Introduction
Rapid economic growth and urbanization processes in East Asia have caused
serious air pollution in the past decades owing to the substantial
consumption of fossil fuel. Anthropogenic emissions (industrial, traffic,
residential, etc.) emit a substantial amount of pollutant gases (SO2,
NO2, NH3, VOCs, etc.) and primary aerosols (Akimoto, 2003,
2006; Kurokawa et al., 2013; Li et al., 2017), resulting in the formation of
PM2.5. Mineral dust particles in the atmosphere also have a detrimental
impact on air quality and on human health, such as reducing visibility and
increasing respiratory morbidity. Beijing is located in the vicinity of dust source regions.
In dry seasons, mineral dust particles emitted from the Taklimakan, the Gobi,
the Mongolia Plateau and the Loess Plateau may transport eastwardly across the
northern part of China (Takemura et al., 2002; Jickells et al., 2005; Uno et
al., 2009), and undergo complex mixing with anthropogenic pollutants.
The environmental and climate effects of these mixing processes are notable
because of dramatic changes in the physical, chemical and optical properties
of mixed particles (Pan et al., 2009). In polluted urban areas, soluble salts
coated on dust aerosols reduce the critical supersaturation, and there is a
stronger tendency for polluted aerosols to serve as CCN (cloud condensation
nuclei), influencing the formation of cloud (Sullivan et al., 2009; Tang et
al., 2016). The soluble salts are derived from the directly trapping of
inorganic salt and from the heterogeneous reactions between reactive gases
(mostly HNO3, HCl, SO2 and NO2) and
alkaline mineral dust. Continuous coating and hygroscopic growth processes on
the surface modify the shapes of dust particles (Li et al., 2011). A recent
study pointed out that the coexistence of NOx and mineral
dust may lead to a gas–particle conversion process and promote the
conversion of SO2 to sulfate (He et al., 2014). There is also
observational evidence that heavy dust mixed with anthropogenic pollution may
trigger new particle formation (Dupart et al., 2012; Nie et al., 2014), which
exaggerates the degradation of regional air quality. The above scientific
discoveries all indicate the importance of study on the mixing states of dust
and pollution aerosols.
Widely used technologies to distinguish aerosol types include high-precision
ground-based lidar (light detection and ranging) systems and satellite-borne
observations (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observation, CALIPSO; Winker et al., 2009; Cesana et al., 2016; Venkata and
Reagan, 2016) that measure a targeted air parcel's volume depolarization
ratio from backward scattering signals. Aerosol types can be distinguished by
the distinct parts formed by data points on a figure of their volume
depolarization ratio (δa=s/p, at 532 nm) of aerosols
versus backscattering color ratio (1064 nm / 532 nm); however, bias in
the classification of internal mixed dust (normally has a large color ratio
and small δa) is sometimes unavoidable since external mixing
of a substantial amount of fine particles
(δa < 0.1) with mineral dust aerosols
(δa > 0.35) can also result in a decrease of
the δa value. To respond to this need, an optical particle
counter with a depolarization module was developed. The single-particle
δ value measure is able to quantitatively investigate the evolution
of the mixing of dust particles during their transport (Pan et al., 2015).
According to the size-resolved δ value of scattering signals,
particles with a spherical shape could be distinguished because the direction
of the polarization of scattering light was identical to the incident light;
for the nonspherical particles, the direction of polarization deviated
significantly. Generally, secondary formatted particles tend to be spherical
with a small δ value, while natural mineral dust has a larger
δ value because of its irregular shape (Kobayashi et al., 2014).
Real-time measurements of δ values on a single-particle basis help to
avoid the misclassification of aerosol types. Sugimoto et al. (2015) found
that the backscattering δ value in polluted dust was smaller compared
to pure Asian dust for measurements in Seoul. A previous study (Pan et al.,
2017) in Beijing indicated that coating processes such as heterogeneous
reactions and hygroscopic growth on the surface of dust particles play a
vital role in the decrease of the depolarization ratio (δ) of
particles in the coarse mode. As far as we know, long-term measurements of
the interaction of anthropogenic pollution and mineral dust and their effect
on dust morphological changes in northern China are still lacking.
