For understanding and assessing aerosol–cloud
interactions and their impact on climate, reliable measurement data on
aerosol particle hygroscopicity and cloud condensation nuclei (CCN) activity
are required. The CCN activity of aerosol particles can be determined by
scanning particle size and supersaturation (S) in CCN measurements. Compared
to an existing differential mobility analyzer (DMA) with CCN activity
measurement, a broad supersaturation scanning CCN (BS2-CCN) system, in which
particles are exposed to a range of S simultaneously, can measure the CCN
activity with a high time resolution. Based on a monotonic relation between
the activation supersaturation of aerosol particles (Saerosol) and the
activated fraction (Fact) of the BS2-CCN measurement, we can derive
κ, a single hygroscopicity parameter, directly. Here, we describe
how the BS2-CCN system can be effectively calibrated and which factors can
affect the calibration curve (Fact-Saerosol). For calibration,
size-resolved CCN measurements with ammonium sulfate and sodium chloride
particles are performed under three different thermal gradient (dT)
conditions (dT=6, 8, and 10 K). We point out key processes that can affect
the calibration curve and thereby need to be considered as follows: first,
the shape of the calibration curve is primarily influenced by Smax, the
maximum S in the activation tube. We need to determine appropriate Smax
depending on the particle size and κ to be investigated. To minimize the
effect of multiply charged particles, a small geometric mean diameter
(Dg) and geometric standard deviation (σg) in
number size distribution are recommended when generating the calibration
aerosols. Last, Fact is affected by particle number concentration and
has a decreasing rate of 0.02 per 100cm-3 due to
the water consumption in the activation tube. For evaluating the BS2-CCN
system, intercomparison experiments between typical DMA-CCN and BS2-CCN
measurements were performed with a laboratory-generated aerosol mixture and
ambient aerosols. Good agreement of κ values between DMA-CCN and
BS2-CCN measurements for both experiments shows that the BS2-CCN system can
measure CCN activity well compared to the existing measurement method and can
measure a broad range of hygroscopicity distributions with a high
time resolution (∼1 s vs. a few minutes for a standard CCN
activity measurement). As the hygroscopicity can be used as a proxy for the
chemical composition, our method can also serve as a complementary approach
for fast and size-resolved detection and estimation of aerosol chemical
composition.
Introduction
Atmospheric aerosol particles affect global climate change in that aerosols
alter the radiative balance by scattering and absorbing shortwave and
longwave radiation directly. Additionally, by serving as cloud condensation
nuclei (CCN), atmospheric aerosol particles influence the radiative budget
by modulating the microphysical structure, lifetime, and coverage of the
clouds. Although the direct and indirect effects of aerosols on climate
change are widely accepted within the scientific community, the assessment
of aerosol–cloud interactions and the quantification of their effect on
climate still remain largely uncertain (IPCC, 2013). Notably, one of the
underlying challenges is to determine the ability of aerosol particles that
act as cloud droplets, CCN activity, which has come up as a rising issue over
the past years (McFiggans et al., 2006; Andreae and Rosenfeld, 2008;
Hiranuma et al., 2011; Paramonov et al., 2013).
The CCN activity, meaning the fraction at which aerosol particles can be activated
to become CCN, can be determined by particle size and chemical composition
at a given water vapor supersaturation (Charlson et al., 2001; Andreae et
al., 2005, 2009; McFiggans et al., 2006; Cai et al., 2018) and can be
parameterized by a single parameter, κ (Petters and Kreidenweis,
2007). The activation S of aerosol particles can be estimated from dry
particle diameter (Dd) and water activity (Köhler, 1936; Su et al.,
2010). Once determined, the parameter κ can greatly simplify
descriptions of the chemical composition effect in models.
κ is closely linked to the chemical compositions of aerosol particles,
especially the ratio of organic to inorganic compositions. Thus, κ
is expected to and has been demonstrated to show a size dependence due to
the change in chemical compositions at different size ranges. However,
size-resolved κ data are still limited due to the relatively slow
response of the commercial instrument when scanning both S and Dd
(Robert and Nenes 2005; Dusek et al., 2006; Moore and Nenes,
2009; Petters
et al., 2009; Svenningsson et al., 2006; Wex et al., 2009; Rose et al.,
2011; Zhao et al., 2015). For example, measurements based on the
continuous-flow streamwise thermal-gradient CCN counter (CCNC) from Droplet
Measurement Technologies (DMT) complete a cycle of size-resolved κ
in about an hour due to the slow temperature stabilization (approximately a few
minutes to stabilize) for changing new S. To solve the low-time-resolution
problem, other techniques have been developed such as scanning flow CCN
analysis (SFCA) that changes flow rate over time in the growth chamber
under a constant temperature gradient (Moore and Nenes, 2009) and an
instantaneous CCN spectrometer with more than 40 channels of supersaturation
resolution from 0.01 % to 1.0 % (Hudson, 1989). Zhang et al. (2021)
introduced a novel measurement technique using a humidity-controlled fast
integrated mobility spectrometer (HFIMS) to measure size-resolved
κ rapidly under subsaturated conditions.
Su et al. (2016) introduced a new concept for the design of a CCN counter, the
broad supersaturation scanning (BS2) approach, for the rapid measurement of
particle hygroscopicity and CCN activity. Compared to the measurement system
of DMT-CCNC that applies a single S at the centerline, aerosol particles of
the BS2-CCN system are introduced with a wider inlet at a low
sheath-to-aerosol flow ratio (SAR) and are exposed to a range of S
simultaneously. Through this new design of the CCNC, we can obtain a monotonic
relation between the activated fraction of aerosols (Fact) and
critical activation supersaturation (Saerosol) and thereby calculate
the size-resolved Saerosol as well as κ directly. A decrease in
time required for scanning S with the BS2-CCN system makes it possible to derive
κ with a high temporal and size resolution. Additionally, a
constant temperature difference minimizes the bias from the potential
volatilization of aerosols in the instrument (Moore and Nenes, 2009).
