To better understand the chemical controls of sub- and super-saturated
aerosol water uptake, we designed and conducted a series of chamber
experiments to investigate the evolution of secondary organic
aerosol (SOA) particle physicochemical
properties during photo-oxidation of single and mixed biogenic (α-pinene, isoprene) and anthropogenic (o-cresol) volatile organic compounds
(VOCs) in the presence of ammonium sulfate seeds. During the 6 h
experiments, the cloud condensation nuclei (CCN) activity at
super-saturation of water (0.1 %–0.5 %), hygroscopic growth
factor at 90 % relative humidity (RH), and non-refractory PM1 chemical composition were
recorded concurrently. Attempts to use the hygroscopicity parameter κ to reconcile water uptake ability below and above water saturation from
various VOC precursor systems were made, aiming to predict the CCN activity
from the sub-saturated hygroscopicity. The thermodynamic model AIOMFAC (aerosol inorganic-organic mixtures functional groups activity coefficients) was
used to simulate κ values of model compound mixtures to compare with
the observation and to isolate the controlling factors of water uptake at
different RHs.
The sub- and super-saturated water uptake (in terms of both κHTDMA and κCCN) were mainly controlled by the SOA mass
fraction, which depended on the SOA production rate of the precursors, and
the SOA composition played a second-order role. For the reconciliation of
κHTDMA and κCCN, the κHTDMA/κCCN ratio increased with the SOA mass fraction and this was observed
in all investigated single and mixed VOC systems, independent of initial VOC
concentrations and sources. For all VOC systems, the mean κHTDMA of aerosol particles was ∼25 % lower than the
κCCN at the beginning of the experiments with inorganic seeds.
With the increase of condensed SOA on inorganic seed particles throughout
the experiments, the discrepancy of κHTDMA and κCCN became weaker (down to ∼0 %) and finally the
mean κHTDMA was ∼60 % higher than κCCN on average when the SOA mass fraction approached ∼0.8. As indicated by AIOMFAC model simulations, non-ideality alone cannot
fully explain the κ discrepancy at high SOA mass fraction (0.8). A
good agreement in κCCN between model and observation was
achieved by doubling the molecular weight of the model compounds or by
reducing the dry particle size in the CCN counter. This indicates that the
evaporation of semi-volatile organics in the CCN counter together with
non-ideality could have led to the observed κ discrepancy. As a
result, the predicted CCN number concentrations from the κHTDMA
and particle number size distribution were ∼10 % lower
than CCN counter measurement on average at the beginning, and further even
turned to an overestimation of ∼20 % on average when the
SOA mass fraction was ∼0.8. This chemical
composition-dependent performances of the κ-Köhler approach on CCN
prediction can introduce a variable uncertainty in predicting cloud droplet
numbers from the sub-saturated water uptake, the influence of which on
models still needs to be investigated.
Introduction
Aerosol-cloud interactions, that is how aerosol particles influence cloud
formation, largely influence Earth's radiation budget and the current climate
projections (Boucher et al., 2013; Lohmann and Feichter, 2005; Bellouin
et al., 2020). Thus, an accurate prediction of cloud condensation nuclei
(CCN) number from aerosol properties is essential for investigating
aerosol–cloud interactions in climate models. However, the reliability of
cloud condensation nuclei (CCN) activity predicted from the aerosol
hygroscopic growth under sub-saturated condition remains unresolved,
(e.g. Cruz and Pandis, 1998; Vanreken et al., 2005; Huff Hartz et al., 2005;
Prenni et al., 2007; Petters et al., 2009; Wex et al., 2009; Ervens et al.,
2007; Good et al., 2010b; Liu et al., 2018). One of the main knowledge gaps
is the precise determination of CCN activity involving complex organic
aerosols. A large portion of organic aerosols are secondary organic aerosols
(SOA) (Zhang et al., 2007; Jimenez et al., 2009), formed from oxidation
of gaseous volatile organic compounds (VOCs) via gas-particle partitioning
(Hallquist et al., 2009)
and aqueous-phase reactions (Ervens et al., 2011; Kuang et al., 2020b).
Although the organic aerosol components are less soluble and consequently
less hygroscopic than the referenced inorganic compounds (e.g. sulfate,
nitrate) (Alfarra et al., 2013; Mcfiggans et al., 2006; Kreidenweis and
Asa-Awuku, 2014; Huff Hartz et al., 2005; King et al., 2009), they can play
an important role in the cloud formation globally (Liu and Wang, 2010;
Rastak et al., 2017) due to its ubiquitous large fraction (20 %–90 %) in
fine particulate matter mass (Kanakidou et al., 2005; Jimenez et al.,
2009; Zhang et al., 2007). Nevertheless, our understanding of its
hygroscopicity and CCN activity remains uncertain, due to the wide range of
solubility, volatility and complex composition of organic compounds from
different sources (Hallquist et al., 2009; Goldstein and Galbally, 2007;
Shrivastava et al., 2017).
Previous laboratory reconciliation studies of aerosol hygroscopicity and CCN
activity were mainly focused on experiments investigating the nucleation of
SOA from single biogenic VOC oxidation (e.g. Prenni et al., 2007; Wex et
al., 2009; Petters et al., 2009; Alfarra et al., 2013; Liu et al., 2018;
Zhao et al., 2016; Duplissy et al., 2008), from anthropogenic VOCs (Zhao
et al., 2016; Liu et al., 2018; Prenni et al., 2007) and a few from
biogenic–anthropogenic VOC mixtures (e.g. Zhao et al., 2016). However, the
findings are not consistent. For biogenic SOA, most studies found that
the single hygroscopicity parameter (ρion, κ) from CCN
activity was 20 %–70 % higher than the that from sub-saturated
hygroscopicity, using oxidation of representative biogenic precursors, such
as monoterpenes (Wex et al., 2009; Liu et al., 2018; Zhao et al., 2016;
Prenni et al., 2007) and sesquiterpenes (Huff Hartz et al.,
2005). They speculated that the higher measured CCN activity of the biogenic
SOA may be caused by the complex composition and variable properties, such
as the suppressed surface tension below that of the pure water induced by
organic surfactants (Wex et al., 2009), the
presence of sparingly soluble organic compounds (Petters et al., 2009;
Prenni et al., 2007), non-ideality-driven liquid–liquid phase separation
(Liu et al., 2018), or joint influences of these factors. In
contrast, Duplissy et al. (2008) found a good
reconciliation of hygroscopicity parameter κ between the
hygroscopicity at 95 % relative humidity (RH) and CCN activity of the SOA from α-pinene oxidation. Moreover, Good et al. (2010b) found that the agreement of κ reconciliation of the SOA from
α-pinene ozonolysis was influenced by the use of three different
custom-built hygroscopicity tandem differential mobility analyser (HTDMA)
for sub-saturated hygroscopicity measurements and the absence/presence of
inorganic seed. For the anthropogenic or biogenic–anthropogenic mixed SOA, Zhao et al. (2016) observed a smaller
discrepancy of κ than for the biogenic SOA, but the measured CCN
activity was still higher than the sub-saturated hygroscopicity
(>20 %). In contrast, Liu et al. (2018) found no
discrepancy for anthropogenic SOA.