In this study, a comprehensive ground-based measurement of depolarization
properties of aerosol particles was performed at an urban site in Beijing
(Fig. 1), at the State Key Laboratory of Atmospheric Boundary Layer Physics
and Atmospheric Chemistry (LAPC; 116.37∘ E, 39.97∘ N),
Institute of Atmospheric Physics/Chinese Academy of Sciences. The
morphological variability of ambient aerosol particles was investigated from
November 2015 to July 2016 using a polarization optical particle counter
(POPC), and the seasonal characteristics of the depolarization ratio (δ) of atmospheric aerosols were explained. Three pollution events including an anthropogenic pollution case, a
typical dust-dominant case and a mixed-type pollution period were classified
according to their trajectory analysis result and size distributions and
δ values, to investigate the
interactions between dust particles and pollutants. The objective of this
study focuses on the variation of the δ value of aerosol particles
and its relationship with secondary pollutants. For the first time this study
represents such long-term observations of ambient aerosol morphology
performed in the megacity of Beijing, and provides more applicable data for
evaluating the mixing processes of atmospheric aerosols and their impact on
the climate.
Observation
Instrument overview
The observation of the depolarization properties of single particles in
Beijing was performed using a polarization optical particle counter (POPC)
(Kobayashi et al., 2014). The instrument was installed on the second floor of
an air-conditioned two-story building. The inlet was ∼50 cm above the
roof of the building, and ambient air was drawn into room through a
1/2 in. stainless tube with a rainproof cap. The total flow rate of inlet
sampling air was set to 13 L min-1 (liters per minute) with a
supporting pump. For POPC, the detecting size range was 0.5–10 µm.
A polarized laser beam at a wavelength of 780 nm illuminated the particles.
The POPC uses a forward scattering light at a scattering angle of 60∘
to determine the size of particles; backward scattering intensity at
120∘ is divided into two components with the polarizer: p-polarized
light is in the plane of the incident and reflected beams, known as the
horizontal polarized light, while s-polarized light is perpendicular to the
plane. Normally, the ratio of the s-polarized signal to that of the total
backward light scattering signal is defined as the depolarization ratio
(δ), which can provide morphological information about the particle
(Muñoz and Hovenier, 2011). The acceptance angle (angle of the
backscattered light received by the polarizer) for the polarization detector
is 45∘. To avoid coincidence error (several particles passing through
the laser beam simultaneously), the sampling flow of the POPC is set to
80 cm3 min-1 (cubic centimeters per minute) with a dilution flow
of 920 cm3 min-1.
During the observation, POPC was calibrated using standard known-size
particles (JSR Life Sciences Corp.) at Dp = 0.048 µm
(SC-0050-D) and 1.005 µm (SC-103-S), and DYNOSHERE polystyrene
standard aerosols at 3.210 µm (SS-032-P), 5.125 µm
(SS-052-P), 7.008 µm (SS-074-P) and 10.14 µm (SS-104-P).
Aerosols were generated by a nebulizer at a flow rate of 3.5 L min-1,
desiccated by passing through a vertically placed 45 cm Perma casing tube
(MD-110-24P, GL Sciences), as the laboratory calibration process in Fig. S1
showed. The δ values of typical spherical particles at
Dp = 5.125, 7.008 and 10.14 µm were found to be 0.075, 0.085
and 0.102, and the δ value was almost zero for the fine-mode particles
(Dp = 0.048, 1.005 µm; Fig. S2). The uncertainties of the
δ value were affected by various factors, including the voltage
variance of power supply (σvol2), the environmental
water content (σWC2) and the complex refraction
index (σnf2) of the aerosols; we estimate the
uncertainty of the δ value to be < 13 %. For comparison,
mass concentrations of pollutants in Beijing were obtained from a
ground-based state control site (116.40∘ E, 39.98∘ N;
2.7 km northeast of LAPC) and corresponding meteorology data from the
climatological station (116.48∘ E, 39.95∘ N; 9.5 km
southeast of LAPC); we thus analyzed the artificial pollution processes and
special mineral dust cases that occurred in Beijing.
The vertical profile of the extinction coefficient and the depolarization
ratio of aerosol particles was concurrently measured using a National
Institute for Environmental Studies (NIES) lidar system
(http://www-lidar.nies.go.jp/AD-Net/, last access: 30 October 2018;
Singh et al., 2008; Shimizu et al., 2016). The polarization Mie lidar is a
powerful instrument for identifying the change of optical properties of
mineral dust (Shimizu et al., 2004). The data from the lidar were processed
at 15 min resolution to derive the volume depolarization ratio at 532 nm,
the attenuated backscattering coefficient at 1064 and 532 nm and the
extinction coefficient estimated for spherical aerosols (mainly air
pollutants) and nonspherical particles (mainly natural dust). The energy is
20 and 30 mJ pulse-1 for the 1064 and 532 nm laser, and the light is
emitted vertically with a pulse repetition of 10 Hz (Shimizu et al., 2016).
