Dynamic properties, i.e.
particle formation rate J6 and particle diameter growth rate GR10,
and timing properties, i.e. starting time (t1) and duration time
interval (Δt) of 247 quantifiable atmospheric new aerosol particle formation (NPF) and growth events
identified in the city centre and near-city background of Budapest over 6
full measurement years, together with related gas-phase H2SO4
proxy, condensation sink (CS) of vapours, basic meteorological data and
concentrations of criteria pollutant gases were derived, evaluated, discussed
and interpreted. In the city centre, nucleation ordinarily starts at
09:15 UTC + 1, and it is maintained for approximately 3 h. The NPF and
growth events produce 4.6 aerosol particles with a diameter of 6 nm in
1 cm3 of air in 1 s and cause the particles with a diameter of 10 nm
to grow at a typical rate of 7.3 nm h-1. Nucleation starts
approximately 1 h earlier in the near-city background, and it shows
substantially smaller J6 (with a median of 2.0 cm-3 s-1) and
GR10 values (with a median of 5.0 nm h-1), while the duration of
nucleation is similar to that in the centre. Monthly distributions of the
dynamic properties and daily maximum H2SO4 proxy do not follow
the mean monthly pattern of the event occurrence frequency. The factors that
control the event occurrence and that govern the intensity of particle
formation and growth are not directly linked. New particle formation and
growth processes advance in a different manner in the city and its close
environment. This could likely be related to diversities in atmospheric
composition, chemistry and physics. Monthly distributions and relationships
among the properties mentioned provided indirect evidence that chemical
species other than H2SO4 largely influence the particle growth
and possibly atmospheric NPF process as well. The J6, GR10 and
Δt can be described by a log-normal distribution function. Most extreme
dynamic properties could not be explained by available single or compound
variables. Approximately 40 % of the NPF and growth events exhibited
broad beginning, which can be an urban feature. For doublets, the later onset
frequently shows more intensive particle formation and growth than the first
onset by a typical factor of approximately 1.5. The first event is attributed
to a regional type, while the second event, superimposed on the first, is often
associated with subregional, thus urban NPF and growth processes.
Introduction
Molecules and molecular fragments in the air collide randomly and can form
electrically neutral or charged clusters. Most clusters decompose shortly.
Chemical stabilizing interactions among certain components within a cluster
can enhance its lifetime, during which it can grow further by additional
molecular collisions through some distinguishable size regimes (Kulmala et
al., 2014). If the diameter of these clusters reaches a critical value of
1.5±0.3 nm (Kulmala et al., 2013), they become thermodynamically
stable, and their further growth turns into a spontaneous process.
Supersaturation is a necessary atmospheric condition for this principal
transformation. It is essentially a phase transition, which takes place in a
dispersed manner in the atmosphere, so it generates an aerosol system. The
newly formed particles grow further by condensation to larger sizes in most
cases due to the existing supersaturation. Photochemical oxidation products
such as H2SO4 (Sipilä et al., 2010), extremely low-volatile
organic compounds (ELVOCs; Ehn et al., 2014; Jokinen et al., 2015) and
highly oxygenated molecules (HOMs; Bianchi et al., 2016; Kirkby et al.,
2016; Tröstl et al., 2016), together with H2O vapour, NH3
(Kirkby et al., 2011), amines (Almeida et al., 2013), other oxidation
products of volatile organic compounds (VOCs; Metzger et al., 2010;
Schobesberger et al., 2013; Riccobono et al., 2014) and some inhibiting
chemical species (e.g. isoprene or NO2; Kiendler-Scharr et al., 2009;
Kerminen et al., 2018), can play an important role in particle formation and
growth. The VOCs include compounds of both anthropogenic and biogenic
origin, mainly isoprenoids such as α-pinene (Kirkby et al., 2016).
In some specific coastal regions, iodine oxides produced from marine biota
are involved (O'Dowd et al., 2002). Atmospheric concentration of these key
compounds at a level that is smaller by 12–14 orders of magnitude than the
concentration of air molecules is already sufficient for the phenomenon
(Kulmala et al., 2014). Relative importance of the organics increases with
particle size (Riipinen et al., 2011; Ehn et al., 2014), and their
supersaturation is maintained by fast gas-phase autooxidation reactions of
VOCs (Crounse et al., 2013). The overall phenomenon is ordinarily confined
in time for 1 d or so, and, therefore, it can be regarded as an event in
time and is referred to as a new aerosol particle formation (NPF) and
consecutive particle diameter growth event.
Such events appear to take place almost everywhere in the world and anytime
(Kulmala et al., 2004; Kerminen et al., 2018; Nieminen et al., 2018). Their
occurrence frequency and, more importantly, their contribution to particle
number concentrations were found to be substantial or determinant in the
global troposphere (Spracklen et al., 2006; Kulmala et al., 2014). Moreover,
their contribution to the number of cloud condensation nuclei (CCN) can be
50 % or even more (Makkonen et al., 2009; Merikanto et al., 2009; Sihto et
al., 2011), which links the events to the climate system and emphasizes their
global relevance (Kerminen et al., 2012; Makkonen et al., 2012; Carslaw et
al., 2013; Gordon et al., 2016). New particle formation and growth events
were proved to be common in polluted air of large cities as well, with a
typical relative occurrence frequency between 10 % and 30 % (Woo et al.,
2001; Baltensperger et al., 2002; Alam et al., 2003; Wehner et al., 2004;
Salma et al., 2011; Dall'Osto et al., 2013; Xiao et al., 2015; Zhang et al.,
2015; Kulmala et al., 2017, Nieminen et al., 2018). The coupling and
relationships between regional and urban (subregional) NPF were
demonstrated at least under favourable orographic conditions (Salma et al.,
2016b). New particle formation can increase the existing particle number
concentrations in city centres by a factor of approximately 2 on nucleation
days, while it can produce 13 %–28 % of ultrafine (UF) particles as a lower
estimate on a longer (e.g. annual) timescale (Salma et al., 2017). Particle
concentrations from NPF are also important when compared to (primary)
particles emitted by their dominant source in cities, namely by road
vehicles with internal combustion engines (Paasonen et al., 2016). These
results jointly suggest that particles from NPF and growth events in cities
can influence not only the urban climate but can contribute to the public's
excess health risk from particle number exposures (Oberdörster et al.,
2005; Braakhuis et al., 2014; Salma et al., 2015) and, furthermore, could
be linked to the role of human actions in all these effects.
Despite these potential points, conclusive interpretation of the data obtained and
results derived specifically for cities have remained hindered so far.
