Multi-year observations of aerosol microphysical and optical properties,
obtained through ground-based remote sensing at 50 China Aerosol Remote
Sensing Network (CARSNET) sites, were used to characterize the aerosol
climatology for representative remote, rural, and urban areas over China to
assess effects on climate. The annual mean effective radii for total
particles (ReffT) decreased from north to south and from rural to urban
sites, and high total particle volumes were found at the urban sites. The
aerosol optical depth at 440 nm (AOD440nm) increased from remote and rural
sites (0.12) to urban sites (0.79), and the extinction Ångström
exponent (EAE440–870 nm) increased from 0.71 at the arid and semi-arid sites
to 1.15 at the urban sites, presumably due to anthropogenic emissions.
Single-scattering albedo (SSA440nm) ranged from 0.88 to 0.92,
indicating slightly to strongly absorbing aerosols. Absorption
AOD440nm values were 0.01 at the remote sites versus 0.07 at the urban
sites. The average direct aerosol radiative effect (DARE) at the bottom of
atmosphere increased from the sites in the remote areas (-24.40 W m-2) to the
urban areas (-103.28 W m-2), indicating increased cooling at the latter.
The DARE for the top of the atmosphere increased from -4.79 W m-2 at the
remote sites to -30.05 W m-2 at the urban sites, indicating overall
cooling effects for the Earth–atmosphere system. A classification method
based on SSA440nm, fine-mode fraction (FMF), and EAE440–870nm showed that coarse-mode particles (mainly dust) were dominant at the
rural sites near the northwestern deserts, while light-absorbing, fine-mode
particles were important at most urban sites. This study will be important
for understanding aerosol climate effects and regional environmental
pollution, and the results will provide useful information for satellite
validation and the improvement of climate modelling.
Introduction
Atmospheric aerosols have important direct effects on climate because they
can scatter and absorb radiant energy and, in so doing, affect the Earth's
energy balance (Charlson et al., 1992; Yang et al., 2016). Meanwhile, the
aerosols can serve as cloud condensation nuclei or ice nuclei to affect
the climate indirectly through aerosol–cloud interactions (Twomey et al.,
1984; Garrett and Zhao, 2006; Zhao et al., 2015; Xie et al., 2013). The
optical properties of the aerosol determine the particles' direct effects on
the Earth's radiative balance and weather–climate change (Ramanathan et al.,
2001; Eck et al., 2005; Myhre, 2009; Zhao et al., 2018; Che et al., 2019a; Li et al., 2016). Aerosol optical
depth (AOD) is one of the key measures of the total aerosol extinction
effects on climate (Breon et al., 2002), and the extinction Ångström
exponent (EAE), with spectral dependence, can be used to obtain the
information about aerosol size distributions (Gobbi et al., 2007; Eck et
al., 1999; Zheng et al., 2017). The aerosols' absorptivity depends on
particle composition and is a key determinant to calculate the direct aerosol
radiative effect (Haywood and Shine, 1995; Li et al., 2016), and the single-scattering albedo (SSA) is a parameter that has the spectral
dependence to distinguish major aerosol particle types (Jacobson et al.,
2000; Dubovik et al., 2002; Gelencser, 2004; Russell et al., 2010;
Giles et al., 2012).
With the recognition of the importance for climate, the aerosol optical
properties have been obtained from ground-based monitoring networks
worldwide; some of the major networks include, the Aerosol Robotic
Network (AERONET; Holben et al., 1998) and its sub-networks, the
PHOtométrie pour le Traitement Opérationnel de Normalisation
Satellitaire (PHOTONS), the Canadian Sun Photometer Network (AEROCAN), and the Iberian
Network for aerosol measurements (RIMA; Goloub et al., 2007; Bokoye et al., 2001;
Prats et al., 2011); the SKYrad Network (SKYNET; Takamura and Nakajima, 2004; Che
et al., 2008); the European aerosol Lidar Network (EARLINET; Pappalardo et al.,
2014); and the Global Atmosphere Watch Programmer-Precision
Filter Radiometers network (GAW-PFR; Wehrli, 2002; Estellés et al., 2012). The
China Aerosol Remote Sensing NETwork (CARSNET), the Chinese Sun
Hazemeter Network (CSHNET), and the Sun–Sky Radiometer Observation Network (SONET) have
been established to measure aerosol optical properties in China (Che et al.,
2009a, 2015; Xin et al., 2007, 2015; Li et al., 2018). Furthermore, aerosol
optical properties have also been used in comprehensive studies of aerosol
physical characteristics and chemical composition in many regions of China
(Che et al., 2009c, 2018; Zhao et al., 2018).
China has become one of the largest aerosol sources in the world associated
with its rapid economic development, and this has caused significant effects
on local environments and regional climate (Che et al., 2005; Xia, 2010; Li
et al., 2016; Yang et al., 2018, 2019b; Zhao et al., 2019; Gui et al., 2019). There have been
numerous studies that have focused on aerosol optical properties obtained
though ground-based remote-sensing methods in China (Luo et al., 2002; Li et
al., 2003; Duan and Mao, 2007). Some previous research has paid more attention to
aerosol's optical properties and its radiative effects over the
urban industrial areas, as well as at coastal sites in northeastern and
eastern China (Wang et al., 2010; Xin et al., 2011; Xia et al., 2007; Zhao
et al., 2016; Wu et al., 2012; Shen et al., 2019). Many studies of aerosol
optical properties were conducted in northern China with high aerosol
loadings, such as the Beijing–Tianjin–Hebei region (Che et al., 2014; Xia et
al., 2013; Fan et al., 2006; Xie et al., 2008; Zhang et al., 2019; Yang et
al., 2019a; Zhao et al., 2018; Zheng et al., 2019). Aerosol optical properties have also been
investigated at Hefei, Shouxian, Nanjing, Taihu, Shanghai, and other sites in
eastern China (Lee et al., 2010; He et al., 2012; Zhuang et al., 2014; Z. Wang et al., 2015; Che et al., 2018). Some studies of aerosol optical
properties have been made in southern and central China (L. C. Wang et al., 2015; Tao et al.,
2014b), and those at remote and rural sites in China provide information on
regional background conditions (Che et al., 2009b; Wang et al., 2010; Zhu et al., 2014; Yuan et al., 2014).
China's vast size, varied terrain, and heterogeneity of aerosol sources has
led to strong temporal and spatial variability in aerosol optical and
physical properties. The mixtures of aerosol types at most sites are
complex, and aerosol populations' sizes and compositions are affected by their
sources, transformations that occur during transportation, and removal
processes (Cao et al., 2007; Wang et al., 2007; Zhang et al., 2013; Wan et
al., 2015). National-scale, ground-based measurements of aerosol
microphysical and optical properties obtained from the sun photometer
provide a better understanding of the aerosols' climate effects over the
different regions of China. The measurements of greatest interest include
aerosol size distributions (volume and aerosol effective radii) and optical
properties (AOD, AE, SSA, absorption AOD) because those data can be
used to evaluate aerosol direct radiative effect.
The aim of this study was focused on the investigation of the climatological
spatial distribution of aerosol microphysical and optical properties over
regional scales using spatial distribution data from the national CARSNET
network. The data were collected at CARSNET sites, which includes sites in
remote, rural, and urban areas, with the same calibration procedures and
calculation algorithms used at all sites. As a result, the data are
directly comparable among sites (Che et al., 2009a), and the results can be
used to characterize the regional distribution and temporal variation in
aerosol optical properties. This research focused on aerosol climate effects
and regional environmental pollution, and the results should be useful for
satellite validations and for the improvement of models in the future. The
remainder of this paper is organized as follows: firstly, Sect. 2
describes the sites in detail and then introduces the methods for the data
processing of the aerosol optical properties and the direct aerosol
radiative effect calculation, through the retrieved aerosol optical
parameters. Section 3 illustrates the aerosol microphysical and optical
properties, as well as its direct aerosol radiative effect. An aerosol type
classification method is proposed according to the aerosol optical
parameters. Section 4 presents the conclusions of the study.
