Introduction
Aerosol particles can influence climate directly and indirectly (Andreae and
Crutzen, 1997; Haywood and Boucher, 2000; IPCC, 2013), and have adverse
impact on human health (Dockery et al., 1993; Laden et al., 2006; Pope and
Dockery, 2006). Atmospheric nucleation of gas-phase precursors to clusters
and then further to nanoparticles is the largest source of atmospheric
aerosol particles (Kulmala et al., 2004b; Zhang et al.,
2012). This phenomenon has been observed in numerous locations around
the world, including areas with a pristine atmosphere, e.g., coastal areas
(O'Dowd et al., 2002), Antarctic/Arctic (Park et al., 2004), remote forest
(Dal Maso et al., 2005), semi-rural locations with very low pollution levels
such as Kent, OH (Kanawade et al., 2012), and heavily polluted cities, such
as Mexico City (Dunn et al., 2004).
The exact mechanism for atmospheric nucleation is still under active
investigation. Field measurements and laboratory studies have shown that
sulfuric acid is a key precursor species for atmospheric nucleation (Weber et al., 1996; Sipila et al., 2010) and that
atmospheric nucleation rate can be significantly promoted in the presence of
other precursors including ammonia (Ball et al., 1999; Benson et al.,
2009), amines (Berndt et al., 2010; Zhao et al., 2011), and organic acids
(Zhang et al., 2004, 2009). At certain locations,
ion-induced nucleation (Yu and Turco, 2001; Lee et al., 2003) or
iodine species (O'Dowd et al., 2002) may also help to explain the
observed new particle formation. Very recently, progress has been made by the
use of a particle size magnifier (PSM) and chemical ionization atmospheric
pressure interface time-of-flight mass spectrometer by
combining the Cosmics Leaving OUtdoor Droplets (CLOUD) chamber experiments
and ambient observations including those in Hyytiälä, Finland,
showing that oxidation products of biogenic emissions, together with
sulfuric acid, contribute to new particle formation in the atmosphere
(Schobesberger et al., 2013; Riccobono et al., 2014).
China suffers severe air pollution, especially high atmospheric particle
loadings in recent years (Chan and Yao, 2008). Among many potential
sources of atmospheric particles, atmospheric nucleation has been suggested
to be a significant source of particles (Matsui et al., 2011; Yue et al.,
2011). Correspondingly, a number of extensive campaigns or long-term
observational studies have been carried out in the Beijing area (e.g., Wu
et al., 2007; Yue et al., 2009; Zhang et al., 2011; Gao et al., 2012) and
Pearl River delta, including Hong Kong (e.g., Guo et al., 2012; Yue et
al., 2013). As one of the most industrialized area of China, one of the most
populated region in the world, and one of the hotspots for particle
pollution, Yangtze River delta (YRD) has only seen a few research activities
on atmospheric nucleation (Du et al., 2012; Herrmann et al., 2014).
Among the few studies, measurements at the station for Observing Regional
Processes of the Earth System, Nanjing University (SORPES-NJU), offered a
first insight for new particle formation in the western part of YRD
(Herrmann et al., 2014). On the other hand, atmospheric nucleation
research in China is still in its infant stage and the latest experimental
techniques are yet to be applied in China. For example, data on freshly
nucleated particles are really sparse, except for those from an air ion
spectrometer employed at SORPES-NJU (Herrmann et al., 2014). To the best
of our knowledge, the use of a PSM, which is able
to study atmospheric nucleation at the size (mobility diameter) down to 1.5±0.4 nm
(Kulmala et al., 2012), has not been reported in a
Chinese location in literature. The lack of key information greatly hinders
a better understanding of nucleation mechanisms in China, where
concentrations of sulfuric acid and basic gases including ammonia and amines
are high (Zheng et al., 2011, 2015) but concentrations of
extremely low-volatility organic compounds formed from biogenic emissions
are yet to be determined.
Direct measurements of atmospheric nucleation rates down to 1.5±0.4 nm
provide a better and more accurate characterization of atmospheric
nucleation; the indirect calculation of atmospheric nucleation rates
from the formation rates of 3 nm particles leads to substantial uncertainty
due to our incomplete understanding of condensational growth and coagulation
scavenging of particles in the 1.5 to 3 nm range (Anttila et al., 2010;
Korhonen et al., 2011). With the growing number of reports of real nucleation
rates in clean atmosphere (e.g., Kulmala et al., 2012; Yu et al.,
2014), it is ideal to measure nucleation rates in a
polluted urban atmosphere to find out how atmospheric nucleation rates vary
under different atmospheric conditions. In addition, the nucleation mechanism
in a polluted urban atmosphere, which is vital to understanding atmospheric
nucleation at a global scale and for atmospheric model development, can be
preliminarily investigated by examining the relationship between the measured
atmospheric nucleation rates and the well-accepted precursor gases that exist
in high concentrations.
In this study, we measured atmospheric nucleation from 25 November 2013
to 25 January 2014 in urban Shanghai with a nano condensation nucleus counter
system (nCNC) and two sets of scanning mobility particle sizers (SMPS).