Several-year-long, semi-continuous, critically evaluated, complex and
coherent data sets are required for this purpose, which have been generating
gradually. As part of this international progress, investigations dedicated
to urban NPF and growth events in Budapest have been going on since
November 2008. Measurements for 5 full years were realized in the city centre
at a fixed location, 1 full year was devoted to measurements in a near-city
background environment, and some other measurements were accomplished in
different urban microenvironments for time intervals of a few months. The
main objectives of this study are to determine, present and analyse the
dynamic properties, i.e. particle formation rate and particle diameter growth
rate, timing properties, i.e. starting time and duration time interval of
nucleation process of NPF and growth events, together with the major sources
and sink of condensing vapours, basic meteorological data and criteria
pollutant gases for 6 years, to investigate and interpret their
relationships, to discuss their monthly distributions, to evaluate and detect
some of their features specific for urban atmospheric environments and to
demonstrate some specific urban influence on the calculation of the
properties. These quantities and relationships are of basic importance in
many atmospheric processes for several reasons. Our goals are in line with
the research needs for global atmospheric nucleation studies (Kerminen et
al., 2018; Nieminen et al., 2018).
Experimental methods
The measurements took place at two urban locations in Budapest, Hungary.
Most measurements were realized at the Budapest platform for Aerosol
Research and Training (BpART) facility (N 47∘28′29.9′′,
E 19∘3′44.6′′; 115 m above mean sea level (a.s.l.; Salma et al.,
2016a). This site represents a well-mixed, average atmospheric environment
for the city centre. The other location was situated at the NW border of
Budapest in a wooded area of the Konkoly Astronomical Observatory of the
Hungarian Academy of Sciences (N 47∘30′00.0′′,
E 18∘57′46.8′′; 478 m a.s.l.). This site characterizes the air masses entering
the city since the prevailing wind direction in the area is NW. The
measurements were accomplished for 6 full-year-long time intervals, i.e.
from 3 November 2008 to 2 November 2009, from 19 January 2012 to 18 January 2013, from
13 November 2013 to 12 November 2014, from 13 November 2014 to 12 November 2015, from
13 November 2015 to 12 November 2016 and from 28 January 2017 to 27 January 2018. In the
measurement year 2012–2013, the instruments were set up in the near-city
background, while in all other years, they were installed in the city
centre. Local time (LT = UTC + 1 or daylight saving time, UTC + 2) was
chosen as the time base of the data unless otherwise indicated because it
had been observed in earlier investigations that the daily activity time
pattern of inhabitants substantially influences many atmospheric processes
in cities (Salma et al., 2014; Sun et al., 2019).
The main measuring system was a flow-switching type differential mobility
particle sizer (DMPS). It consists of a radioactive (60Ni) bipolar
charger, a Nafion semi-permeable membrane dryer, a 28 cm long Vienna-type
differential mobility analyser and a butanol-based condensation particle
counter (TSI, model CPC3775). The sample flow was 2.0 L min-1 in the
high-flow mode and 0.31 L min-1 in the low-flow mode, with sheath air
flow rates 10 times larger than for the sample flows. The DMPS measures
particle number concentrations in an electrical mobility diameter range from
6 to 1000 nm in the dry state of particles (with a relative humidity of
RH <30 %) in 30 channels, which finally yields 27 channels after
averaging 3 overlapping channels when joining the data for the two flow modes.
The time resolution of the measurements was approximately 10 min till
18 January 2013 and 8 min from 13 November 2013 (after a planned update of the
DMPS system). There was no upper size cut-off inlet applied to the sampling
line, and a weather shield and insect net were only attached. The sampling
inlets were identical at both locations except for the height of the
installation above the ground, which was 12.5 m in the city centre and
approximately 1.7 m in the near-city background. The measurements were
performed according to the international technical standard (Wiedensohler et
al., 2012). The availability of the DMPS data over 1-year-long time
intervals is summarized in Table 1.
Synoptic meteorological data for air temperature (T), RH, wind speed (WS) and
wind direction (WD) were obtained from a measurement station of the
Hungarian Meteorological Service (HMS; station no. 12843) by standardized
methods with a time resolution of 1 h. Global solar radiation (GRad) data
were measured by the HMS at a distance of 10 km in an easterly direction with a time
resolution of 1 h. Meteorological data were available in >90 %
of the possible cases in each year. Concentrations of SO2, O3,
NOx and CO were obtained from measurement stations of the National Air
Quality Network in Budapest (in a distance of 4.5 km from the urban site
and of 6.9 km from the near-city background site) located in the upwind
prevailing direction from the measurement sites. They are measured by UV
fluorescence (Ysselbach 43C), UV absorption (Ysselbach 49C),
chemiluminescence (Thermo 42C) and IR absorption methods (Thermo 48i),
respectively, with a time resolution of 1 h. The concentration data were
available in >85 % of the yearly time intervals, and
>98 % of them were above the limit of determination (LOD). It
is worth mentioning that the LOD of the SO2 analyser was approximately
0.2 µg m-3 and that the hourly average SO2 concentration
in the Budapest area is ordinarily distributed without larger spatial
gradients (Salma et al., 2011). For the present study, this was proved by
evaluating the concentration ratios from two different municipal stations
which are in the closest distance from the BpART facility in two different
directions with an angle of 60∘ between them. The mean SO2
concentration ratio and standard deviation (SD) for the two stations were
81±20 % over the 5-year-long measurement time interval.
Data treatment
The measured DMPS data were evaluated according to the procedure protocol
recommended by Kulmala et al. (2012) with some refinements that are related
to urban features (see Sect. 3.1). Particle number concentrations in the
diameter ranges from 6 to 1000 nm (N), from 6 to 25 nm (N6–25), from 6 to
100 nm (N6–100 or UF particles) and from 100 to 1000 nm
(N100–1000) were calculated from the measured and inverted DMPS
concentrations. Particle number size distribution surface plots
jointly showing the variation in particle diameter and particle number concentration
density in time were also derived. Identification and classification of NPF
and growth events was accomplished on these surface plots (Dal Maso et al.,
2005; Németh et al., 2018) on a daily basis into the following main
classes: NPF event days, non-event days, days with undefined character and
days with missing data (for more than 4 h during the midday). The relative
occurrence frequency of events was determined for each month and year as the
ratio of the number of event days to the total number of relevant (i.e.
all - missing) days. A subset of NPF events with uninterrupted evolution in
time, which are called quantifiable (class 1A) events, were further
separated because the time evolution of their size distribution functions
was utilized to determine the dynamic and timing properties with good
accuracy and reliability.