Annual spatial distribution of aerosol volume–size distributions
at the CARSNET sites.
Site description, instruments, and dataSite description
Sun photometers (CE-318, Cimel Electronique, Paris, France; see Appendix A)
were installed at 50 CARSNET sites (Fig. 1) from 2010 to 2017. The stations
were classified as remote, rural, or urban sites based on administrative
division (Appendix Table A1). Three of the remote stations were more
than 3000 m above sea level on the Tibetan Plateau, far from the
anthropogenic influences, and one of them was a northwestern regional
background site in China. The 23 rural sites represent (i) 5 sites of desert
regions affected by mostly dust aerosols rather than anthropogenic
particles, (ii) 2 sites affected by both dust and anthropogenic activities on
the Loess Plateau, and (iii) 16 sites located near or surrounding large
cities with relatively strong impacts from anthropogenic activities in
central and eastern China. The last category is 24 urban sites located in
provincial capitals or heavily populated cities.
Instruments and calibration
The CE-318 sun photometers used in this study were calibrated annually, using
the CARSNET calibration protocol, to verify the accuracy and reliability of
the sky irradiance measurements (Holben et al., 1998; Che et al., 2009a; Tao
et al., 2014a). The reference instruments for CARSNET were periodically
calibrated at Izaña, Tenerife, Spain, located at 28.31∘ N,
16.50∘ W (2391.0 m a.s.l.), in conjunction with the AERONET
program. There are several different types of Cimel instruments that
have been used at the 50 sites in this network: (1) logical type
CE-318 sun photometers (440, 675, 870, 940, 1020, and three polarization bands at 870 nm ), (2) numerical type CE-318 sun photometers (440, 675, 870, 940, 1020 nm, and three polarization bands at 870 nm),
(3) numerical type CE-318 sun photometers at eight wavelengths (340, 380, 440, 500, 675, 870, 940, and 1020 nm), (4) and numerical-type CE-318 sun photometers at nine wavelengths (340, 380, 440, 500, 675, 870, 940, 1020, and 1640 nm).
Measurements used to retrieve AODs were at 340, 380, 440, 500,
675, 870, 1020, and 1640 nm, while the total precipitable water
content was obtained by using those measurements at 940 nm (Holben et al.,
1998; Dubovik and King, 2000). The cloud-screened AOD data were calculated
by using the ASTPwin software, and extinction Ångström exponents
(EAE) were calculated from the instantaneous AODs for wavelengths of 440
and 870 nm (Che et al., 2009a, 2015). Sites with more than three daily AOD
observations and more than 10 monthly AOD observation days were used to
calculate the daily and monthly mean AODs and extinction Ångström
exponents. The fine-mode fraction (FMF) is described as the fraction of fine-mode particles of
total AOD440nm (AODfine440 nm/AOD440nm).
Data processing
The aerosol microphysical and optical properties, including volume size
distributions (dV(r)/dlnr); the total, fine, and coarse-mode aerosol
effective radii (ReffT, ReffF, and ReffC, respectively);
single-scattering albedo (SSA); complex refractive indices; absorption AODs
(AAODs); and absorption Ångström exponents (AAEs), were retrieved
from the observational data from the sky scattering channel of the
sun photometers at 440, 670, 870, and 1020 nm using the algorithms of
Dubovik et al. (2002, 2006). In the process of retrieval, the data of
surface albedo (SA) was interpolated or extrapolated to 440, 670, 870, and 1020 nm based on the daily MCD43C3 data, a product from the
MODIS-Moderate Resolution Imaging Spectroradiometer surface reflectance
(https://ladsweb.modaps.eosdis.nasa.gov/, last access: 31 March 2019). The algorithm used to calculate
aerosol volume size distributions (dV(r)/dlnr) was under the assumption of a
homogeneous distribution of non-spherical particles following the approach
of Dubovik et al. (2006). The sphericity fraction retrieved from the inversions is
defined as spherical particles/(spheroidal particles + spherical
particles) (Giles et al., 2011).
Dubovik et al. (2002, 2006) defined that all the particles with effective
radii < 0.992 µm were considered fine-mode particles, and
those > 0.992 µm were considered coarse-mode particles.
For the total (ReffT), fine-mode (ReffF), and coarse-mode (ReffC)
aerosols, the effective radii are calculated by the following equation:
Reff=∫rminrmaxr3dNrdlnrdlnr∫rminrmaxr2dNrdlnrdlnr,
where rmin denotes 0.05, 0.05, and 0.992 µm and rmax denotes 15,
0.992, and 15 µm of the total, fine-mode, and coarse-mode particles,
respectively.
The coarse (PVC) and fine aerosol particle volumes distributions
(PVF) are calculated according to a bimodal lognormal function described
by Whitey (1978), Shettle and Fenn (1979), and Remer and Kaufman (1998):
dV(r)dlnr=∑i=12Cv,i2πσiexp-lnr-lnrV,i22σi2,
where Cv,i is the volume concentration,
rV,i is the median radius, and σi is the standard
deviation.
The volume median radius is computed by fine-mode and coarse-mode particles as
follows:
lnrV=∫rminrmaxlnrdVrdlnrdlnr∫rminrmaxdVrdlnrdlnr.
Then the standard deviation is calculated from the volume median radius:
σV=∫rminrmax(lnr-lnrV)2dV(r)dlnrdlnr∫rminrmaxdV(r)dlnrdlnr.
The volume concentration (µm3 per µm2) is speculated by the
following equation:
CV=∫rminrmaxdV(r)dlnrdlnr.
The SSA was retrieved only for AOD440nm > 0.40; this was
done to avoid the larger uncertainty inherent in the lower AOD retrieval,
according to Dubovik et al. (2002, 2006). The AAOD and AAE for wavelength
λ were calculated as follows:
6AAOD(λ)=1-SSAλ×AOD(λ),7AAE=-dln[AAOD(λ)]dln(λ).
The total AODs' uncertainty was 0.01 to 0.02 according to Eck et al. (1999).
The accuracy of SSA retrieved from AOD440nm > 0.50 with
a solar zenith angle of > 50 was 0.03 (Dubovik et al., 2002). The
accuracy of the particle volume size distribution was 15 %–25 % between 0.1 µm ≤r≤7.0µm and 25 %–100 % when r < 0.1 µm and r > 7 µm.
Direct aerosol radiative effect (DARE in W m-2) was calculated by the
radiative transfer module under cloud-free conditions, which is similar to
the inversion of AERONET (García et al., 2008, 2012). The DARE at the
bottom of the atmosphere (BOA) and the top of the atmosphere (TOA) was
defined as the difference in the shortwave radiative fluxes with and without
aerosol effects as follows:
8DARETOA=FTOA↑0-FTOA↑,9DAREBOA=FBOA↓-FBOA↓0,
where F and F0 denote the broadband fluxes of including and excluding
aerosols, respectively, at the BOA and TOA. The “↑” and
“↓”mean the upward fluxes and downward fluxes, respectively.
In the radiative transfer module, the absorption and multiple scattering
effects are taken into account during flux calculations using the discrete
ordinates (DISORT) approach (Nakajima and Tanaka, 1988; Stamnes et al.,
1988). Gaseous distributions and single fixed aerosol vertical
distributions (exponential to 1 km), taken from the multilayered US standard 1976
atmosphere, were used in the radiative flux calculations (García et al.,
2008). García et al. (2008) pointed out that the error for the observed
solar radiation at the surface in global was +2.1±3.0 % for an
overestimation of about +9±12 Wm-2. The data used in
preparing the figures for the present paper have been made available as an
Appendix.
