Introduction
New particle formation (NPF) is an important source of secondary aerosols in
the atmosphere (Kulmala et al., 2004a). Field studies and model simulations
have consistently shown that NPF can enhance cloud condensation nuclei (CCN)
concentrations and contribute significantly to the global CCN production
(Wiedensohler et al., 2009; Yue et al., 2011; Spracklen et al., 2008; Pierce
and Adams, 2009; Merikanto et al., 2009; Yu and Luo, 2009; Matsui et al., 2013). NPF
is a two-stage process consisting of the formation of clusters and subsequent
growth to detectable sizes (Kulmala et al., 2000). Recently, chamber
experiments have made substantial progress in revealing the fundamental
processes involved in particle nucleation and growth (Kirkby et al., 2011;
Almeida et al., 2013; Schobesberger et al., 2013; Riccobono et al., 2014;
Ehn et al., 2014; Kürten et al., 2014). However, consistent theories are
still under investigation to quantify the processes physically, chemically,
and dynamically (Kulmala et al., 2013, 2014). For example, the identity and
physico-chemical properties of assisting vapors other than sulfuric acid
(H2SO4) are uncertain so far. It is also uncertain what mechanisms
allow for the assisting vapors to overcome strong Kelvin effect over sub-3 nm
particles. Existing mechanisms include condensation of extremely low
volatility organic compounds (Ehn et al., 2014), nano-Köhler activation
(Kulmala et al., 2004b), heterogeneous chemical reactions (Zhang and Wexler,
2002), heterogeneous nucleation (Wang et al., 2013), and adsorption of
organics on cluster surface (Wang and Wexler, 2013). However, the relative
importance of various mechanisms is unknown.
Direct measurements of size- and time-dependent nucleation rate and growth
rate in sub-3 nm size range are important to constrain the relative
contributions from different mechanisms and precursors. Such measurements
are also important to evaluate the survival probability of new particle to
CCN-active sizes (∼ 100 nm for soluble particles at 0.2 %
super saturation; Pierce and Adams, 2009) and to reveal the limiting factors
in the process. Recently, a series of new instruments have been developed to
measure sub-3 nm aerosol number concentration and chemical composition, such
as condensation particle counters (e.g., PSM, DEG-SMPS, Jiang et al., 2011a;
Sipila et al., 2009; Vanhanen et al., 2011), ion spectrometers (e.g., NAIS,
Asmi et al., 2009), and mass spectrometers (e.g., Cluster-CIMS, APi-TOF,
CI-APi-TOF (chemical ionization atmospheric pressure interface
time-of-flight) mass spectrometer, Jokinen et al., 2012; Junninen et al., 2010; Zhao et al., 2010).
Kuang et al. (2012) developed a de-coupling method to measure size- and time-dependent growth rates of sub-5 nm particles. Their results at two urban
sites in USA showed that size-resolved growth rates increased
approximately linearly with particle size from 1 to 5 nm. Similar results
were also observed in the boreal forest (Kulmala et al., 2013; Lehtipalo et
al., 2014). Based on growth rates measured below 2 nm, Kulmala et al. (2013)
identified three separate size regimes, which were dominated by different
key gas to particle conversion processes.
The relative contribution of different precursors and mechanisms to the
nucleation and growth of 1–3 nm particles may vary greatly with atmospheric
conditions (Riipinen et al., 2012). Therefore, sub-3 nm particle
measurements in a variety of atmospheric conditions, e.g., remote or urban
atmosphere, biogenic, or anthropogenic emission-dominated areas, are
immensely valuable. Unfortunately, such data are very sparse until now
(Jiang et al., 2011b; Kuang et al., 2012; Kulmala et al., 2013; Lehtipalo et
al., 2009, 2010, 2011; Yu et al., 2014a, b). China has suffered from severe
atmospheric particulate matter pollution in recent years (Chan and Yao,
2008; Yue et al., 2011). To the best of our knowledge, only two studies were
conducted in China to measure the occurrence of new particles down to
∼ 1 nm. In these two studies, air ions (Herrmann et al., 2014)
or neutral particles (Xiao et al., 2015) were measured by air ion spectrometer (AIS) or particle size magnifier (PSM) in two
urban locations of Yangtze River Delta (YRD) region. Both studies were
conducted in the winter season.
Here we reported the nucleation and growth of sub-3 nm particles in the
urban atmosphere of Nanjing, China on arbitrarily selected observation days
in the spring, summer, and winter of 2014–2015. Our aim was to (1) provide new
information about the initial steps of NPF based on size- and time-resolved
nucleation rate and growth rate measurements, and (2) find possible limiting
factors behind the seasonal and diurnal variations of nucleation events in
the polluted urban atmosphere.
Methodology
Field measurements
Nanjing is the second largest megacity after Shanghai in the YRD region of
China (Chan and Yao, 2008). The YRD city cluster, covering 2.1×105 km2 land with 170 million residents, is one of the most
populated and industrialized regions in China. Field measurement was
conducted from the third floor (15 m above the ground level) of an academic
building beside a Chinese national meteorology observatory facility on the Nanjing University of Information Science and Technology
(NUIST)
campus (32.20∘ N, 118.71∘ E; symbol (1) in Fig. 1). The sampling
was carried out during the months of May (10–30 20 May14), June (1–15 June
2014), December (24–31 December 2014), February (16–22 February 2015), and
March (1–7 March 2015). A total of 58 measurement days were arbitrarily selected
to represent spring, early summer, and winter seasons, but to avoid any
rain event.
Locations of two urban measurement sites in Nanjing, the second
largest megacity in the Yangtze River Delta region, China, (1) are the NUIST
site and (2) is the summer measurement site.
As part of an intensive summer campaign (12 August–12 September 2014), the
summer measurement was conducted at a local governmental meteorology
observatory platform (32.06∘ N, 118.70∘ E) that is 14 km south of the
NUIST site ((2) in Fig. 1). The instruments were housed in an air-conditioned trailer, using exactly the same sampling inlets as the NUIST
site. The main aim of the summer campaign was to understand the effects of
regional emission control measures during the 2014 Young Olympic Games
(1 August–15 September) on air quality. Since the two sites were located within
the same urban air shed, the measurement provided an opportunity to study
seasonal variation of nucleation and its relationship with meteorological
variables and gaseous precursors.
Sub-3 nm clusters/particles (hereafter referred as particles) were measured
with a nano-condensation nucleus counter system (nCNC) consisting of a
particle size magnifier (PSM; model A10, Airmodus Oy, Finland) and a butanol
condensation particle counter (model A20, Airmodus Oy, Finland). During the
measurement, an ambient airflow of 14 standard liters per minute (standard L min-1) was
drawn into building room or trailer via a 72 cm long and 1.0 cm I.D. diameter
stainless steel (SS) tube, which was extended outside the room/trailer
horizontally. PSM then sampled a split flow of 2.5 standard L min-1 via a SS T-union.
The design of the inlet tubing (length and airflow rate) was to minimize
the transport loss of nano-particles. The size-dependent transport survival
ratios of sub-3 nm particles in the inlet tubing were estimated (67–86 % for 1.4–3.0 nm) and corrected using a particle loss calculator tool
(von der Weiden et al., 2009).
