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JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, D19202, doi:10.1029/2007JD009761, 2008
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Dependence of size-resolved CCN spectra on the mixing state
of nonvolatile cores observed in Tokyo
Mikinori Kuwata1 and Yutaka Kondo1
Received 25 December 2007; revised 10 July 2008; accepted 23 July 2008; published 8 October 2008.
[1] Size-resolved cloud condensation nuclei (CCN) spectra and volatility tandem
differential mobility analyzer (VTDMA) measurements were made simultaneously
in Tokyo in wintertime 2007 to characterize CCN activity near the urban center.
Ambient particles with mobility diameters of 30–200 nm were investigated
at supersaturations (SSs) of 0.32, 0.60, and 0.88%. The size distributions
of the nonvolatile cores of size-selected particles measured by the VTDMA were bimodal;
one mode showed relatively small changes (<10%) in peak diameter by volatilization,
and the other mode showed significant changes in diameter (>10% in peak diameter).
The former mode is referred to as less volatile (LV), and the latter mode is called
more volatile (MV). The main component of nonvolatile cores in Tokyo is known
to be black carbon (BC). Therefore, it is likely that LV particles correspond to soot
particles. The size-resolved CCN spectra were broader than those of (NH4)2SO4,
indicating that the observed particles were not uniformly mixed. In addition,
CCN/CN (CN: condensation nuclei) ratios were smaller than unity after the stepwise
increase. A CCN-LV correlation analysis shows that for small (<80 nm) particles,
the slopes of the correlations were smaller than unity, although the correlations
were significant. This indicates that CCN-inactive fractions are explained by LV particles
and particles coemitted with LV particles (likely primary organic aerosol particles).
For larger particles (>100 nm), the CCN-inactive fractions were close to the LV particle
fractions, suggesting that CCN-inactive particles were composed of fresh soot.
Citation: Kuwata, M., and Y. Kondo (2008), Dependence of size-resolved CCN spectra on the mixing state of nonvolatile cores
observed in Tokyo, J. Geophys. Res., 113, D19202, doi:10.1029/2007JD009761.
1. Introduction
[2] A subset of aerosol particles acts as cloud condensation nuclei (CCN) in cloud processes. The number of
cloud droplets, optical properties, and the lifetime of a
cloud depend on the number concentration of CCN
[Twomey, 1974; Lohmann and Feichter, 2005, and references
therein]. Thus it is important to determine the controlling
factors of CCN number concentration.
[3] The number concentration of CCN depends on the
number size distribution, aerosol chemical composition, and
mixing state [Seinfeld and Pandis, 2006]. In the last few
decades, a number of laboratory experiments [e.g., Cruz
and Pandis, 1998; Dinar et al., 2006] and atmospheric
observations [e.g., Roberts et al., 2002; Medina et al.,
2007] have been conducted to study the dependence of
CCN number concentration and CCN-active particle fraction
on chemical composition and size distribution. However,
most of these studies have mainly focused on internally
mixed particles, and only a few studies have paid attention
to the influence of the mixing state on size-resolved CCN
1
Research Center for Advanced Science and Technology, the University
of Tokyo, Tokyo, Japan.
Copyright 2008 by the American Geophysical Union.
0148-0227/08/2007JD009761$09.00
spectra [e.g., Dusek et al., 2005], although atmospheric
particles are often externally mixed, as is evident from
observations using hygroscopicity tandem differential
mobility analyzers (HTDMAs) [e.g., Cocker et al., 2001;
Massling et al., 2005]. In particular, soot particles, which
are mixtures of black carbon (BC) and primary organic
compounds, have been found to be externally mixed with
other particles in various regions of the world [McMurry et
al., 1996; Clarke et al., 2004; Philippin et al., 2004; Moteki
et al., 2007; Shiraiwa et al., 2007]. Kuwata et al. [2007]
showed that the CCN-inactive fraction of less hygroscopic
particles with mobility diameters of 100 nm selected by a
HTDMA could be explained by soot particles in Tokyo.
