ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-11673-2017Role of atmospheric circulations in haze pollution in December 2016YinZhicongyinzhc@163.comWangHuijunKey Laboratory of Meteorological Disaster, Ministry of Education /
Joint International Research Laboratory of Climate and Environment Change
(ILCEC) / Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters (CIC-FEMD), Nanjing University of Information
Science & Technology, Nanjing 210044, ChinaNansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, ChinaClimate Change Research Center, Chinese Academy of Sciences, Beijing, ChinaZhicong Yin (yinzhc@163.com)28September2017171811673116814June201713June201712August201714August2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/11673/2017/acp-17-11673-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/11673/2017/acp-17-11673-2017.pdf
In the east of China, recent haze pollution has been severe and damaging. In
addition to anthropogenic emissions, atmospheric circulations and local
meteorological conditions were conducive factors. The number of December haze
days over North China and the Huanghuai area has increased sharply since 2010
and was greatest in 2016. During 2016, the most aggressive control measures
for anthropogenic emissions were implemented from 16 to 21 December, but the
most severe haze pollution still occurred, covering approximately 25 % of
the land area of China and lasting for 6 days. The atmospheric circulations
must play critical roles in the sub-seasonal haze events. Actually, the
positive phase of the East Atlantic–West Russia pattern in the middle
troposphere strengthened the anomalous anti-cyclone over the NH area that
confined vertical motion below. The associated southerly anomalies made the
cold air and surface wind speed weaker, but enhanced the humid flow. Thus,
the horizontal and vertical dispersion of atmospheric particulates was
suppressed and the pollutants gathered within a narrow space. In December
2016, these key indices were strongly beneficial for haze occurrence and
combined to result in the severest haze pollution. The influences of the
preceding autumn sea surface temperature near the Gulf of Alaska and the
subtropical eastern Pacific, October–November snow cover in western Siberia,
and associated physical processes on haze pollution are also discussed.
Introduction
Because of its enormous adverse effects, haze pollution has become one of the
most serious environmental problems in China, attracting considerable
scientific and social attention. Increasing anthropogenic emissions have been
contributing to severe haze pollution in China and have mainly impacted on
the long-term trend of haze days (Wang et al., 2013). However, interannual
variations of haze days were affected by meteorological conditions (Wang et
al., 2015; Yang et al., 2016; Wang and Chen, 2016). At present, aerosols have
approached saturation in the atmosphere (Zhang et al., 2013). When the
horizontal and vertical dispersion of atmospheric particulates is impeded,
haze weather tends to occur (Yin et al., 2015a). Therefore, anomalous
atmospheric circulations play a key role in the formation of heavy haze
pollution in winter (December–February; Chen and Wang, 2015). From the
hemispheric and regional perspective, the positive phase of the Arctic
Oscillation (Yin et al., 2015b), the weak East Asia winter monsoon (Li et
al., 2015; Yin et al., 2015b), and the positive phase of the East
Atlantic–West Russia (EA/WR) teleconnection (Yin et al., 2017) contribute to
the occurrence of winter haze by modulating local anti-cyclone anomalies over
North China. As a key local circulation, this anomalous anti-cyclone resulted
in descending motion (Wu et al., 2017) that contributed to a reduction in the
height of the planetary boundary layer (PBL). Other conducive weather
conditions include reduced surface wind speed and enhanced humidity in the
lower atmosphere (Ding et al., 2014). Such weather conditions trap abundant
atmospheric particles and moisture, leading to a high concentration of
pollutants. Furthermore, the frequency and persistence of weather conditions
conducive to the Beijing winter severe haze events were projected to increase
substantially under climate change in the future (Cai et al., 2017).
On the sub-seasonal timescale, haze pollution in December is quite serious
and has distinct characteristics, but it has not received adequate attention.
As shown in Fig. 1, there were eight wide-scale haze pollution events in
China in 2016. During six of these events, the highest PM2.5
concentration was observed in North China. During 2016, haze pollution was
most severe during 16–21 December, with the highest PM2.5 concentration
of 1100 µg m-3 in the whole of North China and the Huanghuai
area (NH, located at 30–41∘ N, 110–120∘ E) where more
than 300 million people live. The affected area was 2680 000 km2.