In this study, we introduce the experimental setup of a materialized BS2-CCN
system, including the newly designed inlet, and describe how the BS2-CCN
system can be effectively calibrated. To validate the performance of the
BS2-CCN system, we perform an intercomparison with the existing DMA-CCN
measurement method using a laboratory-generated aerosol mixture and ambient
aerosols.
MethodConcept of BS2-CCNC
The BS2-CCN counter, a modified commercial DMT-CCNC with a newly designed
inlet system, measures the activation of size-resolved CCN with a high time
resolution (Su et al., 2016). Aerosol particles are introduced with a low SAR
by a wider inlet and distributed over a continuous range of S in the activation
tube, whereas aerosol particles in a commercial DMT-CCNC are forced onto the
centerline facing a single S. The BS2-CCN system reduces the time required
for scanning S and thereby obtains κ with a high temporal and
size resolution by using a monotonic Fact-Saerosol relation.
Figure 1 shows the comparison of supersaturation distribution in the
activation tube of the CCNC, denoted by Stube, between a typical CCNC and a
modified CCNC for the BS2-CCN system. Stube(r) is a function of r,
the radial distance to the centerline of the activation unit, and is the
highest in the centerline (r=0). For a typical CCNC, aerosol particles
pass through the centerline in the activation tube by a narrow inlet and
laminar sheath flow (Fig. 1a). Particles can be activated as CCN (i.e.,
Fact=1) when Saerosol of particles is lower than
Stube(r=0) or cannot be activated (i.e., Fact=0) when
Saerosol is higher than Stube(r=0). For the modified CCNC for the
BS2-CCN system, aerosol particles are introduced in a broad cross section of
the activation tube through a wider inlet of a low sheath-to-aerosol flow
ratio (Fig. 1b). Therefore, aerosol particles show a monotonic dependence
between Fact and Saerosol based on Stube distribution as they
are simultaneously exposed to a wide range of S. The κ value of
certain Dd can be derived by the Fact-Saerosol relation
directly based on κ–Köhler theory. This relation
implies that Saerosol can be directly determined depending on Fact
and, it is therefore essential to get an accurate calibration curve (i.e.,
Fact-Saerosol relation).
Schematics of typical CCN (a, c, e) and BS2-CCN
measurements (b, d, f). (a, b) Contour of supersaturation in
the CCN activation unit and configuration of aerosol and sheath flow; (c, d) distribution of supersaturation in the activation unit
(Stube). r is the radial distance to the
centerline. The shaded areas represent the sheath flow part, and non-shaded
areas represent the aerosol flow part. (e, f) Plot of the activation
supersaturation of aerosol particles Stube against
the activation fraction Fact. Reprinted from Su et al. (2016) under the Creative Commons Attribution 4.0 License.
Experimental setup
Figure 2 shows the schematic plot of the instrumental setup for the BS2-CCN
system. The setup consists of an aerosol classifier, a condensation particle
counter (CPC), a modified DMT-CCNC, and other components to maintain and
monitor the working system. The aerosol sample is firstly dried to RH<30 % with an aerosol dryer before entering the aerosol
classifier (TSI classifier 3080). A sensor to monitor the temperature (T),
pressure (P), and RH of the aerosol sample is placed in the aerosol flow
pathway. A Y-shaped splitter is mounted at the outlet of the aerosol
classifier to split the selected monodispersed aerosols into two aerosol
flows: CPC (TSI CPC 3772) and modified DMT-CCNC (CCN 100, DMT).
Schematic plot of a broad scanning supersaturation cloud
condensation nuclei counter (BS2-CCN) system. An additional setup marked
with a dashed line is for a humidifier experiment.
Compared to the commercial DMT-CCNC, the modified DMT-CCNC has a redesigned
inlet and flow-maintaining system. The newly designed diffusive inlet is
used to distribute aerosol samples widely in the activation tube, as
detailed in Appendix A and Fig. A1. According to the computational dynamic
simulation result (COMSOL Multiphysics, version 5.6) of flow streamline and
the relative particle concentration in Fig. A2, laminar flow inside the
activation tube can be achieved with our new inlet design. Additionally,
this new inlet allows for maintaining stable low sheath-to-aerosol flow
ratios (SARs), for which a monotonic Fact-Saerosol relation can then be
obtained. Compressed air is used to provide the sheath flow with a HEPA
filter in-line to remove all particles in the flow. Its volume flow rate is
controlled by a mass flow controller (MFC, Bronkhorst). The aerosol and
sheath flows are set to 0.46 and 0.04 Lmin-1,
respectively. The calibration curves with the different flow set in
Fig. S1 in the Supplement
show that, if the sample flow is set low, the slope between Fact and
Saerosol gradually decreases due to the narrow S distribution in the
activation tube, making it difficult to obtain a monotonic relationship. It
is noted that the total flow in the modified DMT-CCNC is maintained at 0.5 Lmin-1 by a vacuum pump with an MFC and filter.
To avoid water depletion in the activation tube due to a high particle
number concentration, an aerosol humidifier is placed in the aerosol flow
pathway to pre-humidify the monodispersed aerosol before it enters the
activation tube (dashed line in Fig. 2). Pure water is circulated between
the humidifier and a water reservoir by the bath circulator (Thermo
Scientific).
NCCN is recalculated with bin count data and sample flow as NCCN
values recorded in the instrument software are different from what we
measure due to the separated flow control system in the modified DMT-CCNC. It
can be calculated as follows.