Clearly the complexity of aerosol chemical composition can propagate to
their water uptake behaviour. Consequently, our understanding of the
chemical controls on the sub- and super-saturated water uptake is still
limited, especially for the evolution of the multi-component
organic–inorganic systems. To further improve our understanding on chemical
controls of water uptake of multi-component aerosol particles, we designed
and performed a series of chamber experiments to investigate the evolution
of the chemical composition, the sub- and super-saturated water uptake of
SOA from single and mixed VOCs in the presence of ammonium sulfate seed.
The novelty of the project is its design to investigate SOA formation from
single to mixed precursors, whereas previous studies mainly focused a single
precursor (Voliotis et al., 2022). The
interaction of the mixed precursor could influence SOA properties, therefore
this study takes a further step for lab studies towards the real atmosphere,
where thousands of precursors exist and react at the same time; however,
even the chemical regime and complexity of the chamber studies could deviate
from the real atmosphere. The ultimate goal of this paper was to explore the
change and controlling factors in the water uptake of multi-component seeded
particles as they transformed through the oxidation of the various mixed VOC
systems.
Materials and methodExperiment design
A series of chamber experiments were designed and conducted at Manchester
Aerosol Chamber (MAC) to investigate the impacts of mixing VOCs on SOA
formation mechanisms and aerosol physicochemical properties (e.g. chemical
composition, volatility, water uptake). An overview of the overall project
can be found in Voliotis et al. (2022).
Briefly, this work builds on the concept explored in McFiggans
et al. (2019) using a mixture of the biogenic SOA precursors, α-pinene and isoprene, extended to a ternary system by including o-cresol as
an anthropogenic VOC. o-cresol is both directly emitted anthropogenically or
naturally and is a first generation oxidation product of toluene, both being
abundant aromatic VOCs observed in anthropogenically polluted areas
(Seinfeld and Pandis, 2016). o-cresol is sufficiently close in
reactivity towards OH radicals with α-pinene and isoprene as to
contribute comparable amounts of oxidation products to the mixture
(Coeur-Tourneur et al., 2006; IUPAC, 2022). Additionally, it is a
moderate SOA yield compound (Henry et al., 2008), so any
interactions in the mixture with the oxidation products of the other VOCs
may lead to contrasting interactions to those in the binary high-yield
α-pinene mixture with low-yield isoprene (McFiggans et al., 2019;
Voliotis et al., 2022). VOCs were injected into the chamber with modest
VOC/NOx ratio ranging 4–10 and the mixing ratio of VOCs were chosen
such that they would have the same reactivity towards ⚫OH at the
beginning of the experiment (though clearly not necessarily after the
commencement of photochemistry). In addition, ammonium sulfate particles
were injected as seeds for SOA condensation considering its abundance in
atmosphere (Seinfeld and Pandis, 2016). Characteristic experiments of
each system are chosen and details of the initial conditions are shown in
Table 1. Single VOC isoprene experiments were carried out, but not included
in this study since they had undetectable levels of SOA mass above our
background under the neutrally seeded conditions of our experiments with no
noticeable change to hygroscopicity.
A detailed description and characterization of the MAC facility (e.g.
controlling condition stability, gas/particle wall loss, auxiliary
mechanism, aerosol formation capability) can be found in Shao et al. (2022). Briefly, MAC consists of an 18 m3 FEP Teflon bag supported by movable aluminium frames and runs as a
batch reactor. The chamber is mounted inside the enclosure where the air
conditioning system can well control the temperature
(25 ± 2 ∘C) and RH (50 ± 5 %). For photochemistry experiments, two 6 kW Xenon arc lamps (XBO
6000 W/HSLA OFR, Osram) and five rows (#16 for each row) of halogen bulbs
(Solux 50 W/4700 K, Solux MR16, USA) are used to mimic the solar spectrum of
mid-day of clear sky conditions in June in Manchester and the total actinic
flux between 290 and 600 nm was approximately one-third of the clear-sky solar
radiation in Manchester (Shao et al., 2022). A
reproducible cleaning protocol was conducted, including daily cleaning (fill
chamber with ∼1 ppm of O3 and leave overnight to remove
reactive organics, and perform automatic fill/flush physical cleaning cycles
before and after experiments) and regular harsh cleaning with high
concentration of O3 under strong ultraviolet light. During an
experiment, seed particles are injected and mixed well with a high flow rate
blower and kept well-mixed within chamber by the continual external
agitation of conditioned air through the gap between the enclosure and
chamber. Liquid VOCs (α-pinene, isoprene, o-cresol; Sigma Aldrich, GC
grade ≥99.99 % purity) are injected with syringes through a heated
glass bulb in which the liquids can be vaporized immediately under
∼80∘C and then flushed into chamber with high
purity nitrogen (ECD grade, 99.997 %). NOx (as mostly NO2 in
this study) injection is controlled by a mass flow controller and the
desired RH in chamber is moderated by the mixing of water vapour and dry
purified clean air to chamber to adjust the desired RH condition. A series
of instruments were deployed to record gas precursors (VOC, NOx,
O3) and physicochemical properties of seeded SOA. Details of key
instruments used in this study can be found in Sect. 2.2.
Experimental initial conditions of the various single and mixed
biogenic and anthropogenic VOC systems photochemistry in the presence of
ammonium sulfate seed.
DateVOC type[VOC]0VOC/NOxSeed conc.(ppbV)(µg m-3)*29 March 2019α-pinene3097.267.617 April 2019α-pinene1554.446.213 July 2019α-pinene1035.755.412 April 2019o-cresol400NA40.919 April 2019o-cresol2005.056.010 July 2019o-cresol1334.938.18 April 2019α-pinene & isoprene237 (155/82)9.950.523 April 2019α-pinene & o-cresol355 (155/200)5.942.524 April 2019o-cresol & isoprene282 (82/200)NA57.030 July 2019α-pinene, isoprene, & o-cresol191 (103/55/133)3.745.9
* Calculated mass concentration from volume concentration from DMPS
with a density of 1.77 g cm-3.
NA means no available data due to instrument failure.
Measurements
The measured aerosol particles are dried with a Nafion® drier
(Perma Pure, MD-110-12, Toms River, NJ, USA) to RH <30 % before being
introduced to the following instruments. The sub-saturated water uptake of
aerosol particles was measured by a custom-built hygroscopicity tandem differential mobility analyser (HTDMA) (Good et al., 2010a).