There, the area affected by severe haze was 710 000 km2, which was
close to the total area of the preceding seven episodes in 2016. In addition,
its duration was 6 days, which was approximately twice as long as the other
haze episodes (Fig. 1b). Furthermore, for the past 38 years, the number of
December haze days (DHD) over the NH area (DHDNH) was greatest in
2016 (Fig. 1a). Since 2010, DHDNH has experienced a sharp
increase and reached 21.5 in 2016, meaning that the air was polluted for
approximately 70 % of the days. Because air pollution is regulated and
controlled by the Chinese Government, annual pollutant emissions varied
slowly (Mathews and Tan, 2015), but this could not fully explain the sharp
increase in DHDNH after 2010. In particular, although vehicle
control and production restriction measures were timely,
extensive, and strictly implemented,
haze pollution was still severe during 16–21 December. The effects of
emission reduction measures on air pollutants were efficient and proven
during the 2015 World Championships and Parade (WCP) held in Beijing (Zhou et
al., 2017). Thus, understanding the role of atmospheric circulation in
extreme haze pollution in December 2016 is vital, and this is analyzed in
this paper.
(a) The variation of DHDNH from 1979 to 2016
and (b) the parameters of the main haze processes in China in 2016:
haze (blue bar) and severe haze (red bar) covered area (left y-axis), the maximum
PM2.5 concentration (yellow line, right y-axis, unit: µg m-3) and
the number of days lasted (green line and number).
The data and methods are described in Sect. 2. In Sect. 3, we analyze the
roles of global and regional atmospheric circulations in haze in December
2016. Then, a synoptic case (i.e., the severest haze pollution in 2016) was
studied to understand the physical mechanisms in more detail in Sect. 4. A
discussion of our results and the main conclusions of the study are included
in Sect. 5.
Correlation coefficients between the DHDNH and key
indices from 1979 to 2016 and the ranks of key indices in 2016. The Corr
Coe1 and Corr Coe2 indicated that correlation coefficients were
calculated after and before detrending. The IAC was the
anti-cyclone index that was defined as the
mean Z500 over 105–125, 30–50∘ E. The local PBL, surface wind
speed, and relative humidity were calculated as the mean over the NH area.
All the correlation coefficients were above the 99 % confidence level.
The rank was sorted from largest to smallest, when the Corr Coe was positive.
If the Corr Coe was negative, the rank was calculated from smallest to
largest.
Distribution of the global atmospheric circulation anomalies,
(a) Z500 (shading) and U200 (contour) and (b) SLP (shading)
and SAT (contour) in December 2016. The anomalies here are calculated with
respect to the period from 1981 to 2010.
Datasets and methods
The geopotential height at 500 hPa (Z500), zonal wind at 200 hPa (U200),
wind at 850 hPa, wind at the surface, sea level pressure (SLP), surface air
temperature (SAT), surface relative humidity, and vertical wind (omega) were
available on the website of the National Centers for Environmental
Prediction/National Center for Atmospheric Research (NCEP/NCAR). These
NCEP/NCAR reanalysis I datasets had a horizontal resolution of
2.5∘× 2.5∘ from 1948 to 2016 (Kalnay et al., 1996).
For the representativeness of vertical dispersion, the
1∘× 1∘ height of the PBL was also
used here, but was derived from the ERA-Interim dataset (Dee et al., 2011).
The monthly mean extended reconstructed SST datasets with a horizontal
resolution of 2∘× 2∘ from 1979 to 2016 were
obtained from the National Oceanic and Atmospheric Administration (Smith et
al., 2008). The monthly 1∘ by 1∘ snow cover data were
supported by Rutgers University (Robinson et al., 1993). The EA/WR pattern
consisted of four anomalous centers and the positive phase is associated with
positive anomalous height over Europe and northern
China, and negative anomalies over the central North Atlantic and north of
the Caspian Sea. The EA/WR index was computed by the NOAA climate prediction
center according to the rotated principal component analysis used by Barnston
and Livezey (1987). The routine meteorological measurements included relative
humidity, visibility, and wind speed at the surface that were collected eight
times per day. The temperature profile was collected with a sounding balloon
twice per day. The calculation procedure for the haze data was consistent
with that of Yin et al. (2017), which was mainly based on the observed
visibility and relative humidity. Hourly PM2.5 concentration data were
downloaded from the website of the Ministry of Environmental Protection of
China. Definitions of anomalies are described in the captions for each
figure.