NCCN=Sum of bin countsSample flow×60
The 60 is a unit conversion factor since bin counts are given in particles
per second, while sample flow is in cm-3min-1. Particle number
concentration (i.e., number concentration of condensation nuclei, NCN)
is measured by the CPC. Fact is calculated as the ratio of NCN and
NCCN. Figure S2 in the Supplement is an exemplary Dp scan with ammonium sulfate
particles to examine the time resolution of the BS2-CCN system. Fact of each
diameter is measured every 40 s including stabilization. It is noted
that NCN and NCCN data have 1 s time resolution, and thereby
Fact data with 1 s time resolution are initially available. For
scanning, it takes up to 10 s to stabilize immediately after changing
the particle size. Absolute deviation of Fact is mostly less than 0.05
except when Fact is higher than 0.85. In other words, we could get
a reliable κ value, derived by Fact directly, in 1 s time
resolution after stabilization for Dp scan measurements. Additionally,
if we set a single particle size, we could derive a κ value in 1 s time resolution. However, in this study, for the calibration experiment,
we use 1 min average data including stabilization time to calculate
the Fact value corresponding to each Dd.
Results and discussionCalibrations
For the calibration of the BS2-CCN system, the goal is to determine the
monotonic Fact-Saerosol relation, as discussed above. This can be
obtained from size-resolved CCN measurement with pure calibration
aerosols, e.g., ammonium sulfate and sodium chloride, in which hygroscopic
properties are well known. A specific Saerosol corresponding to each
Dd can be calculated from an approximate solution in Eq. (2).
Saerosol≈100%×exp4A327κDd3-1
The Fact value of each Dd can be measured by the size-resolved CCN
measurement, and thereby we can get a calibration curve,
Fact-Saerosol, relation. Figures 3 and 8 show exemplary calibration
curves obtained in this study. We investigate the impact of several factors
that may affect the calibration results.
Calibration curves (Fact-Saerosol) for three different T gradients (dT=6,
8, and 10 K) with ammonium sulfate (red) and sodium chloride (blue)
particles.
Stube distribution
Stube distribution in the activation tube shows the maximum in the
centerline (i.e., Smax) and the minimum at the edge (Fig. 1). Depending
on the temperature gradient, controlled by the temperature difference (dT)
between the top and the heated bottom of the activation tube, the
distribution of Stube varies, resulting in different shapes of the
calibration curve. Figure 3 shows the calibration curves depending on
temperature gradients (dT=6, 8, and 10 K). For calibration experiments,
we used an aerosol atomizer to generate particles with diameters of 20–300 nm of ammonium sulfate (purity >99.5 %, VWR Chemicals) and
sodium chloride (purity >99.5 %, Sigma-Aldrich) solutions.
According to Fig. 3, the monotonic Fact-Saerosol relation is confirmed
by the investigated aerosol systems. Good agreement between the two calibration
aerosols for all three dT conditions shows the reproducibility and stability
of the BS2 system in measuring CCN activity, supporting its application in
the real atmosphere. Large standard deviations at a high Fact range are
mainly caused by the low particle counts NCN at large diameters (e.g.,
less than 20 cm-3).
Although the calibration curve covers the whole range of Fact, we
suggest using only the center part of the curve from 0.1 to 0.9 of
Fact. This is because a steeper slope at low Fact and the aforementioned
low NCN at high Fact can introduce large uncertainties in the
retrieved Saerosol and κ. Moreover, it is essential to
determine an appropriate dT so that the supersaturation at the centerline,
Smax, is higher than the highest Saerosol of aerosol particles for the
investigated size range and environment. For the dependence of
Saerosol, κ, and Dd, please refer to the
κ–Köhler equation (Petters and Kreidenweis, 2007) and exemplary
Fig. 2 in Wang et al. (2015).
Minimizing the effect of doubly or multiply charged aerosols
Particles of given electrical mobility passed through the DMA are not all
singly charged as a DMA extracts particles with a narrow range of electrical
mobility rather than a geometric diameter. Multiply (mostly doubly) charged
particles with a larger size also penetrate the DMA, accompanying the singly
charged particles with a targeted diameter. As the CCN activation of a
particle strongly depends on its size, a high fraction of doubly charged
particles can directly affect the calibration curve (i.e., Fact-Saerosol relation).
To solve this problem, Frank et al. (2006) suggested a correction method
that subtracts the doubly charged particle distribution in the number size
distribution of polydisperse calibration aerosol from
NCCN/NCN under the assumption of a bipolar
equilibrium charge distribution. Rose et al. (2008) proposed the simple
alternative method that calculates the fraction of activated doubly charged
particles from the lower level of the plateau in the CCN spectrum, assuming
a constant fraction over the whole particle size range. However, these
methods are for the CCN efficiency spectra of typical CCNC using a single
S in the centerline of the activation tube. As BS2-CCNC has a wide range of
S simultaneously in the activation tube and the number concentration of
activated particles is different depending on S, it is quite complicated to
apply existing methods to experimental data. Here, we compare CCNC responses
with and without considering doubly charged aerosol particles and how it
depends on the size distribution of calibration aerosols through calculation
and experiment results.
We use an activation model that describes the CCNC response to the
transferred polydisperse charge-equilibrated particles through an ideal DMA,
similar to Petters et al. (2007). Considering electrical mobilities of
particles classified by DMA and the fraction of particles carrying n
charges (+1, +2) at charge equilibrium, this model calculates an
idealized CCN instrument response with an assumed lognormal particle size
distribution. When calculating the number of particles that activate as CCN,
we need to consider the activation fraction for each aerosol particle size.