Nucleation frequency, nucleation rate (J1.34), condensation sink
(CS), and growth rates (GR) are reported and compared with previous
studies with similar or dissimilar atmospheric environments. In addition,
the potential nucleation mechanism was explored by correlating sulfuric acid
proxy calculated from sulfur dioxide precursor and gas-phase ammonia to
nucleation rate J1.34.
Experimental
Nucleation measurements
Nucleation measurements were carried out on the rooftop of a teaching
building (31∘18′ N, 121∘30′ E) that is about 20 m above ground
on the campus of Fudan University between 25 November 2013 and 25 January
2014. This monitoring site is mostly surrounded by commercial properties and
residential dwellings. The Middle Ring Road, one of the main overhead highways in
Shanghai, lies about 100 m to the south of the site. Hence, the Fudan site
can be treated as a representative urban site influenced by a wide mixture of
emission sources (Wang L. et al., 2013; Ma et al., 2014).
Ambient particle size distributions in the range of 1.34–615 nm were
measured using a combination of one nano condensation nucleus counter system
(model A11, Airmodus, Finland), one nano-SMPS (consisting of one DMA3085 and
one CPC3776, TSI, USA), and one long-SMPS (consisting of one DMA3081 and one
CPC3775, TSI, USA). The instruments were continuously running except for
maintenance and minor instrument breakdown during the campaign.
Ambient air was drawn into a stainless-steel manifold of 5.0 m length and 4 inch inner diameter at a flow rate of
153 cubic feet per minute (CFM) using a blower (Model DJT10U-25M, NUSSUN, China). From this main manifold, 1.75 L min-1 ambient air was drawn
through a 1/4 in. inner diameter stainless-steel tube of 18 cm length, and
diluted with a zero air flow generated by a zero air generator (Model 111,
Thermo, USA) at a ratio of 1 : 1 to reduce the overall relative humidity (RH)
and the number of particles entering PSM, since high RH and particle loading
had an impact on the saturation of diethylene glycol in PSM and hence data
quality. Subsequently, 2.5 L min-1 diluted air was sampled into nCNC. In
addition, 30 L min-1 split flow was drawn from the main manifold through a 1/4 in.
inner diameter conductive silicon tubing of 50 cm length, and then 0.3 and 1.5 L min-1 ambient air from the split flow were drawn
into nano-SMPS and long-SMPS, respectively. The calculated diffusion loss is up to 29 %
for 1.35 nm particles with the above setup and has been taken into account
in the entire size range during data processing.
The nCNC system consists of one PSM (model A10, Airmodus, Finland) and one
butanol Condensation Particle Counter (model A20, Airmodus, Finland),
and was used to measure size distributions of 1.34–3 nm clusters/particles.
Briefly, PSM activates the smallest particles using diethylene glycol as a
working fluid and condensationally grows nanoparticles up to larger than 90
nm in mobility equivalent diameter, after which an external condensation particle counter is used to
further grow the particles to optical sizes and count the grown
particles (Vanhanen et al., 2011). In this study, PSM was used in the
scanning mode in which the saturator flow rate is changed continuously,
giving an activation spectrum of the measured particles to obtain size
distribution of sub-3 nm clusters/particles. A scanning cycle of 100 steps
between saturator flow rates 0.1 and 1 L min-1 and a time resolution of 220 s were
chosen. Sub-3 nm clusters/particles were classified into 5 bins, i.e.,
1.34–1.37, 1.37–1.41, 1.41–1.52, 1.52–1.89, and 1.89–3.0 nm.
Geometric mean values of upper and lower limits of the five bins, i.e.,
1.35, 1.39, 1.46, 1.70, and 2.39 nm, were used to refer to the
five bins, respectively, in the growth rate calculation.
The nano-SMPS measured particles in the size range of 3 to 64 nm and the
long-SMPS recorded particles from 14 to 615 nm. For both SMPSs, 64 size bins
and 5 min time resolution were chosen. The sample flow to sheath flow ratios
for both SMPSs were set at 1 : 10. A comparison analysis on the total
particle concentrations between 14 and 64 nm measured by both nano-SMPS and
long-SMPS showed less than 10 % difference in the size range of
55–64 nm between two SMPSs. Hence, number concentrations of particles in
the size range of 3–615 nm, N3∼615, were calculated from the sum
of N3∼55 obtained from nano-SMPS, N55∼64 from the
arithmetic average of nano-SPMS and long-SMPS, and N64∼615 from
long-SMPS.
At the same site, sulfur dioxide (SO2) was measured by an SO2
analyzer with pulsed UV fluorescence technique (Model 43i, Thermo, USA) and
a time resolution of 5 min; calibration of this SO2 analyzer was
performed every month. A differential
optical absorption spectroscopy system was used to measure the integrated
concentration of NH3 along the optical path between a transmitter
telescope using a 35W Deuterium lamp as the light source and a receiver
telescope (53 m); then the system yielded the average concentration of NH3
by dividing the integrated concentration by the absorption length (Platt
and Stutz, 2008). In this study, the concentration of NH3 was determined
by fitting the reference spectra to the atmospheric spectra in a given window
(205–220 nm) using a nonlinear least-squares method similar to a
previous measurement of HONO and NO2 (Wang S. et al., 2013). Detection limit of NH3 was about 1 ppb with a 3 min integration
time.
