Introduction
Atmospheric aerosol strongly affects the earth's radiative balance by
scattering and absorbing incoming solar radiation, which, however, is still a
large source of uncertainties in global climate forcing assessment (Stocker et al.,
2013). The aerosol optical properties are responsible for the direct aerosol
radiative forcing, depending on aerosol chemical composition and
microphysical properties. Relative to another major component of radiative
forcing, greenhouse gases, the shorter atmospheric lifetime of aerosols
leads to more localized effects and regional differences in aerosol optical
properties. Due to the spatial and temporal differences of aerosol optical
properties caused by the complex distribution of tropospheric aerosols,
field monitoring of aerosol optical properties in different regions around
the world is critical for exploring the variations in aerosol radiative
forcing. Among the major aerosol radiative forcing drivers, mineral dust,
sulfate, nitrate, and organic carbon generally have negative radiative
forcing. Contrarily, absorbing aerosols, like black carbon (BC), can
strongly absorb visible light, enhancing the warming effect of the atmosphere
(Jacobson, 2001; Babu and Moorthy, 2001; Ding et al., 2016).
Light absorption and scattering of different kinds of aerosols have distinct
wavelength dependencies that are approximately proportional to λ-AAE or λ-SAE, respectively, where λ is the
wavelength and AAE and SAE are the Ångström exponents of absorption
and scattering, respectively. Hence, the wavelength dependency of aerosol
light scattering and absorption has been recognized as an efficient index to
distinguish aerosol types (e.g., Russell et al., 2010; Moosmüller and
Chakrabarty, 2011; Devi et al., 2016). For instance, BC can strongly absorb
light at all visible wavelengths, while other light-absorbing aerosols (some
organic aerosol, soil, and dust) absorb more blue light than red light
(Moosmüller et al., 2011; Bond et al., 2013; Ding et al., 2016).
Therefore, the absorption Ångström exponent (AAE) is often related
to the dominant absorbing aerosol type for a mixture of aerosols (Cazorla et
al., 2013). The AAE in externally mixed BC-dominated regions has been
reported to be around 1 (Hegg et al., 2002; Bond and
Bergstrom, 2006; Bond et al., 2013), while it is greater than 1 for some
organic aerosol from biomass smoke and mineral dust due to their diverse
light absorbing abilities at different wavelength ranges (Kirchstetter et
al., 2004; Russell et al., 2010; Valenzuela et al., 2015; Devi et al., 2016).
Moreover, studies have shown that AAE of BC has a large variability,
depending on the size of BC cores and coating thickness (e.g., Lack and
Cappa, 2010). For non-coated BC with small diameters (e.g., 10 nm), AAE is
close to 1, but large BC cores can have AA E less than 1 (e.g., Gyawali et
al., 2009; Lack and Cappa, 2010). For coated BC particles, laboratory
measurements of Schnaiter et al. (2005) reported that thickly coated BC by
α-pinene plus ozone SOA could decrease the AAE of up to 0.8. Coating of BC
by purely scattering material may also result in AAE up to about 1.8
(Gyawali et al., 2009; Lack and Cappa, 2010). The scattering
Ångström exponent (SAE) is often regarded as a qualitative indicator
of the dominating particle size, that is, large values (SAE > 2)
indicate a large contribution of small particles and small values (SAE < 1) a large contribution of large particles. For instance, Delene
and Ogren (2002) reported that the influence of large sea-salt particles led
to lower SAE. However, this interpretation is not quite unambiguous, as
was shown, for example, by Schuster et al. (2006) and Virkkula et al. (2011). The
SSA is the ratio of scattering to extinction coefficient, i.e., the sum of
scattering and absorption coefficients. It equals 1 for purely scattering
aerosol and is clearly lower, approximately 0.3, for pure BC particles (e.g.,
Schnaiter et al., 2003; Mikhailov et al., 2006). SSA varies significantly for
smoke of different origin and age and correlates with the presence of BC in
the combustion products (e.g., Dubovik et al., 2002).
There are several ways to assess the sources of aerosols, for instance by
comparing observed particle concentrations with other tracers. CO is a by-product of the incomplete oxidation. Due to its long lifetime (about 1–2 months) in the troposphere, CO can act as a tracer of anthropogenic emissions (Jennings et al., 1996). A strong positive
correlation between BC and CO has been found in previous studies concerning
source identifications (Pan et al., 2011; Jennings et al., 1996). The BC / CO
ratio is considered a good indicator to determine BC emission and to
recognize source characteristics. Also, the emission ratio of BC and CO
varies significantly from different sources, making it an effective index
for validating emission inventories (Girach et al., 2014). The SO2 / BC
ratio can be also used for assessing the sources since both BC and SO2
are emitted in fossil fuel combustion (Bond et al., 2013).
The Pearl River Delta (PRD) region in southern China has undergone fast
industrialization with increasing emissions of particulate and gaseous
pollutants (Wang et al., 2003). In particular, the growing crisis of high
particulate matter (PM) levels in the Pearl River Delta (PRD) region is of
great concern due to its adverse effects on regional and continental
atmospheric environment (Wang et al., 2009; Ding et al., 2013; Lam et al.,
2005; Liu and Chan, 2002; Verma et al., 2010). Hong Kong is a typical
coastal city in the PRD region. Under the influence of the East Asian
monsoon, this region is controlled by the southerly winds bringing marine
inflow from the South China Sea in summer, while in winter it is downwind
from the North China Plains and East China and is dominated by the continental
outflow (Ding et al., 2013; Lam et al., 2001; Zhou et al., 2013). Thus, it
is an ideal place for exploring the characteristics of optical properties
for continental and marine aerosols.
There have been studies concerning aerosol optical properties and
light-absorbing aerosols in the PRD region. Man and Shih (2001) did field
observations of light-scattering and absorption coefficients from September
1997 to April 1999 in Hong Kong. Cheng et al. (2006a) investigated the
seasonal variation patterns of BC concentrations in Hong Kong as well as the
potential sources of BC by continuous measurement from June 2004 to May 2005,
using a model AE-42 Aethalometer (Magee Scientific Inc., Berkeley, CA).