The HTDMA is used to determine aerosol growth factor (GF) at a certain RH.
Principally, sampled aerosol particles are dried and then selected by the
first differential mobility diameter (DMA1) to get monodisperse aerosol
particles at given size (D0), which are further humidified at 90 %
RH in this study. The humidified aerosol particles enter the second DMA and
a condensation particle counter (CPC) in order to determine the size
distributions. The HTDMA was calibrated and its performance was validated by
(NH4)2SO4 before and after the campaign following the method
of Good et al. (2010a). Finally, the GF
probability density function and mean GF were retrieved
using the TDMAinv method developed by Gysel et al. (2009). To track particle growth, the measured particle size increased from
75 up to 300 nm, depending on the geometric mean diameter of aerosol
populations during SOA formation evolution processes in various VOC systems.
The super-saturated water uptake of aerosol particles, that is the ability
to activate to CCN, was measured by a DMT continuous flow CCN counter
(Roberts and Nenes, 2005). In this study, the CCN counter was
coupled with a DMA and a CPC to obtain the fraction of size-resolved aerosol
particles activating to CCN (FA) at a certain supersaturation. Briefly,
the DMA was used to select monodisperse dried aerosol particles (RH < 30 %), which are fed into the CCN counter and CPC in parallel
to count the activated and total number concentrations of aerosol particles,
respectively. During the experiments, DMA scans from 20 to 550 nm with 20
size bins, splitting the flow to direct the size-selected aerosol particles
through the CCN counter and a CPC to measure the CCN and total particle
number concentrations, respectively. The supersaturation ratio of the CCN
counter is usually set to 0.5 % at the beginning of experiments. With
ongoing SOA formation, the aerosol particles grow. To derive a reliable
activation curve with enough particle number concentration around the
activation size, the set supersaturation ratio decreases accordingly down to
0.1 % during experiments, depending on how fast the SOA forms. The time
resolution for each measurement is 10 min. FA as a function of the dry
particle size (D0) was derived from the ratio of the activated and
total aerosol particles concentrations with a correction of DMA multiple
charge. Finally, the particle size at 50 % activation (DcCCN) was
identified through a sigmoid fit of the FA-D0 curve, which was assumed
to be the critical diameter at the critical supersaturation (ScCCN).
CCN counter was calibrated and its performance was validated by
(NH4)2SO4 before and after the campaign following the
procedure in Good et al. (2010a).
The chemical composition of the non-refractory PM1 components
(NR-PM1, including ammonium NH4, sulfate SO4, nitrate
NO3, SOA) was measured by a high-resolution time-of-flight aerosol mass
spectrometer (HR-ToF-AMS, Aerodyne Research Inc., USA). Detailed instrument
descriptions can be found elsewhere (Decarlo et al., 2006; Jayne et al.,
2000; Allan et al., 2003, 2004). During the experiment period,
HR-ToF-AMS was calibrated and its performance was validated following the
standard procedures (Jayne et al., 2000; Jimenez et al., 2003). In
addition, to obtain the size-resolved chemical composition, a polystyrene
latex sphere calibration was performed to obtain the relationship
between vacuum aerodynamic particle size and its velocity following the
protocol provided at http://cires1.colorado.edu/jimenez-group/wiki/index.php/Field_Data_Analysis_Guide (last access: 24 January 2022).
For the conversion of AMS vacuum aerodynamic diameter to mobility diameter,
firstly, we estimated the density of the non-refractory aerosol particles
using simple mixing rule shown in Eq. (1) assuming the density of
ammonium sulfate (1.77 g cm-3) and SOA (1.4 g cm-3):
ρest=ρAS(1-Fm,SOA)+ρSOAFm,SOA.Fm,SOA is the mass fraction of the SOA. Then, this estimated density is
used to calculate the mobility diameter as shown in Eq. (2)
(Zhang et al., 2005):
Dm≈Dvaρest.
For the MRSOA/PM uncertainty, the choice of SOA density can introduce
uncertainty to ρest, with implications for the mobility diameter.
Previous studies found that the SOA density can range from 1.2 to
1.65 g cm-3 (Kostenidou et al., 2007; Alfarra et al., 2006; Varutbangkul
et al., 2006; Nakao et al., 2013). For example, Kostenidou et al. (2007) reported that the estimated density of SOA from α-pinene,
β-pinene, d-limonene are 1.4–1.65 g cm-3. Nakao et
al. (2013) investigated the SOA from 22 different precursors with a wide
range of carbon number (C5–C15) and found their density ranging from 1.22 to
1.43 g cm-3, negatively related to their molecular size. In this study,
considering the three precursors we used, we take a medium value of density
(1.4 g cm-3). To calculate the uncertainty of the SOA density on
MRSOA/PM, we recalculated with the minimum (maximum) density, 1.2
(1.65) g cm-3, the MRSOA/PM changes within ± 10 %.
κ-Köhler approach
A single parameter κ is used to bridge the sub- and super-saturated
water uptake, which is readily applied to predict cloud properties from
aerosol physicochemical properties in climate models (Fanourgakis et al.,
2019). However, it should be noted that the non-ideality of solution (e.g.
the sparingly soluble SOA, molecular and ionic interactions), the potential
influence of SOA on surface tension, and the difference in co-condensation of
condensable vapours through the systems will influence the results as
previously discussed (Wex et al., 2009; Prenni et al., 2007; Hu et al.,
2018), and will be further discussed in Sect. 3.4 and 3.5.
The hygroscopicity parameter κ from sub-saturated HTDMA and
super-saturated CCN counter are referred to as κHTDMA and κCCN, respectively. κHTDMA was calculated directly through
Eqs. (1)–(2) with the measured GF and dry particle size D0:
3SD=awexp4σMwRTρwD,4κ=VwVs1aw-1=D3-D03D031aw-1,5D=D0GF.
For CCN measurement, κCCN was derived from the computed κ–Dc–Sc relationship at surface tension of water and temperature of 298.15 K
in Petters and Kreidenweis (2007). Here, Dc and Sc represent
the dry diameter of aerosol particle and the critical supersaturation ratio
of water vapour (maxima of the Köhler curve) to activate it to CCN.
Where S(D) is the supersaturation ratio or RH at sub-saturated condition. D
and D0 represents the dry and wet particle diameter, respectively.
aw, σ, Mw, ρw are activity, droplet surface
tension, molecular weight, and density of water, respectively. R and T
represents the universal gas constant and absolute temperature,
respectively. GF is the growth factor at 90 % RH measured by HTDMA.