Results and discussionSpatial distribution of aerosol microphysical properties
A map showing the 50 CARSNET sampling sites and plots of the aerosol volume
size distributions (dV(r)/dlnr) at each of the sites is shown in Fig. 1.
Generally, the annual mean effective radius of total particles (ReffT)
decreased from the inland northwestern areas to the southeastern coastal areas.
Furthermore, the volume concentration of total particles was found to be
substantially higher at the urban sites. The volume of the coarse-mode
particles was considerably larger than that of the fine-mode particles at
the remote, arid, and semi-arid sites and at those sites on the Chinese
Loess Plateau (CLP) or nearby, indicating that those areas were most strongly
affected by larger particles, most likely mineral dust, as discussed below.
Aerosol type classification based on the optical properties.
The average (arithmetic mean) ReffT at the remote sites was about 0.47 µm with the volume about 0.05 µm3 per µm2 (Table 1). A
large ReffT (0.64 µm) was found at Lhasa, and the total aerosol
volume there was 0.05 µm3 per µm2. These results are
consistent with those reports by Li et al. (2018), who found high levels of
coarse-mode particles at Lhasa due to the presence of mineral dust. The two
other remote sites, Akedala and Shangri-La, had smaller average ReffT
values than Lhasa (0.36 and 0.39 µm, respectively), and corresponding
volumes were 0.06 and 0.03 µm3 per µm2. The average
fine-mode effective radius (ReffF) was 0.14 µm at the remote
sites, and fine-mode particle fractional volume (PVF) was 0.01 µm3 per µm2, while the average coarse-mode effective radii
(ReffC) was 2.35 µm and the coarse-mode fractional volume
(PVC) was 0.03 µm3 per µm2. These findings indicated
that the contribution of coarse-mode particles to the total volume of
aerosol was larger at the remote sites. A study by Cong et al. (2009) at the
remote Nam Co site on the Tibetan Plateau showed that dust particles mainly
affected the site in spring, while anthropogenic aerosols were prevalent in
the summer.
The average ReffT at the arid and semi-arid sites (0.55 µm)
was larger than at the remote sites, and the total volume of aerosols at the
arid and semi-arid sites was also large (0.14 µm3 per µm2),
nearly 3 times that at the remote sites. Large ReffT values (0.71 µm) were found at Tazhong, which is near the northwestern deserts, and
the aerosol volume there was also high, 0.30 µm3 per µm2.
Large PVC values were found at the arid and semi-arid sites (0.05–0.27 µm3 per µm2). The arithmetic mean ReffT (0.49 µm) at
the rural sites on or near the CLP had total aerosol volumes (0.15 µm3 per µm2) similar to those at the arid and semi-arid sites. These
results also show a major contribution to the aerosol volumes by coarse-mode
particles at the sites in or near the mineral dust source regions. Bi et al. (2011) similarly found that coarse particles dominated the volume–size
distribution at the Semi-Arid Climate and Environment Observatory of Lanzhou
University (SACOL) on the CLP.
Small ReffT values (0.33 µm) were found at the rural sites in
eastern China, and relatively high aerosol volumes were observed there (0.18 µm3 per µm2). In the Yangtze River Delta (YRD) region, the
ReffF was large range for 0.16–0.17 µm, and the PVF values were
0.12–0.13 µm3 per µm2. At the Mt. Longfeng background site
in northeastern China, the total particle volume was low (0.08 µm3 per µm2), which is consistent with minimal anthropogenic
influences and low aerosol loadings. Compared with the other sites, the
urban areas had relatively low coarse-mode aerosol concentrations, but small
particles were plentiful – the average ReffT was 0.37 µm and total
volume was high at 0.21 µm3 per µm2. The average ReffF of fine-mode particles at the urban sites was 0.16 µm with a PVF
of 0.10 µm3 per µm2 while the ReffC was 2.22 µm
and PVC was 0.11 µm3 per µm2.
The effective radii and PVF values showed strong relationships with
population density and vehicle emissions at the urban sites. High volumes of
fine-mode particles occurred at the northeastern urban site of Shenyang
(ReffT=0.16µm, PVF=0.12µm3 per µm2); at major cities in northern China, including Shijiazhuang
(ReffT=0.16µm, PVF=0.12µm3 per µm2)
and Zhengzhou (ReffT=0.18µm, PVF=0.12µm3 per µm2); at Chengdu, a city in the Sichuan Basin
(ReffT=0.21µm, PVF=0.16µm3 per µm2); and in the urban regions of Nanning (ReffT=0.18µm,
PVF=0.13µm3 per µm2) and Panyu (ReffT=0.16µm, PVF=0.10µm3 per µm2) in southern
China. Overall, these results show that the volumes of fine-mode particles
increased at the urban sites where anthropogenic influences were most
apparent.
Cheng et al. (2015) found different aerosol volume size distributions for
dust and sea salt in Shanghai in eastern China, and they showed that
their relative abundances varied with season and in response to local or
long-range transport. Zhao et al. (2018) reported the effect of sea salt
aerosol on the aerosol absorption and radiative effects in the coastal
region over northeastern China. The particles' hygroscopic growth,
with different compositions observed in special climatic conditions, could
especially affect aerosol microphysical properties with their geographically variable effects
(Zhang et al., 2015; Sun et al., 2010). Like in the YRD region, hygroscopic
growth of fine-mode particles could lead to larger a AOD and the scattering
enhancing reported by Sun et al. (2018) and Che et al. (2018). Xia et al. (2019) observed the aerosol hygroscopic growth of the fine particle scattering
coefficient in Beijing.
Annual spatial distribution of aerosol optical depth (AOD) at 440 nm at the CARSNET sites.
Spatial distributions of AOD and EAE
The spatial distributions of AOD440nm and EAE440–870 nm are
shown in Fig. 2. The AOD440nm increased from the remote and rural sites to
the urban sites, and as one might expect, the remote sites were the least
affected by particle emissions and had the lowest aerosol loadings. For
example, the AOD440nm at the remote stations was low and had an average
value of 0.12. The Lhasa and Shangri-La sites on the Tibetan Plateau had
similar average AOD440nm values of 0.10. These phenomena are similar
to the study of Li et al. (2018), who showed clean air conditions at Lhasa
with AOD < 0.1. Cong et al. (2007, 2009) also found a low AOD (0.05)
at Nam Co, which was comparable to the background levels at other remote
sites.
The AOD440nm values at the arid and semi-arid sites and those on or near the
Loess Plateau ranged from 0.32 to 0.42, which is higher than at the remote
sites. The high AOD440nm at Tazhong (0.60), which is near the deserts
in northwestern China, was likely due to the large aerosol volume of 0.30 µm3 per µm2 (Sect. 3.1) caused by mineral dust. Indeed,
arid and semi-arid regions in northwestern China are important sources of
aeolian dust on a global scale (Bi et al., 2011). Li and Zhang (2012) showed
that the contribution of dust to the average AOD at SACOL near Lanzhou was
28.4 %. Other sites that showed large AOD440nm include regions with
strong anthropogenic influences, such as Dengfeng (0.79) on the North China
Plain, Huimin (0.83) in the YRD (0.83 to 0.87), and Huainan (0.91) on the
Guanzhong Plain.
