Introduction
Atmospheric aerosols affect human life by influencing both air quality and
climate (e.g., Charlson et al., 1992; Menon et al., 2002; Akimoto, 2003; Heal
et al., 2012; IPCC, 2013). Fine particles, especially the submicron ones,
have received lots of attention due to their close connection to climate via
light extinction (Malm et al., 1994), cloud droplet activation (Kerminen et
al., 2005; Wiedensohler et al., 2009; Sihto et al., 2011) and precipitation
formation (Gettelman et al., 2013; Lebo and Feingold, 2014), as well as due
to their adverse effects on human health (Pope et al., 2002; Rao et al.,
2012).
Numerous studies have been conducted all over the world focusing on the
characters of submicron particles, including their chemical composition and
size distribution as well as their formation and growth in the atmosphere
(e.g., Woo et al., 2001; Birmili et al., 2003; Engler et al., 2007; Zhang et
al., 2007; Dal Maso et al., 2008; Laakso et al., 2008; Jimenez et al., 2009;
Komppula et al., 2009; Asmi et al., 2011; Kerminen et al., 2012; Vakkari et
al., 2013; Kulmala et al., 2014; Nie et al., 2014; Nieminen et al., 2014). In
China, studies on submicron particles started about a decade ago. However, to
the best of our knowledge, there are only three studies in China providing
more than 1 year of measurements of aerosol size distributions, two of them
conducted in the North China Plain (Wu et al., 2007; Shen et al., 2011) and one
at Mount Waliguan in remote western China (Kivekäs et al., 2009).
Therefore, knowledge about the temporal variation of submicron particles and
their relationship to meteorology and human activities in China is rather
poor, even in some well-developed regions such as Yangtze River Delta (YRD)
in eastern China.
The YRD has experienced rapid urbanization and industrialization in the last
2 decades, which have induced large amounts of fossil fuel consumption in the
region and resulted in serious air pollution (Chameides et al., 2002; Ding et
al., 2013a, b; Tie and Cao, 2009; Li et al., 2011). In addition, the YRD is a
region influenced by typical Asian monsoons, which dominate the temporal and
spatial variations of particles (Qian et al., 2003; Ding et al., 2013a).
However, previous studies on aerosols in this region were mainly on mass
concentrations and chemical compositions (e.g., Huang et al., 2012; Cheng et
al., 2013; Ding et al., 2013a); studies on the number concentrations and size
distributions were rather limited. In Nanjing, Herrmann et al. (2014)
reported the first result of about 4 months of data on aerosol size
distribution at the SORPES (Station for Observing Regional Processes of the
Earth System) station, a suburban site in Nanjing, and Wang et al. (2014)
reported about 1 month of data at another suburban site. Both studies were
conducted during the cold season. In other regions of the YRD, Gao et
al. (2009) reported an intensive campaign in the early summer of 2005 in
Taicang, a small town near to Shanghai, and Du et al. (2012) reported
wintertime measurements from October 2008 to February 2009 in Shanghai. These
results showed significant differences in both the diurnal patterns and NPF
(new particle formation) characteristics between the two seasons and
emphasize the need of continuous long-term measurements on the number size
distributions of submicron particles in this region.
In the present study, we report 2 years of continuous observation of submicron
particles (6–800 nm) and related quantities (including trace gases,
PM2.5 mass concentration and meteorological data) recorded at the SORPES
site in
suburban Nanjing in the western part of the YRD from December 2011 to November 2013. The aim
of this work is to characterize the temporal variations of particle number
size distributions and occurrence of NPF in the
western part of the YRD, and to improve our understanding of the sources and
processes influencing the atmospheric aerosol population in the developed
region of China.
Experiment and methodology
Site information and measurements
This study was conducted at the SORPES station developed in 2011 (Ding et al., 2013a).
The site is located about 20 km northeast of downtown Nanjing
(118∘57′10′′ E, 32∘07′14′′ N, 40 m above ground
level). With few local sources within its 2–3 km surroundings, it can be considered
as a regional background site in the well-developed YRD of eastern China. More
details of the site, including trace gas, PM2.5 and meteorological
measurements, can be found in Ding et al. (2013a).
The size distribution of submicron particles is measured with a DMPS
(differential mobility particle sizer) constructed at the University of
Helsinki in Finland. This instrument was also involved in the instrument
intercomparison workshops conducted within the European infrastructure
project EUSAAR (European Supersites for Atmospheric Aerosol Research) and
ACTRIS (Aerosols, Clouds, and Trace gases Research Infrastructure Network)
(Wiedensohler et al., 2012). Before entering the inlet of the instrument, the
particles are cut off at 2.5 µm and then dried (using a Nafion tube
from December 2011 to June 2012 and silica gel dryer after June 2012). The
instrument consists of one DMA (differential mobility analyzer) in different
flow rates and one CPC (condensation particle counter, TSI Model 3772). The
DMA segregates the particles into exact narrow size ranges based on different
narrow ranges of electrical mobility of charged particles in the electrical
field. Equilibrium charge is ensured by two americium 241 sources (each about
37 kBq) before particles enter the DMA. The DMPS is a flow-switching-type
differential mobility particle sizer in which two different sample and sheath
air flow rates for the DMA are used to cover a wide size range. In the high
flow mode, the sample air and sheath air flows are 3 and 20 L min-1,
respectively, and in the low flow mode they are 1 and 5 L min-1,
respectively. The high flow mode measures the size from 6 to 100 nm and the
low flow mode measures from 100 to 800 nm. The measurement time interval of
the instrument is 10 min during which the total particle number
concentration is measured by CPC directly and 29 channels (16 for low flow
rate and 13 for high flow rate) are scanned. Weekly maintenance, including
flow rate adjusting and impactor cleaning, is routinely performed. The data
assimilation in two flow modes and the test of data quality are described in
Appendix A.