Telescopes with a diameter of 20 cm (lidar in Beijing) are used to collect
the scattered light from the sky at an observation wavelength of 532 nm.
Geographical location of the observation site in Beijing, PM2.5
emissions in China and the location of major deserts including the Gobi in
East Asia.
Dispersion and trajectory analysis
The long-range and mesoscale dispersion of air parcels over the Asian region
was simulated using the FLEXPART (FLEXible PARTicle) dispersion model.
FLEXPART is a Lagrangian transport and dispersion model
(https://www.flexpart.eu, last access: 15 June 2018) developed by the
Norwegian Institute for Air Research. This model is suitable for the
simulation of a large range of atmospheric transport processes (Stohl et al.,
2005), which can do forward simulation to trace particles from source areas and backward
simulation to track particles from given receptors. The meteorological fields
for FLEXPART are taken from NCEP's (National Centers for Environmental
Prediction) reanalysis GDAS (Global Data Assimilation System) dataset on a
1∘×1∘ grid, which provides global observation
meteorological data at 00:00, 06:00, 12:00 and 18:00 UTC and forecast data
at 03:00, 09:00, 15:00 and 21:00 UTC
(http://nomads.ncep.noaa.gov/pub/data/nccf/com/gfs/prod/, last access:
18 December 2018). During the simulation, 1 unit mass of particles considered
as an air sample was released from the observation site at 150 m above
ground level. The spatial distribution of the footprint region of the air
samples was calculated on the 5 days of backward simulation considering flow
meteorology, turbulent motions, the sub-grid terrain effect and Earth's water
cycle. In addition, a footprint region of air parcels of interest was
simulated used the HYSPLIT (Hybrid Single Particle Lagrangian Integrated
Trajectory) model developed by the NCEP (National Centers for Environmental
Prediction) and NCAR (National Center for Atmospheric Research), which is
based on the Lagrangian transport model
(https://ready.arl.noaa.gov/HYSPLIT_traj.php, last access: 1 November
2018) (Stein et al., 2015). The dataset provided for HYSPLIT is the global
reanalysis data in GDAS format
(ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1, last access: 10
December 2018). It produces meteorological data four times a day, namely, at
00:00, 06:00, 12:00 and 18:00 UTC, and the horizontal resolution is
2.5∘ ×2.5∘. The vertical direction is 17 floors,
ranging from the ground surface to 10 hPa. Elements, including wind,
temperature, humidity, potential height and ground precipitation, are
provided. During the simulation, the trajectory ensemble option starts
multiple trajectories from the first selected starting location. Each member
of the trajectory ensemble is calculated by offsetting the meteorological
data by a fixed grid factor (one meteorological grid point in the horizontal
and 0.01σ units in the vertical). Air samples were released at 150 m
above ground level from LAPC, and the simulation time of the backward
trajectory was 5 days.
Time series of (a) volume size distributions and RH,
(b) size-resolved δ values (solid line:
δ = 0.1) and (c) hourly and monthly averaged δ values for fixed-size particles: Dp = 1 and
5 µm from 29 November 2015 to 29 July 2016. Error bars for monthly
averaged δ in (c) depict the monthly averaged standard deviation
of value.
Results and discussion
Size distribution of ambient aerosols
The hourly-averaged volume size distribution of aerosol from 29 November 2015 to
29 July 2016 is shown in Fig. 2a. The particle size was derived according to
the calibration curve between the forward scattering intensity and standard
spherical particles (Fig. S3). Mass concentrations of particle matters were
reconstructed on the basis of the number concentration of particles measured by
the POPC and particle density. The particle density was assumed to increase
linearly from 1.77 (0.5 µm) to 2.2 g cm-3
(10 µm). To test the accuracy of the POPC detection, the PM2.5
and PM10 inverted by POPC were compared with the observed data from the
Olympic Sport Center state control station (Fig. S4). The correlation
coefficients are 0.91 and 0.89 (significance level: 0.001) for PM2.5 and
PM10, respectively. The result also compared well with a commercial
optical particle counter (KC52, RION, as shown in Fig. S5), especially in the
coarse-mode size range observation.
It can be seen in Fig. 2a that volume size distribution generally had two
size modes during the whole observation periods. The occurrence of fine mode
(peak at ∼1 µm) was accompanied with an increase of RH. The
coarse mode (4–8 µm) mainly occurred in the spring when
the eastward transport of dust events was significant (Lue et al., 2010).