Associated atmospheric circulations in December 2016
Figure 2 shows the distribution of atmospheric circulation anomalies in
December 2016. In the upper troposphere, the East Asia jet stream (EAJS) was
weaker and northward relative to the mean status, indicating that meridional
cold air activity in East Asia was restricted (Chen et al., 2015). As a
result, the land surface of East China was warmer (Fig. 2b). On the
mid-level, there were positive anomalies of Z500 over Europe and North China
and negative centers over the central–North Atlantic and to the north of the
Caspian Sea (Fig. 2a). This Rossby wave train resembled the positive phase of
the EA/WR pattern. To verify the relationship between the DHDNH
and the EA/WR pattern, the correlation coefficient was calculated: it was
0.66 from 1979 to 2016 after removing the linear trend and exceeded the
99 % confidence level (Table 1). This positive correlation was stronger
than that with winter haze days (i.e., 0.43), as analyzed by Yin et
al. (2017). Specific to the positive Z500 anomalies over 105–125,
30–50∘ E (i.e., the easternmost center of the EA/WR pattern), the
correlation coefficient was 0.62 (Table 1). Thus, the anomalous anti-cyclone
over the NH area in December 2016 could efficiently weaken the vertical
motion, resulting in a shallower PBL (Fig. 3a). Furthermore, possible
conducive local weather conditions that included the shallow PBL (impacting
vertical dispersion), low surface wind speed (impacting horizontal
dispersion), and high relative humidity (impacting moisture absorption),
whose correlation coefficients with DHDNH were -0.59, -0.63,
and 0.49 from 1979 to 2016, all passed the 99 % confidence test
(Table 1). Near the surface, the SLP gradient between Eurasia and the western
Pacific decreased (Fig. 2b) and the southerly was induced over the east of China. Warm and
humid airflow from the south caused the surface wind speed to be slower and
the surface relative humidity to be higher (Fig. 3b). Physically, the
positive phase of the EA/WR pattern strengthened the anomalous anti-cyclone
over the NH area and the Japan Sea from the surface to the middle
troposphere, resulting in confined vertical motion. The southerly anomalies
made the cold air and surface wind speed weaker but enhanced the humid flow.
Under control of such atmospheric circulations and local meteorological
conditions, the horizontal and vertical dispersion of atmospheric
particulates was suppressed. Thus, the pollutants gathered within a narrow
space and the haze occurred frequently. In addition, high humidity supported
a beneficial environment for the hygroscopic growth of haze
particles. In December 2016, the
height of the PBL was lowest and the intensity of the anomalous anti-cyclone
over the NH was strongest (Table 1), indicating that the vertical dispersion
condition of pollutants was weakest during the past 38 years. The other key
indices (i.e., the EA/WR index, surface wind speed, and surface relative
humidity) were all in the top six. Thus, the atmospheric circulations and
local meteorological conditions were strongly beneficial for haze occurrence
and combined to result in the severest haze events in December 2016.
Distribution of the regional atmospheric circulation anomalies,
(a) the height of the PBL (shading), and wind at 850 hPa (arrow);
and (b) surface wind speed (shading) and surface relative humidity
(contour) in December 2016. The anomalies here are calculated with respect to
the period from 1981 to 2010.
(a) The measures to limit anthropogenic emissions and
(b) the maximum hourly PM2.5 concentration during 16–21
December 2016. In panel (a), the red rectangle indicates both the
vehicle control and production restriction measures that were implemented,
while the blue triangle indicates the production that was restricted. The
letters in panel (b) were the names of the provinces.
The variation of area-mean visibility (black), surface wind speed
(blue), and surface relative humidity (red, right y-axis). The intensity of
the temperature inversion (T925–T1000) in Beijing is shown as a
gray bar.
Distribution of the global atmospheric circulation anomalies,
(a) Z500 (shading) and U200 (contour); the white dots indicate Z500
anomalies exceeding the 95 % confidence level (t test), and
(b) SLP (shading) and SAT (contour) during 16–21 December 2016; the
white dots indicate SLP anomalies exceeding the 95 % confidence level
(t test). The anomalies here are calculated with respect to the period of
1981–2010.
