Introduction
Atmospheric aerosols have substantial influences on human health, air
quality and climate changes and their loadings have significantly increased
since the preindustrial times (Qin et al., 2001; Forster et al., 2007). Due
to their ability to scatter/absorb solar radiation and act as cloud
condensation nuclei, atmospheric aerosols can affect atmospheric radiation
and dynamics, as well as the Earth's hydrologic cycle, leading to regional
or global climate changes (Forster et al., 2007; Rosenfeld et al., 2008;
Qian et al., 2009; Li et al., 2011; Wang et al., 2014; Guo et al., 2016a).
Light-scattering aerosols have contributed to offsetting the warming effect
of CO2 (Kiehl and Briegleb, 1993) while light-absorbing aerosols such as
black carbon (BC) could further enhance global warming (Jacobson 2002),
especially in the high aerosol regions. Due to the warming effect of BC, the
atmosphere would become more unstable, which might result in changes in
the trend of precipitation in China over the past decades as suggested by
Menon et al. (2002). Furthermore, atmospheric aerosols can be a major
component of haze pollution, altering atmospheric visibility and being
harmful to human health (Chameides and Bergin, 2002).
Observations and modelling studies have been conducted on aerosol optical
properties and radiative forcing, as well as its climate effects on regional
and global scales in the past two decades (e.g. Penner et al., 2001;
Bellouin et al., 2003; Liao and Seinfeld, 2005; Yan et al., 2008; Wu et al.,
2012; Zhuang et al., 2013a, 2014a; Wang et al., 2015; Yu et al., 2016).
Forster et al. (2007) summarized that large uncertainties exist in
estimating the aerosol radiative forcing, especially in climate models. The
bias mostly results from the uncertainties in the simulated aerosol optical
properties (Holler et al., 2003), which, in turn, are related to the aerosol
loadings, profiles, compositions, mixing states and atmospheric
humidity. The 5th IPCC reported that the global mean direct radiative
forcing ranged from -0.85 to +0.15 W m-2 for total aerosols and from
+0.05 to +0.8 W m-2 for BC (IPCC, 2013). This would further lead to
much larger uncertainties in the estimations of the aerosol climate effects.
In east Asia, the range of simulated BC direct radiative forcing is much
larger than the global one, varying from +0.32 to +0.81 W m2
(Zhuang et al., 2013a). The uncertainty could be substantially reduced in a
model if the aerosol optical properties are based on observations or if
observed properties are directly used (Forster et al., 2007).
In the last three decades, China has experienced the most rapid economic
growth worldwide. This leads to high emissions of anthropogenic aerosols and
trace gases (e.g. Guo et al., 2009; Zhang et al., 2009; Xin et al., 2014; Che
et al., 2015). The anthropogenic aerosol emissions in east Asia were
estimated to exceed 1/4 of the global emissions (Streets et al., 2001),
resulting in more diversified aerosol compositions, complex species and
heterogeneous spatial distributions in the region (Zhang et al., 2012),
especially in megacities and urban agglomerations (e.g.
Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD) and Pearl River
Delta (PRD) regions). Uncertainties of the aerosol radiative forcing and
corresponding climate effects in these regions might be much larger than
those of the rest of the world (e.g. Forster et al., 2007; Zhuang et al.,
2013b). In addition, the diurnal variability of aerosol properties has been
suggested due to other major factors leading to such large biases (e.g. Xu et
al., 2016). Therefore, it is necessary to characterize the aerosol optical
properties based on observations in China, as many studies did in recent
years at urban sites and in rural areas (e.g. Bergin et al., 2001; Xu et al.,
2002, 2004, 2012; Zhang et al., 2004, 2015; Yan, 2006; Xia et al., 2007; Li
et al., 2007; Yan et al., 2008; Andreae et al., 2008; He et al., 2009; Wu et
al., 2009, 2012; Wang et al., 2009; Li et al., 2010; Fan et al., 2010; Bai et
al., 2011; Cai et al., 2011; Xiao et al., 2011; Zhuang et al., 2015; Li et
al., 2015a, b; Yu et al., 2016). For example, Bergin et al. (2001), He et
al. (2009) and Zhuang et al. (2015) presented the surface aerosol scattering
and absorption properties in urban area of north and east China and they
suggested that the annual mean 532 nm AAC (aerosol absorption coefficient)
in Beijing was about 56 Mm-1 and it was 41–44 Mm-1 in YRD, which
were much smaller than those in central to south-west China and in PRD (Wu et
al., 2009; Cao et al., 2012; Tao et al., 2014) but much larger than those in
rural and desert regions (Xu et al., 2002, 2004; Yan et al., 2008). In
addition to surface measurements, the columnar optical properties of the
aerosols were also observed (Xia et al., 2007; Zhuang et al., 2014a; Che et
al., 2015). Long-term measurements of the country-wide aerosol optical depths
and Ångström exponents in China from 2002 to 2013 were introduced by
Che et al. (2015). In spite of intensive observation-based studies,
measurements and analyses on aerosol properties in the YRD region, one of the
most populous regions in China, are still rather limited. To fill the gaps in
the current observational network in China and to better understand the
optical properties of urban aerosols in YRD, this study will analyse the
observations of aerosol scattering (SC), back-scattering (Bsp), absorption
(AAC), extinction (EC) coefficients and single-scattering albedo (SSA),
Ångström exponent of scattering (SAE) and absorbing (AAE) aerosols,
as well as aerosol asymmetry parameter (ASP) in the urban area of Nanjing, a
major megacity in YRD. Our ultimate goals are to provide a reference when
estimating aerosol radiative forcing and climate effect as well as
forecasting visibility.
In the following, the method is described in Sect. 2. Results and discussions
are presented in Sect. 3, followed by conclusions in Sect. 4.
Data and methodologies
Sampling station and instruments
The sampling station is located at the Gulou campus of Nanjing University,
urban area of Nanjing (32.05∘ N, 118.78∘ E). It is built on
the roof of a 79.3 m-tall building, around which there are no industrial
pollution sources within a 30 km radius but there are several main roads
with apparent traffic pollution, especially at rush hours. The layout of the
site and the corresponding climatology have been described in Zhu et
al. (2012).
The wavelength-dependent AAC and concentrations of BC were derived from the
measurements using a seven-channel Aethalometer (model AE-31, Magee
Scientific, USA). The wavelength-dependent aerosol SC and Bsp were measured
using a three-wavelength-integrating Nephelometer (Aurora 3000, Australia).
To make a brief comparison between surface and column aerosols, the
wavelength-dependent columnar aerosol optical depth (AOD) was observed using
a Cimel sunphotometer (CE-318). The AE-31 model measures light attenuation at
seven wavelengths, including 370, 470, 520, 590, 660, 880 and 950 nm, with a
desired flow rate of 5.0 L min-1 and a sampling interval of 5 min.