PSM was operated in a continuous scanning mode with a cycle of 240 steps
between saturator flow rates of 0.1 and 1.0 standard L min-1 within 240 s. The
particle cutoff sizes of the nCNC varied with saturation ratios in the
saturator (Vanhanen et al., 2011). A stepwise method was used to invert raw
scanning data to size spectrum (time resolution: 4 min) of sub-3 nm
particles, which were classified evenly into 6 size bins, i.e. 1.4–1.6,
1.6–1.9, 1.9–2.2, 2.2–2.4, 2.4–2.7, and 2.7–3.0 nm. The particle number
concentrations were then smoothed with a moving average filter for
minimizing the effect of noises and fluctuations. The inverted particle
number concentrations in the 6 bins were referred as N1.5, N1.8,
N2.0, N2.3, N2.6, and N2.8, using mean values of upper and
lower size boundaries in each bin. The stepwise method was described in
detail by Lehtipalo et al. (2014).
Particle size distributions in the range from 3 to 750 nm were obtained by
integrating two scanning mobility particle spectrometers (SMPS) with a
nano-SMPS (a TSI differential mobility analyzer DMA3085 and a condensation
particle counter CPC3776; scanning range: 3–64 nm) and a long SMPS (TSI
DMA3081 and CPC3775; scanning range: 64–750 nm). During the summer
campaign, only the long-SMPS was operated to scan particles from 8 to 350 nm.
Scanning cycles of both SMPS systems were 4 min, in order to synchronize
with the nCNC. The SMPSs sampled ambient air from a separate sampling inlet.
The inlet was a 129 cm long and 1.0 cm I.D. horizontally oriented SS tube
with an airflow of 14 standard L min-1. The transport loss of particles in the SMPS
inlets was corrected using size-dependent survival ratios of 85–100 % for
particles > 3 nm.
Sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO), and
nitrogen oxides (NO and NO2) concentrations were measured every 1 min with Thermo Environmental Instruments (model 43i-TLE, 49i, 48i, and
42i, respectively). When gaseous SO2, O3, NO2, and CO data
were not available, hourly SO2, O3, NO2, and CO were obtained
from the nearby local Environmental Protection Agency (EPA) monitoring station.
PM2.5 was monitored with Thermo Scientific TEOM 1405. Meteorological
variables including wind speed, wind direction, relative humidity (RH),
temperature, and solar radiation flux were recorded every 1 h during the
measurement periods. Mean concentrations of PM2.5, SO2, and
O3 were 79 µg m-3, 10, and 48 ppbv, respective, during
the whole measurement period. Therefore, we regard our measurement
environment as a polluted urban atmosphere.
Nucleation event and growth patterns
A criterion was set to determine whether the nCNC detected sub-3 nm particles
in the atmosphere. The criterion was that total particle concentration
reading followed the supersaturation scanning cycle of PSM so that the
highest concentrations were measured at lowest cutoff sizes (see also
Fig. 2 in Lehtipalo et al., 2014). However, it was possible in the
stepwise inversion method that the number concentration fluctuation of
> 3 nm particles within a 4 min scanning cycle was wrongly
inverted to sub-3 nm particles even when sub-3 nm particles actually did not
exist according to the above criterion. As a result, the stepwise inversion
method always reported a background sub-3 nm particle concentration
(Nsub-3; i.e. the sum of N1.5, N1.8, N2.0, N2.3,
N2.6, and N2.8) of 0.5×103–2×103 cm-3 in the nighttime and 3×103–8×103 cm-3 in the daytime. Similar background levels of sub-3 nm
particles during non-NPF periods were also reported by other studies that
used the nCNC (Kulmala et al., 2013; Lehtipalo et al., 2014; Xiao et al.,
2015). Following their procedures, we did not attempt to subtract this
background from Nsub-3 reported in this study.
We defined the sub-3 nm particle event as a sub-3 nm particle occurrence with
a Nsub-3 higher than the background level persisting for longer than 1 h in the atmosphere. In this study, we used sub-3 nm particle event as an
approximate measure of the nucleation event. This is because (1) there was an
approximately positive linear correlation between the Nsub-3 and the nucleation
rate (J1.4 in this study, see next section) with R2 of 0.94 (Fig. 2),
and (2) the Nsub-3 calculation only needs nCNC scanning data and was
thus more readily available than the J1.4 calculation, which needs both nCNC
and SMPS scanning data. A similar definition has been discussed in our
previous studies (Yu et al., 2014a, b). Apparently, a sub-3 nm particle
event did not necessarily always lead to an NPF event, but it indicated the
intensity and frequency of nucleation in the atmosphere. One focus in this
work was to investigate the characteristics of sub-3 nm particle event.
Particle growth after nucleation is crucial to determine if nucleated
particles could grow to CCN-active sizes. We identified two growth patterns
according to size spectrum characteristics in sub-3 nm size range (Fig. 3). In a Type A event (Fig. 3a or b), size distribution n(Dp, t) was
higher at smaller sizes (e.g., 1.4–1.6 nm) than n(Dp, t) at larger
sizes (e.g., 2.7–3.0 nm). The size spectrum below 3 nm thus looked like a
“volcano”. In a Type B event (Fig. 3c or d), n(Dp, t) was lower at
smaller sizes than n(Dp, t) at larger sizes ( “up-side-down volcano”).
For the size range > 3 nm, depending on whether a banana-shape
growth was seen, we further defined Type A1/A2 and Type B1/B2 events:
particles eventually grew to a CCN-active sizes in Type A1 and B1 events,
while in Type A2 and B2 events, banana-shape particle growth to CCN-active
sizes was not seen. Therefore, Type A1 and B1 events were equivalent to
conventional NPF events based on either DMPS or SMPS measurements.
Nsub-3 vs. J1.4 in the eight nucleation events in February, May,
December, and August during 2014–2015. The events were indicated by different
colors (blue: 1 March 2015; green: 18 February 2015; red: 19 February
2015; purple: 15 August 2014; black: 27 December 2014; grey: 15 May 2014;
orange: 20 May 2014; yellow: 16 May 2014).
Size spectra of typical (a) Type A1 event on 15 May 2014,
(b) Type A2 event on 20 May 2014, (c) Type B1 event on 18 February 2015, and
(d) Type B2 event on 19 February 2015, during our measurement period. Size
spectra from 3 to 300 nm (logarithmic scale) and 1.4 to 3 nm (linear scale) were
obtained using SMPS and nCNC, respectively.
Type B size distribution was more unusual since n(Dp, t) of small
particles were less than n(Dp, t) of large particles in the sub-3 nm
size range. We excluded the possibility of deteriorated nCNC detection
efficiencies for small particles due to high particle loading in the sample
air. This is because total number concentrations of nCNC during our
measurements never approached nCNC upper concentration limit 4×105 cm-3, especially in the early stage of nucleation when total
particle concentration was rather low. Our nCNC was also calibrated
periodically using H2SO4–H2O particles in a laboratory flow
tube to ensure the detection efficiency of the nCNC. The different chemical
composition of atmospheric particles could be another factor of lower
detection efficiencies. It is well known that organic substances activate
less readily in diethylene glycol (DEG) (e.g. Kangasluoma et al., 2014). However,
it is accepted in general that larger particles have higher mass fraction of
organics than smaller particles in a NPF process. If organic substances
activate less readily in DEG, it should be even more difficult to activate
larger particles than smaller particles. Therefore, the increasing
n(Dp) with Dp (i.e. upside down volcano) could not be simply due to
lower detection efficiency of organic substances.