According to the POPC observations, five main dust episodes in total (3–5, 16–22 and 30–31 March; 9–10 April; and 28 April–1 May) happened at the site
during the observation period. For the cases on 3–5 and 16–18 March, the volume
size distribution of ambient particles showed two peaks in both the fine and
the coarse mode (Fig. S6), suggesting the interaction of anthropogenic
pollutants and dust particles and the high possibility of the existence of internally mixed
dust particles (discussed in Sect. 3.5). In winter 2015, POPC observed
anthropogenic-dominant pollution cases five times. All of them were related to high emissions for residential heating purposes and unfavorable
air diffusion circumstances (Wang et al., 2015). In summer, the volume
concentration of aerosols in all size modes was significantly low because of
relatively moderate anthropogenic emissions, better diffusing boundary layer
conditions and frequent precipitation. In addition, relatively strong turbulence in
the planetary boundary layer also increased the dry deposition processes of
particles.
Seasonal patterns of the δvalue of
ambient aerosols
Figure 2b illustrates the size-resolved δ value as a function of time
during the observation period. The δ value of particles increased
significantly as the size increased. Episodes influenced by mineral dust
could be easily discerned due to the increase in both the volume
concentration of coarse-mode aerosols and the δ value of fine-mode
aerosols. In general, the δ value of particles in urban Beijing had
prominent seasonal variability, with a summer low and a spring high pattern
due to the different compositions and origins of aerosols and atmospheric
meteorology at the site. The averaged δ value of particles in both
fine and coarse mode was highest in March 2016 (0.26) and lowest in July 2016
(0.19). This seasonal variability was very obvious in fine-mode particles.
Figure 2c shows temporal variations of hourly and monthly averaged
δ values for the typical particle size at 1 and 5 µm, and
the error bar depicts the monthly averaged deviation. For fine particles at
Dp = 1 µm, their δ values in winter and summer were
0.09±0.01 and 0.07±0.01 respectively; however, they could
increase up to 0.2 as they were subject to dust events in spring. The
phenomenon of the coexistence of fine mineral dust with anthropogenic
pollutants has been reported in electro-microscopic studies in the literature
(Li and Shao, 2009; Li et al., 2011). The daily averaged δ value at
Dp = 5 µm varied significantly between 0.12 and 0.4, with a
monthly mean value of 0.3±0.05. We could discern that the
δ values for 5 µm particles in winter could decrease
dramatically down to 0.15; however they had a very small deviation in spring.
This was because water-soluble anthropogenic pollutants in winter and summer
were substantial. Although dV/dlogDp of coarse mode
particles was comparatively low, heterogeneous processes on the surface of
particles in the coarse mode under high RH conditions were inevitable, which
may result in a decrease in the δ value.
Dependence of the hourly-averaged δ value of
particles at Dp = 5 and Dp = 1 µm on the
wind speed and direction in winter (December, January and February: DJF)
2015, spring (March, April and May: MAM) 2016 and summer (June and July: JJ)
2016.
δ value of aerosols from different origins
The dependence of the hourly-averaged δ value of particles at
Dp = 1 µm and Dp = 5 µm on the wind speed and
directions in different seasons is plotted in Fig. 3. It can be seen that the
δ values at both sizes increased when the observation site experienced
a prevailing northwest wind, almost regardless of the season. For the
particles at Dp = 5 µm, δ values in all direction in
spring were generally higher than those in winter and summer because of the
impact of mineral dust aerosols, and the δ values also generally
increased with wind speed, implying the impact of resuspended road dust or
floating dust under strong wind conditions. For particles at
Dp = 1 µm, it was only during the northwest wind period that
the δ values were 40 %–50 % higher than other directions.
This demonstrated that the morphology of particles in the fine mode was only
altered significantly during dust events. We noted that particles with low
δ values in all size modes were observed when the site had a
prevailing southeast wind in summer, indicating the presence of a large
fraction of spherical particles and high ambient water content, which greatly
contributed to the deliquescence process of soluble components in the
atmosphere.
In order to understand the source region of air masses in different pollution
types and further explain the seasonal characteristics of δ values of
atmospheric aerosols, the footprint of the air mass in typical anthropogenic
pollution cases and mineral-dust-dominant cases were analyzed. Generally
speaking, PM2.5 / PM10 ratios have been used for identifying
the sources of primary pollutants (Chan et al., 2005; Pérez et al.,
2008). A higher ratio was generally ascribed to anthropogenic-related
secondary particles (sulfate, nitrate etc.), and a lower ratio indicates
significant contributions of mainly resuspended or fugitive mineral dust
particles due to some mechanical processes (Chan and Yao, 2008; Akyuz and
Cabuk, 2009; Xu et al., 2017). Here, the specific pollution incidents were
identified based on the size-resolved volume distribution and
δ values. The criteria of
PM2.5 > 250 µg m-3 and
PM2.5 / PM10≥0.8, and
PM10 > 150 µg m-3 and
PM2.5 / PM10≤0.4 were chosen for heavy anthropogenic
pollution-dominant and dust-dominant cases, as shown in Table 1.