Calculation of variables characterizing new particle formation
The calculation of particle growth and formation rates along with the
condensation sink were made following the procedure described by Kulmala et
al. (2012). The growth rate (GR) of particles during the NPF events can be
expressed as
GR=ddpdt=ΔdpΔt=dp2-dp1t2-t1,
where dp1 and dp2 are the
representative of the diameter of nucleated particles at the times
t1 and t2, respectively. For calculation,
dp1 and dp2 are defined as the center of the size bin
and t1 and t2 are the times when the
concentration of this size bin reaches the maximum.
The formation rate of particles of diameter dp is obtained
from
Jdp=dNdpdt+CoagSdp×Ndp+GRΔdp×Ndp+Slosses,
where the first term on the right side is the time evolution of the particle
number concentration in the size range [dp, dp+Δdp]. The second term is the coagulation loss approximated
by the product of coagulation sink (CoagSdp) and
the number concentration in the size range [dp, dp+Δdp]. The third term is the growth out of the considered
size range where GR is the observed growth rate. The fourth term represents
additional losses which were not considered in this study.
Having positive correlation with the coagulation sink (CoagS), the
condensation sink (CS) describes the speed at which condensable vapor
molecules condense onto the existing aerosol. It is expressed as
CS=4π∫0dpmaxβmdp′dp′Ndp′ddp′=4πD∑dp′βm,dp′dp′Ndp′,
where D is the diffusion coefficient of the condensing vapor, βm
is a transition-regime correction, d′p is the discrete
diameter and Nd′p is the particle number concentration in
respective size bin. The increase of CS due to the particle hygroscopic
growth was also estimated. The hygroscopic growth factor (GF), i.e., the
ratio of the particle diameter at an ambient relative humidity (RH) to the
corresponding “dry” particle diameter, was determined using the method
described by Laakso et al. (2004),
GF=1-RH100γ(dp).
Here γ(dp) depends on the particle diameter
(dp) and was obtained by a least square fit to hygroscopicity
data. As there were no measured hygroscopic growth data at the SORPES
station, the hygroscopicity data from another site in the north of downtown
Nanjing (Wu et al., 2014) was used.
Sulfuric acid has been identified as the main factor of new particle
formation (Kulmala et al., 2013). Therefore, in this study we used the
semi-empirical equation constructed by Mikkonen et al. (2011) to calculate
the sulfuric acid proxy:
H2SO4=1.30×10-1⋅k⋅Radiation1.10⋅[SO2]0.69⋅CS-0.53⋅RH-1.92,
where k is the reaction rate constant.
Overall statistics for the number concentrations and relevant
parameters calculated based on DMPS measurement at the SORPES site during
December 2011–November 2013.
Annual
Spring
Summer
Autumn
Winter
Total particles (cm-3)
19200a ± 9200b
20600 ± 9000
18000 ± 10800
18000 ± 7600
19900 ± 9300
Nucleation mode (cm-3)
5300 ± 5500
6200 ± 5900
4600 ± 5500
4800 ± 4900
5700 ± 5400
Aitken mode (cm-3)
8000 ± 4400
8500 ± 4000
8100 ± 5700
7600 ± 3900
7800 ± 3900
Accumulation mode (cm-3)
5800 ± 3200
5900 ± 2900
5300 ± 4200
5600 ± 2500
6500 ± 3000
AMD6–800nm (nm)
92 ± 25
89 ± 23
90 ± 26
92 ± 26
97 ± 26
CS (10-2 s-1)
3.8 ± 2.0
4.0 ± 1.7
3.5 ± 2.5
3.6 ± 1.7
4.2 ± 1.9
CSwet (10-2 s-1)
5.3 ± 2.9
5.5 ± 2.6
5.0 ± 3.7
5.1 ± 2.7
5.8 ± 2.7
a Mean; b standard deviation; AMD6–800nm: arithmetic mean diameter
of 6–800 nm particles; CS: condensation sink;
CSwet: condensation sink with the consideration of hygroscopic growth.
Results and discussions
Particle number concentrations and size distributions
Overall results
Figure 1a shows the averaged particle number size distribution during the
studied period. It shows a typical multimodal distribution as a result of
combining of three lognormal distributions in the nucleation (6–30 nm),
Aitken (30–100 nm) and accumulation modes (100–800 nm). Figure 1b
illustrates the average fraction of the particle number, surface and volume
concentration in these three modes. It shows features similar to most other
continental regions in the lower troposphere, i.e., the nucleation- and
Aitken-mode particles (< 100 nm) dominate the number concentration, and
the accumulation-mode particles control the surface and volume concentration
(Raes et al., 2000; Asmi et al., 2011).
(a) Averaged number size distribution and lognormal fitting
curves of three modes, and (b) averaged fraction of the particles
number, surface and volume concentrations in the three modes measured at the
SORPES site during December 2011–November 2013.