Aurora 3000 measures aerosol light scattering, including SC and Bsp at 450,
525 and 635 nm, with a sampling interval of 1 min. CE-318 measures the AOD
from 340 to 1640 nm in the daytime. Routine calibrations and maintenances
were carried out for all these instruments during the sampling periods. R-134
was used as a span gas for Aurora 3000. The aerosol inlet is located about
1 m above the roof. Data to be analysed in this study were measured from
March 2014 to February 2016 for AE-31 and CE-318 and from June 2014 to
February 2016 for Aurora 3000. Meteorological data (such as relative
humidity) during the sampling period are from the National Meteorological
Station of Nanjing (no. 58238).
Calculation of the aerosol optical properties
The wavelength-dependent aerosol absorption coefficient (AAC) and BC mass
concentration can be calculated directly based on the measured light
attenuations (ATN) through a quartz filter matrix (Petzold et al., 1997;
Weingartner et al., 2003; Arnott et al., 2005; Schmid et al., 2006):
σATN,t(λ)=(ATNt(λ)-ATNt-1(λ))Δt×AV,
where A (in m2) is the area of the aerosol-laden filter spot, V is
the volumetric sampling flow rate (in L min-1) and Δt is the
time interval (= 5 min) between t and t-1. σATN is
the AAC without any correction, which is generally larger than the actual one
(σabs) because of the optical interactions of the filter
substrate with the deposited aerosol. Generally, there are two key factors
leading to the bias: (1) multiple scattering of light at the filter fibres
(multiple-scattering effect) and (2) instrumental response with increased
particle loading on the filter (shadowing effect). Thus, the correction is
needed and the calibration factors C and R (shown in Eq. 2) are
introduced against the scattering effect and shadowing effect,
respectively:
σabs,t(λ)=σATN,t(λ)C×R.
Collaud Coen et al. (2010) suggested that AAC corrected from Weingartner et
al. (2003) (WC2003 for short, hereinafter) and Schmid et al. (2006) (SC2006
for short, hereinafter) have good agreements with the one measured by a
Multi-Angle Absorption Photometer. These two corrections are similar to each
other and they use the same R(λ):
Rt(λ)=1f-1×ln(ATNt(λ))-ln10ln50-ln10+1,
where R=1 when ATN ≤ 10 and f=1.2. However, C is
fixed in WC2003 but is wavelength-dependent in SC2006. According to Wu et
al. (2013) and Zhuang et al. (2015), C in Nanjing is 3.48 in WC2003 while
it is 2.95, 3.37, 3.56, 3.79, 3.99, 4.51 and 4.64 at 370, 470, 520, 590, 660,
880 and 950 nm in SC2006. Zhuang et al. (2015) further
suggested that wavelength-dependent AACs corrected by SC2006 might be closer to the real ones than WC2003s in Nanjing, although 532 nm AACs from
these two corrections are close to each other. In addition to the direct method,
AAC can be calculated indirectly:
σabs,t(λ)=[BC]×γ,
where [BC] is the mass concentration of Aethalometer BC (in
µg m-3) without any correction and γ is the conversion
factor determined empirically from linear regression of the Aethalometer BC
concentration versus the aerosol absorption measurement (Yan et al., 2008).
Zhuang et al. (2015) indicated that γ from the linear regression of
the Aethalometer BC concentrations (ng m3) at 880 nm against the light
absorption coefficient (Mm-1) at 532 nm in Nanjing is about
11.05 m2 g-1. It is obvious that only 532 nm AAC can be
addressed from this way. Thus, AACs corrected from SC2006 are used in this
study.
The 10th, 25th, median, 75th and 90th percentiles of 550 nm AAC
(a, Mm-1), 470/660 nm AAE (b),
550 nm SC (c, Mm-1), 550 nm (d, Mm-1) and
450/635 nm SAE (e) in each season from March 2014 to February
2016.
Statistical summary of the surface aerosol optical properties in
Nanjing.
Factors
Max
Min
Mean ± SD
Median
550 nm AAC (Mm-1)
230.648
1.439
29.615 ± 20.454
24.572
550 nm SC (Mm-1)
2493.092
20.673
338.275 ± 228.078
284.379
550 nm Bsp (Mm-1)
300.101
1.401
44.257 ± 27.396
38.206
550 nm EC (Mm-1)
2643.101
31.186
381.958 ± 252.271
321.679
550 nm SSA
0.988
0.404
0.901 ± 0.049
0.908
550 nm ASP
0.908
0.118
0.571 ± 0.088
0.582
470/660 nm AAE
3.256
0.145
1.583 ± 0.228
1.592
450/635 nm SAE
3.344
0.162
1.320 ± 0.407
1.317
AAC: aerosol absorption coefficient. SC: aerosol scattering
coefficient. Bsp: aerosol back-scattering coefficient. SSA: aerosol
single-scattering albedo. ASP: aerosol asymmetry parameter. AAE:
Ångström exponent of absorbing aerosols. SAE:
Ångström exponent of scattering aerosols.
Based on wavelength-dependent AAC and SC, Ångström exponent of
scattering (SAE) and absorbing (AAE) aerosols are estimated by the following:
AAE470/660nm=-log(AAC470nm/AAC660nm)/log(470/660)SAE450/635nm=-log(SC450nm/SC635nm)/log(450/635).
For purposes of comparison, AAC at 450, 525, 532, 550 and 635 nm, SC at 532
and 550 nm as well as Bsp at 532 and 550 nm were further calculated by the
given coefficients and corresponding Ångström exponents:
σλ=σλ0×λλ0-α,
where σλ is the coefficient at wavelength λ and
α is the corresponding Ångström exponents.
Based on wavelength-dependent SC, Bsp, AAC, aerosol asymmetry parameter (ASP),
single-scattering albedo (SSA) and extinction coefficient (EC) are
further estimated:
ASPλ=-7.143889βλ3+7.46443βλ2-3.9356βλ+0.9893SSAλ=SCλSCλ+AACλECλ=SCλ+AACλ,
where βλ is the ratio of Bsp to SC at wavelength λ.
Equation (8) is derived from Andrews et al. (2006).
Results and discussions
It is well known that the temporal variations of the aerosol optical
properties at different wavelengths are generally consistent with each other.
Therefore, only single-wavelength (such as 550 nm) AAC, SC, Bsp, SSA and ASP
are focused when analysing their basic characteristics (including temporal
variations, frequency distributions and changes with wind direction), their
relationships with each other and their relationships with the
meteorological conditions (such as RH and VIS) and columnar AOD.
Seasonal mean ± SD of the surface aerosol optical properties in
Nanjing.