Dynamic and timing properties
Growth rate (GR) of nucleation-mode particles was calculated using a mode fitting
method (Kulmala et al., 2012). Particle number median mobility diameter
(NMMD) of the nucleation mode was obtained from fitting the individual size
distributions using the DoFit algorithm (Hussein et al., 2004). The growth rate was
determined as the slope of the linear line fitted to the time series of the
NMMD data within a time interval around a diameter d, for which the dependency
could be satisfactorily approximated by linear fit. Since the nucleation
mode was mostly estimated by N6–25 in the calculations of the formation
rate (see below), and since the median of the related diameter interval
(from 6 to 25 nm) is close to d=10 nm, GRs for particles with a diameter of
10 nm were determined (GR10). This type of GR can be interpreted as an
average GR as far as the given particle diameter range is concerned, but it
actually expresses the beginning of the growth process only. Particle growth
can slow substantially in time in specific cases, and this can affect the formation rate calculations
considerably (see later).
Time evolution of an aerosol population is described by the general dynamic
equation which was rearranged, simplified and approximated by several
quantities (Kulmala et al., 2001, 2012; Dal Maso et al., 2002; Cai and Jiang, 2017) to express the formation rate J6 of particles
with the smallest detected diameter of dmin=6 nm in a form utilized in
the present evaluation as
J6=dN6–25dt-dNAi,<25dt+CoagS10(N6–25-NAi,<25)1+GR10(25–6)(N6–25-NAi,<25).
The first term on the right side of Eq. (1) expresses the concentration
increment caused by nucleation and particle growth. The particle number
concentration in the size range from 6 to 25 nm (i.e. N6–25) is
usually selected to approximate the nucleation-mode particles
Nnuc≈N6–25. This is a reasonable choice because
it was proved to be an advantageous and effective way of handling fluctuating
data sets since N6–25 often exhibits smaller scatter in time and
less sensitivity than the fitted area of the nucleation mode. It is
implicitly assumed that the intensity of the NPF is constant for a certain
time interval, and, therefore, dN6–25/dt can be
determined as the slope of the linear function of N6–25 versus
time t within an interval for which the dependence could be satisfactorily
approximated by linear fit. A limitation of the relatively wide size range
(6–25 nm) selected can be manifested by disturbances from primary
particles, particularly in urban environments. This is taken into account by
an additional term of NAi,<25, which is discussed below.
The second term on the right side of Eq. (1) expresses the concentration increment caused by
high-temperature emission sources, usually of vehicular road traffic
(Paasonen et al., 2016; Salma et al., 2017) to N6–25, which can
provisionally disturb the assumption of Nnuc≈N6–25. A
typical example of such a situation is shown in Fig. S1a in the Supplement from 10:09 to
12:23 LT. In these specific cases, the contribution of primary emissions was
estimated from the slope of the time series of the fitted peak area of the
Aitken mode below d<25 nm (NAi,<25) in the time region
under consideration. Reliable separation of the nucleation and Aitken modes
from each other was hindered or was not possible for a few individual size
distributions due to overlapping modes and the scatter in the measured
concentration data, and these individual cases were excluded from or skipped
in the time series.
The third term on the right side of Eq. (1) represents the loss of particles
due to coagulation scavenging (with pre-existing particles). The coagulation
scavenging efficiency for particles with a diameter of 10 nm (CoagS10)
was selected to approximate the mean coagulation efficiency of
nucleation-mode particles (CoagSnuc). This diameter was chosen by
considering the median of the related diameter range, which was discussed
above for GR. The coagulation efficiency was calculated from classical
aerosol mechanics by adopting a mass accommodation coefficient of 1 and
utilizing Fuchs' transition-regime correction factor (Kulmala et al.,
2001, 2013; Dal Maso et al., 2005) by using computation
scripts developed at the University of Helsinki. Self-coagulation within the
nucleation mode was neglected due to limited ambient concentrations.
Hygroscopic growth of particles was not considered since this depends on
chemical composition of particles, which is unknown.
The fourth term on the right side of Eq. (1) expresses the growth out of newly
formed particles from the size range through the condensation of vapours. The
GR10 was selected to approximate a representative value at the median
of the particle diameter range considered (Vuollekoski et al., 2012). It is
implicitly assumed that GR10 can be regarded to be constant over the
time interval under consideration. Nevertheless, the growth of
nucleation-mode particles in time is occasionally limited (Fig. S1b). In
these specific cases, the mean relative area of the
nucleation mode below 25 nm was determined by fitting individual size distributions around the time
of the maximum nucleation-mode NMMD, and the ratios were averaged. A
correction in form of the mean relative area was adopted as a multiplication
factor for the growth out term in Eq. (1). On very few days, the growth of
newly formed particles was followed by a decrease in nucleation-mode NMMD
(Salma et al., 2016a). In these cases, the shrinkage rate (with a formal
GR10<0) was derived and adopted in Eq. (1). Relative
contributions of the concentration increment, coagulation loss and growth out
from the diameter interval to J6 are decreasing in this order with mean
values of 71 %, 17 % and 12 %, respectively (Table S1).
The formation and growth rates for the measurement years of 2008–2009 and
2012–2013 were calculated earlier in a slightly different way and
neglecting the urban features discussed above (Salma et al., 2011, 2016b).
To obtain consistent data sets, the dynamic properties for these 2 years
were re-evaluated by adopting the present improved protocol and implementing
the experience gained over the years. The mean new-to-old rate ratios with
SDs for the GR10 and J6 were 1.06±0.32 and 1.23±0.37,
respectively, in the city centre (2008–2009) and 1.04±0.21 and
1.20±0.35, respectively, in the near-city background (2012–2013). It
was the smaller rates that were primarily and sometimes substantially
impacted. The modifications were simultaneously adopted. The subtraction of
particle number concentrations emitted by road traffic from N6–25
usually leads to a decrease in the coagulation loss term and loss term due
to growth out from the diameter range of 6–25 nm. At the same time, the
subtraction can also influence the slope of the concentration change in time
(dNnuc/dt) depending on the actual time evolution of perturbing emission
source. In addition to that, the time interval in which this slope is
considered to be constant was set in a new treatment. It is noted that the
relative contributions of the concentration increment, coagulation loss and
growth out from the diameter interval to J6 have different weights in
propagating their effects. Furthermore, J6 itself also depends on
GR10, which makes the relationships even more complex. These connected
effects explain why the changes resulted in increments. The recalculation
is considered to be a methodological improvement over the years of research.
