Secondary aerosols (SAs, including secondary organic and inorganic aerosols,
SOAs and SIAs) are predominant components of aerosol particles in the North
China Plain (NCP), and their formation has significant impacts on the evolution
of particle size distribution (PNSD) and hygroscopicity. Previous studies
have shown that distinct SA formation mechanisms can dominate under
different relative humidity (RH). This would lead to different influences of
SA formation on the aerosol hygroscopicity and PNSD under different RH
conditions. Based on the measurements of size-resolved particle activation
ratio (SPAR), hygroscopicity distribution (GF-PDF), PM2.5 chemical
composition, PNSD, meteorology and gaseous pollutants in a recent field
campaign, McFAN (Multiphase chemistry experiment in Fogs and Aerosols in the
North China Plain), conducted during the autumn–winter transition period in
2018 at a polluted rural site in the NCP, the influences of SA formation on
cloud condensation nuclei (CCN) activity and CCN number concentration
(NCCN) calculation under different RH conditions were studied. Results
suggest that during daytime, SA formation could lead to a significant
increase in NCCN and a strong diurnal variation in SPAR at
supersaturations lower than 0.07 %. During periods with daytime minimum
RH exceeding 50 % (high RH conditions), SA formation significantly
contributed to the particle mass and size changes in a broad size range of 150 to 1000 nm, leading to NCCN (0.05 %) increases within the size range of
200 to 500 nm and mass concentration growth mainly for particles larger
than 300 nm. During periods with daytime minimum RH below 30 % (low RH
conditions), SA formation mainly contributed to the particle mass and size and
NCCN changes for particles smaller than 300 nm. As a result, under the
same amount of mass increase induced by SA formation, the increase of
NCCN (0.05 %) was stronger under low RH conditions and weaker under
high RH conditions. Moreover, the diurnal variations of the SPAR parameter
(inferred from CCN measurements) due to SA formation varied with RH
conditions, which was one of the largest uncertainties within NCCN
predictions. After considering the SPAR parameter (estimated through the
number fraction of hygroscopic particles or mass fraction of SA), the
relative deviation of NCCN (0.05 %) predictions was reduced to within
30 %. This study highlights the impact of SA formation on CCN activity and
NCCN calculation and provides guidance for future improvements of
CCN predictions in chemical-transport models and climate models.
Introduction
The cloud condensation nuclei (CCN) activity of aerosol particles describes the
ability to activate and grow into cloud droplets at given supersaturations
and thus has important impacts on cloud microphysics and the aerosol
indirect effect on climate. CCN activity is dependent on the physicochemical
properties of aerosol particles, including particle size distributions,
hygroscopicity (determined by chemical composition) and mixing state. Thus,
atmospheric processes influencing these aerosol properties may exert
influences on CCN activity.
Secondary aerosol (SA) formation contributes greatly to aerosol populations
and impacts CCN properties in many ways, generally increasing CCN number
concentrations (NCCN) and leading to changes in the CCN activity
(Wiedensohler et al., 2009; Kerminen et al., 2012; Wu et al., 2015; Farmer
et al., 2015; Ma et al., 2016; Zhang et al., 2019, and references therein).
Differences in precursor and oxidant concentrations as well as SA formation
mechanisms lead to particle size growth in different size ranges (Dal Maso
et al., 2005; Kulmala et al., 2007; Zhang et al., 2012; Farmer et al., 2015;
Cheng et al., 2016; Kuang et al., 2020c), thus impacting CCN activities
in different ways. SA formation includes both the formation and subsequent
growth of new particles (new particle formation, NPF) and the growth of
existing particles. NPF can directly provide particles large enough to act
as CCN (Wiedensohler et al., 2009; Kerminen et al., 2012; Farmer et al.,
2015), generally affecting aerosol particles smaller than 100 nm, thereby
elevating NCCN at higher supersaturations
(SSs > 0.2 %)
(Wiedensohler et al., 2009; Kerminen et al., 2012; Ma et al., 2016; Zhang et
al., 2019, and references therein). SA formation on existing particles,
especially under polluted conditions, significantly adds mass to and changes
the chemical composition of accumulation-mode particles (Farmer et al.,
2015), thus affecting CCN at lower SSs (< 0.2 %) (Wiedensohler et
al., 2009; Mei et al., 2013; Yue et al., 2016; Thalman et al., 2017; Duan et
al., 2018). SSs vary greatly among different clouds categories. Cumulus
clouds are formed under higher SSs and are thus mostly influenced by Aitken-mode particles formed in NPF events (Reuter et al., 2009; Gryspeerdt and
Stier, 2012; Fan et al., 2016; Jia et al., 2019, and references therein).
Stratus clouds and fogs that exert stronger effects on climate and
environment, however, are generally formed at SSs lower than 0.2 %,
indicating that only accumulation-mode particles can serve as CCN (Ditas et
al., 2012; Hammer et al., 2014a, b; Krüger et al., 2014;
Shen et al., 2018). Numerous studies have investigated the impact of NPF on
CCN (Gorden et al., 2016; Ma et al., 2016; Yu et al., 2020, and references
therein); however, only few studies have focused on the influence of SA
formation on CCN activity of accumulation-mode particles, which might
exhibit strong climate and environment impacts and urgently requires
attention.
SA formation affects the CCN activity of accumulation-mode particles, not only by
enlarging their size, but also by changing their chemical compositions. At a
specific particle size, the CCN activity is determined both by the chemical
composition of particles which originally were and stayed this size and
that of particles which grew into this size via added SA mass. These two
groups of particles can exert different variations to CCN activity at the
same particle size (Wiedensohler et al., 2009, and references therein). In
general, the SA formation can increase the hygroscopicity of particles by
adding chemical compounds with lower volatility and higher oxidation state,
which are usually more hydrophilic, thereby enhancing the CCN activity of
accumulation-mode particles (Mei et al., 2013; Yue et al., 2016). However,
CCN activity may also remain unchanged (Wiedensohler et al., 2009) or be
weakened in some cases (Thalman et al., 2017; Duan et al., 2018). In SA
formation observed in central Amazon forests, Thalman et al. (2017) reported
enhanced CCN activity in the dry season and constant CCN activity in the wet
season. In SA formation events under polluted conditions in Guangzhou (Pearl
River Delta, China), Duan et al. (2018) found that bulk CCN activity can be
enhanced in summer due to the formation of large and inorganic-rich
particles but weakened in winter due to the formation of small and
organic-rich particles, where RH seemed to have been an important factor in
the variations of bulk CCN activity due to different particle formation
pathways. Aside from variations of particle chemical composition, changes in
aerosol mixing states caused by SA formation can also change CCN activity
(Su et al., 2010; Rose et al., 2011; Cheng et al., 2012). The fast
condensation of SA components on accumulation-mode particles led to the
turnover of soot particle mixing state from externally to internally mixed,
which contributed mostly to enhancements of CCN activity (Cheng et al.,
2012). Thus, SA formation influences the CCN activity of accumulation-mode
particles through its integrated impacts on their size, hygroscopicity and
mixing state, which requires more detailed and comprehensive investigations.