κ-modelling with AIOMFAC
To study the influence of non-ideality on κHTDMA and κCCN, model calculations were performed using the group contribution
model AIOMFAC (Zuend et al., 2008, 2010, 2011; Zuend and Seinfeld, 2012, 2013) to calculate
activity coefficients. Since the real SOA composition is complex and the
exact chemical composition is unknown, the goal here was not to simulate the
composition as realistically as possible but to create mixtures of model
compounds that cover the experimental range of hygroscopicity. The
hygroscopicity depends solely on the hydrophilicity of the substance
(affecting the activity coefficients) and the number of solute molecules in
a particle (affecting the mole fraction) which is determined by their
molecular weight. Reactivity is not considered in thermodynamic modelling.
The hydrophilicity of a substance depends on its chemical composition, most
importantly on the number of polar functional groups while the exact
arrangement of the functional groups is of minor relevance. Thus the
hydrophilicity can be captured by the O : C ratio, which also determines the
tendency for liquid–liquid phase separation in aerosol particles
(Song et al., 2012). Therefore, by examining model
compound mixtures covering broadly the range of experimentally determined
O : C ratios and realistic molecular weights, the possible range of κ
values can be investigated without the necessity of replicating the real mix
of chemical structures.
The mixtures chosen here contained between two and eight different organic
compounds, most of them α-pinene oxidation products, mainly with
carboxyl (-COOH), hydroxyl (-OH) and/or keto (C=O) functionalities. The
average O : C ratio of the organic mixtures ranges between 0.36 and 0.95,
while the O : C ratio of the experimental SOA ranges between 0.36 ± 0.03
and 0.69 ± 0.05 (Wang et al., 2021b). The
average molar mass of the mixtures was varied in a broad range of 173–478 g mol-1. High molar masses were achieved by artificially dimerizing the
original model compounds by doubling each subgroup of the molecule, similar
to the approach by Zuend and Seinfeld (2012). To isolate the
effect of non-ideality from co-condensation effects, all substances were
assumed to be non-volatile and gas-particle partitioning was not explicitly
modelled. Therefore, the selected substances were chosen to have
sufficiently large molecular weights for allowing partitioning to the
condensed phase. The lower bound of the average molar masses is reached by
model compound mixtures that match the experimentally measured volatility
distribution of the SOA (Voliotis et al., 2021). Even lower average molar
masses or O : C ratios would not alter the drawn conclusion as can be seen in
Sect. 3.5 and Fig. S3 in the Supplement. Tables S1 and
S2 in the Supplement list the monomeric model compounds and all mixture
compositions.
For each mixture, the water-partitioning and potential liquid–liquid phase
separation was calculated with AIOMFAC using the algorithm of Zuend and Seinfeld (2013). In this algorithm, the calculations
are performed for a bulk system. To obtain the corresponding relative
humidity in equilibrium with the droplet (S), the water activity was
multiplied with the Kelvin effect based on the wet diameter D at this
water activity, following Köhler theory (Köhler, 1936) as shown
in Eq. (3). κHTDMA and κCCN were calculated
according to Eq. (4) (Petters and Kreidenweis, 2007).
Vw and aw are taken from the AIOMFAC output at S(D)=90 % and
Sc, respectively.
Results and DiscussionBulk and size-dependent chemical composition
Figures 1 and 2 show the bulk NR-PM1 species and size-resolved organic
mass fraction (MRSOA/PM) measured by HR-ToF-AMS. At the
beginning of experiments before illumination (-1 to 0 h), seed particles are
mainly comprised of sulfate with a small contribution from nitrate (max. 5 %–16 % of NR-PM1) in all investigated VOC systems. The observed
nitrate was mainly inorganic ammonium nitrate and the organic nitrate was
statistically insignificant (a detailed estimation method and discussion can
be found in Wang et al., 2021a). Considering the
small fraction of nitrate in the inorganic seed particles in this study and
comparable water uptake ability with sulfate (Kreidenweis and
Asa-Awuku, 2014), it may be expected that the overall hygroscopicity and CCN
activity will be highly related to the MRSOA/PM. After initiating
illumination, the condensable organic vapours were formed from VOCs
photo-oxidation, which further condensed on the inorganic seed particles
yielding SOA. Therefore, an increasing MRSOA/PM over time was observed,
as shown in Fig. 1. As different VOC systems have different SOA yield and
reactivity with oxidants (Voliotis et al.,
2022), the mass and the production rate of SOA varied with the VOC systems.
After a 6 h photochemistry for the single VOC systems, the
MRSOA/PM approached 0.88 ± 0.01, 0.82 ± 0.01, 0.62 ± 0.01, 0.71 ± 0.01, 0.56 ± 0.02 and 0.52 ± 0.02 (last 0.5 h
of experiments, avg. ± SD) in the α-pinene, 50 %
reactivity α-pinene, 33 % reactivity α-pinene,
o-cresol, 50 % reactivity o-cresol and 33 % reactivity o-cresol systems,
respectively. For the binary and ternary systems, the MRSOA/PM was 0.79 ± 0.01, 0.82 ± 0.01, 0.32 ± 0.01, and 0.78 ± 0.01 in
the α-pinene and isoprene, α-pinene and o-cresol,
o-cresol and isoprene, and α-pinene, o-cresol and isoprene, respectively.
Moreover, a size-dependent chemical composition was observed, with a higher
MRSOA/PM for particles at 75/100 nm than the 200/300 nm particles in
all investigated VOC systems (as shown in Fig. 2). This indicates that the
chemical composition is not uniform across the size distribution. As the
inorganic compounds are much more hygroscopic than the SOA (Kreidenweis
and Asa-Awuku, 2014; Prenni et al., 2007; Alfarra et al., 2013, 2012), aerosol hygroscopicity and CCN activity will vary with
MRSOA/PM. Considering measured dry size differences between the HTDMA
and CCN counter, size-resolved chemical composition was used to ensure
that the paired κHTDMA and κCCN for measurement
reconciliation are with comparable MRSOA/PM.
Mass fraction of chemical species in non-refractory PM1 measured by HR-ToF-AMS during SOA formation evolution in various VOC systems.
Size-resolved SOA mass fraction in non-refractory PM1 (MRSOA/PM1) measured by HR-ToF-AMS during SOA formation evolution in various VOC systems.
Aerosol hygroscopicity under sub-saturated conditions
The GF at 90 % RH was measured by a HTDMA and hygroscopicity parameter
(κHTDMA) was calculated using the κ-Köhler approach (Petters and Kreidenweis, 2007) for all the investigated VOC
systems, as shown in Fig. 3. Before the photochemistry with inorganic seed
only, the GF at 90 % RH (κHTDMA) for the 75/100 nm aerosol
particles were 1.65–1.72 (0.45–0.50) in all VOC systems. This result is
comparable with the predicted GF (κHTDMA) of 1.71 (0.51) of the
(NH4)2SO4 using AIOMFAC with the assumption of non-ideality.