These results suggest that the mixing state of soot has a
significant influence on the size-resolved CCN spectra of
atmospheric particles. In addition, primary organic aerosol
(POA) particles, which are often coemitted with soot, may
not be CCN active. Therefore POA, together with soot, can
significantly influence size-resolved CCN spectra, particularly in urban centers where these particles are actively
emitted. In order to quantify this point, we simultaneously
measured size-resolved CCN spectra (30 – 200 nm) and
mixing state using a volatility tandem differential mobility
analyzer (VTDMA) in Tokyo. In Tokyo, it has been shown
that the main component of nonvolatile cores extracted by a
heater at 400°C is BC [Kondo et al., 2006]. In the following
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Figure 1. Observation system used in this study.
sections, ‘‘BC’’ represents the nonvolatile core extracted at
400°C.
Advanced Science and Technology (RCAST) at the
University of Tokyo. A detailed description of the observation site has been given by Kondo et al. [2006].
2. Experiment
[4] Figure 1 shows the observation system used in this
study. In this system, atmospheric particles were dried to a
relative humidity (RH) of <5% using two diffusion dryers
in series (TSI Model 3062). Dried particles were charged
with a 241Am neutralizer, and their size was classified by a
differential mobility analyzer (DMA1: TSI Model 3081).
The sheath-to-sample flow ratio of all DMAs used in this
study was set to 10:1. Particles with mobility diameters of
30, 40, 60, 80, 100, 150, and 200 nm were selected. Then,
particles were introduced to a condensation particle counter
(CPC: TSI Model 3022), a CCN counter (CCNC: Droplet
Measurement Technologies) [Roberts and Nenes, 2005],
and a heater. The CPC measured the concentration of
condensation nuclei (CN), and the CCNC monitored the
CCN number concentration. The sample flow and sheath
flow rates of the CCNC were set to 45 cm3/min and
455 cm3/min, respectively. The CCNC was operated at
supersaturations (SSs) of 0.32, 0.60, and 0.88%. These
SSs were calibrated using ammonium sulfate particles as
described by Kuwata et al. [2007]. The parameters for the
Pitzer model determined by Clegg et al. [1996] were used to
calculate the water activity of ammonium sulfate particles,
and calculations of the critical SSs were performed at a
temperature of 298.15 K and a surface tension of 72 mN/m.
The VTDMA used in this system was essentially the same as
that used by Kuwata et al. [2007]. The heater temperature
was set to 400°C; thus we can assume that the main
component of nonvolatile cores extracted by the heater
was BC [Kondo et al., 2006]. The size distributions of
nonvolatile cores included in size-selected particles were
measured using DMA2 (TSI Model 3081) and CPC2
(TSI Model 3022). The size distribution was measured
down to 15 nm. An inverse analysis using the STWOM
algorithm [Markowski, 1987] was performed on the raw
data obtained by the DMA2-CPC2 system. The DMA
transfer function developed by Stolzenburg [1988] and the
counting efficiency of the CPC were taken into account
for the calculation.
[5] In addition to the measurements described above, the
number size distribution (10 – 500 nm) was measured using
a wide-range particle spectrometer (WPS 1000XP, MSP
corporation) [Rodrigue et al., 2007]. The internal humidity
sensor of the WPS showed that the RH was kept at 23 ± 3%.
Thus, we regard particles measured by the WPS as almost
entirely dried. The observations were performed between
24 January and 2 February 2007 at the Research Center for
3. Results and Discussion
3.1. Size Distribution of Nonvolatile Cores
[6] Figure 2 shows the average size distribution of
nonvolatile cores included in 100-nm particles observed
using VTDMA. The number size distribution is bimodal;
one mode, which shows a slight change from the original
diameter by volatilization, is called less volatile (LV), and
the other mode is called more volatile (MV). Similar
bimodal size distributions have been observed in Leipzig,
Germany [Philippin et al., 2004; Rose et al., 2006] and in
Tokyo [Kuwata et al., 2007]. Some particles were completely volatilized by the heater, and thus they do not appear
in Figure 2. This portion is defined as completely volatile
(CV). These definitions are the same as those of previous
studies [Sakurai et al., 2003a; Wehner et al., 2004; Kuwata
et al., 2007]. The LV peak was fitted by a lognormal
function as shown in Figure 2, and then the number fraction
of less volatile particles (FLV) was calculated by the
integration of this function. The number fraction of particles
containing nonvolatile cores (FNV) was calculated by integrating the observed size distribution, and the number
fractions of more volatile (FMV) and completely volatile
(FCV) particles were calculated by the following equations:
FMV ¼ FNV FLV
ð1Þ
FCV ¼ 1 FNV :
ð2Þ
Figure 2. Observed VTDMA data for 100-nm particles.