As the aerosol particles in the BS2-CCN system are distributed in a broad
cross section of the activation tube, the activation fraction is calculated by
integrating the activation fraction function and flow velocity over the
cross section of the aerosol flow. The detailed calculation procedure is
described in Sect. S1 in the Supplement.
Calculated ideal activation fraction for lognormally distributed,
charge-equilibrated particles transmitted in the BS2-CCNC system. Shown are the (a)
assumed lognormal particle size distribution (black solid line, left
ordinate, N=2000cm-3, Dg=50 nm, and
σg=15), total activation fraction (red solid
line), activation fractions by singly charged particles (red dashed line),
(b) activation fraction by singly charged particles (red solid line) and
doubly charged particles (red dashed line), and the ratio of [+2]/[+1]
charges (blue solid line), which refers to f(D,n=+2)/f(D,n=+1) with mobility
diameter at charge equilibrium. f(D,n)
is the fraction of particles carrying n charges at charge
equilibrium by Wiedensohler (1988) and (c) activation fractions by doubly
charged particles (Fact_double) for
variant particle size distributions. Information on each particle size
distribution is presented in the legend of the figure.
Figure 4 shows the calculated activation fraction (Fact_total=Fact_single+Fact_double) for ammonium sulfate aerosols and the ratio of [+2]/[+1]
charges at charge equilibrium of an assumed lognormal size distribution
with N=2000cm-3,Dg=50nm, and σg=1.5. Sheath flow (Qsh) and aerosol flow (Qa) of DMA
were set to 10 and 1.5 Lmin-1, respectively, which are the same as the
calibration experiment. It is noted that we used the physicochemical
properties of ammonium sulfate for the calculation and set 0.63 % for
Smax for the S distribution in the activation tube when calculating the
activation fraction of aerosol particles. A small plateau exists in an area
where the Fact is low (Dp<40nm) due to doubly charged
particles; however, the overall effect of multiply charged particles on
Fact is not significant and is a small effect. The maximum
Fact_double in the assumed particle size distribution is
about 0.04. According to Fig. 4c, the Fact_double varies
depending on the particle size distribution. The Fact_double not only increases as the value of Dg increases, but also
increases as the σg increases, even if the Dg is the same.
As the Fact directly affects the calculation of κ for the
BS2-CCN system, the effect of particle size distribution still needs to be
considered even though Fact_double<0.1 for the
Dg=60nm case. Therefore, when generating calibration
aerosols, small Dg and σg in number size distribution are
recommended to minimize the effect of multiply charged particles on the
calibration curve. These effects can also be seen in the calibration
experiment using sodium chloride. Figure 5
presents the calibration curves
and number size distribution of NCN and NCCN of sodium chloride
particles for dT=10 and 8 K. Sodium chloride that has a high κ
value (κ=1.28) shows variant calibration curves depending on the
particle number size distribution, whereas ammonium sulfate shows only small
change in the calibration curves (Fig. S3). The Fact in the calibration
curve is higher for larger peak diameter (Dpeak), and accordingly, the
gap in the calibration curve between the ammonium sulfate and sodium
chloride increases. Specifically, according to Fig. 5a (dT=10K), the
calibration curve of sodium chloride particles with Dpeak of 31 nm
matches that of ammonium sulfate well. However, the calibration curve
of sodium chloride with Dpeak of 37 and 52 nm is inconsistent with
that of ammonium sulfate. This effect is more pronounced at higher
supersaturation conditions. Both calculation and experiment results imply
that the number size distribution of the generated particles should be
considered, especially when using sodium chloride during calibration, and it
is recommended to generate aerosols with Dpeak corresponding to an
Fact less than 0.3. The number size distribution of generated
calibration aerosols can be controlled by adjusting the particle
concentration. Assuming the atmospherically relevant particle number size
distribution with N=1000cm-3, Dg=80nm, and
σg=1.5 from Rose et al. (2011), Fact_double is up to 0.05 (Fig. S4 in the Supplement). It is noted that aerosol with κ
of 0.3 is assumed to be internally mixed, and Smax is set to
0.2 %. Although the effect of doubly charged particles in Fig. S4 is not
significant, the effect of doubly charged particles cannot be ignored if
Dg or σg becomes large in specific environments or
conditions.
Calibration curves of ammonium sulfate and sodium chloride for (a)dT=10 K and (c)dT=8 K as well as the number size distribution of
NCN and NCCN for sodium
chloride particles for (b)dT=10 K and (d)dT=8 K.
Effect of particle number concentration
As the modified CCNC enlarges the cross section of aerosol flow, more
particles can enter the column compared to the standard DMT-CCNC. The
consumption of water vapor in the column by a large number of particles can
change the distribution of the supersaturation and thereby influence the
measured number fraction of activated particles. Therefore, as a simple
test, the Fact values of three different sizes of ammonium sulfate
particles (60, 80, and 120 nm) are measured under the different particle
number concentrations. In this experiment, dT is set to 7.7 (S=0.6%). According to Fig. 6, we can see that Fact decreases with a
rate of about 0.02 per 100cm-3 with the increase in the particle number
concentration. Specifically, Fact decreases by about 2.6 % (60 nm),
1.6 % (80 nm), and 1.1 % (120 nm) per increase of 100 cm-3 in the
number of particles. It is noted that the decrease with particle number is
calculated for a particle number concentration of 300 cm-3 of ammonium
sulfate, and the decrease is expected to be greater if the number
concentration increases. The decreasing rate increases slightly as the
particle size decreases. These results imply that the number concentration
of calibration aerosols can affect the calibration result, and we can get
consistent results even in very low number concentrations from this BS2-CCNC
setup.