Vertical-latitude section (110–120∘ E mean) of wind during
16–21 December 2016: omega (shading) and wind (arrow, omega was magnified
100 times).
The variation of the area-mean anomalous height of the PBL in
December 2016. The anomalies here are twice a day and calculated with respect
to the December mean PBLH from 1981 to 2010.
Distribution of the regional atmospheric circulation anomalies,
surface wind (arrow), and surface relative humidity (shading) during 16–21
December 2016. The white dots indicate surface relative humidity anomalies
exceeding the 95 % confidence level (t test). The anomalies here are
calculated with respect to the period from 1981 to 2010.
(a) The correlation coefficients (shading) between the
preceding autumn SST and DHDNH, and the anomalous SSTs in 2016
(contour) that are calculated with respect to the period from 1979 to 2016,
and (b) the correlation coefficients between SSTEP and
Z500 exceeding the 90 % confidence level (shading), correlated WAF
(arrow), and quasi-geostrophic stream function (contour) at 500 hPa in
December.
A synoptic case study
On 15 December 2016, the Ministry of Environmental Protection of China warned
that severe haze pollution would occur over the NH area in the coming week.
After that, nearly 30 cities were issued an air pollution red (the highest
level) warning, and another 20 cities were issued an orange (the second
level) warning (figure omitted). There was a haze-prone zone located from
southwest to northeast, i.e., from the north of Henan Province to Beijing. In
this haze-prone zone, vehicle control and production restriction measures
were both strictly implemented. In the surrounding cities, the industrial
production was also restricted (Fig. 4a). These regulatory measures were
distributed according to the measured PM2.5 concentration, illustrating
good scientific-based decision-making and management. Anthropogenic emissions
were also more stringently limited at this time than for other haze weather
processes that had occurred during the same year, but the most severe haze
pollution still occurred. The highest PM2.5 concentration (i.e.,
1100 µg m-3) was observed in Shijiazhuang, the provincial
capital of Hebei Province (Fig. 4b). The measured maximum hourly PM2.5
concentrations over the NH area were almost above 500 µg m-3,
which was beyond the level of severe air pollution for China. Furthermore,
there were three groups of stations with PM2.5 concentrations greater
than 700 µg m-3, and these were in the central Shaanxi
Province, the north of Henan Province and the south of Hebei Province, and
the central Shandong Province. In addition, the coverage for this haze
pollution process was quite large. Spreading to the south, PM2.5
concentrations larger than 300 µg m-3 could be observed in
most sites in Jiangsu Province. Around the northern edge of the haze area,
high PM2.5 concentrations occurred in Liaoning Province and Inner
Mongolia.
Low visibility is another representation of haze that is widely used in
meteorology. Area-averaged visibility was lower than 10 km, and haze
pollution was gradually aggravated from 16 to 21 December (Fig. 5). During
16–18 December, the diurnal variation of visibility was obvious; i.e.,
visibility decreased at night and increased a little in the morning. Then,
visibility decreased persistently and to a minimum value on 21 December. The
correlation coefficient between visibility and surface wind speed (surface
relative humidity) was 0.4 (-0.69), passing the 99.99 % confidence
test. The continuous low surface wind speed (< 2 m s-1)
restrained the horizontal dispersion of aerosols, and high humidity in the
environment promoted hygroscopic growth that dramatically reduced visibility.
The intensity of the temperature inversion remained positive for 132 h and
reached 9 ∘C on 20 December, so atmospheric particles were limited
to a shallow PBL and accumulated easily. The meteorological conditions also
showed obvious diurnal variation during the early stage of this haze process.
Relative humidity was continuously above 80 % after 20 December,
resulting in persistent decreasing and a minimum value of visibility.