The data were averaged over the observation period. The
shaded area shows the number fraction of LV particles.
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Table 1. Definitions of Parameters Used for VTDMA Data
Parameter
Definition
Less volatile (LV)
More volatile (MV)
Nonvolatile (NV)
Completely volatile (CV)
Particles
Particles
Particles
Particles
that
that
that
that
have a large nonvolatile core (change in peak diameter of less than 10%)
have a small nonvolatile core (change in peak diameter of more than 10%)
have a nonvolatile core (LV + MV)
do not have a measurable nonvolatile core (nonvolatile core < 15 nm: 1 NV)
FLV
FMV+CV
FNV
Number fraction of less volatile particles
Number fraction of more and completely volatile particles
Number fraction of nonvolatile particles
The terminology used for the VTDMA data is summarized
in Table 1.
[7] Kuwata et al. [2007] have shown that LV particles are
less hygroscopic using a VTDMA system combined with a
HTDMA, and McMurry et al. [1996] have shown that the
shape of less hygroscopic particles is chain aggregate and
mainly composed of carbon. Therefore, it is likely that the
dominant portion of LV particles was freshly emitted soot
(mixture of BC and POA) particles.
3.2. Diurnal Variation of CCN and VTDMA Data
[8] Figures 3a and 3b show the diurnal variations of the
CCN/CN ratios and VTDMA data of 30- and 100-nm
particles averaged over the observation period. A similar
trend with 100-nm particles was observed in the case of
60-, 80-, 150-, and 200-nm particles. It is clear that more
than 90% of 30-nm particles were CV, and they were
CCN-inactive at all SSs employed during the observations.
Thus, diurnal variations of CCN and VTDMA data were
not observed. The diurnal variation of 40-nm particles was
similar to that of 30-nm particles.
[9] Unlike 30-nm particles, the diurnal variation of CCN
and VTDMA data for 100-nm particles were clearly
Figure 3. Diurnal variation of CCN/CN ratios and
VTDMA data for (a) 30-nm and (b) 100-nm particles.
The data were averaged over the observation period.
observed. FLV reached its maximum value in the morning
(0600– 0800). During this time period, the emission of BC
is the highest in Tokyo owing to the morning rush [Kondo
et al., 2006]. Thus, this peak was possibly caused by the
increase of freshly emitted soot particles from motor
vehicles. A similar peak in FLV caused by the morning
rush was also observed in Leipzig, Germany [Rose et al.,
2006]. The diurnal variation of the CCN-active fraction is
similar to the sum of FMV and FCV. In particular, they were
very close at SS = 0.60%. Although they were also similar
at SS = 0.88%, CCN-inactive fractions (1 – CCN/CN)
were smaller than FLV during 0800 – 1200. This difference
was likely due to a slight coating on nonvolatile cores of
the LV-mode particle [Kuwata et al., 2007], as discussed in
more detail in section 3.5.
3.3. Number Size Distribution
[10] Figure 4a shows the average and standard deviations
of number size distributions measured by WPS. The number
size distributions of particles were bimodal. They were
Figure 4. Number size distributions of (a) ambient
particles and (b) CCN, LV, MV, and CV particles. All data
were averaged over the whole observation period. The
colored area in Figure 4a denotes the standard deviation of
the size distribution, and the dashed lines are the fitting
result to lognormal functions (see text for a detailed
explanation).