Compared with the sites just discussed, lower AOD440nm values were found at
the Mt. Longfeng background station on the Northeast China Plain (0.34), the
semi-arid rural site at Tongyu in northeastern China (0.23), and the clean
Xiyong site in southern China (0.41). Zhu et al. (2014) found a low AOD of
0.28 at the North China Plain regional background site. Che et al. (2009c)
pointed out that the large AOD at Lin'an was likely affected by the
high aerosol loadings in YRD region. Among the urban sites in China, large
AOD440nm values were found in the cities with strong influences of
anthropogenic activities, such as the Northeast China Plain (Shenyang 0.89),
North China Plain (Zhengzhou 0.99), central China (Wuhan 1.00) and Sichuan
Basin (Chengdu 1.17); the average value for these sites was 0.79. Lower
AOD440nm values, that is < 0.50, occurred at remote sites in
northwestern China, including Ürümqi (0.42) and Yinchuan (0.37); these sites
are less affected by industrial activities and the population densities are
lower compared with the sites in northern or eastern China.
It is worth noting that the particle emissions in or around the urban sites
could lead to large optical extinctions due to hygroscopic aerosol growth,
especially in summer when the relative humidity is often high. In a related
study, Zhang et al. (2018) found a large AOD of 1.10 at Wuhan in central
China that was linked to secondary aerosol formation under the high
summertime temperatures. Li et al. (2015) similarly concluded that high
temperatures and humidity promoted the formation of fine particles and led to
hygroscopic aerosol growth at Nanjing. Qin et al. (2017) observed a high
AOD500nm of 1.04 at Shijiazhuang and related this to the hygroscopic
growth of aerosol fine-mode particles during polluted days.
Annual spatial distribution of extinction Ångström
exponent (AE) 440–870 nm at the CARSNET sites.
Clear spatial variability in EAE values over China is evident in Fig. 3, and
at the remote sites the average EAEs were 1.03. The EAE at Lhasa (0.77) was
lower than at Akedala (EAE =1.13), which is in an arid region of central
Asia, or at Shangri-La (EAE =1.19) in Tibet. The average coarse-mode
average effective radius (ReffC) at Lhasa was 2.26 µm and the
fractional volume was 0.04 µm3 per µm2, this result suggests
the major components of the large mineral dust particles in aerosol
populations over that region. The smaller sphericity fraction
(∼42.70) and lower FMF (0.66) at Lhasa indicates the
presence of non-spherical aerosol coarse particles compared with the
spherical fine particles in the urban sites.
At arid and semi-arid sites in China, the average EAE value (0.71) was
relatively low and the FMF also was low (0.58). The EAE was extremely low at
Tazhong (0.25), which is in the Taklamakan Desert in the Xinjiang Uygur
Autonomous Region of northwestern China and the sphericity fraction (12.87)
and FMF (0.35) there were lower compared with most of the other sites. This
finding indicates a strong contribution of large particles in this desert
region consistent with large volume of the coarse-mode particles (0.27 µm3 per µm2) noted in Sect. 3.1. The average EAE reached 0.93
at the rural sites near the CLP, and the average value of FMF for those
sites was 0.73. Eck et al. (2005) found especially low EAE values in March
and April (0.3 and 0.4, respectively) at Yulin, China, where the dust
aerosol dominated the optical column.
Large EAEs (1.23) were found at the sites in eastern China, and the FMFs also
were large (0.89) at those sites. This result can be attributed to the
strong impacts of anthropogenic in the more urbanized eastern part of the
country. On the other hand, large EAE values also occurred at the clean
sites in northeastern China, including Mt. Longfeng (1.38), where the
sphericity fraction was 58.5 and the FMF 0.90. This shows that small
particles can have stronger effects in these areas relative to some other
regions of China. The EAE at Lin'an was larger than that at Shangdianzi in
the North China Plain or Mt. Longfeng in Northeastern China for most months
according to data from Che et al. (2009c). At the urban sites, large EAEs
were found at sites in southern China, including Nanning (EAE =1.36,
sphericity fraction =70.12, FMF =0.95), Panyu (EAE =1.43,
sphericity fraction =75.55, FMF =0.93) and Zhuzilin (EAE =1.45,
sphericity fraction = 55.51, FMF = 0.94). This is likely because the
large populations and widespread vehicle ownership in those cities led to
the dominance of fine-mode particles throughout the year. Cheng et al. (2015) found a unimodal distribution of EAE centred in 1.1–1.6 with the
occurrence frequency about 72 %, which indicated an abundance of fine
primary particles at Shanghai in eastern China. At the urban Nanjing site,
which is in eastern central China, small particles were dominant, and the
annual average EAE was 1.21±0.28 (Li et al., 2015).
Annual spatial distribution of fine-mode fraction at the CARSNET
sites.
Spatial distribution of aerosol single-scattering albedo
The spatial distribution of SSA at 440 nm of the 50 CARSNET stations is
shown in Fig. 4. As a frame of reference, Eck et al. (2005) reported that
that SSA440nm from the AERONET retrievals were 0.82 to 0.98 globally.
We note that SSA440nm values in this range reflect slightly to strongly
absorbing aerosols, and these particles originate from multitude sources
(Che et al., 2018). The SSA440nm values decreased from remote and rural to the
urban sites and from west to east, which means that there were higher
percentages of absorbing particles at the urban and eastern stations. The
average SSA440nm at the remote sites was about 0.91, which is
indicative of particles with moderate absorption. The absorbing aerosols at
the remote sites were more likely mineral dust particles because those sites
are less likely to be affected by carbonaceous particles, which also are
absorbing but mainly produced by anthropogenic activities. The
SSA440nm values for the arid and semi-arid sites were 0.89. The relatively
high SSA at Tazhong (0.92) was probably due to slightly absorbing, coarse
mode dust particles (EAE =0.25).
A study by Bi et al. (2011) showed that SSAs increased slightly with
wavelength when dust was present at the SACOL site. Moderately absorbing
particles were found in our study on or near the Chinese Loess Plateau where
the SSA440nm values were typically 0.88 to 0.89. Eck et al. (2005) concluded
that the spectral SSA demonstrated effects of dust at Yulin because the SSA
increased for wavelengths from 440 to 675 nm. At the rural sites in eastern
China, large SSA440nm values mainly occurred at sites in the YRD affected
anthropogenic influences; these include Tonglu (0.93), Xiaoshan (0.93),
Xiyong (0.94). Che et al. (2018) found the slightly absorbing particles came
from industrial activity and anthropogenic sources at YRD region with the
SSA440nm between 0.91 and 0.94.
The average value of SSA440nm at the urban sites was 0.90, which
indicates that particles with moderate absorption dominated the aerosol
populations. Cheng et al. (2015) reported a seasonal range of SSA from 0.88
to 0.91 at Shanghai, with higher values in autumn and winter compared with
spring and summer. Lower SSA440nm values occurred at the urban sites and
industrial regions in northeastern China, such as Shenyang (0.84), Anshan
(0.89), Fuhsun (0.84), which indicates that the particles were more strongly
absorbing in that region. On the other hand, higher SSA440nm values were
found at urban sites in southern China, including Nanning (0.92), Panyu
(0.90) and Zhuzilin (0.96), and this indicates that the particles at those
sites were slightly or weakly absorbing.
Moreover, we found that the SSA440nm spatial distribution reflected the
percentages of absorbing aerosols at the urban sites both in northern and
eastern China. The reports of Dubovik and King (2000), Dubovik et al. (2002, 2006) showed that
SSA values vary with both particle size and composition, and Su et al. (2017) used the variations in SSA with wavelength to indicate the presence
of brown carbon aerosols at Tianjin, a coastal megacity in China. Qin et al. (2017) suggested that the small SSAs found at Shijiazhuang indicated the
presence of fine-mode absorbing particles, such as brown carbon. Zhuang et
al. (2014) reported that the SSA at the Nanjing urban site ranged from 0.90
to 0.95, and the aerosol was more absorbing in autumn, possibly due to the
biomass burning emission in the YRD. As evident in the results presented in
Sect. 3.1, one can see that the ReffT, ReffF and ReffC
between northeastern and southern China was very similar. For example, at
Shenyang, a megacity in northeastern China, the effective radii of total,
fine- and coarse-mode particles were 0.31, 0.16, 2.23 µm and the
corresponding volumes were 0.22, 0.12, 0.10 µm3 per µm2,
respectively. At Hangzhou in the YRD region, the ReffT, ReffF and ReffC were 0.30, 0.17, and 2.21 µm with volumes of about 0.22,
0.12, and 0.10 µm3 per µm2, respectively. Therefore, the
different SSA440nm distributions in the two regions may be attributed
to the special aerosol composition related to the urban industrial
background of northeastern China (lower SSA440nm) and more
anthropogenic sources in eastern China (higher
SSA440nm).
