Geophysical Research Letters
Supporting Information for
Size-dependent Hygroscopicity Parameter () and Chemical Composition of
Secondary Organic Cloud Condensation Nuclei
D. F. Zhao1, A. Buchholz1, a, B. Kortner1, P. Schlag1, F. Rubach1, b, A. Kiendler-Scharr1,
R. Tillmann1, A. Wahner1, J. M. Flores2, Y. Rudich2, Å. K. Watne3, M. Hallquist3, J. Wildt4,
Th. F. Mentel1
1Institute
for Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Jülich, Jülich, 52425,
Germany
2Department
of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
3Department
4Institute
aNow
of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, SE-41296,
Sweden
of Bio- and Geosciences, IBG-2, Forschungszentrum Jülich, Jülich, 52425, Germany
at Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland
bNow
at Max-Planck-Institute for Chemistry, Mainz, Germany
Correspondence to: Th. F. Mentel (t.mentel@fz-juelich.de)
Contents of this file
Text S1 to S4
Figures S1 to S4
Tables S1-S9
Introduction
Text S1 and S2 present experimental details and CCN measurement details. Text S3
presents the equations to investigate the influence of Kelvin effect on the relative amount
of low volatility and high volatility compounds. Text S4 provides the equations of Köhler theory.
Fig. S1 is included to show critical dry diameter at various supersaturations. Fig. S2
shows the dependence of CCN activity () of SOA from different precursors besides the
1
one in the main manuscript. Fig. S3 shows the dependence of SOA chemical
composition in additional experiment. Fig. S4 shows the CCN calibration data.
Table S1 shows the summary of the experiments. Table S2 and S3 shows the
theoretical CCN data and experimental CCN data of (NH4)2SO4 for calibration. Table S4
shows the summary of the previous studies of SOA from α-pinene. . Table S5, S6, S7,
S8, S9 present the data in Figure 1, 2a, 2b, 3 and 4 of the main manuscript, respectively.
Text S1. Experimental details
The experiments were carried out in the outdoor atmosphere simulation chamber
SAPHIR, a 270 m3 double-wall Teflon chamber of cylindrical shape at the
Forschungszentrum Jülich, Germany (surface to volume ratio: 0.88 m-1). The chamber
uses natural sunlight as light source and is equipped with a louvre system which can be
used to simulate dark processes when closed. A continuous flow 7-9 m3 hr-1 of synthetic
air keeps the chamber at a slight overpressure of ~50 Pa and compensates for the losses
by sampling air of various instruments. The OH radical comes from photolysis of HONO
emitted by the photolytic process of Teflon wall. Typical OH concentration is several 106
molecules cm-3 for the experiments in this study, which is close to the ambient daytime
OH level. The details of the chamber have been previously described [Bohn et al., 2005;
Rohrer et al., 2005].
For the experiments described here, the chamber was equipped with
instrumentation for gas phase and particulate phase characterization, and for measuring
chamber parameters such as temperature, relative humidity, flow rate and photolysis
frequencies. In a typical experiment, the starting RH was 75% and can vary between 30%
and 75% over the whole experiment due to the diurnal temperature change and the
dilution by compensation flow. The typical peak J(O1D) measured from a spectral
radiometer is about 1.510-5 s-1.
The details of each experiment are summarized in Table S1.