Solar radiation intensity measured by a pyranometer (Kipp & Zonen CMP6,
Netherlands) was obtained from the Shanghai Pudong Environmental Monitoring
Centre (31∘14′ N, 121∘32′ E, about 8.78 km
from the Fudan site).
Data processing
Nucleation rate, formation rate of 3 nm
particles (J3), growth rate, and condensation sink
In this study, PSM allowed measurements of clusters/particles down to 1.34 nm.
Hence, atmospheric nucleation rate, J1.34, defined as the flux of
particles growing over 1.34 nm, can be calculated by taking into account the
coagulation losses and condensational growth out of the considered size
range (Kulmala et al., 2012):
J1.34=dN1.34∼3dt+CoagSdp=2nm⋅N1.34∼3+11.66nmGR1.34∼3⋅N1.34∼3,
where CoagSdp=2nm represents coagulation sink of 2 nm particles,
an approximation for that of the size interval 1.34–3 nm, and
GR1.34∼3 represents the apparent clusters/particle growth rate
between 1.34 and 3 nm.
Formation rate of 3 nm particles was calculated in a similar way (Sihto et
al., 2006; Kulmala et al., 2012), providing a comparison with previous
studies:
J3=dN3∼6dt+CoagSdp=4nm⋅N3∼6+13nmGR3∼6⋅N3∼6,
where CoagSdp=4nm represents coagulation sink of 4 nm particles,
an approximation for that of the size interval 3–6 nm.
Growth rate is defined as the rate of change in the
diameter of a growing particle population, using the maximum-concentration
method (Kulmala et al., 2012):
GR=ddpdt=ΔdpΔt=dp2-dp1t2-t1,
where dp1 and dp2 are the representative particle
diameters at times t1 and t2, respectively.
Condensation sink describes the condensing vapor sink caused by the
particle population (Kulmala et al., 2012):
CS=4πD∫0dpmaxβm,dpdpNdpddp=4πD∑dpβm,dpdpNdp,
where D is the diffusion coefficient of the condensing vapor, usually
assumed to be sulfuric acid (0.104 cm2 s-1 used in this study),
and βm,dp is the transitional regime correction factor.
Sulfuric acid
Sulfuric acid has been accepted as a key gas-phase precursor for atmospheric
nucleation and contributes to the subsequent growth of newly formed
particles (Weber et al., 1996; Sipila et al., 2010). The
accurate measurement of gas-phase sulfuric acid requires application of
chemical ionization mass spectrometry using nitrates as reagent ions
(Eisele and Tanner, 1993), which was not possessed by this research
group during this study. Instead, the sulfuric acid proxy
[H2SO4]
was estimated based on local solar radiation n level radiation, SO2
concentration [SO2], condensation sink, and relatively humidity
(Mikkonen et al., 2011):
[H2SO4]=8.21×10-3⋅k⋅radiation⋅[SO2]0.62⋅CS⋅RH-0.13,
where k is the temperature-dependent reaction-rate constant. The relative
error between calculated sulfuric acid proxy and measured sulfuric acid
concentration is estimated to be 42 % (Mikkonen
et al., 2011). The time resolution of calculated sulfuric acid proxy was 1 h since that of temperature and relative humidity was 1.
Condensation of sulfuric acid contributes to the growth of newly formed
particles. The growth of clusters/particles due to condensation of sulfuric
acid, GRH2SO4, can be estimated by the following equations
(Nieminen et al., 2010):
GRH2SO4=γ2ρv1+dvdp28kTπ1/21mp+1mv1/2mv[H2SO4]
and
γ=43⋅Kn⋅βm,dp,
where ρv and dv are the condensed phase density and molecule
diameter of H2SO4, respectively; mp and mv are
particle and H2SO4 vapor molecule masses, respectively; γ
is a correction factor; and Kn is the Knudsen number (Lehtinen and Kulmala,
2003). For this calculation, particle density ρp=1.83 g cm-3 was used.
The particle growth due to the hydration of H2SO4 is taken into
account by assuming that sulfuric acid is instantaneously equilibrated with
gas-phase water. During our campaign, daily average RH varied between 28.7 and 60.0 %. Hence, using the H2SO4-hydrate distribution data
given by Kurtén et al. (2007), the density and mass of the average
hydrated H2SO4 molecule at 50 % relative humidity is calculated
and further used in Eq. (6).
Results and discussion
Classification of new particle formation (NPF) events
Figure 1 presents a contour plot for particle size distributions of
3–615 nm and a number concentration plot of sub-3 nm clusters/particles,
N1.34∼3, during 25 November 2013–25 January 2014. Data were
occasionally missing because of maintenance and minor breakdown of
instruments. From the figure, frequent bursts of sub-3 nm particles were
evident, with concentrations up to 8.0×104 cm-3 around noontime. However, similar to previous studies (Kulmala et al., 2007, 2013; Yu
et al., 2014), not all sub-3 nm particles eventually underwent a continuous
growth to larger sizes. In this study, we define an observation day with
appearances of sub-3 nm clusters/particles over a time span of hours and
subsequent growth to larger sizes for a few hours as a NPF event day. In this
case, a NPF day will present a banana-shaped contour plot of particle size
distributions obtained from SMPS (Dal Maso et al., 2005). We focus on
characteristics and potential mechanisms of these events.