Representative cases of heavy anthropogenic pollution and dust
episodes in Beijing.
Selected pollution
Year/month/day
PM2.5
PM10
PM2.5 / PM10
AQI
cases
(µg m-3)
(µg m-3)
Anthropogenic-dominant
2015/12/01
490
578
0.85
476
cases
2015/12/23
255
298
0.86
305
2015/12/25
477
510
0.94
485
2015/12/29
279
338
0.83
329
2016/01/02
266
299
0.89
316
Dust-dominated
2016/03/05
58
290
0.20
170
cases
2016/03/06
73
182
0.40
116
2016/03/28
70
195
0.36
123
2016/04/09
54
245
0.22
148
2016/04/10
41
192
0.21
121
2016/05/05
62
153
0.40
102
2016/05/06
57
182
0.31
116
The 5-day backward trajectories were calculated from HYSPLIT ensemble
calculations, resulting in 27 members for all possible offsets around the
release point. The different directions from which the air mass originated
over Beijing are shown in Fig. 4. For mineral-dust-dominant episodes, the air mass
mainly originated from large areas in western Mongolia and the Gobi Desert, and
the footprint pattern represented comparatively large dust loading in the
atmosphere in spring, while for the anthropogenic-pollution-dominant period, air
mass passed through the Beijing–Tianjin–Hebei region, where anthropogenic emission
was significantly strong. Note that the RH along the trajectories was
13.9 % on average during dust-dominant cases (mostly in
springtime) and 87.6 % in anthropogenic-pollution-dominant cases (mostly
in wintertime). It means that the origin of aerosol particles and their
interaction with water vapor as well as consecutively heterogeneous reactions
can lead to pronounced morphological changes of particles.
Proportion of different directions from which the air mass over
Beijing originated in varying pollution types: (a) the severe
anthropogenic-pollution-dominant case and (b) the dust-dominant case.
The red ellipse represents the major source region of air mass arriving at
the site.
δ variability of atmospheric aerosols on clean and substandard
days
Figure 5a shows the number of substandard days that daily-averaged PM2.5
exceeds 75 µg m-3, the secondary standard of the Chinese Ambient
Air Quality Standard. Figure 5b shows the mean mass concentrations of
PM2.5, PM10 and the PM2.5 / PM10 ratio on substandard
days. About 26.7 % of substandard days featured high atmospheric
loading of coarse-mode particles (PM2.5 / PM10 < 0.6)
in winter and spring. There were 18 substandard days in December 2015 with
mean values of 199.6 and 240.1 µg m-3 for PM2.5 and
PM10. A high PM2.5 / PM10 ratio (0.83) suggested that
anthropogenic pollutants were dominant. We found that the median δ value
of the particles at Dp = 5 µm (0.27) during substandard days
in the winter of 2015 was 12.9 % lower than that (0.31) on clean days, as
shown in Fig. 5c. It indicated that during the substandard days the dust
particles were more likely to be modified in shape due to the coexistence of
huge amount of pollutants. The second most pollution days occurred in March
(15 days) and April (14 days), and the PM2.5 / PM10 ratio was 0.67
and 0.65 respectively. The δ value of particles at
Dp = 5 µm particles was almost the same for substandard and
clean days. This demonstrated that almost all of the mineral dust particles
were in irregular shape.
(a) The number of poor air quality days, (b) daily
averaged mass concentration of PM2.5 and PM10 on the poor air
quality days and the ratio of PM2.5 / PM10, (c) a box
plot of particle δvalues of Dp = 5 and
(d) Dp = 1 µm on clean days
(PM2.5 < 35 µg m-3) and poor air quality
days.
The peak of δ value of the particles at Dp = 1 µm occurred in April, 0.15±0.03 (0.13±0.02) on substandard
(clean) days (Fig. 5d). The high 90th percentile value indicated that the
observation site was influenced by several intensive dust events in spring.