After the commencement of photochemistry, the MRSOA/PM increased over
time. Consequently, the GF (κHTDMA) decreased accordingly due
to the less hygroscopic nature of SOA compared with the one of inorganic
compounds (Kreidenweis and Asa-Awuku, 2014; Prenni et al., 2007; Alfarra
et al., 2013, 2012; Varutbangkul et al., 2006).
Time series of GF and κ at different measured particle size during SOA formation evolution in various VOC systems.
As expected, the rate of change and magnitude of the GF (κHTDMA) decreases over time depends on the change of MRSOA/PM in
all VOC systems. For example, for the α-pinene system, the
MRSOA/PM increased substantially from ∼0 to 0.72 within
an hour of the experiment (as shown in Fig. 1a); correspondingly, the GF
(κHTDMA) decreased from 1.65–1.72 (0.45–0.50) to
∼1.15 (∼0.1) (as shown in Fig. 3a). In
comparison, for the o-cresol and isoprene system, it took 6 h for the
MRSOA/PM to increase to 0.33, and accordingly, the GF (κHTDMA) decreased slowly to 1.44–1.53 (0.28–0.36) after the
6 h experiment. Moreover, consistent with the observed higher
MRSOA/PM for smaller size in Sect. 3.1, Fig. 3 shows evidence that the
GF (κHTDMA) is size dependent, up to ∼0.2
(∼0.1) lower in 100 nm than in 200 nm aerosol particles,
measured adjacently. This is consistent with the non-uniform size-dependent
particle chemical composition in our chamber studies. Consideration of
size-resolved chemical composition is very important for the aerosol
physical and optical properties where both chemical composition and particle
size can play a role.
CCN potential under super-saturated conditions
CCN activity above water saturation was simultaneously recorded by CCN
counter during the experiments of all investigated VOC systems. Figure 4 shows
the relationship of the critical supersaturation of water vapour (Sc), the
dry particle size, and the κCCN. It provides the required Sc to
activate 50 % of a given size of dry particles (DcCCN), for which
this CCN activation potential can be represented by a single hygroscopicity
parameter (κCCN) (Petters and Kreidenweis,
2007). At the beginning of experiments before photochemistry, the κCCN was mainly 0.55–0.65 in all investigated VOC systems, which is
comparable with predicted κ of 0.61 from AIOMFAC. After initiating
photochemistry, a declining trend of κCCN over time was
observed as the continuous condensation of less hygroscopic/CCN-active
SOA, consistent with the trends of sub-saturated water uptake in Sect. 3.2.
For example, for the α-pinene system as shown in Fig. 4a, the
κCCN decreased from 0.64 to ∼0.1 within an hour,
whereas the κCCN decreased from 0.55 to 0.23 after the 6 h
oxidation for the o-cresol and isoprene system. This significant differences
between different VOC systems are highly related to the production rate of
SOA and the corresponding change of MRSOA/PM over time. It is worth
noting that the set-point Sc in CCN counter was changed from 0.1 %–0.5 %
during the experiments to follow the particle growth and ensure sufficient
data points are collected for the activation curve to accurately determine
the DcCCN.
Critical supersaturation as a function of dry particle size (D50) measured by CCN counter during SOA formation evolution in various VOC systems. Contour lines represent hygroscopicity κ, calculated by following the method in Petters and Kreidenweis (2007).
CCN prediction from the sub-saturated conditions
This section illustrates the reconciliation of the aerosol hygroscopicity
and CCN activity, and its relationship with the aerosol chemical composition
in various VOC systems to investigate the performance of the κ-Köhler approach in predicting CCN activity from sub-saturated aerosol
hygroscopicity. As shown in Sect. 3.1–3.2, the aerosol chemical composition
is size dependent. It is essential to ensure the chemical composition is
comparable for HTDMA and CCN measurements for the reconciliation study if
their measured dry particle sizes are different. Therefore, we selected the
synchronized HTDMA/CCN data pairs only when the 10 min moving average of
MRSOA/PM for the measured particle sizes were within 5 %. An example
of selected data pairs in the α-pinene, isoprene, and o-cresol system is
shown in Fig. S1. In addition to the hygroscopicity parameter κ, the critical diameter (DcHpre) was predicted from κHTDMA following the κ–Dc–Sc relationship in Sect. 2.3 under the
critical supersaturation of the paired CCN measurement. Further, by assuming
all particles larger than DcHpre be activated at the given DcHpre,
the CCN number was predicted based on the DcHpre and particle number
size distribution.
Figure 5 shows a summary of (a) κHTDMA, (b) κCCN,
(c) κHTDMA/κCCN, (d) κHTDMA-κCCN, (e) DcHpre/DcCCN, and (f) NccnHpre/NccnCCN as
a function of the organic mass fraction in various VOC systems (except for
α-pinene and 33 % α-pinene systems due to CCN instrument
failure). Similar trends of the investigated parameters as a function of
MRSOA/PM were observed in all VOC systems. As shown in panels (a), (b), the
hygroscopicity parameter κHTDMA and κCCN decreased
with the increase of MRSOA/PM in all VOC systems, indicating aerosol
particles became less hygroscopic and CCN active modified by the
increasingly condensed SOA. For a summary of all data points binned with a
MRSOA/PM of 0.1, the black solid circles and grey lines represent the
average and standard deviation of the categorized data points. The overall
κHTDMA (κCCN) declined from 0.46 ± 0.02
(0.61 ± 0.07) to 0.14 ± 0.03 (0.09 ± 0.01) when the
MRSOA/PM increased from ∼0 to ∼0.8.
(a)κHTDMA, (b)κCCN, (c)κHTDMA/κCCN, (d)κHTDMA-κCCN, (e, f) critical diameter and CCN number concentration between HTDMA prediction using κ-Köhler theory and CCN measurement, as a function of MRSOA/PM in various investigated VOC systems. The error bar of κHTDMA and κCCN in (a) and (b) represent measurement uncertainty following the method in Irwin et al. (2010). The uncertainty in κHTDMA and κCCN then propagates to the uncertainty of parameters shown in (c)–(f).