The assumptions and estimations above usually represent a reasonable
approximation to reality. The N6–25 is derived from the experimental
data in a straightforward way, and the GR10 and the corrections for primary
particles and limited particle growth depend on the quality of the size
distribution fitting as well, while the CoagS10 is determined using
a theoretical model. The resulting accuracies of the dynamic properties, in
particular of J6, look rather complicated. They also depend on the
spatial heterogeneity in the investigated air masses, particularly for the
observations performed at the fixed site, size and time resolution of the
concentrations measured, diameter range of the size distributions,
fluctuations in the experimental data, selection of the particle diameter
interval, choice of the time interval of interest (for linear fits),
sensitivity of the models to the input uncertainties (Vuollekoski et al.,
2012) and also on the extent of the validity of the assumptions applied
under highly polluted conditions (Cai and Jiang, 2017). The situation is
further complicated with the fact that the dynamic (and also the timing)
properties are connected to each other. Finally, it is important to
recognize that some NPF and growth curves on the surface plots have a rather
broad starting time interval (Fig. S1b and S1c). They occur in a
considerable abundance in cities, e.g. in 40 % of all quantifiable events
in Budapest (Sect. 4.4). This may yield badly defined or composite dynamic
properties, whose uncertainty can have principle limitations which can
prevail on the experimental and model uncertainties.
Timing properties of NPF and growth events are increasingly recognized, and
they can provide valuable information, even if they are estimated indirectly
from the observed diameter interval >1.5 nm (Sect. 1). The
earliest estimated time of the beginning of a nucleation (t1) and the
latest estimated time of the beginning of a nucleation (t2) were derived
using a comparative method (Németh and Salma, 2014) based on the variation
in the content of the first size channel of the DMPS system. Both time
parameters include a time shift that accounts for the particle growth from
the stable neutral cluster mode at approximately 2 nm to the smallest
detectable diameter limit of the DMPS systems (6 nm in our case) by adopting
the GR value in the size window nearest to it in size space. The difference
Δt=t2-t1 is considered to be the duration time interval of
the nucleation process. It represents the time interval during which new
aerosol particles are generated in the air. The timing properties are
expressed in UTC+1, and their uncertainty is regarded to be ca. 30 min
under ordinary NPF and growth situations.
Sources and sink
The relative effects and role of gas-phase H2SO4 were estimated by
its proximity measure (proxy value) containing both its major source and sink
terms under steady-state conditions according to Petäjä et
al. (2009). It was calculated for GRad >10 W m-2.
Formally, it is possible to convert the H2SO4 proxy values to
H2SO4 concentrations by an empirical scaling factor of
k=1.4×10-7× GRad-0.70, where GRad is expressed in units of W m-2 (Petäjä et al., 2009). The factor was, however,
derived for a remote boreal site, and, therefore, we prefer not to perform
the conversion since urban areas are expected to differ from the boreal
regions. The conversion was applied only to estimate the order of average
H2SO4 atmospheric concentration levels. The results derived by
utilizing the proxy are subject to larger uncertainties than for the other
properties because of these limitations, but they may indicate well gross
tendencies.
Condensation sink for vapour molecules onto the surface of existing aerosol
particles was computed for discrete size distributions as described in
earlier papers (Kulmala et al., 2001; Dal Maso et al., 2002, 2005) and
summarized by Kulmala et al. (2013). The equilibrium vapour pressure of the
condensing species was assumed to be negligible at the surface of the
particles, thus similar to sulfuric acid. Dry particle diameters were
considered in the calculations.
Results and discussion
Annual median total particle number concentrations (N) for each measurement
year are summarized in Table 1. The data for the city centre indicate a
moderate decreasing trend. The mean UF/N ratios with SD for the same
measurement time intervals were 67±14 % and 79±6 %,
75±10 %, 75±11 %, 76±11 % and 80±10 %,
respectively. The values correspond to ordinary urban atmospheric
environments in Europe (Putaud et al., 2010, Sun et al., 2019). An overview
of the number of classified days for each measurement year is also given in
Table 1. The availability of the daily size distribution surface plots with
respect to all days ensures that the data are representative on yearly and
monthly timescales, except for the months August and September 2015, when
there were missing days in larger ratios. The number of quantifiable event
days (248 cases) is also considerable, which helps us to arrive at a firm
conclusion for the NPF and growth events as well.
Annual median total particle number concentrations
(in 103 cm-3), number of days with NPF and growth events, quantifiable event
days, non-event days, undefined days, missing days and the coverage (in %) of relevant days in the near-city background and city centre
separately for the 1-year-long measurement time intervals.
It was previously shown that the NPF and growth events observed in the city
centre of Budapest and its background ordinarily happen above a larger
territory or region in the Carpathian Basin (Németh and Salma, 2014),
and they are linked to each other as a spatially coherent and joint
atmospheric phenomenon (Salma et al., 2016b). From the point of the
occurrence frequency distribution, they can, therefore, be evaluated jointly
in the first approximation. An overall monthly mean relative occurrence
frequency of nucleation days derived for all 6 measurement years is shown in
Fig. 1. The annual mean frequency with SD was 22±5 %, which is
considerable and is in line with other urban sites (Sect. 1). The monthly
mean frequency has a temporal variation, which can be characterized by a
noteworthy pattern. The mean monthly dependency exhibits an absolute and a
local minimum in January (5.6 %) and August (21 %), respectively, and an absolute and a local maximum in April (40 %) and September (31 %),
respectively. Nevertheless, the SDs of the monthly means indicate prominent
variability from year to year. The pattern can be related to multivariate
relationships and complex interplay among the influencing factors, which
include the air temperature (January is the coldest month, while August is
the warmest month in the Carpathian Basin) and enhanced emission of biogenic
VOCs in springtime (March–April) and early autumn (September) as well
(Salma et al., 2016b). It is noted that the findings derived for the
separate city-centre data set are very similar to the results presented
above.
Monthly mean relative occurrence frequency of NPF and growth
events for the joint 6-year-long data set. The error bars show ±1 standard deviation, the horizontal line in cyan indicates the overall annual
mean frequency, the yellow bands represent ±1 standard deviation of
the annual mean, and the smooth curve in red serves to guide the eye.
The properties and variables studied were derived in full time resolution.
They were averaged in several ways for different conditions and for various
purposes to obtain typical average descriptive characteristics. In one case
(31 August 2016), the NPF and growth event could reliably be identified,
while the measured absolute particle number concentrations could not be
validated due to experimental difficulties, and, therefore, it was left out from
the further calculations. Similarly, there were one and four events with
unusually and extraordinarily large dynamic properties in the measurement
years 2014–2015 and 2017–2018, respectively. More specifically, five
individual J6 data when expressed in units of cm-3 s-1 and
one individual GR10 data when given in nm h-1 were >20
(Table 3). These extremes were left out from the overview statistics to
maintain the representativity (they could be influenced by some unknown extra
or very local sources) and to fulfil the basic requirements of
correlation analysis better. If an event showed a double beginning then the dynamic
properties for the first onset were considered in the basic overview since
this onset is of regional relevance (Salma et al., 2016b). The extreme NPF
and growth events and the characteristics for the second onsets were,
however, evaluated separately and are discussed in detail and interpreted in
Sect. 4.4.