Cheng et al. (2008) presented the 1-month record of aerosol optical
measurements with related chemical apportionment at Xinken in the PRD region and
reported a relatively low SSA at this polluted rural site. Mixing states of
light-absorbing aerosols were also investigated using optical closure
experiments during the campaign (Cheng et al., 2006b; Tan et al., 2016).
However, long-term observations of several key aerosol optical properties
(including wavelength dependencies of light scattering and absorption
SSA), and studies on the relationships between optical properties and particle
size as well as their quantitative linkage to multi-scale transport) have
been limited in Hong Kong over the past decade.
In this study, we aim at demonstrating the temporal variations of aerosol
optical properties at a coastal station in Hong Kong and investigating the
relationships between aerosol optical properties and size distributions
based on field observations. Source analyses are conducted by comparing
observed BC–CO ratios as well as the SO2–BC ratios.
Transport pattern and origins of aerosols were quantitatively studied based
on Lagrangian particle dispersion modeling (LPDM). Characteristics of local aerosol optical properties dominated by
different aerosol source regions were also compared and illustrated.
Methodology
Sampling site
The Hok Tsui (HT) monitoring station is situated on the southeast tip of
Hong Kong Island facing the South China Sea (22.22∘ N, 114.25∘ E 60 m above sea level) with an almost vertical
drop to the sea. This station has a view of the sea for over 180∘
from the northeast to the southwest and is 20 km away from the urban area of Hong
Kong on the northwest. Owing to the characteristics of the location
mentioned above, it is an ideal background monitoring site for identifying
both the long-range transport of polluted continental/marine air mass caused
by anthropogenic emissions and relatively clean marine air mass in different
seasons. For more details about the HT site, please refer to Wang et al. (2009) and papers cited therein.
Absorption measurement
Light-absorption measurement was conducted using a model AE-31 Aethalometer
(Magee Scientific Company, Berkeley, California, USA) from 1 February 2012 to
30 September 2013 and 1 March 2014 to 28 February 2015. Sample air was
obtained through a stainless steel inlet with a PM2.5 cut-off,
protected with a rain cap. Prior to entering the instrument, sample air was
heated to ensure a moderate relative humidity. The sample inlet was
approximately 1.5 m above the roof of the measurement station building,
which was about 4 m above the ground. The sample flow provided by the
internal pump was set to 4.0 L min-1. The AE-31 Aethalometer performs continuous
measurements of BC concentrations at seven wavelengths (370, 470, 520, 590, 660, 880 and 950 nm) with a time resolution of 5 min. In
this work, without specific notes, BC concentrations refer to the
aethalometer data measured at λ= 880 nm. Sample flow on the
Aethalometer display was checked once a week to ensure the flow was within
0.2 L min-1 of previous week and flow calibration was conducted once a month
using an independent flowmeter. The inlet cyclone was cleaned every month.
In order to correct the systematic errors of filter-based absorption
technique, the light absorption coefficients (σap) at all
wavelengths were calculated by using the method presented by Collaud Coen et al. (2010), where the Cref factor was set to be 4.26 according to the
value from Cabauw (CAB) station reported in the same paper. CAB station is
located near populated and industrialized areas, which was to some extent
similar to Hok Tsui station (near most of cities in the Pearl River Delta
region). The reported average Cref value at CAB was
4.26 ± 0.11, and it varies from 2.60 to 4.75 (Collaud Coen et al., 2010). There was
no
MAAP (Multi-Angle Absorption Photometer) or any other reference absorption instrument available, so determining
Cref at Hok Tsui was not possible and the published mean Cref at
CAB station was used. However, to present an upper estimate for σap, the Cref , calculated as 3.51 for the clean marine site of
Mace Head (MHD); Collaud Coen et al., 2010), was also used and the respective
average σap and SSA are presented in the discussions. Since the
Cref is responsible for the largest uncertainty in the calculation of
σap (Collaud Coen et al., 2010) we did not make further
uncertain analyses by using the uncertainties related to the other factors
within the algorithm. Absorption coefficients were presented under standard
temperature and pressure (STP; 273.15 K, 1013 hPa). Measured BC
concentrations were corrected following the algorithm presented by Virkkula
et al. (2007).
Scattering measurement
Light-scattering coefficients (σsp) at wavelength of 450,
550 and 700 nm were measured using an integrating nephelometer (model
3563, TSI Inc, St. Paul, MN, USA). The averaging time was set to 5 min.
Calibration was conducted once a month using CO2 and filtered air as
described in the user manual. An internal heater was used to maintain a
moderate relative humidity during measurement. Raw σsp data
were corrected for truncation errors following the method from Anderson and
Ogren, (1998), where the scattering coefficients were determined by
calculating the Ångström exponents from uncorrected scattering
coefficients and the correction factors with no-cut inlet. Scattering
coefficients were then corrected to STP using pressure and temperature
readings from the nephelometer.
The σsp and σap data were used for
calculating SSA as SSA =σsp/(σsp+σap). The
nephelometer took its sample from a total suspended particle inlet (TSP) but
the Aethalometer through a PM2.5 inlet, so it may seem somewhat
uncertain which size range the SSA represents. However, BC is the most
important light-absorbing constituent in aerosol particles and it is well
known that it is in the submicron size range. In larger particles there
might be some light-absorbing dust particles, but their contribution at this
site can be considered to be negligible. Therefore it is reasonable to claim
that the absorption coefficients derived from the aethalometer data
represent absorption in the full TSP size range even if there was a
PM2.5 inlet for the Aethalometer. Since the scattering coefficients
were measured after a TSP inlet, it is also reasonable to say that the SSA
represents that of TSP.