The North China Plain (NCP) frequently experiences severe aerosol pollution
due to both strong emissions of primary aerosol and strong SA formation
caused by the abundance of gaseous precursors and oxidants (Zheng et al., 2015; Liu et al., 2010; Huang et al., 2014; Xu et al., 2019). In the SA
formation events on the NCP, both aqueous-phase processes and gas-phase
photochemical processes can play important roles, depending on atmospheric
conditions such as RH (Hu et al., 2016; Xu et al., 2017a; Wang et al.,
2019). A recent observational study on the NCP found that SA formation
dominantly contributed to different particle size since SA formation
mechanisms varied with RH conditions (Kuang et al., 2020c). Under dry
conditions, SAs were mainly formed through gas-phase photochemical processing
and mostly added mass to accumulation-mode particles. While under high RH
conditions or supersaturated conditions, SA was also formed in the aqueous
phase, contributing to the formation of both accumulation-mode and coarse-mode particles. The difference in particle size where SA formation took
place and the difference in SA chemical compositions could result in
distinct variations of CCN activity, which has not been evaluated yet. In
this study, we will study the influence of SA formation on the size-resolved
particle activation ratio (SPAR) of accumulation-mode particles in the NCP
under different RH conditions, which fills a gap of knowledge within CCN
studies in the NCP and may provide guidance for the improvement of current
CCN parameterization schemes in chemical-transport and climate models.
MethodMeasurementsSite
Under the framework of McFAN (Multiphase chemistry experiment in Fogs and
Aerosols in the North China Plain) (Li et al., 2021), from 16 November to 16 December 2018, physical and chemical properties of
ambient aerosol particles as well as meteorological parameters were
continuously measured at the Gucheng site in Dingxing County, Hebei
Province, China. This site is an Ecological and Agricultural Meteorology
Station (39∘09′ N, 115∘44′ E) of the Chinese Academy of
Meteorological Sciences, which is located between Beijing
(∼ 100 km) and Baoding (∼ 40 km), two megacities in the North
China Plain, and surrounded by farmlands and small towns. Measurements at
this site can represent the polluted background conditions of the NCP well.
All aerosol measurement instruments were placed in a container with
temperature maintained at 24 ∘C, while conventional trace gas
instruments including CO were housed in an air-conditioned room on a
two-story building located ∼ 80 m to the south of the
container, with no taller buildings between them blocking the air flow.
Instrumentation
In this study, ambient aerosol was sampled by an inlet system consisting of
a PM10 inlet (Rupprecht & Patashnick Co., Inc., Thermo, 16.67 L min-1), a
Nafion dryer that dried relative humidity to below 30 % and an isokinetic
flow splitter directing the air sample to each instrument.
A DMA-CCNC (differential mobility analyzer–cloud condensation nuclei counter) system measured SPAR at five supersaturations (SSs), 0.05 %,
0.07 %, 0.2 %, 0.44 % and 0.81 %, with a running time of 20 min for
0.05 % and 10 min for the other SSs. This system consisted of a
differential mobility analyzer (DMA model 3081; TSI, Inc, MN USA), a
condensation particle counter (CPC model 3772; TSI, Inc., MN USA) and a
continuous-flow CCN counter (model CCN200, Droplet Measurement Technologies,
USA; Roberts and Nenes, 2005). The system was operated in a size-scanning
mode over the particle size range from 9 to 400 nm. SPAR can be obtained by
combining the measurements of CPC and CCNC at different particle size. The
sample and sheath flow rate of the DMA were set to 1 and 5 L min-1,
respectively; hence the resultant measured particle diameter ranged from 9 to 500 nm. Since the low number concentration of particles above 300 nm
could lead to large uncertainty in CCNC counting, the measurements for
particles larger than 300 nm were excluded, except for 0.05 % SS. In order
to characterize the variations of particles with low hygroscopicity of about
0.1, SPAR measurement up to about 400 nm is used at 0.05 % SS. There are
12 size distribution scans during a complete 1 h cycle, with four scans
for the first SS and two scans for each of the remaining four SSs. Only the last scan
for each SS is used as the CCNC needs time for SS stabilization. The SSs of
CCNC were calibrated with monodispersed ammonium sulfate particles (Rose et
al., 2008) both before and after the campaign. The flow rates were checked
regularly (every few days) during the campaign, as the flows (sample flow
and sheath flow) of the instrument can affect both the counting of droplets
and the SS in the column. A modified algorithm based on Hagen and Alofs (1983) and Deng et al. (2011, 2013) was used to correct the influence of
multiple-charge particles and DMA transfer function on SPAR. Details about
the system are described in Ma et al. (2016), and the description about the
inversion method can be found in the Supplement.
Non-refractory particulate matter (NR-PM) including SO42-,
NO3-, NH4+, Cl- and organics with dry aerodynamic
diameters below 2.5 µm was measured by an Aerodyne Time-of-Flight
Aerosol Chemical Speciation Monitor (ToF-ACSM hereafter) equipped with a
PM2.5 aerodynamic lens (Williams et al., 2010) and a capture vaporizer
(Xu et al., 2017b; Hu et al., 2017a) at 2 min time resolution. The
ToF-ACSM data were analyzed with standard data analysis software
(Tofware v2.5.13; https://sites.google.com/site/ariacsm/, last access: 21 January 2020). The organic mass spectra from m/z 12 to 214 were analyzed with
an Igor Pro-based positive matrix factorization (PMF) evaluation tool
(v3.04) and then evaluated following the procedures described in Zhang et
al. (2011). The chosen five-factor solution includes four primary factors,
i.e., hydrocarbon-like OA (HOA), cooking OA (COA), biomass burning OA (BBOA)
and coal combustion OA (CCOA), and a secondary factor, i.e., oxygenated OA
(OOA). More detailed descriptions on the ACSM measurements and data analysis
can be found in Kuang et al. (2020b) and Sun et al. (2020).
A hygroscopocity-tandem differential mobility analyzer (HTDMA; Tan et al., 2013)
measured the size-resolved aerosol growth factor (GF) at 90 % RH. The
sampled particles were subsequently charged by a neutralizer (Kr85, TSI
Inc.) and size-selected by a DMA (DMA1, model 3081L, TSI Inc.). A Nafion
humidifier (model PD-70T-24ss, Perma Pure Inc., USA) was used to humidify
the monodisperse particles with a specific diameter (Dd) at a fixed RH
of (90 ± 0.44) %, and then the number size distribution of the
humidified particles (Dwet) was measured by another DMA (DMA2, model
3081L, TSI Inc.) and a condensation particle counter (CPC, model 3772, TSI
Inc.). Thus, the GF of the particles can be calculated as
GF=DwetDd.
During the campaign, four dry mobility diameters (60, 100, 150 and 200 nm)
were selected for the HTDMA measurements. A full scan takes about 1 h in
order to cover the four sizes. Regular calibration using standard
polystyrene latex spheres and ammonium sulfate was performed to ensure the
instrument functioned normally. The tandem differential mobility analyzer
(TDMA) inversion algorithm (Gysel et al., 2009) was applied to calculate the
probability density function of GF (GF-PDF). More details about this system
can be found in Cai et al. (2018) and Hong et al. (2018).