As shown in Table 1, the average total particle number concentration (NC)
over the diameter range 6–800 nm during the 2-year period was
19 200 ± 9200 cm-3, with the values of
5300 ± 5500 cm-3 in the nucleation mode (6–30 nm),
8000 ± 4400 cm-3 in the Aitken mode (30–100 nm) and
5800 ± 3200 cm-3 in the accumulation mode (100–800 nm),
respectively. The NCs of total particles at SORPES in Nanjing are comparable to
those measured at the urban site (about 32 700 cm-3 in the size range
of 3–1000 nm) (Wu et al., 2008) and at a rural site in Beijing (about
11 500 cm-3 in the size range of 3–1000 nm) (Shen et al., 2011),
while about 10 times higher than those measured at Mount Waliguan, a remote
background site in western China (about 2100 cm-3 in the size range of
12–570 nm) (Kivekäs et al., 2009). One typical feature at SORPES is the
high concentration of accumulation-mode particles, which can be up to several
times the typical concentrations measured in Europe or North America (200–2900 cm-3 compared to 5700 cm-3 at SORPES in the size range of
100–500 nm) (Stanier et al., 2004; Asmi et al., 2011; Wang et al., 2011).
Seasonal variations
The average seasonal variations of particle number concentrations during the
2 years of measurements are presented in Fig. 2. Nucleation-mode particles had
the highest concentrations in winter (December–January) and spring (April).
Aitken-mode particles showed similar patterns with an additional peak in
July. Accumulation-mode particles showed high concentrations in January and
June and low concentrations in July, which is similar to the seasonal
variation of PM2.5 concentrations reported by Ding et al. (2013a).
Accounting for almost 70 % of the total particles, the nucleation- and
Aitken-mode particles dominated the seasonal cycle of the total particle
number concentration. The exact values and standard deviations of the
particle number concentrations in spring (March–May), summer (June–August),
autumn (September–November) and winter (December–February) are given in
Table 1. Seasonally, the nucleation- and Aitken-mode particles showed the
highest concentrations in spring (6200 cm-3 ± 5900 cm-3 and
8500 cm-3 ± 4000 cm-3, respectively), whereas the highest
concentrations of accumulation-mode particles were observed in winter
(6500 cm-3 ± 3000 cm-3). The arithmetic mean diameter (AMD)
of the particles and condensation sink (CS) revealed also seasonal cycles
(Table 1), with the highest values observed in winter
(AMD6–800nm: 97 nm ± 26 nm, CS:
4.2 × 10-2 s-1 ± 1.9 × 10-2 s-1, CSwet:
5.8 × 10-2 s-1 ± 2.7 × 10-2 s-1).
Given that the hygroscopicity measurements were conducted at another site in
Nanjing (Wu et al., 2014), the hygroscopic growth associated with the
calculation of CS at the SORPES station might have large uncertainties. We
therefore deployed the CS without considering the hygroscopic growth in the
following discussion.
The averaged seasonal variations of (a) the total
particle number concentration and number concentrations in the (b) nucleation, (c) Aitken and (d) accumulation modes. Note:
bold solid lines are the monthly medians and shaded areas represent the
25th–75th percentile range. The diamond markers represent the
monthly average.
Averaged seasonal variations of (a) temperature
and pressure and (b) radiation and rainfall during the entire
measurement period.
Generally, the seasonal patterns of NCs of submicron particles at SORPES were
related to the long-range transport associated with the Asian monsoon climate
and also anthropogenic emissions. Figure 3 presents the seasonal variations
of four meteorological variables (temperature, pressure, radiation and
rainfall) during the 2-year measurement period. In winter, few rains and
a low boundary layer favor the accumulation of pollutants and result in high
particle loadings. In addition, the winter heating in northern China would bring
aged accumulation-mode particles to Nanjing via the regional transports
(Zhang et al., 2009; Li et al., 2011). In summer, the predominantly rainy and
unstable weather (e.g., convection and monsoon precipitation) leads to low
particle number concentrations, especially for accumulation-mode particles.
Radiation, which drives NPF events, influences the NCs of nucleation and
Aitken-mode particles. Moreover, an evident holiday effect can also influence
the observed temporal variation. For example, the low particle loadings in
all the modes can be identified in February (see Fig. 2) when the Chinese have
the winter break to celebrate the Spring Festival (Ding et al., 2013a).
Averaged diurnal cycles of particle number size
distributions for (a) spring, (b) summer, (c) autumn and (d) winter at the SORPES station during the 2-year
measurement period.
Biomass burning (BB) is an important source of accumulation-mode particles in
early summer (Ding et al., 2013a, b), so the burning of wheat straw in
northern and middle parts of eastern China (Wu et al., 2008; Shen et al., 2011)
is the plausible cause for the particle NCs peak observed in June (Fig. 2d).
Here, we defined BB events as potassium concentration
K+ > 2 µg cm-3 and K+ / PM2.5
ratio > 0.02 (K+ was measured using MARGA; Nie et al., 2015). The
average NC of accumulation-mode particles during BB events in June was
31 700 cm-3, which is almost 6 times higher than the corresponding NC
in non-BB event days (5300 cm-3). Relatively large (> 100 nm)
particles are emitted directly from BB (Reid et al., 2005; Li et al., 2007),
or formed rapidly after emissions by the combination of NPF and various
particle growth processes (Hennigan et al., 2012; Vakkari et al., 2014). Such
particles are able to promote atmospheric heterogeneous chemistry by
providing a large surface area (Nie et al., 2015), influence the global
climate by enhancing the cloud condensation nuclei capacity (Hennigan et al., 2012), and even
change the everyday weather (Ding et al., 2013b).
