Collocated measurements using a condensation particle counter,
differential mobility particle sizer and cloud condensation nuclei counter
were realised in parallel in central Budapest from 15 April 2019
to 14 April 2020 to gain insight into the cloud activation properties of
urban aerosol particles. The median total particle number concentration was
10.1 × 103 cm-3. The median concentrations of cloud
condensation nuclei (CCN) at water vapour supersaturation (S) values of 0.1 %, 0.2 %,
0.3 %, 0.5 % and 1.0 % were 0.59, 1.09, 1.39, 1.80 and 2.5 × 103 cm-3, respectively. The CCN concentrations represented 7–27 % of all
particles. The CCN concentrations were considerably larger but the
activation fractions were systematically substantially smaller than observed in
regional or remote locations. The effective critical dry particle diameters
(dc,eff) were derived utilising the CCN concentrations and particle
number size distributions. Their median values at the five supersaturation values considered were 207, 149, 126, 105 and 80 nm,
respectively; all of these diameters were positioned within the accumulation mode of the
typical particle number size distribution. Their frequency distributions
revealed a single peak for which the geometric standard deviation increased
monotonically with S. This broadening indicated high time variability in the
activating properties of smaller particles. The frequency distributions also
showed fine structure, with several compositional elements that seemed to reveal a consistent or monotonical tendency with S. The relationships between the critical S and
dc,eff suggest that urban aerosol particles in Budapest with
diameters larger than approximately 130 nm showed similar hydroscopicity to corresponding continental aerosol particles, whereas smaller particles in Budapest were less hygroscopic than corresponding continental aerosol particles. Only modest seasonal cycling in CCN concentrations and
activation fractions was seen, and only for large S values.
This cycling likely reflects changes in the number concentration,
chemical composition and mixing state of the particles. The seasonal
dependencies of dc,eff were featureless, indicating that the droplet activation properties of the urban
particles remained more or less the same throughout
the year. This is again different from what is seen in non-urban locations. Hygroscopicity parameters (κ values) were computed without
determining the time-dependent chemical composition of the particles. The median values for κ
were 0.15, 0.10, 0.07, 0.04 and 0.02, respectively, at the five supersaturation values considered. The averages suggested
that the larger particles were considerably more hygroscopic than
the smaller particles. We found that the κ values for the urban aerosol were substantially
smaller than those previously reported for aerosols in regional or remote locations. All of these characteristics can be linked to the specific source
composition of particles in cities. The relatively large variability in the
hygroscopicity parameters for a given S emphasises that the individual
values represent the CCN population in ambient air while the average hygroscopicity parameter mainly corresponds to particles with sizes close to the effective critical
dry particle diameter.
Introduction and objectives
Water is the most abundant vapour in the troposphere. Its condensation onto
aerosol particles is the only relevant pathway for cloud or fog droplet
formation at water vapour supersaturation (S) values that occur in the ambient air
(Pruppacher and Klett, 2000). The S values in clouds are usually less than 1 %,
with a median value of between 0.1 % and 0.2 %. The number and size of the
generated droplets depend on both the particle properties and the local S value
(Andreae and Rosenfeld, 2008). Only a subset of aerosol particles are able
to grow to droplets at a given S; they are called the cloud condensation nuclei
(CCN) for this S. The aerosol properties that primarily influence the ability to grow droplets
are the size of the aerosol particle and, to a lesser degree, its chemical
composition and mixing state (Dusek et al., 2006). S is mainly governed
by cloud dynamics and the amount of cloud droplets present. The droplets
act as a sink for water vapour and the amount of droplets present also depends on the CCN concentration.
Different updraft velocities and droplet populations in clouds result in
different S values, which can also change the activation process. As a consequence,
droplet formation can be limited by the availability of CCN and/or the
updraft velocity. The former case ordinarily prevails in the global
troposphere at concentrations of < 9 × 103 cm-3
and is called the CCN-limited regime (Rosenfeld et al., 2014).
CCN modify the intensity and other properties of the sunlight reaching
the Earth's surface indirectly, through cloud droplets. These properties are
primarily influenced by the droplet number, droplet size and cloud residence time
(Andreae and Rosenfeld, 2008; Rosenfeld et al., 2008, 2014). CCN also
influence the hydrological cycle (including the amount and
intensity of precipitation), vegetation and its interactions with the carbon cycle, as well
as atmospheric chemistry, physics and dynamics. Moreover, it is this
indirect effect of aerosols that contributes the most uncertainty to
global radiative forcing calculations (e.g. Carslaw et al., 2013). This is
particularly important given that number concentrations of particles seem to
be increasing globally due to anthropogenic activities (Andreae et al.,
2005). Concentrations of CCN can vary considerably over space and time.
Dedicated studies of CCN have been performed in field experiments at several
locations in the world and in various laboratories (e.g. Dusek et al., 2006;
McFiggans et al., 2006; Hudson, 2007; Rose et al., 2008, 2010; Kuwata and
Kondo, 2008; Pringle et al., 2010; Wex et al., 2010; Burkart et al., 2011;
Sihto et al., 2011; Jurányi et al., 2011; Kerminen et al., 2012; Topping
and McFiggans, 2012; Paramonov et al., 2015; Herenz et al., 2018; Schmale et
al., 2018). Despite their importance, our knowledge of aerosol–water vapour
interactions at supersaturations that are typical of atmospheric conditions and
cloud microphysics is still insufficient. Longer-term studies
(e.g. those lasting 1 year) are preferred to understand these processes and
their consequences. Broad regional coverage is also needed to achieve
representative results. Data sets for the environmental category of
large cities are particularly scarce.