Text S2. Detailed description of CCN activation measurement
The setup of CCN activation measurement consisted of a cloud condensation nuclei
counter (CCNC, Droplet Measurement Technique, USA) and a condensation particle
counter (CPC3786, TSI, USA), which were operated in parallel after a differential
mobility analyzer (DMA). This method, also known as Scanning Mobility CCN analysis
[Moore et al., 2010], has been successfully used previously [Asa-Awuku et al., 2008; AsaAwuku et al., 2009; Asa-Awuku et al., 2010; Buchholz, 2010; Engelhart et al., 2008;
Engelhart et al., 2011; Padro et al., 2007; Zhao et al., 2010; Zhao et al., 2015]. Particles
2
first passed through the DMA and the outgoing flow was split to CPC and CCNC to
measure simultaneously the concentrations of condensation nuclei (CN) and CCN,
respectively. Particles with diameters (Dp) between 10 nm and 500 nm were studied by
scanning the DMA while the supersaturation (SS) remained constant. In each scan, for
each particle size class, the activated fraction (af, CCN/CN) was calculated from the
measured CCN and CN concentrations after multi-charge correction. The fraction of
multiple charged particles was calculated using the measured size distribution for each
size class according to a natural charge distribution [Wiedensohler, 1988]. After multicharge correction, af for the single charge particles was obtained as a function of Dp,
which was fitted with a cumulative Gaussian error function [Rose et al., 2008]. The
critical dry diameter (Dcrit) at a given SS is determined from the turning point of the
function. At each supersaturation (SS), three full size scans were made, and four to five
different supersaturations in the range of 0.1%-1% were used with the exact SS
depending on the particle size distribution.
The time matching between the CCN and the CPC was carefully done using a
method similar to Moore et al. [2010]. The time delay between the CCN and CPC was
measured by changing the particle concentration immediately via an abrupt change of
particle size (DMA voltage) of poly dispersed (NH4)2SO4 aerosol. In the data analysis,
the raw SMPS data (time resolution 0.1 s) was used. The exact delay time between the
CCN and the CPC was obtained by adjusting the delay time (in the step of 0.1s) to
achieve the optimal overlap of the larger size parts of size distribution between CCN and
CPC which includes an up scan and down scan. This method provides an accuracy of
0.1s for the time delay. In addition, the tubings from DMA to CCN and CPC were kept
constant.
For each SS the average Dcrit of the three scans was obtained. The SS was
calibrated using theoretical SS vs. Dcrit of (NH4)2SO4 particles. The theoretical values
from Rose et al. [2008] (OS1 data set, c.f. Table S2) were used. Typical calibration data
are shown in Table S3 and Figure S4. From Dcrit at given SS, the hygroscopicity
parameter  was calculated using the method in Petters and Kreidenweis [2007].
Text S3. Equations related to the influence of Kelvin effect on the relative amount
of low volatility and high volatility compounds
According to the Raoult law,
𝐶𝑖,𝑔 = 𝑥𝑖,𝑙 𝐾𝑒 𝐶𝑖0 ,
(S1)
where Ci,g is the gas phase concentration of species i, xi,l is the liquid phase molar fraction
of species i, Ci0 is the saturated concentration of species i and Ke is the Kelvin effect
term.
3
4𝑉 𝜎
𝐾𝑒 = exp⁡(𝑅𝑇𝐷𝑙 ),
(S2)
𝑝
Where Vl is the partial molar volume (assuming different species have the same partial
molar volume),  is the surface tension, R is the gas constant, T is the temperature and Dp
is the diameter of the particle.
Xi,l can be obtained from the Ci,l, the liquid phase concentration of species i, and Ct, the
total molar concentration of all species in the liquid phase,
𝑥𝑖,𝑙 =
𝐶𝑖,𝑙
.
𝐶𝑡
(S3)
Substituting Eq. S3 into S1 and rearranging, one can get
𝐶𝑖,𝑔
𝐶𝑖,𝑙
=
𝐶𝑖0
𝐶𝑡
𝐾𝑒 .
(S4)
Using Ci,t, total concentration of species i in the gas and particle phase, one can get
𝐶𝑖,𝑔 = 𝐶𝑖,𝑡 − 𝐶𝑖,𝑙 .
