The strictest ever clean air action (CAA) plan has been implemented by
the Chinese government since 2013 to alleviate the severe haze pollution. The
PM2.5 mass concentration was found to largely be reduced in response to
emission mitigation policies, but the response of particle number concentrations
(PNCs) to CAA was less evaluated in the previous studies, which may
be significantly different from PM2.5 mass due to newly formed particle impacts.
In this work, the first in situ observation of particle number size distributions
(PNSDs) during 2012–2019 in urban Lanzhou was used to analyze long-term PNC
variations and CAA impacts. The average number of particles in nucleation
(N13-25, particle number in the size range of 13–25 nm), Aitken
(N25-100, particle number in the size range of 25–100 nm) and
accumulation (N100-800, particle number in the size range of 100–800 nm)
modes were respectively 2514.0, 10 768.7 and 3258.4 cm-3, and N25-100 accounted for about 65.1 % of total PNCs during
the campaign. The k-means clustering technique was used to classify the hourly
mean PNSDs into six clusters, and each cluster corresponded to a specific
source and influencing factor. The polluted clusters governed the winter
PNCs before 2016, and their occurrence was less and less frequent after
2016, which was largely dominated by reduction in primary emissions.
However, the contribution of new particle formation (NPF) events to summer
N13-25 decreased from 50 % to about 10 % during 2013 to 2015 and
then increased to reach around 60 % in 2019. The trends of size-resolved
PNCs for each cluster were quantified by Theil–Sen regression. The
size-segregated PNCs exhibited downward trends for all clusters during
2012–2015, especially in spring. The annual relative slopes of spring PNCs
varied from -54.7 % to -17.2 %, -42.6 % to -14.1 %, and -40.7 % to
-17.5 % per year for 13–25, 25–100, and 100–800 nm size ranges, and the
reduction in the polluted clusters was much larger than NPF clusters. The
ultrafine particle number was increased, and the amplitude was much greater
during 2016–2019. The annual relative slopes of N13-25 varied between
8.0 % in fall and 135.5 % in spring for the NPF cluster. In response to CAA,
the increased daytime net radiation, higher ambient temperature and lower
relative humidity at noon for NPF events also could partly explain the
higher N13-25 induced by the more frequent nucleation events after 2016,
especially in spring. The air masses were mainly from the adjacent regions of
urban Lanzhou and less affected by long-range transport for NPF events, and
thus the particles were not easily grown by coagulation during transport
processes, which was helpful for the occurrence of NPF events. Therefore, some
effective measures to cooperatively control particle number concentration and mass should
be taken for the Chinese megacities.
Introduction
China has been experiencing large-scale and long-lasting winter haze
pollution due to fast-growing economy and urbanization in past decades. The
high concentration of aerosols perturbs the radiative balance of the atmosphere
and surface by directly scattering and absorbing solar radiation or by
indirectly altering cloud optical properties and cloud lifetime serving as
condensation nuclei and ice nuclei (Andreae and Rosenfeld, 2008; Gao et al.,
2015; Li et al., 2017). The adverse effect of deteriorated air quality on
public health is of the greatest concern in China (Hu et al., 2017;
Lelieveld et al., 2015). The present air quality standards consider particle
mass instead of number concentration (WHO, 2000). However, compared to the
larger aerosol particles, the ultrafine particles (UFPs, diameter < 100 nm) scarcely contribute to aerosol mass, while they share the largest
number fraction in urban areas (Hussein et al., 2004; Wehner et al., 2004).
The toxicity of UFPs is enhanced by the large surface area due to high
number concentrations, and they can penetrate deep into the lungs, ending up in
the blood circulation (Oberdörster et al., 2005; Schmid and Stoeger,
2016).
Aerosols' ability to efficiently scatter or absorb light depends not only on
their chemical composition but on their sizes as well (Asmi et al., 2013).
Liu et al. (2020) indicated that coating plays an important role in light
absorption. The amplification of black carbon absorption by the coating
increased from 1.21 to 1.75 with increasing aerodynamic diameter
(Dae) due to the thicker coating of BC-containing particles with a
larger Dae. Their study highlights the strong dependence of the
microphysical and optical properties of BC on size. The more recent study of
Zhao et al. (2021) found that interdecadal AOD had a negative trend from 2009
to 2018, which may be related to the variation in particle size
distribution. Some aerosol monitoring networks were established around the
world for long-term measurements of climate-relevant aerosol properties,
such as Geophysical Monitoring for Climate Change (GMCC, Bodhaine, 1983) and
Global Atmosphere Watch (GAW, Rose et al., 2021). The particle number
concentrations (PNCs) and size distributions (PNSDs), considered to be
primary indicators of human impacts on atmospheric composition, were the main
aerosol properties measured at the networks. Based on long-term in situ measurements
at the network sites, many studies on particle number and size distributions
have been conducted since the 1990s in Europe and North America (Asmi et
al., 2011; Birmili et al., 2016; Dal Maso et al., 2008; Heintzenberg et al.,
2011; Krecl et al., 2017; Kulmala et al., 2004; Makela et al., 1997; Sun et
al., 2020; Wiedensohler et al., 2012). Their studies indicated that the
annual, weekly and diurnal cycles largely depended on station type and
geographic location. The more recent study of Schmale et al. (2018) also
well illustrated the importance of measuring the PNSD over long-enough time
periods in contrasting micro-environments for the understanding of
aerosol–climate interactions and the improvement of their representation in
numerical models. Sun et al. (2020) determined long-term trends of PNCs
during 2009–2018 for 16 sites ranging from roadside to high-Alpine
environments, and the annual relative slope varied from -17.2 % to
-1.7 %, -7.8 % to -1.1 % and -11.1 % to -1.2 % per year for 10–30, 30–200 and 200–800 nm size bins, respectively. The downward trends of
PNCs were found to be dominated by the reductions in various anthropogenic
emissions, while meteorology impacts were less important or negligible.
However, a few long-term PNSD measurements in the developing countries
mainly concentrated on urban Beijing since 2004 (Wang et al., 2013; Wehner
et al., 2004), the North China Plain since 2008 (Shen et al., 2011) and
Mount Waliguan since 2005 (Kivekas et al., 2009). Aitken mode particles
(25–100 nm) were found to account for about half of total PNCs in urban
areas in China (Wu et al., 2008), and the number of accumulation mode particles (100–1000 nm) was around 4 times higher than that in developed countries (Wehner
et al., 2008; Wu et al., 2008), indicating largely different PNSD
characteristics in China compared to Europe and North America.
PM2.5 (particulate matter with aerodynamic diameter less than 2.5 µm) decreased by 30 %–50 % across China over the 2013–2018 period in
response to the Air Pollution Prevention and Control Action Plan (APPCAP) in
2013 implemented by the Chinese central government (Zhai et al., 2019). Compared
to PM2.5 mass concentrations, the particle number concentrations were
more directly affected by newly formed particles (Dal Maso et al., 2008;
Dinoi et al., 2021), and new particle formation (NPF) events contributed
about 54 % of total PNCs in Leipzig, Germany (Ma and Birmili, 2015). Guo
et al. (2014) tried to reveal the causal connection between NPF and haze
pollution and reported that NPF trends precede winter haze episodes in
Beijing. The more recent study by Kulmala et al. (2021) found that over
65 % of the number concentrations of haze particles resulted from NPF
events in Beijing, and their findings suggested that almost all present-day
haze episodes originated from NPF, mainly since primary emission
considerably decreased during recent year. PNSDs were considered to be
better indicators of the strength of emission sources (Vu et al., 2015), but
they were more easily modulated by aerosol dynamic processes, such as
nucleation, coagulation, volatilization and condensation (Birmili et al.,
2010; Kulmala, 2003). Nucleation and coagulation were largely affected by
the coagulation sink (CoagS), and CoagS significantly decreased due to the large
reduction of PM2.5 mass concentrations in response to APPCAP.
Therefore, the response of particle number concentration in different size bins
to emission mitigation policies may be different from PM2.5 mass
concentration.
The long-term PNSDs measurements were mainly conducted before APPCAP in
China, and the response of particle number
concentrations to the strictest ever air pollution control policies
implemented by the Chinese central government has not been widely reported. Lanzhou, as one of the most
polluted cities around the world with special basin terrain, won the Award for Today's
Transformative Step 2015 awarded by the United Nations due to significant
improvement in urban air quality (Zhao et al., 2018). The atmospheric
horizontal and vertical dispersion conditions inside the basin are poor due
to weak winds and strong multi-layer temperature inversion induced by basin
terrain (Pandolfi et al., 2014). Therefore, the air pollutants were easily
trapped inside the basin and hard to disperse to the upper air. Furthermore,
basin aerosol pollution was more controlled by vertical than horizontal
dispersion compared to the plain (Zhao et al., 2019). Based on a unique
PNSD dataset for the period of 2012–2019 in urban Lanzhou in western China,
this study investigates the long-term trends of PNCs in different modes to
evaluate the role of emission reduction and meteorology in PNC variations.
The results of this study may be important for the policymakers to
cooperatively prevent and control heavy particle mass and number
concentrations in Chinese megacities.
Lanzhou, located at the intersection of the Tibetan Plateau, the Loess Plateau and
the Mongolian Plateau, is in a long valley running from the east to the west. The
urban area is encircled by the hills rising from 200 to 600 m and thus
forming saddle-shaped basin terrain (Fig. 1). The weak winds and
multi-layer temperature inversion occurred frequently due to terrain
impacts, and thus the air pollutants are trapped inside the basin (Chu et
al., 2008). It was thought to be one of the most polluted cities around the
world (WHO, 2018), and a photochemical smog episode (PSE) was observed in the
1980s at the Xigu District of urban Lanzhou, which was the first time a PSE was
observed in China (Chen et al., 1986). The observation campaign was
conducted from September 2012 to August 2019 on the rooftop of a 32 m high
research building of the Northwest Institute of Eco-Environment and
Resources (NIEER), Chinese Academy of Sciences. There are two major roads
with a traffic volume of more than 2000 cars per hour near the observation site
(Fig. 1). The NIEER is surrounded by residential and commercial buildings,
with no local industrial sources around the site (Zhao et al.,
2015a), and thus the measurement site can represent the urban background.
PNSD, criteria air pollutants and meteorology data
Five-minute particle number concentrations and size distributions (13–800 nm) were
measured continuously using a scanning mobility particle sizer (Model 3936, TSI,
USA) for about 7 years at the urban site from September 2012 to August 2019.