The anomalies of atmospheric circulation during 16–21 December were similar
to those throughout December, but they were more evident and much stronger
than the mean status in December 2016 that resulted in the severest December
haze. The EAJS was weaker than the mean status and moved northward, resulting
in weak cold air activity and a warmer surface (Fig. 6). In the middle
troposphere, the EA/WR pattern could be clearly recognized, and the anomalous
anti-cyclone over North China and Japan was very strong. Under their
influence, there was a descending motion from 30 to 55∘ N (Fig. 7),
and the height of the PBL was approximately 400 m lower than the mean status
of December (Fig. 8). Furthermore, the anomalous height was almost negative
in all months. In addition to vertical accumulation, there was northward and
horizontal transportation of atmospheric particles from the surface to
950 hPa (Fig. 7). Near the surface, the SLP of the mid–high latitude was
distributed as the positive phase of the Arctic Oscillation (AO) pattern and the cold air of the polar regions was toward
the Aleutian Islands, so the cold air was difficult to move southward to the
NH area. The gradient of SLP (SAT) between Eurasia and the western Pacific
receded. The stimulated southerly over the East China coastal area not only
weakened the surface wind speed, but also led to high humidity over the NH
area. In summary, during 16–21 December, atmospheric circulations resulted
in local weather conditions highly conducive to severe haze pollution over
the NH area.
Variation of the DHDNH (black), EA/WR pattern (red),
and SSTEP (blue) indices from 1979 to 2016. The solid lines indicate
the indices whose linear trends were removed and the symbols without lines
were the original indices.
(a) The correlation coefficients (shading) between the
October–November snow cover and DHDNH. The dots indicate the
correlation coefficients exceeding the 95 % confidence level (t-test),
and (b) the correlation coefficients between snowWS and
Z500 exceeding the 90 % confidence level (shading), correlated WAF
(arrow), and quasi-geostrophic stream function (contour) at 500 hPa in
December.
Variation of the DHDNH (black) and SnowWS (blue) indices from 1979 to 2016. The solid lines indicate the indices
whose linear trends were removed and the symbols without lines are the
original indices.
Discussion and conclusions
The most forceful controlling measures of anthropogenic emissions in 2016
were implemented during 16–21 December, but the severest haze pollution
still occurred, covering approximately 25 % of the land area of China and
lasting for 6 days. The highest PM2.5 concentration observed was
1100 µg m-3. Thus, it was hypothesized that atmospheric
circulation must play a critical role. Our results verified that a weaker and
northward EAJS led to weak cold air activity. In the middle troposphere, the
positive phase of the EA/WR pattern was evident, and it stimulated a
descending motion from 30 to 55∘ N and a lower PBL over the NH area.
Near the surface, the positive phase of the AO pattern made the cold air move
southward. The anomalous southerly not only weakened the surface wind speed,
but also led to high humidity over the NH area. The atmospheric circulations
were very conducive to severe haze pollution over the NH area. During all of
December, the number of DHDNH increased sharply from 2010 and was
greatest in 2016. The associated atmospheric circulations that were verified
by climatic correlation analysis were similar. In other words, there was a
weaker EAJS in the upper troposphere, a positive phase of the EA/WR pattern
in the middle troposphere, and conducive local weather conditions (lower PBL,
low surface wind speed, and abundant moisture).
The preceding autumn SST in the Pacific significantly influenced the winter
haze days in North China (Yin et al., 2016) and could partly explain the
severe haze pollution during the winter of 2014 (Yin et al., 2017). For
December, the significantly correlated SST with DHDNH was located
near the Gulf of Alaska and the subtropical eastern Pacific (Fig. 10a). The
preceding autumn SST of these two areas was averaged as an index
(SSTEP), and the correlation coefficients with December Z500 were
calculated and are shown in Fig. 10b. The EA/WR pattern, especially the
anomalous anti-cyclone over NH and Japan, was obvious. The correlation
coefficient between SSTEP and the EA/WR index (DHDNH)
was 0.48 (0.55) after detrending; thus, we speculated that the
SSTEP influenced DHDNH by modulating the EA/WR
pattern. The positive SST anomalies near the Gulf of Alaska and the
subtropical eastern Pacific could impact the wave activity flux (WAF) and
(Fig. 10b) stimulated a Rossby wave-like pattern propagating from the eastern
Pacific, through North America and the Atlantic, and to East Asia. The
atmospheric action centered over the North Atlantic and Eurasia overlapped
with that of the EA/WR teleconnection pattern. Therefore, the positive phase
of the EA/WR pattern could be stimulated or enhanced by positive
SSTEP and then lead to weak ventilation conditions that were
beneficial for the occurrence of haze. In 2016, the positive
SSTEP in autumn were consistent with the positive correlation
fields, leading to more DHDNH. Furthermore, the DHDNH
varied with an obvious decreasing trend from 2006 to 2010, and with a
dramatic increasing trend after 2010. The variation of the EA/WR and
SSTEP index exhibited similar features. During the most recent 10
years, the EA/WR pattern was distributed as its strongest negative phase in
2010 and strongest positive phase in 2016, which was consistent with the
variation of DHDNH (Fig. 11). The variation of the EA/WR pattern
could largely explain the trend break of DHDNH. As shown by Gao
and Chen (2017), October SST anomalies near the Gulf of Alaska and the
subtropical eastern Pacific contributed to the haze pollution over North
China in October 2016. Impacts of the SSTEP on ventilation
conditions were robust and could continue into December. Furthermore, the
relationships with autumn SST in the Atlantic were also found to be weaker
and significant only in small regions (figure omitted).
