ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-2971-2017Attributions of meteorological and emission factors to the 2015 winter
severe haze pollution episodes in China's Jing-Jin-Ji areaLiuTingtingGongSunlingsunling@camscma.cnHeJianjunYuMengWangQifengLiHuairuiLiuWeiZhangJieLiLeiWangXuguanLiShuliLuYanliDuHaitaoWangYaqiangZhouChunhongLiuHongliZhaoQichaoSchool of Mechanical Engineering, Hangzhou Dianzi University,
Hangzhou, ChinaState Key Laboratory of Severe Weather & Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing, ChinaLangfang Bureau of Environmental Protection, Langfang, Hebei, ChinaLangfang Academy of Eco Industrialization for Wisdom Environment,
Langfang, Hebei, ChinaLangfang Bureau of Meteorology, Langfang, Hebei, ChinaSunling Gong (sunling@camscma.cn)27February2017174297129805September201621September201623January201731January2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/2971/2017/acp-17-2971-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/2971/2017/acp-17-2971-2017.pdf
In the 2015 winter month of December, northern China witnessed the most
severe air pollution phenomena since the 2013 winter haze events occurred.
This triggered the first-ever red alert in the air pollution control
history of Beijing, with an instantaneous fine particulate matter (PM2.5) concentration over 1 mg m-3.
Air quality observations reveal large
temporal–spatial variations in PM2.5 concentrations over the
Beijing–Tianjin–Hebei (Jing-Jin-Ji) area between 2014 and 2015. Compared to
2014, the PM2.5 concentrations over the area decreased
significantly in all months except November and December of 2015, with an increase of 36 % in
December. Analysis shows that the PM2.5 concentrations are
significantly correlated with the local meteorological parameters in the
Jing-Jin-Ji area such as the stable conditions, relative humidity (RH), and wind
field. A comparison of two month simulations (December 2014 and 2015)
with the same emission data was performed to explore and quantify the
meteorological impacts on the PM2.5 over the Jing-Jin-Ji area.
Observation and modeling results show that the worsening meteorological
conditions are the main reasons behind this unusual increase of air
pollutant concentrations and that the emission control measures taken during this
period of time have contributed to mitigate the air pollution
(∼ 9 %) in the region. This work provides a scientific
insight into the emission control measures vs. the meteorology impacts for the
period.
Introduction
Severe air pollution has been observed in China for the last 15–20 years,
with an annual mean concentration of fine particulate matter (PM2.5)
ranging from 80 to 120 µg m-3 and over 1000 µg m-3
during some heavy haze episodes. The haze phenomenon has become a major pollution
problem in many China cities (Han et al., 2013; Wang et al., 2015), which
causes wide public concern and has an adverse impact on human health and the
environment (Gurjar et al., 2010; Kan et al., 2012). Therefore, it is
necessary to comprehensively investigate the emission sources,
meteorological factors, and other characteristics of the PM2.5
pollution in China and provide more effective control measures (Wang et al.,
2008; Zhang et al., 2014).
Since the strict control measures of air pollutants over the country were
enforced in 2013 by the government, a steady decrease of air pollutant
concentrations has been observed with the annual mean PM2.5 concentration
dropping from about 85 µg m-3 in 2014 to
80 µg m-3 in 2015 for Beijing, from 86 to 70 µg m-3 for Tianjin, and
from 118 to 88 µg m-3 for Shijiazhuang (three typical cities in north China,
http://www.mep.gov.cn/gkml/). Meteorological conditions, especially the
large-scale circulations, are important factors in determining the variation
of pollution levels (He et al., 2016a; Jia et al., 2015). Significant regional
transport, caused by special meteorological conditions, is critical for the
formation of severe haze in the winter of 2015 in Beijing (Sun et al., 2016). A number
of papers have tried to analyze the meteorological contributions (He et al.,
2017; Liao et al., 2015; Wang et al., 2013; Zeng et al., 2014) for individual
cases but could not consider the emission changes for a comprehensive
analysis. The most recent consensus is that these decreases can be partially
attributed to the difference in the meteorological conditions but should largely
be attributed to the control measures taken. However, due to the
complex interactions between pollution sources and meteorology, the
quantitative contributions for each factor remain to be separated.