Dust aerosols with light-absorbing properties occur more frequently in spring in
northeastern China than in more southern regions (Zhao et al., 2018).
Anthropogenic emissions from seasonal biomass burning and residential
heating are two other main factors that affect aerosol composition between
the two regions (Che et al., 2018). There was high
percentage of absorbing aerosols at the northeastern sites, especially in winter,
more than likely caused by emissions of carbonaceous aerosol from
residential heating (Zhao et al., 2015). Climatic conditions are also the
main factors affecting the absorption characteristics of aerosols in
different regions of northern and southern China. The increased light scattering
could well be due to the particles hygroscopic growth demonstrated in other
studies. For example, Mai et al. (2018) found that AODs and SSAs both
increased with relative humidity at Guangdong in the Pearl River Delta (PRD) region, which
suggests that condensational growth can affect aerosol optical
properties.
Annual spatial distribution of the single-scattering albedo (SSA)
at 440 nm at the CARSNET sites.
Spatial distributions of absorption aerosol optical depth (AAOD)
The spatial distribution of AAOD at 440 nm, shown in Fig. 5, indicates that,
overall, the AAOD440nm values increased from north to south and from
remote and rural to urban sites. Lower AAOD440nm values were found at the remote
stations, where the average value was 0.01. The AAOD440nm at Akedala, a
remote site in northwestern China, was 0.02, and that was higher than at
Shangri-La or Lhasa (0.01), both of which are on the Tibetan Plateau. The
low AAOD440nm values throughout that region indicate that the aerosol
population was not strongly absorbing. Compared with these three sites, the
average AAOD440nm values at the arid and semi-arid sites were higher (0.03);
for example, an AAOD440nm of 0.05 was found at Tazhong, which is
adjacent to the desert, and that indicates that the aerosol particles were
more absorbing. As discussed in Sect. 3.2 and 3.3, dust aerosols likely
make a significant contribution to aerosol light absorption in the areas
impacted by the desert areas.
The low AAOD440nm found at Xilinhot (0.02) was probably due to the low
aerosol loadings (AOD440nm=0.21) in this region. The
AAOD440nm values at the Mt. Gaolan and Yulin rural sites, which are on or around
the CLP, were about 0.04 and 0.03, respectively, and the particles were
moderately absorbing (SSA =0.89). The large AAOD440nm at Datong
(0.09) can be explained by the high AOD440nm (0.58) there. Indeed,
large AAOD440nm values were found at rural sites in eastern China, where
there were high AODs and low SSAs, as noted in Sect. 3.2 and 3.3. Of these
sites, Dengfeng (AOD440nm=0.08) and Huimin (AOD440nm=0.08)
are located on the North China Plain, while Huainan (AOD440nm=0.10)
is on the Guanzhong Plain. Lower AAOD440nm values, from 0.02 to 0.03, occurred
at Tongyu (0.03), which is in a semi-arid region in northeastern China, at
the Mt. Longfeng (0.03) regional background site on the Northeast China
Plain, at the Yushe rural site in northern China (0.03), and at the clean
Xiyong site in the PRD (0.02).
Several urban sites showed AAOD440nm values greater than 0.10: these
include Fushun (0.11) and Shenyang (0.14) in northeastern China, Lanzhou
(0.10) in northwestern China, and Nanjing (0.10) and Wuhan (0.11) in
eastern and central China. Lower AAOD440nm values occurred in other urban
areas, such as Yinchuan (AAOD440nm=0.02, AOD440nm=0.37) in
the northwest and Zhuzilin (AAOD440nm=0.03, AOD440nm=0.66)
in the PRD; both of these sites had relatively low AOD440 values, indicating
weaker anthropogenic influences compared with metropolitan regions in some
other areas. We note that there are significant uncertainties in relating
aerosol absorbing properties to particle types, such as black carbon,
organic matter, and mineral dust (Russell et al., 2010; Giles et al.,
2012). Nonetheless, the information presented here on the spatial
distribution of AAODs over China may be useful for further
investigations into the relationships between light absorption and particle
type (Liu et al., 2018; Schuster et al., 2016a, b).
Annual spatial distribution of absorption aerosol optical depth
(AAOD) at 440 nm at the CARSNET sites.
Spatial distribution of direct aerosol radiative effect at the Earth's
surface and top of the atmosphere
The spatial distributions of the DAREs calculated for both the bottom and
top of the atmosphere are shown in Fig. 6. Overall, the DARE-BOAs increased
from northwest to southeast and from rural to urban sites, consistent with
impacts from the densely populated regions around the sites. The average
DARE-BOA at the remote sites was -24.40 W m-2, and, in comparison, a
higher DARE-BOA (-33.65 W m-2) occurred at Akedala, which occurred in a
remote region of northwestern China. The AOD440nm at Akedala was
relatively low (0.17) and the SSA moderate (0.90). The moderate absorption
of aerosol could lead to more strong surface cooling effects with little
higher DARE-BOA than the other remote sites. The DARE-BOAs for Lhasa and
Shangri-La were -22.13 and -17.43 W m-2, respectively. These results
indicate weaker surface cooling effects at the remote sites relative to
other regions because the aerosol loadings were relatively low, as indicated
by AOD440nm values of < 0.20.
The average DARE-BOTs at the arid and semi-arid sites in China were about
-56.43 W m-2, and those high DARE-BOAs can be explained by the
moderately absorbing particles (SSA =0.89) and large AOD440nm values
(0.32) compared with the remote sites. A large DARE-BOA (-91.20 W m-2)
occurred at the Tazhong site near the northwestern deserts, and there the
high AOD (0.60) and the slight absorption of mineral dust (SSA =0.92)
imply substantial surface cooling. The average DARE-BOA for rural sites on
the Chinese Loess Plateau or surrounding area was -74.67 W m-2, which also
implies cooling at the surface.
Several rural sites in northern and eastern China had large DARE-BOA values;
these include Huimin (-111.58 W m-2), Dengfeng (-104.78 W m-2), and
Huainan (-129.17 W m-2), and at those sites the AODs were high, from
0.80 to 0.90, and the SSAs were ∼0.89. These results show
stronger surface cooling effects at sites influenced by anthropogenic
emissions compared with the remote sites or those near the deserts. The
large negative DARE-BOA values (-103.28 W m-2) at the urban sites
indicate that the combination of high AOD440nm values (0.79) and moderate
SSAs (0.90) can cause significant surface cooling. Indeed, anthropogenic
emissions presumably led to the high DARE-BOAs at urban sites, including
Shenyang (-144.88 W m-2) and Fushun (-116.91 W m-2) on the
Northeast China Plain, Xi'an on the Guanzhong Plain (-132.55 W m-2),
Chengdu in the Sichuan Basin (-110.42 W m-2), Lanzhou in the western
region (-126.17 W m-2), and Nanjing (-143.38 W m-2) and Wuhan
(-171.80 W m-2) in central China. These results indicate that
anthropogenic aerosols can cause significant direct radiative effects at
urban sites.
Annual spatial distribution of direct aerosol radiative effect at
the bottom of the atmosphere at the CARSNET sites.