The Eurasian snowpack and atmospheric circulation dominant modes were stably
coupled from autumn to the subsequent spring (Sun, 2017), so the role of the
preceding October–November (ON) snow cover was also examined (Fig. 12). The
snow cover over western Siberia (SnowWS) was significantly
correlated with DHDNH (EA/WR; Fig. 12a); i.e., the correlation
coefficient was 0.52 (0.45) after detrending (Table 1). More snow was
correlated with a higher albedo, resulting in a colder land surface. When
there was higher SnowWS, negative Z500 anomalies over western
Siberia and positive anomalies over eastern China, i.e., the two active
centers of the EA/WR pattern in the east, were significantly stimulated. The
WAF associated with positive SnowWS anomalies was evidently
induced near western Siberia and efficiently propagated westwards, and
stimulated an obvious anti-cyclone over Baikal Lake and the NH area
(Fig. 12b). The SnowWS varied similarly with DHDNH
and achieved its maximum (minimum) in 2016 (2010). As revealed by Wang et
al. (2015) and Yin et al. (2017), the preceding autumn Arctic sea ice has a
close relationship with the winter haze days in the east of China. The
climatic relationship between the Arctic sea ice and DHDNH and
the anomalies in December were also examined and found to be insignificant
(figure omitted). This may be due to the relationship being different in the
early and late winter, which requires more research in the future. The
detailed mechanisms between the external forcings and haze pollution should
be studied physically and dynamically in future work. Furthermore, although
anthropogenic emissions were limited during haze pollution events, there were
still aerosols being discharged into the atmosphere by the dense population
and industry before and during 16–21 December. There is little doubt that
the high concentration of PM2.5 was the fundamental cause of haze
pollution, and the associated atmospheric anomalies played key roles in the
severe haze pollution events. The previous accumulation of atmospheric
particles also contributed to the occurrence of haze pollution events. As
revealed in this study, it was difficult to modify the simultaneous
atmospheric circulations, which significantly contributed to the haze.
Therefore, the controlling measures of anthropogenic emissions should be
implemented in advance to reduce the stock of aerosols in the atmosphere.
Atmospheric data are available from the
NCEP/NCAR data archive:
http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
(NCEP/NCAR, 2017). SST data are downloaded from
http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v4.html
(NOAA, 2017). Snow cover data can be downloaded from Rutgers University:
http://climate.rutgers.edu/snowcover/ (Rutgers, 2017). The monthly
EA/WR and WP indices (CPC, 2017) can be downloaded from NOAA's Climate
Prediction Center:
http://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml. The
ground observations are from the website http://data.cma.cn/ (CMA,
2017). The monthly PBLH data are available on the ERA-Interim website:
http://www.ecmwf.int/en/research/climate-reanalysis/era-interim
(ERA-Interim, 2017). The atmospheric composition data can be obtained from
the authors.
The authors declare that they have no conflict of interest.
Acknowledgements
This research was supported by the National Key Research and Development Plan
(2016YFA0600703), the National Natural Science Foundation of China (41421004,
41705058), the CAS-PKU Partnership Program, the Startup Foundation
for Introducing Talent of Nanjing University of Information Science and
Technology (20172007), the KLME Open Foundation (KLME1607), and the Priority
Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
Edited by: Jianping Huang
Reviewed by: two anonymous referees
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