Anthropogenic pollution was dominant on substandard days in summer for which PM2.5 accounts for 0.76 and 0.87 in PM10 in June and July. The δ value of aerosols at Dp = 1 and 5 µm in summer was 0.07±0.01 and 0.27±0.03, and there was no apparent difference for
substandard and clean days. This was because under the humid and high
oxidizing environment in summertime, catalyzing processes (Nie et al., 2012;
Dupart et al., 2012) on the surface of mineral dust aerosols affect the
hygroscopicity of dust and affect the evolution of particle morphology. Previous
studies found that mineral dust coexisting with NOx can
promote the conversion of SO2 to sulfate (He et al., 2014). Nie et
al. (2014) also found that mixed plumes provide abundant reactive species,
and dust-induced photocatalytic reactions accelerate oxidization in
SO2 and volatile organic compounds (VOCs). This implies
that it is highly likely that pollution days in northern China induce internal
mixing of dust and pollutants, especially in a high humid atmospheric
environment, and dust-related heterogeneous processes on the dust surface can
aggravate the deterioration of air quality as a feedback.
δ value of different types of aerosols
Li et al. (2011) showed that the surface of mineral dust aerosols provides a
suitable space for heterogeneous reactions with gaseous pollutants, leading
to changes in the size, shape and chemical components. On the basis of
depolarization properties of single particles, the evolution of the mixing state
of dust particles could be estimated properly. Here, three pollution cases
were carefully chosen considering the variation of both the volume size
distribution and the δ value: anthropogenic pollution, a typical
dust-dominant case and a mixed-type pollution period (coexistence of
anthropogenic pollutants and dust particles in the atmosphere that caused
severe air pollution). They are denoted as case A on 22–23 December 2015,
case B on 9–10 April 2016 and case C on 4–6 March 2016, respectively. The vertical profile of the extinction coefficient and
the depolarization ratio are shown in Fig. 8, and the evolution of depolarization
properties of dust particles was simultaneously observed by POPC. In this
study, we simulated a 5-day footprint region of air mass based on the FLEXPART
model. The inert particles were released at 09:00 UTC each day on
23 December 2015, 9 April 2016 and 4 March 2016. The releasing point was at
LAPC in Beijing at 150 m above ground level. The footprint regions for the
three cases are shown in Fig. 6a–c. The variation of the volume concentration of
particles as a function of both the δ value and particle sizes is
depicted in Fig. 6d–f. For better comparison, the standard volume
concentration was normalized to a maximum value of 1 using the following formula:
normalized value = (truth value – minimum) / (maximum – minimum).
Backward trajectories from Beijing calculated by the FLEXPART
dispersion model for (a) the anthropogenic pollution case,
(b) the dust-dominant case and (c) the mixed pollution
period. Variation in the standard δvalue as a function
of particle size is shown for corresponding episodes: (d),
(e) and (f).
Anthropogenic-pollution-dominant period
In case A, daily-averaged PM2.5 was 281.3 µg m-3, with a
PM2.5 / PM10 ratio of 0.72. The volume concentration of
particles in this period had a peak in the submicron range, with a
δ value of < 0.1 (Fig. 6d), reflecting the predominance of
secondary formation pollutants. As shown in Fig. 7, the δ values of
particles with Dp less than 2 µm were normally less than 0.12 and
increased gradually to ∼0.27 (Dp > 4 µm),
implying the influence from dust aerosol, even in the typical
anthropogenic-pollution-dominant period. The footprint of air mass covered
the west of the North China Plain, which is characterized by heavy
industrialization and high emissions (Zhao et al., 2012). As suggested by
many previous studies (Ilten and Selici, 2008; Wang et al., 2013; Zhang et
al., 2016; Chang and Zhan, 2017), unfavorable meteorological conditions
played an important role in the occurrence of severe pollution. In case A,
the RH ranged 80 % and 90 % from 00:00 LST on 22 December to
12:00 LST on 23 December, and diffusion conditions were weak (wind speed
< 1.5 m s-1) (Fig. S7). Air mass was mostly stagnant within a
high emission region, resulting in the substantial formation of secondary
pollutants from primary pollutant precursors in the atmosphere (Wang et al.,
2014).
(a) Volume and (b) δvalue
size distribution of aerosols observed in the study cases.
Dust-dominant case
The typical dust-dominant case occurred on 9–10 April 2016. The main body of
the dust plume arrived at the site on 9 April with a daily-averaged PM10
of 273.6 µg m-3 and a PM2.5 / PM10 ratio of
0.33. Footprint analysis shows that air mass originated from western Mongolia
and was transported rapidly by a strong wind (∼5 m s-1). The
vertical structure of the dust extinction coefficient determined by
ground-based lidar measurement indicated that the dust plume presents a
layered structure when it arrived at the observation site (Fig. 8a–b). The
lowest dust layer in the altitudes < 700 m first arrived at Beijing
at 06:00 LST on 9 April containing a huge amount of coarse-mode particles,
with hourly averaged PM2.5-10 reaching 395 µg m-3 at
noon. This layer was lifted up and became mixed with the dust layer at a
height of 1 km in the afternoon of 9 April. The impact of anthropogenic
pollutants on this dust event was weak for a smaller amount of pollutants.