Diurnal pattern in different seasons
The diurnal cycles of particle number size distributions had similar patterns
in spring, summer and autumn, which were connected to the events of new
particle formation and growth, showing the typical “banana” shape during
daytime (Fig. 4a–c). However, obvious differences could be observed for the
starting time and strength of NPF in different seasons. It needs to be
pointed out that in the averaged diurnal pattern in spring, summer and autumn
(Fig. 4a–c) the NPF had a relatively low number concentration of 6–15 nm
particles, which is different from a typical NPF event. The main reason for
this feature is that the averaged patterns include both event and non-event
days and that sub-100 nm particles tend to grow also during non-event days
without a clear formation of new sub-15 nm particles. A detailed discussion
on NPF events will be given in Sect. 3.2. Compared with other seasons, the
high peaks of NCs around 20–30 and 100 nm during the wintertime rush
hours in the morning and late afternoon (Fig. 4d) suggest that local
combustion sources, such as vehicle emissions, played a more important role in
the diurnal cycle of the total particle concentration than sources like
regional NPF and growth.
In order to investigate the detailed diurnal variations of the particle NCs
in different modes, we compared the diurnal patterns of the three modes in
seasons: spring and winter (Fig. 5a–c). For nucleation-mode particles, peak
concentrations appeared at noontime in spring but in the early morning or
late afternoon in winter (Fig. 5a). The later suggests a possible influence from human
activities, such as vehicle emissions under conditions of a low mixing layer.
For the Aitken-mode particles, in spring the highest concentrations were seen
about 2 h after the appearance of the peak in the nucleation mode,
which was due to the growth of nucleated particles to larger sizes (Fig. 5b).
In winter, Aitken-mode particles had peaks at the same time or a bit later
than the nucleation-mode particles did. The diurnal variation of the
accumulation-mode particle number concentration (Fig. 5c) was controlled by
the development of the boundary layer in both seasons, having a pattern
similar to that of the PM2.5 concentration (Ding et al., 2013a).
Averaged diurnal variations of particle number
concentrations in (a) nucleation mode, (b) Aitken mode and
(c) accumulation mode in spring, winter and the entire period during
the 2-year measurement period.
Mean air mass backward trajectories of five clusters
showing on maps of emission inventory of (a) monoterpenes in eastern China and
(b) anthropogenic VOCs in the YRD. Note: points of trajectories represent the
6-hourly location. The percentages of each cluster are tabulated in the
map. The monoterpene data were calculated by MEGAN (Model of Emissions of
Gases and Aerosols from Nature) with MM5 (the Fifth-Generation Mesoscale
Model) providing meteorological data and the anthropogenic VOC data were
accessed from the MEIC (Multi-resolution Emission Inventory for China) database
(http://www.meicmodel.org/).
The influences of air masses
Figure 6 shows backward trajectory cluster analysis for the SORPES site.
Using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT)
model version 4.9 (Draxler and Hess, 1998) driven with Global Data
Assimilation System (GDAS) output, five clusters of trajectories were identified
based on hourly 3-day backward trajectories for the period of
December 2011–November 2013. Here we calculated the trajectories for 3 days
by considering a rough turnover time of about 2.4 days for 200 nm particles
(Tunved et al., 2005). The results showed that air masses arriving at the
SORPES site generally came from the inland continent (14.8 % of the air
masses belonging to the cluster C2 and 13.0 % to the cluster C3), coastal
northern China (C1: 30.6 %, C4 and C5 together: 41.6 % in total). The
two continental air masses had distinguished source regions, C2 passed
over the North China Plain and C3 traveled over a large area of BVOCs (biogenic volatile organic
compounds). For the
coastal air masses, C5 was much more local compared to C4, but the latter just
crossed the city cluster along Nanjing–Shanghai axis and might have an
additional marine signature. The air mass transport pattern was controlled by
the Asian monsoon (Ding et al., 2013a), with the winter monsoon bringing regional
pollution from the North China Plain (C2, C1 and C5) and the summer monsoon
bringing the YRD regional pollution (C4) or biogenic emissions from the south
of China to the site (C3). Being located in the most western part of the YRD,
SORPES is a unique site to investigate the impact of different regional air
masses.
The average behavior of the particles number size distributions and diurnal
variations of NCs in the three modes in the five air mass clusters are shown
in Fig. 7. The coastal air masses (C1) had the lowest accumulation-mode
particle loading during the whole day (Fig. 7d). The continental air masses
(C2 and C3) had highest levels of nucleation-mode particles (Fig. 7b),
probably associated with the dry and sunny weather. Since C3 air masses
occurred mostly in summer and traveled over regions abundant in BVOC
emissions, these air masses preferred NPF and resulted in the highest
concentrations of nucleation-mode and Aitken-mode particles (Fig. 7b, c). The YRD
air masses (C4 and C5), passing through the YRD city clusters, brought the
highest accumulation-mode particle loadings (Fig. 7d) and lowered
concentrations of nucleation-mode particles (Fig. 7b) due to the high
coagulation/condensation sinks. C5 air masses, with more local air and less
influences of marine air, displayed larger concentrations of accumulation-mode particles and lower concentrations of Aitken-mode particles (Fig. 7c,
d).