Particle size measurement and the use of the size distributions
An Ultrafine Particle Monitor (UFPM, model 3031, TSI Inc.) was used to
measure the number size distribution of particles in the size range of 20 to
800 nm with six size bins of mobility diameter: 20–30,
30–50, 50–70, 70–100,
100–200, and 200–800 nm. The operating
principle of a UFPM is based on the diffusion charging of particles,
followed by size segregation within a differential mobility analyzer (DMA)
and the detection of the aerosol via a sensitive electrometer. The UFPM
was equipped with a model 3031200 environmental sampling system. The sample
inlet was placed 2.0 m above the ground. Ambient air was continuously drawn
through a size selective PM10 inlet at a standard flow rate of 16.7 L min-1. The sample then passed through a PM1 cyclone to remove larger
particles. The main sample stream was subsampled into the UFP at a flow rate
of 5 L min-1. A Nafion dryer was installed upstream of the UFP to ensure
proper conditioning of the aerosol and to minimize effects due to water
vapor. The remaining 11.7 L min-1 of make-up air, drawn through a vacuum pump
and exhausted, was routed through the Nafion dryer as purge air. The
averaging time was set to 15 min.
The total mass concentrations of particles with a mobility diameter less than
800 nm were calculated using the following equation:
PM1=∑i=1nNiρiπ6Dp,i3,
where Ni is the number concentration in each size bin, ρi
is the density of particles assumed to be 1.7 g cm-3, and Dp,i is
the geometric mean of the upper and lower limit diameter in each size bin.
For spherical particles the aerodynamic diameter
(Da) is calculated from the mobility diameter
(Dm) as Da = Dmρp/ρ0, where ρp is the density of the particle and
ρoi̇s the density of water. For Dm = 0.8 µm and ρp= 1.7 g cm-3, this yields
Da = 1.0 µm. In the results, therefore, the
mass concentration calculated from the number size distributions was denoted as PM1.
The size distributions were used for calculating scattering coefficients
from the following equation:
σsp(λ)=∫Qsp(λ,Dp,m)πDp24n(Dp)dDp,
where the scattering efficiencies (Qsp) were calculated by using the BHMIE
code (Bohren and Huffman, 1983). We assumed that the Dp of each
particle is equal to the geometric mean of the upper and lower limit
diameter in its size bin for modeling, and the aerosol is ammonium sulfate
with the refractive index m = mr= 1.52 (Chamaillard et al.,
2006). The refractive index used in the modeling could, in principle, be
varied and iterated until the measured and modeled scattering coefficients
match, as was done, for example, by Virkkula et al. (2011). However, due to the
different size ranges and low number of size bins of the size distributions,
this kind of iteration is not reasonable for the data in this work.
Both the PM1 and the σsp calculated from the number size
distributions have uncertainties due to the uncertainties of the UFPM. The first is the wide range of particle diameters within the size
bins and the use of the geometric mean of the bin limits for the whole bin.
This yields the highest uncertainty for the bin that measures particles in
the size range 200–800 nm that can easily be calculated assuming all
particles in that size range were 800 nm instead of the geometric mean 400 nm. This calculation is theoretical in the real atmosphere, however, and
yields unrealistically high uncertainties and will not be analyzed further.
Another source of uncertainty is related to the instrument itself. Hillemann
et al. (2014) found that the number of concentrations measured by UFPM are
typically within a range of ±20 % from the reference values
measured with a scanning mobility particle sizer (SMPS). Also, Gómez-Moreno et al. (2015) compared the
UFPM with an SMPS and found that the size distributions measured by UFPM and SMPS
were similar in the sense that the peak concentrations were observed at the
same size. In the same study it was also observed that in the size channels
corresponding to particle diameters < 100 nm the UFP overestimated
the number concentrations and in the two largest channels it underestimated
the number concentrations. These are the channels that measure the particles
that have the highest mass and that scatter light most efficiently. It may
therefore be argued that both the PM1 and the modeled σsp
are underestimated.
It was mentioned above that the PM1 concentrations were calculated by
using the density of 1.7 g cm-3, which deserves reasoning. The densities
of major inorganic aerosol compounds such as ammonium sulfate and sodium
chloride are 1.76 and 2.165 g cm-3 (e.g., Tang, 1996). Zhang et al. (2008) estimated that the density of
sulfuric acid-coated soot is 1.7 g cm-3. Ambient aerosols contain many unknown compounds (such as organics) and also some water (even after drying to
RH < 50 %). Therefore, densities of ambient aerosols can vary within a certain range based on their compositions.
Densities of real atmospheric aerosols have been measured in several
campaigns. Quinn et al. (2001) determined aerosol densities on a cruise
across the Atlantic Ocean. The density of submicron aerosols, averaged from
observations at very different regions, was 1.73 ± 0.24 g cm-3.
Pitz et al. (2003) determined the mean apparent particle density of 1.6 ± 0.5 g cm-3 for urban aerosol. Saarikoski et al. (2005) found
that at a boreal forest site the average density was 1.66 ± 0.13 g cm-3. Based on these publications it is reasonable to use the density
value of 1.7 g cm-3 for the estimation of aerosol mass concentration from the
number size distributions of particles smaller than 800 nm of the mobility
diameter. It has to be noted. However, there is uncertanty, in the aerosol mass concentration since
the density was not measured at this site.
(a) Map showing the location of Hok Tsui (HT) monitoring station
with emission inventory in Asia. (b) Locations of monitoring stations
mentioned in this paper and (c) wind rose plot at WGL in Hong Kong.
Supporting measurements
CO data were used to help analyze aerosol sources since they typically
originate from incomplete combustion like BC. Hourly mixing ratios of
carbon monoxide were measured with a nondispersive infrared absorption
instrument (Teledyne API model 300) at Hok Tsui station.
In addition to the measurements at the HT station, the following supporting
data measured at two nearby sites were used in the analyses. SO2 is
the precursor of sulfate, the most important light-scattering constituent
and it is also one of the major pollutants of ship emission. PM2.5
concentrations can be used for a semi-quantitative quality check of the
aerosol mass concentrations calculated from the size distributions. Hourly
SO2 and PM2.5 concentrations at Eastern Station (about 7 km away
from HT station, the location shown in Fig. 1b), were downloaded from the
open-access dataset from the website of the Hong Kong Environmental Protection
Department (HKEPD).