(S5)
Substituting Eq. S5 into S4 and rearranging, one can get
𝐶𝑖,𝑙 =
𝐶𝑡
𝐶𝑖0 𝐾𝑒 +𝐶𝑡
𝐶𝑖,𝑡
(S6)
Similarly, for species j,
𝐶𝑗,𝑙 =
𝐶𝑖,𝑙
𝐶𝑗,𝑙
=
𝐶𝑡
𝐶𝑗0 𝐾𝑒 +𝐶𝑡
𝐶𝑡
𝐶0
𝐾
𝑖 𝑒 +𝐶𝑡
𝐶𝑡
𝐶0
𝑗 𝐾𝑒 +𝐶𝑡
𝐶𝑗,𝑡
(S7)
𝐶𝑖,𝑡
(S8)
𝐶𝑗,𝑡
After rearrangement, one can get
𝐶𝑖,𝑙
𝐶𝑗,𝑙
𝐶0
= 𝐶𝑗0 (1 +
𝑖
(𝐶𝑖0 −𝐶𝑗0 )𝐶𝑡 )
𝐶𝑖0 𝐶𝑗0 𝐾𝑒 +𝐶𝑗0 𝐶𝑡
)∙
𝐶𝑖,𝑡
𝐶𝑗,𝑡
(S9)
Assuming Ci0>Cj0, i.e., species i is more volatile than species j, when particle size
decreases, Ke increases and Ci,l/Cj,l decreases. This means that small particles have
relatively more low volatility compounds.
Text S4. Equations related to -Köhler theory
Petters and Kreidenweis [2007] proposed the theory to parameterize CCN activity data
using .  is defined in the following equation.
1
𝑉𝑠
= 1+𝜅
𝑎𝑤
𝑉𝑤
aw, Vs, and Vw is the water activity, volume of solute and volume of water in the activated
droplet, respectively.
The following equation is derived in the -Köhler theory.
4
𝐷3 − 𝐷𝑑3
4𝑀𝑤 𝜎𝑠𝑜𝑙
𝑆= 3
exp⁡
(
)
𝑅𝑇𝜌𝑤 𝐷
𝐷 − 𝐷𝑑3 (1 − 𝜅)
S: saturation ratio, S=SS+1;
D: droplet diameter;
Dd: dry particle diameter;
Mw: molecular weight of water;
sol: surface tension of droplet solution;
w: density of water.
R: gas constant (8.314 J mol-1 K-1)
T: temperature.
5
200
Dcrit (nm)
150
100
50
0.0
0.2
0.4
0.6
0.8
1.0
SS (%)
0
0
2
4
6
Time (h)
8
10
Figure S1. (a) Critical diameter (Dcrit) of SOA formed by α-pinene photooxidation at
various supersaturations (SS) for the experiment shown in Fig. 1 (the gray shaded area
indicates the dark period). Supersaturation corresponded to Dcrit.
6
0.25
A
0.25
10
8
0.20
6
0.15
4


8
2
0.10
Time (h)
4
10
0.20
Time (h)
6
0.15
B
2
0.10
0
0
0.05
0.05
0.00
0.00
0
50
100
150
200
250
0
50
100
150
Dcrit (nm)
0.25
200
250
300
Dcrit (nm)
C
0.25
10
8

2
0.10
6
0.15
4
Time (h)
4
8
0.20
Time (h)
6
0.15
10

0.20
D
2
0.10
0
0
0.05
0.05
0.00
0.00
0
50
100
150
Dcrit (nm)
200
250
0
50
100
150
200
250
Dcrit (nm)
Figure S2. CCN activity of SOA represented by hygroscopicity parameter  as a function
of critical diameter (Dcrit). Particles were generated by photooxidation of various
precursors: (a) direct boreal tree emissions, (b) monoterpene (α-pinene/limonene=1,
molar ratio), (c) toluene, (d) mixture of monoterpene (α -pinene/limonene=1, molar ratio)
and toluene. At the same time of photochemical aging, there was a trend of smaller
particles having a higher hygroscopicity () for all systems. The dash lines are only for
guiding the eyes.
7
0.12
0.22
f44
ff43
0.20
0.08
0.18
0.06
0.16
0.04
0.14
0.02
0.12
f43
f44
0.10
50
100
150
200
250
300
Dpg (nm)
Figure S3. f44 of SOA from the photooxidation of monoterpene (α-pinene:limonene=1:1,
molar ratio) and xylene-d10 mixture as a function of particle geometric diameter (Dpg).