The aerosol and sheath flow rates were set to 0.3 and 3 L min-1, respectively. The sampling inlet was mounted 1.5 m above the
rooftop. The diffusional and gravitational losses for the inlet lines of
the scanning mobility particle sizer (SMPS) were calibrated during the campaign. The SMPS's mobility was calibrated with monodisperse aerosols prior to their deployment in the field. The
impactor was cleaned every day, and aerosol and sheath rates were examined
with a bubble flow meter to ensure the good performance of the instrument.
Each PNSD was parameterized with a least-squares log-normal fitting
method providing parameters of two to three log-normal modes (Birmili et al., 2001).
Three modes (i=1, 2, 3) were used: the nucleation mode
(13–25 nm), the Aitken mode (25–100 nm) and the accumulation mode (100–800 nm). The log-normal distribution is expressed as (Seinfeld and
Pandis, 2006)
dNdlogDp=∑i=1nNi2πlogσiexp-logDp-logD‾p,i22logσi2,
where Ni is the total number concentration of the mode i,
D‾p,i is the median diameter of mode i, σi is
the geometric mean standard deviation of the distribution and n is the number
of the modes. In this study “log” means log 10.
The hourly averaged concentrations of the criteria air pollutants
(PM2.5, PM10, SO2, NO2, O3, CO) were measured at the
Lanzhou Biological Preparations Institute, which is around 2.8 km away from the
observation site. SO2, NO2, CO and O3 are measured by the
ultraviolet fluorescence method, the chemiluminescence method, the
non-dispersive infrared absorption method and the UV-spectrophotometry
method, respectively. PM2.5 and PM10 are measured by the micro-oscillating balance method. The 10 min meteorological parameters including
temperature, relative humidity, wind speed and direction, precipitation and
raindrop size distribution, and solar radiation were monitored by an
automatic meteorological station co-located with the observation site. All
the online data were hourly averaged and are presented in local time (China standard time = UTC + 8) throughout this paper.
Identification of NPF events and calculation of the relevant parameters
Referring to the methods presented in Dal Maso et al. (2005), NPF events were
identified for a day. Number concentration sharply increased in the
nucleation mode size range (13–25 nm) and prevailed for at least 1 h.
Additionally, the particle size was required to increase during the next few
hours. The parameters describing NPF events such as formation and growth
rates (JD and GR hereafter) and condensation and coagulation sinks (CS and
CoagS
hereafter) were calculated in this study. GR can be calculated with the time
evolution of the geometric mean diameter (GMD) of the nucleation mode obtained by
parameterizing PNSD, and it can be expressed as
GR=dGMDdt.
The formation rates (JD) can be calculated by
JD=dNnucdt+Fcoag,
where the first term in the right-hand side (dNnuc/dt) represents the
observed change in the number concentration of newly formed particles (Zhao
et al., 2021). The second term is the loss of newly formed particles induced
by coagulation scavenging and can be obtained with
Fcoag=CoagSnucNnuc.
The coagulation sink of nucleation mode particles (CoagSnuc) is defined as
CoagSDp=∫KDp′,DpnDp′dDp′,
where K(Dp′,Dp) is the coagulation coefficient of particles with
sizes of Dp and Dp′, calculated by the method of Fuchs (1964).
The reference size (Dp) is assumed to be the GMD of the nucleation mode. An
average CoagSnucNnuc over each formation period was taken during the
campaign.
The condensation sink (CS) can be expressed as
CS=2πD∑βmDp,iDp,iNi,
where Dp,i and Ni are the particle diameter and the corresponding
number concentration in size class i. D is the diffusion coefficient of the
condensing vapor, usually assumed to be sulfuric acid. βm
represents a transition-regime correction (Kulmala et al., 2012),
βm=1+Kn1+1.677Kn+1.333Kn2,
defined as a function of the Knudsen number, Kn=2λ/Dp,i. Furthermore, based on the method presented in Dada et al. (2020), the H2SO4 proxy was calculated to estimate the changes in the
NPF precursors over the study period, and the equation is given as follows:
H2SO4=-CS2⋅9.9×10-9+CS2⋅9.9×10-92+SO29.9×10-91.6×10-9⋅GlobRad1/2,
where CS is calculated by Eq. (6). SO2 concentrations are measured
using the ultraviolet fluorescence method, and global radiation (GlobRad) was
measured by an SMP3 pyranometer (Kipp and Zonen, the Netherlands) during the
campaign. In addition, the peak sizes of PNSDs are determined as mode
diameters.
Trend analysis methods
Referring to the method used in the study of Sun et al. (2020), a customized
Sen–Theil trend estimator was used to analyze the long-term trends of PNCs
in nucleation, Aitken, and accumulation modes; the concentrations of the
criteria air pollutants; and meteorological parameters in this study. The
technique can calculate the true slope of the parameters by considering the
impact of their seasonal, weekly, and diurnal cycles and avoid the effect of
outliers and missing values. The change rates for the hourly or daily time
series can be calculated with
mi,k=xi+Δt-xiΔt,
where k is the integer. Δt is equal to the product of k and 364 d
(52 weeks), indicating that data points from two different years are
compared only if they were measured in the same hour of the day, day of the
week and season the year.
A continuous 7.5-year dataset was evaluated in this investigation. Except
for the instrument maintenance and relocation, 80 % of the data were
effective. The continuous PNSD dataset was integrated to calculate PNCs in
different size bins. In this study, diameter ranges for the nucleation mode,
Aitken mode and accumulation mode were determined as 13–25, 25–100
and 100–800 nm, respectively (Dal Maso et al., 2005). The total PNCs covered
from 13 to 800 nm in mobility diameter.
Overview of the particle number concentration
Sources and origins of particles in the three modes may largely vary in specific micro-environments. Nucleation mode particles are from
atmospheric nucleation events which are closely related to the low-volatility
condensable gases such as water and sulfuric acid and growth of the smaller
aerosol particles (Kulmala, 2003). Aitken mode particles are primarily
emitted from combustion processes, such as coal combustion for domestic
heating in wintertime and also from hygroscopic growth and coagulation of
nucleation mode particles. For a relatively clean environment, the growth of
nucleation mode particles is predominant due to a smaller coagulation sink (Rose
et al., 2021), while the primary emissions are more important at the highly
polluted urban areas (Hussein et al., 2004). Accumulation mode particles
originate from the coagulation and hygroscopic growth of Aitken mode particles
and long-range transport from the highly polluted areas.
Time series of daily average particle number in the three modes
(N13-25, N25-100 and N100-800), aerosol optical properties (AOD,
Alpha), the criteria air pollutants (PM2.5, O3, SO2,
NO2), and basic meteorological parameters and the corresponding
probability density functions in urban Lanzhou during the campaign. The
frequencies of missing values and the statistics are shown in each subplot.
T, RH and Rn represent temperature, relative humidity and net radiation,
respectively.
Figure 2 shows variation of particle number in nucleation mode
(N13-25), Aitken mode (N25-100), and accumulation mode
(N100-800); aerosol optical properties (AOD, Alpha); criteria air
pollutants (PM2.5, O3, SO2, and NO2); and basic
meteorological parameters (wind speed, relative humidity, temperature, net
radiation) during the entire measurement campaign. The probability density
functions and the corresponding statistical parameters are also given in
Fig. 2. The mean PM2.5 and O3, SO2, and NO2 concentrations
were 49.9, 44.8, 22.7 and
57.5 µg m-3 during 2014–2019, and mean values of wind speed,
temperature, relative humidity and net radiation were 1.6 m s-1, 10.9 ∘C, 44.6 % and 44.4 W m-2, respectively. The mean values
for N13-25, N25-100 and N100-800 were 2514.0, 10 768.7 and 3258.4 cm-3, respectively. Aitken mode particles,
accounting for 65.1 % of total PNCs, were significantly higher than the
other modes, and the differences were much larger than the results in
European cities (Cusack et al., 2013; Leoni et al., 2018), urban Beijing (Wu
et al., 2008) and the North China Plain (Shen et al., 2011), which may be
related to the fact that the particles at 3–12 and 800–1000 nm were not
covered in nucleation and accumulation modes for our measurement campaign.
The average particle number size distribution (PNSD) surface plots in four
seasons for each year during the campaign are presented in Figs. S1–S4 to
highlight the overall similarities and differences of each year during the
study period. The mode diameter of PNSD shifted to smaller particle size in
four seasons from 2012 to 2019. The particle number in Aitken and accumulation
modes declined significantly in autumn and winter during the study periods, which could be
due to the strictest emission control policies in recent years.
However, in spring and summer, the nucleation mode particle number increased
significantly after 2016, which can be partly modulated by NPF events. The
impacts of emission control and NPF events are discussed in the following
sections in more detail.
Overview of experimentally determined particle number
concentrations in the troposphere around the world. The duration of the
measurement campaign was at least longer than 1 year (12 months).
ContinentDiameter, city, site and periodNumber concentrations ReferenceAsiaDiameter range (nm) Lanzhou, urban, 7.5 years13–25 251425–100 10 769100–800 3258This workDiameter range (nm) Beijing, urban, 3 years3–20 500020–100 12300100–800 6400Wang et al. (2013)Diameter range (nm) Waliguan, remote rural, 22 months12–21 57021–95 106095–570 430Kivekas et al. (2009)Diameter range (nm) Shangdianzi, rural, 1.5 years3–25 361025–100 4430100–1000 3470Shen et al. (2011)Diameter range (nm) Kanpur, urban, 4 years20–100 12400100–685 18 900Kanawade et al. (2014)Diameter range (nm) Delhi, urban, 1.25 years12–25 894025–100 21 690100–560 11 690Gani et al. (2020)EuropeDiameter range (nm) Värriö, rural, 3 years8–25 14325–90 42990–460 304Dal Maso et al. (2008)Diameter range (nm) Copenhagen, rural, 3 years8–30 77030–100 1813100–700 751von Bismarck-Osten et al. (2013)Diameter range (nm) Leipzig, roadside, 3 years8–30 569230–100 4962100–700 2242von Bismarck-Osten et al. (2013)Diameter range (nm) Helsinki, urban background, 3 years8–30 308030–100 3099100–700 1053von Bismarck-Osten et al. (2013)Diameter range (nm) London, urban background, 3 years8–30 163230–100 3825100–700 1437von Bismarck-Osten et al. (2013)North AmericaDiameter range (nm) Rochester, urban, 8 years10–50 473050–100 1838100–500 1073Wang et al. (2011)Diameter range (nm) Pittsburgh, urban, 1 year3–20 970020–100 10 100100–1000 2188Stanier et al. (2004)
Until now, numerous measurements of sub-micron PNSDs have been carried out
at a variety of locations to examine their variations and key influencing
factors. Table 1 summarizes experimentally determined particle number
concentrations in the troposphere for the measurement campaigns conducted
over time periods longer than 1 year across the globe. The mean number concentrations in the
three modes were much lower than those in urban Beijing (Wang et al., 2013)
and significantly higher than those at a remote background station, Mt.