Contour plot for particle size distributions of 3–615 nm and plot
of number concentrations of sub-3 nm clusters/particles (N1.34∼3)
during 25 November 2013–25 January 2014. Data were occasionally missing
because of the maintenance and minor breakdown of instruments. NPF events are
illustrated with shadows.
According to the classification, there were 13 event days during the 62-day
campaign, as illustrated by the shadow in Fig. 1. Although nCNC data were
partially unavailable on 26 December 2013 and completely unavailable on 24
January 2014, these 2 days are still defined as NPF days since a
distinctive banana-shaped contour plot for particle distributions between
3 and 615 nm existed. 18 December 2013 was not regarded as a NPF day since
N1.34∼3 was not recorded and the growth of 3–20 nm particles
was relatively short-lived.
Among these NPF events, five NPF events occurred in November, three in December,
and five in January. The averaged frequency for NPF events was 21.0 % during
the 62-day campaign. Our NPF frequency in Shanghai is larger than the
average value in winter 1996–2003 at SMEAR II station, Hyytiälä,
Finland (Dal Maso et al., 2005), likely because nucleation events in
Hyytiälä have recently been related to oxidation products of
biogenic emissions (Kulmala et al., 1998; Schobesberger et al., 2013;
Riccobono et al., 2014) and photochemistry of volatile organic compounds is
less intensive in winter months. This frequency is also higher than that in
winter in semi-rural Kent, OH, during which transport of sulfur dioxide from
the east-southeast power plant to Kent is not favored (Kanawade
et al., 2012). NPF events occurred at a frequency of around 40 % during
November–December 2004 in Beijing (Wu et al., 2007), much more often than in
Shanghai. On the other hand, NPF frequency in Shanghai is remarkably close
to that measured in winter 2012 in Nanjing, which is also located in YRD (Herrmann et al., 2014).
Number concentrations of particles in different size ranges, i.e.,
N1.34∼3, N3∼7, and N7∼30, respectively, on a
NPF day (11 December 2013) and an obvious non-NPF day (7 January 2014) are
further examined to illustrate features of a NPF event, as shown in Fig. 2.
On the NPF day, 1.34–3 nm particles appeared as early as 7 a.m. UTC+8 in the
morning, i.e., right after sunrise (6:42 a.m. UTC+8 on 11 December 2013), reached
its maximum just before noontime, and spanned for almost the whole daytime
(sunset at 4:52 p.m., 11 December 2013), suggesting that photochemistry
products likely contribute to the formation of smallest particles. This size
distribution of atmospheric neutral and charged clusters/particles by a
scanning PSM is identical to that measured in Hyytiälä, Finland
(Kulmala et al., 2013). On the same NPF day, 3–7 and 7–30 nm particles
appeared much later, resembling previous findings only with SMPS measurements
(e.g., Yue et al., 2010). The lag in peaking times of N1.34∼3,
N3∼7, and N7∼30 on the NPF day clearly indicated the
continuous growth of clusters/particles, the reduction of particles due to
coagulation during the growth, and the diverse sources of particles in the
size range of 7–30 nm. In contrast, N1.34∼3 and N3∼7
showed a flat profile on the non-NPF day. The minor enhancement in N7∼30 between 10 a.m. and 5 p.m. on the non-NPF day was not due to growth of
newly formed particles. Instead, direct emission of 7–30 nm particles from
transportation activity likely explained their appearance.
Profiles of N1.34∼3, N3∼7, and N7∼30
from 6 a.m. to 6 p.m. UTC +8 on a NPF day (11 December 2013) and a non-NPF day (7
January 2014).
General characteristics of NPF events
Table 1 summaries characteristics of each NPF event observed in this
campaign, including J1.34, J3, GR1.35∼2.39 (from the
bin of 1.34–1.37 nm to the bin of 1.890–3.0 nm), GR2.39∼7,
GR7∼20, CS, [H2SO4], N1.34∼3, and total
number concentrations of particles N1.34∼615, and compares the
mean values to those in selected other studies. Nucleation rate J1.34
and formation rate of 3 nm particles were 112.4–271.0 and
2.3–19.2 cm-3 s-1, respectively. Nucleation rate J1.34 in Shanghai is
obviously significantly larger than 1.4 cm-3 s-1 in
Hyytiälä, Finland with a pristine atmosphere (Kulmala et
al., 2012) and 1.3 cm-3 s-1 in Kent, OH, with relatively lower
levels of pollutants (Yu et al., 2014). Direct comparison of our
nucleation rate with that in a Chinese location is not feasible because no
previous reports are available. However, Herrmann et al. (2014) reported
a J2 of 33.2 cm-3 s-1 at the SORPES-NJU station in Nanjing,
China. Together with their results, we conclude that strong nucleation
events occur geographically widely in the YRD region.
Formation rate of 3 nm particles has been more routinely reported.