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Table 2. Statistics of the Number Size Distribution Averaged Over the Observation Period Obtained by Fitting to Two Lognormal
Functions
Number size distribution
N1 (cm3)
dp1 (nm)
log s1
N2 (cm3)
dp2 (nm)
log s2
Ntotal (cm3)
3240 ± 1720
29 ± 7
0.21 ± 0.05
2960 ± 1850
77 ± 27
0.26 ± 0.06
6130 ± 2340
deconvoluted by two lognormal functions, which are
expressed as follows:
ðlogðdp =dp1 ÞÞ2
N1
nðlog dp Þ ¼ pffiffiffiffiffiffi
exp 2p log s1
2ðlog s1 Þ2
!
ðlogðdp =dp2 ÞÞ2
:
exp 2ðlog s2 Þ2
!
N2
þ pffiffiffiffiffiffi
2p log s2
ð3Þ
[11] The fitting result of the average size distribution is
shown as dashed lines in Figure 4a. The fitting parameters
are summarized in Table 2. On average, the peak diameters
of the smaller and larger modes were 29 and 77 nm,
respectively. The number concentration obtained by integrating the number size distribution was 6130 ± 2340 cm3.
This concentration may not represent climatological values,
considering the relatively short observation period (10 days).
[12] Figure 4b shows the number size distributions of
ambient particles, CCN, LV, MV, and CV particles
averaged over the observation period. We multiplied
CCN/CN, FLV, FMV, and FCV by the number size distribution measured by WPS to obtain the concentrations.
Figure 4b clearly shows that almost all the smaller-mode
particles were CV. Thus, particles in this mode were likely
composed of volatile compounds at 400°C such as sulfate,
nitrate, and organics. Hasegawa et al. [2005] measured the
number size distribution (14.9 – 742 nm) at Ikegami-Shincho crossing in Kawasaki City, which is 16 km southsoutheast of RCAST. They observed a bimodal size distribution with peak diameters around 30 and 90 nm. They
employed a thermodenuder operated at 250°C to investigate
the chemical composition. The smaller mode (peak diameter of 30 nm) disappeared at 250°C, and the larger mode
(peak diameter of 90 nm) remained in the thermodenuder.
Their results are consistent with our data in that the smaller
mode did not include nonvolatile cores larger than 15 nm.
Takegawa et al. [2006] employed an Aerodyne aerosol
mass spectrometer (AMS) to study size-resolved chemical
composition in Tokyo. They observed that small (vacuum
aerodynamic diameters <100 nm) particles were enriched
by organics. In this size range, mass concentrations of
organics were higher than those of sulfate by more than a
factor three. In addition, the m/z = 57 signal (characteristic
signal of hydrocarbons) was significant, and the m/z =
44 signal (characteristic signal of oxygenated organic compounds) was insignificant. These results indicate that most
of the smaller-mode particles did not contain significant
amount of BC, and particles in this mode were mainly
composed of hydrocarbon-like organic compounds. Lubricating oil emitted from motor vehicles may constitute this
mode [Sakurai et al., 2003b].
[13] The CCN number concentration (NCCN) of 30- to
200-nm particles averaged over the observation period is
shown in Table 3. We multiplied size-resolved CCN/CN
ratios by the number size distributions to obtain NCCN.
Thus, number concentrations of CCN larger than 200 nm
are not included in the values shown in Table 3. The average
number concentration of particles larger than 200 nm (200–
500 nm) was 142 ± 68 cm3. This value gives a measure of
the underestimation of NCCN. The concentrations shown in
Table 3 are comparable to those measured in field experiments to study aerosol impacts on cloud formation [e.g.,
Conant et al., 2004]. They are also in the ranges of
concentration similar to those used for cloud microphysical
models [Kuba and Fujiyoshi, 2006]. Therefore, the present
CCN spectra results are linked to their impacts on cloud
formation processes to some extent by reference to these
earlier studies, although we focus on characterizing the CCN
spectra in this work.
[14] The CCN size distribution shown in Figure 4b is
nearly monomodal because the smaller mode does not
contribute to the CCN number concentration significantly.
This trend becomes clearer at lower SS. The peak diameters
of the size distributions were about 100 nm (SS = 0.32%),
80 nm (SS = 0.60%), and 70 nm (SS = 0.88%). For particles
larger than 100 nm, the CCN number concentration was
close to the sum of CV and MV particles and was smaller
than the particle number concentration. This indicates that
LV particles were not CCN-active, and they affected the
CCN size distribution.