Formation rate and growth rate calculations with a simplified GDE
method
Conventional appearance-time method determined growth rates (hereafter,
GR) during the initial period of NPF by finding the time steps when
newly formed particles appeared at certain size bins and calculating the
GR from the time differences between successive size bins (Kulmala et al.,
2012; Lehtipalo et al., 2014). This method was often not applicable to the
NPF event with high GR below 3 nm, e.g., 0.3 nm/4 min (i.e. 4.5 nm h-1)
with size intervals of 0.3 nm and scanning time intervals of 4 min in our
measurements. Furthermore, sub-3 nm particles were often generated
persistently throughout the daytime period. Maximum concentrations in the
sub-3 nm size bins could appear around noontime, which is a few hours later
than the onset of nucleation. Therefore, we were not able to pinpoint
correctly maximum or 50 % maximum concentrations at the onset of
nucleation.
The rapid growth of small particles in the urban atmosphere was the
motivation that we used an alternative method to calculate growth rate and
formation rate. Here, we analyzed eight events (listed in Table 1, including
both Type A1/A2 and B1/B2 events) in detail, for which complete size spectra
from 1.4 to 750 nm were available without distorted, broken or noisy data.
A total of 8 size bins were classified: 6 evenly-divided size bins in sub-3 nm
and 2 size bins in 3–30 nm (3–10 and 10–30 nm). For an aerosol population
that is growing through simultaneous condensation and coagulation, the aerosol
general dynamic equation (GDE) describes the evolution of number
concentration in a size bin between particle diameters Dp1 and
Dp2 (Dp2 > Dp1) as
dN(Dp1,Dp2,t)dt=JDp1,t-JDp2,t-CoagSnkDp1,Dp2,t+CoagSrcDp1,Dp2,t,
where N(Dp1,Dp2,t) is the number
concentration from Dp1 to Dp2, inverted from nCNC or SMPS scanning
data. J is condensational growth flux (i.e. particle formation rate) across
the lower (Dp,1) or upper (Dp,2) boundaries of a size bin. In the
first size bin of 1.4–1.6 nm, J(1.4 nm, t), or
simply J1.4, is the unknown formation rate of the smallest particles
that we measured.
Activation diameter (Dp,act), maximum growth rate in
1.4–3 nm (GRmax,1.4–3), overall growth rate in 1.4–3 nm
(GR1.4–3), overall growth rate in 3–20 nm
(GR3–20), nucleation rate (J1.4), condensation sink
(CS), and temperature (T) of selected nucleation events. Estimated
gas-phase condensing vapor concentrations Celvoc, pure
saturation concentration of condensing vapor over flat surface
Celvoc∗, and the Mikkonen H2SO4 proxy are shown
in right three columns. All data were for the time periods with maximum
nucleation rates.
Type
Date
Dp,act
GRmax,1.4–3
GR1.4–3
GR3–20
J1.4
T
CS
Mikkonen
Celvoc
Celvoc∗
(nm)
(nm h-1)
(nm h-1)
(nm h -1)
(cm-3s-1)
(∘C)
(10-2 s-1)
H2SO4 proxy
(cm-3)
(cm-3)
(cm-3)
A1
15 May 2014
2.4
6.4
3.6
7.7
3.0 × 102
20.8
1.6
2.9 × 107
3.5 × 107
6.3 × 106
A1
15 Aug 2014
2.4
14.5
7.1
7.7
2.0 × 102
26.1
1.8
3.1 × 107
8.5 × 107
2.1 × 107
A2
16 May 2014
2.4
3.8
1.9
0
95
25.3
1.9
1.4 × 107
2.5 × 107
4.6 × 106
A2
20 May 2014
2.2
2.9
1.6
0
92
24.1
1.9
1.3 × 107
1.7 × 107
3.3 × 106
B1
18 Feb 2015
1.6
25.9
4.4
6.0
1.1 × 103
8.2
3.3
3.9 × 107
1.4 × 108
3.0 × 107
B1
27 Dec 2014
1.6
17.7
4.2
5.5
1.9 × 102
7.6
2.8
3.5 × 107
1.1 × 108
2.2 × 107
B2
19 Feb 2015
1.9
25.0
8.9
10.1
8.0 × 102
7.4
3.2
3.7 × 107
1.7 × 108
5.2 × 107
B2
4 Mar 2015
1.9
18.0
5.8
8.7
2.5 × 103
3.9
2.2
4.8 × 107
1.3 × 108
1.1 × 107
CoagSnk(Dp1, Dp2,t) and CoagSrc(Dp1,
Dp2, t) are the sink and source terms defining the coagulation out of and
into the size bin between Dp1 and Dp2. Assuming bin k has lower
boundary Dp1 and upper boundary Dp2,
CoagSnkDp1,Dp2,t=N(k,t)∑i=198(1-θk,i,k)Kk,iN(i,t),CoagSrcDp1,Dp2,t=12∑i=1k-1∑j=1k-1θi,j,kKi,jN(j,t)N(i,t).
Here N(i,t) is the number concentration of bin i.
Ki,j is the coagulation kernel for a collision between
particles from bins i and j. Probability coefficient θi,j,k=1, if the volume sum of two coagulating
particles (vi+vj, here
the volume is calculated from the center diameter of a bin) is within the
volume boundaries of bin k. Otherwise θi,j,k=0. The particle coagulation of total 98 bins
was considered, but the coagulation terms were only needed to be calculated
for the smallest 8 bins from 1.4 to 30 nm. According to our calculation,
CoagSrc(Dp1, Dp2, t) accounted for only 0–0.8 % of the total particle
flux into a bin (i.e. CoagSrc(Dp1, Dp2, t) + J(Dp1,t)) in the sub-3 nm size
range. This implied that self-coagulation played a negligible role and most
of the production flux into a bin is due to condensational growth from gas
molecules.
The GDE here is the same as the Eq. (1) by Kuang et al. (2012). In their
method, gaseous H2SO4 was measured simultaneously and a constant
GR(Dp, t) / GRH2SO4(Dp,t) ratio at a given size over time was
assumed. Their GR(Dp, t) was then solved by fitting the GDE to the
measured size distributions. In our study, however, we did not measure
gaseous H2SO4. Instead, J(30 nm, t) in
the largest size bin, which is the condensational growth flux out of 30 nm,
was set to zero. This simplification was valid in the four Type A2/B2 events
when particles never grew to > 30 nm (4 March, 19 February,
20 and 16 May). In the remaining four Type A1/B1 events (18 February,
27 December, 15 May, and 15 August), this was also valid during the early NPF period
when particles did not grow out of 30 nm and during the late NPF period when
particles grew out of 30 nm completely. During the middle period of events
(usually around 11:00–14:00 LT, local time), J(30 nm, t) was
underestimated and thus J1.4 could be regarded as a lower estimate. In
the four Type A2/B2 events, our calculation showed that J10 was only
0–4 % of J1.4. Xiao et al. (2015) and Kulmala et al. (2013) measured
both J1.5 and J3 using the appearance-time method. Their J3 was less
than 7 % of J1.5. Furthermore, J30/J1.4 ratio should be even
smaller than J10/J1.4 or J3/J1.5 ratios, considering the eight events
were carefully selected to ensure all sub-30 nm particles were grown
from nucleation (not emitted directly from emission sources like vehicular
engine). All these evidences supported the fact that even if J30 was set to 0,
J1.4 would not be underestimated more than 7 % when particles grew to > 30 nm on 18 February, 27 December, 15 May, and 15 August.