Dpg is derived from the vacuum dynamic diameter of AMS divided by particle density. f44
decreased with particle size while f43 increased with size. The dashed line is only for
guiding the eyes.
8
2
Theoretical value (Rose et al. 2008)
Calibrated SS
1
9
8
7
SS(%)
6
5
4
3
2
0.1
9
8
2
3
4
5
6
7
8
Dcrit (nm)
9
2
100
(a)
1.0
SS theoretical (%)
0.8
y=0.8303x+0.0022
0.6
2
R =0.9993
0.4
0.2
0.0
0.0
0.2
0.4
0.6
SS set (%)
0.8
1.0
(b)
Figure S4. (a) Theoretical values of SS vs. Dcrit of (NH4)2SO4 particles used for CCN
calibration (solid line, c.f. Table S2) from Rose et al. [2008] (OS1 data set) and one
typical (NH4)2SO4 calibration data in this study (red cross for the data after calibrated
with theoretical data, c.f. Table S3). (b) The relationship between the theoretical SS and
set SS (c.f. Table S3).
9
Experiment
SOA precursor
No.
1
-pinene
2
Monoterpene
3a
4
5
6
7
a
VOC (ppb)
O3
added(ppb)b
-pinene (40)
160
-pinene (48), limonene (48)
monoterpene (0.6), sesquiterpene (8.1),
Plant emissions
stress emissions (4.7), others (2.2)
Toluene
toluene (85)
Xylene
Xylene (30)
Monoterpene/toluene -pinene (4), limonene (4), toluene (85)
Monoterpene/pα-pinene (42), limonene (42), p-xylene-d10
xylene-d10
(90)
Experiment description
200
Photooxidation &
ozonolysis
Photooxidation
82
Photooxidation
0
0
60
Photooxidation
Photooxidation
Photooxidation
200
Photooxidation
: The experiment was done in the JPAC chamber and all the other experiments were done in the SAPHIR chamber.
b
: In the experiments #2, 3, 6, 7, O3 was added to the chamber immediately before the roof was opened. In the experiment #1, O3 was
first added to the chamber to initiate ozonolysis and after around 5 h, the roof was opened to start photooxidation.
Table S1. Summary of the experiments in this study.
10
Dcrit (nm)
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
SS (%)
1.843682
0.968073
0.615428
0.43389
0.326443
0.256822
0.208739
0.173924
0.147775
0.127551
0.111538
0.098604
0.087979
0.079131
0.071663
0.065256
0.059825
0.055065
0.050907
Table S2. Theoretical values of SS~Dcrit of (NH4)2SO4 particles used for CCN calibration
in this study (from Rose et al. 2008, OS1 data set).
SS set (%)
Dcrit (nm)
0.1
0.15
0.3
0.6
0.9
138.4
112.2
70.8
46.5
35.1
SS_theoretical
(%)
0.0897
0.1242
0.2541
0.4890
0.7560
Table S3. Typical (NH4)2SO4 calibration data.
11
Oxidation
condition
Source
 depedencea  value
[VanReken et al., 2005]
-1b
0.014-0.091e
Ozonolysis
[Hartz et al., 2005]
[Prenni et al., 2007]
x
0
0.028-0.229f
0.1
Ozonolysis
Ozonolysis
[Engelhart et al., 2008]
x
0.11-0.14g
Ozonolysis
[Duplissy et al., 2008]
[Wex et al., 2009]
x
-1
0.09-0.12
0.1±0.04
Photooxidation
Ozonolysis
[Juranyi et al., 2009]
0c
0.091±0.01
Photooxidation
[Massoli et al., 2010]
Frosch [Frosch et al., 2011]
[Lambe et al., 2011a]
[Lambe et al., 2011b]
x
1
x
x
0.13-0.24d
0.06-0.16d
0.12-0.23d
0.12-0.18
Photooxidation
Photooxidation
Photooxidation
Photooxidation
 was not reported.  derived from the data
decreases with SS.
 was not reported.
 does not depend on SS.
 was not reported and no data on SS
dependence available.