The DARE-TOAs increased from north to south and from rural to urban sites,
and the average DARE-TOA for the remote stations was low, about -4.79 W m-2 (Fig. 7). The DARE-TOAs at Lhasa and Shangri-La were -5.04 and -8.93 W m-2, respectively. A notably small DARE-TOA was
found at Akedala (-0.42 W m-2), indicating that the effects of the
aerosol on the temperature of Earth–atmosphere system there would be weak.
The average DARE-TOA at the arid and semi-arid sites was -10.17 W m-2.
The large DARE-TOA found at Tazhong (-23.49 W m-2) could represent the
larger contribution of slightly absorbing mineral aerosols (SSA =0.92)
and a large AOD (0.60); this indicates more cooling at the surface through the
absorption and scattering solar radiation compared with the less impacted
sites. This is consistent with the results for Tazhong discussed in Sect. 3.1, which showed high volumes of coarse-mode particles with large radii.
The average DARE-TOA at rural sites on the Chinese Loess Plateau or nearby
was about -14.56 W m-2. Although the SSA440nm were close between Mt. Gaolan
and Yulin, about 0.89, the TOAs were quite different (Mt. Gaolan -20.87 W m-2; Yulin -9.09 W m-2), which could be due to the different
AOD440nm, about 0.36 and 0.32, respectively. In rural eastern China, the
DARE-TOA was about -32.40 W m-2, and, to put this in context, Che et al. (2018) found DARE-TOAs of -40 W m-2 at rural sites in the YRD
region, which is indicative of a relatively strong cooling effect. Low
DARE-TOAs were found at the Mt. Longfeng rural site in northeastern China
(DARE-TOA =-11.34, AOD440nm=0.34, SSA =0.89) and at the
Tongyu semi-arid site in northeastern China (DARE-TOA =-8.87,
AOD440nm=0.23, SSA =0.88), where the aerosol loadings were
relatively low and the absorption was moderate.
At the urban sites at central and eastern China, the average DARE-TOA values
were about -30.05 W m-2. Higher DARE-TOAs occurred at Anshan on the
Northeast China Plain (-39.66 W m-2), Chengdu in the Sichuan Basin (-52.21 W m-2), Hangzhou in the YRD (-40.16 W m-2), Jiaozuo (-39.35 W m-2) and Zhengzhou (-46.18 W m-2) on the North China Plain, and
Zhuzilin (-40.15 W m-2) in the PRD region. The high DARE-TOA values at
these urban sites imply relatively strong cooling effects due to higher
aerosol loadings in the atmosphere.
Annual spatial distribution of direct aerosol radiative effect at
the top of the atmosphere at the CARSNET sites.
Annual spatial distribution of the aerosol type classification of
types I–VII at the CARSNET sites.
Spatial distributions of aerosol mixing properties
The spatial distribution of aerosol mixing properties (Fig. 8) was
obtained by using the SSA440nm, FMF, and EAE results to classify the
particles based on size and absorbing properties. In previous studies by
Zheng et al. (2017) and Che et al. (2018), the particles in this study were
grouped into eight types as shown in Table 2. Moreover, the FMF has been
provided to give the particle size information in the group of the
particles.
At the remote Akedala and Lhasa sites (FMF =0.70–0.78 and SSA440nm=0.85), the percentages of mixed absorbing particles (Type V) were
35 %–40 %, while at Shangri-la (FMF =0.76, SSA440nm=0.84) the
percentage was slightly lower, 24.62 %. The characteristics of the
particles at these remote, high-altitude sites were probably affected by the
rugged topography, which would promote particle mixing. The proportion of
coarse-mode particles (mainly dust)with moderate to strong absorption
(Group VII) was highest at the arid and semi-arid sites. The percent
abundances of Group VII particles were 57.90 % at Dunhuang (AE =0.26,
SSA440nm=0.85, FMF =0.43) and 58.52 % at Tazhong (AE =0.20,
SSA 440nm=0.87, FMF =0.37), respectively. Mixed absorbing
particles (Type V) and strongly absorbing dust particles (Group VII)
accounted for 30 % to 70 % of the aerosol in the rural sites on or near the
CLP. The percentages of mixed absorbing particles (Type V) at Mt. Gaolan, Yulin,
and Datong were 31.98 %, 45.22 %, and 29.04 %, respectively, and the
average FMFs at those sites ranged from 0.70 to 0.76.
The proportions of the coarse-mode aerosols with strongly absorbing properties in Group
VII was about 35.23 % at Mt. Gaolan and 21.21 % at Yulin, which was mainly
dust particles, with the FMFs at those sites being 0.43 and 0.48,
respectively. The proportion of coarse-mode particles with strongly
absorbing properties in Group VII and coarse-mode particles with weakly absorbing properties in
Group VIII at the rural sites in eastern China was < 11 %. These
patterns indicated that there are differences between the eastern region and
northwestern China because in the east coarse-mode particles only make a
minor contribution to aerosol absorption. The percentage of fine-mode
particles with weakly absorbing properties in Type IV and mixed absorbing particles in
Type V combined to be about ∼50 % at the eastern sites. This
result suggests that mixed aerosols originated from a variety of sources and
that many of the sites were affected by anthropogenic emissions from
megacities upwind.
The fine-mode particles with absorbing properties in Types I, II, III, and V at most of the urban sites accounted
for 50 % to 90 %. The percentages of these four
particle types combined were especially large in eastern China; for example,
at Panyu, particle Types I–IV composed 90.83 % of the total and the FMF
there was 0.90–0.94, while at Zhuzilin, the percentage of Types I–IV was
92.55 % and the FMF was 0.92–0.94. These results are another indication
that fine-mode particles are important for light absorption in urban areas.
In contrast, the Lanzhou and Ürümqi urban sites were less affected by
absorbing fine particles because the percentages of Type I–IV particles
were only 19.73 % and 18.36 %, respectively. The mixed absorbing Type V
particles accounted for large percentages of the total at Lanzhou
(48.80 %, EAE =0.88, SSA =0.82, FMF =0.73) and at Ürümqi
(59.39 %, EAE =0.94, SSA =0.84, FMF =0.75). Different from the
other urban sites, these patterns show that larger particles had significant
contributions to the aerosol absorption at these two northwestern sites.
Conclusions
Aerosol microphysical and its optical properties obtained from the
ground-based sun photometer deployed at 50 CARSNET stations were used to
begin the development of their climatology characteristics and to
investigate potential aerosol–climate effects over vast area of China.
Direct aerosol radiative effects (DAREs) at the bottom and at the top of the
atmosphere were calculated, and eight types of aerosols were classified
based on the particle size and absorbing properties. The annual mean values
of the ReffT decreased from the arid and semi-arid sites (0.55 µm)
to the urban sites (0.37 µm). The aerosol volumes increased from the
remote sites (0.05 µm3 per µm2) to the urban sites (0.21 µm3 per µm2). The volumes of coarse-mode particles were
larger than those for the fine mode at the remote and arid and semi-arid
sites – this can be explained by the greater relative abundances of mineral
dust compared with pollution-derived particles at those sites. At the urban
sites, where anthropogenic influences were relatively strong, the proportion
of fine-mode particles increased gradually with aerosol volume.
The AOD440nm progressively increased from the remote sites (0.12), to
the arid and semi-arid sites (0.32), to rural sites in eastern China (0.70),
and finally to the urban sites (0.79), which were the ones most strongly
affected by anthropogenic activities. The average EAE440–870 nm values at
the arid and semi-arid sites were relatively low (0.71), which indicates an
important contribution of larger particles to the aerosol extinction in
those regions. The consistently large EAE440–870 nm values at the urban
sites (> 1.20) and the high FMFs that those sites (0.88) are
evidence that fine-mode particles are prevalent throughout year. The average
SSA440nm values at the remote, rural, and urban sites were relatively
similar, averaging about 0.89, and this indicates the particles were
moderately absorbing.