Waliguan, one of the global GAW sites in China (Kivekas et al., 2009). The
sub-micron particle number concentration was much lower compared to the most
polluted cities in India, such as Delhi (Gani et al., 2020) and Kanpur
(Kanawade et al., 2014), especially for Aitken and accumulation modes. The number of
particles in Aitken and accumulation modes at Asian cities were even
higher than those at the urban sites in Europe and North America, which may
largely be related to poor visibility in Asian cities according to Mie
scattering theory. For nucleation mode, the situation is the opposite, which may
be because newly formed particles were rapidly scavenged by coagulation with larger particles in highly polluted cities (von Bismarck-Osten
et al., 2013; Wang et al., 2011).
Trends of PNCs, criteria air pollutants and meteorological parameters
Besides primary emissions from human activities in urban areas, particle
number concentration was easily affected by secondary newly formed
particles, which were closely related to meteorological conditions such as
temperature, relative humidity and new radiation (Zhao et al., 2015a).
Figure S5 shows inter-annual variations of monthly averaged particle
numbers, criteria air pollutants, and wind speed during 2012–2019, and it
normalizes the time series data (N13-25, N25-100, N100-800,
PM2.5, O3, SO2, NO2 and wind speed) to fix values to
equal 100 at the beginning of September 2012. The particle number in the
three size ranges declined largely during 2012–2015 (Period I), and summer
N13-25 decreased by around 75 % in 2015 compared to that in 2013,
while that in winter varied less during Period I. The N25-100 and
N100-800 reduced more in winter than in summer due to emission
control impacts. The number of nucleation mode particles (N13-25)
increased significantly during 2016–2019 (Period II), which was consistent
with O3 while showing the opposite trend with declining PM2.5
during Period II. The strongly declining aerosol radiative effect due to the
strict air pollution controls resulted in an unprecedented rapid increasing
trend in surface solar radiation over China during 2014–2019 (Shi et al.,
2021), which may promote the formation of secondary air pollutants.
The particle number in the Aitken and accumulation modes (N25-100,
N100-800) firstly increased during 2016–2017 and then decreased from
2018 to 2019, and their variations were consistent with the primary emitted
pollutants (SO2, NO2), indicating that N25-100 and
N100-800 variations during 2016–2018 were mainly modulated by primary
emissions. Sun et al. (2020) analyzed the long-term trends of particle
number concentrations at 16 observational sites in Germany from 2009 to
2018, and number concentrations of particles in the three modes were found
to have significant decreasing trends in response to emission mitigation
policies. The contrasting response of nucleation mode particles to mitigation
policies between China and Germany may be related to the fact of the increased
reduction of the coagulation sink due to the strictest ever clean air action plan in
China, and thus NPF event occurred easily due to the lower coagulation scavenging
effects (Gani et al., 2020). The variation in wind speed was not significant
during the entire measurement campaign.
Mean diurnal (a) and annual (b) variations of
particle number in 13–25 size bins (N13-25) as wind directions in the two
contrasting periods (before vs. after January 2016).
In view of the contrasting PNC trends between periods I and II, the following
analyses compared mean diurnal and annual variations of particle number in
the three size bins (N13-25, N25-100 and N100-800), PM2.5
and O3 as wind directions before and after January 2016 (Figs. 3, S6–S9). The most obvious increase in N13-25 was during 12:00–16:00 in
the summer months after January 2016 compared to before January 2016, and the
largest increase corresponded to easterly, southerly and southwesterly
winds, especially for the annual cycles with the more significantly
increased N13-25 for southeasterly winds. The large conditional probability function (CPF) values of
N13-25 mainly corresponded to southerly winds (Fig. S10), which can
support the above results. The N25-100 difference between the two periods
(2012–2015 vs. 2016–2019) was much less significant than N13-25, and the
most obvious N25-100 increase occurred in morning and evening rush hours
for northeasterly winds (Fig. S6), which could be supported by the results
of polar plots (Fig. S10). Figure 4 illustrates mean particle number size
distributions by varying wind directions, and the number of Aitken mode particles
for north northeasterly winds (0–45 ∘C) was
higher than that for the other wind directions. The emissions from
jammed traffic and winter domestic heating with traditional stoves at Keji
Street, about 500 m away from the sampling site, could be transported to the
station as northeasterly winds. In addition, the larger increase in
N25-100 at 13:00 for easterly, southerly and southwesterly winds was
consistent with N13-25 possibly due to newly formed particle growth
impacts.
The N100-800 and PM2.5 trends from Period I to II in diurnal and
annual cycles were opposite to those in N13-25 with a significant reduction at
noon in the summer months for southerly winds (Figs. S7, S8 and S10), which were
mainly affected by the clean air action plan (Li et al., 2021). Gani et al. (2020)
studied particle number concentrations and size distributions in the polluted
megacity of Delhi in India and pointed out that strategies that only
target accumulation mode particles (which constitute much of the fine
PM2.5 mass) may even lead to an increase in the UFP concentrations as
the coagulation sink decreases. Furthermore, O3 increased more
significantly in the afternoon in summer months after January than before January
2016, and wind directions for the largest increased O3 concentrations
were consistent with nucleation mode particles (Fig. S9), which further
confirmed that the increased N13-25 from Period I to II was induced by
more frequent nucleation events. Compared to before January 2016, the more
favorable meteorological conditions after January 2016 such as the much
drier air (Fig. S5), higher ambient temperature (Fig. S5) and stronger
solar radiation (Fig. S12) for southerly winds also helped to form new
particles, which could be supported by our previous work in the same site
(Zhao et al., 2015a).
Typical particle number size distributions influenced by varying factors
Besides the chemical composition of airborne particles, the information derived
from particle number size distributions (PNSDs) is beginning to play an
important role in source apportionment studies (Vu et al., 2015) due to the
obvious difference in diameters for the particles from varying sources. The
hourly average PNSDs during the entire measurement campaign were classified
into six clusters by the k-means clustering technique, and the mean PNSD for each
typical type was shown in Fig. 5. As shown in Fig. 5, the shape and
mode diameter of PNSDs were largely different among the clusters. Mode
diameters varied from ∼ 20 nm for Cluster B to 70 nm for
Cluster F, and more than a quarter of PNSDs were sorted into Cluster A with a mode diameter of ∼ 55 nm, while Cluster B occurred less frequently with
a mode diameter of ∼ 20 nm. The sources and key factors
influencing each cluster of PNSD can be better determined by average
annual and diurnal variations of occurrence frequencies, as well as the
corresponding air pollutants and meteorological parameters for the clusters
(Fig. 6, Tables 2 and 3).
Mean particle number size distributions by each sector of wind
directions with an interval of 45∘ during 2012–2019.
Mean values of particle number in the three modes (N13-25,
N25-100 and N100-800), AOD, the concentrations of six criteria air
pollutants (PM2.5, PM10, O3, SO2, NO2 and CO) and
the condensation sink (CS) for each cluster.
About 70 % of clusters A and F are in the cold seasons (October–December
and January–March) with the almost opposite diurnal pattern between the two
clusters (Fig. 6). Clusters A and F had the highest number concentrations
of accumulation mode particles (N100-800) and mass concentrations of
particulate matter (PM2.5, PM10) and gaseous pollutants (SO2,
NO2, CO), while they had the lowest particle number concentrations in nucleation mode
(N13-25) and O3 mass concentrations among the clusters (Table 2),
suggesting that the two polluted clusters may be mainly impacted by primary
emissions from human activities, which is defined and abbreviated as
Pollut_C in the following analyses. This can also be
confirmed by the larger geometric median diameters for the three modes
(GMDnuc, GMDAit and GMDacc) compared to the other
clusters, as well as the high particle number concentrations in the morning and evening
rush hours (Fig. S13). Compared to other clusters, the weaker winds and
net radiation, lower ambient temperature, and higher relative humidity
indicated that the severe air pollution for clusters A and F was
significantly affected by stable air and poor diffusion conditions.
Furthermore, as illustrated in Fig. 7, Cluster F (A) accounted for more
than 40 % (60 %) of all clusters during 5 h after the occurrence of
Cluster A (F), and the more frequently synchronous occurrence between
clusters A and F may be related to the pollution process from
light–severe–light episodes. From the perspective of inter-annual
variations in occurrence frequency, Pollut_C
occurred increasingly less frequently from 2014 to 2019, possibly due to implementation of the clean air
action plan (Fig. 6a), which is analyzed in detail in the following
section.
Mean particle number size distribution for each typical cluster
obtained by the k-means clustering method. The occurrence frequencies of
clusters A–F were calculated during 2012–2019.
(a) Inter-annual, (b) average annual and (c) diurnal variations
of occurrence frequencies for clusters A–F during the measurement campaign.
Mean values of meteorological parameters (wind speed, temperature,
relative humidity and net radiation) and geometric median diameters
(GMDnuc, GMDAit and GMDacc are for nucleation, Aitken and
accumulation modes) fitted by Eq. (1) for each cluster. WS, T, RH and Rn
are the abbreviations of wind speed, temperature, relative humidity and net
radiation, respectively.
The clusters B and E mainly appeared in the daytime in the warmer months,
and the occurrence frequency had a sharp peak in the afternoon (Fig. 6b, c), but the peak for Cluster E lagged by around 2 h compared to that for
Cluster B. The frequency of Cluster E during 2 h after the occurrence of
Cluster B was larger than 80 %, and the mode diameter of Cluster E
(∼ 30 nm) was only larger than that of Cluster B
(∼ 20 nm); thus, it was inferred that Cluster B represented
secondary new particle formation (NPF) event impacts, while Cluster E was
influenced by subsequent new particle growth events. The inference could
be confirmed by the highest particle number in nucleation mode and O3
mass concentration among the clusters (Table 2). The sharply increased
nucleation mode particles at 09:00 were followed by a subsequent growth to
accumulation mode indicated by the typical banana-shaped temporal
development of the number size distribution (Fig. S13, Boy and Kulmala,
2002), which also supported the above inference. In addition, the reduced
coagulation sink and low number concentrations of particles in
accumulation mode as well as low PM2.5 and PM10 mass induced by higher
wind speed helped to form secondary new particles (Tables 2 and 3). The
more recent study of Gani et al. (2020) investigated particle number
concentrations and size distribution in a polluted megacity, Delhi, and
found that reduction in mass concentration in the highly polluted megacity
may not produce a proportional reduction in PNCs and may even lead to an
increase in the UFP concentrations as the coagulation sink decreases. The
mean AOD of 0.39 for Cluster B was significantly lower than that for the
other clusters (Table 2), which resulted in the higher atmospheric
transparency and thus stronger net radiation (223.55 W m-2) and higher
ambient temperature (20.77∘C). The drier air was conducive to
detecting NPF events, and newly formed particle growth was limited by hygroscopicity under low-RH environments. The occurrence frequency for the two
clusters first decreased from 2013 to 2015 and then increased until 2019,
which contrasted with Pollut_C during the campaign.