The study presented here deals with the cloud droplet activation properties
of aerosol particles in a continental Central European city, Budapest, which
has 2.2 million inhabitants in its metropolitan area and is the largest
city in the Carpathian Basin. Online aerosol and meteorological measurements
have been performed in a semicontinuous manner at the Budapest platform for
Aerosol Research and Training (BpART) Laboratory for more than a decade
(Salma et al., 2011; Mikkonen et al., 2020). Essential
instruments for this task include a differential mobility particle sizer (DMPS) and a
condensation particle counter (CPC). They have also been complemented by a
continuous-flow cloud condensation nuclei counter (CCNc) since 2018. The ability to obtain
long-term particle number size distributions, total
particle number concentrations and CCN data at various S values facilitates the
utilisation of special data validation methods and joint
evaluation procedures.
The main objective of the present study was to gain insight into the cloud activation properties of urban aerosol particles based on 1 year of measurements
in central Budapest. Specifically, in this paper we report, discuss, explain and interpret
the measured time series and descriptive statistics of CCN
concentrations, activated fractions of aerosol particles, effective
activation dry particle diameters and effective hygroscopicity parameters
under various supersaturated conditions, and we discuss some collective
consequences of the data sets.
Methods
The time interval considered in this study was 15 April 2019 to 14 April 2020. CPC, DMPS, CCNc and meteorological
measurements were performed on 100 %, 99 %, 85 % and 100 % of the relevant days,
respectively. The CCNc was out of operation in January 2020. We also mention for completeness that the study period also included the emergency
phase (from 12 to 27 March 2020, 16 d) and the period
(from 28 March to the end of the measurement year; 18 d) in which movement was restricted during the first
outbreak of the COVID-19 pandemic in Hungary (Salma et al., 2020b). Local
time (LT = UTC+1 or daylight-saving time, UTC+2) was chosen as the time
base for the data because it has been observed that the daily activity
pattern of the inhabitants of a city strongly influences many of the atmospheric processes in
that city (Salma et al., 2014; Mikkonen et al., 2020).
Experimental part
All measurements were performed at the BpART Laboratory (47∘28′30′′ N, 19∘3′45′′ E, 115 m above mean sea level) of Eötvös Loránd University (Salma et al., 2016). Due to its geographical
location and meteorological conditions, this location site is representative of the
average atmospheric environment for central Budapest. Thus, it can be regarded as an urban
background. Local sources comprise residential and household
emissions, including seasonal heating, vehicle traffic exhaust and some
industrial sources (Salma et al., 2017, 2020a, b). Long-range transport
of air masses can also play a role over shorter time intervals. The
measurement site is located 85 m from the River Danube. The sampling inlets
of the instruments were between 12 and 13 m above
street level and protected by a weather shield and insect net. The
laboratory was air conditioned and maintained at (20±3) ∘C.
The CPC instrument deployed (TSI, model 3752, USA) was operated with an
aerosol inlet flow of 1.5 L min-1 and recorded concentrations of
particles with diameters above 4 nm using n-butanol as a working fluid. Its
sampling inlet was a stainless-steel tube with a diameter of 6.35 mm
(1/4 in.) and a length of ca. 1.6 m. Mean particle number
concentration (NCPC) data with a time resolution of 1 min were extracted
from its extended database. According to the nominal specification of the CPC, the difference between the concentrations measured by two
identical instruments operating in the single-particle counting mode with a
data averaging interval of > 30 s is less than ±10 %.
The DMPS system utilised was a laboratory-made flow-switching-type device
(University of Helsinki, Finland). It measured particle number
concentrations in the electrical mobility diameter range from 6 to 1000 nm in
the dry state (when the relative humidity, RH, was < 30 %)
in 30 channels with a time resolution of 8 min (Salma et al., 2011, 2016).
Its main components included a radioactive (60Ni) bipolar diffusion
charger, a Nafion semipermeable membrane monotube dryer, a 28 cm long
Vienna-type differential mobility analyser and a butanol-based CPC (TSI,
model 3775, USA). The aerosol flow rates in the high and low modes were 2.0 and 0.31 L min-1, respectively. The sheath flows were 10 times larger than the
aerosol flows. The sampling inlet was made of copper tube with a diameter of
6 mm and a length of ca. 1.9 m. The measurements were realised
semicontinuously according to international technical standards
(Wiedensohler et al., 2012; Schmale et al., 2017).
The CCNc system implemented was a DMT-200 instrument (Droplet Measurement
Technologies, USA) that contained two vertical condensation chambers, A and B,
with a cylindrical shape (inner diameter 2.3 cm, length 50 cm; Roberts and
Nenes, 2005; Rose et al., 2008). The porous internal walls of the chambers were
continuously wetted with liquid water from peristaltic pumps. A linear
positive temperature gradient was established along the cylinders and
controlled at the top, middle and bottom zones of the chambers. The aerosol
sample flow was continuously guided through the centre of each chamber and
surrounded by a filtered sheath air flow. The flows proceeded from top to bottom
under laminar conditions and near-ambient air pressure (P). As the flows passed
through the chambers, heat and water vapour were transported from the
internal wall surface towards the centre of each chamber. Because water
molecules diffuse faster than air molecules (transferring the heat), a
constant S value was obtained along the axes. The S value could be adjusted
by changing the temperature gradient. The particles were
exposed to this S for ca. 10 s, and the particles that activated at a critical
S that was lower than the adjusted value formed droplets that were substantially
larger than the inactivated particles. The droplets were detected at the chamber exit
by optical particle counters, yielding size distributions in the 0.75–10 µm diameter range. Droplets larger than 1 µm
were considered to be activated CCN, and the concentration of particles
in this size interval was negligible.
The total air flow rate was set to 500 cm3 min-1 and the ratio
of the sample flow rate to the sheath flow rate was 1 : 10. The S value was
shifted from 0.1 % to 0.2 %, 0.3 %, 0.5 % and then 1.0 % within a measuring cycle, with durations at each S value of 12, 5, 5, 5 and 5 min, respectively. The data measured by the system were recorded every 1 s.
The CCN concentrations (NCCN,S) obtained by the two
chambers at a given S were accepted provided they did not differ by more than 15 %. The system was run in
polydisperse operation mode, largely according to the ACTRIS standard
operating protocol (Gysel and Stratmann, 2013).