Equation (1) requires the balance of condensational growth (J), coagulation
terms (CoagSnk and CoagSrc), and the changing rate of particle number
concentration (dN/ dt). Using Eq. (1) we can therefore calculate the
nucleation rate J(1.4 nm, t) and formation rates
J(Dp, t) across all size bin boundaries from 1.6
to 10 nm. After the formation rates J(Dp,t) were
obtained, GR(Dp,t) was calculated from J(Dp,t)/n(Dp,t), where
n(Dp,t) is the size distribution calculated
as n(Dp,t)=dN(t)dDp for each
size bin. On the other hand, the appearance-time method could still be
applied to (1) the size range of > 3 nm where size intervals were
large (2–6 nm), and (2) the size range of < 3 nm when GR was small.
The results from the appearance-time method will also be shown in the next
section.
Results and discussion
Sections 3.1, 3.2–3.4, 3.5, and 3.6 were organized,
respectively, to address the following four issues: (1) seasonal variation,
diurnal variation, and limiting factors of nucleation events (represented by
sub-3 nm particle event) in the polluted urban atmosphere; (2) time- and
size-dependent nucleation rate and growth rate of sub-3 nm particles, and
their implications for nucleation and growth mechanisms; (3) inhibited
particle growth to CCN-active sizes in strong nucleation events of Type B2;
and (4) the comparison with other two studies involving sub-3 nm particle
measurements in the YRD megacities.
Mean and standard deviation of event-averaged Nsub-3,
anthropogenic trace gases (SO2, NO2, CO and O3), PM2.5,
and meteorological variables (temperature, RH, wind speed, WS, solar
radiation, and radiation × SO2/ PM2.5) for nucleation
events in spring (n=17), summer (n=3) and winter (n=14). Nucleation
frequency (the percentage of event days out of total measurement days) was
also shown.
Seasonal and diurnal variations of nucleation event
As seen from Fig. 2, there was an approximate linear correlation between
Nsub-3 and J1.4 with the slope of Nsub-3/J1.4
equal to ∼ 160. This seemed to suggest that the average
residence time of new particles in the sub-3 nm size range was 160 s
before they were scavenged due to coagulation or grew out of 3 nm. The sub-3 nm particles observed at the present work were thus formed in situ in the
urban atmosphere and not likely to be carried-over by air transport. In this
section we used a sub-3 nm particle event as an approximate measure of
nucleation.
We observed significant seasonal characteristics of nucleation event (Fig. 4). Nucleation was rare and weak in summer, while it was commonly observed
in all other seasons. During our measurements from 2014 to 2015, nucleation
events occurred on 81 % of all spring observation days (May 2014), 53 %
in early summer (June 2014), 10 % in summer (August and September 2014),
and 64 % in winter (December 2014, February and March 2015). We compared
intensity (Nsub-3) and frequency of nucleation events, as well as
meteorological variables (temperature, RH, wind speed, and solar radiation
flux) and gaseous pollutants (SO2, NO2, CO, and O3) for
spring, summer, and winter seasons. June was not shown in Fig. 4 for
comparison, because it was a transit season from spring (May) to summer
(August and September). The data were first averaged over the entire event
period for each event, and we then used event-averaged data to create box
and whistler plots for the three seasons. PM2.5 was used here as a
surrogate of condensational sink (CS), because of the more ready availability
of PM2.5 data than SMPS data.
As shown in Fig. 4, nucleation in summer was characterized by the lowest
frequency, the lowest Nsub-3 (2.2×104 cm-3), and a short
nucleation period (only 1–2 h). Strict emission control measures during
the 2014 Youth Olympic Games resulted in relatively low PM2.5 level (32 ± 8 µg m-3), which should favor nucleation. However,
relatively low SO2 concentration (1.4 ± 0.6 ppbv), high
temperature (26 ± 2 ∘C), and high RH (74.3 ± 4.2 %)
might not be in favor of nucleation. A simple H2SO4 proxy
(Radiation × SO2/ PM2.5) indicated that summer
H2SO4 concentration was likely to be the lowest among the three seasons, which could explain low nucleation intensity/frequency.
We further examined diurnal variations of Nsub-3 and other variables on
event and non-event days in winter (Fig. 5). Since nucleation in winter
was characterized by a Type B event (“up-side-down volcano” below 3 nm),
event days were further divided into Type B1 and Type B2 events depending on
whether banana-shape particle growth was seen. The difference between Type B1 and B2 will be discussed later in Sect. 3.5. During the non-event days,
Nsub-3 ranged from 2.4×103 cm-3 in the night to
8.0×103 cm-3 in the day, which was close to background levels.
During the event days, Nsub-3 in the night was close to that of
non-event days, but could reach 8×104–20×104 cm-3 in the middle of the day. This was more than 10 times higher than
those on the non-event days. From Fig. 5 we can see that non-event day had
higher concentrations of anthropogenic precursors (indicated by SO2,
NO2, and CO), but nucleation seemed to be limited by higher
pre-existing particle surface area (indicated by PM2.5), higher
temperature and RH, and lower radiation flux. Photochemistry indicator
O3 was also lower during non-event days.
Diurnal variations of mean Nsub-3, anthropogenic trace gases
(SO2, NO2, CO, and O3), PM2.5, and meteorological
variables (temperature, RH, wind speed, and solar radiation flux) on
non-event days (n=8, blue line) and event days (n=3 for Type B1 event,
red line and n=6 for Type B2 event, green line) during winter measurement
period.
Nucleation in spring was characterized by the highest frequency (81 %) among
all seasons. The highest gaseous pollutant concentration of (H2SO4
proxy, SO2, NO2, CO, and O3) and radiation seemed to be the
favorable factors to explain this. However, Nsub-3 in spring
(3.3×104 cm-3) was much lower than that in winter
(11.2×104 cm-3). Unfavorable factors included high
pre-existing particle surface area (PM2.5: 112 ± 68 µg m-3) and high temperature (27 ± 4 ∘C) in spring. Integrating
the above seasonal and diurnal variation information in Figs. 4 and 5, we tentatively identified that the limiting factors for nucleation in our
urban atmosphere were (1) radiation, temperature, RH, and CS in winter and
spring, and (2) temperature, RH, and available gaseous precursors in summer.
Out of the total of 90 measurement days, 4 March 2015 in winter was the only day
that we observed significant nocturnal nucleation. Sunrise and sunset were
at 06:29 and 18:00 LT on 4 March, but nucleation was observed
persistently from 04:00–20:00 LT. Nsub-3 increased from
3.5×103 cm-3 at 04:00 LT to 6.3×104 cm-3 before sunrise. During 10:00–11:00 LT, peak Nsub-3 reached
3×104 cm-3, 3 times higher than the average of all other
event days in winter. Apparently, nocturnal nucleation on 4 March could not
be explained as carry-over of daytime particles nor being associated with
photochemistry. This implied the existence of a certain dark nucleation
source. There are a number of observations that have also shown nighttime
particle formation events in various atmospheric conditions (Junninen et
al., 2008; Lehtipalo et al., 2011; Lee et al., 2008; Ortega et al., 2009,
2012; Russell et al., 2007; Suni et al., 2008; Svenningsson et al., 2008; Yu
et al., 2014a), but the mechanisms behind the nocturnal nucleation are yet
still highly speculative. With our instrument capability in this work, we
could not deduce any valuable information on the nocturnal nucleation
mechanism, except that we found that the air mass on 4 March was relatively
clean (both CS and gases, mean CS: 0.15 s-1), and temperature and RH (mean:
4.4 ∘C and 33 %) were favorable for nucleation.
Size- and time-dependent formation rates of sub-3 nm particles
We observed 23 Type A events and 9 Type B events during the measurements.