Clusters B and E were abbreviated as NPF_C for the
following analyses. The condensation sink (CS) ranged from 2.12×10-3 s-1 for Cluster F to 1.38×10-2 s-1 for
Cluster B during the campaign (Table 2). The CS values for clusters B and F,
representing new particle formation and growth events, were much higher than
those for the other clusters, but they were even lower than CS during NPF events in
the North China Plain (0.02 s-1, Shen et al., 2011). Therefore, the large
PNSD discrepancy among the clusters may be less influenced by the condensation
sink during the measurement campaign. It is possible that CS was not a key factor in modulating
the occurrence of NPF events in urban Lanzhou in western China and that NPF was mainly
affected by meteorological variables and coagulation effects (Table 3).
The mean PNSD for Cluster C was much wider and more flat than that for the
other clusters, and thus it was hard to determine the mode diameter,
especially for the PNSDs from dawn to noon (Figs. 5 and S13). The number
of particles in nucleation mode (N13-25) was only lower than
NPF_C, while that in accumulation mode (N100-800) was only
lower than Pollut_C. The cluster occurred more easily in
the morning in the warm months, which was consistent with most of the
clusters except the polluted clusters A and F. Additionally, except for
Cluster F, the occurrence frequency of the other clusters was comparable and
ranged from about 15 % to 28 % during 1–12 h after Cluster C. The
frequency of Cluster C also varied less during 1–12 h after clusters A,
D, E and F (Fig. 7). Combining this with the modest
particle number concentrations in the three modes, criteria air
pollutants and meteorological parameters, it was inferred that Cluster C
represents the urban background PNSD, and thus it is defined as
UB_C in the following analyses. Cluster D occurred more frequently
in the morning and evening rush hours in the warm seasons (Fig. 6), and the corresponding mean particle number in Aitken mode
(N25-100) was the second highest ever – just behind Cluster A, which may
be impacted by motor vehicle emissions from the nearby roads. The mode
diameter of ∼ 40 nm was only larger than NPF_C
(clusters A and E), and it appeared frequently after Cluster E with the high
concentration of particles in Aitken mode in the afternoon (Fig. S13)
possibly due to new particle growth impacts. Therefore, Cluster D was
jointly influenced by motor vehicle emissions and NPF events and is
defined as VE_NPF_C in the following
section.
Occurrence frequency of the other clusters at the subsequent 1–12 h after each cluster appeared during the entire measurement campaign.
For example, the frequencies of clusters B, C, D, E and F in the subsequent
1–12 h when Cluster A appeared (the first column of Fig. 7) can be
calculated by the equation NA,i,j=Ni,j/∑Ni×100 % (i=1,2…12,j represents the other
clusters except Cluster A, i.e., clusters B, C, D, E and F). The calculation
is similar when the other clusters appeared during the campaign.
From the perspectives of the variation in mode diameter among the clusters
(Fig. 5) and the variation in frequency during 1–12 h after each
cluster (Fig. 7), the NPF_C was closely followed by
Pollut_C during the measurement campaign, and the clusters
can be ranked by temporal order as B → E → D → A → F.
Therefore, NPF events significantly contributed to haze episodes in the
subsequent 1–2 d, which may be increasingly obvious mainly due to
considerably decreased emissions of primary particles during recent years in
response to the clean air action plan. Guo et al. (2014) first reported that
atmospheric NPF tends to precede winter haze episodes in Beijing, and then
the latest study of Kulmala et al. (2021) investigated how NPF and subsequent
particle growth affect the initial steps of haze formation in Beijing. Their
findings showed that reducing the subsequent growth rate of freshly formed
particles by a factor of 3–5 would delay the buildup of haze episodes by
1–3 d.
Impact of the clean air action plan on PNC variations
The response of PM2.5 mass to the clean air action plan has been evaluated in
many previous studies, and PM2.5 was found to decrease by
30 %–50 % across China during 2013–2018 due to the implementation of
emission control policies (Zhai et al., 2019). The impact of the policies on
particle number may be more complex compared to PM mass since more fine
particles cannot rapidly grow by coagulation with the reduced coarse
particles (Gani et al., 2020). However, the response of PNCs to the
restricted emissions was only analyzed by some short-term measurements
during some important and international meetings and activities such as the
Summer Olympic Games in 2008, the Asia-Pacific Economic Cooperation (APEC)
in 2014 and China's Victory Day parade in 2015 (Chen et al., 2015; Shen et al.,
2016; Wang et al., 2013). The long-term in situ measurements of PNSDs and mass
concentrations of the criteria air pollutants were essential to understand
the emission control impacts and to reveal the mechanism. Figures 8, S14 and
S15 show the trends of daily mean particle number in the three modes as wind
directions for each cluster based on 7.5 years of measurement. The number of
particles in nucleation mode (N13-25) first decreased from 2012 to 2015
and then increased rapidly after 2016. The N13-25 changing trend for
NPF_C (clusters B and E) was more significant compared
to that for the other clusters, especially for southeasterly winds. The
specific winds corresponded to more PM2.5 reduction on summer afternoons
after 2016 than before 2016 due to the impact of emission mitigation policies (Fig. S8), and thus NPF events, represented by NPF_C, were easily
detected by chemical reactions due to reduced coagulation sink. More
solar radiation reached near-surface air as a result of reduced PM2.5
mass (Shi et al., 2021), and thus ambient temperature increased and relative
humidity declined (Fig. S11), which also favored the occurrence of NPF events
(Zhao et al., 2015a).
Trends of daily mean particle number in nucleation mode
(N13-25) as wind directions for each cluster during the entire
measurement campaign.
At our sampling site, N25-100 was easily influenced by growth of newly
formed particles and primary emissions from human activities. The
N25-100 trends were similar to N13-25 for clusters B, D and E, and
the increasing trends were also more significant after 2016 for
southwesterly winds (Fig. S14), which represented NPF impacts. Dependence
of N25-100 on wind directions was not obvious for clusters A, C and F,
and thus the trends may be related to variations in primary emissions.
Unlike nucleation and Aitken modes, particle number in accumulation mode
(N100-800) depended less on wind directions. Furthermore,
N100-800 was the lowest and less varied for NPF_C, while
that for clusters A, C and D had a similar trend to N25-100, and that
for Cluster F, the most polluted cluster, had a downward trend during the
campaign due to the implementation of the strictest ever the clean air action plan.
Therefore, the response of particle number to air pollution control may
be significantly different for each size fraction, which may be closely related to
the variations in coagulation sink and meteorological conditions induced by
reduced primary emissions. This is discussed in detail in the following
section.
To better evaluate variations in particle number concentrations and emission
control impacts, Figs. S16, S17 and S18 show variations of the contributions
of each cluster to monthly averaged PNCs in 13–25, 25–100 and 100–800 nm during the campaign, respectively. Pollut_C (clusters A
and F) dominated the winter PNCs in different size bins before 2016, and
their occurrence was less and less frequent after 2016, especially for the
most polluted Cluster F, which was largely dominated by a reduction in primary
emissions. In contrast to Pollut_C, as a main cluster
representing NPF events, the contribution of Cluster B to summer
N13-25 decreased from 50 % to about 10 % during 2013 to 2015 and
then increased to reach around 60 % in 2019. For Cluster C representing
urban background, its frequency varied less during the entire measurement
campaign. The particle number was dominated by primary emissions before
2016, and thereafter that was controlled by NPF events, which was partly due
to emission control. In response to air pollution control, the reduction in
coarse particles could promote secondary new particle formation due to the reduced
coagulation sink (Gani et al., 2020). NPF events were largely dependent on
PM mass concentrations mainly contributed by coarse aerosol particles.
Accumulation mode particle number concentrations in cities of developing
countries are generally higher than those in many western cities (Gani et
al., 2020; Wu et al., 2008), and thus the response of NPF events to emission
control may be significantly different between the cities of developed and
developing countries. For example, Sun et al. (2020) found coincidently
downward trends of particle number and black carbon mass concentrations at
16 observational sites in Germany from 2009 to 2018 due to reduced
anthropogenic emissions. Gani et al. (2020) pointed out that strategies that
only target accumulation mode particles in a polluted megacity in India may
even lead to an increase in the UFP concentrations as the coagulation sink
decreases. Shen et al. (2016) also found that PM1 mass concentration
was significantly reduced while NPF event frequency was much higher during
the short-term emission control period.
Seasonal variations in the trends of PNCs in three modes for the
two contrasting periods (before vs. after January 2016) for each cluster.
The annual change is calculated by Theil–Sen regression, and the calculation
is shown in Sect. 2.4 (Trend analysis methods). MAM, JJA,
SON and DJF refer to spring, summer, fall and winter, respectively.
We also quantitatively evaluated the changing trends of particle number in
the three modes by Theil–Sen regression. In view of the contrasting trends,
the observation period was divided into two sub-periods, i.e., before and
after January 2016. Figures 9 and 10 illustrate the seasonal and diurnal
variations of the trends of PNCs for each cluster during each sub-period. For
Period I (2012–2015), PNCs in the three size bins exhibited downward trends
for all clusters, especially in spring. The annual relative slopes of spring
PNCs varied from -54.7 % for Cluster F to -17.2 % for Cluster B, from
-42.6 % for Cluster A to -14.1 % for Cluster B, and from -40.7 % for
Cluster A to -17.5 % for Cluster B per year for 13–25, 25–100, and 100–800 nm size ranges (Fig. 9). The PNCs for Pollut_C (clusters A
and F) decreased by about 40 % in the morning and evening rush hours,
which was much higher than in the other hours of the day. Therefore, the
increased reduction in PNCs for Pollut_C may be closely related to
emission control policies. The much larger PNC reduction in this study than
in Germany may be due to the strictest emission
mitigation policies ever implemented in Chinese cities (Sun et al., 2020). In contrast to Period
I, the UFP number increased and the amplitude was greater during Period
II (2016–2019). The annual relative slopes of N13-25 varied between
5.1 % (fall) and 314.4 % (winter), 8.0 (fall) and 135.5 % (spring),
11.3 % (fall) and 184.3 % (winter), -4.5 (fall) and 59.1 % (summer),
6.3 % (fall) and 30.3 % (spring), and 3.6 % (fall) and 15.7 %
(spring) for clusters A–F. The maximum increase of N13-25 was in the
spring afternoon for NPF_C, which may be governed by NPF
events due to the reduced coagulation sink corresponding to low N100-800.