In addition to the overall trend, the κHTDMA (κCCN) at the same MRSOA/PM were different in the different VOC
systems which indicated that the SOA composition played a second-order role
in the hygroscopicity (CCN activity). A higher κHTDMA (κCCN) of the multi-component SOA–inorganic mixtures at the same
MRSOA/PM indicated a higher κ of the SOA, according to the
Zdanovski–Stokes–Robinson (ZSR) mixing rule of κ demonstrated in Petters and Kreidenweis (2007). In this study, the κHTDMA (κCCN) (indicating a higher κ of the SOA),
in the α-pinene, isoprene, and o-cresol and 33 % o-cresol systems were
the highest, which are higher than other VOC systems by 0–0.2 (0–0.3),
depending on the MRSOA/PM. In contrast, the κHTDMA
(κCCN) in o-cresol and 50 % reactivity o-cresol were usually
the lowest at the same level of MRSOA/PM, whereas the 50 %
reactivity α-pinene; α-pinene and isoprene; and
o-cresol and isoprene seated in the middle. Previous studies found the
sub-saturated aerosol water uptake (κ) increases with chemical ageing
of SOA from single precursor oxidation and showed a positive relationship
with SOA oxidation state (e.g. O : C ratio or f44, fraction of m/z 44 in total
organic signal) (Jimenez et al., 2009; Massoli et al., 2010; Lambe et
al., 2011; Zhao et al., 2016; Duplissy et al., 2011; Kuang et al., 2020a),
but no clear relationship involving multiple precursors with various
oxidation state (Alfarra et al., 2013; Zhao et al., 2016). In addition, Wang et al. (2019) found that the positive
relation between water uptake at super-saturated conditions and oxidation
state (O : C) can be attributed to lower molecular weight of organic species
rather than higher solubility at higher oxidation level. To illustrate the
relationship between κ of SOA and the oxidation state, the κorg was deduced with ZSR method and the κ of ammonium sulfate
from AIOMFAC assuming volume additivity. Two main messages are shown in Fig. S2. Firstly, the calculated κorg from HTDMA and CCN counter
varied with VOC systems ranging from -0.2 to 0.2. The ZSR method assumes
that components are independent and the water uptake by individual
components are additive. Therefore, the negative values of the κorg indicates the existence of interactions between inorganic and
organic substances and thus results in less water uptake than the case
without interactions in ZSR method (Zardini et al.,
2008). Secondly, the calculated κorg at sub- and super-saturated
conditions showed no clear relationship with oxidation state of SOA (f44)
when various VOC systems were compared, which is consistent with previous
studies involving multiple precursors (Alfarra et al., 2013; Zhao et al.,
2016). Other factors might have influenced the results and warrant further
investigations, such as organic mass loading, molecular weight (Cappa et
al., 2011; Petters et al., 2017), solubility (Petters et al., 2009; Ruehl
and Wilson, 2014; Huff Hartz et al., 2006), surface tension (Ovadnevaite
et al., 2017; Bzdek et al., 2020; Ruehl et al., 2016; Lowe et al., 2019), and
co-condensation (Kulmala et al., 1993; Topping et al., 2013; Hu et al.,
2018), and will be discussed in Sect. 3.5.
Panels (c), (d) in Fig. 5 show the ratio (κHTDMA/κCCN)
and the absolute difference (κHTDMA-κCCN) of
κ derived from HTDMA and CCN counter as a function of MRSOA/PM.
Interestingly, a clear co-increase of κHTDMA/κCCN
(κHTDMA-κCCN) with the MRSOA/PM was observed
in all VOC systems. The overall κHTDMA/κCCN for
all VOC systems increased from 0.76 ± 0.08 to 1.62 ± 0.26 with
the MRSOA/PM increasing from ∼0 to ∼0.8,
and, correspondingly, the κHTDMA-κCCN increased
from -0.15± 0.06 to 0.05 ± 0.02. This means the averaged
κHTDMA was ∼25 % (16 %–32 %) lower
than κCCN with inorganic compounds at the beginning of the
experiments, but this discrepancy decreased down to ∼0 with
the increasing MRSOA/PM and even became higher than κCCN
by ∼60 % (36 %–88 %) at MRSOA/PM of
∼0.8 (as shown in Fig. 5c). These results indicated that the
performances of κ-Köhler approach on the reconciliation study of
sub- and super-saturated water uptake varied with the MRSOA/PM.
The discrepancy in the κHTDMA and κCCN can
influence the prediction of CCN activity from sub-saturated hygroscopicity
(κHTDMA) using the κ-Köhler approach. As shown in Fig. 5e, the predicted critical diameter (DcHpre) was 5 %–20 % (avg.
∼10 %) higher than the measured DcCCN at
MRSOA/PM of 0.02, and the DcHpre/DcCCN decreased gradually to
0.8–1 (avg. ∼0.9) as MRSOA/PM approached 0.8. As a
result, the predicted CCN number concentration from sub-saturated water
uptake was underestimated by 0 %–20 % (avg. ∼10 %) at
MRSOA/PM of 0.02. This underestimation of CCN number became weaker
(averaged value almost down to ∼0) with MRSOA/PM
increased to 0.2–0.4 due to SOA condensation, and the underestimation even
reversed to an overestimation by up to 40 % (avg. 20 %) with
MRSOA/PM of ∼0.8 (as shown in Fig. 5f). It is worth
noting that the prediction of critical diameter and CCN number concentration
from κHTDMA are based on the concurrently measured critical
super-saturation and particle number size distribution. This dependence of
κHTDMA/κCCN ratio on chemical composition can have
a varied impact on the uncertainty of the predicted CCN activity from
sub-saturated κHTDMA at different super-saturation ratio of
water vapour and/or different particle number size distribution as measured
above. Because the activated CCN number concentration is determined by all
the three factors: the κHTDMA, water super-saturation ratio and
particle size distribution. If at different super-saturation ratio of water
vapour and/or different particle number size distribution as measured in
this study, the uncertainty of the predicted CCN activity from sub-saturated
κHTDMA can change. Indeed, this discrepancy trend between
κHTDMA and κCCN could introduce a varied impact on
the CCN prediction, which needs further investigations.
Analysis of the model results and discussion of the κ-discrepancy
As demonstrated above, the κHTDMA was, on average,
∼ 25 % lower than the κCCN of the inorganic
seeds when the MRSOA/PM was ∼0, which is consistent
with the thermodynamic model results from AIOMFAC with the assumption of
non-ideality (both κ were 0.72 if assuming ideality).
To examine the influence of non-ideality at higher organic mass fractions,
model calculations with AIOMFAC were performed to explore whether the mean
experimental κHTDMA (0.14 ± 0.03) and κCCN
(0.09 ± 0.01) at MRSOA/PM=0.8 can be reproduced by
including non-ideality. To this purpose, 17 model compound mixtures of
average O : C ratios between 0.36 and 0.95 and average molar masses between
173 and 478 g mol-1 were designed, that cover the hygroscopicity range spanned
by the SOA products. For none of these mixtures the experimental κHTDMA and κCCN at MRSOA/PM=0.8 could be met.