The meteorological measurements took place onsite at the BpART Lab. Air
temperature (T), RH, wind speed (WS), wind direction (WD), P and global solar
radiation (GRad) data were obtained by standardised meteorological sensors
(HD52.3D17, Delta OHM, Italy and an SMP3 pyranometer, Kipp and Zonen, the
Netherlands) with a time resolution of 1 min (see the Supplement).
Data treatment and validation
The measured DMPS data were inverted into discrete size distributions that
were utilised to calculate particle number concentrations in the diameter
ranges 6–25 nm (N6–25), 6–100 nm (N6–100), 30–1000 nm (N30–1000) and 6–1000 nm (N6–1000). These size
intervals were selected to represent various important particle source types and to enable comparisons with earlier results. The
extraction, treatment and processing of the measured CCNc data – including the
date and time, NCCN,S, flow rates and activation temperatures
(Ta=(T1+T2)/2, where T1 and T2 are the read wall
temperatures in the top and middle zones of the condensation chambers; Gysel
and Stratmann, 2013) – were accomplished for each S stage using the
laboratory-developed computer software AeroSoLutions.
Averaging of the individual measurements was performed from the end of
each S stage in a backward direction over a set time span at a stable
temperature in the condensation chambers. The averaging times were
the result of examining several randomly selected time dependencies of the CCN
concentration and temperature in different seasons. The averaging
interval was preselected for each S, and was ordinarily set to 90, 210,
210, 180 and 150 s, respectively, for S values of 0.1 %, 0.2 %, 0.3 %, 0.5 % and 1.0 %.
The averaging was monitored within the data treatment to ensure proper functioning, and it was
refined in some cases when necessary. Warning flags were generated for
suspicious data during data processing, and the filtered data
were checked separately. The two data sets for chambers A and B were
averaged if their ratio was between 80 and 120 %. Otherwise, one of the two
data sets was chosen on the basis of its time evolution. For an S of 0.1 % (i.e. for small CCN concentrations), another averaging criterion, namely
ABS(NCCN,A-NCCN,B)/min(SDCCN,A,SDCCN,B)<5, was
utilised instead of the concentration ratio. The limits were based upon tests with concentrations in typical measurement intervals. The criteria
represented sensible and pragmatic approaches, although alternative thresholds
could also be set. Finally, it was checked that the average CCN
concentration increased monotonically with S within a measurement cycle.
The time resolution of all experimental data derived from the CCNc
instrument was 32 min.
The N6–1000 data from the DMPS system were compared to the CPC
concentrations, which were averaged over the corresponding DMPS measuring
cycle. Due to a difference between the lowest diameters that can be measured by the two instruments (6 vs. 4 nm, respectively), measurements from the instruments were assumed to agree if the contribution of nucleation-mode particles to the total number of
particles was negligible. Additional factors such as larger particle
transport losses along the longer path in the DMPS system and the potentially
different response times of the two CPCs involved in the instruments could
also contribute to the observed concentration discrepancy (Salma et al.,
2016). The comparison was realised by evaluating the
NCPC/N6–1000 ratio as a function of the N6–30/N6–1000 ratio.
The intercept of the regression line for the resulting plot was considered the correction
factor for the DMPS system (Sect. 3.1).
For validation purposes, the CCN concentrations at an S of 1.0 %
(NCCN,1.0%) were compared to the particle number concentrations. These
two concentrations would be expected to be similar if most particles activate at
this S. In a previous survey, certain criteria were set to exclude the time
intervals during which very small, hence, non-activating particles were present in
larger concentrations (Schmale et al., 2017). The comparison was performed
when the concentration ratio of particles <30 nm in diameter to all particles was either <10 % or between 10 % and 20 %.
These two criteria proved useful for remote or regional locations. They do not seem to be applicable for urban data sets since
the annual N30–1000/N6–1000 means and standard deviations (SDs)
are much lower in urban areas than in more distant environments. In Budapest, for
instance, they were (52±15) %. This also meant that the proportion of the DMPS data that fulfilled either of the two criteria above was
very small: only 2 % annually. This is due to the relatively
large and persistent contributions from the high-temperature emission sources of
particles that are typically present in cities. The representativeness of any
conclusion drawn for the whole data set based on such a limited amount of data
is statistically questionable.
To avoid this weak point, we propose another criterion for urban or
polluted environments as a compromise between the larger number of
cases and the constrained contribution of small and thus non-activating
particles: the NCCN,1.0% data are only compared to the N30–1000
data if
N30–1000/N6–1000>70 %. This was fulfilled by more of the DMPS concentrations and yielded more robust statistics, although
the contribution from smaller particles remained higher than for the
original criteria. The limit value of 70 % employed to express this compromise was determined in a pragmatic
manner; alternative values could also be set. The size distribution spectrum for which the date and time were the closest (within 20 min) and earlier than or equal to that of the CCN concentration was
considered. This procedure is discussed further in Sect. 3.1.
Particle hygroscopicity
For atmospheric aerosol, the activated fraction of particles tends to
increase gradually with the dry particle diameter (as a sigmoid function,
instead of the step function valid for internally mixed monodisperse
particles). This is primarily because atmospheric particles are
often external mixtures or because chemical composition changes with particle
size (Dusek et al., 2006; Rose et al., 2010). A threshold activation
diameter called the effective critical dry particle diameter
(dc,eff) is defined in these cases as the particle size at which 50 % of the
dry particles activate at a given S (Rose et al., 2008, 2010).
The effective critical dry particle diameter was determined from collocated
polydisperse CCN and particle number size distribution measurements as
(Sihto et al., 2011; Kerminen et al., 2012; Schmale et al., 2018)
NCCN,S=∑i=dc,effdmaxNi,
where dmax is the largest dry particle diameter measured by the sizing
instrument (DMPS here) and Ni is the number of particles in size
channel i of the instrument. Hence, the concentrations were summed from the
largest particle size (dmax) to smaller diameters until the
measured CCN concentration was obtained. In order to estimate
dc,eff with higher accuracy, a logarithmic interpolation was
accomplished between the last two diameters of the summation. The size
distribution spectrum for which the date and time were the closest (within 20 min) and earlier than or equal to that of the CCN concentration was considered.