The winter N13-25 increased significantly for Cluster A during
Period II, especially in the morning and evening rush hours, suggesting the
impact of primary emissions from motor vehicles. The annual slope of
N100-800 varied less compared to that of ultrafine particles in
seasonal and diurnal cycles.
Diurnal variations in the trends of PNCs in three modes for the
two contrasting periods (before vs. after January 2016) for each cluster.
The annual change is calculated by Theil–Sen regression, and the calculation
is shown in Sect. 2.4 (Trend analysis methods).
Inter-annual statistics of the trends of NPF frequency, mode
diameter, formation (dNnuc/dt, Fcoag) and growth rates, CS and
H2SO4 proxy, and number concentrations in the three bins
(N13-25, N25-100, N100-800) during the campaign. The lines
inside the box denote the median slope; the two whiskers and the top and
bottom of the box denote the 5th and 95th and the 75th and 25th percentiles.
To better analyze long-term trend of NPF events and the relevant parameters
during 2012–2019, Fig. 11 illustrates the inter-annual statistics of the
trends of NPF frequency, mode diameter, and formation and growth rates.
Furthermore, condensation and coagulation sinks (CS, CoagS) and H2SO4
proxy were also calculated over the study period. Similar to the opposite
N13-25 trend between the two contrasting periods (Figs. 8–9), the
occurrence frequency of NPF events decreased from ∼ 30 % to
less than 5 % until 2016 and then increased to more than 30 % in 2019.
Particles have become much finer since 2015 due to more frequent
NPF events (Fig. 11b). The temporal variations of PNCs in nucleation mode
(dNnuc/dt, Fig. 11c) and the coagulation scavenging effect (Fcoag, Fig. 11d) followed similar inter-annual variations of NPF frequency. The
contribution of coagulation loss flux Fcoag to the total observed rate was
on average 37 %, which was close to the average ratio of coagulation loss
to formation rate in urban Beijing, 0.41 (Yue et al., 2010), suggesting that
coagulation loss was as important as dNnuc/dt. The formation rate
(JD) ranged from 0.2 to 16.2 cm-3 s-1 in urban Lanzhou, which
was lower than the observations at some urban sites, such as in Beijing,
3.3–81.4 cm-3 s-1 (Wu et al., 2007), and St. Louis, with a mean
value of 17.0 cm-3 s-1 (Qian et al., 2007), but much higher than
in regional nucleation episodes (0.01–10 cm-3 s-1) at most
other sites (Kulmala et al., 2004).
Compared to JD, GR varied less in inter-annual scale and ranged from
0.5 to 14.9 nm h-1, which is slightly higher than that in urban Beijing,
0.3–11.2 nm h-1 (Wu et al., 2007), and also within the range of the typical
particle growth rate of 1–20 nm h-1 in mid-latitudes (Kulmala
et al., 2004). The mean CS values were between 0 and 0.01 s-1 with less
fluctuation during the campaign (Fig. 11f). Shen et al. (2011) found that
the mean value of CS was 0.02 s-1 during NPF events in the North China Plain,
which was much higher than our results. Therefore, NPF events were less
impacted by the condensation sink during our campaign. The less-varied N100-800
during 2015–2019 compared to that during 2012–2014 may be related to the
condensation of low-volatility vapors, which resulted in a relatively high
condensation sink (Fig. 11j). Based on the method presented in Dada et al. (2020), we also calculated the H2SO4 proxy to estimate the changes in
the NPF precursors over the study period (Fig. 11g). The H2SO4
proxy varied from 3.3×107 to 6.0×108 cm-3,
with average concentration of 2.5×108 cm-3 over the
study period, which was slightly higher than that in urban Beijing (Dada et
al., 2020) due to more coal combustion and basin terrain in urban Lanzhou.
Normalized diurnal variations of net radiation (Rn),
N13-25, temperature, relative humidity, and wind speed and direction for
each cluster during the campaign. The shading shows the estimated 95 %
confidence intervals.
Role of meteorology and air masses
NPF events predominantly occurred under dry and sunny weather conditions
(Birmili and Wiedensohler, 2000; Kerminen et al., 2018). According to a
relatively recent review on regional NPF in different environments of the
global troposphere, the observed factors that favor the occurrence of
regional NPF include a high intensity of solar radiation, low RH, high
gas-phase sulfuric acid concentration and low pre-existing aerosol loading
(Kerminen et al., 2018). Possible reasons for the apparently close
connection between the ambient RH and occurrence of NPF have been proposed,
including the typically negative feedback of high RH on the solar radiation
intensity, photochemical reactions and atmospheric lifetime of aerosol
precursor vapors. The effect of the ambient temperature (T) on NPF shows very
different responses between different studies, which is probably related to
the simultaneous presence of several temperature-dependent processes that
may either enhance or suppress NPF. Therefore, the meteorological parameters
affect NPF process by modulating the condensation and coagulation sink.
Figures 12 and S19 show the diurnal and inter-annual variations in
meteorological parameters such as net radiation, temperature, relative
humidity, and wind speed and direction for each cluster during the campaign
to better understand response of PNCs to meteorology. The peak of net
radiation and N13-25 coincided at noon for NPF_C, and
their peaks were significantly higher than those for the other clusters (Fig. 12). The increased daylight net radiation for Cluster B could also partly
explain the higher N13-25 induced by the more frequent NPF events after
2016, especially in spring (Figs. S19 and S20). The higher ambient
temperature and lower relative humidity at noon and the larger daily ranges
for NPF_C also indicated that dry and hot air in a sunny day
was conducive to forming new particles. In addition, NPF events corresponding
to large southeasterly winds may be because accumulation mode particles were
dispersed and diluted by strong winds and thus coagulation sink decreased,
which can be supported by the above results. According to the empirically based
mathematical function between number concentrations of fine particles (FPs,
diameter < 2.5 µm) and meteorological variables, Hussein et al. (2006) found that the predicted number concentrations of accumulation mode
particles follow this relationship more closely than those of UFPs due to
the origin and type of aerosol particles in the accumulation mode size
range, being mainly regional and long-range transported. The main limitation
of the mathematical function in their study was during NPF events,
indicating that particles in nucleation and accumulation modes were
differently dependent on meteorological variables.
A general finding was that changes in aerosol were related to air mass
changes (Birmili et al., 2001), and dust aerosols from the Gobi Desert at the Hexi
Corridor could be transported to Lanzhou and affect urban PM pollution
(Zhao et al., 2015b). Figure S21 illustrates gridded back trajectory
frequencies with hexagonal binning for each cluster to explore the impacts
of air mass on variations of particle number. The huge discrepancy of back
trajectory frequencies among the six clusters suggested that the air mass
history has a significant impact on urban particle number concentrations and
size distributions. For example, back trajectories were mainly from the
adjacent regions of urban Lanzhou and were less affected by long-range transport
for NPF_C, and thus the particles were not easily grown by
coagulation during transport processes, which was conducive to the occurrence of
NPF events. In urban Beijing, Wang et al. (2013) also indicated that mean
total PNCs from the northern directions were higher than the air masses that came
from other directions, while more volume concentrations were observed for
the air masses from the southwest and the south. Therefore, particle number
size distributions in urban Lanzhou were partly affected by air mass
conditions.
Summary and conclusions
The first in situ observations of particle number size distributions (PNSDs) in the
size range of 13–800 nm were conducted from 2012 to 2019 in urban Lanzhou, a
typical valley city in western China. Meanwhile, the mass concentrations of the
criteria air pollutants (PM2.5, PM10, O3, SO2, NO2,
and CO), AOD and meteorological variables (temperature, relative humidity,
wind speed and direction, and net radiation) were also measured during the
campaign. The customized Sen–Theil trend estimator and k-means clustering
technique were used to explore the trends of PNCs and the criteria air
pollutants, as well as to reveal the contributions of variations in primary
emissions due to the clean air action plan and secondary formation to PNCs. Some
novel findings were obtained as follows.
The mean values for particle number in nucleation (N13-25), Aitken
(N25-100) and accumulation modes (N100-800) were respectively 2514.0, 10 768.7, and 3258.4 cm-3, and N25-100
accounted for about 65.1 % of total PNCs during the campaign. The particle
number in the three modes declined largely during 2012–2015; for example, summer
N13-25 decreased by around 75 % in 2015 compared to that in 2013.
However, N13-25 increased significantly during 2016–2019, which was
consistent with O3 while showing the opposite trend with declining
PM2.5 during the period. The most obvious increase in N13-25 was
during 12:00–16:00 in summer months, and the largest increase corresponded
to easterly, southerly and southeasterly winds. N25-100 and
N100-800 first increased during 2016–2017 and then decreased until
2019, and their variations were consistent with the primary emitted
pollutants (SO2, NO2). The N25-100 difference between the two
periods (2012–2015 vs. 2016–2019) was much less significant than
N13-25, and the most obvious N25-100 increase occurred in the morning
and evening rush hours for northeasterly winds. In diurnal and annual
cycles, the N100-800 and PM2.5 trends for the two periods were
opposite to N13-25, with a significant reduction at noon in the summer
months for southerly winds, and thus the reduced coagulation sink was
conducive to the occurrence of NPF events.
The k-means clustering technique was used to classify the hourly average PNSDs
into six clusters during the measurement campaign. The shape and mode
diameter of PNSDs were largely different among the clusters with varying
mode diameters from ∼ 20 to 70 nm. According to the annual
and diurnal variations of occurrence frequency, PNSD, the corresponding air
pollutants and meteorological parameters, the sources and key influencing
factors were determined for each cluster. The two most polluted clusters (A
and F), Pollut_C, were mainly affected by the primary
emissions from human activities and poor diffusion conditions. Cluster B was
followed by Cluster E, and N13-25 had a sharp peak in the afternoon in
the warm months, and thus the two clusters represented new particle
formation and growth event impacts. Cluster C suggested an urban background
PNSD, while Cluster D was jointly affected by motor vehicle emissions and
NPF events. The response of particle number to air pollution control was
largely different for each size fraction, which may be closely related to
the variations in coagulation sink and meteorological conditions induced by
reduced primary emissions. Based on trends of daily mean particle number in
the three modes as wind directions for each cluster, the contributions of
primary emissions and secondary formation to PNCs were evaluated in this
study. The southeasterly winds corresponded to more PM2.5 reduction on
summer afternoons in response to emission control policies, and thus more
solar radiation reached the ground surface, which promoted NPF occurrence due to
the reduced coagulation sink. The polluted clusters governed the winter PNCs
before 2016, and their occurrence was less and less frequent after 2016,
which was largely dominated by the reduction in primary emissions. However, the
contribution of NPF events to summer N13-25 decreased from 50 % to
about 10 % during 2013 to 2015 and then increased to reach around 60 %
in 2019.