Factors
MAM
JJA
SON
DJF
550 nm AAC (Mm-1)
26.954 ± 18.632
19.653 ± 15.689
33.474 ± 19.686
37.958 ± 21.892
550 nm SC (Mm-1)
318.998 ± 202.264
340.865 ± 226.151
294.624 ± 200.052
385.137 ± 255.282
550 nm Bsp (Mm-1)
42.995 ± 23.580
36.990 ± 25.067
38.684 ± 23.017
54.786 ± 30.974
550 nm EC (Mm-1)
341.279 ± 209.315
370.236 ± 248.125
351.887 ± 244.267
422.569 ± 273.565
550 nm SSA
0.915 ± 0.043
0.933 ± 0.049
0.874 ± 0.053
0.890 ± 0.040
550 nm ASP
0.553 ± 0.086
0.638 ± 0.069
0.566 ± 0.079
0.540 ± 0.083
470/660 nm AAE
1.571 ± 0.172
1.488 ± 0.263
1.524 ± 0.277
1.701 ± 0.156
450/635 nm SAE
1.097 ± 0.320
1.337 ± 0.428
1.544 ± 0.352
1.235 ± 0.383
Temporal variations of the aerosol optical properties
The aerosol absorption coefficient (AAC) was directly obtained from the
measurement of AE-31 and the scattering and back-scattering coefficients (SC
and Bsp), which were directly measured from Aurora 3000. Based on
wavelength-dependent AAC and SC, the Ångström exponent of absorbing
(AAE at 470/660 nm) and scattering (SAE at 450/635 nm) aerosols were
estimated according to Eqs. (5) and (6). Based on AAC, SC and Bsp,
wavelength-dependent aerosol asymmetry parameter (ASP), single-scattering
albedo (SSA) and extinction coefficient (EC) are further estimated using Eqs.
8–10 and analysed. Table 1 lists the statistical summary of surface aerosol
optical properties in the urban area of Nanjing during the sampling period.
The annual mean AAC, SC, Bsp, EC, SSA and ASP at 550 nm, AAE at
470/660 nm and SAE at 450/635 nm are 29.615, 338.275, 44.257,
381.958 Mm-1, 0.901, 0.571, 1.583 and 1.320, with a standard deviation
of 20.454, 228.078, 27.396 and 252.271 Mm-1, 0.049, 0.088, 0.228 and
0.407, respectively.
Figure 1 shows the 10th, 25th, median, 75th and 90th percentile values of the
550 nm AAC, SC, Bsp, 470/660 nm-AAE and 450/635 nm-SAE in the urban
area of Nanjing in each season from March 2014 to February 2016. Default
values of aerosol scattering properties in spring 2014 are blank because the
measurements of Aurora 3000 started from June 2014. The figure suggests that
AAC, SC, Bsp, AAE and SAE have substantially seasonal variations. A high
level of AAC appears in winter (DJF) while a lower level is found in summer
(JJA) (Fig. 1a). The temporal variability of Bsp is similar to that of AAC
(Fig. 1d). According to Zhang et al. (2009), emissions of the aerosols and
trace gases in China are larger in winter than in other seasons, especially
for carbonaceous aerosols (Fig. 1c in Zhuang et al., 2013b). Thus, the higher
AAC values in winter than in summer might result from the higher aerosol
emissions, lower boundary height (Guo et al., 2016b) and less rainfall.
However, possibly due to the impacts of hygroscopic growth of aerosol caused
by higher RH in summer and dust aerosol in spring (Zhuang et al., 2014a), SC
is considerably large in these two seasons (Fig. 1c). Thus, the lowest SC is
found in autumn in both 2014 and 2015. AAE has a similar seasonality to AAC.
Due to relatively higher RH, a small value of AAE is found in JJA while the
larger ones appear in the other seasons (Fig. 1b), which is different from
the seasonality of SAE. SAE is larger in warmer seasons but is smaller in the
other seasons. Scattering aerosols, including inorganic and partially organic
components, mainly come from gas-to-particle transformation, so that they
have smaller sizes (larger AE) compared to the primary aerosols (such as dust
and BC). The efficiency of gas-to-particle transformation is higher in warmer
seasons. The observations of the aerosol compositions at the site showed that
seasonal mean inorganic aerosols, including sulfate, nitrate and ammonium,
account for about 50 % of the total PM2.5 in spring and might be
higher than 50 % in the other seasons (Zhuang et al., 2014b). Thus, SAE
in summer and autumn is large (Fig. 1e). RH can impose substantial influences
on scattering aerosols. SAE might be much larger than the current values in
these two seasons if the hygroscopic growth were excluded. Seasonal mean RH
is about 75.41 and 70.86 % in JJA and SON, respectively, to a certain
degree leading to higher values of SAE in autumn than in summer. The figure
also suggests that the aerosol absorption coefficient and scattering
coefficient as well as their sizes in 2014 are higher than in 2015. The
observed RH difference in these 2 years at least partly accounts for the
variation of aerosol absorption coefficient and scattering coefficient as
well as their sizes. A comparison of RH between 2014 and 2015 indicates that
RH is 79.49 and 72.86 % in JJA and SON, respectively, in 2014, larger
than in 2015 (71.33 % in JJA and 69.03 in SON).
The dominant and maximum frequencies as well as corresponding ranges
of the aerosol optical properties.
The aerosol optical
The dominant
The maximum
properties
Bins
Frequencies
Bins
Frequencies
AAC
9–45 Mm-1
73 %
9–21 Mm-1
32.9 %
SC
60–390 Mm-1
67 %
170–280 Mm-1
24.04 %
Bsp
15–60 Mm-1
69 %
30–45 Mm-1
26.45 %
SSA
0.87–0.97
73 %
0.91–0.93
18.64 %
AAE
1.4–1.8
71 %
1.5–1.6
20.9 %
SAE
0.96–1.68
62 %
1.32–1.5
18.06 %
ASP
0.48–0.69
81 %
0.55–0.62
34 %
Seasonal means (markers) and corresponding standard deviations
(error bars) of wavelength-dependent AAC (a, Mm-1), SC
(b, solid mark, Mm-1), Bsp (b, open mark, Mm-1),
EC (c, Mm-1), SSA (e) and ASP (f) at 450,
532, 550, 635 nm, as well as AAE at 470/660 nm (d, red solid mark)
and SAE at 450/635 nm (d, green open mark).
Diurnal variations of 550 nm AAC (a, Mm-1), SC
(b, Mm-1), Bsp (c, Mm-1), SSA (d),
ASP (g), 470/660 nm AAE (e) and 450/635 nm
SAE (f) during the study period.
Frequency (%) distributions of 550 nm AAC (a),
SC (b), Bsp (c), SSA (d), ASP (g),
470/660 nm AAE (e) and 450/635 nm SAE (f) on annual
(shaded bar) and seasonal (coloured marks) scales.