The different size distribution patterns (Fig. 3) were probably linked to
the mechanism or intensity of nucleation and growth. To address this issue,
we first compared the formation rates and growth rates in two types of
events. Formation rates J of 1.4, 1.6, 1.9, 2.2, 2.4, 2.7, and 3.0 nm
particles were shown in Fig. 6 (upper panels) for typical Type A and Type B events.
It is obvious that J1.4 was much higher on 18 February (Type B) than that on 15 May (Type A). A clear time dependence of J was observed.
For example, J1.4 was 60 cm-3 s-1 at the onset of the
nucleation event on 15 May and increased to 300 cm-3 s-1 in the
middle of the day. In the Type B event on 19 February, the initial and peak
J1.4 were 2.1×102 and 1.2×103 cm-3 s-1, respectively. Therefore, our method provided more
information of nucleation than conventional calculation methods that usually
showed only an averaged J at the onset of a nucleation event. Our method was
also different from Kulmala et al. (2013). Their time-dependent formation
rate on an event day was equal to size distribution n(Dp, t) times a
constant growth rate at the onset of the event obtained with the appearance-time method.
The diurnal variation of J implied that nucleation was probably linked to
sunlight induced photochemistry. We calculated the correlations between
J1.4 and an H2SO4 proxy for the eight events of our interest. The
H2SO4 proxy was calculated following [H2SO4] =8.21×10-3k⋅Radiation⋅ [SO2]0.62⋅ (CS ⋅ RH)-0.13 (Eq. 8 of Mikkonen et al.,
2011), where k is the temperature-dependent reaction-rate constant. Figure 7a
shows that good linear correlation was usually seen for every single event
with R2 ranging from 0.72 to 0.86 for six out of eight events. A moderate
R2 of 0.56 was obtained for 15 August. R2 was lowest (0.34) on
4 March 2015. This is not surprising because we know 4 March was the only day
with nocturnal nucleation during the measurement period. The H2SO4
proxy was also calculated using the derivation of Petäjä et al. (2009), which resulted in lower R2 of log J1.4
vs. log[H2SO4] for all eight events. Therefore, in this study we used
the Mikkonen H2SO4 proxy, as it was derived with a more comprehensive
data sets than Petäjä et al. (2009). The slopes of log J1.4 vs.
log[H2SO4] were close to 1 in all events (0.82–1.17, excluding 4 March), indicating activation theory can explain the nucleation mechanism in our
urban atmosphere.
Upper: formation rates (or equivalently, particle growth fluxes)
of 1.4, 1.6, 1.9, 2.2, 2.4, 2.7, and 3.0 nm cluster/particles on 15 May 2014
(Type A1 event) and 18 February 2015 (Type B1 event). Middle: particle size
distribution (dN/ dlogDp, green square) selected during the two events
(09:36 and 10:02 LT). Lower: particle growth rates measured during the
same time periods (GRmeas, red square). Also shown in the figure were
H2SO4 proxy (black square) and growth rates calculated from the
H2SO4 proxy (GRH2SO4, dashed black line), as well as the
calculated ELVOC concentration (Celvoc, red square,
see Eq. 5) during the same time periods. Dashed boxes in the lower panels
highlighted the size distributions and growth rates between 1.4 and 3 nm
measured with nCNC.
(a) Correlations between log J1.4 and log [H2SO4]
for the eight events. H2SO4 proxy was calculated according to
Mikkonen et al. (2011). J1.4 and [H2SO4] were synchronized to
1 h that was the time resolution of solar radiation data. The colored
lines showed linear fits to the data of every single event. (b) The same
data set as panel (a), but with symbol color to indicate ambient temperature. Two
black lines showed the linear dependences of J1.4=10-4.1× [H2SO4]
and J1.4=10-6.3× [H2SO4], between which most of data points located.
If data points of all eight events were put together, the linear correlation
between H2SO4 proxy and J1.4 deteriorated (slope = 1.1,
R2= 0.17, Fig. 7b). In spite of considerable scattering, most data
points are located between J1.4=10-4.1× [H2SO4]
and J1.4=10-6.3× [H2SO4]. An interesting
finding was that the scattering of J1.4 vs. [H2SO4] proxy among
all eight events was probably due to temperature or season change (Fig. 7b).
More specifically, with the same level of H2SO4 proxy, J1.4
was higher in winter with lower temperatures than in spring/summer with
higher temperatures. There were two possibilities behind the deteriorated
linear correlation between the H2SO4 proxy and J1.4: (1) inaccurate
H2SO4 proxy and (2) other varying factors in nucleation
mechanism. First, it was very likely that H2SO4 concentrations in
our polluted urban atmosphere were overestimated by the H2SO4
proxy of Mikkonen et al. (2011), which was based on statistic regression of
historical data sets from a relatively clean Europe/USA atmosphere. The extent
of overestimation may vary with the levels of predictor variables (e.g.,
SO2, temperature, CS). Mean SO2 mixing ratios were 10.5 and 7.3 ppbv
in spring/summer and winter during our measurements, respectively. These
were 1 order of magnitude higher than SO2 mixing ratios at the six European
and USA sites (mean values: 0.23–3.4 ppbv, Mikkonen et al., 2011).
Our CS in the eight events was on the order of magnitude of 10-2 s-1, again higher than 10-3 s-1
in Mikkonen et al. (2011). Mikkonen et al. (2011) had already pointed out that the predictive
ability was lower for long-term data due to atmospheric condition changes in
different seasons.
Second, organic condensing vapor concentrations in particle growth events
were higher in winter than those in spring/summer (Table 1, see Sect. 3.4). If the organics were also involved in nucleation, J1.4 should be
enhanced in winter. The enhancement of nucleation by organics (most likely
anthropogenic organics in our urban atmosphere) could be supported by the
comparison of J1.4 dependences on H2SO4 between our study and
the measurements in the boreal forest: besides possible H2SO4
overestimation, J1.4=10-4.1× [H2SO4]–10-6.3× [H2SO4] in our sites was much higher than
J1.5=1.06×10-7 [H2SO4]1.1 in
Hyytiälä during active aerosol formation periods (Kulmala et al.,
2013). At last, low temperature itself might enhance nucleation in winter
(Brus et al., 2011) via increasing the saturation ratios of all nucleation
precursors (e.g., water, H2SO4, organics).
Size- and time-dependent growth rates of sub-3 nm particles
Particle size distribution n(Dp) and corresponding GR(Dp) at an
instant in time during the events were shown in Fig. 6 middle and lower
panels. A local minimum of n(Dp) at 2.4 nm, followed by a local maximum
somewhere between 2.5 and 10 nm, was seen on 15 May 2014. Such size
distribution characteristics on 15 May 2014, as well as on all other Type A
event days, was also observed by Kulmala et al. (2013) in the boreal forest
(Fig. 1A and S9A in their paper) and by Jiang et al. (2011b) in the urban
area of Atlanta, USA (Fig. 1 in their paper). We further examined the
growth rates in the size range of 1–3 nm on 15 May 2014. It was shown that
there was a local maximum of GR(Dp) at 2.4 nm. This could explain why
n(Dp) was increasing in 2.4–3 nm size range: when particle
condensational flow out of a size bin was slowed down, it was possible that
particles flowing into the size bin accumulated, leading to particle number
increase in the bin.