Theil–Sen regression was used to quantitatively evaluate the changing trends
of size-resolved PNCs, and they exhibited downward trends for all clusters
during 2012–2015, especially in spring. The annual relative slopes of spring
PNCs varied from -54.7 % for Cluster F to -17.2 % for Cluster B,
from -42.6 % for Cluster A to -14.1 % for Cluster B, and from -40.7 % for
Cluster A to -17.5 % for Cluster B per year for the 13–25, 25–100, and 100–800 nm size ranges. The UFPs number was increased, and the amplitude was greater
during 2016–2019. The annual relative slopes of N13-25 varied between
5.1 % (fall) and 314.4 % (winter), between 8.0 (fall) and 135.5 % (spring),
between 11.3 % (fall) and 184.3 % (winter), between -4.5 (fall) and 59.1 % (summer), between 6.3 % (fall) and 30.3 % (spring), and between 3.6 % (fall) and 15.7 %
(spring) for clusters A–F. The increased daytime net radiation, higher
ambient temperature and lower relative humidity at noon for NPF events also
could partly explain the higher N13-25 induced by the more frequent
nucleation events after 2016, especially in spring. The air mass history had
a significant impact on urban PNSDs. The back trajectories were mainly from
the adjacent regions of urban Lanzhou and less affected by long-range
transport for NPF events, and thus the particles were not easily grown by
coagulation during transport processes, which was helpful for the occurrence of
NPF events. In this study, the measurement campaign was conducted at a
Chinese cities in western China, but the similar PNCs trends and influencing
factors should be expected in other Chinese cities. In future work, we will
establish the PNSD observation network in some megacities to better
evaluate the response of PNCs to emission mitigation policies in China.
Code and data availability
The data used and the code for processing the data in this work can be
obtained by contacting the corresponding author (zhaosp@lzb.ac.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-14959-2021-supplement.
Author contributions
SZ and YY designed the study. SZ analyzed the data
with help from YY and DQ. DY and LD collected
and analyzed the particle number size distributions and meteorology data during
the campaign. JL conducted the field experiment.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank Longxiang Dong, Jin Xie and Chenchen Ma for their contributions to the experiment. We thank the editor and two anonymous referees for their suggestions, which improved the paper.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 42075185 and 41605103), the Youth Innovation Promotion Association of the Chinese Academy of Sciences (grant no. 2017462), and the Gansu Science and Technology Program key projects (grant nos. 20JR10RA037 and 18JR2RA005), the CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellows (2016LH0020).
Review statement
This paper was edited by Veli-Matti Kerminen and reviewed by two anonymous referees.
ReferencesAndreae, M. O. and Rosenfeld, D.: Aerosol-cloud-precipitation interactions,
Part 1. The nature and sources of cloud-active aerosols, Earth-Sci. Rev., 89, 13–41, 10.1016/j.earscirev.2008.03.001, 2008.Asmi, A., Wiedensohler, A., Laj, P., Fjaeraa, A.-M., Sellegri, K., Birmili, W., Weingartner, E., Baltensperger, U., Zdimal, V., Zikova, N., Putaud, J.-P., Marinoni, A., Tunved, P., Hansson, H.-C., Fiebig, M., Kivekäs, N., Lihavainen, H., Asmi, E., Ulevicius, V., Aalto, P. P., Swietlicki, E., Kristensson, A., Mihalopoulos, N., Kalivitis, N., Kalapov, I., Kiss, G., de Leeuw, G., Henzing, B., Harrison, R. M., Beddows, D., O'Dowd, C., Jennings, S. G., Flentje, H., Weinhold, K., Meinhardt, F., Ries, L., and Kulmala, M.: Number size distributions and seasonality of submicron particles in Europe 2008–2009, Atmos. Chem. Phys., 11, 5505–5538, 10.5194/acp-11-5505-2011, 2011.Asmi, A., Collaud Coen, M., Ogren, J. A., Andrews, E., Sheridan, P., Jefferson, A., Weingartner, E., Baltensperger, U., Bukowiecki, N., Lihavainen, H., Kivekäs, N., Asmi, E., Aalto, P. P., Kulmala, M., Wiedensohler, A., Birmili, W., Hamed, A., O'Dowd, C., G Jennings, S., Weller, R., Flentje, H., Fjaeraa, A. M., Fiebig, M., Myhre, C. L., Hallar, A. G., Swietlicki, E., Kristensson, A., and Laj, P.: Aerosol decadal trends – Part 2: In-situ aerosol particle number concentrations at GAW and ACTRIS stations, Atmos. Chem. Phys., 13, 895–916, 10.5194/acp-13-895-2013, 2013.
Birmili, W. and Wiedensohler, A.: New particle formation in the continental
boundary layer: Meteorological and gas phase parameter influence,
Geophys. Res. Lett., 27, 3325–3328, 2000.
Birmili, W., Wiedensohler, A., Heintzenberg, J., and Lehmann, K.:
Atmospheric particle number size distribution in central Europe: Statistical
relations to air mass and meteorology, J. Geophys. Res., 32,
5–18, 2001.Birmili, W., Heinke, K., Pitz, M., Matschullat, J., Wiedensohler, A., Cyrys, J., Wichmann, H.-E., and Peters, A.: Particle number size distributions in urban air before and after volatilisation, Atmos. Chem. Phys., 10, 4643–4660, 10.5194/acp-10-4643-2010, 2010.Birmili, W., Weinhold, K., Rasch, F., Sonntag, A., Sun, J., Merkel, M., Wiedensohler, A., Bastian, S., Schladitz, A., Löschau, G., Cyrys, J., Pitz, M., Gu, J., Kusch, T., Flentje, H., Quass, U., Kaminski, H., Kuhlbusch, T. A. J., Meinhardt, F., Schwerin, A., Bath, O., Ries, L., Gerwig, H., Wirtz, K., and Fiebig, M.: Long-term observations of tropospheric particle number size distributions and equivalent black carbon mass concentrations in the German Ultrafine Aerosol Network (GUAN), Earth Syst. Sci. Data, 8, 355–382, 10.5194/essd-8-355-2016, 2016.Bodhaine, B. A.: Aerosol measurements at four background sites, J. Geophys. Res., 88, 10753–10768, 10.1029/JC088iC15p10753,
1983.Boy, M. and Kulmala, M.: Nucleation events in the continental boundary layer: Influence of physical and meteorological parameters, Atmos. Chem. Phys., 2, 1–16, 10.5194/acp-2-1-2002, 2002.
Chen, C. H., Huang, J. G., Ren, Z. H., and Peng, X. A.: Meteorological
conditions of photochemical smog pollution during summer in Xigu industrial
area, Acta Sci. Circumst., 6, 334–342, 1986 (in Chinese).Chen, C., Sun, Y. L., Xu, W. Q., Du, W., Zhou, L. B., Han, T. T., Wang, Q. Q., Fu, P. Q., Wang, Z. F., Gao, Z. Q., Zhang, Q., and Worsnop, D. R.: Characteristics and sources of submicron aerosols above the urban canopy (260 m) in Beijing, China, during the 2014 APEC summit, Atmos. Chem. Phys., 15, 12879–12895, 10.5194/acp-15-12879-2015, 2015.
Chu, P. C., Chen, Y. C., Lu, S. H., Li, Z. C., and Lu Y. Q.: Particulate air
pollution in Lanzhou China, Environ. Int., 34, 698–713, 2008.Cusack, M., Pérez, N., Pey, J., Alastuey, A., and Querol, X.: Source apportionment of fine PM and sub-micron particle number concentrations at a regional background site in the western Mediterranean: a 2.5 year study, Atmos. Chem. Phys., 13, 5173–5187, 10.5194/acp-13-5173-2013, 2013.Dada, L., Ylivinkka, I., Baalbaki, R., Li, C., Guo, Y., Yan, C., Yao, L., Sarnela, N., Jokinen, T., Daellenbach, K. R., Yin, R., Deng, C., Chu, B., Nieminen, T., Wang, Y., Lin, Z., Thakur, R. C., Kontkanen, J., Stolzenburg, D., Sipilä, M., Hussein, T., Paasonen, P., Bianchi, F., Salma, I., Weidinger, T., Pikridas, M., Sciare, J., Jiang, J., Liu, Y., Petäjä, T., Kerminen, V.-M., and Kulmala, M.: Sources and sinks driving sulfuric acid concentrations in contrasting environments: implications on proxy calculations, Atmos. Chem. Phys., 20, 11747–11766, 10.5194/acp-20-11747-2020, 2020.
Dal Maso, M., Kulmala, M., Riipinen, I., Wagner, R., Hussein, T., Aalto, P.
P., and Lehtinen, K. E. J.: Formation and growth of fresh atmospheric
aerosols: eight years of aerosol size distribution data from SMEAR II,
Hyytiala, Finland, Boreal Environ. Res., 10, 323–336, 2005.
Dal Maso, M., Hyvärinen, A., Komppula, M., Tunved, P., Kerminen, V.-M.,
Lihavainen, H., Öviisanen, Y., Hansson, H.-C., and Kulmala, M.: Annual
and interannual variation in boreal forest aerosol particle number and
volume concentration and their connection to particle formation, Tellus B, 60, 495–508, 2008.Dinoi, A., Weinhold, K., Wiedensohler, A., and Contini, D.: Study of new
particle formation events in southern Italy, Atmos. Environ., 244,
117920, 10.1016/j.atmosenv.2020.117920, 2021.