Similarly to J1.34, J3 in Shanghai is significantly larger than
0.61 cm-3 s-1 in Hyytiälä, Finland (Kulmala
et al., 2012), and generally comparable to 3.3–81.4 and 1.1–22.4
in Beijing (Wu et al., 2007; Yue et al., 2009), 3.6–6.9
in Hong Kong (Guo et al., 2012), and
2.4–4.0 cm-3 s-1 in a back-garden rural site in Pearl River delta (Yue et al., 2013). The fast reduction from
J1.34 to J3 was likely due to the presence of a large background
particle number as shown in Table 1.
The large background particle number concentrations corresponded to the high
condensation sink (0.030–0.10 s-1) observed during the
campaign. As shown in Table 1, CS in Shanghai is much larger than
(0.05–0.35)×10-2 s-1 in Hyytiälä, Finland
(Kulmala et al., 2012), and 0.8×10-2 s-1 in
Kent, OH (Yu et al., 2014), but comparable to (0.58–8.4) ×10-2 s-1 in Beijing (Wu et al., 2007; Yue et al., 2009;
Zhang et al., 2011; Gao et al., 2012), (1.0–6.2) ×10-2 s-1 in Hong Kong (Guo et al., 2012), 2.4×10-2 s-1 in Nanjing (Herrmann et al., 2014), and
(3.5–4.6) ×10-2 s-1 in an urban site in Pearl River delta (Yue et al., 2013). High sulfuric acid
proxy ([H2SO4] of (2.3–6.4) ×107 molecules cm-3) existed to promote the NPF events. Measurements of gas-phase
sulfuric acid by a chemical ionization mass spectrometer during the
CAREBeijing 2008 Campaign, a time period with strict air-quality control
regulations, reported peak concentrations of sulfuric acid up to the order
of 107 molecules cm-3 (Zheng et al., 2011), smaller than our
sulfuric acid proxy. Uncertainty may well exist for our sulfuric acid proxy
that was calculated from the concentrations of sulfur dioxide and radiation
intensity. However, judging from CS and [H2SO4] together, it is
clear that the condensable vapor in Shanghai is likely a main impetus for
observed strong new particle formation events.
GR1.35∼2.39, GR2.39∼7, and GR7∼20 were in the
range of 0.49–8.1, 3.1–35.7, and 4.5–38.3 nm h-1, respectively. The
arithmetic average values of GR1.35∼2.39, GR2.39∼7, and
GR7∼20 were 2.0±2.7 (1 standard deviation), 10.9±9.8, and
11.4±9.7 nm h-1, respectively, which are comparable to
3–20 nm h-1 for nucleation mode particles in another sulfur-rich
city, Atlanta, GA (Stolzenburg et al., 2005). In addition, GR1.35∼2.39 in Shanghai is smaller than the growth rates (5.5–7.6 nm h-1)
for particles in 1∼3 nm geometric diameter range in Atlanta (Kuang et
al., 2012). A closer examination of growth rates was performed by dividing
GR1.35∼2.39 into growth of clusters/particles from one bin to
another, i.e., GR1.35∼1.39 (1.6±1.0 nm h-1 from the bin
of 1.34–1.37 nm to the bin of 1.37–1.41 nm), GR1.39∼1.46
(1.4±2.2 nm h-1 from 1.37–1.41 to 1.41–1.52 nm),
GR1.46∼1.70 (7.2±7.1 nm h-1 from 1.41–1.52 to
1.52–1.89 nm), and GR1.70∼2.39(9.0±11.4 nm h-1 from
1.52–1.89 to 1.89–3.0 nm). These growth rates show a clear size-dependent
particle growth (Fig. 3), owing to the nano-Köhler activation that
suggests a faster growth for activated nanoparticles due to a decreasing
Kelvin effect and, thus, an enhanced condensation flux (Kulmala et al.,
2004b), Kelvin effect, and surface or volume-controlled reaction corrected for
the Kelvin effect on surface or volume concentrations (Kuang et al., 2012).
Similar observations have been reported in previous studies using nCNC
(Kulmala et al., 2013) and diethylene glycol-based ultrafine condensation particle counter (DEG UCPC) (Kuang et al., 2012). Our
GR2.39∼7 is larger than 6.3 nm h-1 in Nanjing (Herrmann et
al., 2014), and our GR7∼20 is close to the upper bound of those in
urban Beijing (Wu et al., 2007; Yue et al., 2009; Zhang et al., 2011; Gao et
al., 2012) and generally larger than 1.5–8.4 nm h-1 in Hong Kong
(Guo et al., 2012), indicating that high concentrations of condensable vapors
existed. In addition, our growth rates suggest that the smallest clusters
(the bin of 1.34–1.37 nm), if not scavenged by larger particles, would grow
to 3 nm within ∼ 12 min and to 20 nm within ∼ 2 h.
Nucleation rate (J1.34), formation rate of 3 nm particles
(J3), particle growth rates (GR1.35∼2.39, GR2.39∼7,
and GR7∼20), condensation sink (CS), sulfuric acid proxy
([H2SO4]), number concentrations of 1.34–3 nm
clusters/particles (N1.34∼3), and total number concentrations of
particles (N1.34∼615), of each NPF event during this campaign.