We saw more unusual behaviors of n(Dp) and GR(Dp) in the Type B
event on 18 February (Fig. 6 right panels): GR(Dp) decreased
monotonically in the size range of 1.4–3 nm, and accordingly n(Dp)
increased monotonically at the same time. A high GR(Dp) of 25 nm h-1
was observed at 1.6 nm and GR(Dp) decreased rapidly to 1.7 nm h-1 at
∼ 3 nm. If we consider that GR(Dp) below 1.6 nm would
eventually decrease due to strong Kelvin effect of all possible precursors
(H2SO4 or organics), the overall trend of GR(Dp) in the Type B
event was in fact the same as Type A: for the smallest clusters, growth rate
was small (possibly below 1 nm h-1) and increased with Dp. It
reached a local maximum somewhere between 1 and 3 nm, after which
GR(Dp) decreased with Dp. For a typical NPF event, GR(Dp) would
eventually increase again after a local minimum between 3 and 10 nm. The
difference between the Type A event (18 February) and Type B event (15 May)
was the Dp of local maximum GR(Dp) (2.4 vs. 1.6 nm).
The interesting behaviors of n(Dp) and GR (Dp) in our urban atmosphere
were different from the stereotyped understanding that steady-state cluster
size distribution n(Dp) decreases with Dp in nucleation and GR
increases monotonically with Dp in an NPF event. It should be pointed
out that if we calculated the overall GR in 1.4–3 nm, GR1.4–3 was
3.6 nm h-1 on 15 May and 4.4 nm h-1 on 18 February, which were still
smaller than GR3–20 during the initial period of the events (7.7
and 6.0 nm h-1, calculated using appearance-time method). Table 1
showed that a faster GR3–20 than GR1.4–3 were quite common, except
in two events on 16 and 20 May when particles did not grow beyond 3 nm.
Overall, GR was still increasing with increasing Dp.
Kuang et al. (2012) reported a local maximum of GR at ∼ 2.6 nm in an NPF event measured in Atlanta, USA (Fig. 1b in their paper).
In this study we further point out that GR could decrease monotonically with
Dp in 1–3 nm range in strong nucleation events. Our GR was calculated from
a simplified GDE method, however, the decease of GR in 1–3 nm size range could
be easily inferred from the size spectra shown in
the middle panels of Figs. 3 or 6: for a Dp2 that was larger than Dp1, particle
formation rate J(Dp2) must be smaller than J(Dp1). If we observed a
higher n(Dp2) than n(Dp1), GR(Dp) that was equal to
J(Dp)/n(Dp) must be smaller at Dp2 than Dp1.
Growth rate due to condensing organic vapor on newly formed nuclei in
sub-3 nm sizes
Apparently, the complicated growth rate behaviors in our polluted urban
atmosphere cannot be explained by H2SO4 condensation alone, not
only because H2SO4 condensational growth rate (GRH2SO4,
calculated from the H2SO4 proxy and shown as black dashed lines in
Fig. 6) was smaller than the measured growth rate (GRmeas), but also
because GRH2SO4 curve should follow a monotonically decreasing trend in
> 1 nm sizes assuming a collision-only condensational growth
without vaporization (Nieminen et al., 2010).
Nano-Köhler theory (Anttila et al., 2004; Kulmala et al., 2004b, c)
suggests that when a soluble organic vapor is dissolved in newly formed
nuclei of aqueous-phase sulfate at certain size between 1 and 3 nm, the surface
organic vapor pressure is lowered and thus assists the growth of the nuclei.
Here, we continued our discussion based on the nano-Köhler theory to
provide an explanation of GR behaviours observed in our urban atmosphere. We
first subtract GRH2SO4 from GRmeas to obtain the growth rate due to a
condensing organic vapor (hereafter, denoted as ELVOC – extremely low
volatility organic compound):
GRmeas,elvoc=GRmeas-GRH2SO4,
where GRH2SO4 is calculated from the H2SO4 proxy concentration
[H2SO4] following Nieminen et al. (2010):
GRH2SO4=γ2ρv,H2SO41+Dv,H2SO4Dp28kTπ121mp+1mv,H2SO412mv,H2SO4H2SO4
and all parameters in Eq. (4) are taken from Nieminen et al. (2010) for
H2SO4.
The size-dependent growth rate due to the uptake of ELVOC was expressed as
GRelvoc=γ2ρv,elvoc1+Dv,elvocDp28kTπ121mp+1mv,elvoc12mv,elvocCelvoc-Csurface,
where Celvoc is gas-phase ELVOC
concentration far from the particle. The net uptake of ELVOC is driven by
the difference of Celvoc and equilibrium surface
concentration over the particle Csurface.
Csurface is determined by the pure component
saturation vapor pressure Celvoc∗,
particle curvature exp4σvkTDp and particle composition:
Csurface=Celvoc∗exp4σvkTDpxDp.
xDp is the mole fraction of water-soluble ELVOC in the pseudobinary
solution consisting of ELVOC and the aqueous sulfate nuclei. The
pseudobinary solution was treated ideal here. An example of xDp as a
function of Dp was shown in Fig. 8a. Nuclei activation diameter
Dp,act is the size that ELVOC fraction begins to increase significantly.
For Dp < Dp,act, xDp is approximated with a fixed
value (x0). For Dp > Dp,act, xDp increases
significantly with the organics being added to the sulfate core of
Dp,act size. The size-dependent xDpis approximated as
xDp=x0,Dp<Dp,actx0+(Dp3-Dp,act3)/velvoc(Dp3-Dp,act3)/velvoc+Dp,act3/vsulfate,Dp≥Dp,act,
Considering strong Kelvin effect, Csurface decreases with increasing
Dp for Dp < Dp,act (Fig. 8b dashed
black line). For Dp > Dp,act, the rapidly
increasing organic fraction in the small size regime of 2–3 nm raises the
equilibrium Csurface of ELVOC first. Then for 3–6 nm
particles that are dominated by organics,
Csurface decreases to merge with the
Kelvin curve of a pure organic droplet (red line, Fig. 8b). The complete
equilibrium curve of Csurface in 1–6 nm (dashed + solid
black lines) was shown in Fig. 8c. The blue line represented the
calculated
Celvoc-Csurface. The
trend of Celvoc-Csurface coincided with the size dependence of the measured GR corrected by
H2SO4 (GRmeas,elvoc, Fig. 8d blue circle). Dp,act
corresponded to the size with local maximum GRmeas,elvoc.
(a) Mole fraction of organics (xDp) in a binary solution
of sulfate nuclei and activating organics (ELVOC) in a new particle. Nuclei
activation diameter Dp,act is the size where ELVOC begins to dilute the
nuclei. (b) Kelvin equilibrium curves over a pure organic droplet (red line)
and a binary solution with a fixed organic fraction (green line), nano-Köhler curve for Dp > Dp,act (black solid line) and
surface concentration Csurface for Dp < Dp,act (black
dashed line). (c) Gas phase concentration of the organic vapor
(Celvoc, cyan line), surface concentration
C∞ (black line), and
Celvoc-Csurface (blue
line). (d) Growth rate GRH2SO4 due to H2SO4 (Mikkonen proxy) and growth rate due to organic vapor GRelvoc,meas, calculated
as GRmeas- GRH2SO4.