Fuchs, N. A.: The mechanics of aerosols, Pergamon, Pp, xiv, 408, 1964.Gani, S., Bhandari, S., Patel, K., Seraj, S., Soni, P., Arub, Z., Habib, G., Hildebrandt Ruiz, L., and Apte, J. S.: Particle number concentrations and size distribution in a polluted megacity: the Delhi Aerosol Supersite study, Atmos. Chem. Phys., 20, 8533–8549, 10.5194/acp-20-8533-2020, 2020.Gao, Y., Zhang, M., Liu, Z., Wang, L., Wang, P., Xia, X., Tao, M., and Zhu, L.: Modeling the feedback between aerosol and meteorological variables in the atmospheric boundary layer during a severe fog–haze event over the North China Plain, Atmos. Chem. Phys., 15, 4279–4295, 10.5194/acp-15-4279-2015, 2015.Guo, S., Hu, M., Zamora, M. L., Peng, J., Shang, D., Zheng, J., Du, Z., Wu,
Z., Shao, M., Zeng, L., Molina, M. J., and Zhang, R. Y.: Elucidating severe
urban haze formation in China, P. Natl. Acad. Sci. USA, 111, 17373–17378,
10.1073/pnas.1419604111, 2014.Heintzenberg, J., Birmili, W., Otto, R., Andreae, M. O., Mayer, J.-C., Chi, X., and Panov, A.: Aerosol particle number size distributions and particulate light absorption at the ZOTTO tall tower (Siberia), 2006–2009, Atmos. Chem. Phys., 11, 8703–8719, 10.5194/acp-11-8703-2011, 2011.
Hu, J., Huang, L., Chen, M., Liao, H., Zhang, H., Wang, S., Zhang Q., and
Ying, Q.: Premature mortality attributable to particulate matter in China:
source contributions and responses to reductions, Environ. Sci. Tech., 51, 9950–9959, 2017.Hussein, T., Puustinen, A., Aalto, P. P., Mäkelä, J. M., Hämeri, K., and Kulmala, M.: Urban aerosol number size distributions, Atmos. Chem. Phys., 4, 391–411, 10.5194/acp-4-391-2004, 2004.
Hussein, T., Karppinen, A., Kukkonen, J., Harkonen, J., Aalto, P. P.,
Hämeri, K., Kerminen, V.-M., and Kulmala, M.: Meteorological dependence
of size-fractionated number concentrations of urban aerosol particles,
Atmos. Environ., 40, 1427–1440, 2006.
Kanawade, V. P., Tripathi, S. N., Bhattu, D., and Shamjad, P. M.: Sub-micron
particle number size distributions characteristics at an urban location,
Kanpur, in the Indo-Gangetic Plain, Atmos. Res., 147, 121–132,
2014.Kerminen, V.-M., Chen, X., Vakkari, V., Petäjä, T., Kulmala, M., and
Bianchi, F.: Atmospheric new particle formation and growth: review of field
observations, Environ. Res. Lett., 13, 103003,
10.1088/1748-9326/aadf3c, 2018.Kivekäs, N., Sun, J., Zhan, M., Kerminen, V.-M., Hyvärinen, A., Komppula, M., Viisanen, Y., Hong, N., Zhang, Y., Kulmala, M., Zhang, X.-C., Deli-Geer, and Lihavainen, H.: Long term particle size distribution measurements at Mount Waliguan, a high-altitude site in inland China, Atmos. Chem. Phys., 9, 5461–5474, 10.5194/acp-9-5461-2009, 2009.
Krecl, P., Johansson, C., Targino, A. C., Strom, J., and Burman, L.: Trends
in black carbon and size-resolved particle number concentrations and vehicle
emission factors under real-world conditions, Atmos. Environ., 165,
155–168, 2017.
Kulmala, M.: How particles nucleate and grow, Science, 302, 1000–1001,
2003.
Kulmala, M., Vehkamäki, H., Petäjä, T., Dal Maso, M., Lauri, A.,
Kerminen, V. M., Birmili, W., and McMurry, P. H.: Formation and growth rates
of ultrafine atmospheric particles: a review of observations, J. Aerosol Sci., 35, 143–176, 2004.
Kulmala, M., Petaja, T., Nieminen, T., Sipila, M., Manninen, H. E.,
Lehtipalo, K., Dal Maso, M., Aalto, P. P., Junninen, H., Paasonen, P.,
Riipinen, I., Lehtinen, K. E. J., Laaksonen, A., and Kerminen, V. M.:
Measurement of the nucleation of atmospheric aerosol particles, Nat. Protocol., 7, 1651–1667, 2012.Kulmala, M., Dada, L., Daellenbach, K. R., Yan, C., Stolzenburg, D.,
Kontkanen, J., Ezhova, E., Hakala, S., Tuovinen, S., Kokkonen, T. V.,
Kurppa, M., Cai, R. L., Zhou, Y., Yin, R., Baalbaki, R., Chan, T., Chu, B.,
Deng, C., Fu, Y., Ge, M. F., He, H., Heikkinen, L., Junninen, H., Liu, Y.,
Lu, Y., Nie, W., Rusanen, A., Vakkari, V., Wang, Y., Yang, G., Yao, L.,
Zheng, J., Kujansuu, J., Kangasluoma, J., Petaja, T., Paasonen, P., Jarvi,
L., Worsnop, D., Ding, A. J., Liu, Y., Wang, L., Jiang, J. K., Bianchi, F.,
and Kerminen, V.-M.: Is reducing new particle formation a plausible solution
to mitigate particulate air pollution in Beijing and other Chinese
megacities?, Faraday Discuss., 226, 334–347, 10.1039/d0fd00078g,
2021.Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D.. and Pozzer, A.: The
contribution of outdoor air pollution sources to premature mortality on a
global scale, Nature, 525, 367, 10.1038/nature15371, 2015.
Leoni, C., Pokorna, P., Hovorka, J., Masiol, M., Topinka, J., Zhao, Y. J.,
Krumal, K., Cliff, S., Mikuska, P., and Hopke, P. K.: Source apportionment
of aerosol particles at a European air pollution hot spot using particle
number size distributions and chemical composition, Environ. Pollut.,
234, 145–154, 2018.Li, J., Gao, W., Cao, L., Xiao, Y., Zhang, Y., Zhao, S., Liu, Z., Liu, Z.,
Tang, G., Ji, D., Hu, B., Song, T., He, L., Hu, M., and Wang, Y. S.:
Significant changes in autumn and winter aerosol composition and sources in
Beijing from 2012 to 2018: Effects of clean air actions, Environ. Pollut., 268, 115855, 10.1016/j.envpol.2020.115855, 2021.Li, Z., Rosenfeld, D., and Fan, J.: Aerosols and their impact on radiation,
clouds, precipitation, and severe weather events, Oxford Research
Encyclopedias, Oxford University Press, Oxford, USA, 10.1093/acrefore/9780199389414.013.126, 2017.Liu, H., Pan, X. L., Wu, Y., Ji, D. S., Tian, Y., Chen, X. S., and Wang, Z.
F.: Size-resolved mixing state and optical properties of black carbon at an
urban site in Beijing, Sci. Total Environ., 749, 141523, 10.1016/j.scitotenv.2020.141523,
2020.
Ma, N. and Birmili, W.: Estimating the contribution of photochemical
particle formation to ultrafine particle number averages in an urban
atmosphere, Sci. Total Environ., 512, 154–166, 2015.
Makela, J. M., Aalto, P., Jokinen, V., Pohja, T., Nissinen, A., Palmroth,
S., Markkanen, T., Seitsonen, K., Lihavainen, H., and Kulmala, M.:
Observations of ultrafine aerosol particle formation and growth in boreal
forest, Geophys. Res. Lett., 24, 1219–1222, 1997.
Oberdörster, G., Oberdörster, E., and Oberdörster, J.:
Nanotoxicology: An emerging discipline evolving from studies of ultrafine
particles, Environ. Health Perspec., 113, 823–839, 2005.
Pandolfi, M., Querol, X., Alastuey, A., Jimenez, J. L., Jorba, O., Day, D.,
Ortega, A., Cubison, M. J., Comerón, A., Sicard, M., Mohr, C.,
Prévôt, A. S. H., Minguillón, M. C., Pey, J., Baldasano, J. M.,
Burkhart, J. F., Seco, R., Peñuelas, J., van Drooge, B. L.,
Artiñano, B., Di Marco, C., Nemitz, E., Schallhart, S., Metzger, A.,
Hansel, A., Lorente, J., Ng, S., Jayne, J., and Szidat, S.: Effects of
sources and meteorology on particulate matter in the Western Mediterranean
Basin: An overview of the DAURE campaign, J. Geophys. Res.-Atmos., 119, 4978–5010, 2014.
Qian, S., Sakurai, H., and McMurry, P. H.: Characteristics of regional
nucleation events in urban East St. Louis, Atmos. Environ., 41,
4119–4127, 2007.Rose, C., Collaud Coen, M., Andrews, E., Lin, Y., Bossert, I., Lund Myhre, C., Tuch, T., Wiedensohler, A., Fiebig, M., Aalto, P., Alastuey, A., Alonso-Blanco, E., Andrade, M., Artíñano, B., Arsov, T., Baltensperger, U., Bastian, S., Bath, O., Beukes, J. P., Brem, B. T., Bukowiecki, N., Casquero-Vera, J. A., Conil, S., Eleftheriadis, K., Favez, O., Flentje, H., Gini, M. I., Gómez-Moreno, F. J., Gysel-Beer, M., Hallar, A. G., Kalapov, I., Kalivitis, N., Kasper-Giebl, A., Keywood, M., Kim, J. E., Kim, S.-W., Kristensson, A., Kulmala, M., Lihavainen, H., Lin, N.-H., Lyamani, H., Marinoni, A., Martins Dos Santos, S., Mayol-Bracero, O. L., Meinhardt, F., Merkel, M., Metzger, J.-M., Mihalopoulos, N., Ondracek, J., Pandolfi, M., Pérez, N., Petäjä, T., Petit, J.-E., Picard, D., Pichon, J.-M., Pont, V., Putaud, J.-P., Reisen, F., Sellegri, K., Sharma, S., Schauer, G., Sheridan, P., Sherman, J. P., Schwerin, A., Sohmer, R., Sorribas, M., Sun, J., Tulet, P., Vakkari, V., van Zyl, P. G., Velarde, F., Villani, P., Vratolis, S., Wagner, Z., Wang, S.-H., Weinhold, K., Weller, R., Yela, M., Zdimal, V., and Laj, P.: Seasonality of the particle number concentration and size distribution: a global analysis retrieved from the network of Global Atmosphere Watch (GAW) near-surface observatories, Atmos. Chem. Phys. Discuss. [preprint], 10.5194/acp-2020-1311, in review, 2021.
Sabaliauskas, K., Jeong, C.-H., Yao, X., Jun, Y.-S., and Evans, G.: Cluster
analysis of roadside ultra?ne particle size distributions, Atmos.
