 ± 0.00
18.81 ± 1.53
0.20 ± 0.01
0.34 ± 0.02
0.42 %
63.33 ± 3.65
0.94 ± 0.01
18.02 ± 2.84
0.26 ± 0.03
0.30 ± 0.04
64.06 ± 1.24
0.95 ± 0.00
16.21 ± 0.81
0.25 ± 0.01
0.29 ± 0.01
0.80 %
44.78 ± 2.51
0.94 ± 0.01
14.08 ± 0.98
0.29 ± 0.01
0.24 ± 0.03
45.67 ± 1.29
0.95 ± 0.01
13.82 ± 1.17
0.30 ± 0.02
0.22 ± 0.02
Maximum activated fraction
In general, aerosols with a more uniform and homogenous chemical composition
or with a core-shell structure would have a higher MAF. The MAF under polluted and
background conditions are denoted by MAF_POL and MAF_BG in Table 1. Values
of MAF_BG and MAF_POL range from 0.95–0.98 and from 0.94–0.98, respectively.
No significant discrepancies in MAF are observed between polluted and
background conditions Observations show that particles can activate to CCN
completely when particle diameters are greater than 300 nm even at SS = 0.08 %. This suggests
that a smaller portion of 1- MAF (2–6 %) is caused
by the error in the CDF fit, which will lead to a lower MAF than expected.
Derived hygroscopicity parameter, κa, as
a function of particle diameter, Da, under polluted (POL) and background
(BG) conditions. Bottom: percent change in κa due to
pollution as a function of Da. Error bars represent one standard
deviation calculated over the entire measurement period.
Cumulative Gaussian distribution function standard deviations
CDF standard deviations (σ) are general indicators for the extent of
external mixing and the heterogeneity of particle composition for aerosols
in the size range around Da. CDF σ under polluted and background
conditions are denoted as σ_POL and σ_BG in Table 1, respectively.
Under ideal conditions, σ equals zero for an internally mixed, fully
mono-dispersed aerosol with particles of homogeneous chemical composition.
According to Rose et al. (2008), even after correcting for the DMA transfer
function, calibration aerosols composed of high-purity ammonium sulfate
exhibit small non-zero σ values that correspond to ∼ 3 % of Da. This can be attributed to heterogeneities of the water
vapor SS profile in the CCNc or other non-idealities, such as DMA transfer
function and particle shape effects. Thus, “heterogeneity parameter”
values of σ/Da= 3 % indicate internally mixed CCN,
whereas higher values indicate external mixtures of particles. Heterogeneity
parameters under polluted and background conditions are denoted as σ/Da_POL
and σ/Da_BG in Table 1, respectively. They range from 17–30 %,
which is much higher than the 3 % observed for pure ammonium sulfate,
indicating that the particles were externally mixed with respect to their
solute content.
Derived average hygroscopicity parameter dependence on activation diameter
Figure 2 shows the dependence of κa on Da under background
and polluted conditions. κa_POL and κa_BG are defined as
the average hygroscopicity of CCN-active particles in the size range around
Da under polluted and background conditions, respectively. For
background days, larger particles were on average more hygroscopic than
smaller particles, i.e., κa_BG increases substantially from about
0.22 at 30–60 nm to about 0.38 at a size range of 120–180 nm. This is
consistent with field results observed in Guangzhou, South China by Rose et al. (2010). However, compared
to κa_BG, κa_POL shows a relatively flat trend as the particle size diameter
increases and error bars are larger. This suggests that under polluted
conditions, particle composition and their mixing state is complex and
diverse. In this case, larger particles are even less hygroscopic than
smaller particles. One possible reason for changes in κa under
polluted conditions may be the presence of a high amount of organics freshly
emitted from biomass burning (Andreae and Rosenfeld, 2008; Petters et al.,
2009; Rose et al., 2010) which would coat the larger particles and render
them less hygroscopic. Overall, κ for polluted aerosols is
about 20 % lower than that for clean aerosol particles in the
accumulation size range. For particles in the nucleation or Aitken size
range, κa for polluted particles is slightly higher than that
for particles in the background cases. Based on laboratory experiments,
Petters et al. (2009) examined the hygroscopic properties of particles
freshly emitted from biomass burning. They found that κ was a
function of particle size, with 250 nm particles being generally weakly
hygroscopic and sub-100 nm particles being more hygroscopic. During the
field campaign, polluted cases occurred when particles were mainly
biomass burning aerosols. The laboratory results, to some extent, can thus
explain our field measurements. Further investigations, including laboratory
experiments and field measurements of size-resolved chemical composition,
are needed to confirm and clarify this.