Clusters of 96 h back trajectories arriving at the study site at
100 m in JJA (a) and DJF (b) simulated by the HYSPLIT
model. The means with standard deviations of the aerosol optical properties
at each cluster of back trajectories in both JJA and DJF are plotted in
(c) and (d).
Figure 2 plots the seasonal mean values with standard deviations of AAC, SC,
Bsp, EC, SSA, ASP, AAE at 470/660 nm and SAE at 450/635 nm. AAC, SC,
Bsp and EC increase with decreasing wavelength in four seasons. Changes in
SSA and ASP with increasing wavelength are different in different seasons.
SSA increases with increasing wavelength in colder seasons but much less in
JJA and SON. ASP increases with wavelength in JJA, contrary to in other
seasons. The figure also suggests that seasonal variation of EC is more
consistent with SCs, with large values in JJA and DJF (370.236 and
422.569 Mm-1 at 550 nm). The largest values of SSA and ASP appear in
JJA (0.933 and 0.638 at 550 nm), implying that aerosols in the urban area of
Nanjing are more scattering and have a stronger forward-scattering ability in
JJA than in other seasons. The urban aerosols are more absorbent in SON and
DJF in Nanjing (550 nm SSA is no more than 0.89). The seasonal variation of
SSA is determined by the variations of both AAC and SC. As mentioned above,
AAC is highest in winter and lowest in summer, to a great degree due to the
seasonality of the emissions. SC would have the same variation as AAC if only
emissions were taken into account. Zhang et al. (2009) indicates that the
emission seasonality of carbonaceous aerosols are much stronger than the
trace gases (such SO2 and NOx), and they show that the
anthropogenic emission rate in winter is about 1.87 times that in summer for
black carbon but only about 1.2 for SO2 in China. This is also supported
by Sun et al. (2015), who found that the concentration of black carbon
aerosol in north China was much higher in winter due to enhanced emissions
based on 1-year observations. Thus, the different emission seasonal
variations between black carbon and trace gases alone would cause a lower SSA
in winter compared to that in summer. What is more, both a higher efficiency
of gas-to-particle transformation and higher level of RH in summer are in
favour of a much larger SC, which to a certain degree could further enlarge
SSA in summer. The smaller SSA in the colder season might mainly be caused by
a higher emission of absorbing aerosol.
Seasonal mean 550 nm AAC, SC, Bsp, EC, SSA and ASP, 470/660 nm AAE and
450/635 nm SAE as well as corresponding standard deviations are listed in
Table 2. It suggests that seasonal mean 550 nm AAC, SC, Bsp, EC, SSA and
ASP vary from 19.65 to 37.96, 294.62 to 385.14, 36.99
to 54.79 and 341.3 to 422.57 Mm-1, 0.874 to 0.933 and 0.54 to
0.64, respectively. Seasonal mean AAE and SAE vary from 1.49 to 1.70 and 1.1
to 1.54, respectively. AAC and Bsp in DJF are about 2 and 1.5 times of those
in JJA, respectively. SSA in JJA is about 6.75 % larger than in SON.
In addition to seasonality, the aerosol optical properties near the surface
at urban Nanjing have substantial diurnal variations (Fig. 3), especially for
the coefficients (AAC, SC, Bsp and EC). The diurnal variation of EC, which is
consistent with SC, is not shown in the figure. AAC levels are usually high
at the rush hours around 07:00–09:00 LT and around 09:00–11:00 LT but low
in the afternoon (Fig. 3a). At 08:00 LT, mean 550 nm AAC is about as large
as 34 Mm-1, while at 02:00, it is about 23 Mm-1. SC and Bsp
(Fig. 3b, c), to some extent, have diurnal variations similar to AACs. Their
lowest values also appear in the afternoon (about 280 Mm-1 for SC and
38 Mm-1 for Bsp). However, only one peak of the aerosol scattering
coefficient is found in the early morning (about 379 Mm-1 for Sc and
48 Mm-1 for Bsp) and it is about 1–2 h earlier than its absorption
coefficient, possibly owing to the different emissions between these two
types of aerosols. Absorbing aerosols in urban Nanjing mainly come from the
vehicle emissions because of the developed transportation network, resulting
in two peaks of AAC within 1 day (Zhuang et al., 2015). Scattering aerosol
loadings are somewhat less affected by traffic emissions, especially at
night-time. Their precursors, such as SO2 and NOx, mostly come from coal
combustion and industrial emissions in urban Nanjing based on source
apportionment. Therefore, there is no peak for SC or Bsp before midnight,
although their values are considerably large (about 350 and 46 Mm-1).
Different diurnal cycles between AAC and SC were also observed in the
suburban area of Nanjing (Yu et al., 2016). Diurnal variations of AAC, SC and
Bsp might be highly affected by the diurnal cycles of the boundary layer. The
small coefficients in the afternoon are mostly induced by a well-developed
mixing layer (Zhuang et al., 2014b). Generally, the boundary layer becomes
more and more stable after sunset and its height lowers, which is conducive
to the accumulation of air pollutants at night-time, especially during the
period from midnight to sunrise. Therefore, SC usually peaks in early morning
and the peak appears at different times in different seasons (05:00 LT in
JJA and 08:00–09:00 LT in DJF). The daytime peak of AAC appears at
07:00 LT in JJA and at 09:00 LT in DJF. The diurnal variation of SSA also
reflects the difference between AAC and SC (Fig. 3d), implying that aerosols
in urban Nanjing are more scattering after midnight (SSA is about 0.91) but
more absorbing before noon and midnight (SSA is about 0.89). Scattering
aerosols mainly come from strong chemical production (gas-to-particle
transformation) in the daytime, which to some extent might offset the
dilution effect of the boundary on SC, thus leading to a relatively larger
SSA in the afternoon. The figure further shows that both AAE (Fig. 3e) and
SAE (Fig. 3f) in the daytime are slightly larger than those after midnight
because both absorbing and scattering aerosols are fresher in the daytime
whereas they are more aged before sunrise. Diurnal variations of SAE and AAE
are relatively weaker compared to the corresponding coefficients. In addition
to aerosol loadings, the level of Bsp is also affected by the size of the
aerosols, as suggested by Yu et al. (2016), and so is ASP (Fig. 3g). The
diurnal cycle of ASP is similar to that of Bsp but is opposite to that of
SAE. A large ASP appears in the early morning (0.587) and the lower ASP in
the afternoon (0.552).