No data on SS dependence available.
 decreases with increasing SS.
 does not depend on SS. But Dcrit only
covers >100 nm.
No data on SS dependence available.
Smaller  at larger sizes.
No data on SS dependence available.
No data on SS dependence available.
[Alfarra et al., 2013]
x
0.10-0.17
Photooxidation
No data on SS dependence available.
Findings
Setup
CIT 28 m3 chamber
CMU 103 chamber
UCR 7m3 chamber
CMU 12 m3 chamber
PSI 27m3 chamber
12L flow reactor
PSI 27m3 chamber
Aerosol flow reactor
PSI 27m3 chamber
15L PAM flow reactor
15L PAM flow reactor
Univ. Manchester 18m3
chamber
Table S4. Summary of the previous studies on the CCN activity of SOA from α-pinene.
: “0” means  does not depend on supersaturation (SS). “-1” means  decreases with increasing SS. “1” means  increases with
increasing SS. “x” means that author did not investigate or report the dependence of  on SS.
b
:  was not reported, but SS~Dcrit data was available to derive .
c
: Dcrit only covers the size range of >100 nm and author stated that “note that they (dependence) might appear at sizes smaller than the
ones measured here.”
d
: Values are read from the figures in the literature.
e
:  was not reported in the original paper.  here was from Petters and Kreidenweis [2007].
f
:  was not reported in the original paper.  here was from Petters and Kreidenweis [2007].
g
:  was not reported in the original paper.  here was from [Engelhart et al., 2011].
a
12
Time
1.8
2.2
2.4
2.6
3
3.2
3.4
3.6
4
4.2
4.4
4.6
5
5.2
5.4
6
6.2
6.4
7
7.25
7.45
7.65
8.05
8.25
8.45
9.05
9.25
9.45
SS
0.83
0.33
0.50
0.67
0.17
0.33
0.50
0.67
0.17
0.33
0.50
0.67
0.17
0.33
0.50
0.17
0.33
0.50
0.17
0.33
0.42
0.50
0.17
0.33
0.42
0.17
0.33
0.42
Dcrit (nm)
48.4
103.1
74.1
54.8
190.0
101.7
74.3
55.8
185.8
100.1
73.6
55.1
178.7
97.5
72.3
173.3
97.7
72.4
173.6
94.3
80.1
69.0
158.7
89.5
77.0
159.2
93.1
76.1

0.16
0.10
0.12
0.17
0.07
0.11
0.12
0.16
0.07
0.11
0.13
0.17
0.08
0.12
0.13
0.09
0.12
0.13
0.09
0.14
0.14
0.15
0.11
0.16
0.16
0.11
0.14
0.17
_stdev
0.015
0.002
0.005
0.027
0.006
0.003
0.006
0.018
0.011
0.002
0.003
0.019
0.003
0.006
0.007
0.005
0.005
0.006
0.011
0.003
0.010
0.001
0.003
0.006
0.009
0.003
0.006
0.009
Table S5. Data in Figure 1 of main manuscript
13
Dpg (nm)
73.1
79.8
86.9
94.3
102.1
110.2
118.6
127.4
136.5
146.0
155.9
166.1
176.7
187.7
f43
0.156
0.150
0.147
0.156
0.152
0.144
0.150
0.146
0.145
0.153
0.151
0.147
0.155
0.169
f43_
0.024
0.006
0.010
0.008
0.006
0.004
0.005
0.006
0.003
0.004
0.016
0.008
0.006
0.003
f44
0.092
0.082
0.084
0.074
0.078
0.082
0.074
0.077
0.075
0.064
0.068
0.076
0.062
0.052
f44_
0.010
0.015
0.012
0.015
0.003
0.002
0.003
0.003
0.003
0.008
0.004
0.019
0.029
0.004
Table S6. Data in Figure 2a of the main manuscript
14
Dpg (nm)
94.8
108.9
124.1
140.4
157.8
176.5
196.4
217.5
240.0
263.7
288.7
f43
0.184
0.186
0.191
0.190
0.191
0.194
0.194
0.197
0.201
0.200
0.202
f43_
0.009
0.012
0.007
0.007
0.005
0.004
0.004
0.005
0.008
0.005
0.010
f44
0.067
0.058
0.053
0.055
0.056
0.050
0.048
0.048
0.043
0.046
0.039
f44_
0.008
0.013
0.008
0.006
0.007
0.007
0.005
0.004
0.010
0.007
0.009
Table S7. Data in Figure 2b of the main manuscript
15
Table S8. Data in the Figure 3 of the main manuscript (“2015GL066497-ts08.xlsx”).