We fitted GRelvoc with GRmeas,elvoc in sub-3 nm sizes at an
instant in time by adjusting three free parameters in Eq. (5): x0,
Celvoc, and Celvoc∗. Other parameters like
surface tension (0.02 Nm-1) and molar volume (135.5 cm3 mol-1) of ELVOC were taken from Kulmala et al. (2004b). Molecule
diameter dv (0.8 nm) and condensed-phase density ρv
(1.5 g cm-3) of ELVOC were taken from Ehn et al. (2014). Uptake coefficient γ
was calculated following Nieminen et al. (2010). The fitting results in Fig. 9 showed that the dependence of
GRmeas,elvoc on Dp below 3 nm could be well reproduced
by Eq. (5) for both Type A and Type B events. Free parameter x0 determined the
magnitude of the dashed black line in Fig. 8b. x0 was fitted to be
0.07–0.42 for the eight events. C∞ was sensitive to
the local maximum GRmeas,elvoc at the Dp,act.
Celvoc, and Celvoc∗ determined the
local minimum GRmeas,elvoc at the right side of Dp,act.
Therefore, C∗ and C∞
were basically determined by the measured GR (local maximum and local minimum)
and not sensitive to x0. As shown in Table 1, the condensing
organic vapor concentrations Celvoc were
1.7×107–1.7×108 cm-3. The saturation
vapor concentrations Celvoc∗ were
3.3×106–5.2×107 cm-3; they were within
the orders of magnitude of 107–108 cm-3 and 106–107 cm-3 suggested by Kulmala et al. (2004b), respectively.
Comparisons of measured (GRelvoc,meas, black circle) and fitted
(GRelvoc, red line) growth rates from Eq. (4) for typical Type A1, A2,
B1, and B2 events. Also shown were growth rates calculated from appearance-time method (blue cross) for sub-3 nm particles when growth rate was
relatively small or for larger particles with large size intervals.
For comparison, the GR calculated from the appearance-time method was also shown
in Fig. 9 (blue cross) for > 3 nm particles on 15 May, 18 and 19 February, as well as for sub-3 nm particles on 20 May when
particle growth was relatively slow. It can be seen that the GR on 20 May
calculated from the two methods agreed well with each other, lending credit
to our GDE method. The GR in > 10 nm sizes was usually
underestimated by GRelvoc. This could be interpreted as other condensing
vapors with higher volatility may contribute to particle growth in the
larger particles. It should be noted that the appearance-time method
followed the time steps when newly formed particles appeared in successive
size bins and thus the GR calculated from the appearance-time method as not
the growth rates at the same instant in time.
For all the eight nucleation events, Table 1 summarizes the measured values of
overall growth rate in 1.4–3 nm (GR1.4–3), maximum growth rate in 1.4–3 nm
(GRmax,1.4–3), overall growth rate in 3–20 nm (GR3–20), nucleation
rate (J1.4), activation diameter (Dp,act), CS, and temperature (T)
during the event periods with maximum nucleation rates. Corresponding
estimates of the Mikkonen H2SO4 proxy, Celvoc, and
Celvoc∗ were shown in the right three columns. It can be seen that in comparison with more conventional Type A
events, Type B events usually occurred with (1) higher J1.4,
GRmax,1.4–3, GR1.4–3, Celvoc, and CS; (2) smaller
Dp,act; and (3) lower T. However, the H2SO4 proxy and
GR3–20 were similar in Type A and Type B events. Based on these
estimations, we concluded that higher ELVOC concentration
Celvoc was the key factor leading to the higher
J1.4 and GR1.4–3, which in turn resulted in the different size
spectrum pattern in Type B events (“up-side-down volcano”) from in Type A
events (“volcano”).
It should be noted that the organic vapor concentrations
Celvoc in this study were not directly
measured, but estimated based on Eqs. (4) and (5). Celvoc,
[H2SO4], mole fraction xDp, and growth rates calculated
using Eqs. (4) and (5) were for an instant in time. Aerosol dynamic processes, such
as nucleation, coagulation, and the condensation growth of H2SO4
and water vapor, were not considered explicitly in Eqs. (4) and (5). In addition,
bulk thermodynamics was applied in Eq. (5) for extremely small
clusters/particles of sub-3 nm sizes. Therefore, although our calculation
provided a possibility to explain the size dependence of growth rate
observed in the polluted urban atmosphere, Celvoc in
this study was subject to uncertainties in (1) the growth rate derived from
the GDE method, (2) the theory by which the growth rate was related to the
organic vapor concentration, and (3) H2SO4 level, which was
calculated using Mikkonen proxy.
Inhibited particle growth to CCN-active sizes in strong nucleation
events of Type B2
Type B2 was a strong nucleation event that produced rather high concentrations
of new particles in sub-20 nm size range (Fig. 3d). High concentrations of
activating vapor in these events (e.g., C∞:
1.4–2.0×108 cm-3 on 18 February and 4 March) should
favor a banana-shape NPF event with fast growth of particles > 20 nm, due to weakened Kelvin effect. However, it was puzzling to us why new
particles accumulated in 2–20 nm and did not grow further on Type B2 event
days (see Fig. 3d). We first examined the air mass trajectory
characteristics of Type B2 events. Compared with Type B1, Type B2 was
characterized by long-range transport air masses from far north of China and
Mongolia. The lumped trajectories with insignificant wind direction change
implied that the air mass in Type B2 event was quite uniform. In addition,
meteorological and chemical variables (high solar radiation flux and wind
speed, low temperature, PM2.5, SO2, NO2, CO, and O3;
green lines in Fig. 5) collectively suggested that Type B2 was a typical
regional event in homogeneous cold air masses. Therefore, the interrupted
growth of new particles was not likely to be a result of wind direction
change.
As seen from Fig. 5, meteorological variables on Type B2 days were
generally more favorable in aiding particle growth than on Type B1 days:
lower PM2.5, lower temperature, and higher solar radiation flux. The
unfavorable factors in Type B2 events, however, included lower
concentrations of SO2, NO2, and CO (anthropogenic emission
indicators), lower secondary photochemical product indicators O3 and
lower particle phase sulfate in 100–1000 nm (X. Ge, personal communication,
2015; X. Ge conducted simultaneous AMS measurement during our measurement
periods). All of these evidences suggested that further particle growth in Type B2 events was limited by certain condensing vapor other than ELVOC.
Consequently, although there was a pool of sub-20 nm particles, they were
not further “activated” due to the low availability of this condensing
vapor. Following the terminology of Donahue et al. (2011, 2012), we called
this condensing vapor LVOC (low volatility organic compounds).
The above hypothesis was sound if we considered that the identity of LVOC
for the growth of particles > 20 nm could be different from ELVOC
for sub-3 nm particle growth. Hirsikko et al. (2005) observed that
GR3–20 demonstrated an opposite seasonal cycle to GR1.3–3:
GR3–20 was higher in summer, whereas GR1.3–3 was higher in winter.
This suggested that the condensing vapors were different in identity for
particles of different sizes. Hirsikko et al. (2005) attributed the
condensing vapors for GR3–20 to biogenetic VOCs. In our urban atmosphere,
according to Fig. 5, LVOC was more likely to be from anthropogenic sources
associated with SO2, NOx, and CO emissions. A picture of the growth
process was thus like this: ELVOC of lower volatility, lower concentration,
and higher water solubility activated inorganic nuclei and accelerated
particle growth in smaller sizes. This in turn assisted in the condensation
of LVOC of high volatility, low solubility, but with larger amount of mass.
The further growth of particles > 20 nm, which means significant
increment of particle mass, needed continuous supply of LVOC from the
anthropogenic sources. On the Type B2 days, LVOC supply was not adequate
(low SO2, CO and NOx). As a result, continuous banana-shape
particle growth did not take place.