Particle number size distributions (PNSDs) were measured by combining the
measurements of a scanning mobility particle sizer (SMPS; TSI model 3080)
and an aerodynamic particle sizer (APS; TSI Inc., Model 3321), that measured
particle mobility diameter size distributions in the range of 12 to 760 nm and particle aerodynamic diameter size distribution in the range of 700 nm to 10 µm, respectively. A commercial instrument from Thermo
Electronics (Model 48C) was used to measure CO concentration. Besides
monthly multipoint calibrations and weekly zero-span check, additional
6-hourly zero checks were also performed for the CO instrument.
Data processingAerosol hygroscopicity and cloud activation: κ-Köhler theory
The ability of particles to act as CCN and their dependence on particle size
and particle chemical composition on CCN activity can be described by the
Köhler theory (Köhler, 1936). A hygroscopic parameter κ is
calculated based on the κ-Köhler theory (Petters and
Kreidenweis, 2007) to evaluate the influence of particle chemical
compositions:
κ=Dwet3-Dd3Dd31Sexp4σs/aMwRTρwDwet-1,
where S represents the saturation ratio, ρw is the density of
water, Mw is the molecular weight of water, σs/a is the
surface tension of the solution–air interface, R is the universal gas
constant, T is the temperature, Dd is the diameter of dry particle and
Dwet is the diameter of the humidified particle. In this study,
σs/a is assumed to be the surface tension of the pure water–air
interface. Based on the κ-Köhler theory, the surface equilibrium
water vapor saturation ratio of particles with a specific κ at
different wet particle size can be calculated, and the maximum value of
the surface equilibrium saturation ratio (which is generally supersaturated) is
defined as the critical SS for CCN activation. As a result, the variation of
the critical diameter (Da) for particles with different hygroscopicity
(or GF at a specific RH) at different SSs can be determined.
Aerosol growth factor and its probability density function
In practice, the growth factor probability density function (GF-PDF) was
inversed from the measured GF distribution using a TDMAinv algorithm (Gysel
et al., 2009). After obtaining the GF-PDF, the ensemble average GF and
corresponding critical diameter under a certain SS (Da,GF) can be
calculated. Furthermore, the number fraction and the weighted-average GF of
hygroscopic particles (κ> 0.1 and GF(90 %,
200 nm) > 1.22) were calculated as
2NFhygro=∫1.2∞PDF(GF)×dGF3GFhygro=∫1.2∞GF×PDF(GF)×dGF.
Based on the κ-Köhler theory, the hygroscopicity parameter
κ and corresponding critical diameter (Da,hygro) under a
certain SS for particles with GFhygro can be calculated. As
GFhygro is higher than the average GF, Da,hygro is smaller
than Da,GF.
Calculations of aerosol hygroscopicity from aerosol
chemical composition measurements
For the calculation of aerosol hygroscopicity parameter κ based on
measured chemical composition data (κchem), detailed
information on the chemical species is needed. The ACSM can only provide
bulk mass concentrations of SO42-, NO3-, NH4+ and
Cl- ions and organic components, which cannot be used to calculate size-resolved hygroscopicity. However, in the North China Plain, accumulation-mode particles are the dominant contributors to the bulk particle mass
concentration (Liu et al., 2014; Xu et al., 2015; Hu et al., 2017b), and thus
the bulk chemical compositions can be used as a proxy for those of
accumulation-mode particles. For the inorganic ions, a simplified ion
pairing scheme was used to convert ion mass concentrations to mass
concentrations of corresponding inorganic salts (Gysel et al., 2007; Wu et
al., 2016). Thus, mass concentrations of SO42-, NO3-,
NH4+ and Cl- are specified into ammonium sulfate (AS),
ammonium nitrate (AN), ammonium chloride (AC) and ammonium bisulfate (ABS),
for which the κ values under supersaturated conditions were
specified according to Petters and Kreidenweis (2007). For a given internal
mixture of different aerosol chemical species, the
Zdanovskii–Stokes–Robinson (ZSR) mixing rule can be applied to predict the
overall κchem using volume fractions of each chemical species
(εi) (Petters and Kreidenweis, 2007):
κchem=∑iκi⋅εi,
where κi and εi represent the hygroscopicity
parameter κ and volume fraction of chemical component i in the
mixture. Based on Eq. (2), κchem can be calculated as follows:
κchem=κASεAS+κANεAN+κABSεABS+κACεAC+κBCεBC+κOrgεOrg,
where κBC is assumed to be zero as black carbon is
non-hygroscopic. κorg and εorg represent
κ and volume fraction of total organics. The values of
hygroscopicity parameter for inorganic compounds can be found in Table 1 of
Petters and Kreidenweis (2007). Large variations in κorg have been
reported in former studies, and a linear relationship between κorg and organic aerosol oxidation state (f44) was detected in our
campaign (Kuang et al., 2020b), which was adopted to calculate κorg in this study:
κOrg=1.04×f44-0.02.
It should be noted that the κ-Köhler theory is not perfect, even
for inorganic compounds. Numerous studies have been focusing on the
performance of its application on measurements under different RH conditions
(Liu et al., 2011; Wang et al., 2017). And κorg used in this
study was determined by the measurement of a humidified nephelometer at RH of
85 % in Kuang et al. (2020b), due to the lack of κorg measured under supersaturated conditions. In this study, we focus
on the variations of κ values derived from HTDMA and CCN measurement
during the SA formation events, rather than the closure between κ
values derived using different techniques, which will be addressed in an
upcoming study.
Fitting parameterization scheme of SPAR
In general, the variation in CCN activity of a particle population can be
attributed to the variation in the number fraction of hygroscopic particles
or its hygroscopicity, which can be indicated by fitting parameters of SPAR
curve parameterization. SPAR curves are often parameterized using a
sigmoidal function with three parameters. This parameterization assumes
aerosols to be an external mixture of apparently hygroscopic particles that
can act as CCN and non-hygroscopic particles that cannot be measured by CCNC
within the measured particle size range below 400 nm (Rose et al., 2010).
SPAR (Ra(Dp)) at a specific SS can be described as follows (Rose et al.,
2008):
Ra(Dp)=MAF21+erfDp-Da2πσ,
where erf is the error function, MAF is the asymptote of the measured SPAR
curve at large particle sizes, Da is the midpoint activation diameter
and is associated with the hygroscopicity of CCN and σ is the standard
deviation of the cumulative Gaussian distribution function and indicates the
heterogeneity of CCN hygroscopicity. As reported by Jiang et al. (2021),
based on the investigation of the covariations between SPAR curves and
parameterized hygroscopicity distribution, it was found that the MAF can be
used to estimate the number fraction of hygroscopic (thus CCN-active)
particles, for aerosol hygroscopicity distributions generally observed in
the ambient atmosphere, and thus half MAF can be used to represent the number
fraction of CCN to total particles at particle size around Da.
Although the influence of particles whose κ is less than 0.1 on SPAR
cannot be considered in this parameterization scheme, significant deviations
were only found under higher SSs (Tao et al., 2020) and need not be
considered under the low SSs discussed in this study.