3.4. Comparison of CCN and VTDMA Data
[15] Figure 5a shows the size-resolved CCN/CN ratios at
each SS averaged over the observation period. The activation curves for (NH4)2SO4 obtained in the calibration of the
instrument are also shown in Figure 5a for comparison.
CCN/CN ratios increased with increasing diameter because
(1) larger particles contained more water-soluble molecules
and ions and (2) the magnitude of the Kelvin effect
decreases with increasing diameter.
[16] The activation curves are shifted to larger sizes
compared with that of (NH4)2SO4 particles. In addition,
they become significantly broader than that of (NH4)2SO4.
The shift shows that chemical compounds that had larger
critical dry diameters than that of (NH4)2SO4 were contained
in the particles. As shown by Kuwata et al. [2008], the critical
dry diameter becomes larger when water-insoluble compounds (water-insoluble organic compounds and BC) are
internally mixed with inorganic compounds. In addition,
King et al. [2007] have measured the critical SS of internally
mixed (NH4)2SO4 secondary organic aerosol particles. They
showed that the critical SS increases with the increase of the
organic mass fraction, which is equivalent to larger critical
dry diameters.
[17] The broader size distribution shows that the chemical
composition of the particles was not uniform. Namely, some
Table 3. CCN Number Concentration (NCCN) of 30- to 200-nm
Particles Averaged Over the Observation Period
Supersaturations
0.32%
NCCN (30 – 200 nm)
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3
1030 ± 620 (cm )
0.60%
0.88%
1760 ± 1000
2370 ± 1260
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KUWATA AND KONDO: DEPENDENCE OF CCN ON THE MIXING STATE
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the number fractions of MV and CV particles (FMV+CV)
crossed around 80 – 100 nm. For larger particles, the
CCN/CN ratios agreed with FMV+CV to within 10%. At
SS = 0.32%, CCN/CN and FMV+CV did not cross. For
this SS, CCN/CN and FMV+CV were similar for particles
larger than 150 nm.
Figure 5. Size distributions of (a) CCN/CN ratios of
ambient particles (solid lines) and (NH4)2SO4 particles
(dashed lines) and (b) comparison of CCN/CN ratios and
VTDMA results. All data were averaged over the whole
observation period.
particles were enriched by water-soluble compounds, and
others were enriched by water-insoluble compounds (only
the particles enriched by water-soluble compounds can act
as CCN).
[18] In addition, incomplete CCN activation after the
step increase in CCN/CN ratio was observed. For instance,
CCN/CN ratios at 100 nm were 0.57 (SS = 0.32%) to 0.78
(SS = 0.88%), although a relatively sharp increase in
CCN/CN ratio was observed at 60– 80 nm (SS = 0.32%)
and 30– 60 nm (SS = 0.88%). This can be explained by
external mixing of soot particles as detailed below.
[19] Figure 5b shows the size distributions of CCN/CN,
FLV, FMV, and FCV. As discussed above, the CCN-active
fraction increased with increasing diameter. However, it
did not reach unity after the step increase. The VTDMA
data also show a clear size dependence. FLV was only 4%
at 30 nm. FLV increased with increasing diameter between
30 and 100 nm. FLV reached a maximum around 100 to
150 nm (FLV = 25%), and it was smaller than the
maximum value at 200 nm (FLV = 18%). A similar size
dependence of FLV was also reported in German cities
[Wehner et al., 2004; Rose et al., 2006]. At SS = 0.60 and
0.88%, the number fractions of CCN-active particles and
3.5. Correlation of CCN-Inactive Fraction
and LV Fraction
[20] For a more quantitative analysis of CCN and
VTDMA data, correlations of the CCN-inactive fraction
(1 - CCN/CN) and FLV were investigated. Figure 6 shows
correlations of the CCN-inactive fraction and FLV at SS =
0.60%. In Figure 6, correlations for 30- and 40-nm
particles are not shown, as they were not CCN-active at
this SS (Figure 5). In general, correlations are good (r2 =
0.44 0.86) at all diameters shown in Figure 6. In
particular, in the cases of 100-, 150-, and 200-nm particles,
the CCN-inactive fractions and FLV agree to within 20%.