Average and standard deviation (error bar) of
Fact depending on the number concentration NCN
for 60 (black), 80 (red), and 120 nm (blue) of ammonium sulfate
particles under the dT=7.7 K (0.6 % S) condition. Dashed lines indicate
linear regression lines.
To examine the necessity of a humidifier to avoid water consumption in
the activation tube, an aerosol humidifier is additionally installed in the
aerosol flow pathway. The setup is described in Fig. 2, and the part is
marked by the blue dashed line. Monodispersed aerosols are pre-humidified by
the humidifier that is composed of a Nafion tube and a bath circulator
before the aerosol flow enters the BS2-CCN system. Particle number
concentrations are controlled within the range of ∼300cm-3. Figure 7 shows the calibration curves for three dT conditions
(dT=6, 8, and 10 K) with a humidifier (blue dots) and without a humidifier
(black dots). For dT=6 K, the calibration curves with the humidifier
system (WH) and without the humidifier system (NH) are almost identical.
Although Fact values of WH are slightly higher than those of NH under
higher dT conditions (i.e., dT=8 and 10 K), differences are not
significant as number concentrations of calibration aerosols are not so high.
In other words, a compact instrumental setup without the pre-humidifier
system is sufficient for the BS2-CCNC calibration experiment as well as the
measurement if aerosol particles are kept below ∼3×102cm-3. Otherwise, we need
a pre-humidifier system for conditions of high aerosol number concentration to
avoid the decrease in Fact. It is noted that particle concentrations
below ∼3×103cm-3 are
recommended to avoid counting errors for a calibration experiment with a typical
DMT-CCNC (Rose et al., 2008). We usually observe a low number
concentration for aerosol particles during size-resolved CCN measurements,
and Fact is not highly variable within that range. However, we still
need to consider this effect, especially for the region of high number
concentration and/or high-number-concentration cases like new particle
formation (NPF) events and the transport of pollution.
Calibration curves (Fact-Saerosol) for three different T gradients: dT=6
(circle), 8 (square), and 10 K (diamond) with a humidifier system (WH) and
without a humidifier system (NH).
Fitting procedure of calibration curve
As selected diameters for calibration are limited and cannot cover the whole range of
Fact values, a curve fitting procedure is necessary. The equation for
curve fitting (Eq. 3), the relationship between Fact and Saerosol,
can be derived based on Eq. (S8) in the Supplement and the cosine function of Stube
distribution in Sect. S1 in the Supplement.
F(x)=a×acos(b×x)-cF(x) and x correspond to Saerosol and Fact,
respectively. Coefficients of a, b, and c are what we need to
obtain from the curve fitting procedure. They are calculated using a
nonlinear least square method (MATLAB curve fitting toolbox 3.5.8). It is
noted that values lower than 0.05 for Fact, showing a large
discrepancy between reference curves and experimental data, are excluded for
curve fitting.
Figure 8 shows fitting curves and experimental data from ammonium sulfate
particles for three dT conditions (dT=6, 8, and 10 K). Coefficients and
goodness of fit for each curve are presented in Table 1. For assessing the
goodness of fit, three statistical parameters were used: error sum of
squares (SSE), coefficient of determination (R2), and root mean square
error (RMSE). According to Fig. 8 and Table 1, fitting curves cover most of
the experiment data, and good fitting results are shown for all three dT
conditions. It implies that Eq. (3) covers the experimental data well and
appears suitable for the calibration curve of the BS2-CCN system. Also,
Saerosol corresponding to the Fact can be directly obtained
through this curve. Furthermore, we can calculate the effective particle
hygroscopicity parameter, κ, based on
κ–Köhler theory (Petters and Kreidenweis, 2007). Figure S5 in the Supplement presents
κ values that correspond to the Fact values of a particle
ranging from 50 to 150 nm. They are calculated with a fitting curve
equation of the dT=8 K condition shown in Fig. 8 and Table 1.
Experimental data (square dot with error bar) and fitting curves
(dashed lines) for three different dT conditions: dT=6 (black), 8
(blue), and 10 K (red). Ammonium sulfate particles are used for
calibration.
Coefficients and goodness of fit for calibration curves for
three different dT conditions (dT=10, 8, and 6 K). Three
different statistical values, including error sum of squares (SSE),
coefficient of determination (R2), and root mean
square error (RMSE), are used to judge the goodness of fit.
∗ Coefficients and goodness of fit were calculated by the MATLAB curve
fitting toolbox 3.5.8.
As Fact values obtained from size-resolved CCN measurements use
1 min average data, the calibration curve could have a range and not a single
curve line. To consider the range of Fact, we add two more calibration
curve lines applying data points of Fact_low (average –
standard deviation, μ-σ) and Fact_high(μ+σ). Figure S6 shows the range of the calibration curve
for the dT=8 K condition. All of the calibration curves are obtained using
Eq. (3) for curve fitting. When the Fact is measured at a certain
Dp, three κ values, denoted as κhigh, κavg, and κlow, can be calculated based on these three curves.
Specifically, κhigh is derived from the curve that is obtained
from data points of Fact_low and vice versa.
Evaluation and application of the BS2-CCN system
To evaluate the BS2-CCN system, we performed two intercomparison
experiments between BS2-CCN and standard DMA-CCN measurements (denoted as
DMA-CCN) with a laboratory-generated aerosol mixture and ambient aerosols.