POPC analysis shows no feature of internally mixed dust particles. The
dVdlogDp has a peak at 5 µm with a mean δ value of 0.34,
consistent with the result in western Japan (Pan et al., 2015). The
δ values of aerosols in coarse mode were found to be about 3 times
larger than the calibration result (0.07–0.1) for standard spherical
aerosols (Fig. S2), which suggests the coarse-mode particles at the site were
nonspherical. Note that the δ values (0.18±0.02) of particles in
fine mode were twice higher than those during the
anthropogenic-pollutant-dominant case, indicating the presence of irregular
dust particles.
Time–height indications of the dust extinction coefficient and the
δvalue at 532 nm derived from polarization-sensitive
lidar measurement in Beijing in case B and case C.
Mixed pollution case
During the occurrence of the dust event on 4 March, daily-averaged PM10
was 376.3 µg m-3 and PM2.5 / PM10 was 0.19, and
the dust plume existed at an altitude of up to 3.5 km on 5 March. At
12:00 UTC on 5 March, the main body of the dust plume descended to an
altitude of less than 1.5 km (Fig. 8c). The PM2.5 / PM10 ratio
concurrently increased to 49 % because of mixing with a higher
concentration of pollutants and the rapid gravitational settlement of large
particles. The δ value (0.28) for particles at 5 µm
decreased 17.6 % at midnight on 5 March compared to 0.34 on 4 March
(Fig. S8). The 5-day back trajectory implied that the air masses were from a
convergence air flow of deviating northwest and south streams, being affected
by both mineral dust in midwestern China and emissions of anthropogenic
pollution in eastern China. The dV/dlogDp in case C has
two peaks in both the submicron range (0.9 µm) and the coarse-mode
range (4.5 µm), corresponding to δ values of < 0.1
and 0.3±0.2 in Fig. 6f. Note that the averaged δ value for
particles at Dp larger than 4 µm was about 0.3, which is
11.8 % lower compared to case B. T matrix simulations indicated that
the aspect ratios of the dust particles were estimated to be 1.48
(δ = 0.30) (Fig. S9), presuming that the dust particles had a
spheroid shape.
Scatter diagram between the backscattering color ratio (1064 nm/
532 nm) and the particle depolarization ratio at 532 nm for case B
(dust-dominant case) and C (polluted dust case) and an anthropogenic-dominant
case. The error bars indicate estimates of statistical error. The
observations results in Seoul in a previous study are displayed in the plot.
Figure 9 shows the scatter diagram of the averaged particle depolarization
ratio at 532 nm versus the ratio
of the backscattering coefficient at 1064 nm / 532 nm on the two dust
cases and the anthropogenic-dominant case according to lidar measurement. For
comparison, the results from an observation study of mixed-type pollution in
Seoul (Sugimoto et al., 2015) were also plotted in the figure. As shown, the
daily averaged δ value for dust aerosols in case C was 0.26±0.1,
with a backscattering averaged color ratio of 1.21±0.4. It was ∼36.6 % lower than the δ value in case B (0.41±0.14), but
the color ratio (1.32±0.14) was relatively consistent with case C. The
air pollution aerosols had a color ratio of 0.32±0.25 and a
δ value of 0.1±0.05. The results of the δ value in pure
dust and polluted dust plume were similar to the study in Seoul, even though
the coarse-mode aerosols observed in Seoul had a smaller size range due to
the gravitational settlement of large particles during longer range
transport. One possible reason for the decrease in the δ value was
that the mineral dust was involved in the internal mixing process through the
trapping or heterogeneous reactions. However, we cannot eliminate the
possibility that the decrease of δ was just caused by the external
mixing of a huge amount of dust and anthropogenic pollutants. Because lidar
observations only provide an averaged δ value of all the particles in
the detecting volume, the external mixing of dust particles with substantial
amounts of spherical secondary anthropogenic particles could also result in a
lower δ value. This means the environmental impact of transported
Asian dust in polluted areas in East Asia may be underestimated since the
shadow area of lidar improperly recognizes
polluted dust particles.