Table S9. Data in the Figure 4 of the main manuscript (“2015GL066497-ts09.xlsx”).
16
References
Alfarra, M. R., N. Good, K. P. Wyche, J. E. Hamilton, P. S. Monks, A. C. Lewis, and G.
McFiggans (2013), Water uptake is independent of the inferred composition of secondary
aerosols derived from multiple biogenic VOCs, Atmos. Chem. Phys., 13(23), 1176911789.
Asa-Awuku, A., A. P. Sullivan, C. J. Hennigan, R. J. Weber, and A. Nenes (2008),
Investigation of molar volume and surfactant characteristics of water-soluble organic
compounds in biomass burning aerosol, Atmos. Chem. Phys., 8(4), 799-812.
Asa-Awuku, A., G. J. Engelhart, B. H. Lee, S. N. Pandis, and A. Nenes (2009), Relating
CCN activity, volatility, and droplet growth kinetics of beta-caryophyllene secondary
organic aerosol, Atmos. Chem. Phys., 9(3), 795-812.
Asa-Awuku, A., A. Nenes, S. Gao, R. C. Flagan, and J. H. Seinfeld (2010), Watersoluble SOA from Alkene ozonolysis: composition and droplet activation kinetics
inferences from analysis of CCN activity, Atmos. Chem. Phys., 10(4), 1585-1597.
Bohn, B., F. Rohrer, T. Brauers, and A. Wahner (2005), Actinometric measurements of
NO2 photolysis frequencies in the atmosphere simulation chamber SAPHIR, Atmos.
Chem. Phys., 5, 493-503.
Buchholz, A. (2010), Secondary organic aerosols: Chemical aging, hygroscopicity, and
cloud droplet activation, University of Cologne, Cologne.
Duplissy, J., et al. (2008), Cloud forming potential of secondary organic aerosol under
near atmospheric conditions, Geophys. Res. Lett., 35(3).
Engelhart, G. J., A. Asa-Awuku, A. Nenes, and S. N. Pandis (2008), CCN activity and
droplet growth kinetics of fresh and aged monoterpene secondary organic aerosol, Atmos.
Chem. Phys., 8(14), 3937-3949.
Engelhart, G. J., R. H. Moore, A. Nenes, and S. N. Pandis (2011), Cloud condensation
nuclei activity of isoprene secondary organic aerosol, J. Geophys. Res.-Atmos., 116.
Frosch, M., M. Bilde, P. F. DeCarlo, Z. Juranyi, T. Tritscher, J. Dommen, N. M.
Donahue, M. Gysel, E. Weingartner, and U. Baltensperger (2011), Relating cloud
condensation nuclei activity and oxidation level of alpha-pinene secondary organic
aerosols, J. Geophys. Res.-Atmos., 116.
Hartz, K. E. H., T. Rosenorn, S. R. Ferchak, T. M. Raymond, M. Bilde, N. M. Donahue,
and S. N. Pandis (2005), Cloud condensation nuclei activation of monoterpene and
sesquiterpene secondary organic aerosol, J. Geophys. Res.-Atmos., 110(D14).
Juranyi, Z., et al. (2009), Influence of gas-to-particle partitioning on the hygroscopic and
droplet activation behaviour of alpha-pinene secondary organic aerosol, Phys. Chem.