Date
J1.34
J3
GR1.35∼2.39
GR2.39∼7
GR7∼20
CSe
[H2SO4]e
N1.34∼3f
N1.34∼615f
(cm-3 s-1)
(cm-3 s-1)
(nm h-1)
(nm h-1)
(nm h-1)
(10-2 s-1)
(107 cm-3)
(104 cm-3)
(104 cm-3)
Ref.
25 November 2013
NAa
10.6
NA
12.4b
38.3
4.7
2.7
3.3g
6.3
this study
26 November 2013
NA
2.3
NA
0.32b
NA
5.9
2.6
NA
NA
this study
28 November 2013
185.1
13.4
0.94
35.7
4.6
5.7
3.6
1.6
4.2
this study
29 November 2013
271.0
3.9
1.7
10.6
4.5
6.3
4.3
2.1
4.5
this study
30 November 2013
NA
NA
0.82
3.4
10.2
NA
3.1
1.5
NA
this study
10 November 2013
268.4
10.0
0.49
18.6
21.0
9.9
5.5
1.4
4.6
this study
11 December 2013
219.0
19.2
NA
5.1
9.6
10.2
6.4
1.1
4.5
this study
12 December 2013
190.3
7.6
NA
3.1
12.3
8.8
4.5
1.1
4.1
this study
9 January 2014
136.2
NA
8.1
NA
9.5
3.7
2.3
1.6
3.8
this study
13 January 2014
NA
2.7
NA
6.3
1.9
3.0
2.3
1.5
3.4
this study
15 January 2014
121.9
NA
0.56
NA
9.7
4.2
4.1
1.5
4.3
this study
21 January 2014
112.4
9.2
1.5
11.9
7.5
4.9
3.7
1.1
3.9
this study
24 January 2014
NA
8.1
NA
12.2b
7.8
4.7
3.4
1.7
4.2
this study
Mean
188.0
8.7
2.0
10.9
11.4
6.0
3.7
1.5
4.4
this study
Hyytiälä
1.4
0.61
1.4
3.9
4.9
0.05–0.35
Kulmala et al. (2012)
Kent, OH
1.3
0.8
0.9
Yu et al. (2014)
Atlanta
3–20c
Stolzenburg et al. (2005)
Atlanta
5.5–7.6
Kuang et al. (2012)
Beijing
1.2–8.0c
2.4–3.6
Gao et al. (2012)
Beijing
3.3–81.4
0.1–11.2c
0.58–4.3
Wu et al. (2007)
Beijing
2.7–13.9c
0.6–8.4
Zhang et al. (2011)
Beijing
1.1–22.4
1.2–5.6c
0.9–5.3
Yue et al. (2009)
Nanjing
33.2h
1.1i
6.3
8d
2.4
Herrmann et al. (2014)
Hong Kong
3.6–6.9
1.5–8.4c
1.0–6.2
Guo et al. (2012)
4.0–22.7 (rural)
2.3–3.3 (rural)
Pearl River delta
2.4–4.0 (rural)
10.1–18.9 (urban)
3.5–4.6 (urban)
Yue et al. (2013)
a Data were not available or could not be accurately
determined; b results were calculated from nano-SMPS data; c shown
here is GR3∼30; d shown here is GR7∼30; e daytime average (from 6:00 a.m. to 6:00 p.m.);
f 24 h average; g average values between 10 a.m. and 4 p.m.;
h shown here is J2;
i shown here is J6.
Averaged particle size evolution on NPF days. Arithmetic mean of
particle growth rates are given with 1 standard deviation.
Potential mechanisms for NPF events
As shown in Table 1, nucleation rate (J1.34) in this study is
typically larger than 100 cm-3 s-1, suggesting that the
ion-induced nucleation was not a main mechanism for observed fast nucleation
(Hirsikko et al., 2011; Riccobono et al., 2014). The 2012 winter study
at the SORPES-NJU station that is also located at YRD shows that the ratio
of J2 between ions and total particles (ions plus neutral particles)
was 0.002, also indicating the minor role of ion-induced nucleation
(Herrmann et al., 2014). Hence, it is likely that nucleation of neutral
precursor molecules actually largely determined the observed NPF events.
Correlations between logJ1.34 and log[H2SO4] (Fig. 4) and
between logJ1.34 and log[NH3] (Fig. 5) were examined to
elucidate potential mechanisms for our NPF events. Since J1.34 could not be accurately determined on some of the NPF days, the number of
data points in both figures was less than the actual number of NPF events
that have been observed. Daily peak concentration of sulfuric acid proxy and
daytime (6 a.m.–6 p.m.) averages of ammonia were used as approximations for
their effective concentrations on a NPF day since there was no peak
concentration for ammonia. The correlation (R2=0.62) between
logJ1.34 and log[NH3] is better than that (R2=0.38)
between logJ1.34 and log[H2SO4], and slopes are
0.57±0.17 and 0.65±0.28, respectively.
Correlation between logJ1.34 and log[H2SO4].
Daily peak concentration of sulfuric acid proxy was used as an approximation
for its effective concentration on a NPF day. The error bar corresponds to a
42 % uncertainty of sulfuric acid proxy according to Mikkonen et
al. (2011).