(a) Particle number size distributions of five clusters
(C1, C2, C3, C4 and C5 air masses, respectively); diurnal variations of
particle number concentration of five clusters in (b) nucleation
(6–30 nm), (c) Aitken (30–100 nm) and (d) accumulation
modes (100–800 nm).
Statistics of NPF events, J6 and GR6–30nm
at the SORPES station during the 2-year measurement period (SD: standard deviation).
Spring
Summer
Autumn
Winter
Event classification
Class I
52
44
64
10
Class II
42
29
24
5
Non-event
75
60
89
115
Undefined
3
3
1
4
J6 (cm-3 s-1)
Mean ± SD
3.6 ± 2.4
2.1 ± 1.4
2.1 ± 1.9
1.8 ± 1.6
GR6–30nm (nm h-1)
Mean ± SD
10.0 ± 3.4
12.8 ± 4.4
8.9 ± 2.9
9.5 ± 3.3
New particle formation (NPF)
Population statistic about NPF
The sampling days during the 2 years of measurements were classified into NPF-event days, non-event days and undefined days with the criterion of whether a
nucleation burst occurred or not. By following the method used by Dal Maso et
al. (2005) and Kulmala et al. (2012), the event days are further classified
into Class I when the formation and growth rate can be calculated with
confidence, and Class II when the formation and growth rate cannot be
calculated or done in accurate ways.
The numbers of the four types of sampling days are given in Table 2 and the
percentages of them in each month are shown in Fig. 8a. Due to instrument
maintenance, data from 111 days of the 2-year period were unavailable for
the event's classification. Only few of the days at SORPES were considered as
undefined days (11 days), for which it was hard to determine whether a NPF
event occurred or not. Overall, NPF-event days (including Class I and
Class II days) accounted for 44 % of the sampling days. This frequency is
a bit higher than observed in the other two long-term measurements in China
(urban Beijing ∼ 40 %, SDZ ∼ 37 %). In spring, summer
and autumn, NPF events took place in about half of the sampling days (55, 54
and 49 %, respectively), which is more frequent than at other measurement
sites in China, including Taicang (44 %), Hong Kong (34 %) and
Xinken (26 %) (Gao et al., 2009; Guo et al., 2012; Liu et al., 2008). A
higher frequency of NPF events in the warm season is similar to what has been
observed in most other sites all over the world and is mainly because of
higher radiation and stronger biogenic activity during that time of the year
(Manninen et al., 2010). In contrast, in winter only 15 NPF-event days during
the 2-year measurement period were identified. This frequency is similar to
another work in Shanghai (Du et al., 2012) but quite different from that in
Beijing where winter is the second season favorable to NPF (Wu et al., 2007;
Shen et al., 2011). One explanation for this could be that there are more
“clean” days in winter in Beijing because of frequent cold fronts (Wehner
et al., 2008). In June, continuous rainy days (“plum rain” in China) with
low radiation also inhibit the NPF events. There was a great difference in
the frequency of NPF events in summer between the 2 years: 39 % in 2012
and 66 % in 2013 (Fig. 8a).
Monthly time series of (a) the fraction of Class I, Class II NPF events, undefined and non-event days, (b) start
time and end time of Class I event days, (c and d) J6
and growth rate during Class I event days. Note: Dashed lines in Fig. 8b
represent the sunrise and sunset time. Bold solid lines in Fig. 8c and d
are the median values and shaded areas represent the 25th or 75th
percentiles. Red circles and blue circles in Fig. 8c and d are the days
that selected for further investigation in Sect. 3.2.3.
(a–c) Meteorological variables, (d–f) gaseous
pollutants, (g) condensation sink and (h) H2SO4
proxy during event (red) and non-event (blue) days in different seasons.
Note: bars are the mean value. The bold sticks and whiskers indicate the median values
and the 25th and 75th percentiles, respectively.
Figure 8b gives the variability of start and end times of the Class I event
days. Since the cutoff diameter of the DMPS was 6 nm, the start and end
times were defined here as the times when the 6–7 nm particles started to
increase and decrease back to the background level (i.e.,
∼ 50 cm-3). Generally, the start time was somewhere between the
sunrise and midday, and no evident nocturnal events were identified. The
seasonal variation of the start time followed that of sunrise, which is
similar to what has been reported elsewhere around the world (e.g., Woo et al., 2001; Boy
and Kulmala et al., 2002; Kulmala et al., 2004; Hamed et al., 2007; Wu et
al., 2007). However, on some days the start time was just 1 h after the
sunrise. Most of such events took place in summer and were associated with
marine air masses that had been transported over the polluted area in the YRD.
This topic will be studied further in our future work.
The formation rate of 6 nm particles (J6) and growth rate of 6–30 nm
particles (GR6–30nm) during the Class I event days are
illustrated in Fig. 8c and d. The statistical results are given in Table 2.