The DMA-CCN is widely used for size-resolved CCN measurements
in the aerosol community to examine CCN activity (Cai et al., 2018; Deng
et al., 2011; Moore et al., 2011; Rose et al., 2011; Pöhlker et al.,
2016, 2018; Thalman et al., 2017, and references therein). For DMA-CCN
measurement, the critical diameter (Dc) at which Fact becomes 0.5 is
determined by scanning Dp under a given constant supersaturation (i.e.,
Dp scan), and κ can be calculated with a given S and Dc
based on κ–Köhler theory (Petters and
Kreidenweis, 2007). In other words, in DMA-CCN measurement, we can obtain
a single Dc value for each Dp scan and convert it to single κ, but in BS2-CCN measurement, we can obtain κ values at all
selected particle sizes of each Dp scan by converting Fact values
using the calibration curve. Figure S7 in the Supplement presents a schematic plot of the
instrumental setup for intercomparison experiments. Specifically, selected
monodisperse aerosol flows by the DMA (TSI classifier 3080) are split into
three parallel lines and fed into the CPC, modified CCNC (CCN-100, DMT for
BS2-CCNC), and DMT-CCNC (CCN-200, DMT for DMA-CCNC). Before entering the
DMA, all aerosol particles are dried (RH<30 %) by the Nafion
tube aerosol dryer and neutralized by a krypton-85 (Kr 85) bipolar charger.
All instruments were installed in the laboratory at the Max Planck Institute
for Chemistry, Mainz, Germany. A detailed set of experiments for each
intercomparison experiment is described in the following section.
Ammonium sulfate and succinic acid mixture
We used an atomizer to generate internally mixed nanoparticles with
diameters of 30–160 nm by spraying a mixed solution of succinic acid and
ammonium sulfate; each pure component was completely dissolved in pure
water obtained from a Milli-Q water purification system. This mixture was
chosen to mimic a typical atmospheric aerosol composed of ammonium sulfate
and organic acids. For the mixture, molar ratios of ammonium sulfate and
succinic acid used in this study were 1:1, 3:1, and 1:3. The experiments
were performed under the dT=8 K (0.63 % S) condition. The sample flow rates
of CCNCs were 0.04 and 0.46 Lmin-1 for the DMA-CCN and BS2-CCN
measurements, respectively. The total flow rate (sample + sheath) was 0.5 Lmin-1 for both instruments. As the κ of ammonium sulfate is higher
than that of succinic acid (i.e., more hygroscopic), a mixture of ammonium
sulfate and succinic acid in a ratio of 3:1 shows the highest κ
value among the three mixtures and vice versa. Table 2 presents details of the
comparison for all three solutions. The κ values of the DMA-CCN
measurement for all three solutions are within the range of κ for
the BS2-CCN measurement but are slightly lower (less than about 10 % of
relative deviation) than the κavg. These results imply that we
can measure κ of an aerosol mixture quantitatively well from
BS2-CCN measurements compared to the existing measurement method with DMA-CCN.
κavg from DMA-CCN and
κlow, κavg, and κhigh
from BS2-CCN measurements for the dT=8K (S=0.63%)
condition for mixtures of ammonium sulfate and succinic acid.
κlow and κhigh are derived from the range of calibration curves
in Fig. S6.
Time series of (a)Fact, (b)κ
distribution of BS2-CCN measurement for the 0.63 % S (dT=8 K) condition, and
(c) hourly averaged κ values for DMA-CCN (red dots, bar indicates
the standard deviation of κ) and BS2-CCN (grey shaded area for the
range between κavg, indicated by the black solid line, and
κlow, indicated by the black dashed line) measurements. The
κhigh is excluded in this figure. Measurement
period was 13 July–16 July 2020.
Ambient aerosol measurement
An intercomparison experiment with ambient aerosols was performed from 13
July to 16 July 2020. The instrumental setup and flow system were the same
as in Sect. 3.1. For Dp scan (fixed S), 19 dry diameters of 40–250 nm were selected for each scan. Each scan took a total of 22 min,
including 1 min for each diameter and 3 min for stabilization. The S of CCNC
was set to 0.63 % (dT=8 K) for BS2-CCN measurement and 0.4 % for
DMA-CCN measurement. The S from the DMA-CCN measurement was set slightly lower
because the comparison with κ from the BS2-CCN measurement is difficult if
the critical diameter is too small, which is obtained from the DMA-CCN
measurement. It is noted that κ values from BS2-CCN
measurement were calculated based on the fitting curve for dT=8 K, as
shown in Fig. S6 and Table 1. The double-charge effect is not considered in
the intercomparison experiment. Figure 9 shows the times series of
Fact and κ distributions from the BS2-CCN measurement. Here κavg, derived from Fact_avg, is referred to as
κ. For κ calculation, high-Fact (>0.85) and
low-Fact (<0.1) data are excluded. Fact distribution
showed a clear size dependency: low Fact for small particles and high
Fact for large particles. Since the κ value is calculated based
on the Fact value, the κ value appeared to be increasing at the
time when the Fact was increased and vice versa. The average κ
values of particles for the BS2-CCN measurement exhibit diurnal variability
that increases during the daytime and decreases at nighttime within the
range of 0.11 to 0.32 (Figs. 9 and S8 in the Supplement) and has an average value of 0.18.
For DMA-CCN measurement, average Dc and κ were about 80 nm and
0.17, respectively, during the measurement under the 0.4 % S condition.
Figure S9 in the Supplement presents the average CCN efficiency spectra and the cumulative
particle hygroscopicity distribution, H(κ,Dd), from DMA-CCN
measurements. Particularly, the κ values of a significant portion of
particles were distributed between 0.1 and 0.3, which was consistent with
the result of the BS2-CCN measurement. Figure 9c presents the
hourly averaged κ value from BS2-CCN and DMA-CCN measurements. Unlike
BS2-CCN measurement results, which show κ distribution of various
particle sizes, DMA-CCN measurement allows a single κ value to be
produced per each Dp scan cycle. Therefore, as shown in Fig. 9c, we
used a κ value from the BS2-CCN measurement by selecting the particle
diameter close to the average Dc from the DMA-CCN measurement for the
intercomparison. Compared to the κ values from the DMA-CCN
measurement, those from the BS2-CCN measurement showed good agreement,
keeping up with the increasing and decreasing variability. Additionally, a
direct intercomparison was carried out through the 1:1 scatterplot between
the κ of BS2-CCN and DMA-CCN measurements in Fig. 10. All the
detailed intercomparison results, including the ratio between
κ of DMA-CCN and BS2-CCN measurements as well as the goodness of fit of
the linear regression line for each scatterplot, are presented in Table 3.