Most ambient studies showed that nucleation rate J is proportional to the
first or second power of the concentration of gas-phase sulfuric acid, i.e.,
J=A⋅[H2SO4]P where P is equal to 1 or 2,
conventionally interpreted as the number of sulfuric acid molecules in the
critical nucleus, and A is a pre-exponential factor (McMurry et al.,
2005; Sihto et al., 2006; Erupe et al., 2010). Our P of 0.65±0.28 is of a significant uncertainty, which could come from the
uncertainty during the calculation of sulfuric acid proxy [H2SO4] and the
scarcity of our data points. The upper limit of our P indicates that nucleation occurs after activation of clusters containing
one molecule of sulfuric acid, with subsequent growth involving other
species (Kulmala et al., 2006). The lower limit, however,
would imply a less important role of sulfuric acid in the critical nucleus
during our campaign, which is unlikely to be true according to numerous
previous studies (Weber et al., 1996; Sipila et al., 2010; Yu and Hallar,
2014). Kupiainen-Määttä et al. (2014) recently
reported that the number of molecules in a critical cluster cannot be
determined by a slope analysis in atmospherically relevant applications,
underscoring the need to further explore the exact nucleation mechanism.
Herrmann et al. (2014) also calculated the sulfuric acid proxy, related
it to observed nucleation rates, and speculated that the sulfuric acid
exponent might be well over 2, which underscores the reliability of
calculation methods in a Chinese location. Hence, our preliminary result
should be further tested with actual measurements of gas-phase sulfuric
acid, although it does indicate the key role of sulfuric acid in NPF events. On the
one hand, linear correlation between logJ and log[NH3] was
observed in a previous nucleation study in Atlanta, GA, with a slope of 1.17
(McMurry et al., 2005); on the other hand, a clear relationship was not perceived in
Kent, OH (Erupe et al., 2010). This discrepancy may come from the
level of ammonia that has been predicted to have a saturation threshold
(Napari et al., 2002) and/or the co-existing sulfuric acid
concentration (Benson et al., 2009). Nevertheless, our correlation
between logJ and log[NH3] suggests that ammonia also participated in
the nucleation. A recent chemical ionization mass spectrometer (CIMS) study (Zheng et al., 2015)
observed good correlations between NH3 and amines at an urban site in
Nanjing, China. Hence, it is plausible that amines may contribute to
nucleation in our site in Shanghai, too.
Correlation between logJ1.34 and log[NH3].
Daytime average of ammonia was used as an approximation for its effective
concentration on a NPF day. The error bar represents the standard deviation
of the daytime average concentration of ammonia.
The subsequent growth of newly formed particles can be partially attributed
to condensation of sulfuric acid. The theoretical maximum growth rate of
1.34–3 nm clusters/particles due to condensation of hydrated sulfuric acid
at 50 % RH (GRH2SO4(1.34∼3)), calculated according
to Eqs. (6) and (7), was 3.9±1.3 nm h-1 on average. This rate is
larger than the observed growth rates of clusters/particles from the bin of
1.34–1.37 nm to the bin of 1.89–3.0 nm (GR1.35∼2.39), being 2.0±2.7 nm h-1, indicating that sulfuric acid proxy was enough to
explain the observed growth for particles under 3 nm. Similar calculation of
GRH2SO4(3∼7) and GRH2SO4(7∼20) yielded 2.8±0.94 and 2.2±0.74 nm h-1, respectively.
In Fig. 6, relative contributions of sulfuric acid to growth of particles in
the range of 3–7 and 7–20 nm on each NPF day is presented.
Since 7 nm particles reached their maximum earlier than 3 nm particles on 9
and 15 January 2014, there was no calculated GR3∼7 and
hence no ratios available on these 2 days. In addition, condensation of
hydrated sulfuric acid was more prominent for 3–7 nm particles on 6 NPF
days (25, 26, and 30 November, and 10, 11, and 12
December 2013), whereas it was more significant for 7–20 nm particles on
the other 5 NPF days (28, 29 November 2013, and 13, 21, and 24 January 2014). On average, condensation of gas-phase
hydrated sulfuric acid explained 39.1 % of GR2.39∼7 and
29.0 % of GR7∼20. The rest of growth might be
largely attributed to condensation of extremely low volatility organic
compounds (Ehn et al., 2014) and potentially heterogeneous reactions of
organics on nanoparticle surface (Wang et al., 2010, 2011).
Relative contribution of sulfuric acid to growth of particles in the
range of 3–7 and 7–20 nm, respectively, on each NPF day.