The hourly averaged meteorological parameters, including air temperature,
relative humidity (RH), wind direction, wind speed, and precipitation were
obtained from the dataset in the HKEPD in which meteorological data from the
nearest meteorological station (Waglan Island, WGL) were used for analyzing this paper. The location of the WGL station is shown in
Fig. 1b.
Backward Lagrangian particle dispersion modeling (LPDM)
Transport and dispersion simulations were conducted using a Lagrangian
particle dispersion modeling (LPDM) following the method developed by Ding
et al., (2013). LPDM was conducted by using the Hybrid Single-Particle
Lagrangian Integrated Trajectory (HYSPLIT) model, developed in the Air
Resource Laboratory (ARL) of the US National Oceanic and Atmospheric
Administration (Draxler and Hess, 1998; Stein et al., 2015). In each simulation,
particles were released at a height of 100 m above the ground level at the
site and went backward in time for a 7-day period. LPDM calculations were driven
with GDAS (Global Data Assimilation System) data (http://ready.arl.noaa.gov/HYSPLIT.php). Particle positions were calculated
in each hour and gridded concentrations were in a spatial resolution of
0.01∘ latitude by 0.01∘ longitude.
Knowing the transport characteristics of air masses, the next step was to
explore the source profile of light absorbing particles affecting the
regional aerosol optical properties in Hong Kong. Since BC is the most
significant light-absorbing constituent of aerosols, the PSC of BC to
observed air masses was calculated using the MIX Asian emission inventory (Li et
al., 2017) together with LPDM results. The MIX emission inventory has a
horizontal grid resolution of 0.25∘ × 0.25∘ in
longitude and latitude and it is considered the anthropogenic emissions from
transportation, residential, industry, and power generation in continental
area. In each grid cell, the emission rate was multiplied by the footprint
retroplume, and the sum of this potential source contribution of all grid
cells can provide the total BC concentration resulting from emissions during
a certain period (Ding et al., 2013). The maps of averaged source
contribution profile of BC in different seasons were calculated covering
70–140∘ in longitude and 0–50∘
in latitude. This method to calculate the PSC of target pollutants has been
adopted in a previous study by Ding et al. (2013). The major advantage of
this method is that it captures the potential contribution of target
pollutants to the receptor due to the transport of air mass containing the
information of anthropogenic emissions.
Statistical summary of data measured at Hok Tsui station.
Scattering coefficients (σsp) and absorption coefficients
(σap) at λ= 550 nm are corrected to STP (1013 mbar,
273.15 K), Ångström exponents of scattering and absorption (SAE,
AAE), single-scattering albedo (SSA), total particle number concentration, (Ntotal) geometric mean diameter (GMD), and PM1).
Percentile
AVG ± SD
5
25
50
75
95
σap, 550 nm (Mm-1)
8.3 ± 6.1
2.2
4.0
6.6
11.0
19.3
σsp, 550 nm (Mm-1)
151 ± 100
23
75
134
206
331
SSA (550 nm)
0.93 ± 0.05
0.84
0.92
0.94
0.96
0.98
BC (µg m-3)
1.4 ± 1.1
0.2
0.6
1.2
1.9
3.4
CO (ppbv)
272 ± 185
59
109
242
373
623
AAE
1.0 ± 0.2
0.5
0.9
1.1
1.2
1.4
SAE
1.4 ± 0.4
0.6
1.1
1.4
1.6
2.0
Ntotal (cm-3)
7790 ± 4300
2980
5060
6830
9410
15 710
GMD (nm)
67 ± 17
43
55
65
77
98
PM1 (µg m-3)
22 ± 19
3
10
18
29
55
In this study, the MIX emission inventory provided relatively high spatial
resolution of BC emission rates, considering its major anthropogenic sources
in China and nearby Asian countries. However, marine emission is not
included in the MIX database. To investigate the possible influence of
marine sources, like ship emissions, on the observed aerosol concentrations
at this coastal site, we used the observed aerosol concentrations together
with the LPDM footprint. We used the following concentration-weighted
equation to calculate the potential source contribution from each grid cell:
Ax(i,j)=∑t=1n(xt⋅Rt(i,j))∑t=1nRt(i,j),
where x is the selected optical property or other parameters, and we chose
σap, σsp and PM1 in this study. R
represents the retroplume with 3-day backward time, while t is the time
step and n is the total number of the time steps. The
interpretation of Eq. (3) is that it shows the average value of the
property x observed at the receiving site when air masses have come from
over grid cell i,j. The method is analogous to that presented by Stohl et al. (1996) and the concentration-weighted trajectory (CWT) methods reviewed
by Cheng et al. (2015). The major difference is that in the present approach, the footprints were used instead of single trajectories for each time step.
Summarization of aerosol light-scattering coefficients, absorption
coefficients and single-scattering albedo observed in this study and
reported in other studies.
Site
Period
σap
σsp
SSA
Instrumentation
References
Hok Tsui,
Feb 2012–Feb 2015
8.3 ± 6.1
151 ± 100
0.93 ± 0.05
AE31, Magee Scientific
This work
Hong Kong
Nephelometer, TSI, Inc.
(rural, coastal)
Cape D'Aguilar
Nov 1997–Feb 1998
25.72
64.77
PSAP, Radiance Research
Man and Shih (2001)
(Hok Tsui),
Mar–Apr 1998
15.79
38.65
Nephelometer,
Hong Kong
May–Aug 1998
6.03
8.71
Radiance Research
(rural, coastal)
Sep–Oct 1998
18.98
70.91
Nov 1998–Feb 1999
31.22
96.75
Xinken, PRD,
Oct–Nov 2004
70 ± 42
333 ± 137
0.83 ± 0.05
MAAP, Thermo, Inc.
Cheng et al. (2008)
China (non-urban,
Nephelometer, TSI, Inc.
regionally polluted)
Shangdianzi,
Sep 2003–Jan 2005
17.54 ± 13.44
174.6 ± 189.1
0.88 ± 0.05
AE31, Magee Scientific
Yan et al., (2008)
China (rural)
Nephelometer, EcoTech
Lin'an,
Nov 1999
23 ± 14
353 ± 202
0.93 ± 0.04
PSAP, Radiance Research
Xu et al. (2002)
China (rural)
Nephelometer, Radiance Research
Granada,
Dec 2005–Nov 2007
21 ± 10
60 ± 30
0.68 ± 0.07
MAAP, Thermo, Inc.