It should be noted that the assumption of internally mixed particles is
rarely met in urban environments, including Budapest (Enroth et al., 2018).
However, the approximation involves various influences that largely compensate for each other, so
it still gives reasonable results for such environments (Kammermann et al., 2010).
The cloud droplet activation of aerosol particles refers to indefinite diameter
growth (i.e. up to the droplet sizes) due to the condensation of water
vapour at a constant saturation ratio (s=p/p0, where p is the partial
vapour pressure of water over a droplet solution and p0 is the
saturation vapour pressure of water over pure water with a flat surface).
The conditions for the S (with S=s-1) at which the droplets stay in
equilibrium with the water vapour (Seq) can be identified using the Köhler model
(e.g. Pruppacher and Klett, 2000; McFiggans et al., 2006). To calculate the
composition-dependent Seq as a function of the droplet diameter
(dwet) for a given dry particle diameter ds, most of the controlling
variables are further simplified and approximated within different types of
thermodynamic parametrisations. In the present study, the effective
hygroscopicity model was adopted (Petters and Kreidenweis, 2007). Laboratory
and field measurements together with modelling considerations indicate
that this parametrisation is reliable for both sub- and
supersaturated conditions (Rose et al., 2008; Merikanto et al., 2009;
Rissler et al., 2010; Sihto et al., 2011; Kerminen et al., 2012; Schmale et
al., 2018).
Seq can be expressed as follows by assuming volume additivity of the solute and
water in the droplet and spherical shapes for the dry solute particle and
solution droplet (Petters and Kreidenweis, 2007):
Seq=dwet3-ds3dwet3-ds3(1-κ)expAdwet-1,
where
A=4σd/aMwRTρw.
Here, κ, σd/a, Mw, ρw, R and T are the
hygroscopicity parameter, surface tension of the droplet–air interface,
molar mass of water (0.018015 kg mol-1), density of water,
universal gas constant (8.3145 J mol-1 K-1) and absolute
temperature of the droplet and air in thermodynamic equilibrium,
respectively. σd/a was assumed to be that of pure water.
Some organic chemical species in atmospheric aerosol particles, such as
humic-like substances, are surface active and can lower the surface tension
of droplets (Facchini et al., 1999; Ovadnevaite et al., 2017). This
depression is mainly controlled by the diffusion of surfactants from the bulk of
the droplet to its surface. It takes several hours to reach
thermodynamic equilibrium at medium concentrations (Salma et al., 2006).
This implies that possible alterations due to the lower surface
tension compared to that of pure water are small with respect to estimated experimental
uncertainties; they may also be compensated for by some surface/bulk partitioning
effects (Sorjamaa et al., 2004). The surface tension of pure water seems,
therefore, to be a reasonable approximation to reality under the conditions
considered in the present study.
The κ values can be computed by solving Eq. (2). This contains several
independent variables, i.e. T, ds and dwet in addition to
Seq and κ. S is controlled by the CCNc instrument;
T can be expressed by the activation temperature in the condensation
chamber (Ta, Sect. 2.2). For a polydisperse atmospheric aerosol, ds
can be approximated by dc,eff (Eq. 1; Rose et al., 2008). An additional
independent relationship, namely the fact that the dependency of Seq on
dwet exhibits a maximum (of Sc at a diameter of dc), is
also exploited to solve Eq. (2). The κ values were computed in an
iterative manner by varying both κ and dwet until the calculated
S values were equivalent to the adjusted S values and showed a
maximum (Jurányi et al., 2010; Rose et al., 2010).
When the volume occupied by the solute can be neglected with respect to the
water volume at the activation stage, Sc can be approximated for
κ>0.2 as follows (Petters and Kreidenweis, 2007):
lnSc=4A271κdc3.
The time resolution of all modelled data was 32 min, which typically resulted in 13.6 × 103 counts in each data set at each S level.
Results and discussion
The relevant meteorological properties for each month are summarised in
Table S1 in the Supplement to give an impression of the
actual atmospheric environment at the study location. They also indicate that typical weather conditions
were present in Budapest during the measurement year, with no extraordinary situations.
Data quality
The DMPS systematically measured smaller total particle number
concentrations (N6–1000) than those measured by the CPC (NCPC), as discussed in Sect. 2.2. The intercept (a) and slope (b) and their SDs of the regression line of the
NCPC/N6–1000 ratio vs. the N6–30/N6–1000 ratio were
a=1.33±0.01 and b=0.17±0.02, respectively, and the Pearson
coefficient of correlation (R) between the concentration sets was 0.943. As a
result of the comparison, a size-independent multiplication correction
factor of 1.33 was adopted for the inverted DMPS data.
A scatter plot of the DMPS N30–1000 data for which
N30–1000/N6–1000>70 % (N30–1000,>70%; see also
Sect. 2.2) versus the NCCN,1.0% data is shown in Fig. 1a. It can be seen
that all measured particle number concentrations were larger than those for the
CCN. The N30–1000,>70%/NCCN,1.0% ratio as a function of
NCCN,1.0% (Fig. 1b) did not indicate a systematic difference between the
two instruments. Most concentration ratios with larger values (i.e. from ca. 3 to
7) that appear in all panels of the figure were isolated cases.
They are most likely related to the time difference between the actual DMPS
and CCNc data. Since the instruments have time resolutions of ca. 8 and
32 min, respectively, a compared pair of values could have a time difference
of up to 16 min (if the first possible DMPS-measured spectrum was missing).