Environ., 70, 64–74, 2013.Schmale, J., Henning, S., Decesari, S., Henzing, B., Keskinen, H., Sellegri, K., Ovadnevaite, J., Pöhlker, M. L., Brito, J., Bougiatioti, A., Kristensson, A., Kalivitis, N., Stavroulas, I., Carbone, S., Jefferson, A., Park, M., Schlag, P., Iwamoto, Y., Aalto, P., Äijälä, M., Bukowiecki, N., Ehn, M., Frank, G., Fröhlich, R., Frumau, A., Herrmann, E., Herrmann, H., Holzinger, R., Kos, G., Kulmala, M., Mihalopoulos, N., Nenes, A., O'Dowd, C., Petäjä, T., Picard, D., Pöhlker, C., Pöschl, U., Poulain, L., Prévôt, A. S. H., Swietlicki, E., Andreae, M. O., Artaxo, P., Wiedensohler, A., Ogren, J., Matsuki, A., Yum, S. S., Stratmann, F., Baltensperger, U., and Gysel, M.: Long-term cloud condensation nuclei number concentration, particle number size distribution and chemical composition measurements at regionally representative observatories, Atmos. Chem. Phys., 18, 2853–2881, 10.5194/acp-18-2853-2018, 2018.
Schmid, O. and Stoeger, T.: Surface area is the biologically most effective
dose metric for acute nanoparticle toxicity in the lung, J. Aerosol
Sci., 99, 133–143, 2016.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics of air
pollution, John Wiley and Sons, Inc., New York, USA, 2006.Shen, X. J., Sun, J. Y., Zhang, Y. M., Wehner, B., Nowak, A., Tuch, T., Zhang, X. C., Wang, T. T., Zhou, H. G., Zhang, X. L., Dong, F., Birmili, W., and Wiedensohler, A.: First long-term study of particle number size distributions and new particle formation events of regional aerosol in the North China Plain, Atmos. Chem. Phys., 11, 1565–1580, 10.5194/acp-11-1565-2011, 2011.Shen, X. J., Sun, J. Y., Zhang, X. Y., Zhang, Y. M., Zhang, L., Fan, R. X.,
Zhang, Z. X., Zhang, X. L., Zhou, H. G., Zhou, L. Y., Dong, F., and Shi, Q.
F.: The influence of emission control on particle number size distribution
and new particle formation during China's V-Day parade in 2015, Sci. Total Environ, 573, 409–419, 10.1016/j.scitotenv.2016.08.085, 2016.Shi, H. R., Zhang, J. Q., Zhao, B., Xia, X. A., Hu, B., Chen, H. B., Wei,
J., Liu, M. Q., Bian, Y. X., Fu, D. S., Gu, Y., and Liou, K.-N.: Surface
Brightening in Eastern and Central China Since the Implementation of the
Clean Air Action in 2013: Causes and Implications, Geophys. Res. Lett., 48, e2020GL091105, 10.1029/2020GL091105, 2021.
Stanier, C. O., Khlystov, A. Y., and Pandis, S. N.: Ambient aerosol size
distributions and number concentrationsmeasured during the Pittsburgh Air
Quality Study (PAQS), Atmos. Environ., 38, 275–3284, 2004.Sun, J., Birmili, W., Hermann, M., Tuch, T., Weinhold, K., Merkel, M., Rasch, F., Müller, T., Schladitz, A., Bastian, S., Löschau, G., Cyrys, J., Gu, J., Flentje, H., Briel, B., Asbach, C., Kaminski, H., Ries, L., Sohmer, R., Gerwig, H., Wirtz, K., Meinhardt, F., Schwerin, A., Bath, O., Ma, N., and Wiedensohler, A.: Decreasing trends of particle number and black carbon mass concentrations at 16 observational sites in Germany from 2009 to 2018, Atmos. Chem. Phys., 20, 7049–7068, 10.5194/acp-20-7049-2020, 2020.Tunved, P., Ström, J., and Hansson, H.-C.: An investigation of processes controlling the evolution of the boundary layer aerosol size distribution properties at the Swedish background station Aspvreten, Atmos. Chem. Phys., 4, 2581–2592, 10.5194/acp-4-2581-2004, 2004.
von Bismarck-Osten, C., Birmili, W., Ketzel, M., Massling, A.,
Petäjä, T., and Weber, S.: Characterization of parameters in?uencing
the spatio-temporal variability of urban particle number size distributions
in four European cities, Atmos. Environ., 77, 415–429, 2013.
Vu, T. V., Delgado-Saborit, J. M., and Harrison, R. M.: Review: Particle
number size distributions from seven major sources and implications for
source apportionment studies, Atmos. Environ., 122, 114–132, 2015.
Wang, Y., Hopke, P. K., Chalupa, D. C., and Utell, M. J.: Long-term study of
urban ultrafine particles and other pollutants, Atmos. Environ.,
45, 7672–7680, 2011.Wang, Z. B., Hu, M., Wu, Z. J., Yue, D. L., He, L. Y., Huang, X. F., Liu, X. G., and Wiedensohler, A.: Long-term measurements of particle number size distributions and the relationships with air mass history and source apportionment in the summer of Beijing, Atmos. Chem. Phys., 13, 10159–10170, 10.5194/acp-13-10159-2013, 2013.Wehner, B., Wiedensohler, A., Tuch, T. M., Wu, Z. J., Hu, M., Slanina, J.,
and Kiang, C. S.: Variability of the aerosol number size distribution in
Beijing, China: New particle formation, dust storms, and high continental
background, Geophys. Res. Lett., 31, L22108, 10.1029/2004GL021596, 2004.Wehner, B., Birmili, W., Ditas, F., Wu, Z., Hu, M., Liu, X., Mao, J., Sugimoto, N., and Wiedensohler, A.: Relationships between submicrometer particulate air pollution and air mass history in Beijing, China, 2004–2006, Atmos. Chem. Phys., 8, 6155–6168, 10.5194/acp-8-6155-2008, 2008.
WHO: Air quality guidelines for Europe, 2nd Edn., Copenhagen, World Health
Organization Regional Office for Europe, WHO Regional Publications, European
Series No. 91, 2000.World Health Organization (WHO), Ambient Air Quality Database, available at: https://whoairquality.shinyapps.io/AmbientAirQualityDatabase/ (last access: 5 July 2014), 2018.Wiedensohler, A., Birmili, W., Nowak, A., Sonntag, A., Weinhold, K., Merkel, M., Wehner, B., Tuch, T., Pfeifer, S., Fiebig, M., Fjäraa, A. M., Asmi, E., Sellegri, K., Depuy, R., Venzac, H., Villani, P., Laj, P., Aalto, P., Ogren, J. A., Swietlicki, E., Williams, P., Roldin, P., Quincey, P., Hüglin, C., Fierz-Schmidhauser, R., Gysel, M., Weingartner, E., Riccobono, F., Santos, S., Grüning, C., Faloon, K., Beddows, D., Harrison, R., Monahan, C., Jennings, S. G., O'Dowd, C. D., Marinoni, A., Horn, H.-G., Keck, L., Jiang, J., Scheckman, J., McMurry, P. H., Deng, Z., Zhao, C. S., Moerman, M., Henzing, B., de Leeuw, G., Löschau, G., and Bastian, S.: Mobility particle size spectrometers: harmonization of technical standards and data structure to facilitate high quality long-term observations of atmospheric particle number size distributions, Atmos. Meas. Tech., 5, 657–685, 10.5194/amt-5-657-2012, 2012.Wu, Z. J., Hu, M., Liu, S., Wehner, B., Bauer, S., ßling, A. M.,
Wiedensohler, A., Petäjä, T., Dal Maso, M., and Kulmala, M.: New
particle formation in Beijing, China: Statistical analysis of a 1-year data
set, J. Geophys. Res., 112, D09209,
10.1029/2006JD007406, 2007.
Wu, Z. J., Hu, M., Lin, P., Liu, S., Wehner, B., and Wiedensohler, A.:
Particle number size distribution in the urban atmosphere of Beijing, China,
Atmos. Environ., 42, 7967–7980, 2008.Yue, D. L., Hu, M., Zhang, R. Y., Wang, Z. B., Zheng, J., Wu, Z. J., Wiedensohler, A., He, L. Y., Huang, X. F., and Zhu, T.: The roles of sulfuric acid in new particle formation and growth in the mega-city of Beijing, Atmos. Chem. Phys., 10, 4953–4960, 10.5194/acp-10-4953-2010, 2010.Zhai, S., Jacob, D. J., Wang, X., Shen, L., Li, K., Zhang, Y., Gui, K., Zhao, T., and Liao, H.: Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology, Atmos. Chem. Phys., 19, 11031–11041, 10.5194/acp-19-11031-2019, 2019.Zhao, H. J., Gui, K., Ma, Y. J., Wang, Y. F., Wang, Y. Q., Wang, H., Zheng,
Y., Li, L., Zhang, L., Che, H. Z., and Zhang, X. Y.: Climatology and trends
of aerosol optical depth with different particle size and shape in northeast
China from 2001 to 2018, Sci. Total Environ., 763, 142979, 10.1016/j.scitotenv.2020.142979, 2021.
Zhao, S. P., Yu, Y., and Qin, D. H.: From highly polluted inland city of
China to “Lanzhou Blue”: The air-pollution characteristics, Sciences in Cold
and Arid Regions, 10, 12–26, 2018.
Zhao, S. P., Yu, Y., Yin, D. Y., and He, J. J.: Meteorological dependence of
particle number concentrations in an urban area of complex terrain,
Northwestern China, Atmos. Res., 164, 304–317, 2015a.
Zhao, S. P., Yu, Y., Xia, D. S., Yin, D. Y., He, J. J., Liu, N., and Li, F.:
Urban particle size distributions during two contrasting dust events
originating from Taklimakan and Gobi Deserts, Environ. Pollut., 207,
107–122, 2015b.
Zhao, S. P., Yu, Y., Yin, D. Y., He, J. J., Liu, N., Qu, J. J., and Xiao, J.
H.: Annual and diurnal variations of gaseous and particulate pollutants in
31 provincial capital cities based on in situ air quality monitoring data
from China National Environmental Monitoring Center, Environ. Int., 86, 92–106, 2016.Zhao, S. P., Yu, Y., Yin, D., Yu, Z., Dong, L. X., Mao, Z., He, J. J., Yang,
J., Li, P., and Qin, D. H.: Concentrations, optical and radiative properties
of carbonaceous aerosols over urban Lanzhou, a typical valley city: Results
from in-situ observations and numerical model, Atmos. Environ., 213,
470–484, 2019.
Zhao, S. P., Yu, Y., Yin, D. Y., and Qin, D. H.: Contrasting response of
ultrafine particle number and PM2.5 mass concentrations to Clean Air
Action in China, Geophys. Res. Lett., 48, e2021GL093886,
10.1029/2021GL093886, 2021.