Ranges, averages and standard deviations of aerosol particle
formation rate J6, particle diameter growth rate GR10, starting
time (t1) and duration time interval (Δt=t2-t1) of the nucleation process of quantifiable NPF and growth
events in the near-city background and city centre separately for the 1-year-long measurement time intervals and for the joint 5-year-long city centre
data set.
EnvironmentBackgroundCentre Time2012–2008–2013–2014–2015–2017–All 5interval201320092014201520162018yearsFormation rate J6 (cm-3 s-1) Minimum0.481.471.130.811.191.600.81Median2.04.23.54.44.66.34.6Maximum5.615.917.818.015.317.318.0Mean2.24.75.25.65.06.65.6SD1.32.63.74.23.73.33.8Growth rate GR10 (nm h-1) Minimum3.03.73.12.83.23.32.8Median5.07.66.66.58.07.57.3Maximum9.817.419.018.015.519.819.8Mean5.27.87.27.37.78.07.6SD1.42.62.83.23.02.82.9Starting time, t1 (HH : mm UTC + 1) Minimum05:5107:1406:4405:4807:3105:5705:48Median08:1909:2609:2208:4809:4509:1809:15Maximum11:0911:3812:2111:2312:4512:1512:45Mean08:1709:2709:2508:4910:0209:2409:19SD01:1101:0501:2601:2201:2301:3601:26Duration time, Δt (HH : mm) Minimum01:2300:5200:4200:3101:0301:2600:31Median03:1602:3602:0403:5302:3103:4902:57Maximum06:4406:0405:3407:4606:0507:5507:55Mean03:3002:4402:1403:5202:5803:5703:18SD01:4001:1101:0101:4001:4701:3901:40Ranges and averages
Ranges and averages with SDs of formation rate J6, growth rate
GR10, starting time of nucleation (t1) and duration time interval
of nucleation (Δt) are summarized in Table 2 for separate measurement
years and for the joint 5-year-long city centre data set. In the city
centre, nucleation generally starts at 09:15 UTC + 1, and it is typically
maintained for approximately 3 h. The NPF and growth events ordinarily
produce 5.6 new aerosol particles with a diameter of 6 nm in 1 cm3 of
air in 1 s and cause the particles with a diameter of 10 nm to grow at a
typical rate of 7.6 nm h-1. The statistics for J6 and GR10 are
based on 199 and 203 events, respectively. The corresponding data for the
separate years show considerable variability without obvious trends or
tendencies. The differences between the years can likely be related to
changes in actual atmospheric chemical and physical situations and
conditions and to the resulting modifications in the sensitive balance and
delicate coupling among them from year to year. Spread of the individual
data for GR10 is smaller than for J6; the relative SDs for the
joint 5-year-long city centre data set were 38 % and 68 %, respectively.
The dynamic properties and t1 data tend to be smaller in the near-city
background than in the city centre. In general, nucleation starts 1 h
earlier in the background, and the events typically show significantly
smaller J6 (with a median of 2.0 cm-3 s-1) and GR10 (with
a median of 5.0 nm h-1). Duration of the nucleation is very similar to
that in the city centre. All starting times of nucleation were larger than
(in a few cases, very close to) the time of the sunrise. This implies that
no nocturnal NPF and growth event has been identified in Budapest so far.
The particle growth process (the so-called “banana” curve) could be traced
usually for a longer time interval (up to 1.5 d) in the background than in
the centre.
These results are in line with ideas on atmospheric nucleation and
the consecutive particle growth process (e.g. Kulmala et al., 2014; Zhang et
al., 2015; Kerminen et al., 2018). It was observed in a recent overview
study (Nieminen et al., 2018) that the formation rate of 10–25 nm particles
increased with the extent of anthropogenic influence, and in general, it was
1–2 orders of magnitude larger in cities than at sites in remote and clean
environments.
Ranges and averages with SDs of some related atmospheric properties, namely
of mean condensation sink (CS) averaged for the time interval from t1 to t2, daily
maximum gas-phase H2SO4 proxy, daily mean T and RH (Table S2) and
of daily median concentrations of SO2 (as the major precursor of
gas-phase H2SO4), O3 (as an indicator of photochemical
activity), NOx and CO gases (as indicators of anthropogenic combustion
activities and road vehicle emissions) (Table S3) were also derived for
quantifiable NPF and growth event days and are further evaluated. The
annual mean CS values exhibited decreasing tendency in the city centre over
the years. The individual values remained below approximately 20×10-3 s-1, which agrees well with the results of our earlier study
(Salma et al., 2016b) according to which the CS suppresses NPF above this
level in the Carpathian Basin. Maximum H2SO4 proxy values reached
substantially higher levels (by a factor of approximately 2) in the
near-city background than in the city centre, mainly due to the differences
in the CS and [SO2]. The differences between the two sites are
particularly evident when considering their smallest values. The largest
variability in the annual average values was observed for the proxy. Median
concentration of H2SO4 molecules was roughly estimated to be
approximately 5×105 cm-3 by adopting the scaling factor,
although it is largely uncertain due to the limitations of the factor
(Sect. 3.2). The air T displayed quite similar and comparable values over the years
at both sites. The discussion of its overall effect on the dynamic
properties is accomplished in Sect. 4.2, where the monthly distributions are
presented. Some events happened at daily mean temperatures below zero. The
daily mean RH and its SD for the city centre and near-city background were
54±11 % and 64±12 %, respectively. There were events that
occurred at RHs as high as 90 %. Relationships of the dynamic properties
with T and RH are also obscured by the strong seasonal cycle of these
meteorological data and by the fact that air masses arriving at the
receptor site in different trajectories are often characterized by distinct
levels of meteorological data.
As far as the pollutant gases are concerned (Table S3), SO2 showed
somewhat smaller daily median values, and O3 exhibited substantially
smaller levels on event days in the city centre than in the near-city
background, while concentrations of NOx and CO were obviously larger in
the city than in its close background. The differences can primarily be
explained by intensity and spatial distribution of their major sources and
atmospheric chemical reactions, and the joined concentration data resemble
typical situations without photochemical smog episodes in cities. There was
no obvious decrease in SO2 concentration during these years in contrast
with an earlier decreasing trend from the mid-1980s till about 2000.