NPF and aerosol surface area
NPF events in urban environments are of special interest since the
pre-existing particle surface may significantly scavenge the newly formed
particles and change the probability of NPF. We plot number concentrations of
1.34–10 nm particles (N1.34∼10), sulfuric acid proxy
([H2SO4]), ammonia ([NH3]), and aerosol
surface area with shadowed blocks representing NPF events in Fig. 7. Note
that N1.34∼10 was used as an approximation for nucleation and
subsequent growth while excluding particles from direct emission. The average
daytime (6 a.m.–6 p.m.) N1.34∼10 on NPF days was (2.7±2.1×104 cm-3, much larger than (1.5±1.0)×104 cm-3 on the rest days of the campaign, indicating that a
stronger input of particles from nucleation processes on NPF days. However,
daytime [H2SO4] did not show an apparent difference between
on NPF days ((3.7±1.2)×107 molecules cm-3) and on the
rest days ((3.9±2.5)×107 molecules cm-3). For example,
an episode with high daily sulfuric acid proxy during 19 December 2013 and 16
January 2014 did not lead to any NPF events. Instead, observed NPF events
occurred on days with low aerosol surface area levels and moderate
[H2SO4]. During our campaign, NPF days were characterized
with low aerosol surface area ((7.8±3.8) ×108 nm2 cm-3), whereas the average was (10.4±4.7)×108 nm2 cm-3 on the rest days of the campaign. Ammonia varied
dramatically, even within a single day. NPF events occurred on days with
around 10-fold difference in ammonia concentrations. According to ammonia's
profile and its positive correlation with J1.34, we speculate that
ammonia was involved in nucleation but was not the driving force.
Examination of these parameters
was performed from 12 to 14 January 2014 because the 3 days were characterized with similar
meteorological conditions. The average daytime concentration of sulfuric
acid proxy was 2.8, 2.3, and 1.0×107 molecules cm-3 on 12, 13, and 14 January 2014, respectively. No NPF
event was observed on 14 January at least partially because of the low
sulfuric acid proxy. Appearance of a NPF event on 13 January and
non-appearance on 12 January could be explained by the high aerosol
surface area on 12 January, with maximum aerosol surface area up to 1.8×109 nm2 cm-3, although similar sulfuric acid
proxies existed between the 2 days. Hence, we conclude that,
qualitatively, NPF processes in urban Shanghai occurred with low levels of
aerosol surface and that high sulfuric acid favored NPF events when aerosol
surface area was low. This conclusion is identical to that drawn from a
Mexico City study where NPF events observed in the city correlated with
elevated concentrations of sulfur dioxide and low particulate matter mass
concentrations in the afternoon hours (Dunn et al., 2004).
Number concentrations of 1.34–10 nm particles (N1.34∼10),
sulfuric acid proxy ([H2SO4]), concentrations of ammonia,
and aerosol surface area during the campaign. NPF events are illustrated with
shadows.
Summary and conclusions
Atmospheric new particle formation is a significant source of atmospheric
aerosol particles. Understanding NPF under the current levels of air
pollution in China is of special scientific interest because the exact
nucleation mechanism under urban environment remains elusive. From 25 November 2013 to 25 January 2014, a combination of one nCNC, one
nano-SPMS, and one long-SPMS has been utilized to investigate atmospheric
nucleation by measuring particle size distributions in the range of 1.34–615 nm
in urban Shanghai, located in the east Yangtze River delta. During this
62-day campaign, 13 NPF events were identified with strong burst of sub-3 nm
particles and subsequent fast growth of newly formed particles. Together
with nucleation frequency (21 %), the obtained nucleation rate J1.34
(112.4–271.0 cm-3 s-1), condensation sink (0.030–0.10 s-1),
and aerosol surface area ((7.8±3.8) ×108 nm2 cm-3) on NPF event days clearly indicate that strong
atmospheric new particle formation occurred in winter in urban Shanghai, with
competition between promotion from condensable vapors and scavenging by
preexisting particles. The absolute values of J1.34 and CS are 1
to 2 orders of magnitude larger than those at locations with a pristine
atmosphere (e.g., Kulmala et al., 2012) and semi-rural locations
with very low pollution levels such as Kent, OH (Yu et al., 2014), as a
reflection of the significantly altered atmospheric background.
Our preliminary exploration of the nucleation mechanism indicated that
nucleation rate J1.34 was proportional to a 0.65±0.28 power of
sulfuric acid proxy. It is hence likely that observed NPF events could be
explained by the activation theory. As Herrmann et al. (2014) doubted
reliability of sulfuric acid proxy, accurate measurements of gas-phase
sulfuric acid instead of calculation of a proxy is necessary to achieve an
unambiguous conclusion. The positive correlation between J1.34 and
gas-phase ammonia hints at the involvement of ammonia in new particle
formation, but its exact role cannot be determined without measurements of
nucleating clusters.
A size-dependent particle growth in the range of 1.34–20 nm has been observed
in this study, consistent with predictions from nano-Köhler theory
(Kulmala et al., 2004a) and Kelvin-limited diffusion, surface, and
volume growth laws (Kuang et al., 2012). Sulfuric acid proxy was
enough to explain the observed growth for particles under 3 nm and
contributed to 39.1 of GR2.39∼7 and 29.0 % of GR7∼20. The rest of the growth could be largely attributed to
condensation of extremely low volatility organic compounds (Ehn et al.,
2014) and potentially heterogeneous reactions of organics on nanoparticle
surface (Wang et al., 2010, 2011).
Clearly, further long-term measurements with integrated state-of-the-art
measurement techniques are necessary to draw a comprehensive picture of
atmospheric nucleation events in China. Currently, atmospheric sub-3 nm
particle measurements are still scarce in China, other than measurements
of gas-phase sulfuric acid, nucleating clusters, and other potential
precursors. Nevertheless, our study offers the very first measurement of sub-3 nm particles in urban Shanghai and provides some of the preliminary
mechanisms for NPF events in China.