The formation rates were highest in spring with the value of
3.6 ± 2.4 cm-3 s-1, followed by summer
(2.1 ± 1.4 cm-3 s-1) and autumn
(2.1 ± 1.9 cm-3 s-1), whereas the lowest formation rates
were observed in winter (1.8 ± 1.6 cm-3 s-1). The maximum
formation rate was 10.9 cm-3 s-1 on 3 April 2013. The observed
formation rates in Nanjing are comparable to other measurements in China,
e.g., 3.3–81.4 cm-3 s-1 in Beijing (for J3),
0.7–72.7 cm-3 s-1 with mean value of 8 cm-3 s-1 at
SDZ (Shangdianzi)
(for J3), 3.4 cm-3 s-1 (3 October–5 November 2004) in the PRD
(Pearl River Delta)
(for J3), 3.8 cm-3 s-1 (25 October–29 November 2010) in
Hong Kong (for J5.5 on Class I days), and about 2.2 cm-3 s-1
(5 May–2 June 2005) in Shanghai (for J10) (Wu et al., 2007; Shen et
al., 2011; Liu et al., 2008; Guo et al., 2012; Gao et al., 2009). Concerning
the nuclei growth rates, the highest values of 12.8 ± 4.4 nm h-1
were observed in summer, followed by spring (10.0 ± 3.4 nm h-1), winter (9.5 ± 3.3 nm h-1) and autumn
(8.9 ± 2.9 nm h-1). The maximum growth rate was
22.9 nm h-1, observed on 29 August 2013. The values of growth rates
presented in this study for Nanjing are slightly higher than those reported for the
other two long-term measurements in China, i.e., 0.1–11.2 nm h-1 in
Beijing and 0.3–14.5 nm h-1 with mean value of 4.3 nm h-1 at
SDZ (Wu et al., 2007; Shen et al., 2011).
Conditions favoring NPF
In Fig. 9, we compare the NPF-related parameters between the event (Class I
and Class II) and non-event days in different seasons. As shown in Fig. 9a
and b, higher temperatures and lower RH favored the NPF events. Having a
higher temperature on event days is similar to observations made in Germany
and Italy (Birmili and Wiedensohler, 2000; Birmili et al., 2003; Hamed et al.,
2007) but different from observations made in Finland or in the tropopause
region (Boy and Kulmala, 2002; Hamed et al., 2007; Young et al., 2007). A
lower RH on event days is similar to what has been observed in most
boundary layer stations (Boy and Kulmala, 2002; Birmili et al., 2003; Hamed
et al., 2007; Guo et al., 2012). In general, a low RH is related to sunny
days with strong radiation, which favor the formation of OH (Hamed et al.,
2007). In addition, a low RH will decrease the condensation sink by slowing
the hygroscopic growth (Hamed et al., 2011).
Scatter plot (a) between J6 and RH and
(b) between GR6–30nm and RH, color coded with O3
concentration. Note: linear fits for the data when the O3 concentration
is higher than 57 ppbv (the 75th percentile) or lower than 32 ppbv (the
25th percentile) are shown in Fig. 10b, respectively.
Radiation and O3 concentrations were higher on event days than on
non-event days (Fig. 9c, d), indicating that the observed NPF events in the YRD
were typically photochemically influenced. In line with most boundary layer
stations (Manninen et al., 2010), lower PM2.5 concentrations and CS
favored the occurrence of NPF events (Fig. 9e, g). On average, higher
SO2 concentrations were observed on event days in spring and summer,
while the events in autumn and winter favored lower SO2 concentrations
(Fig. 9f). This result was in accordance with our previous study conducted in
the wintertime, which suggested that NPF occurs preferably under conditions of lower
SO2 concentrations (Herrmann et al., 2014). In autumn and winter, the
SO2 peaks were always accompanied with a high PM2.5 concentration
in the YRD. Therefore, the observed lower SO2 concentrations on event days
in autumn and winter are understandable as the pre-existing particles play a
more important role. The proxy of H2SO4 was significantly higher on
event days than on non-event days, suggesting the sulfuric acid was the main
driver of NPF events at SORPES.
Statistics of NPF events in different air mass clusters.
Class I
Class II
Non-event
Undefined
C1
52
25
82
3
C2
25
19
48
0
C3
32
22
12
0
C4
33
13
69
4
C5
20
11
72
3
Correlation coefficients of J6 and GR6–30nm
with the main meteorological parameters and air pollutants.
Temp.
RH
Radiation
O3
PM2.5
SO2
NOx
CS
(∘C)
(%)
(W m-2)
(ppbv)
(µg m-3)
(ppbv)
(ppbv)
(10-2 s-1)
J6 (cm-3 s-1)
0.10
-0.31*
0.32*
0.17*
0.01
0.11
-0.01
0.06
GR6–30nm (nm h-1)
0.36*
0.27*
0.27*
0.38*
0.07
0.06
-0.14
0.38*
* The correlation coefficient passes the statistical significant test
(p < 0.05).
The statistics of the NPF events in different air mass clusters are given
in Table 3. The events influenced by two or more air mass groups, 17 % of the sampling days, were not included in this statistic. Air
masses in C3 revealed the most frequent NPF (54 event days and 12 non-event
days). As illustrated in Sect. 3.1.4, C3 air masses took place usually in
summer and brought large amounts of BVOCs (e.g., monoterpenes) from southern
China (Fig. 6a). BVOC emissions have previously been observed to contribute
to the formation and growth of new particles (Birmili et al., 2003; Tunved et
al., 2006; Fu and Kawamura, 2011; Kamens et al., 2011). Air masses in C4 and
C5, which passed through a polluted YRD area with lots of pre-existing
particles, generally had less NPF events, further indicating that the
polluted YRD plume would suppress the formation of new particles. In winter,
when solar radiation is low, no NPF events occurred in the YRD area masses
(Herrmann et al., 2014).