Frequencies of the aerosol optical properties
The frequency of the aerosol optical properties is presented in Fig. 4 and
Table 3. Similarly, the frequency of EC is not shown in the figure because it
has similar pattern to SCs. Almost all of them follow a unimodal pattern. As
listed in Table 3, the dominant ranges for all the aerosol optical properties
are distributed around their annual mean values with different widths and
they account for at least 60 % of the total samplings during the entire
study period. The maximum frequencies of 32.9 % (AAC), 24.04 % (SC),
26.45 % (Bsp), 18.64 % (SSA), 20.9 % (AAE), 18.06 % (SAE) and
34 % (ASP) occur in the ranges from 9 to 21, 170 to 280 and 30 to
45 Mm-1, 0.91 to 93, 1.5 to 1.6, 1.32 to 1.5 and 0.55 to 0.62,
respectively. Frequency distributions of the aerosol optical properties have
substantially seasonal variations. The frequency peaks of the properties
would be more concentrated at lower/higher ranges if their seasonal means are
smaller/larger. As shown in Fig. 4a, c and e, relatively larger values or the
peaks of frequencies for AAC, Bsp and AAE are concentrated in lower value
ranges in JJA but in higher value ranges in the other seasons. Moisture
absorption growth of absorbing aerosols leads to a leftward shift in an AAE
frequency curve in JJA. Effects of dust aerosol also might result in a
leftward shift in a SC frequency curve in spring (Fig. 4f). Furthermore, due
to dust and RH, SC is considerably large in MAM and JJA, leading to
relatively larger frequencies of SC distributed at larger SC ranges compared
with the ones of AAC. As mentioned above, aerosols in urban Nanjing are more
scattering and have a stronger forward-scattering ability in JJA than in the
other seasons, thus larger frequencies occur more at higher-value ranges of
SSA and ASP in JJA.
Aerosol optical properties in different wind directions
The east Asian monsoon is active in midlatitudes. Nanjing could be affected
by the east Asian summer monsoon in JJA and by the winter monsoon in DJF.
Airflows in these two seasons are significantly different (Fig. 5a, b),
altering the aerosol optical properties in different seasons. Air masses
mostly come from the oceans (about 77 %) in JJA and from continental
regions in the north and north-west of China (57 %) in DJF. Only a few
percentages of air masses are from the northern region of China in JJA.
Additionally, considerable air masses arriving at the site are from the local
areas (cluster 1 in JJA) or from places near Nanjing (cluster 1 in DJF).
Therefore, the aerosol optical properties at the study site are characterized
differently with different air masses in the two seasons.
The aerosol optical properties both in Nanjing and at other sites of
China.
Site
Period
AAC (Mm-1)
SC (Mm-1)
ASP
SSA
Method
References
Nanjing (urban)
2014.3–2016.2
29.6 (550 nm)
338.3 (550 nm)
0.57 (550 nm)
0.9 (550 nm)
AE-31a Aurora 3000b
This study
Beijing (urban)
2005–2006
56 (532 nm)
288 (525 nm)
–
0.8 (525 nm)
AE-16c M9003d
He et al. (2009)
Beijing (rural)
2003–2005
17.5 (525 nm)
174.6 (525 nm)
–
0.88 (525 nm)
AE-31a M9003d
Yan et al. (2008)
Xi'an (urban)
2009
–
525 (520 nm)
–
–
Auroral 1000e
Cao et al. (2012)
Chengdu (urban)
2011
96 (532 nm)
456 (520 nm)
0.82
AE-31a Aurora 1000Gf
Tao et al. (2014)
Wuhan (urban)
2009.12–2014.03
119 (520 nm)
377 (550 nm)
–
0.73 (520 nm)
AE-31a Model 3563g
Gong et al. (2015)
Xinken (rural)
2004.10–2011.05
70 (550 nm)
333 (550 nm)
–
0.83 (550 nm)
MAAPh Model 3563g
Cheng et al. (2008)
Tongyu (rural)
Spring, 2010
7.61 (520 nm)
89.2 (520 nm)
–
0.9 (520 nm)
AE-31a Aurora 3000b
Wu et al. (2012)
Spring, 2011
7.01 (520 nm)
85.3 (520 nm)
–
Nanjing (suburban)
2011.03–04
28.1 (532 nm)
329.3 (550 nm)
–
0.89 (532 nm)
iPASS Model 3563d
Yu et al. (2016)
Shanghai (urban)
2010.12–2011.03
66 (532 nm)
293 (532 nm)
–
0.81 (532 nm)
AE-31a Model 3563g
Xu et al. (2012)
Shouxian (rural)
2008.5–12
29 (550 nm)
401 (550 nm)
–
0.92 (550 nm)
Model PSAPjModel 3563g
Fan et al. (2010)
Lanzhou (urban)
Winter 2001, 2002
–
226 (550 nm)
–
–
Model 3563d
Zhang et al. (2004)
Panyu (urban)
Spring and winter, 2008
84.03 and 188.8 (532 nm)
–
–
–
AE-31a
Wu et al. (2013)
Dongguan (suburban)
Spring and winter, 2008
47.1 and 95.53 (532 nm)
–
–
–
AE-31a
Wu et al. (2013)
Maofengshan (rural)
Spring and winter, 2008
26.45 and 28.77 (532 nm)
–
–
–
AE-31a
Wu et al. (2013)
Yongxing Island
Spring and winter, 2008
7.21 and 8.37 (532 nm)
–
–
–
AE-31a
Wu et al. (2013)
a Seven-channel Aethalometer
(model AE-31, Magee Scientific, USA).
b Three-wavelength-integrating Nephelometer (Model Aurora 3000,
Australia). c Aethalometer AE16.
d Nephelometer M9003. e Integrating
Nephelometer (Model Aurora 1000). f Integrating
Nephelometer (Model Aurora 1000G). g Integrating
Nephelometer (Model 3563, TSI, USA). h Multi-angle
Absorption Photometer (MAAP, Thermo, Inc., Waltham, MA, USA, Model
5012). i Photo acoustic Soot Spectrometer (PASS 1, DMT,
USA). j Particle/Soot Absorption Photometer.
As suggested by Zhuang et al. (2014b), high BC loadings in early June 2012
were observed at the site in Nanjing when the air masses were from a
north-westerly direction, in which serious biomass burning was detected.
Therefore, the aerosol optical properties are further analysed by their
origins in both JJA and DJF (Fig 5c, d). In JJA, seasonal mean AAC, SC, Bsp,
SSA, ASP, AAE and SAE are about 19.65, 340.87 and 36.99 Mm-1, 0.93,
0.64, 1.49 and 1.34, respectively (Table 2). The dominant air masses are from
local areas (cluster 1 in Fig. 5a) and the East China Sea
(passing through urban agglomeration regions (cluster 2) and
less-developed regions (cluster 3) of the Yangtze River Delta YRD),
accounting for 90 % of the total characteristics of the aerosol optical
properties in urban Nanjing. All the values of the properties in the first
three clusters are closer to their season means. Aerosol absorption and
scattering coefficients from local emissions are larger than those in the
other clusters. Although air masses in cluster 2 and cluster 3 come from the
oceans and have the same level of relative humidity (RH), differences still
exist between the clusters. The air masses have to cross the urban
agglomeration (from Shanghai to Nanjing) of YRD when they arrive in Nanjing
in cluster 2 but past less-developed regions (north Jiangsu Province) in
cluster 3. In YRD, emissions of the aerosols and trace gases are much
stronger in urban agglomeration regions than those in other areas as
suggested in Zhang et al. (2009) and Zhuang et al. (2013b). Therefore, AAC
and SC in cluster 2 are larger than in cluster 3 to some extent (Fig. 5a, c).