It should be noted that the meaning of MAF can be different with regards to the SS, and SPAR
measurement up to about 400 nm is needed for the MAF fitting for SPAR at SS
of 0.05 % to represent the particles with κ values higher than 0.1.
For SPAR at SS of 0.8 %, MAF should be 1 at 400 nm diameter. However, a
MAF of 1 in this case can lead to overestimations of hygroscopic particle
number fraction due to the significant difference between SPAR curves and
sigmoidal fitting curves. In the former study on SPAR fitting curves in the
NCP, it was found that a fitting parameterization with the combination of
two sigmoidal fitting curves was needed for SPAR fitting at SSs higher than
0.4 % (Tao et al., 2020). However, in this study, we investigate SA
formation on accumulation-mode particles and particle CCN activity at SSs
below 0.1 %, under which condition non-hygroscopic particles smaller than
400 nm are typically CCN-inactive. The MAF fitted in the particle size range
below 400 nm was used to indicate the variations of SPAR that was of the
main focus here in this work. In addition, due to the very low NCCN in particle size ranges larger than 400 nm, the deviations of NCCN due to
the limited range of measured particle size are also very small.
ResultsOverview of the measurements
The time series of meteorological parameters, SPAR, NCCN at SS of
0.05 % and mass concentration of non-refractory particulate matter of
PM2.5 (NR-PM2.5), PM2.5 SA (inorganic compounds and OOA) and
PM2.5 PA (primary aerosol, defined as the sum of POA) are shown in Fig. 1. The mass concentration of OOA and four POAs were quantified by the ACSM
PMF analysis (Zhang et al., 2011). During the campaign, PM2.5 PA was
generally lower than 100 µg m-3 under both high and low RH
periods. Meanwhile, PM2.5 SA can approach about 400 µg m-3,
especially during the strong SA formation events under high RH conditions,
but can be lower than 100 µg m-3 under low RH conditions. Strong
diurnal variations were found in SPAR with varying meteorological
parameters. During the whole period, the wind speed was generally lower than
4 m s-1, which is in favor of aerosol particle accumulation and SA
formation on existing particles. However, RH, NCCN (0.05 %),
PM2.5 SA and NR-PM2.5 mass concentrations revealed very distinct
levels before and after 4 December, and thus the whole campaign was
divided into two stages with different RH and SA pollution conditions:
higher RH and stronger SA pollution before 4 December and lower RH and
lighter SA pollution after 4 December. In the following discussions, the
high RH stage corresponds to days before 4 December with daily maximum and
minimum RH higher than 75 % and 50 %, respectively. Two events that
occurred during 25 to 27 November (Event 1) and 30 November
to 2 December (Event 2), respectively, displayed especially high RH
conditions with successive nighttime fogs (blue shaded areas). The low RH
stage corresponds to the period after 4 December with daily maximum and
minimum RH below 70 % and 30 %, which was represented by two events that occurred during 9 to 11 December (Event 3) and 13 to
15 December (Event 4), respectively. These events were selected based on
the similarity of PM2.5 concentration and evolution, while the time
window was fixed to 2 d for the convenience of intercomparison. In
addition, during these events, the wind speed was generally low, the RH
followed a general diurnal variations and SA mass grew steadily and
continuously. Thus the interference of the variations of air mass and
short-term local emissions can be eliminated, and the influence of SA
formation can be highlighted. It should be noted that variations of
NCCN at 0.07 % were similar to those at 0.05 %, which followed the
variations of SA mass concentration, while at higher SSs, the variations of
NCCN differed from those of SA mass concentration, especially under
high RH conditions, suggesting different responses of CCN activity towards
distinct SA formation processes. As reported in Kuang et al. (2020c), during
the high RH stage, aqueous-phase SA formation was promoted, leading to
persistent increases in NCCN(0.05 % and 0.07 %), mass concentration
of NR-PM2.5 and especially mass concentration of PM2.5 SA during
Events 1 and 2. During the low RH stage, the SA formation dominantly occurred
in the gas-phase, that generated much less SA than aqueous-phase formation
(Kuang et al., 2020c). Thus, the persistent increases of NCCN (0.05 %
and 0.07 %) and PM2.5 during Events 3 and 4 were much weaker than those
in Events 1 and 2. Due to the different SA mass fractions, SPAR during the
high RH stage was generally higher than that during the low RH stage.
However, the ratios between NCCN (0.05 %) and mass concentration of
PM2.5 SA or NR-PM2.5 were lower during the high RH period and
demonstrated strong decreases, especially in Event 1 and 2. The response of
CCN activity and NCCN (0.05 %) to the different SA formation
mechanisms will be discussed comprehensively in the following parts.
Overview of the measurements during the campaign: (a) dots represent wind speed, with color indicating wind direction, and black lines represent RH; (b) SPAR under SS of 0.05 %; (c) blue, green and yellow dots represent NCCN under SS of 0.05 % and 0.07 % and mass concentration of NR-PM2.5, respectively; (d) blue, green and yellow dots represent NCCN under SS of 0.2 %, 0.44 % and 0.81 %, respectively; (e) blue and
yellow dots represent the mass concentration of PM2.5 PA and PM2.5 SA
respectively; (f) blue and yellow dots represent the ratio between NCCN and mass concentration of NR-PM2.5 and PM2.5 SA, respectively. There were four events with significant enhancements of NCCN during the blue shaded periods.
The influence of different secondary aerosol formation on the diurnal variation of CCN activity
The diurnal averages of PNSD, SPAR at SS of 0.05 %, GF-PDF for 200 nm
particle and mass fraction of particle chemical compositions during high RH
periods before 4 December, low RH periods after 4 December and the four
events are shown in Fig. 2, respectively. It should be noted that the “high (or low)
RH events” is used to refer to the SA formation events under high (or low)
RH conditions for convenience, and it does not mean that RH caused variations
of CCN behavior. As can be seen in Fig. 2(1b) and (2b), different
variations of SPAR due to SA formation can be found during the periods with
different RH conditions. The average diurnal variations of these parameters
for the entire high RH stage and low RH stage as shown in Fig. 2(1a)–(1d)
and (2a)–(2d) revealed similar but more smoothed variations as in the four
selected events. The four events are discussed and intercompared in the
following to magnify the differences under distinct RH conditions. For
accumulation-mode particles, particle number concentrations were higher
during daytime in high RH events, while stronger diurnal variations occurred
in low RH events. Simultaneous daytime increases in particle SPAR in the size range from 200 to 400 nm, GF-PDF in the GF range from 1.2 to 1.8 and SA mass
fraction were found in all four events, suggesting that SA formation led to
increasing hygroscopic particle number concentration, which in turn
enhanced particle CCN activity. This effect was more pronounced in Events 1
and 2 than in Events 3 and 4. In Events 1 and 2, SPAR values were generally
higher than 0.4 at 200 nm and reached the maximum of 1 during noontime at
300 nm. A hygroscopic mode with GF > 1.4 was found throughout the
day, which dominated aerosol hygroscopicity during daytime. Mass fractions of
SA were generally higher than 70 % and reach a maximum of 80 % at noon, while in Events 3 and 4, SPAR at 200 nm was lower than 0.4 at night, and the
maximum SPAR at 300 nm was lower than 0.9. A particle mode with GF < 1.1 dominates particle hygroscopicity, and the mass fraction of SA was lower
than 60 % and 30 % at noon and at night, respectively. However, a stronger
daytime increase of SA mass fraction and accordingly larger variation in
SPAR was observed in Events 3 and 4 than in Events 1 and 2.