The averaged retroplumes (i.e., 100 m footprint) of the
selected events: (a) high J6, (b) low J6,
(c) high GR, (d) low GR days (the dates are marked in Fig. 8c and d.). Note: the red areas in the maps
show the locations and sizes of the cities.
Factors influencing particle formation and growth rates
To investigate the factors that influence the formation rate and growth rate,
the correlation coefficients of J6 and GR6–30nm with
meteorological quantities and gaseous pollutants were calculated (Table 4).
The correlation coefficients that passed the statistical significance test
(p < 0.05) were highlighted by asterisk in Table 4. The particle
formation rate (J6) was negatively correlated with RH and positively
correlated with both radiation and O3. No significant correlation
between J6 and SO2 was seen at SORPES. The particle growth rate was
positively correlated with temperature, RH, radiation, O3 and CS
(p < 0.05). It is worth noting here that while lower values of RH and CS
appeared to favor the occurrence of a NPF event, higher values of these two
quantities clearly favored the particle growth. This suggests that the new
formation and growth are influenced, at least to some extent, by different
processes and vapors (Yli-Juuti et al., 2011; Rose et al., 2015). The scatter
plots of J6–RH and GR6–30nm–RH color coded with
O3 mixing ratios are shown in Fig. 10. The negative correlation between
J6 and RH did not depend on the O3 concentration (Fig. 10a),
whereas for GR6–30nm an obvious difference in the
GR6–30nm–RH slope could be identified for different
levels of the O3 mixing ratio (Fig. 10b). Here the good GR–RH
relationship may also be influenced by factors other than the O3 mixing
ratio, such as the transport compounds acting as precursors for the vapors
responsible for the particle growth. Because of the influence of the summer
monsoon, air masses from the southeast and southwest directions are generally
much humid than those from the north (Ding et al., 2013a). These southerly air masses could also
be accompanied with a high concentration of BVOCs or anthropogenic VOCs and
their oxidants. Ding et al. (2013a) found that air masses from the YRD city
clusters were always associated with high concentrations of O3, which
should also contain high concentrations of anthropogenic VOCs.
In order to study further the event with a high or low value of
J6/GR6–30nm, we conducted Lagrangian dispersion
modeling for the selected days marked in Fig. 8c and d by using the method
developed by Ding et al. (2013c), based on the HYSPLIT model, to study the
influence of air masses. Figure 11 gives the footprint, i.e., a retroplume at an
altitude of 100 m, of the selected high and low
J6/GR6–30nm days. Air masses had an obvious
influence on the formation rate and growth rate. Most low J6 days and
high GR days occurred in the air masses passing over the polluted YRD area,
while all the high J6 days and low GR days appeared in air masses that
did not go through the YRD area. This further suggests the differences in the
particle formation and growth processes could partly explain the positive
correlation between the GR and RH. In addition to the high RH in YRD air
masses, the high anthropogenic VOC concentration may play a more important
role in enhancing the particle growth. Our finding that the polluted YRD
plume induces a high GR is consistent with the studies reporting relatively
high particle growth rates under urban conditions (Kulmala and Kerminen,
2008; Peng et al., 2014).
Causes on the high frequency of NPF in August 2013
As shown in Fig. 8, a higher frequency of NPF events occurred in July and
August of 2013 compared with the same months in 2012. In August of 2013, the
frequency of Class I NPF events was highest during the 2-year measurement
period, with 17 Class I events observed among the 24 analyzed days. Figure 12
shows the time series of particle number size distribution, O3 and
PM2.5 concentrations, and radiation. The daily average O3
concentration gradually increased in early August with an hourly maximum
value of up to 165 ppbv on 12 August 2013. Accompanied with this O3
episode, the geometric mean diameter (GMD) of submicron particles and
PM2.5 concentration also increased (Fig. 12a, b). Interestingly, there
were continuously multi-day NPF events in the first half of this month, even
during 11–13 August when PM2.5 reached 70–80 µg m-3.
During 17–24 August, there were also notable NPF events. Contrary to this,
few NPF took place in August 2012.
Time series of (a) particle number size
distribution, (b) O3 and PM2.5 concentrations,
(c) temperature and RH, and (d) intensity of radiation
measured at the SORPES site in August 2013.
(a) Average geopotential height and wind vectors at the
925 hPa level during August 2013. (b) Differences in geopotential
height and wind vectors between August 2013 and August 2012 at the 925 hPa level.
Examination of the average geopotential height and wind vectors at the 925 hPa
level during the two Augusts suggests that in 2013 the
subtropical (Pacific) high moved more to the west than that in 2012, causing a
positive anomaly (high pressure) and anticyclone over southeastern China
(Fig. 13). As a result, the YRD experienced a continuous
heat wave with humid and hot air transported from the south and southwest.
In order to further understand the air masses' history during the events in
2013, Fig. 14 gives the averaged “footprint” (i.e., 100 m retroplume) for
the episode period. The air masses can be divided into three time periods.