Monthly distributions
Distributions of the monthly mean J6, GR10, daily maximum gas-phase
H2SO4 proxy, mean CS, daily mean air T and RH and daily median
SO2, O3, NOx and CO concentrations for quantifiable NPF and
growth events for the joint city centre data sets are shown in Fig. 2. The
distributions – eminently for J6, GR10, H2SO4 proxy and
SO2 – do not follow the monthly pattern of the event occurrence
frequency at all (cf. Fig. 1). Instead, the J6, GR10 and
H2SO4 proxy tend to exhibit larger values in summer months, and
the temporal changes over the other months are smooth and do not show
distinctive features. The elevations are substantial; the estimated maximum
level was larger than the baseline by a factor of 2.1 for the J6 and by
a factor of approximately 1.4 for the GR10 and H2SO4 proxy.
Intensity of solar radiation, its seasonal cycling, concentration of
atmospheric precursors in different months, biogenic processes,
anthropogenic activities and the fact that rate coefficients of many thermal
chemical/physicochemical processes in nature (including GR; Paasonen et
al., 2018) increase with T could play an important role in explaining the
distributions.
Distribution of monthly mean aerosol particle formation rate J6
in units of cm-3 s-1 and particle diameter growth rate GR10
in units of nm h-1(a), mean condensation sink for vapours
(CS) in units of s-1 averaged over the nucleation time interval
(t1, t2) and daily maximum gas-phase H2SO4 proxy in units of µg m-5 W s (b), daily mean air temperature
(T) in units of ∘C and daily mean relative humidity (RH) in
% (c) and daily median concentrations of SO2,
O3, NOx and CO for quantifiable NPF and growth
events in the city centre for the joint 5-year-long time interval. The error
bars are shown for one side and indicate 1 standard deviation. The number of individual data averaged in each month is displayed next to the symbols. The
horizontal lines indicate the overall mean. The non-linear curves assist to
guide the eye.
Distributions of ratios for monthly median concentrations of
N6–100, N100–1000, SO2, O3,
NOx and CO, and for monthly mean condensation sink for
vapours (CS), global solar radiation (GRad), air temperature (T) and
relative humidity (RH) on NPF event days to that on non-event days in the
city centre for the joint 5-year-long time interval. The horizontal lines
represent annual mean ratios.
The differences in the GRad (and some other properties) are, however, biased
by the seasonal cycle of solar electromagnetic radiation via the seasonal
variation of NPF occurrence frequency. Nevertheless, the misalignment among
the monthly distributions of NPF and growth event occurrence frequency and
all the other properties indicates that the occurrence or its basic causes
are not linked with the dynamic properties in a straightforward or linear
manner in the Carpathian Basin, including Budapest.
Some of our results are in line with other observations according to which
GR exhibited almost exclusively a summer maximum, while some other findings
are different in the sense that the seasonal variability in particle
formation rate was quite modest and could not be established earlier
(Nieminen et al., 2018). There is one more aspect which may be worth
realizing in this respect. A large fraction of compounds contributing to NPF
and growth in cities can originate from anthropogenic precursors (Vakkari et
al., 2015). Their emissions may peak any time of the year depending on human
habits and requirements (Nieminen et al., 2018). Nevertheless, the fact that
our monthly distributions of the dynamic properties in urban environments
follow the universal summer maximum behaviour may indicate the overall
prevailing role of atmospheric photochemistry coupled with biogenic
emissions of aerosol precursor vapours.
The monthly mean J6, GR10 and H2SO4 proxy data still have
considerable uncertainty, which makes their interpretation not yet
completely conclusive. The uncertainties are influenced by inherent
fluctuations in the primary data sets, enhancing effects caused by combining
some individual primary data into compound variables (such as
H2SO4 proxy), number of data items available for different
properties and months, variations in other or unknown relevant environmental
conditions and by the variability in relative nucleation occurrence
frequency from year to year. The resulting uncertainties are expected to
decrease with the length of the available data sets, which emphasizes the
need to continue the measurements.
The monthly distributions of CS and SO2 and NOx concentrations
could be represented by constant values of the overall means and SDs of
(9.4±4.3)×10-3 s-1, 4.7±2.1µg m-3 and
81±38µg m-3, respectively, with an acceptable
accuracy. This suggests that these variables in Budapest do not critically
or substantially affect the dynamic properties (or the event occurrence).
Monthly distributions of air T and O3 concentration showed a maximum
over summer months, while RH reflected the T tendency. In addition, monthly
averages of T on event days and on non-event days were similar. Both higher
biogenic emissions and typically stronger photochemistry are expected during
the summer, which enhance the production rate of nucleating and condensing
vapours, while there is practically nothing extra in the first approximation
(except for extreme Ts) that would suppress the dynamical properties
(Kerminen et al., 2018). As a result of these complex effects, the dynamic
rates showed a summer maximum. This is consistent with the results from
other urban and non-urban studies (Nieminen et al., 2018). The distribution of
CO was more variable and without obvious tendentious temporal structure or
feature than for the other gases, and, therefore, its interpretation has been encumbered so far. However, this does not seem to substantially affect the
dynamic properties.
Distributions of monthly average ratios of major variables on NPF event days
to that on non-event days for the joint city centre data set are summarized
in Fig. 3. It is noted that the differences in the number of non-event days
and event days are the largest in winter and smallest in spring (Fig. 1).
The annual mean ratios for N6–100, GRad, SO2 and O3 were above
unity, and for N100–1000 and RH, they were below unity, while the value of
CS, NOx and CO were close to each other on both types of days.
Ultrafine particles are generated by NPF and growth processes in a
considerable amount; their concentration was larger by 23 % on event days
than on non-event days. This agrees with our earlier assessment of the NPF
contribution as a single source of particles based on the nucleation strength
factor NSFGEN of 13 % as a lower estimate (Salma et al., 2017). The
other variables of the first group above represent conditions which favour
atmospheric nucleation and particle growth, i.e., strong solar radiation,
precursor gas and general photochemical activity, respectively. Particles in
the size range of 100–1000 nm (the pre-existing particles with a relatively
long residence time) express condensation and scavenging sink, which
represents a competing process to nucleation. There is also evidence that RH
acts against continental NPF processes (Hamed et al., 2011).
It is also seen in Fig. 3 that NPF and growth events in winter took place
preferably when N100–1000, CS, RH, NOx, and CO concentrations were
especially low and O3 concentration was unusually large. It can be
explained by considering that the basic preconditions of NPF events are
realized by the ratio of source and sink terms for condensing vapours. The
source strength in winter is often decreased substantially in the Budapest
area (Salma et al., 2017) due to lower solar radiation and less (biogenic)
chemical precursors in the air. Nevertheless, NPF can still occur if the
sink becomes even smaller. This also explains the relatively low event
day-to-non-event day ratios for N6–100 observed in winter months. Full
exploitation of the database by multi-statistical and other methods has been
in progress and is to be published in a separate article.