Diurnal variation of (a) PNSD, (b) SPAR at SS of 0.05 %, (c) GF-PDF at 200 nm and (d) mass fraction of different PM2.5 chemical species
during high RH periods before 4 December (1), low RH periods after
4 December (2) and the four events (3–6), including OA factors:
hydrocarbon-like OA (HOA), cooking OA (COA), biomass burning OA (BBOA), coal
combustion OA (CCOA) and oxygenated OA (OOA).
Besides SS of 0.05 %, variations of SPAR at SSs of 0.07 % and 0.2 %
are also shown in Figs. S1 and S2 in the Supplement. And as shown in Figs. S1 and S2, the variations of SPAR and NCCN/ PM at SS of 0.07 % are
similar but lighter, compared with those at SS of 0.05 %, while for SS of
0.2 %, the difference of SPAR between different periods or events is
smaller (Fig. S1) and so were the diurnal variations of SPAR and GF-PDF at
a particle size of 100 nm (Fig. S2). Because CCN activity at SS of 0.2 % was
strong enough (indicated by SPAR value close to 1) in the particle size range
where the SA formation dominates, the different SA formation under
high or low RH conditions cannot lead to significant variations of CCN
activity at SS of 0.2 %. In summary, based on CCN measurements in this
study, the RH-dependent influence of SA formation on CCN activity can be
found obviously at SSs of 0.05 % and 0.07 %. As the variations of SPAR
at SS of 0.07 % were quite similar to those at SS of 0.05 %, further
analysis was only based on CCN activity at SS of 0.05 %.
(a) The averages of SPAR curves at SS of 0.05 % in three different time periods (blue: 00:00–08:00 LT; green: 08:00–12:00 LT; red: 12:00–16:00 LT) during high (squares with solid line, event 1 and 2) and low (dots with dashed line, event 3 and 4) RH events. Diurnal variation of (b)Da and (c) MAF under high (blue) and low (yellow) RH conditions. The blue, green and red shades correspond to the three periods in panel (a). Error bars
indicate the standard deviations of data.
In Fig. 3a, a detailed comparison of particle CCN activity during SA formation
events of NCCN enhancements at SS of 0.05 % under different RH
conditions shows the variations of SPAR curves. Particle CCN activity
in Events 1 and 2 were combined due to their similar diurnal variations (as
shown in Fig. 2). Besides SPAR curves (Fig. 3a), corresponding fitting
parameters of the SPAR curve including Da and MAF were also shown in
Fig. 3b and c, respectively, as enhanced SPAR for particle population can
be attributed to hygroscopic particle number fraction increase (MAF
increase) or enhancement of hygroscopic particle hygroscopicity (Da
decrease). The same as demonstrated in Fig. 2, SPAR was generally higher, and
thus particle CCN activity (0.05 %) was generally stronger in high RH
events than those in low RH events. However, as shown in Fig. 3a, the
difference between SPAR in high and low RH events at 300 nm decreased from
0.2 to 0.1 during the SA formation, indicating a stronger enhancement
in low RH events, probably due to both the stronger increase of SA mass
fraction and the higher nighttime PA mass fraction (Fig. 2e). Furthermore,
in high RH events, there were daytime enhancements of SPAR within the 150
to 300 nm size range, as was indicated by the daytime increase of MAF and
decrease of Da, which mainly resulted from number fraction and
hygroscopicity increases of CCN-active particles, while in low RH events,
the daytime enhancement of SPAR was only observed for particles larger than
200 nm. This can be attributed to the strong increase of MAF and the slight
decrease of Da, which indicates significant increasing number fraction,
yet slightly enhanced hygroscopicity of hygroscopic particles, respectively.
Overall, the enhancement of SPAR was weaker but occurred at a broader
particle size range in high RH events than in low RH events, as shown in
Fig. 3a. This is in accordance with the previous results from Kuang et al. (2020c), suggesting that SA formation occurred mainly in the aqueous phase
within a broad particle size range (up to 1 µm) in high RH events,
while SA formation dominantly proceeded via gas-phase reactions and
contributed to aerosol sizes smaller than 300 nm in low RH events. At SS of
0.05 % (Fig. 3a), the variation of SPAR from 08:00–12:00 to 12:00–16:00 local time (LT)
in particle sizes smaller than 200 nm was very small during low RH events,
suggesting a smaller CCN activity enhancement due to SA formation compared
with high RH events. In detail, the different variations of SPAR in
high and low RH events indicated by MAF and Da (shown in Fig. 3b and
c) suggested different variations of hygroscopicity, number fraction and
size of SA particles. Before SA formation, there was a significant
difference between the MAF in high and low RH events, which disappeared
after the SA formation. The stronger variations in MAF in low RH events
suggested stronger enhancement of number concentration of formed SA
particles. As for Da during SA formation, there were similar, little
decreases in both high and low RH events, suggesting similar hygroscopicity
of the SA formed under low and high RH conditions. Thus differences of SPAR
and the resultant NCCN during low and high RH events were mainly due to
the different variations of number fraction of formed SA particles.
During different RH events, the average diurnal variation of (a) the ratios between particle mass concentration (dots with solid lines; blue: NR-PM2.5; yellow: PM2.5 SA; green: PM2.5 PA) and CO concentration and the ratio between NCCN at SS of 0.05 % and CO concentration (squares with solid line); (b) the ratios between particle volume concentration (Vconc) of different particle size range (indicated by colors) and CO concentration; (c) the ratios between NCCN of different particle size range at SS of 0.05 % (indicated by colors) and CO concentration; (d) the ratios between particle number concentration (Nconc) of different particle size range (indicated by colors) and CO concentration; (e) SPAR of different particle size range (indicated by colors); (f) the ratios between NCCN at SS of 0.05 % (black: bulk NCCN; yellow: NCCN with particle size larger than 300 nm; blue: NCCN with particle size smaller than 300 nm) and mass concentration of NR-PM2.5 SA and the ratios between NCCN and mass concentration of NR-PM2.5 (dashed lines).