Lyamani et al., (2010)
Spain (urban)
Nephelometer, TSI, Inc.
Alomar station,
Jun–Aug 2008
0.40 ± 0.27
5.41 ± 3.55
0.91 ± 0.05
PSAP, Radiance Research
Mogo et al. (2012)
Norway (background,
Nephelometer, TSI, Inc.
coastal)
Comparison of mean concentration of BC with other studies.
Site
Environment
Period
Inlet
BC (µg m-3)
Instrumentation
References
Hok Tsui,
Rural, coastal
Feb 2012–Feb 2015
PM2.5
1.4 ± 1.1
AE31, Magee
This work
Hong Kong
Scientific
Cape D'Aguilar
Rural, coastal
Jun 2004–May 2005
PM2.5
2.4 ± 1.8
AE42, Magee
Cheng et al. (2006a)
(Hok Tsui),
Scientific
Hong Kong
Yongxing Island,
Oceanic rural,
May–Jun 2008
PM2.5
0.54 (rainy season)
Aethalometer,
Yu et al. (2013)
China
(South China Sea)
0.67 (dry season)
Magee Scientific
Maofengshan,
Rural, PRD
May–Jun 2008
PM10
2.62 (rainy season)
Aethalometer,
Yu et al. (2013)
China
2.88 (dry season)
Magee Scientific
Toulon, France
Semi-urban, coastal
Jun 2005–Oct 2006
PM2.5
0.95 (winter)
AE31,
Saha and Despiau (2009)
0.45 (summer)
Magee Scientific
Hyytiälä,
Boreal forest
Dec 2004–Dec 2008
PM2.5
0.32 ± 0.34
AE31,
Hyvarinen et al. (2011)
Finland
Magee Scientific
Voerde-Spellen,
Rural
Sep–Oct 1997
PM2.5
0.8 ± 0.3
AE-10 IM, G1V
Kuhlbusch et al. (2001)
Germany
Preila,
Rural, coastal
Mar–Apr 2002
PM2.5
0.84
AE40,
Andriejauskienė (2008)
Lithuania
Magee Scientific
Results and discussions
Aerosol optical properties and their relationships with particle
size
Overall results of aerosol optical properties and related
parameters
Table 1 shows a basic statistical summary of all measured parameters. The
light absorption coefficients at λ= 550 nm were interpolated
between the σap at 520 and 590 nm. The mean absorption and
scattering coefficients at λ= 550 nm during the whole measurement
period were 8.3 ± 6.1 and 151 ± 100 Mm-1,
respectively. As mentioned in the methods, the above-mentioned σap was calculated by using the Cref of CAB. If, instead, we use
the Cref of MHD, σap= 10.1 ± 6.1, which may be
considered as an upper estimate. Table 2 summarizes
the light-scattering and absorption coefficients and single-scattering
albedos observed in this study and in selected other studies using
comparable instruments (Man and Shih, 2001; Xu et al., 2002; Yan et al.,
2008). On average, the σap was lower than that measured at
Lin'an regional background station in the rural area of the Yangtze River
Delta region (Xu et al., 2002). Compared to the value measured at Hok Tsui 15
years ago, σap was lower than that observed in Hok Tsui, from
November 1997 to February 1999 (Man and Shih, 2001). Being the most significant
light-absorbing constituent of aerosols, a similar decrease of BC
concentration was also found. Table 3 presents the
mean BC mass concentrations reported in other comparable studies. The
overall average of BC mass concentrations in this study was 1.4 ± 1.1 µg m-3 (Table 1), which was lower than
the values observed at same site in 2004–2005 (with a mean of 2.4 µg m-3 using a AE-42 Aethalometer; Cheng et al., 2006a). A decreasing trend
of BC concentration was found at the Panyu station in the PRD region with a
decreasing rate of approximately 1 µg m-3 per year from 2004 to
2007 (Wu et al., 2009). Compared to the other rural sites in South
China, BC levels in Hok Tsui station were lower than the concentrations
measured at a rural site in the center of the PRD region, yet higher than those
on Yongxing Island, an oceanic rural site in the middle of the South China
Sea (Yu et al., 2013). BC concentrations were also higher than those
measured in European coastal stations (Saha and Despiau, 2009;
Andriejauskienė, 2008). The σsp was comparable to that
obtained at Shangdianzi station in a suburb of Beijing, but much higher
than the value at Hok Tsui station measured a decade ago (Yan et al., 2008;
Man and Shih, 2001). The overall average SSA550 nm was 0.93 ± 0.05, which was comparable to that in a rural station, Lin'an, China (Xu et
al., 2002) but higher than those measured in a suburban station in northern
China (Mean SSA525 nm= 0.88; Yan et al., 2008). In addition, as above
with σap, a lower estimate for SSA = 0.92 ± 0.05 can be
obtained by using the Cref of MHD in the calculations. This shows that
even by changing the Cref by ∼ 20 % the SSA is very
high, which is reasonable at a site at the sea. CO mixing ratios in Hok Tsui
station were comparable to those measured at the same site in 1994–1996 (Lam et
al., 2001).
Summary of seasonal average value of target pollutants.