Overall, dust aerosols with light-absorbing properties (in spring) and emissions
from biomass burning and residential heating during the colder months were
the main factors that led to spatial differences in the percentages of
absorbing aerosols over China. The AAOD440nm values increased from the
remote sites (0.01), to the arid and semi-arid sites (0.03), to the rural sites
of eastern China (0.05), and finally to the urban sites (0.07). High
AAOD440nm values were caused by light-absorbing dust aerosols at the rural
sites and by the strong anthropogenic emissions in the metropolitan areas.
The spatial patterns in the absorbing aerosols were not only affected by the
chemical composition of the aerosol but also by physical effects imposed by
topography, weather, and climate.
The average DARE-BOA values were -24.40 W m-2 at the remote sites,
-56.43 W m-2 at the arid and semi-arid sites, -74.67 W m-2 at the
sites on the CLP or nearby, -85.25 W m-2 at the rural sites in eastern
China, and -103.28 W m-2 at the urban sites. The larger DARE-BOA values
at the urban sites imply stronger cooling effects from anthropogenic
emissions compared with those from mineral dust at the remote sites or those
near the desert. Moreover, larger DARE-TOAs also occurred at the urban
sites (-30.05 W m-2), which indicates strong cooling effects due to the
large aerosol extinctions between the Earth–atmosphere system displayed by the
moderate to strong light absorption. Mixed-absorbing particles were the most
abundant aerosol type in the remote and rural sites on or near the Chinese
Loess Plateau and in eastern China. Mineral dust particles with moderate to
strong absorbing properties were dominant at the arid and semi-arid sites, while
absorbing fine-mode particles accounted for 50 % to 90 % of the aerosol at most urban sites.
The results of this study have considerable value for ground-truthing
satellite observations and for validating aerosol models. Moreover, the
results have also provided significant information on aerosol optical and
radiative properties for different types of sites covering a broad expanse
of China. These results also are a major step towards developing climatology
for aerosol microphysical and optical properties for China and even East
Asia.
Data availability
The detailed data used in the study have been deposited in the figshare database (Che et al., 2019b, 10.6084/m9.figshare.9731339.v2).
Site information for the 50 CARSNET sites used in this study.
No.Site nameLongitudeLatitudeAltitudeSite informationObs. NumPeriodRemote sites (three sites) 1Akedala47.1287.97562.055 km west of Fuhai county, Xinjiang Province,9472010–2017and 250–300 km southeast of Kazakhstan2Lhasa29.6791.133663.0In the centre of Lhasa, Qinghai–Tibetan Plateau.4372012–20173Shangri-La28.0299.733583.012 km northeast of Shangri-La county, Diqing area,3252013–2017Yunnan ProvinceArid and semi-arid sites (six sites) 4Dunhuang40.1594.681139.01.5 km northeast of Dunhuang, Gansu Province,20302012–2017near the Kumtag Desert in China5Ejina41.95101.07940.5West of Inner Mongolia, near Mongolia,19702013–2017and Badanjilin Desert6Minqin38.63103.081367.0In Minqin county, east of the Tenggeli desert and4812013–2017north of the Badanjilin Desert, Gansu Province7Tazhong39.0083.671099.4In the middle of Taklamakan Desert,12792013–2017Xinjiang Province8Xilinhot43.95116.121003.05 km southeast of Xilinhot, near Hunshandake,14642013–2017Inner Mongolia Province,9Tongyu44.42122.87151.0In Tonyu, west of Jilin Province8172010–2011Rural sites on (or near) the Chinese Loess Plateau (three sites) 10Mt. Gaolan36.00103.852161.65 km north of Lanzhou in Gansu Province7692015–201611Yulin38.43109.201135.010 km north of Yulin in Shaanxi Province7162010–201612Datong40.10113.331067.3Within 9 km of Datong but within an area of rapid9142014–2017urbanization, Shanxi ProvinceRural sites in eastern China (15 sites) 13Changde29.17111.70565.018 km northwest of Changde, Hunan Province3442013–201614Dongtan31.52121.9610.0On Chongmin Island, 30km east of Shanghai9862012–201615Chun'an29.61119.05171.4151 km southwest of Hangzhou, Zhejiang Province12862011–201516Huimin37.48117.5311.7100 km northeast of Jinan, Shandong Province22432009–201717Lin'an30.30119.73138.6150 km northeast of Shanghai and 50 km west18342011–2015of Hangzhou, Zhejiang Province18Mt.Longfeng44.73127.60330.5In Wuchang county, 175 km northeast of Harbin,15152012–2016Heilongjiang Province19Fuyang30.07119.9517.044.1 km southwest of Hangzhou, Zhejiang Province7102014–201520Shangdianzi40.65117.12293.0In Miyun county, 150 km northeast of Beijing15202014–201721Yushe37.07112.981041.51.5 km east of Yushe, Shanxi Province14792013–201722Dengfeng34.46113.02350.075 km southwest of Zhengzhou, Henan Province712201323Huainan32.65117.0252.0Central Hefei, Anhui Province7942014–201524Jiande29.45119.2889.0Southwest of Hangzhou, Zhejiang Province15502011–201525Tonglu29.80119.6446.1100 km northwest of Hangzhou, Zhejiang Province17172011–201526Xiaoshan30.16120.2514.0South of Hangzhou, Zhejiang Province6002014–201527Xiyong22.28114.33155.2East of Shenzhen, Guangdong Province1892016Urban sites (23 sites) 28Anshan41.08123.0023.0In Anshan, central Liaoning Province1932009–201329Beijing-Nanjiao39.80116.4731.3In southeastern Beijing17322014–201730Beijing-CAMS39.93116.32106.0Chinese Academy of Meteorological Sciences,11132012–2018Beijing31Chengdu30.65104.03496.0In Chengdu, Sichuan Province552014–201532Dalian38.90121.6391.5Southeastern coastal city in Liaoning Province7362012–201533Fushun41.88123.9580.0In Fushun, central Liaoning Province2312009–201334Hangzhou30.23120.1742.0In Hangzhou, Zhengjiang Province16632011–201535Hefei31.98116.3892.0In Hefei, Anhui Province197201636Jiaozuo35.18113.25113.0Central Jiaozuo, Henan Province9812016–201737Lanzhou36.05103.881517.3In Lanzhou, Gansu Province14932013–201738Nanjing32.05118.7799.3In Nanjing, Jiangsu Province12582007–201539Nanning22.82108.35172.0In Nanning, Guangxi Province2862013–201740Panyu23113.35145.0In district of Guangzhou,4362012–2016Guangdong Province
Continued.
No.Site nameLongitudeLatitudeAltitudeSite informationObs. NumPeriod41Shanghai31.22121.5514.0In the Pudong district of Shanghai144201642Shenyang41.77123.5060.0In Shenyang, central Liaoning Province5412009–201343Tianjin39.10117.173.3Northern coastal city on the North China Plain17052013–201744Ürümqi43.7887.62935.0In Ürümqi, Xinjiang Province14112012–201745Xi'an34.43108.97363.020 km north of the centre of Xi'an but within the Jing River6522012–2016industrial district, Shaanxi Province46Yinchuan38.48106.221111.5In Yinchuan, Ningxia Province124201747Zhengzhou34.78113.6899.0In Zhengzhou, Henan Province14852013–201748Shijiazhuang38.03114.5375.0Central Shijiazhuang,11782015–2017Hebei Province49Wuhan30.32114.2130Central Wuhan,2202008Hubei Province50Zhuzilin22.32114.0063.0Central Shenzhen,9152010–2017Guangdong Province
Annual data for aerosol microphysical properties and optical and
direct radiative parameters.