As there were different influences of SA formation on both CCN activity at
SS of 0.05 % and PNSD under different RH conditions, different variation
of NCCN (0.05 %) due to SA formation can also be expected. Figure 4
displays the diurnal variation of PM2.5 mass concentration, volume
concentration (Vconc), number concentration (Nconc) and NCCN (0.05 %)
(all divided by CO to partially compensate for changes in planetary boundary
layer height), as well as the NCCN/ PM2.5 mass concentration ratio
and SPAR during high and low RH events, respectively. Variables in Fig. 4
are also presented in Fig. S3 averaged for the entire high RH and low RH
stages, respectively. Compared with the selected case events featuring
significant NCCN enhancement (Fig. 4(1c)–(2c)), the diurnal variations
averaged for the entire high and low RH stages were similar, with higher
levels of particle mass concentration but weaker enhancement of SA and
NCCN, indicating a similar but weakened impact of SA formation on CCN
activity due to the interference of other aerosol processes. Hereinafter, we
discuss the variations in the four events to magnify the discrepancies of SA
formation under high RH and low RH conditions and its distinct impact on
NCCN. The Vconc size distribution variations can be used as a proxy for
the evolution of NR-PM2.5 size distributions, considering the
relatively small variations in particle density (ranging from 1.2 to 1.8 and
with relative variations within 20 %; Hu et al., 2012; Zhao et al.,
2019). The variations of the ratio between NCCN (in different particle
size range) and the mass concentration of PM2.5 SA (referred to as
NCCN/ SA) or NR-PM2.5 (referred to as NCCN/ NR) can be used to
evaluate the response of NCCN to SA formation.
During high RH events, normalized NCCN (0.05 %) increased by
∼ 50 % from 08:00 to 14:00 LT, with a similar increase in
normalized PM2.5 SA mass concentration (Fig. 4(1a)). As the PM2.5
PA mass concentration decrease was much smaller than the SA increase, the
NR-PM2.5 mass concentration increase can be expected to be similar to
the SA increase. As reported by Kuang et al. (2020c), SAs during daytime were
mainly formed at larger particle sizes, featuring Vconc increase in the
particle size range of 400 to 1000 nm. In Fig. 4(1d), significant increases
of particle number concentration (Nconc) in particle size range of 150 to
1000 nm can be observed. At larger particle size, the increase of Nconc led
to a stronger increase of Vconc, which is why there were simultaneous but much
weaker increases of Vconc in the particle size range of 150 to 300 nm compared
with increases of those in the particle size of larger than 300 nm (Fig. 4(1b)).
This suggests that PM2.5 SA mainly contributed to particle sizes of
larger than 300 nm. In addition, because the SA formation enhanced
hygroscopicity and number fraction of CCN-active particles in the particle size
range of 150 to 300 nm, simultaneous enhancements of SPAR can be found
throughout the measured particle size range of 180 to 300 nm (Fig. 4(1e)).
By combining the enhancements of Nconc and SPAR in measured particle size
ranges, there were increases of NCCN from 200 to 500 nm (Fig. 4(1c)).
Thus while SA formation processes contributed to their volume (mass) and
hygroscopicity increase, it had no further impact on NCCN. As a result,
NCCN (> 300 nm) / SA, NCCN (< 300 nm) / SA,
NCCN (> 300 nm) / NR and NCCN (< 300 nm) / NR all
decreased during the SA formation (Fig. 4(1f)), suggesting that weakening
enhancement of NCCN (0.05 %) in SA formation under high RH condition
as SA formation mainly added mass to already CCN-active particles .
During low RH events, weaker increases of both NCCN (0.05 %) and
PM2.5 SA mass concentration from 08:00 to 14:00 LT were found (Fig. 4(2a)).
At the same time, PA mass decreased by 50 %, and the variation of total NR
mass was small. Under low RH conditions, SA formation mainly contributed to
mass enhancements of smaller particle sizes (Kuang et al., 2020c). Vconc
increased mostly in the range of 150 to 300 nm (Fig. 4(2b)), while Nconc
only increased within 300 nm (Fig. 4(2d)), suggesting that PM2.5 SA
mainly formed in the particle size range below 300 nm. SA formation mainly
enhanced the number fraction of CCN-active particles in the particle size range of 200 to
300 nm, as SPAR only revealed evident enhancement (Fig. 4(2e)) and NCCN
only significantly increased (Fig. 4(2c)) in that size range. As a result,
although NCCN (> 300 nm) / SA decreased similar to that under
high RH conditions, NCCN (< 300 nm) / SA and NCCN
(> 300 nm) / NR generally stayed constant, and NCCN (< 300 nm) / NR even increased during SA formation in daytime (Fig. 4(2f)). The
ratio between bulk NCCN and mass concentration of NR-PM2.5 became
larger due to the SA formation, suggesting stronger enhancement of
NCCN (0.05 %) in SA formation under low RH conditions because SA
formation mainly added mass to CCN-inactive particles and turned them into
CCN-active particles.
In summary, during the campaign in this study, two kinds of SA formation
events were observed under different RH conditions with different variations
of PM and NCCN at SSs lower than 0.07 %. Under high RH conditions,
there was strong secondary inorganic aerosol (SIA)-dominated SA formation, leading to stronger
enhancements of CCN-active particle number fraction and NCCN.
Meanwhile, under low RH conditions, there was moderate secondary organic aerosol (SOA)-dominated SA
formation, with moderate enhancements of CCN-active particle number fraction
and NCCN. However, for a unit amount of SA formation, the increase of
NCCN was stronger under low RH conditions and weaker under high RH
conditions. This was because SA formation under low RH conditions was more
concentrated on particle sizes smaller than 300 nm and added more mass to
CCN-inactive particles, turning them into CCN-active particles. In addition,
strong and distinct diurnal variations of CCN activity of particles were
observed during different SA formation processes, whose effects on NCCN
calculation need to be evaluated further.
The influence of diurnal variation of CCN activity on NCCN
prediction
Since PNSD measurements are generally simpler and more common than NCCN measurements, NCCN is usually estimated from real-time PNSD combined
with parameterized CCN activity. In former sections, it was already
manifested that SA formation under different RH conditions led to distinct
variations in PNSD and SPAR at SS of 0.05 % and hence different variations in
NCCN. Thus, it is important for the prediction of NCCN to quantify sensitivity towards changes in PNSD and SPAR during SA formation
processes under different RH conditions.
(a) The averaged SPAR at SS of 0.05 % during the campaign (green scatters), the corresponding fitting curve (green line) and the averaged fitting parameters (dotted line for Da and dashed line for MAF). The blue and yellow shaded areas represent the variations of SPAR before 4 December and after 4 December, respectively. The ratio between calculated NCCN and measured NCCN(b) before and (c) after 4 December. Bars represent 1 standard deviation, and colors represent different calculation of SPAR curves: green represents average SPAR during the campaign (AvgSPAR), yellow represents SPAR calculated with average Da and real-time MAF (AvgDa) and blue represents SPAR calculated with average MAF and real-time Da (AvgMAF).