The trends of the simulation results are exemplified in Fig. 6 for four out
of the 17 mixtures, which combine low (O : C = 0.36) and high (O : C = 0.66)
oxidation with low and high molecular weights. Most of the low molecular
weight compounds are identified α-pinene oxidation products, while
the high molecular weight compounds are artificial dimers of the monomeric
compounds. Further details regarding the four mixtures can be found in the
Supplement under mixture numbers 5 (red line in Fig. 6), 6 (yellow), 14 (blue), and
15 (cyan). For the monomeric SOA with O : C = 0.66, a calculation assuming
solution ideality (activity coefficients set to one) was also performed. It
can be seen that the assumption of solution ideality leads to an
overestimation of κHTDMA and κCCN for all organic
mass fractions including the inorganic seed (MRSOA/PM of
∼0). In AIOMFAC, the ideal aqueous ammonium sulfate solution
is calculated as fully dissociated into 2 NH4++1SO42- (corresponding with van 't Hoff factor of three) with
activity coefficients set to one. At activation, ideal solution conditions
would be expected, as the particles are strongly diluted. However, for
ammonium sulfate a large difference in κCCN between the ideal
and non-ideal model calculation can be observed. This difference suggests
some association of the ions in solution, possibly to N2H7+
and HSO4- (Atwood et al., 2002). AIOMFAC accounts for
concentration and composition dependent speciation of ammonium sulfate in
solution through the activity coefficients, which have been adjusted during
the parameterization process to bring the model output in agreement with the
experimental data (Zuend et al., 2008). Including
non-ideality leads to an overall better agreement of κHTDMA at
all organic mass fractions (Fig. 6a). At high organic mass fractions
(MRSOA/PM=0.8), best agreement of κHTDMA is reached
for the simulations with O : C = 0.66 irrespective of the molecular weight
(i.e. monomers and dimers). In contrast to that, Fig. 6b shows the best
agreement of κCCN at MRSOA/PM=0.8 for the model
mixture with dimers with average O : C = 0.36, which is the one that agrees
least with the observed κHTDMA values. As a result, the κHTDMA/κCCN (Fig. 6c) and κHTDMA-κCCN (Fig. 6d) at MRSOA/PM=0.8 could not be reproduced with
the model compound mixtures shown in this figure. Overall, only mixtures
with dimers and low O : C ratios were able to match the experimental range of
κCCN, yet, only dimer mixtures with rather high O : C ratios were
able to fully match κHTDMA. Thus, among all 17 examined
mixtures, none was found where the modelled κHTDMA and κCCN values were both within the standard deviation range of the
experimental values (see Fig. S3), indicating that non-ideality
alone cannot account for the discrepancy between κHTDMA and
κCCN.
Influence of non-ideality on (a)κHTDMA, (b)κCCN, (c)κHTDMA/κCCN, and (d)κHTDMA-κCCN analysed by comparison of model and
experiment: Solid coloured lines show model results using AIOMFAC activity
coefficients. The dashed red line shows the model result assuming an ideal
solution for the same model compounds as the red solid line. The average O : C
ratios of the model compound mixtures are given in the legend, the average
molar masses are: 173 (red), 347 (yellow), 185 (blue), and 369 (cyan) g mol-1.
High molar masses were achieved by artificially dimerizing all organic
compounds in the model calculations, labelled “dimers” in the legend. Grey
dots and lines in the background show all experimental data points, and
their mean and standard deviation, respectively. The uncertainty of the
experimental data points is shown exemplarily for some points.
Previous studies found that some organic compounds are strongly
surface-active, and can lower the surface tension of the droplet below the
value of pure water even at activation (Ovadnevaite et al., 2011; Bzdek
et al., 2020; Gérard et al., 2019). While the effect of a lowered
surface tension on hygroscopic growth is negligible, assuming a lowered
surface tension at supersaturated conditions would lead to a reduction in
Sc. In the experiment, however, a higher Sc was measured than κHTDMA would suggest (see Fig. S4 in the Supplement). Therefore, a lowered
surface tension cannot explain the observed discrepancy in κ at high
MRSOA/PM. Calculating κCCN with the assumption of a lower
surface tension would even lead to a higher κCCN thus
increasing the discrepancy rather than reducing it.
Thermodenuder measurements showed that the examined SOA contained a
substantial fraction of semi-volatile compounds (Voliotis et al., 2021). Differences in the
design of the HTDMA and CCN counter could have influenced the fate of the
semi-volatile compounds, thereby explaining the observed κ
discrepancy. The semi-volatile compounds in the gas phase (e.g. organics,
HNO3) can co-condense with water vapour on aerosol particles and enhance
the water uptake (Rudolf et al., 1991, 2001; Hu et al.,
2018; Topping et al., 2013; Wang et al., 2020; Gunthe et al., 2021). This
enhancement is more significant at higher relative humidity. In addition,
aerosol particles grow larger at higher relative humidity in the CCN counter
and dilute the solute concentrations in the particle phase, which further
facilitates the partitioning of semi-volatile compounds into the particle
phase, creating a positive feedback. Therefore, equal organics in the gas
phase and equal temperature in both instruments would result in κCCN>κHTDMA, which contrasts with the
observation in this study. However, if the gas phase is diluted or if the
temperature is increased, semi-volatile compounds in the particle phase can
also evaporate and thereby decrease the water uptake when re-equilibrating
(Hu et al., 2018). The observed higher κHTDMA
than κCCN can be explained, if the organic concentration in the
gas phase was significantly higher in the HTDMA than in the CCN counter
and/or if the temperature in the CCN counter was higher than in the HTDMA.
The sampled aerosols from the chamber were dried to RH <30 %
before splitting and entering the HTDMA and CCN counter. During the drying
process, semi-volatile compounds can co-evaporate with water to the gas
phase. Water vapour was then removed through the Nafion membrane, but this
pre-treatment was the same for both instruments. In our setup, the sheath
air flows of the two DMAs in the HTDMA are closed-loop, which means that the
sheath air is filtered and recirculated and will reach equilibrium with the
sample air including gaseous organic compounds. In commercial CCN counters,
the sheath air is produced by splitting the sample air and filtering it
(Roberts and Nenes, 2005) and thus, contains organic gases.
However, the DMA for size selection before the CCN counter uses dry clean
air as sheath air, which can dilute the aerosol flow and thereby result in
the evaporation of organic compounds. Gaseous organic substances can deposit
on the filters in both instruments and deposited material from previous
experiments can desorb or evaporate from the filters, which could have
influenced the sheath air composition.
After selecting a given size of aerosol by the first DMA, the aerosol went
through the conditioned humid environment. The temperature was decreased to
18 ∘C to reach the set RH in HTDMA (Good et al.,
2010a), which will facilitate the co-condensation of the semi-volatile
compounds and the growth of the aerosol particles by lowering the saturation
vapour pressure due to temperature drop (Hu et al., 2018).