No.SiteaReffTaReffFaReffCaVolTaVolFaVolCaAODTbEAEaFMFaSSATaImageaRealaAAODaBOAaTOARemote sites (3 sites) 1Akedala0.360.142.450.060.020.040.171.130.810.900.01171.45400.02-33.65-0.422Lhasa0.640.132.260.050.010.040.100.770.660.900.01061.55410.01-22.13-5.043Shangri-La0.390.142.330.030.010.020.101.190.850.930.00861.46260.01-17.43-8.93Average0.470.142.350.050.010.030.121.030.770.910.01031.49020.01-24.40-4.79Arid and semi-arid sites (6 sites) 4Dunhuang0.620.141.520.150.020.130.330.480.440.880.01031.54910.04-63.61-8.965Ejina0.560.141.780.110.020.090.240.640.520.890.01161.52650.03-47.66-7.206Minqin0.560.131.870.130.020.110.300.680.590.860.01451.54300.04-59.83-5.017Tazhong0.710.141.380.300.030.270.600.250.350.920.00541.52570.05-91.20-23.498Xilinhot0.480.132.450.080.020.050.211.030.780.890.01391.51830.02-37.14-7.479Tongyu0.390.132.360.070.020.050.231.160.820.880.01791.53770.03-39.13-8.87Average0.550.141.890.140.020.120.320.710.580.890.01231.53340.03-56.43-10.17Rural sites on the Chinese Loess Plateau or nearby (3 sites) 10Mt. Gaolan0.580.142.030.160.030.130.360.810.640.890.01081.51540.04-59.36-20.8711Yulin0.530.152.050.110.030.080.320.840.720.890.01221.50700.03-56.81-9.0912Datong0.350.132.150.190.090.100.581.150.830.860.01711.49050.09-107.86-13.71Average0.490.142.080.150.050.100.420.930.730.880.01341.50430.05-74.67-14.56Rural sites in eastern China (15 sites) 13Changde0.320.162.180.140.070.070.581.150.880.930.01011.46190.04-75.33-31.4414Dongtan0.370.162.120.170.080.090.621.210.860.930.00801.46240.04-79.41-33.1815Chun'an0.300.182.300.190.120.080.811.220.920.940.00661.40950.04-86.49-46.4816Huimin0.360.152.070.220.100.120.831.140.860.890.01471.48520.08-111.58-25.4917Lin'an0.290.172.240.210.120.090.871.290.910.930.00891.41720.06-93.09-41.7318Mt.Longfeng0.280.152.440.080.040.040.341.380.900.890.01651.46470.03-51.17-11.3419Fuyang0.290.172.280.210.130.090.891.310.920.940.00701.41470.05-91.69-42.2920Shangdianzi0.400.152.330.120.050.070.431.170.860.890.01481.48400.04-59.99-20.5821Yushe0.410.152.180.140.060.080.501.070.840.920.00901.48780.03-66.72-25.9922Dengfeng0.390.152.030.230.090.130.791.020.830.890.01311.47820.08-104.78-35.8423Huainan0.300.172.250.210.130.080.911.170.920.880.01661.43080.10-129.17-24.4424Jiande0.290.172.180.200.120.080.841.340.910.920.00991.40850.06-91.06-40.0725Tonglu0.290.172.200.200.120.080.831.310.910.930.00911.42690.06-89.82-41.2826Xiaoshan0.280.172.240.220.130.090.871.350.910.930.00821.41340.06-95.23-40.3927Xiyong0.330.162.430.110.060.050.411.320.890.940.00741.40720.02-53.18-25.45Average0.330.162.230.180.090.080.701.230.890.920.01071.44350.05-85.25-32.40Urban sites (23 sites) 28Anshan0.360.172.240.260.120.140.941.120.860.890.01581.47590.10-117.99-39.6629Beijing-Nanjiao0.450.152.330.190.070.120.651.120.840.920.01001.49390.05-82.06-29.4330Beijing-CAMS0.500.162.370.190.070.120.651.120.790.900.01151.51080.05-72.66-29.1031Chengdu0.340.212.260.260.160.101.171.120.920.970.00331.41160.04-110.42-52.2132Dalian0.350.162.240.160.080.090.621.220.870.930.00951.45840.04-75.50-37.4233Fushun0.380.172.340.220.090.120.801.120.870.840.02441.49540.11-116.91-19.5934Hangzhou0.300.172.210.220.120.100.871.300.900.910.01091.43370.07-31.57-40.1635Hefei0.290.152.370.180.100.080.691.280.900.850.01951.42530.10-105.83-19.2236Jiaozuo0.350.162.170.200.100.100.761.140.880.910.01051.47220.05-92.29-39.3537Lanzhou0.540.142.040.280.060.220.660.810.660.830.01971.51930.10-126.17-13.8138Nanjing0.330.162.160.250.120.120.941.130.880.880.01541.44460.10-143.38-28.2939Nanning0.300.182.530.200.130.060.971.360.950.920.01071.42720.07-121.92-33.3540Panyu0.260.162.290.160.100.060.691.430.930.900.01371.41550.07-96.03-26.5641Shanghai0.400.151.930.190.080.110.681.100.840.880.01421.48140.07-106.89-24.3442Shenyang0.310.162.230.220.120.100.891.200.900.840.02531.45890.14-144.88-15.0243Tianjin0.420.162.260.230.100.130.831.110.860.890.01341.49570.07-108.09-33.2644Ürümqi0.480.142.140.150.040.100.420.930.750.850.01921.53710.05-70.55-11.7445Xi'an0.370.161.850.260.110.150.980.980.820.880.01501.48880.10-132.55-35.9346Yinchuan0.380.142.020.110.040.070.371.120.810.940.00541.49300.02-48.67-21.8947Zhengzhou0.430.182.220.280.120.160.991.100.860.950.00451.46260.04-101.10-46.1848Shijiazhuang0.400.162.280.260.120.140.951.090.870.880.01541.47540.09-125.05-33.6649Wuhan0.340.172.220.220.120.101.001.160.910.880.01961.47790.11-171.80-20.4050Zhuzilin0.270.172.450.150.090.050.661.450.940.960.00491.44380.03-73.16-40.65Average0.370.162.220.210.100.110.791.150.860.900.01361.46950.07-103.28-30.05
a Optical parameters at a wavelength of 440 nm.
b Ångström exponents between 440 and 870 nm.
Author contributions
All authors contributed to shaping the ideas and reviewing the paper. HC,
XX, and XZ designed and implemented the research and prepared the
manuscript. HC, HZ, YW, and HW contributed to analysis of the CARSNET dataset.
HC, XX, JZ, OD, BNH, PG, and ECA contributed to the CARSNET data retrieval.
HC, BQ, WG, HY, RZ, LY, JC, YZ, KG, and XZ carried out the CARSNET
observations. OD, BNH, PG, and ECA provided constructive comments on this
research.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by grants from the National Science Fund for
Distinguished Young Scholars (41825011), the National Key R&D Program
Pilot Projects of China (2016YFA0601901), National Natural Science
Foundation of China (41590874), the CAMS Basis Research Project (2017Z011),
the European Union Seventh Framework Programme (FP7/2007-2013) under grant
agreement no. 262254, AERONET-Europe ACTRIS-2 program, and the European Union's
Horizon 2020 research and innovation programme under grant agreement no.
654109.
Financial support
This research has been supported by the National Science Fund for Distinguished Young Scholars (grant no. 41825011), the National Key R & D Program Pilot Projects of China (grant no. 2016YFA0601901), the National Natural Science Foundation of China (grant no. 41590874), the European Union's Horizon 2020 research and innovation programme (grant no. 654109), and the European Union Seventh Framework Programme (FP7/2007-2013).
Review statement
This paper was edited by Xiaohong Liu and reviewed by three anonymous referees.
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