In this study, NCCN was mostly determined by PNSD, as was generally the
case in former studies (Dusek et al., 2006). During SA formation events,
however, the variation of CCN activity also contributed significantly to the
deviation of NCCN calculation. In former discussions, CCN activity
(indicated by SPAR) at 0.05 % SS revealed significant diurnal variations
during this campaign, which were different during SA formation under
distinct RH conditions. The ratio of NCCN calculated based on campaign-averaged SPAR (NCCN_cal) to those measured at 0.05 %
SS (NCCN_meas) before and after 4 December are shown
in Fig. 5. SPAR is determined by the variation of Da and MAF, which
reflects changes in the hygroscopicity and number fraction of hygroscopic
particles. Thus, to investigate the respective influences of MAF and Da
variations on NCCN predictions, NCCN_AvgMAF (or
NCCN_avgDa) was calculated based on the real-time PNSD
and SPAR estimated by replacing MAF (or Da) in Eq. (7) with the campaign-averaged value. During the high RH stage, underestimation of daytime
NCCN_cal can reach up to 20 %, since SPAR variations
due to CCN activity enhancement were not considered. Similar deviations of
both NCCN_AvgMAF and NCCN_avgDa from
NCCN_meas were detected, suggesting that both MAF and
Da variations contributed to NCCN_cal
underestimation under high RH conditions. During the low RH stage, up to
50 % overestimation existed in NCCN_AvgSPAR outside SA
formation time periods. Only NCCN_AvgMAF displayed
similar deviations from NCCN_meas as
NCCN_AvgSPAR, indicating that differences between
NCCN_cal and NCCN_meas were mainly
contributed by variations in MAF brought on by significant CCN-active
particles number fraction growth due to SA formation. To be noted,
NCCN_AvgSPAR before and after 4 December were both
calculated based on the SPAR averaged over the entire campaign (green dots
in Fig. 5a), since the applicability of campaign-averaged SPAR in NCCN
calculations was confirmed by many former studies in the NCP (Deng et al.,
2013; Wang et al., 2013; Ma et al., 2016). During low RH periods, SPAR was
generally lower than the campaign-averaged SPAR, and the ratio between the
calculated and measured NCCN was systematically higher (lasting for
the whole night). In summary, SA formation processes can induce significant
deviation to NCCN prediction that varied with RH conditions and mainly
resulted from the variation in MAF. Thus, for accurate NCCN
estimations, considering the variation of MAF (changes in the fraction of
the hygroscopic particles) is highly essential.
As SOA is generally considered to be more hygroscopic than POA (Frosch et
al., 2011; Lambe et al., 2011; Kuang et al., 2020a), the increase of
hygroscopic particles or SA particles (both SIA and SOA) were considered to
be the cause for the increase of SPAR within the 200 to 300 nm size range (Fig. 2). In order to account for the variations of hygroscopic particles or SA
particles in NCCN calculation, in the following part, the number fraction
of hygroscopic particles (GF(90 %, 200 nm)> 1.22, NFhygro)
measured by HTDMA and the mass fraction of SA particles (MFSA) measured by
ACSM in this campaign were used to represent MAF variations and to provide
calculation of NCCN at SS of 0.05 % with smaller deviations combined
with PNSD measurement. It should be noted that in order to highlight the application of
using MFSA as an estimation of MAF variations in NCCN calculation,
the campaign-averaged Da from SPAR curves was used.
(a) The comparison between calculated NCCN based on κ derived from bulk particle chemical compositions (NCCN_chem) and measured NCCN at SS of 0.05 %. (b) The correlation between MAF and mass fraction of secondary aerosol (MFSA). (c) The comparison between calculated NCCN based on SPAR derived from real-time MFSA and average Da (NCCN_MF) and measured NCCN. The dashed black lines represent the relative deviation of 30 %. (d) The diurnal variations of the ratio between the calculated and measured NCCN during the whole campaign based on different methods (green: NCCN_chem; blue: NCCN calculated based on SPAR derived from averaged MFSA and average Da; yellow: NCCN_MF).
Based on the bulk hygroscopicity derived from particle chemical compositions
measurements (κchem), a critical diameter for CCN activation
can be calculated based on κ-Köhler theory. With this critical
diameter, NCCN (0.05 %) can be predicted incorporating measured PNSD
(NCCN_Chem). The κ value of accumulation-mode particles
derived from chemical composition of the bulk aerosol might bear significant
uncertainties, which leads to significant deviations of NCCN
prediction. However, in practice, chemical composition measurements
specifically for accumulation-mode particles are not common; thus bulk
aerosol chemical compositions are commonly applied in CCN studies as
substitute (Zhang et al., 2014, 2016; Che et al., 2017; Cai et
al., 2018), especially when particle hygroscopicity measurements are
lacking. As can be seen in Fig. 6a, NCCN_meas at 0.05 %
SS was strongly underestimated by NCCN_Chem, especially
at lower NCCN_meas (∼ 102 cm-3), which is similar to the results of studies that encountered
high fractions of organics (Chang et al., 2010; Kawana et al., 2016). This
deviation between NCCN_meas and NCCN_Chem may have resulted from the hypothesis of internal mixing state and
the difference of particle hygroscopicity derived by particle chemical
composition measurements and CCN activity. Figure 6b depicts the correlation
between mass fraction of SA (MFSA) and MAF at 0.05 % SS. MFSA
was generally positively correlated to MAF (r=0.8) with slight
underestimations, suggesting externally mixed SA-dominated CCN-active
particles. Thus, in the prediction of NCCN, real-time SPAR can be
calculated from campaign-averaged Da and MAF assumed to be equal to
real-time MFSA (NCCN_MF). As displayed in Fig. 6c, the underestimation and correlation between NCCN_cal and NCCN_meas were improved after introducing
MFSA into NCCN calculation. Additionally, the diurnal variations
of the NCCN_cal/NCCN_meas ratio based on
different methods of NCCN calculation during the whole campaign are
shown in Fig. 6d. By considering real-time MFSA variations, the
deviation of calculated NCCN (real-time MF) can be reduced throughout
the day, compared to NCCN_Chem (real-time chem).
Meanwhile, using an averaged MFSA to estimate SPAR and NCCN could
also reduce deviations of calculated NCCN (averaged MF); however, it demonstrated a much stronger diurnal variation than the deviation of
NCCN_MF.
(a) The comparison between calculated NCCN based on κ derived from bulk GF at 200 nm (NCCN_GF) and measured NCCN at SS of 0.05 %. (b) The correlation between MAF and number fraction of hygroscopic particles (NFhygro, GF > 1.2). (c) The comparison between calculated NCCN based on SPAR derived from real-time NFhygro and average Da (NCCN_NF) and measured NCCN. The dashed black lines represent the relative deviation of 30 %. (d) The diurnal variations of the ratio between the calculated and measured NCCN during the whole campaign based on different methods (green: NCCN_GF; blue: NCCN based on SPAR derived from averaged NFhygro and average Da; yellow: NCCN_NF).