This shows that the CCN-inactive fraction is quantitatively
explained by FLV in these cases. The slopes are significantly lower than unity for smaller particles (60 and
80 nm), although correlations are still good (r2 = 0.64
and 0.86, respectively). This indicates that (1) LV particles
were CCN-inactive at these diameters and (2) CCN-inactive
MV and CV particles had the same origin as LV particles.
Hydrophobic POA particles likely correspond to this
CCN-inactive fraction, as discussed in detail in section 3.6.
[ 21 ] Figure 7 summarizes the analysis shown in
sections 3.4 and 3.5 (SS = 0.60%). Particles smaller than
40 nm were not CCN-active. The stepwise CCN activation
occurred between 40 and 60 nm. Between 60 and 100 nm,
the CCN-inactive fractions were accounted for by LV and
POA particles. For diameters larger than 100 nm, the
CCN-inactive particles almost exactly corresponded to
the LV particles. Similar features were seen for the other
SSs, with some shifts in diameter representing the different
regimes, as detailed below.
[22] The correlations between FLV and CCN-inactive
fractions are summarized in Figure 8. At SS = 0.88%,
the slopes for small (40- and 60-nm) particles were much
smaller than unity (0.10 0.52), although correlations
were significant (r2 = 0.75 at 60 nm). At SS = 0.60 and
0.32%, this region shifted to 60– 80 nm and 80– 100 nm,
respectively.
[23] For 80- and 100-nm particles, the FLV and CCNinactive fractions correlate very well at all SS (r2 =
0.56 0.86), and the CCN-inactive fraction and FLV agree to
within 20% at 80 nm (SS = 0.88%) and 100 nm (SS =
0.60 and 0.88%). This indicates that the CCN-inactive
fraction is quantitatively explained by FLV in these cases.
The CCN-inactive fraction and FLV also agreed to within
20% at 150 nm (SS = 0.32 and 0.60%) and 200 nm (SS =
0.32 and 0.60%). However, the slopes exceed 1.4 at
150 nm (SS = 0.88%) and 200 nm (SS = 0.88%). This
indicates that a significant portion of LV particles with
these diameters were CCN-active at SS = 0.88%. In these
cases, slight coatings on LV particles possibly led to the
CCN activation, as shown by Kuwata et al. [2007]. The
main component of nonvolatile particles in Tokyo is BC.
Therefore, these observational results show that the CCN-
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Figure 6. Correlations of FLV and CCN-inactive (SS = 0.60%) fraction (1-CCN/CN).
inactive particles (80 – 200 nm) can be explained by freshly
emitted soot particles as a first approximation.
3.6. CCN Inactive POA Particles
[24] As shown in Figures 6 and 8, CCN-inactive fractions
correlated well with FLV even when the slopes were much
smaller than unity. This shows that a significant portion of
MV and/or CV particles were CCN-inactive, and they were
coemitted with LV particles. It has been shown that waterinsoluble organic aerosol (WIOA), hydrocarbon-like organic
aerosol (HOA), and POA have represented a very similar set
of species in the aerosol in Tokyo [Takegawa et al., 2006;
Miyazaki et al., 2006; Kondo et al., 2007]. They were also
well correlated with BC. Considering this, we assumed that
CCN-inactive particles consisted of LV (soot) and POA
particles. Then, the CCN-inactive POA fractions (F(CCN
inactive POA)) can be estimated as follows as a function of
diameter and SS:
FðCCN inactive POAÞ
6 of 8
¼
1
FLV
CCN inactive fraction
!
1 FLV ðaverageÞ;
ð4Þ
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KUWATA AND KONDO: DEPENDENCE OF CCN ON THE MIXING STATE
Figure 7. Schematic of CCN size-distributions (SS =
0.60%) discussed in section 3.5. Data shown are the same as
those in Figure 4b.
where (FLV/CCN inactive fraction) corresponds to the
slopes shown in Figure 8. The calculated values are
summarized in Figure 9. The values range from 0.10
(80 nm, SS = 0.60%) to 0.60 (40 nm, SS = 0.88%). This
demonstrates that not only LV particles but also POA
particles should be taken into account when studying the
CCN activity of small (<80 nm) particles. F(CCN-inactive
POA) at a given diameter decreases with increasing SS.