Chem. Phys., 11(36), 8091-8097.
17
Lambe, A. T., T. B. Onasch, P. Massoli, D. R. Croasdale, J. P. Wright, A. T. Ahern, L. R.
Williams, D. R. Worsnop, W. H. Brune, and P. Davidovits (2011a), Laboratory studies of
the chemical composition and cloud condensation nuclei (CCN) activity of secondary
organic aerosol (SOA) and oxidized primary organic aerosol (OPOA), Atmos. Chem.
Phys., 11(17), 8913-8928.
Lambe, A. T., et al. (2011b), Characterization of aerosol photooxidation flow reactors:
heterogeneous oxidation, secondary organic aerosol formation and cloud condensation
nuclei activity measurements, Atmos. Meas. Tech., 4(3), 445-461.
Massoli, P., et al. (2010), Relationship between aerosol oxidation level and hygroscopic
properties of laboratory generated secondary organic aerosol (SOA) particles, Geophys.
Res. Lett., 37.
Moore, R. H., A. Nenes, and J. Medina (2010), Scanning Mobility CCN Analysis-A
Method for Fast Measurements of Size-Resolved CCN Distributions and Activation
Kinetics, Aerosol Sci. Technol., 44(10), 861-871.
Padro, L. T., A. Asa-Awuku, R. Morrison, and A. Nenes (2007), Inferring
thermodynamic properties from CCN activation experiments: single-component and
binary aerosols, Atmos. Chem. Phys., 7(19), 5263-5274.
Petters, M. D., and S. M. Kreidenweis (2007), A single parameter representation of
hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7(8),
1961-1971.
Prenni, A. J., M. D. Petters, S. M. Kreidenweis, P. J. DeMott, and P. J. Ziemann (2007),
Cloud droplet activation of secondary organic aerosol, J. Geophys. Res.-Atmos.,
112(D10).
Rohrer, F., B. Bohn, T. Brauers, D. Bruning, F. J. Johnen, A. Wahner, and J. Kleffmann
(2005), Characterisation of the photolytic HONO-source in the atmosphere simulation
chamber SAPHIR, Atmos. Chem. Phys., 5, 2189-2201.
Rose, D., S. S. Gunthe, E. Mikhailov, G. P. Frank, U. Dusek, M. O. Andreae, and U.
Poschl (2008), Calibration and measurement uncertainties of a continuous-flow cloud
condensation nuclei counter (DMT-CCNC): CCN activation of ammonium sulfate and
sodium chloride aerosol particles in theory and experiment, Atmos. Chem. Phys., 8(5),
1153-1179.
VanReken, T. M., N. L. Ng, R. C. Flagan, and J. H. Seinfeld (2005), Cloud condensation
nucleus activation properties of biogenic secondary organic aerosol, J. Geophys. Res.Atmos., 110(D7).
Wex, H., M. D. Petters, C. M. Carrico, E. Hallbauer, A. Massling, G. R. McMeeking, L.
Poulain, Z. Wu, S. M. Kreidenweis, and F. Stratmann (2009), Towards closing the gap
between hygroscopic growth and activation for secondary organic aerosol: Part 1Evidence from measurements, Atmos. Chem. Phys., 9(12), 3987-3997.
18
Wiedensohler, A. (1988), An approximation of the bipolar charge-distribution for
particles in the sub-micron size range, J. Aerosol Sci., 19(3), 387-389.
Zhao, D. F., et al. (2010), Novel method of generation of Ca(HCO3)(2) and CaCO3
aerosols and first determination of hygroscopic and cloud condensation nuclei activation
properties, Atmos. Chem. Phys., 10(17), 8601-8616.
Zhao, D. F., et al. (2015), Cloud condensation nuclei activity, droplet growth kinetics and
hygroscopicity of biogenic and anthropogenic Secondary Organic Aerosol (SOA), Atmos.
Chem. Phys. Discuss., 15(14), 19903-19945.
19