Based on the bulk hygroscopicity derived from GF measurements (κGF) at 200 nm, Da can be calculated based on the κ-Köhler theory, which can be applied to predict NCCN at 0.05 %
SS (NCCN_GF) in combination with measured PNSD. Figure 7a
reveals that NCCN_meas was strongly underestimated by
NCCN_GF (by more than 30 %), which might have resulted
from the hypothesis of internal mixing state and the difference of particle
hygroscopicity derived by GF and particle CCN activity measured under
different water vapor saturated conditions. Figure 7b depicts the positive
correlation between NFhygro and MAF at 0.05 % SS, which was weaker
than that between MFSA and MAF. Similarly to before, NFhygro was
applied as a proxy for MAF in the NCCN calculation, which also improved
the underestimation and correlation between NCCN_cal and
NCCN_meas (Fig. 7c). Also, the campaign-averaged
Da in Fig. 5a was used to calculate SPAR curves and NCCN. The
diurnal variations of the NCCN_cal/NCCN_meas ratio based on different methods of
NCCN calculation during the whole campaign are shown in Fig. 7d. By
considering the real-time variation of NFhygro, the deviation of
NCCN_NF (real-time NF) was mainly reduced during
nighttime compared to NCCN_GF (real-time GF). Meanwhile,
applying an averaged NFhygro to estimate SPAR and NCCN reduced
the deviations of calculated NCCN (averaged NF) during nighttime as
well, but its deviations demonstrated stronger diurnal variations than those
of NCCN_NF. If GF-PDF were directly used to calculate
NCCN, NCCN_cal would agree well with measured
NCCN (Fig. S4) because in this way the mixing state of aerosol would
have been accounted for. However, compared to the approach using GF-PDF,
NFhygro is easier to apply in NCCN calculation and can yield
similar accuracies.
In summary, MAF exhibited strong diurnal variation that varied under
different RH conditions due to different SA formation mechanisms, which
contributed most to NCCN estimation deviations if unaccounted for. The
diurnal variations of MAF at the five measured SSs (Fig. S5) revealed
significant diurnal variations at low SSs (0.05 % and 0.07 %) that were
dependent on RH conditions, while only small diurnal variations that were
insensitive to the RH conditions were detected at SSs above 0.2 %. In
general, MAF became lower at lower SSs, especially during nighttime. As the
fraction of CCN-active particles was generally hygroscopic and composed of
secondary compounds, positive correlation was found between MAF, MFSA
and NFhygro. Although a good prediction of NCCN (0.05 %) was
achieved by applying an averaged MAF (Figs. 5, 6d and 7d), in practice, this
would still require CCN measurements or HTDMA/chemical composition
measurements as proxies. Additionally, deviations of NCCN_cal based on the averaged MAF can be large under low RH conditions (Fig. 5c), while time-dependent MAF can eliminate a great part of these
deviations. Thus, by replacing MAF with real-time MFSA or NFhygro
when deriving SPAR curves, the relative deviation of NCCN (0.05 %)
calculation can be reduced. The proposed NCCN parameterization using
MFSA can also be easily adopted by chemical-transport and climate
models, improving their representation of NCCN changes due to distinct
SA formation processes.
Conclusions
SA formation drives the development of haze pollution in the NCP and can
result in significant variations of PNSD and aerosol hygroscopicity. Studies
in the NCP have shown that the mechanism of SA formation can be affected by
relative humidity (RH) and thus has different influences on the aerosol
hygroscopicity and PNSD under distinct RH conditions. The difference in
particle size where SA formation is taking place and the different chemical
compositions of formed SA can result in different variations of CCN
activity. Thus, it is essential to study the influence of SA formation on
the CCN activity of existing accumulation-mode particles under different RH
conditions in the NCP. As NCCN is often predicted based on real-time
PNSD and parameterized SPAR, the influence of varying SPAR in distinct SA
formation processes on NCCN calculation needs to be evaluated in
detail.
Based on the measurements of CCN activity, particle hygroscopicity, particle
chemical composition and PNSD during the McFAN campaign in Gucheng in winter 2018,
the influences of SA formation on CCN activity and NCCN calculation
under different RH conditions were investigated, especially at SSs lower than
0.07 %. Two kinds of SA formation events were identified under different
RH conditions, with distinct variations in PM and NCCN at 0.05 % SS.
Under high RH conditions, which correspond to the periods with minimum RH
higher than 50 % in daytime, strong SA formation and NCCN (0.05 %)
enhancements with strong hygroscopic particles and SIA-dominated
contribution to SA (> 70 %) were found, while under low RH
conditions, which correspond to the periods with daytime minimum RH below
30 %, moderate SA formation and NCCN (0.05 %) enhancements with
moderately hygroscopic particles and SOA dominated contribution to SA were
found. However, the increase of NCCN under the same amount of SA
formation was stronger under low RH conditions and weaker under high RH
conditions. This was because the formation of SA under low RH conditions was
more concentrated in the particle size range smaller than 300 nm and added more
mass to CCN-inactive particles, turning them into CCN-active ones after SA
formation.
In addition, strong diurnal variations of the CCN activity of particles at
0.05 % SS due to the strong SA formation were also observed, both varying
with RH conditions. NCCN (0.05 %) was significantly underestimated
when MAF (SPAR parameter) variations were not considered. As the fraction of
CCN-active particles was generally hygroscopic and composed of secondary
compounds, there was good correlation among MAF inferred from measurements
of CCN activity, particle hygroscopicity and particle chemical compositions.
Thus, the relative deviation of NCCN (0.05 %) estimation can be
reduced by applying measurements of particle hygroscopicity or particle
chemical compositions as a proxy for aerosol mixing state.
This study can further the understanding of the impact of SA formation on
CCN activity and NCCN calculation, specifically for SA formation on
existing particles, which can strongly affect cloud microphysics properties
in stratus clouds and fogs. The investigation of the influence of SA
formation on the CCN activity of existing particles in this study is important
for improving NCCN parameterizations in chemical-transport and climate
models, so that they can account for the large variations induced by SA
formation processes.
Data availability
The data used in this study are available from
10.5281/zenodo.4706227 (Tao and Ma, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-7409-2021-supplement.
Author contributions
JT, YK and NM designed this research. JT performed the data analysis and
wrote the manuscript. YC, HS, NM, YK, JT and JH planned this campaign. JT
and YZ conducted the CCN measurements. YS and YH conducted the ACSM
measurements and the ACSM PMF analysis. JH and QL conducted the HTDMA
measurements. LX and YZ conducted the particle number size distribution
measurements. WX conducted the measurements of CO and meteorological
parameters. YC, HS, YS, YK and NM contributed to the revisions of this
manuscript, and all other coauthors have contributed to this paper in
different ways.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge the National Key Research and Development Program of China
(grant no. 2017YFC0210104), the National Natural Science Foundation of China
(grant nos. 91644218 and 41805110), the Guangdong Innovative and
Entrepreneurial Research Team Program (Research team on atmospheric
environmental roles and effects of carbonaceous species: 2016ZT06N263),
the Special Fund Project for Science and Technology Innovation Strategy of
Guangdong Province (2019B121205004) and the Basic Research Fund of CAMS
(2020Z002).
Financial support
This research has been supported by the Ministry of Science and Technology of the People's Republic of China (grant no. 2017YFC0210104), the National Natural Science Foundation of China (grant nos. 91644218 and 41805110), the Guangdong Innovative and Entrepreneurial Research Team Program (Research team on atmospheric environmental roles and effects of carbonaceous species, grant no. 2016ZT06N263), the Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province (grant no. 2019B121205004), and the Basic Research Fund of CAMS
(grant no. 2020Z002).
Review statement
This paper was edited by Markus Petters and reviewed by two anonymous referees.
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