During this time, the particle number concentrations could change
substantially. Actual atmospheric concentrations can vary rapidly because of
changes in the intensities of some important anthropogenic emission sources in
the vicinity, changes in physical removal processes and changes in local meteorological
conditions (such as WS, which influences particle transport). Dynamic concentration variability is often observed in cities, and manifests as the sudden appearance of stripes in the particle number size
distribution surface plot (e.g. Fig. 10 in Salma et al., 2016).
Relationship between the concentration of particles with
diameters >30 nm that contribute >70 % of all particles (N30–1000,>70%) and the CCN
concentration at a supersaturation of 1.0 % (NCCN,1.0%) (a); the
number of the data points considered (n), their coefficient of correlation
(R) and the slope (b) and SD of the regression line (in cyan) are also
indicated. The line of equality and the dashed grey lines indicate the range
of uncertainty expected solely from particle
counting (±15 %). The N30–1000,>70%/NCCN,1.0% ratio is also shown as
functions of the variables NCCN,1.0%(b) and N30–1000,>70%(c),
with the mean indicated by the blue line in each plot. The box and whisker plot (d) summarises
the maximum and minimum (triangles pointing upward and downward,
respectively), the first and 99th percentiles (bullets), the mean and SD (blue line
and the horizontal borders of the box, respectively), and the median (red line)
of the N30–1000,>70%/NCCN,1.0% ratio.
The plot of the N30–1000,>70%/NCCN,1.0% ratio as a function of
N30–1000,>70% (Fig. 1c) suggested that the ratio slightly
increased with concentration, in particular when N30–1000,>70% was above 104 cm-3. N30–1000,>70% is expected to agree with NCCN,1.0%
(to within approximately ±15 %; Sect. 2.1) if the number of particles
that are >30 nm and exhibit low hygroscopicity is
negligible with respect to N30–1000,>70%. The opposite can easily
be true in cities, including Budapest. This argument is backed by the fact
that the R between N30–1000,>70% and N6–25 was significant (0.875)
during the measurement year. Most of the time, the latter size fraction mainly contains
freshly emitted particles from road vehicles (Salma et
al., 2017), and these particles typically exhibit low hygroscopicity (Burkart et al.,
2011; Rose et al., 2011; Enroth et al., 2018). They contribute to
N30–1000,>70% as well. Another indication that low-hygroscopicity chemical species are abundant
is the low contribution of water-soluble organic carbon
(WSOC) and the high contribution of elemental carbon (soot) to organic carbon
(OC) in central Budapest. These ratios are moderately related to the general hygroscopicity. The former ratio was found to be substantially lower (WSOC / OC ranged from 20 %
to 39 %) and the latter ratio was considerably larger (EC / OC ranged
from 14 % to 20 %) in comparison with those of aerosols that are
chemically aged or found above regional or remote areas (Salma et
al., 2007, 2020a and references therein).
All of the above imply that the importance of the average N30–1000,>70%/NCCN,1.0% ratio is rivalled by or even superseded by the importance of the slope of
the regression line and R between the two data sets as
quality assurance metrics. The mean ratio and SD, and the median ratio
N30–1000,>70%/NCCN,1.0%, the slope of the regression line and
SD, and the coefficient of correlation for the overall data set were
1.73±0.67, 1.56, 1.10±0.02 and 0.894, respectively. Our set of
quality indicators are in accord with the results of data quality checks
elaborated for a number of other mainly regional locations (Schmale et al.,
2017). They jointly suggest that the CCNc and DMPS instruments were
operating in a coherent manner and that the CCNc instrument performed
reasonably well over the whole measurement year.
Concentrations and their ratios
The basic statistical measures of the particle number concentrations in
different size fractions over the whole measurement year are summarised in
Table 1. The mean ratio and SD of N6–100/N6–1000 were (81±10) %. The concentrations are comparable with but somewhat larger than our
previous annual results, while the ratios agree well with the previous data
(Mikkonen et al., 2020). The median particle number size distribution is
shown in Fig. S1 in the Supplement.
Ranges, medians and means with SDs of the particle number
concentrations in the diameter ranges 6–25 nm (N6–25), 6–100 nm (N6–100), 30–1000 nm (N30–1000), 30–1000 nm
if they contributed >70 % of all particles
(N30–1000,>70%), and 6–1000 nm (N6–1000); all values are in units of
103 cm-3.
For the sake of completeness, we note that the median N6–100 and N6–1000
values during the period with restricted movement (associated with the first outbreak of the COVID-19
pandemic) were smaller than the annual medians by 72 % and 79 %,
respectively, while the N30–1000 values for the period with restricted movement and the whole year were similar. The mean
N6–100/N6–1000 and SD of (75±12) % indicate that the
contribution from ultrafine particles substantially decreased (see the previous
paragraph). All this is in accordance with the conclusions of a more
extensive study we dedicated to this issue (Salma et al., 2020b).
Basic statistical measures of the CCN concentrations at different S values over
the whole measurement year are surveyed in Table 2. We mention for
completeness that while some of the CCN concentrations at S values of 0.5 % and 1.0 % were above 9 × 103 cm-3 (Sect. 1), these corresponded to only 10 cases
(0.073 % of all relevant data) and 59 cases (0.43 %), respectively,
whereas the related κ values were rather low (Sect. 3.5). Therefore,
the CCN-limited regime of droplet activation was realised. The median
concentration changed monotonically from 0.59 × 103 to
2.5 × 103 cm-3 with S and showed a levelling-off tendency.
The medians were fitted (in Origin Pro 2017 software using the Levenberg–Marquardt iteration
algorithm) by a power law function of form
NCCN,S=c×Sk, where S is the supersaturation in %, to obtain the so-called traditional CCN spectrum (Pruppacher and
Klett, 2000). The constant c corresponds to the CCN concentration at an S of
1.0 %. Knowledge of these two parameters is sufficient for some cloud
microphysics applications. The resulting fitted parameters c and k and their SDs
were (2.81±0.12) × 103 cm-3 and 0.52±0.05,
respectively. The coefficient c agreed with the measured average
NCCN,1.0% (Table 2). The exponent k was within the range reported
for other continental locations (k= 0.4–0.9; Pruppacher and Klett, 2000).