In contrast to that, the temperature in the CCN counter is designed to
increase to keep a certain water saturation (Roberts and Nenes,
2005), which is not favourable for co-condensation and could even have led
to evaporation of semi-volatile compounds. A loss of organic mass in the CCN
counter is equivalent to a smaller dry diameter of the particles, which
results in a higher critical supersaturation and thus a lower κCCN. A decrease of ∼15 % of the dry diameter could
explain the observed κ discrepancy at MRSOA/PM=0.8. Setting
the volume loss equal to a mass loss, the 15 % decrease in diameter is
equivalent to a 39 % mass loss, which approximately corresponds to the
total loss of the organic mass in the logC*=2 volatility bin and
half of the mass in the logC*=1 volatility bin of the measured
volatility distribution of α-pinene SOA
(Voliotis et al., 2021). Partial evaporation in
the CCN counter is also in good agreement with the fact that only model
mixtures with dimeric compounds were able to reproduce the observed κCCN. As the high average molar mass required to match κCCN contradicts the measured volatility distribution, this gives
further support to the assumption of a loss of molecules in the CCN counter,
as this reduces the particle size and increases the average molar mass. Note
that a higher molar mass of the organics has the same effect as the absolute
loss of molecules, since both lead to an overall smaller number of organic
molecules in the particle, which reduces the Raoult effect. Thus, solution
non-ideality together with evaporation of semi-volatile compounds in the CCN
counter is a plausible explanation of the observed discrepancy between
κHTDMA and κCCN. Further factors that may have
biased the κ-measurements include co-condensation of semi-volatile
compounds in the HTDMA, the dilution of the sheath air in the size-selection
DMA before the CCN counter and the influence of the filtering on the sheath
air composition in both instruments. This exemplifies how challenging the
physicochemical characterization of semi-volatile organic aerosols is.
Further investigations are needed to clearly quantify possible effects of
co-condensation and evaporation of semi-volatile compounds in HTDMAs and CCN
counters to support this explanation of observed κ discrepancies.
Conclusions
In this study, we designed and performed a series of chamber experiments to
improve our understanding of the chemical controls of the sub- and
super-saturated water uptake in the evolution of the SOA formation from mixed
precursors in the presence of ammonium sulfate seed. The yield and
reactivity of the SOA precursors controlled the SOA production rate in
different VOC systems, and therefore the increase of organic mass fraction
(MRSOA/PM). Our results showed that the MRSOA/PM is the main
factor influencing the hygroscopicity and CCN activity in terms of κ, and the SOA composition plays a second-order role. At the same level of
MRSOA/PM, the order of overall κHTDMA and κCCN, from highest to lowest, were α-pinene, isoprene, and o-cresol and 33 % o-cresol >α-pinene; α-pinene and isoprene; and o-cresol and isoprene >o-cresol and 50 %
reactivity o-cresol systems. There is no clear relationship between the
κ of SOA deduced by ZSR method and oxidation level (f44).
During the SOA formation process in all VOC systems, size-resolved chemical
composition was observed, for which the smaller particles have higher
MRSOA/PM. To avoid the influences of composition differences on the
reconciliation study of sub- and super-saturated water uptake, the
synchronized HTDMA and CCN data pairs with a comparable chemical composition
were selected according to the size-resolved chemical composition.
In the reconciliation, we found the discrepancy between κHTDMA
and κCCN varied with the MRSOA/PM. Consequently, the
performance of the κ-Köhler approach on CCN activity prediction from
sub-saturated condition also changed with the MRSOA/PM. This trend was
observed in all investigated VOC systems, regardless of the VOC sources and
initial concentrations. For all investigated VOC systems, the averaged
κHTDMA/κCCN increased from 0.76 ± 0.08 to
1.62 ± 0.26 when the MRSOA/PM increased from ∼0 to
∼0.8, meanwhile the mean absolute difference (κHTDMA-κCCN) increased from -0.15± 0.06 to 0.05±0.02. To explain these trends, AIOMFAC model calculations for
representative model mixtures were performed. The increasing κHTDMA/κCCN with increasing MRSOA/PM cannot be
explained by potential surface tension reduction of organics as this effect
will yield higher κCCN and even increase the discrepancy. The
non-ideality of mixed organic–inorganic solutions and the different
co-condensation or evaporation behaviour of semi-volatile organic substances
in the two measurement setups could be plausible reasons for the
discrepancy. Further experimental investigations on how HTDMAs and CCN
counters respond to condensable vapours are of great importance to better
understand this discrepancy.
In addition, we estimated the influences of this κ discrepancy trend
on the prediction of CCN number concentration from the sub-saturated
hygroscopicity (κHTDMA). The predicted mean CCN number
concentration was underestimated by ∼10 % at MRSOA/PM
of ∼0. This underestimation of CCN number disappeared with an
increase of MRSOA/PM to 0.2–0.4 due to SOA condensation, and
ultimately turned to an overestimation by ∼20 % in average
with MRSOA/PM of ∼0.8. It is worth noting that the
influences of the κ discrepancy trend on CCN activity prediction
were estimated based on the current measurements of critical super-saturation
and particle number size distribution. Broader impacts of this
chemical-dependent performance of the κ-Köhler approach in cloud
properties prediction under various atmospheric conditions should be
analysed in climate models to better project aerosol-induced climate
effects.
Data availability
The observational dataset of this study is available upon request from
corresponding authors.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-4149-2022-supplement.
Author contributions
YW conceived this study. GM, MRA, YW, AV and YS co-designed the
chamber experiments. YW, AV, YS and MD conducted the chamber
experiments. DH offered in-kind training on operation and data analysis
of HTDMA and CCN counter for YW. During chamber experiments, YW performed
HTDMA and CCN counter measurements used in this study, conducted data
integration and analysis, and wrote the manuscript. YC provided helpful
discussions. JK and CM designed and analysed AIOMFAC model simulations.
GM and MRA proofread and improved the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Simulation chambers as tools in atmospheric research (AMT/ACP/GMD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We acknowledge AMF and AMOF for providing the SMPS instrument. Yu Wang acknowledges the joint scholarship of The University of Manchester and Chinese Scholarship Council. We thank Dr. Harald Saathoff for the editing work and two anonymous referees for their useful comments.
Financial support
Manchester Aerosol Chamber was financially supported by EUROCHAMP 2020. M. Rami Alfarra was funded by the UK National Centre for Atmospheric Sciences (NACS). Aristeidis Voliotis was funded by the Natural Environment Research Council (NERC) EAO Doctoral Training Partnership. Judith Kleinheins was funded by the Swiss National Foundation (project number: 200021L_197149).
Review statement
This paper was edited by Harald Saathoff and reviewed by two anonymous referees.
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