Winter
Spring
Summer
Autumn
AVG ± SD
MED
AVG ± SD
MED
AVG ± SD
MED
AVG ± SD
MED
σap, 550nm (Mm-1)
10.9 ± 7.1
9.6
7.5 ± 4.8
6.4
5.5 ± 5.8
3.8
7.4 ± 4.5
6.3
σsp, 550nm (Mm-1)
193 ± 102
176
148 ± 89
133
64 ± 62
49
140 ± 82
130
SSA (550 nm)
0.94 ± 0.03
0.95
0.93 ± 0.05
0.95
0.90 ± 0.06
0.92
0.94 ± 0.03
0.95
BC (µg m-3)
2.0 ± 1.2
1.8
1.3 ± 1.0
1.2
0.9 ± 1.1
0.6
1.5 ± 0.9
1.4
CO (ppbv)
459 ± 186
423
275 ± 154
262
117 ± 84
91
270 ± 134
252
AAE
1.1 ± 0.2
1.1
1.0 ± 0.3
1.0
0.7 ± 0.4
0.7
0.9 ± 0.3
0.8
SAE
1.3 ± 0.3
1.3
1.2 ± 0.5
1.2
1.4 ± 0.6
1.5
1.7 ± 0.4
1.8
Ntotal (cm-3)
7690 ± 3821
6768
8620 ± 4868
7540
7003 ± 4460
5943
7808 ± 3970
7077
GMD (nm)
72 ± 17
70
68 ± 15
66
58 ± 17
54
69 ± 17
68
PM1 (µg m-3)
27 ± 19
22
25 ± 19
21
13 ± 20
7
22 ± 14
19
(a) Seasonal cycle of scattering coefficient,
σsp;
absorption coefficient, σap; single-scattering albedo; SSA;
temperature; and precipitation; where bold solid lines represent median
values, diamonds show the monthly averages, and thin solid lines are
percentiles of 75 and 25 %. (b) Seasonal cycles of BC, CO,
SO2, and PM2.5 concentrations, where blue solid lines represent
median values, diamonds show the monthly averages, the boxes are 25th
and 75th percentiles, and the thin bars represent 10th and
90th percentiles.
Temporal variations and overall characteristics
The seasonal cycles of target parameters were analyzed based on
hourly-averaged data classified as four seasons: winter (December–February),
spring (March–May), summer (June–August), and autumn
(September–November). Seasonal averaged values of selected parameters are
listed in Table 4. The highest σap and
σsp values were observed in winter (10.9 ± 7.1 and 193.5 ± 102 Mm-1, respectively), which were more than twice
that of summer. A similar pattern was observed in a previous study in Hong
Kong in 1997–1999 (Man and Shih, 2001). Compared with other rural/background
sites, the average SSA550 nm at Hok Tsui was 0.94 ± 0.03 during
autumn, which was higher than that measured at Xinken, PRD, China, in the same
season (0.83 ± 0.05), while this value was 0.90 ± 0.06 in summer, which was slightly lower than that observed at a coastal station in Norway
;also in summer (0.91 ± 0.05, Mogo et al., 2012).
(a) Seasonal mean value of 1-SSA in 36 wind sectors during the
whole period. (b) Map of averaged 7-day retroplume when SSA is below 0.9
compared with (c) averaged 7-day retroplume during the whole period.
Averaged diurnal variations of (a) σsp, (b) σap,
(c) BC, (d) CO, (e) SAE, (f) AAE, (g) SSA, and (h) PM1 in four seasons.
Figure 2 presents the monthly variation of measured
optical properties and meteorological parameters. A clear seasonal cycle of
aerosol optical properties is shown with σap and σsp, having peaked in January and reaching the lowest level in July. While it was lighter in winter, the
aerosol was the darkest in summer, especially in August, with a seasonal mean
SSA of 0.87. Averaged seasonal values of
1-SSA in 36 wind sectors are presented in Fig. 3a.
These figures show the disparity of SSA from different wind directions.
Overall, air plume coming from the southwest to the north
(225–360∘) had higher 1-SSA, i.e., lower SSA, than that from the
east (45–135∘). Ding et al. (2013) reported that the contribution
of anthropogenic emissions from Guangdong and Hong Kong was the highest in
August, which means more freshly emitted urban aerosols were brought to the
monitoring station with lower SSA in this month (Cheng et al., 2008). The main
synoptic process contributing to this kind of sub-regional transport is
tropical cyclones. Ding et al. (2004) explained the mechanism of how these
tropical cyclones influence the development of sea–land breeze and also explained
further about sub-regional and urban air mass accumulation in the South China. Zhang et al. (2013) found an important influence of tropical cyclones in ozone and
haze pollution in this region in summer, based on an analysis of 13-year
data.
(a) Scatter plot of SSA550 nm and AAE, (b) SSA550 nm and
GMD, and (c) SSA550 nm and SAE450_700nm, color-coded with BC mass fraction of PM1.
(a) Scatter plot of simulated σsp submicron
particles and observed σsp at λ= 550 nm. (b) Average
number size distribution and (c) scattering size distribution during the
whole period.
Another possible reason for the relatively low SSA in August is that the air
mass came mainly from the southwest of the site
(Fig. 1), a main waterway for ocean-going vessels
in Hong Kong (Yau et al., 2012). These vessels emitted considerable amount
of light-absorbing carbon from diesel engines during combustion. Similar
pattern was also observed in the seasonal diagrams of BC, SO2,
PM2.5, and CO, which are typical components of ship exhaust
(Fig. 2, Hong Kong Air Pollutant Emission Inventory
for 2013 from Hong Kong Environmental Protection Department:
http://www.epd.gov.hk/epd/english/environmentinhk/air/data/emission_inve.html).
Map showing spatial distribution of BC / CO emission ratio with grid
resolution of (0.25∘ × 0.25∘) from MIX Asian emission
inventory (Li et al., 2017).
Figure 3b demonstrates the averaged 7-day retroplume
of the times when SSA was lower than 0.9. Compared with the overall averaged
7-day retroplume during the whole measurement period
(Fig. 3c), darker aerosols were mostly from two
main types of regions in the vicinity: one was the nearby continental area with fresh polluted air masses from urban Hong Kong and neighboring PRD
cities; another branch was from the ocean side. Fresh emission of passing
ships or fast transport from southern Asia could lead to higher proportion
of BC in the air plumes and thus caused lower SSA.
Figure 4 shows the diurnal cycles of σap, σsp , BC, CO, and PM1 for four seasons. There
was an increase inσap after sunrise with a peak occurring before
noontime. It might be associated with a combined effect of increased human
activities and turbulence mixing in the boundary layer in the morning. This
pattern was more significant in summer, although the pollution level was
relatively low. This phenomenon supports the explanation of turbulent mixing
from a middle or upper planetary boundary layer (PBL) because of a stronger
vertical mixing in summer. The PM1 also showed a daytime maximum
concentration but with the peak in the afternoon
(Fig. 4c). For σsp, morning peaks were
not as significant as σap. The decrease in σap in
the early afternoon might be caused by a further development of PBL or mixing
layer, in which the air pollutants experienced a substantial dilution,
resulting in lower concentrations of pollutants at the ground surface.