The fitted function reproduced the experimental data at higher S values (>0.2 %) satisfactorily, while the ratio of the value from the fitted function to the corresponding experimental value became 1.25 at an S of 0.1 %. A comparison of the concentrations and effective critical dry particle diameters with those observed in other locations is
given in
Sect. 3.3.
The mean activation fractions (AF=NCCN,S/N6–1000) of the
particles increased monotonically from 7 % to 27 % with S and showed some
levelling off (Fig. 2). The activation curve was obtained by
fitting the experimental data with the two-parameter logarithm function
AF=a×ln(S)+b, where a and b are fitting parameters (Paramonov
et al., 2015) using the Levenberg–Marquardt
iteration algorithm in the Origin Pro 2017 software. The shape of the fit curve was similar to that for the CCN
concentrations. This is typical of non-coastal locations, where a
multicomponent mixture of particle sources yield more or less balanced and
therefore similar curves (Schmale et al., 2018). Figure 2 also contains the
annual activation curve obtained in a synthesis study (performed within the European
Aerosol Cloud Climate and Air Quality Interactions project) by fitting the
mean AFs for several regional and remote locations with an identical
function (Fig. 5 in Paramonov et al., 2015). It can be seen that the curves
for the urban and other sites were rather different from each other in
magnitude or placement. The urban AFs were
systematically much smaller than those for the regional and remote locations. This was
also witnessed for other regional results (Sihto et al., 2011),
and could be an urban feature. The low AF in cities is probably explained
by larger particle number concentrations, higher abundances of small
particles that do not activate, and a chemical composition that typically leads to lower hygroscopicity than for regional aerosols.
At the same time, the relative SDs (RSDs) of our mean values were relatively
high (between 45 % and 70 %), which points to considerable
variability in both N6–1000 and NCCN,S over time. It also hints that the
prediction of CCN concentrations based solely on particle number
concentrations and mean AFs is unlikely to be reliable for urban
environments. Moreover, the annual curves do not necessary capture the
variability at shorter or seasonal scales.
Ranges, medians and means with SDs of the CCN
concentration (in × 103 cm-3) at supersaturations of
0.1 %, 0.2 %, 0.3 %, 0.5 % and 1.0 %.
Basic statistical measures of the effective critical dry particle diameter
at different S values over the whole measurement year are displayed in Table 3. The
median dc,eff decreased from 207 to 80 nm with S. All diameters were
positioned within the accumulation mode of the median particle number size
distribution (Fig. S1). The monthly mean number median mobility diameters
for the Aitken and accumulation modes were typically 26 and 93 nm,
respectively, with identical geometric SDs (GSDs) of 2.1 (Salma et al.,
2011). The broadening was caused by the averaging of the individual size
distributions. Considering the minimum of the dc,eff data, some
diameters, in particular for S values of 0.5 % and 1.0 %, could be
shifted to the Aitken mode.
Ranges, medians and means with SDs of the effective
critical dry particle diameter (in nm) at supersaturations of 0.1 %,
0.2 %, 0.3 %, 0.5 % and 1.0 %.
The present average diameters and CCN concentrations were larger than those reported for
coastal or rural background, forested, or remote environments (Henning et
al., 2002; Komppula et al., 2005; Paramonov et al., 2015; Schmale et al.,
2018). This confirmed that the water activation properties of aerosol particles depend on their type. Our data were comparable with those obtained for other urban sites (Kuwata and
Kondo, 2008; Rose et al., 2010; Burkart et al., 2011; Meng et al., 2014).
Variations within the location category are likely to be associated with
relatively large differences between urban aerosol properties. The mean
contribution and SD of ultrafine particles were, for instance,
N6–100/N6–1000=(81±10) % in Budapest and
N13–100/N13–929=75 % in Vienna. The present dc,eff data
are also contrasted with the computed results for the simulated global
continental mean κ value and SD (0.27±0.21; Pringle et al.,
2010) in Fig. 3. The lines shown in the figure were obtained using the parameters given in Sect. 2.3. The individual data points belong to various parallel lines with a theoretical
slope of -3/2. They suggest that the urban aerosol particles in Budapest
with diameters larger than approximately 130 nm show similar
hygroscopicity to the continental aerosol in general, whereas the smaller
particles appear to be less hygroscopic than the corresponding continental aerosol particles. The differences are even larger
when the European continental aerosol is considered (κ=0.36; Pringle
et al., 2010). The data points tend toward a limiting
relationship for insoluble but wettable (hydrophilic) particles (e.g. freshly emitted soot particles; Rose et al., 2011) with
decreasing diameter.
Experimentally determined mean activated fraction of total particles
(N6–1000) and its SD for central Budapest at supersaturations of
0.1 %, 0.2 %, 0.3 %, 0.5 % and 1.0 % (blue dots) together with
the curve fit to the data (blue line). The fitting function used was
AF=a×ln(S)+b, with parameters of a=34.8±0.4 and
b=26.7±0.3. The black line, shown for comparison,
corresponds to the same fitting function but with mean parameters of a=22
and b=69, as obtained for several selected regional and remote sites
in a EUCAARI synthesis study (Table 4; Paramonov et al., 2015). The yellow
band represents the 95 % confidence interval.
The tendentious dependency of the deviation of the experimentally derived
(dc,eff, Sc) data points from the line for the simulated global
continental mean κ (Fig. 3) also point to a size dependence of the
chemical composition, and this dependence is likely to be more pronounced for urban particles.
All this is in line with the major source types, such as vehicle
emissions, biomass burning, and new particle formation and diameter growth
(NPF) events (Salma et al., 2014, 2017, 2020a, b), as well as the particle
number size distributions in Budapest (Salma et al., 2011). Photochemical
processing may also play a role through chemical ageing (Furutani et al.,
2008). As a result, urban sources often result in external mixtures of
particles.