For instance, the values at 60 nm are calculated to be
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Figure 9. Size distributions of FLV and F(CCN-inactive
POA) at each SS.
0.31 (0.60%) and 0.16 (SS = 0.88%), respectively. This
indicates that larger portions of POA particles are activated
as CCN at higher SS. This CCN activation of POA
particles may be caused by the mixing of trace amounts of
primary water-soluble compounds (e.g., sulfate) and/or
oxidation of POA in the atmosphere.
[25] In summary, at diameters larger than 100 nm, CCNinactive fractions were basically explained by FLV, which
ranged between 18 and 25%. Thus, the CCN number
concentration is overestimated by about 20% in this size
range if this fraction is ignored in estimating CCN number
concentration from the observed particle size distribution
(Figure 5b). For smaller (<100 nm) particles, POA particle
fractions (10 – 60%) also need to be taken into account to
explain the CCN inactive fraction after the stepwise
increase in the CCN/CN ratio (Figure 9).
4. Conclusion
Figure 8. Summary of the correlations of FLV and CCNinactive (1 - CCN/CN) fractions.
[26] We simultaneously performed size-resolved CCN
(SS = 0.32, 0.60, and 0.88%), VTDMA, and particle
number size distribution measurements in Tokyo in wintertime 2007. The aims of this study were to characterize CCN
size spectra at different SSs and understand them in terms
of the mixing state of particles. The average number
concentration of particles was 6130 ± 2340 cm3. The
number size distribution was bimodal, with peak diameters
of 29 and 77 nm. A comparison with VTDMA and previous
studies indicated that particles of the smaller mode were
mainly composed of hydrocarbon-like organic compounds.
The number size distribution of CCN was obtained by
multiplying CCN/CN ratios by the number size distribution.
Unlike the number size distribution of particles, the CCN
size distribution was monomodal, because the particles in
the smaller mode were not activated.
[27] CCN/CN ratios increased with increasing diameter,
and stepwise increases were observed at around 40– 80 nm.
Compared with (NH4)2SO4 particles, the stepwise increase
was much broader and shifted to larger size. This indicates
that a significant portion of organic compounds and BC
were contained in the particles and the mixing state was not
uniform. In addition, the activation curves did not reach
unity after the stepwise increases.
[28] Near the end of the stepwise increase in CCN/CN
(60 – 80 nm), the fractions of CCN-inactive particles closely
correlated with those of LV particles, although the slopes
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were smaller than unity (e.g., slope = 0.35, and r2 = 0.64 at
60 nm (SS = 0.60%)). This demonstrates that the CCNinactive particles were composed of soot particles and particles coemitted with soot (most likely primary organic
aerosol particles). The contributions of POA particles to the
total CCN-inactive particles were estimated to be 10–60%,
depending on the SS and diameter. Consideration of the effect
of POA is critical because the CCN spectra peaked in this size
rage.
[29] After the stepwise increase (>80 nm), the CCN/CN
ratios were very close to FMV+CV. Namely, CCN-inactive
particles were nearly identical to the LV (soot) particles.
The average values of FLV were 25% at 100 and 150 nm.
NCCN is significantly underestimated if it is estimated
directly from the observed particle size distribution without considering the mixing state of soot particles.
[30] We have demonstrated the importance of the effect
of soot and POA particles in controlling CCN concentrations. This understanding is useful, particularly in estimating CCN concentrations in urban areas, where fractions
of fresh soot and POA particles are significant.
[31] Acknowledgments. We thank M. Kajino for helpful discussion
of this paper. This work was supported by the Ministry of Education,
Culture, Sports, Science, and Technology (MEXT) and the global
environment research fund of the Japanese Ministry of the Environment
(B-083). M. Kuwata thanks the Japan Society for the Promotion of
Science (JSPS) for a JSPS Research Fellowship for Young Scientists.
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M. Kuwata and Y. Kondo, Research Center for Advanced Science and
Technology, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo
153-8904, Japan. ([email protected]; [email protected])
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