Diurnal variations and fluctuations of CO mixing ratios show a similar
pattern with σap but a relatively smaller variability.
Seasonal cycles of ΔBC / ΔCO and SO2 / BC ratios
from observations. Reference values of emission ratios from different source
types are shown in the column with light-yellow background.
Optical properties and their relationships with particle size
Wavelength dependencies of aerosol light scattering and absorption are
closely related to aerosol size and dominating aerosol types. To find out
the difference of light absorbing materials, Fig. 5a displays the relationship of SSA with AAE, color-coded with a BC mass
fraction of submicron particles (PM1 was calculated from the particle
number size distributions measured with the UFPM.) It shows that
aerosols with high SSA had a lower BC fraction and that AAE varied greatly in
the lower value region, indicating the dominance of scattering particles.
Such air masses were likely of longer transport time and the BC
aerosols had mixed well with light-scattering aerosols during transport.
Contrarily, the low SSA values mostly occurred when AAE were closely
distributed around 1.0 and in these cases BC took up a higher proportion
(red dots in Fig. 5a), showing freshly emitted BC plumes.
Figure 5b and c demonstrate the relationships between particle size and
their scattering Ångström exponents as well as their darkness. It
can be observed that SAE generally increased with decreasing SSA. Dark
aerosols with low SSA were mostly small in size with low GMD but a high BC
fraction. These small dark aerosols had higher SAE (1.5 to 2.0). The wide
range of SAE was possibly due to the mixed control by continental aerosols
and large sea-salt aerosols.
Figure 6 shows the scatter plot of σsp
calculated using Eq. (2) versus the measured σsp. The
slope of σsp, submicron/σsp, obs was 0.86,
indicating that submicron particles were the major light-scattering
components in the air masses arriving at the Hok Tsui station. For most of
the time in the study period, the simulated σsp was lower than the
observed σsp. This is probably because the particle size
distribution data from the UFPM only used the scanned
submicron particles with mobility diameter from 20 to 800 nm (see
Fig. 6b and c) in the calculation, but the nephelometer, equipped
with a TSP inlet, measured light-scattering coefficients from all particles
with a wider-sized range. The relatively limited number of particle size bins
in the UFPM probably also leads to uncertainties for the calculation
of σsp. Hence, this result can only provide a rough image of the
relationships between particle light scattering and their size distribution
at the Hok Tsui station. It can be observed that particles with Dp less
than 200 nm contributed the largest fraction of the total number of
submicron particles but very little to the total scattering, whereas the
small amount of larger particles (Dp: 200–800 nm) contributed the most
to the total light scattering.
Scatter plot of hourly BC and CO in four wind sectors.
Map of averaged 7-day retroplume in (a) winter,
(b) spring,
(c) summer, and (d) autumn.
The scatter plot (Fig. 6a) also shows that there were clusters of data where
the modeled and measured σsp fit close to the 1:1 line and
clusters where the measured σsp was clearly larger than those
modeled on. After computing the averaged retroplume of these clusters, it
was found that the former data cluster is mostly associated with polluted
continental air and the latter with stronger winds and sea-salt particles
(figures were not shown).
Source identification
Figure 7 shows the spatial distribution of BC / CO
emission ratios in East China and the nearby regions calculated using the
MIX emission inventory. It can be seen that the BC / CO emission ratio was higher
in Shanxi Province (higher than 25 ng m-3 ppbv-1), Taiwan
(approximately 20 ng m-3 ppbv-1), and the regions along the coastline of
East China. As reported by previous studies, the BC / CO emission ratio from
industrial coal burning ranges from 1.9 to 20 and it was
5.6–13.3 ng m-3 ppbv-1 from open biomass burning (Wang et
al., 2011; Zhang et al., 2009). For diesel vehicles, the BC / CO emission ratio
was 14–39 and it was 15.6 ng m-3 ppbv-1 for
ship emission calculated from a previous study in southern Asia (Dickerson et
al., 2002). A strong correlation between BC and CO and a high slope of
27 × 10-3 g BC g CO-1 were found from a previous study using
C-130 aircraft flew over the Arabian Sea and northern Indian Ocean
(Dickerson et al., 2002; Mayol-Bracero et al., 2002).
In this study, ΔBC / ΔCO and SO2 / BC ratios were
investigated to study the source characteristics and the freshness of the
fuel combustion sources. ΔBC / ΔCO (net growth of BC and CO:
total concentration minus regional baseline. (Spackman et al., 2008) and
SO2 / BC were calculated with 1 h time resolution. The baseline of BC
and CO was determined as 1.25th percentiles of data in each month (Pan
et al., 2011). Monthly variation of ΔBC / ΔCO is displayed in
Fig. 8 together with SO2 / BC to demonstrate the
fuel burning emission profile since SO2 is a co-emitted species of
fossil fuel combustion (Bond et al., 2013). Reference emission ratios of
BC / CO and SO2 / BC from previous studies (Bond et al., 2013; Li et al.,
2015) are also plotted in Fig. 8.
(a, b) :Map of averaged potential source contribution of BC with a
backward-transport age of (a) 2 days, (b) 7 days for the whole measurement
period. (c, d) :averaged 7-day PSC in (c) summer, (d) winter.