Probability distribution functions of Da under
background (_BG, left panels) and polluted (_POL, right panels) conditions at five SS levels (0.08–0.80 %) during
the size-resolved CCN measurement period.
Probability distribution functions of activation diameter and average hygroscopicity parameter
Figure 3 shows probability distribution functions (PDFs) for Da under
background conditions and during pollution events. Da_POL are mainly
distributed in the ranges of about 185–205, 163–180, 95–120, 65–75 and 45–55 nm at
SS levels of 0.08, 0.11, 0.23, 0.42 and 0.80 %,
respectively. At each SS level, the PDFs of Da_POL have a wider distribution than the PDFs of
Da_BG. At each SS level, the PDFs of Da_POL extend to large particle sizes
indicating the impact by pollution. The largest variation in
Da_BG and Da_POL is seen at SS = 0.08 % and 0.11 %, respectively. One reason for
this is the weakened impact of chemical composition on CCN activity at high
SS levels, i.e., the solute effect. The other reason is the larger uncertainties that arise from making measurements at low SS levels.
Probability distribution functions of κa under
background (_BG, left panels) and polluted (_POL, right panels) conditions for
different particle size ranges during the size-resolved CCN measurement
period.
Figure 4 shows PDFs of κa under background conditions and
during pollution events. The PDF of κa_POL has a large variation at
each size range around Dp and shows two modes. For example,
κa_POL for particles around 48 nm shows two peaks at
∼ 0.15 and ∼ 0.23. Peak values of
∼ 0.26 and ∼ 0.32 are seen for particles around
Dp= 198 nm. Most κa_POL values are less than 0.3.
This suggests externally mixed, but less hygroscopic particles are prevalent during
pollution events. Less variation is seen in the PDFs of κa_BG. One mode is seen with peak values of 0.23, 0.30, 0.35, 0.35, and
0.38 for Dp= 46, 64, 92, 152, and 179 nm, respectively. Most
κa_BG values are greater than 0.3 when Dp> 60 nm, indicating that the particles are more hygroscopic with a relatively
homogeneous composition.
Cloud condensation nuclei closure tests
In this section, we compare observed total NCCN with corresponding
values that were estimated on the basis of aerosol particle number size
distributions measured both in parallel and not in parallel and assuming a
uniform particle composition. Closure was achieved when estimated and observed
NCCN agreed quantitatively within the range of their uncertainties.
Parallel observation closure test
In parallel observation (PO) closure tests, the measured CCN efficiency spectrum is first
multiplied by the measured CN size distribution, which yields the CCN size
distribution. This is then integrated over the whole size range to obtain
the observed total CCN concentration (CCN_Obs). Size-resolved
NCCN are calculated by multiplying the campaign-averaged CCN efficiency
spectrum with simultaneously measured CN number size distributions.
Estimated total NCCN (CCN_Estimated) are obtained by the
stepwise integration of size-resolved NCCN from 10 to 700 nm. With this
comparison, the influence of the variation in chemical composition on the
CCN concentration can be investigated because the CN size distribution is
the same for both parameters.
Estimated NCCN as a function of observed NCCN at
different SS levels in the parallel observation (PO) closure test. The green
solid line is the 1:1 line.