It is noted that κhigh, showing an excessively high value compared
to that from the DMA-CCN measurement, is excluded in Figs. 9c and 10. Based on the good agreement on the κ value and the results
of three different statistical values to judge the goodness of fitting,
including a residual sum of squares, Pearson's r, and R2, we conclude that we can
obtain reliable and quantitative κ data, as well as the κ
variability, from BS2-CCN measurements compared to the existing DMA-CCN
measurement method. The possible reason for the discrepancy in κ between
BS2-CCN and DMA-CCN measurements is the multimode κ
distribution. According to Fig. S9, we can infer that aerosols were
externally mixed and not a single mode. The BS2-CCN system alone cannot resolve a
bimodal or multimodal κ distribution, and thereby when particles at
a certain size are externally mixed, lower Fact, resulting in lower
κ, can be observed. In particular, κ values from the BS2-CCN
measurement are slightly higher than those from the DMA-CCN measurement between 15
and 16 July when externally mixed aerosols were frequently observed. Su et
al. (2016) pointed out through a simulation that BS2-CCN measurements can
underestimate κ when particles are externally mixed. The uncertainty
of Fact values for each measurement caused by the uncertainty of each
CCNC can be another possible reason for the discrepancy. The Fact value
is essential for both measurements as κ is directly determined by
Fact for BS2-CCN, and Dc from a DMA-CCN measurement can be changed
depending on Fact. The selected diameters from BS2-CCN measurements for
comparison in Figs. 9c and 10 do not perfectly match the Dc from
DMA-CCN measurements because Dc in this study is not fixed due to the
Dp scan method, which scans Dp with a constant S. The difference of
time resolution can also be a cause of the discrepancy, although the impact
may not be significant in this study. As we use 1 min average data per
particle size, we can obtain κ values for every 1 min for the
BS2-CCN measurement but only every 19 min for the DMA-CCN measurement.
Therefore, a difference in κ is expected if there are fast changes in
the aerosol. Nevertheless, good correlation and correspondence of κ
values between DMA-CCN and BS2-CCN measurements from both intercomparison
experiments imply that we can obtain a high time resolution with reliable
hygroscopicity data from the BS2-CCN system. These powerful advantages allow
for applying the BS2-CCN system preferably to ship and aircraft measurements
requiring high time resolution, as well as ground-based measurements for
κ distribution with a broad particle size range.
Scatterplot of κ between DMA-CCN and BS-CCN measurements.
The black square and grey square dots indicate κavg
(calculated from the calibration curve of Fact,
μ) and κlow (calculated from the
calibration curve of Fact_high, μ+σ) for the BS2-CCN measurement,
respectively. For comparison, the κ value from a BS2-CCN measurement at the diameter adjacent to the critical diameter obtained from the DMA-CCN measurement was selected. The black dashed line is a 1:1 line for
clarity.
The average and standard deviation of the ratio between κ from the
DMA-CCN and BS2-CCN measurement and the goodness of fit for the linear regression
line. Three different statistical values are used
to evaluate the goodness of fit: residual sum of squares, Pearson
correlation coefficient, and coefficient of determination (R2).
Fact_lowFact_meanFact_high(μ-σ)(μ)(μ+σ)Ratio1.17±0.171.05±0.120.98±0.10(κBS2-CCN/κDMA-CCN)Residual sum of squares0.040.020.02Pearson's r0.750.830.86R20.570.700.74Summary and conclusion
In this study, we implement a new concept for the design of CCN counters, which is a
broadening supersaturation scanning BS2-CCN system, for rapid hygroscopicity
measurement and describe how to calibrate this system. Compared to
typical CCN counters, particles are exposed to a range of S simultaneously in
an activation tube with a newly designed inlet and low sheath-to-aerosol
flow ratio (SAR). Through this system, we can obtain a monotonic relation
between Fact and Saerosol. Based on the κ–Köhler
theory, κ can be derived directly through the calibration curve
(i.e., Fact-Saerosol relation) when we measure the Fact
value at a certain S.
For calibration, ammonium sulfate and sodium chloride, which are representative
calibration aerosols for CCNC, are used under three different dT conditions.
It can be inferred from consistent results between the two calibration aerosols
as well as reference curves for all three dT conditions that the
experimental setting of the BS2-CCN system suggested in this study is
appropriate and can apply to real measurements. We also examine factors
that can affect the calibration curves. First, Smax, the maximum
S in the activation tube, determines the Stube distribution in the
activation tube, and the shape of the calibration curve changes accordingly.
The range of particle sizes and κ values that we can measure
depends on the Smax value. Specifically, a high Smax can cover a
wide range of particle sizes and κ, but a steeper slope of the
calibration curve can lead to high sensitivity of Saerosol
corresponding to the measured Fact. Therefore, depending on the
particle size and environment to be investigated, we need to determine an
appropriate Smax. Calculation and experimental results confirm that
multiply charged particles have a small but measurable effect on the
Fact value and show that the effect depends on the size distribution of
the particles. To minimize the effect of multiply charged particles,
small Dg and σg in number size distribution are recommended
when generating calibration particles. This effect is more pronounced for
sodium chloride with very high hygroscopicity and/or higher dT conditions.