Critical supersaturation and effective critical dry
particle diameter data pairs (blue dots) and SDs (blue error bars) determined experimentally in
central Budapest, and the dependency calculated for the simulated global
continental mean κ and SD of 0.27±0.21 (the black line represents the mean κ value and the yellow band represents ±1 SD). The relationship for insoluble but wettable particles (κ=0,
the Kelvin term) is also shown (grey line) for comparison.
The frequency distribution of dc,eff for a particular S can be described by a lognormal
distribution function. The normalised differential distributions of the
dc,eff data for the five S values considered in this work are shown in Fig. 4. They were derived by
partitioning all the diameter data into 71 intervals of width
0.0243 on a logarithmic scale between 10 and 500 nm. The selected interval width proved to
be a reasonable compromise between good statistics and good data
resolution. The distributions exhibit single peaks with geometric SDs
that increase monotonically with S: 1.14, 1.16, 1.20, 1.22 and 1.27,
respectively. The broadening indicates that the droplet
activation properties of the smaller particles showed greater variability.
The peaks exhibit fine structure. They seem to contain submodes. This
is probably because there were mixtures of particles with different activation
properties. The submodes could have been produced by sources that result in
particles with different chemical compositions and mixing states. These
differences may not necessarily show up in particle number size
distributions, but they can lead to diverse activation properties. Several
compositional elements of the fine structure (e.g. the maximum or the
relative peak areas) changed in a tendentious manner with S. Exact
identification and interpretation of this fine structure is beyond the objectives of the present
work but will be included in an upcoming study that will deal with
the relationships of major source types such as vehicle emissions, NPF
events and biomass burning to the activation properties of CCN together with
their diurnal variability and air mass trajectories.
Differential frequency distributions of the effective
critical dry particle diameter at supersaturations of 0.1 %, 0.2 %, 0.3 %, 0.5 %
and 1.0 %; the frequencies are normalised to the total counts of diameter data.
Seasonal cycling
The time series of the experimental data showed high variability over time.
The monthly medians seemed to be advantageous for investigating
possible seasonal cycling (Fig. 5). It should be noted that the medians were selected
rather than the means and SDs for this task since atmospheric concentrations can be described by lognormal distribution functions (Sect. 3.2).
The months were organised into spring (MAM), summer (JJA),
autumn (SON) and winter (DJF) seasons. Stricter chronological ordering of
the months seems to be more advantageous for source-related or dynamic
studies on various timescales.
For each S value considered, the dependencies for the separate variables were similar to each other. Variations were most pronounced with larger S values, which are
the least relevant for ambient air. This already indicates that
seasonal cycling does not substantially influence
aerosol–water vapour interactions under ordinary environmental conditions.
The CCN concentrations were somewhat smaller from May to September and
somewhat larger in the other months; the minimum was typically in May.
These intervals coincided with the non-heating (formally from 15 April to 15 October) and heating (the rest of the year) seasons in Hungary. However, in an
exception to this rule, the concentrations in February were unexpectedly small. The AFs
were smaller in May (and perhaps also in June) and September (and
perhaps also in October) and larger in the other months. A comparison of
the seasonal variations in the monthly median NCCN,S and AF to the seasonal variation in the total
particle number implied that the seasonal variations in the former two
properties were not mainly due to the seasonal variations in the particle number
concentration. No obvious dependency of the monthly median dc,eff
was established since the seasonal variation in this parameter was featureless. This
lack of seasonal cycling meant that the particles in Budapest exhibited more or less
similar droplet activation behaviour throughout the year, which differs from what is seen in
some non-urban locations (Pringle et al., 2010; Sihto et al., 2011;
Paramonov et al., 2013, 2015; Schmale et al., 2018). It should also be noted that
the dc,eff values for an S of 0.1 % were segregated somewhat from
the dc,eff values for other S values, and that March and April 2020 were
unusual in that they coincided with the first outbreak of the COVID-19 pandemic in Hungary.
A greater understanding of seasonal dependencies requires longer measurements since the related
properties can also be influenced by interannual variability.
Time series of the monthly median CCN concentration and the
total particle number concentration (N6–1000) (a), the activation fraction (b) and the effective critical dry particle diameter (c) at supersaturations of
0.1 %, 0.2 %, 0.3 %, 0.5 % and 1.0 %.
Supersaturations of ca. 0.1 % ordinarily occur in warm stratiform clouds,
and these S values only activate larger (d>200 nm) particles. The chemical
composition of these particles is usually more stable than
that of smaller particles over the year due to, for instance, chemical and physical ageing and
particle mixing processes. Therefore, it can be concluded that differences
in chemical composition do not seem to play a crucial role in cloud
activation properties, even in cities.
Hygroscopicity parameters
The basic statistical measures of the κ values for different S values over
the whole measurement year are given in Table 4. All characteristics
decreased monotonically and showed a levelling-off tendency with S. The
averages implied that, in general, the larger particles exhibited higher
hygroscopicity than the smaller particles. When the
dc,eff data for each S value were also considered, the present hygroscopicity parameters
were found to agree fairly well with the values derived previously from volatility and
hygroscopicity tandem differential mobility analyser (VH-TDMA) measurements
performed under subsaturated conditions (RH = 90 %) at the same site (Enroth
et al., 2018). In that study, the nearly hydrophobic particles exhibited a
mean κ value of 0.033. The mode typically contained 69 % of
the particles at a dry diameter of 50 nm, and κ seemed to be
independent of the particle diameter in the range from 50 to 145 nm. The
less hygroscopic particles showed a larger mean κ value of 0.20 and
typically contributed 59 % of the particles at 145 nm.
Ranges, medians and means with SDs of the hygroscopicity
parameter at supersaturations of 0.1 %, 0.2 %, 0.3 %, 0.5 % and 1.0 %.