Aerosols from these two clusters are more scattering than the local ones.
There are two clusters (cluster 4 and 5 in Fig. 5a) from the remote areas in
JJA. Aerosol loadings are relatively small when the air masses come from
these two clusters. The size of the aerosols is finer (larger AAE in cluster
5 and SAE in cluster 4 and 5 in Fig. 5c). ASP varying with the clusters
coincides with RH varying with the clusters (Fig. 5c), implying that RH might
influence ASP significantly. In DJF, seasonal mean AAC, SC, Bsp, SSA, ASP,
AAE and SAE are about 37.96, 385.14 and 54.79 Mm-1, 0.89, 0.54, 1.70
and 1.24, respectively (Table 2). Similarly to JJA, the aerosol absorption
and scattering coefficients are the largest, of which AAC, SC and Bsp are
about 1.3 times their seasonal means (Fig. 5d), when the air masses are local
or from the regions (cluster 1 in Fig. 5b) near Nanjing in DJF. AAC, SC, Bsp,
SSA and ASP are small but AAE and SAE are large if air masses are from remote
areas. Aerosols are the smallest, most absorbing and finest when the air
masses are from near Lake Baikal. ASP varying with the clusters also
coincides with RH varying with the clusters in this season (Fig. 5d), further
implying the effect of RH on ASP.
Substantial studies on aerosol optical properties have been carried out in
China from monthly to annual scales. Table 4 lists some annual and seasonal
statistics of measured surface aerosol optical properties from literature.
Annual and seasonal means listed in the table are comparable to some extent,
although the observational periods and instruments are different. It suggests
that AACs and SCs in urban areas are much higher than those in rural and
remote areas. In Beijing (the centre of the Beijing–Tianjin–Hebei region),
annual mean AAC and SC were 56 and 288 Mm-1 in urban site during the
period from 2005 to 2006 (He et al., 2009), which were much larger than the
ones (17.5 and 174.6 Mm-1) in rural area (Yan et al., 2008). In Chengdu
(Tao et al., 2014), Xi'an (Cao et al., 2012) and Wuhan (Gong et al., 2015),
which are in the centre from south-west to central China, the annual mean
scattering coefficients in these cities exceeded 450, 520 and 370 Mm-1,
respectively. In the Pearl River Delta (PRD) region, seasonal mean AAC at
532 nm was about 84 and 188 Mm-1 at an urban site (Panyu), about 47
and 95 Mm-1 at a suburban site (Dongguan), about 26 and 28 Mm-1
at a rural site, and only 7.21 and 8.37 Mm-1 at a remote site (Yongxing
Island), in spring and winter, respectively (Wu et al., 2013). Additionally,
aerosols in urban areas are more absorbing. The aerosol absorptions in urban
areas have stronger seasonality than those in rural areas (Table 4). Urban
aerosols in Nanjing on an annual scale are somewhat lower but more scattering
than in most cities in China. In addition to annual and seasonal means, there
are considerable studies on monthly mean aerosol optical properties (e.g.
Bergin et al., 2001; Xu et al., 2002, 2004; Li et al., 2007, 2015a, b;
Andreae et al., 2008). A few studies on the aerosol optical properties in
Nanjing have been carried out previously (Zhuang et al., 2014a, 2015; Yu et
al., 2016) based on observations. They were more focused on the columnar
aerosols (Zhuang et al., 2014a), single optical property (Zhuang et al.,
2015), or shortening observations (two months in Yu et al., 2016). Here,
substantial analyses of the key optical properties of the surface aerosol, to
a certain degree, fill the gaps in the studies of aerosols in Nanjing, even
in YRD.
Relationships between 550 nm AAC and SC (solids square in blue) and
between 550 nm Bsp and SC (solid cycles in gray) in spring (a),
summer (b), autumn (c) and winter (d).
Relationships between the 550 nm ASP and SC in different
RH levels.
Relationship among aerosol optical properties, relative humidity
and visibility
The relationships between SC and AAC, and SC and Bsp are presented by season
in Fig. 6. As shown in Figs. 3 and 4, these three types of coefficients have
similar diurnal and frequency distributions. The linear correlation
coefficient varies from 0.93 to 0.97 for SC and Bsp and from 0.66 to 0.87 for
SC and AAC in urban Nanjing. It is obvious that relations between SC and Bsp
are much better than those between SC and AAC in all seasons. The correlation
between AAC and SC becomes poorer in MAM (0.66) and JJA (0.78) because the
scattering aerosols are more affected by dust in spring and SC is more
affected by RH in summer. The linear correlation coefficients between SC and
AAC and between SC and Bsp in MAM at the site were a little smaller than in
suburban Nanjing (Yu et al., 2016) in the same season in 2011. The slope of
the fitting between Bsp and SC represents the levels of ASP. Analysis (not
shown) suggests that ASP has a significant anti-correlation with the ratio of
Bsp to SC (linear R=-0.98). Thus, a greater slope of curve represents a
smaller ASP, thus less forward-scattering of the aerosols.
Relationships between the monthly mean values of 491 nm SSA and
extinction Ångström exponent (EAE) at 491/863 nm (a) and
between the monthly mean values of the SSA difference (863–491 nm) and EAE
at 491/863 nm (b).
The correlations between ASP and SC under different RH conditions are
illustrated in Fig. 7, showing that ASP has a quasi-log-normal distribution
with SC, especially in lower RH conditions. ASP increases monotonically with
increasing SC in low-RH ranges (Fig. 7a, b, RH < 60 %) and ASP
mostly concentrates on small SC regions when RH is less than 40 %
(Fig. 7a), implying that fine particles dominate the most in low-RH
conditions, as also suggested by Andrews et al. (2006) and Babu et
al. (2012). The correlation between ASP and SC becomes poorer with increasing
RH (Fig. 7c), indicating that both fine and coarse aerosols might be equally
important to the total SC.
Seasonal variations of RH (a, %) and linear correlations
between AAE and RH (b, light blue, upper), between SAE and RH
(b, green, middle) and between ASP and RH (b, deep blue,
lower).
Relationships between SC and visibility (open cycles) and between EC
and visibility (solid cycles) in different RH levels in spring (a),
summer (b), autumn (c) and winter (d).
Relationships between SSA and visibility (solid cycles) and
between ASP and visibility (solid squares) in different RH and AAE levels in
spring (a), summer (b), autumn (c) and winter (d).