Comparison with two other sub-3 nm particle studies in the YRD
megacities
Herrmann et al. (2014), Xiao et al. (2015), and this study investigated sub-3 nm
particle occurrences in the polluted megacities (Nanjing and Shanghai)
of the YRD region. Our study had three advantages over previous studies: (1) we
derived time- and size-resolved nucleation rate and growth rate, and thus
provide more information about NPF, (2) we decoupled the nucleation and
growth processes by differentiating sub-3 nm particle events from
conventional NPF events. This allowed us to investigate the controlling
factors in the two processes respectively, (3) our measurement period covered
spring, summer, and winter seasons, and thus wider ranges of environmental
condition variables. Here we compared the results from the three studies.
The objective was to investigate how these independent studies contribute
collectively to the understanding of NPF in the YRD megacities.
First, we compared the NPF frequency, nucleation rate, and growth rate. All
three studies detected NPF events on about 20 % of winter observation
days. Including summer and spring with fewer events, we observed the overall
NPF frequency was 10 % in Nanjing. Using sub-3 nm particle event as an
approximate measure of nucleation, we found nucleation frequency actually
much higher (47 % of all observation days). Xiao et al. (2015) observed
that average J1.34 at the onset of winter nucleation events was 188 cm-3 s-1
in Shanghai. Using a different GDE method, we found that
the event-averaged J1.4 ranged from 20 to 500 cm-3 s-1 in the eight
events. Clear diurnal variations of J1.4 was observed with peak values
up to 2500 cm-3 s-1. In the size range of 3–30 nm, GR was quite close
in all three studies, ranging from 4.5–11 nm h-1. In the sub-3 nm size
range, however, our median GR1.4–3 was 4.3 nm h-1, which was higher
than median GR1.35–2.39 of 0.94 nm h-1 observed in Shanghai.
From these comparisons we concluded that (1) nucleation rate in the polluted
YRD urban area was clearly higher than those typically observed in most
remote or moderately polluted environments (Jiang et al., 2011b; Kuang et
al., 2012; Kulmala et al., 2013; Lehtipalo et al., 2009, 2010, 2011; Yu et
al., 2014a, b); (2) our results showed a wider range of nucleation rate (a
few to 2.5×103 cm-3 s-1) than Xiao et al. (2015)
and Herrmann et al. (2014), not only because our data covered 3 seasons, but
also because our time-resolved J included the entire nucleation period.
(3) GR in the sub-3 nm size range was higher in our study than in Shanghai, partly
because the GDE method allowed one to determine higher GR than the appearance-time
method.
Second, we compared the NPF mechanisms in this polluted area reported by the
three studies. Based on winter data only, Herrmann et al. (2014) did not
find any correlation between temperature and nucleation in Nanjing. But if
combining data from different seasons, we found significant negative
correlation between J1.4 and temperature (Fig. 7b), implying atmospheric
nucleation was not favored under high temperature conditions. Herrmann et
al. (2014) suggested SO2 was excessive for the winter NPF in Nanjing.
We further pointed out that SO2 may not be excessive in summer and is
an unfavorable factor of rare summer nucleation event. Based only on winter
observations, Herrmann et al. (2014) and Xiao et al. (2015) identified
radiation or H2SO4 as the main driving force of NPF and CS as the
main obstacle. While we recognized this, we concluded that other
environmental variables like temperature and RH can also control the
occurrence of atmospheric nucleation under various atmospheric conditions.
Furthermore, for the subsequent growth of sub-3 nm particles to CCN-active
sizes, the supply of anthropogenic gaseous precursors other that
H2SO4 can also become a limiting factor.
Last, all the three studies tried to correlate Mikkonen's
H2SO4 proxy to nucleation rate or growth rate. Only our study
produced a significant correlation between J and [H2SO4]n
(R2= 0.56–0.86) with n= 0.82–1.2 (Fig. 7a). The better correlation
was mainly because we used hourly J and [H2SO4] data, whereas the
other two studies had fewer data points, i.e., one J value for each event. Xiao
et al. (2015) proposed that the H2SO4 proxy was sufficient to explain
their observed GR1.34–3. In contrast, our study suggested that other
condensing vapors were needed to explain GR in both sub-3 nm and > 3 nm size ranges.
Conclusion
NPF can contribute to CCN only after going through nucleation, initial
growth steps, and subsequent growth to CCN-active sizes. This study provided
the evidences of limiting factors in these processes in a polluted urban
atmosphere in China. We observed atmospheric nucleation events on 42 out of
total 90 observation days, but particles could grow to CCN-active sizes on
only 9 days, which was equivalent to nine conventional NPF events. In summer,
strict emission control measures during the 2014 Youth Olympic Games
resulted in relatively low PM2.5 and anthropogenic trace gases
(SO2, NO2, CO, and O3) levels. Infrequent nucleation was thus
limited by both low concentrations of gaseous precursors and high
temperature and RH in summer. In more polluted winter and spring atmosphere,
precursor supply was not limiting anymore; nucleation occurred once
meteorological conditions were favorable (i.e. low CS and temperature/RH,
higher solar radiation). However, for the further growth of sub-3 nm
particles to CCN-active sizes, anthropogenic gaseous precursors again became
limiting factors.
A simplified GDE method was used in this study to calculate particle
formation rates first and then growth rates. Nucleation events were strong
in the polluted urban atmosphere of Nanjing. Initial J1.4 at the onset
and peak J1.4 at the noontime could be up to 2.1×102
and 2.5×103 cm-3 s-1,
respectively, during the eight nucleation events selected from different
seasons. The diurnal variation of J1.4 implied that nucleation was
usually linked to sunlight induced photochemistry. Time-dependent
J1.4 showed good linear correlations with the H2SO4 proxy for
every single event, except a day with significant nocturnal nucleation.
However, the correlation between J1.4 and the H2SO4 proxy for
all eight events deteriorated, which might reflect the effect of temperature or
assisting vapor concentration in the nucleation. The deteriorated
correlation could also be due to the lower predictive ability of the
H2SO4 proxy in the polluted urban atmosphere for different
seasons.
In all nucleation events, a local maximum growth rate was observed between
1 and 3 nm with GR up to 25 nm h-1. This means GR was not monotonically
increasing with particle size. The overall GR1.4–3, however, was still
smaller than GR3–20, if particles could grow beyond 3 nm. However, it
should be noted that the existence of local maxima GR in sub-3 nm is highly
sensitive to the uncertainty of size distribution derivation, i.e. the
moving average filter was used to smooth original noisy raw data of
N1.5, N1.8, N2.0, N2.3, N2.6, and N2.8. The noise is
due to the nature of sub-3 nm cluster dynamics, environmental conditions, and
instrumental uncertainties. On the other hand, the GR observation is
potentially real and might give new insight into cluster dynamics in
polluted environments. The local maximum growth rate was interpreted as the
solvation effect of organic activating vapor in newly formed inorganic
nuclei. Based on our estimation, high ELVOC concentration of 2.3×107–2.0×108 cm-3 was expected to be the key
factor leading to high GR1.4–3. The varying GR of new particles in turn
resulted in the different particle growth patterns that we observed in
Nanjing.
Our results call for a more robust proxy of gaseous H2SO4 to be
developed for polluted urban conditions. The study also highlighted the
importance of estimating or measuring activating organic vapor levels (using
CI-APi-TOF, for example) in the initial growth steps of atmospheric NPF. Our
year-round measurement data provided valuable size evolution data of sub-3 nm
clusters/particles to evaluate previous aerosol dynamic models of new
particle formation. A robust dynamic model was needed to appropriately treat
all aerosol and gas-phase processes in the initial growth steps.