Implication on heterogeneous processes on dust particles
Coarse-mode particles observed in northern China were reported to contain a
large amount of Ca (Yuan et al., 2006; Geng et al., 2014), generally existing
in the form of CaCO3 (component of calcite), which is the most
widely investigated component of mineral dust particles. Previous studies
indicate that the interaction between water vapor and CaCO3
particles was significant. Hatch et al. (2008) showed that the mass of
adsorbed water on CaCO3 is equal to ∼8 % of the mass of
dry CaCO3 particles at 78 % RH. Studies also found that
one–nine monolayers of adsorbed water are formed on CaCO3
particles at 50 %–95 % RH (Gustafsson et al., 2005; Ma et al.,
2012). Actually, the composition of dust particles is complex and may also
contain SiO2 (component of quartz, illite, feldspar), Al2O3
(illite), CaO (feldspar) and aluminosilicate (kaolinite), etc. These
substances are comparatively insoluble and not sensitive to water vapor in
the air (Tang et al., 2016). This means the heterogeneous reactions and
trapping process on the dust surface were closely related to the dust sources
and residential time in the atmosphere. For example, Wang et al. (2011) found
that the ratio of the Ca component in mineral dust in Beijing was high when
air mass was from the Loess Plateau, while chemical components of dust from
desert areas contained more crustal element
oxides such as SiO2, Al2O3 and Fe2O3
(Ta et al., 2003). Dust with inert components requires longer transport or
residential times before the morphology changes.
Scatter diagram of the relationship between the δ values
of dust particles (at Dp = 5 µm), vapor content (RH) and
PM2.5 / PM10 in the air.
Here, we investigate the vital roles that ambient air humidity and air
pollution content in the air played in the morphological changes of dust
particles in Beijing area. The high PM2.5 / PM10 ratio
indicates that the components of soluble inorganic salts or reactive gases
loading in the atmosphere were highly likely to be relatively high in the
case of high humidity. It also indicates a high collision probability between
pollutants and dust. Therefore, the morphology and corresponding
δ values of particles should be affected. According to our 8-month
observation in Beijing, the δ value had a general trend of
increasing with particle size but decreasing with the
PM2.5 / PM10 ratio (Fig. S10). The extent of the decrease was
not equal for different particle sizes: for particles at
Dp = 5 µm, the δ value was ∼0.3, while
PM2.5 / PM10 was less than 0.6 and decreased by ∼20 %
when PM2.5 / PM10 increased to 1; for particles at
Dp = 1 µm, the δ value decreased by 42.9 % from
0.14 to 0.08. In fact, atmospheric humidity played a vital role in the
observed δ value decrease. Figure 10 shows the relationship between
the δ values of dust particles (Dp = 5 µm), vapor
content (RH) and PM2.5 / PM10 in the air. It can be seen that
the δ value of particles in coarse mode decreased as the
PM2.5 / PM10 ratio increased, especially under high RH
conditions. For particles at Dp = 5 µm, their δ value
was generally 0.28–0.35 when the ambient air was dry (RH
< 10 %), and it decreased by ∼28.6 % when the ambient
RH increased to > 90 %. The δ value of particles at
Dp = 3 µm decreased by ∼36.4 % (Fig. S11). For
small dust particles, the interaction of water vapor and pollutants on the
surface was more obvious in the change of morphology and reduction in
δ value. This negative relationship reflected the spheroidization of
the dust particles as a result of the hygroscopic properties of mineral dust
aerosols. Polluted air generally contains abundant HNO3 (Liang et
al., 2007; Shi et al., 2014). Li and Shao (2009) reported that mineral
particles were mainly covered with coating including Ca(NO3)2,
Mg(NO3)2, and NaNO3 in northern China. Deeper
interaction between alkaline mineral dust and reactive acidic gases and the
trapping process of atmospheric secondary inorganic salt modified the
hydrophilic state of dust aerosols. According to Sullivan et al. (2009), pure
CaCO3 with a diameter of ∼2 µm needs
supersaturation values of 0.6 to 0.9 to be activated, while the soluble salts
of Ca(NO3)2 and CaCl2 are more likely to be activated
with supersaturation values ranging from 0.07 to 0.4. This means that the
more dust particles become involved in chemical mixing or coagulation
processes, the more easily they become hydrophilic and are incorporated into
cloud processes and affect regional and global climate (Shi et al., 2008;
Koehler et al., 2009). Further, the theoretical simulation of Ishimoto et
al. (2010) indicated that a change in refractive index could also affect the
δ value; the simulation results depicted that the δ value
showed a leveling off tendency at 0.31±0.02 for the coarse modal
particles (Fig. S12), which means variations in the particle's refractive
index can only explain limited depolarization variability (6 %). At
present, observation studies for single-particle δ values combined
with aerosol chemical composition analysis are still few in number. To
clarify that the chemical process happened during mixed pollution, more
observational and experimental results in the lab are needed.