Relationships between surface EC at 550 nm and column AOD at 500 nm
in spring (a), summer (b), autumn (c) and winter (d).
Figure 8 shows the relationships between the SSA at 491 nm and extinction
Ångström exponent (EAE) at 491/863 nm (Fig. 8a) as well as between
SSA (863–491 nm) (short for dSSA) and EAE at 491/863 nm (Fig. 8b).
Overall, SSA or dSSA to a certain degree have an anti-correlation with EAE in
the urban area of Nanjing, especially the latter. Linear correlation
coefficient is about -0.13 between SSA and EAE and about -0.75 between
dSSA and EAE. Relationships between the SSA (or dSSA) and EAE to some extent
reflect the aerosol types and sources as indicated by Russell et al. (2014),
who proposed a method to identify the aerosol types based on the columnar
aerosol optical properties (including SSA, EAE and the real refractive index)
from the Aerosol Robotic Network (AERONET) retrievals. They suggested the
following.
The polluted dust aerosol had smaller EAE (near 1.0) and SSA ranged from
0.85 to 0.95.
The urban aerosols had larger EAE values (around 1.4) and SSA
ranges (0.86–1.0) compared with the dust aerosols.
The biomass
burning aerosol (dark type) had the largest EAE (exceeding 1.5) but smaller
SSA (about 0.85).
If there were two kind of aerosols having
nearly identical coordinates in SSA and EAE, further information (such as the
real refractive index) should be used (Russell et al., 2014). Based on this
method, the figure further implies that, in addition to local emissions,
aerosols in the urban area of Nanjing might also be affected substantially by the
long-distance transported dust (or polluted dust) in spring and be influenced
to some extent by biomass burning in autumn.
Atmospheric humidity has significant influences on the growth of particulate
matter, subsequently affecting the sizes and absorbing/scattering abilities
of the aerosols. As shown in Fig. 7a, c, high levels of SC are likely found
in high RH ranges. Seasonal mean RH is largest in summer but lowest in winter
(Fig. 9a). In summer, both trace gases and particulate matters have lower
emission rates as suggested by Zhang et al. (2009). Furthermore, PBL height
and precipitation mostly have larger values in this season than in other
seasons. Thus, these three factors would result in smaller aerosol loadings
in summer (such as Bsp is the smallest in this season). However, contrary to
Bsp, SC in summer is larger than in spring and autumn, which might mainly
result from the effects of high RH (Fig. 1c, d), although gas-to-particle
transformation also contribute to SC to a certain degree in this season.
Zhang et al. (2015) indicated that SC and Bsp in YRD would increase by 50 and
25 % as the RH increased from 40 to 85 % and the increment would
become larger if there were considerable amounts of nitrate in fine
particles. Nitrate in the urban area of Nanjing accounts for more than
20 % (as much as sulfate) of the total PM2.1 (Zhuang et al., 2014a).
RH might also affect the size of the aerosols. The smallest AAE in JJA always
corresponds to the highest RH, and vice versa (Figs. 1b and 9a). These
results are consistent with Zhuang et al. (2014a), in which characteristics
of columnar aerosol optical properties were investigated. Figure 9b further
shows that AAE and SAE decrease monotonically with increasing RH. The
correlation between ASP and RH is opposite to that between aerosol
Ångström exponent and RH. The linear correlation coefficients are
-0.36, -0.15 and 0.6 between AAE and RH, SAE and RH, and ASP and RH,
respectively, in the urban area of Nanjing. ASP and RH are highly correlated
with each other, which is also reflected in Figs. 2f, 5c, d and 9a, implying
that RH might have considerable influence on the aerosol forward-scattering
coefficient, hence on SC. These results could be used to correct the aerosol
optical parameters in numerical models for estimating the aerosol radiative
forcing in east China as suggested by Andrews et al. (2006), in the hope of
reducing uncertainties in such estimations.
High levels of aerosol loadings would directly affect the visibility (VIS),
which is one of the factors to be concerned about in current air quality
forecasting in China. The forecast accuracy of visibility or haze pollution
would be increased significantly if the effects of aerosols on visibility can
be figured out. Instead of the loadings of the particulate matter, the
aerosol optical properties here are used when investigating the aerosol
effects on VIS.
Figure 10 shows the relations between extinction coefficient (EC) and VIS and
between SC and VIS by season under different RH levels. Atmospheric VIS is
found to decrease exponentially with increasing EC or SC in all seasons. The
lapse rate of VIS with EC or SC is much larger in spring and summer than in
autumn and winter. The lower VIS always appears at higher RH ranges and vice
versa. In small VIS regions (such as < 4 km), VIS values are much
smaller in JJA than those in the other seasons under the same SC level,
implying the strong effects of RH on VIS. The effect of AAC on VIS has
substantial seasonality and it is strong in SON but weak in MAM and JJA as
illustrated in the fitting lines in the figure. A study on the effects of PM
on VIS might be more reasonable if it used the aerosol optical properties
rather than its mass concentrations. The linear correlation coefficient
between EC and VIS varies from -0.69 (in JJA) to -0.87 (in DJF), and
between SC and VIS, it varies from -0.71 (in JJA) to -0.87 (in DJF) in
the urban area of Nanjing.
In addition to the SC or EC, the aerosol SSA and ASP also have good
relationships with VIS as shown in Fig. 11, in which the effects of RH and
SAE are also included (larger markers represent smaller SAE, but larger size
of the aerosols). The aerosols would become coarser, less absorbing and more
forward scattering to some extent with increasing RH, which subsequently
further exacerbates the deterioration of visibility in all seasons. The
linear correlation coefficients vary from -0.48 (in JJA) to -0.73 (in
SON) between SSA and VIS and -0.47 (in JJA) to -0.80 (in MAM) between ASP
and VIS in urban Nanjing. These results additionally illustrate that the
scattering aerosols are still key factors affecting the atmospheric
visibility, although the absorbing aerosols might have considerable influence
on VIS in some seasons (Fig. 10c). The results in this study further indicate
that effects of aerosols on air quality are complex.
A comparison between surface aerosol extinction coefficient and columnar AOD
is performed (Fig. 12). Differences exist between EC and AOD, although they
are well correlated with each other in each season. AOD to some extent is
less affected by the development of boundary layer and more affected by the
transport of aerosols compared to EC at the surface. The seasonal mean EC is
large, both in JJA and in DJF, while the largest AOD is only found in JJA,
which is possibly related to higher boundary layer height in JJA. A lower
boundary layer would lead to more aerosol accumulation at the surface thus
result in its smaller column burden. These differences (high surface aerosol
loadings but low AOD) have also been simulated by a regional climate
chemistry model in Zhuang et al. (2011, 2013). Overall, high AOD level
corresponds to large EC value in each season, implying that aerosols in the
upper layers mostly come from surface emissions in urban Nanjing. In some
cases, long-distance transport of aerosols might contribute significantly to
the AOD as shown in Fig. 12a, in which AOD exceeds 2; meanwhile EC is found
to appear in low-value ranges. The slope of the linear fitting is larger in
JJA (about 0.0016) than in the other seasons (all about 0.001), indicating
that for a given value of EC, AOD would be higher in JJA, possibly because of
higher humidity in summer. The columnar water vapour in summer is about 2 to
5 times of that in the other seasons.