As in Fig. 5, but for the non-parallel observation (NPO)
closure test. The dashed green lines indicate the boundaries representing
± 30 % deviations of NCCN-estimated from NCCN-observed.
Activation diameter, Da, maximum activated fraction, MAF, and standard deviation, σ, as a function of super saturation, SS.
Measurements made under background conditions (22–23 June
2013, NCN < 15 000 cm-3) and polluted conditions (14–15
June 2013, NCN > 15 000 cm-3). Bulk CCN activation
ratios at SS = 0.2, 0.5, and 0.8 % as a function of NCN
under background and polluted conditions are shown in (a) and (b),
respectively. Diurnal variations in AR, derived from κchem and
the fraction of total organic mass signal at f44, under background and
polluted conditions are shown in (c) and (d), respectively. Mass
concentrations of black carbon (BC), organics, nitrate (NO3-),
ammonium (NH4+), sulfate (SO42-), chloride (Cl-)
ions, etc., under background and polluted conditions are shown
in (e) and (f), respectively. NCN for nucleation (10–30 nm), Aitken
(30–130 nm), and accumulation modes (130–700 nm) under background and
polluted conditions are shown in (g) and (h), respectively.
Figure 5 shows CCN_Estimated as a function of
CCN_Obs at SS levels ranging from 0.08 to 0.80 %.
Estimated and measured total NCCN agree well. The mean slope and
correlation coefficient (R2) are 0.99 and 0.97, respectively, at the
five SS levels. A ∼ 3–4 % underestimation occurs at SS
levels of 0.08 and 0.11 %. One reason for this slight underestimation
is that size-resolved ARs are more variable at low SS levels than at higher
SS levels. Also, compared to total activated CCN number concentrations at
high SS levels, there are fewer NCCN at low SS levels, leading
to larger uncertainties or to a lower correlation. Overall, CCN closure can
be achieved by using campaign-averaged CCN efficiency spectra. In this PO
closure test, the influence of variations in chemical composition on CCN
concentrations is insignificant.
Non-parallel observation closure test
Mean CCN efficiency spectra derived from size-resolved CCN measurements
taken on 7–21 July 2013 are used to estimate total CCN number concentrations
based on CN size distribution measurements taken on 1–25 June 2013. This is
referred to as a non-parallel observation (NPO) closure test. The average measured CCN efficiency
spectrum (corresponding to spectra in Fig. 1) is multiplied by the measured
CN size distribution which yields the CCN size distribution. This is
integrated over the whole size range (10–700 nm) to obtain the estimated
total CCN concentration. The mean CCN efficiency spectra at SS levels of
0.23 and 0.80 % (Fig. 1) is used to estimate the total CCN
concentration during 1–25 June 2013. The mean CCN efficiency spectra at SS = 0.51 % is derived using the exponential relationships developed from
plotting the three CDF fit parameters as a function of SS (see Fig. 7).
AR measured at SS = 0.23 % as a function of
(a) κchem under background conditions, (b) accumulation mode
NCN under background conditions, (c) f44 under polluted
conditions, and (d) accumulation mode NCN under polluted conditions.
The accumulation mode size range is 130–700 nm in this study.
Estimated total NCCN as a function of measured bulk NCCN at SS
levels of 0.23, 0.51, and 0.80 % are shown in Fig. 6. The lower
slope at SS = 0.23 % indicates that the estimation on the basis of NPO
closure underestimates about 7 % of the observed NCCN. The closure is
considerably improved at higher SS levels. A reasonable correlation between
estimated and measured total NCCN is seen (R2=0.6-0.8), which
suggests temporal variations in chemical composition and mixing state of
aerosol particles. In addition, there are uncertainties due to measuring
bulk and size-resolved CCN. Overall, uncertainties in this NPO CCN closure
study range from 30–40 %. Caution is needed when using data from any
short-term experiment at a single site to do CCN parameterizations for
large-scale applications. It is necessary to conduct long-term CCN
measurements at more regional sites, especially those that are heavily
polluted.