Relationships
Pearson's coefficients of correlation (R) between J6 and GR10
revealed a significant linear relationship for all annual data
sets (the mean R and SD were 0.768±0.099, number of data pairs
n=243). This confirms that formation of new aerosol particle and their
growth to larger sizes are tightly and positively linked together. It should
be noted that J6 and GR10 are not completely independent variables
(see Eq. 1 and Table S1). The linear relationship between the dynamic
properties was observed under different atmospheric conditions in many
environments (Nieminen et al., 2018).
The dynamic properties can also be coupled to the concentrations of aerosol
precursor compounds and properties of a pre-existing particle population,
thus to the atmospheric environment (Kerminen et al., 2018). It is, therefore,
sensible to investigate the city centre and near-city background data
separately. Scatter plots between J6 and GR10 for the 1-year-long
measurement time intervals are shown in Fig. 4. For the city centre, the
regression lines follow the line with a slope of 1 in all 5 years. The mean
slope (b) with SD for the joint 5-year-long city centre data set was
b=0.94±0.07 expressed formally in units of
cm-3 s-1 nm-1 h. At the same time, the regression line for the near-city
background deviated significantly with a
b=0.67±0.10 cm-3 s-1 nm-1 h from the J6 versus GR10 dependency for the city
centre. This can imply that NPF and growth processes advance in a different
manner in these two environments. This is likely related to the differences
between the city and its close environment as far as the atmospheric
composition (for instance, the VOC and NOx concentrations), chemistry
and physics and other delicate conditions are concerned (Paasonen et al.,
2018). The narrower range and smaller number of individual dynamic
properties available for the near-city background relative to those in the
city centre represent some inherent limitation or weakness in the
explanation, and, therefore, it can only strictly be regarded as a working
hypothesis.
Scatter plots for aerosol particle formation rate J6 and
particle diameter growth rate GR10 in city centre (a, c–f) and
near-city background (b) separately for the 1-year-long measurement time
intervals. Number of data points (n), their coefficient of correlation (R) and
the intercept (a) and slope (b) of the regression line with standard
deviations are also indicated. The lines in black represent the line with a
slope of 1, and the solid lines in red show the regression lines, while the
dashed parts in red are extrapolated from the regression line.
The intercepts (a) of the regression lines were identical for all data sets
within their uncertainty interval. The mean intercept and SD were estimated
to be -1.7±0.8 cm-3 s-1. This finding is interpreted as
the existence of a minimum GR or more exactly of a minimally required GR
that leads to J6>0. Particles that exhibit at least this
level of GR can escape coagulation mainly with larger particles and reach
the detectable diameter (6 nm in our case) by condensational growth. The
minimal GR was derived as GRmin=-a/b, and its mean and SD are
1.8±1.0 nm h-1 for the conditions ordinarily present in the
Budapest air. Nucleation processes which are initiated under circumstances
that cause the newly formed particle with a diameter of 10 nm to grow at a
rate of <GRmin are normally not observed. Anyway, these are
expected to be events with relatively small J6 (weak phenomena) due to
the relationship between GR10 and J6. The events with GR larger but
close to this limit could still be masked by fluctuating experimental data.
Their identification and evaluation can be made feasible by decreasing the
lower measurement diameter limit of DMPS systems down to 3 nm or by
using different instruments such as a particle size magnifier or neutral cluster and
air ion spectrometer.
Correlations between individual H2SO4 proxy values on one side and
J6 or GR10 on the other side were not significant. This is
consistent with the corresponding conclusion of Sect. 4.2 and with the
earlier results according to which the mean contribution of H2SO4
condensation to the particle GR10 was only 12.3 % in Budapest (Salma
et al., 2016b). The lack of correlation and the average concentrations of
SO2 derived separately for event and non-event days suggest that this
precursor gas is ordinarily available in excess, and, therefore, it is
usually not the lack of SO2 gas itself which limits the NPF and growth
events in Budapest. Instead, the reaction rate of oxidation of SO2 to
H2SO4 in the gas phase – likely governed by photochemical
conditions – and chemical species other than H2SO4 can have
a larger influence on the particle growth. The role of H2SO4 in the
nucleation process and early particle growth could still be determinant or
relevant.
Coefficients of correlation between CS on one side and J6 or GR10
on the other side for the joint city centre data sets were modest
(R=0.41 and 0.32, respectively, with n=194 and 197, respectively). This is
simply related to the fact that larger GR values are typical of polluted
urban air (Kulmala et al., 2017) since particles capable of escaping
coagulation scavenging need to grow faster in comparison to cleaner
environments, and the enhanced requirements for the growth are linked to
increased formation rates as well. It should be noted here that the GR of
newly formed particles to larger sizes is primarily coupled to (1) CS, which
is further linked to the entire aerosol particle population (including the
newly formed particles, thus the NPF itself), (2) to the total concentration
and some physicochemical properties of non-volatile gaseous compounds and (3) to their production rate in the gas phase from aerosol precursor compounds
(e.g. Kerminen et al., 2018). These couplings could result in rather complex
behaviour, and their understanding is essential when analysing atmospheric
observations.
Date, new particle formation rate
J6 (in units of cm-3 s-1), particle diameter growth rate
GR10 (nm h-1), starting time t1 of nucleation (HH : mm
UTC + 1), duration time interval Δt=t2-t1 of nucleation
(HH : mm), mean condensation sink CS during the nucleation process
(10-3 s-1), daily maximum gas-phase H2SO4 proxy
(104µg m-5 W s), daily mean air temperature T
(∘C), daily mean relative humidity RH (%), daily median
concentrations of SO2, O3, NOx
(µg m-3) and CO (mg m-3) gases and the type of the
onset for extreme quantifiable NPF and growth events. The numbers in
italics indicate the values which are above the 98 % percentile of the
corresponding data sets. NA: not available.
As far as the pollutant gases are concerned, no correlation could be
identified between J6 or GR10 on one side and the gas
concentrations on the other side. The coefficients of correlation between CS
and NOx or CO were modest (R=0.37 and 0.42, respectively, with
n=164 and 152, respectively), while correlation of NOx and CO on one
side with WS was also modest but negative (R=-0.32 and -0.42,
respectively, with n=167 and 155, respectively). The former relationships
can be explained by the fact that vehicular road traffic in cities is a
considerable and common source of NOx, CO and primary particles
(Paasonen et al., 2016), and the emitted particles largely contribute to CS
levels. The latter relationships are linked to the effect of large-scale air
mass transport (often connected to high WSs) on urban air pollution or air
quality.