Conclusions
In this study, the near-surface aerosol optical properties, including aerosol
scattering (SC), back-scattering (Bsp), absorption (AAC) and extinction (EC)
coefficients, single-scattering albedo (SSA), scattering (SAE) and absorbing
(AAE) Ångström exponent, as well as asymmetry parameter (ASP), are
investigated based on the measurements with the seven-channel Aethalometer
(model AE-31, Magee Scientific, USA) and three-wavelength-integrating
Nephelometer (Aurora 3000, Australia) in the urban area of Nanjing.
In the urban area of Nanjing, the annual mean EC, SSA and ASP at 550 nm are
381.958 Mm-1, 0.901, 0.571, respectively. SC, which accounts for about
90 % of EC, is about 1 order of magnitude larger than AAC, implying that
EC to a great degree has similar temporal variation and frequency
distribution to SC. Absorbing aerosol is finer than the scattering one. AAE
at 470/660 nm is about 1.58, about 0.2 larger than SAE. All of the above
have substantially seasonal and diurnal variations. Both the aerosol
absorption and scattering coefficients have the largest values in winter due
to the higher emissions. However, SC also has higher values in summer and
spring likely due to higher relative humidity (RH), efficiency of
gas-to-particle transformation in summer and the effect of dust in spring,
respectively. High RH in summer results in the lowest AAE and largest ASP
being found and it also leads to a relatively smaller SAE, although a large
number of fine scattering aerosols could be produced through intensive
gas-to-particle transformation in this season. Seasonality of SSA is
co-determined by AAC and SC, showing the largest value in summer and lowest
value in autumn. AAC, SC, Bsp and EC have more substantial diurnal variations
than SSA, AAE, SAE and ASP. Because of traffic emissions, AACs are high at
morning rush hours (around 09:00 and 21:00 LT) but low in the afternoon when
the boundary layer is being well developed. SC and Bsp usually peak in the
early morning before sunrise (1–2 h earlier than AACs) and reach
the bottom in the afternoon. High levels of SC and Bsp are mostly caused by
accumulation of air pollution at night-time from midnight to sunrise. The
diurnal variation of SSA is also dependent on AAC and SC. SSA is large after
midnight and noon. AAE and SAE in the daytime are slightly larger than after
midnight because both absorbing and scattering aerosols are fresher in the
daytime but more aged before sunrise. ASP is related to the size of the
aerosols and its diurnal variation is the opposite of SAEs but similar to
Bsps.
The seasonal and diurnal observations of the aerosol optical properties are
of great importance to the modelling community. In addition to the aerosol
emission rates, compositions, mixing states and profiles, uncertainties of
the aerosol seasonality and diurnal variations might also lead to large
biases when investigating the aerosol radiative forcing and climate effects.
Xu et al. (2016) showed that the aerosol direct radiative forcing would be
underestimated both at the TOA and surface by 2.0 and 38.8 W m-2 if
the diurnal variation were excluded. Large biases of the aerosol forcing
would subsequently result in substantial uncertainties of the climate
responses to the aerosol. Analyses of the seasonal and diurnal variations of
the aerosol optical properties in this study to some extent are valuable to
the modelling-based research on the aerosol climate effects.
Frequency analysis indicates that almost all of the aerosol optical
properties follow a unimodal pattern in the urban area of Nanjing. The ranges
around their averages, with different widths, account for more than 60 %
of the total samplings. Frequency distributions of the aerosol optical
properties also have substantial seasonality. The frequency peak of a
property would be more concentrated among lower/higher ranges if the seasonal
mean is smaller/larger. Back trajectory analysis suggests that aerosols in
Nanjing are mainly from the local and regional emissions around YRD in
summer, while sources include both local emissions and transport from central
and north China in winter. In JJA, aerosols are more scattering when air
masses come from the East China Sea and finer if air masses come from remote
areas. In DJF, AAC, SC, Bsp, SSA and ASP are low while AAE and SAE are high
in urban Nanjing under the conditions of air masses being transported from
remote areas. ASP variation with the clusters is consistent with RH in both
JJA and DJF.
The correlation between SC and Bsp is much better than between SC and AAC in
all seasons. In spring, these relationships are a little weaker than those in
suburban Nanjing. ASP has a quasi-log-normal distribution with SC under RH
conditions being lower than 60 % and increasing monotonically with
increasing SC. It would be mostly concentrated at small SC regions when RH is
less than 40 % because finer particles dominate under low-RH conditions.
The correlation between ASP and SC becomes weaker with increasing RH,
indicating that both fine and coarse aerosols might be equally important to
the total SC in high RH conditions. Atmospheric humidity can significantly
modulate aerosol optical properties. Due to the effects of RH in summer, the
aerosol would become coarser and its forward-scattering efficiency would be
stronger with increasing in RH. The linear correlation coefficients are
-0.36, -0.15 and 0.6 between AAE and RH, SAE and RH, and ASP and RH,
respectively, in the urban area of Nanjing. Comparisons also indicate that
seasonal variation of surface aerosol EC (high in JJA and DJF) is different
from its columnar optical depth (AOD, high in JJA and low in DJF), even
though they are closely correlated to each other within each season. Overall,
high AOD level corresponding to a large EC value in each season implies that
aerosols in upper layers are mostly from surface emissions. AOD would be
higher in JJA than in other seasons in a condition with fixed EC, possibly
due to the effects of high humidity.
Overall, the scattering aerosols are still the key factor in affecting the
atmospheric visibility (VIS), although the absorbing aerosol has considerable
contributions in some seasons. The linear correlation coefficient between EC
and VIS varies from -0.69 to -0.87, close to those between SC and VIS.
VIS is found to decrease exponentially with increasing EC or SC in all
seasons. Its lapse rate along with EC or SC is much larger in spring and
summer than in autumn and winter. In small VIS regions (i.e.
VIS < 4 km), VIS values are much smaller in JJA than in other
seasons if the SC levels are the same, further indicating the strong effect
of RH on VIS. The aerosol SSA and ASP could also affect VIS. Large SSA and
ASP might further exacerbate the deterioration of visibility. The linear
correlation coefficients between seasonal SSA and VIS varies from -0.48 to
-0.73 and from -0.47 to -0.80 between ASP and VIS in the urban area of
Nanjing.