Case study: cloud condensation nuclei activation and chemical composition
The behavior of CCN activation under background and polluted conditions is
examined. Two cases are selected: one case with total NCN < 15 000 cm-3 (background)
and another case with total NCN > 15 000 cm-3 (polluted). Trends in bulk CCN activation as
NCN increases are different for the background and polluted cases. Bulk
AR at the three SS levels (0.23, 0.51, and 0.80 %) increases as
total NCN increases for background cases (Fig. 8a) and decreases as
total NCN increases for polluted cases (Fig. 8b). For the background
cases, changes in bulk AR are dependent on changes in κchem (Fig. 8c). A good correlation between AR_
0.23 and κchem (R2 > 0.7) is seen in Fig. 9. A
high correlation between bulk AR and κchem, when total NCN
is low, is observed during the campaign (Fig. 10). In these cases,
organics account for ∼ 30 % of the total particle mass
concentration and concentrations of soluble inorganics are high (Fig. 8e).
In particular, the mass concentration of nitrate is higher than that for
organics and accounts for the largest mass fraction when κchem
reaches a maximum with a mean value of ∼ 0.45. The f44,
which is the fraction of total organic mass signal at m/z 44, is not correlated
with AR (Fig. 8c). The m/z 44 signal is mostly due to acids (Takegawa et al.,
2007; Duplissy et al., 2011) or acid-derived species, such as esters, and
f44 is closely related to the organic oxidation level, i.e., O : C ratio
(Aiken et al., 2008). Oxidized/aged acids are generally more hygroscopic and
easily activated. Therefore, the lower correlation between f44 and AR
implies that organics under low NCN conditions are less hygroscopic. CN
number concentrations in the nucleation, Aitken, and accumulation modes are
shown in Fig. 8g (polluted) and Fig. 8h (background). Under background
conditions, bulk AR at SS = 0.23 % is more correlated (R2=0.5)
with changes in NCN in the accumulation mode (Fig. 9), suggesting that
most aerosol particles with sizes > 100 nm can be activated.
Smaller particles with Aitken diameters of < 40 nm at the given SS
levels (0.23–0.80 %) are not as easily activated, if at all, so no
correlation is seen (Fig. 8g).
Under polluted conditions, there is little dependence of changes in bulk AR
with changes in κchem(Fig. 8d). Bulk AR at SS = 0.23 % is moderately
correlated with f44 (R2=0.5, Fig. 9). As
stated above, f44 is always related to the organic oxidation level.
Usually, oxidized/aged acids are more hygroscopic and easily activated. The
correlation betweenf44 and bulk AR suggests that the organics
contribution from oxidized or aged aerosols play a significant role in CCN
activity (Jimenez et al., 2009). A bias is introduced by using a
parameterized function derived from observations made at other sites with
different aerosol types to describe the particle hygroscopicity and
activation properties due to the complexity of the organic aerosol fraction
and its tendency to evolve with atmospheric oxidative processing and aerosol
aging (e.g., Padró et al., 2010; Engelhart et al., 2011, 2012; Asa-Awuku
et al., 2011). Under polluted conditions, the bulk AR_0.2 is
more correlated with changes in accumulation mode particles (R2= ∼ 0.3).
Overall, based on the case study, one cannot use a parameterized formula
using only total NCN to estimate total CCN number concentrations. If
observations such as size-resolved CCN and size-resolved chemical composition
are not available, the possibility of using bulk κchem and
f44 in combination with bulk NCN > 100 nm to
parameterize CCN number concentrations is implied by the case study. Further
field investigations are needed to demonstrate and confirm the relationship
between bulk AR and particle size and composition.
Time series of bulk AR at SS = 0.23 %, derived
κchem, and NCN from 19–24 June 2013. Green shaded areas
highlight periods with high correlations between bulk AR and
κchem.