In Hong Kong, major SO2 emission was from navigation and public
electricity generation, contributing 50 and 47 % to total SO2
emission (Emission Inventory 2013, HKEPD,
http://www.epd.gov.hk/epd/english/environmentinhk/air/data/emission_inve.html). However, these two sources only took up 19 and 6 % of CO
emission and the largest contributor of CO reported in the emission
inventory was road transport (59 %). As shown in
Fig. 8, ΔBC / ΔCO and SO2 / BC
ratios presented similar monthly variation patterns. The monthly mean
ΔBC / ΔCO ranged from 1.5 to 20 µg m-3 ppbv-1 during the whole study period. The highest values occurred in
summer months for both ΔBC / ΔCO and SO2 / BC and the
ratios were relatively lower in winter. Since SO2 has a short lifetime,
which can easily deposit and transform into secondary aerosols, the
synchronous elevation of ΔBC / ΔCO and SO2 / BC in summer
indicates that freshly emitted anthropogenic pollutants might be more easily
influenced by the air masses in this coastal area. The decrease of ΔBC / ΔCO and SO2 / BC in winter provided the evidence that this
area was under the influence of contaminated air masses from a longer
distance. Figure 9 displays the scatter plot of BC
vs. CO in four wind sectors, giving an image of the freshness of polluted
air masses and the intensities of combustion emissions from different
directions. For wind directions from 180 to 360∘, the
data points show a good positive correlation, suggesting that most of the BC
and CO emission sources in these areas were closer to the measurement site.
Data points in the 0–180∘ wind direction sector were much more
scattered with a lower slope of It should be 'delta BC / delta CO'
of air pollutants coming from the northeast to the southeast. Through the
transport of air plume, diffusion and deposition of air pollutants would
decrease their concentrations that arrive to the receptor, and therefore lower
the slope of ΔBC / ΔCO and their correlation coefficients.
Map of average propertyretroplume for
(a) σap,
(b) σsp, and (c) PM1 (the non-colored areas were where the
total retroplume was smaller than 10-12 mass m-3 h-1 (i.e., air
plumes barely passed through these regions). Due to the different time
period of valid data from UFP, the non-colored areas were slightly different
in (c). (d) Density map showing the ship routes near Hong Kong during 2013
and 2014.
(a) Scatter plots of BC and CO. (b) σsp and SAE
from different source regions during episodes GH: Guangdong and Hong Kong;
SP: Ship; NC: North China; and the AGC: aged continental area.
To investigate the transport pattern of air masses that arrived at the site
during the study period, Fig. 10 shows averaged
7-day retroplume for four seasons. As presented in Ding et al. (2013),
it shows a distinguished different transport pattern under the influence of
Asian monsoons. During summer, the majority of air masses came from the south
and nearby PRD cities. Due to the dominance of relatively clean marine air,
emission from passing ships or local activities in adjacent regions could
make visible effects on the temporal variations of air pollutants. The
source distribution was more complicated during winter. Driven by the winter
monsoon, cold and dry air masses that were transported along the coastline of East
China and from central China took up a higher proportion in winter months
(Ding et al., 2013). There were also air masses passing through Taiwan
and the East China Sea during the cold season
(Fig. 10a).
Since BC is the most significant light-absorbing constituent of aerosols, to
evaluate the potential source contribution of light absorbing particles on
regional optical properties, averaged PSC maps of BC for different transport
times and seasons were calculated using the method described in Sect. 2.6
and illustrated in Fig. 11. However, here we only
calculated the PSC from emission over land because the available emission
inventory from the MIX database is mainly focused on land areas. As shown in
Figs. 11a and 10b, BC concentrations were influenced
by the transport from nearby cities within a short time, especially Shenzhen
and urban Hong Kong. Long-range transport of BC from the South and East
China also played an important contribution. It was also shown that BC coming
from the continental area through longer distances took up a higher proportion of
the pollutant level in winter (Fig. 11d) than that
of the summer (Fig. 11c). During summer, local
emission was the biggest BC contributor.
Figures 12a–11c illustrate the average levels of
σap, σsp and PM1 and the corresponding
frequency of occurrence for air masses passing through different regions
calculated using Eq. (3). Together with the shipping routes density
map (Fig. 12d), it can be observed that the high
levels of σap and σsp were closely associated with
the congested shipping lanes in the maritime space nearby Hong Kong. The
high σap and σsp were especially visible in the
northeast due to the prevailing northeasterly wind from autumn to spring,
transporting ship exhausts mainly through the Taiwan Strait. The belt-like
zone with higher σap and σsp was likely to reveal ship emission. As shown in Fig. 12d,
there were dense shipping routes between Hong Kong and Singapore
traveling through the Xisha Islands in the South China Sea where the
routes were similar to the high σap area in the south. During
summer, Hong Kong was influenced by the southerly and southwesterly wind,
bringing clean marine air to this region for most of time and leading to lower pollutant levels (Wang et al., 2009; Ding et al., 2013). Figure 12 indicates that Hong Kong could be affected
by the passing vessels in the South China Sea due to controlling wind
direction, driven by the summer monsoon.
Analyses of selected episodes
Figure 13 demonstrates the aerosol optical properties
and BC-CO correlations associated with air masses from different source
regions during selected episodes. The major source regions were Guangdong
and Hong Kong (GH), ship emission (SP), North China (NC), and the aged
continental area (AGC). The selection of the episodes was done by combining
the footprints using LPDM and the variation trend of aerosol optical
properties and PM2.5. The air pollution plumes coming from Guangdong
and urban Hong Kong had the highest BC and CO concentrations
(Fig. 13a), indicating a higher level of emission
intensity and stronger light-extinction ability of aerosols from these
regions. The slope of BC vs. CO was highest from ship emission (0.012 µg m-3 ppbv-1), with high correlation
(r2= 0.84), showing that the ship emission source was close to the measurement station and its exhausts
could largely affect the pollution level.
Figure 13b displays that Ångström exponents
of scattering, from Guangdong and Hong Kong, were relatively high, compared to that from the aged continental area and North China,
proving the dominance of smaller particles of emissions from PRD cities and
passing ships. BC-containing particles transported from North and East
China went through longer coating and deposition processes, which enlarged
their size but decreased their concentrations when arriving to the measurement
site. This can further explain the lower SSA in summer months.
Overall, the analyses suggest that aerosols from different source regions
could cause great variances on regional aerosol optical properties. Thus,
more ground observations of aerosol optical properties are needed to fully
understand the characteristics of different types of atmospheric aerosols
and provide reference datasets for further investigation of aerosol radiative
forcing and climatic effects.