Lastly, we examine the effect of particle number concentration on the
calibration curve. The activated particle number fraction decreases with a
rate of about 0.02 per 100cm-3 within ∼300cm-3 of
ammonium sulfate particles, and the decreasing rate is expected to be much
higher when the concentration is higher due to the water consumption in the
activation tube. It implies that we need to generate fewer particles for the
calibration experiment of the BS2-CCN system compared to that of a typical
CCNC. Particles below ∼3×102cm-3 are
recommended for generating calibration aerosols. If the number of particles
is high, the pre-humidifier system is helpful to avoid decreasing Fact.
As selected particles for the calibration experiment cannot cover the whole
Fact range, a curve fitting procedure is essential. We propose the
equation for curve fitting (Eq. 3) based on the equation of Stube
distribution. It is noted that values lower than 0.05 for Fact are
excluded. Good results of statistical parameters to judge the goodness of
fit are shown for all three different dT conditions. With these curves, we
performed two intercomparison experiments between DMA-CCN and BS2-CCN
measurements for evaluation: a laboratory-generated aerosol mixture and ambient
aerosol measurement. Firstly, κ values of the mixture of ammonium
sulfate and succinic acid with three different molar ratios were compared.
The κ values of the BS2-CCN measurements agree well with those of
DMA-CCN measurements. For ambient aerosol measurement, S of CCNC was set to
0.63 % (dT=8 K) for BS2-CCN measurement and 0.4 % for DMA-CCN
measurement. For BS2-CCN measurement, the κ distribution between 70
and 120 nm diameter showed a size dependency, with low κ for small
particles and high κ for large particles, and a distinct diurnal
variability that increases during the daytime and decreases during the
nighttime. Also, the κ values from the BS2-CCN measurement corresponded to
and correlated well with those from the DMA-CCN measurement. It can be concluded
from these results that the BS2-CCN system can measure κ
quantitatively well compared to the existing measurement method and can even
measure a broad range of κ distributions with high time resolution.
The BS2-CCN system, a simple modification of the commercial design of a CCNC,
uses constant supersaturation and flow during measurement, so it is
technically simple, and thereby we can obtain stable data with a high time
resolution. And the low SAR of the BS2-CCN system provides sufficient counting
statistics for size-resolved measurement, in which particle concentrations
are generally low. In addition, the calibration of the BS2-CCN system is not
complicated and has many similarities compared to the existing method, so it
is easy to apply. The advantages of the fast response and stability, as well
as the relatively simple calibration method of the BS2-CCN system, make it
possible to apply not only to long-term observations but also extensive
measurements including aircraft, ship, and ground. Lastly, as the
hygroscopicity can be used as a proxy for the chemical composition, our
method can also serve as a complementary approach for fast and size-resolved
estimation of aerosol chemical composition.
Newly designed diffusive inlet
The newly designed diffusive inlet is comprised of the main body
(101) and a sheath flow straightener (102), as shown in
Fig. A1. The main body of the diffusive inlet is constructed from the
conductive material. An aerosol inlet (103) is placed at the center
top of the main body. Downstream of the aerosol inlet is a funnel-shaped
region (105) where the cross section of the aerosol is smoothly
expanded. The angle (106) of the wall of the funnel-shaped region
is small enough to keep a laminar flow. The wall of the funnel-shaped region
is polished to minimize air turbulence and particle deposition. The inlet of
sheath air (104) is placed at the side of the main body. A flow
straighter (102), located downstream of the sheath air
inlet, is made up of a single or double screen of fine nylon mesh to
straighten the sheath flow and lead to a laminar flow. This inlet is mounted
at the top of the column of the activation tube. At the lower end of the
main body, there are two rubber O-rings (107) to keep the
activation tube airtight.
(a) Front view of an embodiment of the
diffusive inlet. (b) Longitudinal sectional (the cross section in X–Z
surface) view of (a). Each number in the figure is as follows:
main body (101), a sheath flow straightener (102), an aerosol
inlet (103), a funnel-shaped region (105) where the cross section of the
aerosol is smoothly expanded, the angle (106) of the wall of the
funnel-shaped region, the inlet of sheath air (104) at the side of the main
body, and two rubber O-rings (107) at the lower end of the main body to keep
the activation tube airtight.
Result of a computational fluid dynamics simulation with original
inlet design. The figure presents the half-side of a longitudinal sectional
view of Fig. A1b, and the x and y axes present the length of the inlet
(units are meters). A solid black line and the color bar indicate the flow
streamline in the velocity field and the relative particle concentration
[molm-3], respectively. It is noted that aerosol and sheath flow go
from the bottom (-y) to the top (+y), as marked in the figure.
Data availability
Data can be downloaded from Edmond, the open research data repository of the Max
Planck Society
(https://edmond.mpdl.mpg.de/imeji/collection/pohD2XdTlrMwzka7, Kim, 2021), and raw
data are available upon request from the corresponding author (h.su@mpic.de).
The supplement related to this article is available online at: https://doi.org/10.5194/amt-14-6991-2021-supplement.
Author contributions
HS and YC had the initial idea, and HS, YC, and NM designed the BS2
instrument. TFM provided the CCNC instrument and discussed the new system.
NK organized and performed all experiments. MLP provided the instrument for
the intercomparison experiment. OK supported the intercomparison experiment.
TK provided technical support for experiments and CFD simulation. NK wrote
the paper. All coauthors discussed and results and commented on the paper.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work was supported by the Max Planck Society (MPG).
Financial support
The article processing charges for this open-access publication were covered by the Max Planck Society.
Review statement
This paper was edited by Pierre Herckes and reviewed by two anonymous referees.
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