During 6–11 August, the air masses came from the southwest with a high value
of BVOCs and they also passed through the downtown of Nanjing. During this
period, O3 was produced and accumulated with enough precursors and
strong solar radiation. High O3 concentrations also caused a strong
atmospheric oxidation capacity, which caused an increase in the GMD of
submicron particles together with an increase in the PM2.5 mass
concentration. On 11 August, the air masses' transport pathway was changed,
with air masses coming mainly from the southeast and the YRD city cluster, which
is the most polluted area with high value of anthropogenic VOCs and other
pollution gases. Therefore, the O3 concentration continued to increase
until 12 August and then maintained a high level until 19 August. A common
character of the air masses during both of these two periods was that the air
was transported over regions with high biogenic and anthropogenic emissions
(See Figs. 6a, b and 14). During 19–22 August, the air masses were mainly
from the northeast and had a high humidity that caused cloudy days with low
radiation and high wet deposition. The O3 concentration, GMD of
submicron particles and PM2.5 mass concentration therefore sharply
decreased on 19 August.
Retroplumes from 4 to 22 August 2013 identified with three
main periods. Note: red points denote the urban area.
Despite the high levels of GMD, PM2.5 and CS (which is not shown in
Fig. 12), Class I NPF events occurred every day during the whole O3
episode (from 4 to 19 August 2013). As the GMD and PM2.5 increased, the
particle formation rates became lower. This means that the high values of GMD
and PM2.5 suppressed new particle formation but could not stop the
occurrence of NPF event altogether under such an atmospheric condition of a
high oxidization capacity. Because of lower pre-existing particle loading
after 19 August, new particle formation continued, although the radiation
intensity and atmospheric ozone oxidation capacity were lower. Another
obvious character for August 2013 was that, during the whole month, the
particle growth rate had a relatively high correlation with RH (r=0.54), supporting the positive correlation between GR and RH illustrated
above (Fig. 15).
Here the year-to-year difference in aerosol size distributions and NPF
characteristics suggests that large-scale circulations together with
meteorological factors had a strong impact on the aerosol number
concentration. Extreme meteorological conditions are able to reshape the
seasonal profile of the aerosol number concentration and NPF, which means
that measurements in a specific year cannot gain a full picture of seasonal
profiles. Given the fact that there are only a limited number of
measurements covering more than 1 year, especially in China, this work
highlights the importance of long-term continuous measurement.
Summary
This study reports a 2-year measurement (from December 2011 to
November 2013) period of submicron particles (6–800 nm) at the SORPES
station located in suburban Nanjing in the western part of the YRD, eastern China,
with the aims of characterizing the temporal variation of the particle number
concentration and size distribution and understanding the new particle
formation occurring in such a polluted monsoon area.
The average total number concentrations was 19 200 ± 9200
(mean ± standard deviation) cm-3, with
5300 ± 5500 cm-3 in the nucleation mode (6–30 nm),
8000 ± 4400 cm-3 in the Aitken mode (30–100 nm) and 5800 ± 3200 cm-3 in the accumulation mode (100–800 nm). Seasonal
variations of NC and size distribution were influenced by the Asian monsoon,
anthropogenic activities and the atmospheric oxidation capacity. The diurnal
pattern of the particle number size distribution in winter showed peaks at
the normal rush hours, suggesting the emissions of
vehicles as the direct source. Air mass long-range transportation played a clear role in
influencing the particle number concentration: coastal air masses had the lowest
concentrations of accumulation-mode particles but relatively high
concentrations of nucleation-mode particles, continental air masses had the
highest concentrations of nucleation-mode particles with frequent new
particle formation, and YRD air masses had the highest concentrations of
accumulation-mode particles and lowest concentration of nucleation-mode
particles because of the elevated coagulation/condensation sinks.
Time series of RH and the GR6–30nm of NPF days
(Class I and Class II) in August 2013.
NPF events were observed on 44 % of the analyzed days with the highest
frequency in spring, followed by summer and autumn but only 15 event days in
winter. The average formation rates of 6 nm particles were 3.6 ± 2.4,
2.1 ± 1.4, 2.1 ± 1.9 and 1.8 ± 1.6 cm-3 s-1 in
spring, summer, autumn and winter, respectively, and the corresponding
particle growth rates were 10.0 ± 3.4, 12.8 ± 4.4,
8.9 ± 2.9 and 9.5 ± 3.3 nm h-1. A higher temperature,
radiation intensity and O3 concentration together with a lower RH, PM2.5 and CS seemed to favor the occurrence of new particle
formation events. Sulfuric acid appeared to play a key role in NPF events at
SORPES. Trajectory analysis suggested that BVOC chemistry contributed to the
new particle formation and growth. The particle formation rate was negatively
correlated with RH and positively correlated with radiation and O3 while
particle growth rate was positively correlated with temperature, RH,
radiation, O3 and CS. Both particle formation and growth rate depended
on the air mass origin, with low J6 and high GR typical for polluted YRD
air masses and high J6 and low GR for clean air masses.
The observed frequency of NPF events and particle growth rate in summer
showed a strong year-to-year variation under the influence of different
large-scale circulations, such as the intensity and location of a subtropical
high pressure system. Long-range transport, meteorological parameters and
photochemical pollutants promoted the atmospheric new
particle formation and growth in the summer of 2013
compared with the previous year. To quantitatively understand the processes
controlling the aerosol number concentration and size distribution, or to
predict their behavior, additional modeling work on NPF that relies on
long-term observations should be conducted in future.