The average κ values were considerably smaller than those in regional or
remote locations (Paramonov et al., 2015; Schmale et al., 2018). Only a
few hygroscopicity parameters specifically for urban environments have been reported,
and even fewer for city centres (Gunthe et al., 2011; Rose et al., 2010,
2011; Meng et al., 2014; Arub et al., 2020). The present data can also be
linked to the average or effective hygroscopicity parameters found in field
measurements and chamber studies for fresh soot particles (<0.01),
for a secondary organic aerosol (approximately 0.10) and for an inorganic
aerosol fraction (ca. 0.64) (Rose et al., 2011). These all point to a high abundance of
freshly emitted and externally mixed soot particles with very
low hygroscopicity in central Budapest.
The range of κ values increased with S and, more
importantly, became rather large (a factor of ca. 103 for 1.0 %), even when compared with aerosol properties typically driven by
atmospheric dynamics. This can be illustrated by the relationships between
κ and dc,eff for different S values (Fig. 6). The data sets create
separate lines or narrow stripes with a theoretical slope of -3 over the
main range of the variables considered, assuming that the other
physicochemical properties such as dwet, Ta, σd/a and
ρw do not change substantially. The line for an S of 1.0 % bends
at low κ and large dc,eff, in accordance
with the κ–Köhler model (Eq. 2). It can be seen that the data
pairs for a given S level do indeed cover wide intervals of the
variables. Such variability in κ suggests that it would be a rough approximation to use
a single characteristic value for a given S; an effective κ
parameter or κ as a function of particle size would be preferable instead
(Paramonov et al., 2015). The average hygroscopicity parameter represents
particles with sizes close to the effective critical dry particle
diameter. Furthermore, the distribution of the data pairs along each line
is not completely symmetric with respect to the median, confirming
the fine structure of the frequency distributions (Sect. 3.3). The three
characteristic points (the 10th, 50th and 90th percentiles) on
the lines indicate a broadening of the frequency distribution with S. Frequency distributions of the hygroscopicity parameter in 71 intervals of width 0.0571 plotted on a logarithmic scale between 10-4 and 100
are shown in Fig. S2. These largely reflect the behaviour and tendencies observed for
the effective critical dry particle diameter (Fig. 4), since they were
computed using dc,eff.
Relationship of the hygroscopicity parameter to the
effective critical dry particle diameter (dc,eff) derived for
supersaturations of 0.1 %, 0.2 %, 0.3 %, 0.5 % and 1.0 %. The three diamond
symbols that appear along each line of data represent, in order
of increasing dc,eff, the data pairs
for the 10th and 90th percentiles, the 50th and
50th percentiles, and the 90th and 10th percentiles.
Conclusions
Concentrations of CCN at various S values and particle number size distributions
were measured in parallel in a continental Central European
urban environment over the course of 1 year. The effective cloud droplet activation
properties of the aerosol population were determined from the available
experimental data without measuring the time-resolved chemical composition.
The results indicated several distinguishing features of this urban aerosol. The average CCN
concentrations were substantially larger and the average effective
critical dry particle diameters and activation fractions were systematically considerably smaller than for non-urban sites. Particles with
diameters of ca. 130 nm showed relatively low hygroscopicity,
and the difference in hygroscopicity between urban aerosol particles and non-urban aerosol particles increased with decreasing particle size. These characteristic features of the urban aerosol are probably
related to the high abundance of freshly emitted less-hygroscopic particles,
including soot, and to substantial differences in the size-dependent chemical
composition and mixing states of particles in cities. The seasonal
dependencies of NCCN and the activation fraction at various S values were only
weakly influenced by the total particle number concentration. The results achieved
here represent the first information of this type for a city in the
Carpathian Basin, and contribute to our general knowledge and
understanding of urban atmospheric environments.
The measurements were performed at a fixed location in central Budapest that
can be regarded as an urban background site. The obtained results and
conclusions are representative of the average or overall atmosphere of the
city centre. Some urban microenvironments such as kerbside sites, street
canyons, road junctions and suburban areas exhibit diverse particle size distributions
and chemical compositions, so they would be expected to differ somewhat in the hygroscopic
properties of aerosol particles in those environments. Furthermore, some relevant
meteorological conditions can vary within the urban canopy due to,
for instance, the heat islands that are often realised in large cities. At the same time,
the related atmospheric processes likely occur on larger spatial scales than
the city itself or its central part. From this point of view, the data
derived for the urban background appear to be a sensible starting
approximation to reality. Further dedicated studies preferably involving
surface measurements, satellite products and modelling could contribute to a greater
understanding of the challenging issue of the urban climate.
The water uptake properties of urban aerosol particles under both sub- and
supersaturated conditions are increasingly of interest because of their
relevance in urban climate considerations and in the modelling of particle deposition
in the human respiratory system. The κ values determined
here are to be further utilised in health-related studies.
After gaining experience of the operation and calibration of the dual-chamber
CCNc measurement system, we plan to extend one of its chambers by using a DMA and
CPC setup so that we can perform both polydisperse and monodisperse measurements
in parallel, which are expected to supply further valuable knowledge on the
mixing states of particles. This is especially important since urban aerosol
particles typically comprise externally mixed carbonaceous particles with
very distinct hygroscopic properties. This is relevant in general
and could also support or facilitate the association of hygroscopicity
parameters with major source types in cities and with multistatistical
apportionment methods.
Data availability
The observational data are available from the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-11289-2021-supplement.
Author contributions
IS conceived, designed and led the research; WT and AZGy performed the
measurements; MV developed the ASL data evaluation software; IS, MV and WT
accomplished the data treatment and prepared the figures; IS, WT, MV and
AZGy interpreted the results; IS wrote the manuscript, taking into account comments from all the
coauthors.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Financial support
This research has been supported by the Hungarian Research,
Development and Innovation Office (grant no. K132254) and by the European
Regional Development Fund and the Hungarian Government
(GINOP-2.3.2-15-2016-00028).
Review statement
This paper was edited by Ivan Kourtchev and reviewed by two anonymous referees.
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