Extreme and multiple events
The data sets of J6, GR10 and Δt containing all 247
individual values each could be characterized by a log-normal distribution
function. This is demonstrated by a log-probability graph for J6 in
Fig. S2 as an example. The coefficient of determination, median and geometric
standard deviation for J6, GR10 and Δt data sets were 0.990,
4.0 cm-3 and 2.3; 0.993, 6.8 nm h-1 and 1.46; and 0.998, 02:57
(0.123 d) and 1.74, respectively. It is noted that the findings derived for
the separate city centre data set are very similar to the results presented
above.
One of the major properties of this distribution type is that it contains
relatively large individual data with considerably high abundances. There
were five individual J6 and five individual GR10 data above the 98 %
percentile of the data sets, which belonged to nine separate NPF and growth
events (days). Their specifications, properties and parameters are
summarized in Table 3. All these events occurred in the city centre from
April to September. The medians of J6, GR10, CS and air T for the
subsets of these 9 extreme event days were larger by factors of 5.2, 2.4,
1.5 and 1.4, respectively, than for the city centre data. At the same time,
the medians of the other atmospheric properties and concentrations in these
two respective data sets agreed to within approximately 10 %. There was a
single event associated with an extreme H2SO4 proxy (of
23×105µg m-5 W s) and relatively low NOx
concentration (44 µg m-3), which indicate exceptionally
favourable conditions for NPF and growth. In addition to this case, there
were only a few days that were characterized by an unusually large CS
(23×10-3 s-1) – which could in turn be linked to higher
dynamic rates (Sect. 4.3) – or by somewhat larger SO2
(8.1 µg m-3) or lower NOx concentration
(34 µg m-3). For all
the other events, however, no simple or compound property of the
investigated variables could explain the extreme rates. Instead, they may be
related to some other chemical species and/or atmospheric processes, which
were not including in the present study.
Each quantifiable NPF and growth event was labelled as ordinary or broad by
visual inspection of its beginning part. If the width of the beginning was
smaller than approximately 2 h, or there was a determinant single growth
curve (rib) on the size distribution surface plot, then the onset was
labelled as ordinary, otherwise as broad (Fig. S1b and S1c for broad
onsets). Broad onsets can be generated by (1) a long-lasting nucleation
process, (2) nucleation that has been disrupted and has started over due to changing
atmospheric and meteorological conditions or (3) multiple nucleation
processes close to each other in time (Salma et al., 2016b). The broad
onsets were specified as doublets if the nucleation mode could be separated
into two submodes by size distribution fitting. Approximately 40 % of all
quantifiable events had a broad onset. This indicates that events with
broad or multiple onsets are abundant in the urban environment, which could be
an important difference from remote or clean atmospheres.
For ca. 10 % of all quantifiable event days, it was feasible to calculate
two sets of dynamic properties for onsets 1 and 2 with reasonable accuracy.
In the near-city background, the medians of J6 and GR10 for
onset 1 were similar to the corresponding medians for the whole near-city
background data set, while for onset 2, they were substantially larger,
namely 4.1 cm-3 s-1 and 10.0 nm h-1, respectively
(cf. Table 2). Actually, the latter values were closer to the medians of the city
centre than for the near-city background. Approximately 75 % of the
doublets resulted in individual onset 2 / onset 1 ratios larger than unity.
Their overall median ratios for J6 and GR10 were similar and
approximately 1.2, while for the near-city background, they were about 2.
The results are in line with our earlier conclusion according to which the
second onsets (if it is a new formation process and not just an event that has started over) are more intensive than the first onsets (Salma et al., 2016b). These
particles also grow faster. This can be explained by the fact that the first
event is of a regional scale since its dynamic properties resemble those of
the regional background (Yli-Juuti et al., 2009), while the later event can
be characterized by values typical of the city centre (Salma et al.,
2016b). The later event (or events) is mainly caused and governed by
subregional processes. These findings are also coherent with a previous
observation of NPF and growth events with multiple onsets in semi-clean
savannah and industrial environments (Hirsikko et al., 2013), and they also
fit well into the existing ideas on mixing regional and urban air parcels
that exhibit different properties such as precursor concentrations, T and RH
(Kulmala et al., 2017).
Conclusions
Magnitude of the particle number concentration level produced solely by NPF
and growth can roughly be estimated by considering the median J6, median
duration of nucleation Δt (their distribution function is log-normal;
Table 2) and the mean coagulation loss of these particles Fcoag
(0.17; Sect. 3.1 and Table S1) as J6×Δt×(1-Fcoag). In central Budapest, it yields a concentration of
104 cm-3. This is in line with another result achieved using the nucleation strength factor (Salma et al., 2017). More importantly, the
estimated concentration is comparable to the annual median atmospheric
concentrations (Table 1). This simple example indicates that the phenomenon
is relevant not only for aerosol load and climate issues on regional or
global spatial scales, which were first recognized. It is sensible also to
study the effects of NPF and growth events on urban climate and health risk
for inhabitants since they produce a large fraction of particles, even in
cities.
Similar recognitions have led urban atmospheric nucleation
studies to emerge. As part of this international progress, here we have presented a
considerable variety of contributions, which became feasible thanks to
gradually generating, multi-year-long, critically evaluated, complex and
coherent data sets. Dynamic and timing properties of 247 NPF and growth
events were studied together with supporting aerosol properties,
meteorological data and pollutant gas concentrations in the near-city background
and city centre of Budapest for 6 years. The results and conclusions derived
form an important component that is based on atmospheric observations. The
present study can also be considered the first step toward a larger and
more comprehensive statistical evaluation process.
Further dedicated research including sophisticated measurements, data
evaluations and modelling studies is required to find and identify
additional chemical species and their processes and to account for their
multifactorial role in more detail. Such a measurement campaign focusing on
chemical composition of molecular clusters, precursors and nucleating
vapours by applying recent expedient instruments in Budapest over the months
of the highest expected event occurrence has only been realized within a
framework of international cooperation. Its prospective results can hopefully
provide additional valuable information for some of the conclusion base on
indirect evidence for the time being and can further clarify the overall
picture on the urban multicomponent nucleation and growth phenomenon.
Data availability
The observational data used in this paper are available on request from the
corresponding author or on the website of the Budapest platform for Aerosol
Research and Training (http://salma.web.elte.hu/BpArt/, last access: 30 April 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-5835-2019-supplement.
Author contributions
IS designed the study, performed most data analysis, interpreted the
results and wrote the paper. ZN performed most measurements and data
treatment and contributed to the data analysis.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank Markku Kulmala and his research team at the University of
Helsinki for their cooperation. Financial support by the National Research,
Development and Innovation Office, Hungary (contracts K116788 and PD124283),
and by the European Regional Development Fund and the Hungarian Government
(GINOP-2.3.2-15-2016-00028) is gratefully acknowledged.
Review statement
This paper was edited by Xavier Querol and reviewed by six anonymous referees.
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