The year 2015 was an unusual year in terms of the air pollution situation in
northern China, which was in the middle of a global El Niño–Southern
Oscillation (ENSO) event (Varotsos et al., 2016). Unusual
climate and extreme weather happened everywhere. It was found that the El
Niño event had a significant effect on air pollution in eastern China
(Chang et al., 2016). In the first half of 2015, a steady decrease in major air
pollutants was observed compared to those in 2014 in northern China. However,
in the last two months a dramatic increase was observed. The PM2.5
concentration reached as high as 1000 µg m-3 in Beijing and
triggered the first-ever red alert of severe air pollution in the city.
Would this unusual increase of air pollution have anything to do with the special
meteorological conditions and the El Niño event? Additionally, what
impact did emission control measures have?
This paper presents an analysis and modeling study of air pollution
conditions in December 2015 in the Beijing–Tianjin–Hebei (Jing-Jin-Ji) area
located in north China, and it explores the major reasons behind these unusual
increases from both the meteorological and emission points of view. To
evaluate the contribution of meteorological factors to the severe
pollution in December 2015, the wind speed convergence lines (WSCL), the static
wind frequency (SWF, wind speed less than 1 m s-1) data, and other parameters were specifically
investigated and compared with data for the same period in 2014. An analysis
of this heavy haze pollution episode was also simulated with the Chinese
Unified Atmospheric Chemistry Environment (CUACE) model (Gong and Zhang,
2008). The aim of this study was to provide information on the impact degree
and the mechanism of meteorology variations and emission changes on the
PM2.5 haze pollution in this region.
Methodology
The research starts with the analysis of air pollution levels between 2014
and 2015 with a focus on the last month of each year. The difference lays
the foundation for the investigation, where the meteorology factors that
most influence air pollution levels such as stable conditions,
wind speed (WS) and direction, and the relative humidity are probed. This
provides a qualitative description of the reasons for pollution changes
from 2014 to 2015. Based on EAR-Interim reanalysis data from the European Centre
for Medium-Range Weather Forecasts, the potential effect of ENSO on
atmospheric circulation and air quality in the Jing-Jin-Ji area is also
thoroughly investigated. In order to quantify the meteorology impacts, a
modeling study where the pollution level changes are considered to be caused only by
meteorology is carried out with the same emission data for 2014
and 2015 . The impact of emission changes on air pollution due to the meteorology can then
be inferred from the difference between the observed pollution level changes
and the modeled level changes.
(a) Monthly mean PM2.5 concentrations in December 2015
and (b) the change of monthly mean PM2.5 concentrations in
December between 2015 and 2014 over the Jing-Jin-Ji area.
Comparison of monthly average PM2.5 concentrations in 2015 and
2014 in (a) Beijing, (b) Tianjin,
(c) Shijiazhuang,
and (d) the Jing-Jin-Ji area.
Air quality observations
The observational pollution data used in this study were from the near real
time monitoring stations of the Ministry of Environmental Protection
across northern China (http://www.cnemc.cn/) with hourly
concentrations of six major pollutants: particulate matter with an
aerodynamic diameter of less than 2.5 and 10 µm (PM2.5 and
PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon
monoxide (CO), and ozone (O3). The monthly and annual mean concentrations
of PM2.5 in three typical cities (Beijing, Tianjin, and Shijiazhuang)
and the Jing-Jin-Ji area were investigated. The PM2.5 concentration in
the Jing-Jin-Ji area is represented by the regional average for the following 13 cities:
Beijing, Tianjin, Shijiazhuang, Handan, Xingtai, Hengshui, Cangzhou, Baoding,
Langfang, Tangshan, Qinhuangdao, Chengde, and Zhangjiakou. The spatial
distribution of the 13 cities is shown in Fig. 1. Based on the
whole-year data for 2014 and 2015, the annual mean concentrations of PM2.5
decreased overall (Fig. 2). For the three typical cities of Beijing, Tianjin,
and Shijiazhuang, the annual mean PM2.5 concentrations in 2015 are 5.7,
18.5, and 29.2 % lower than those in 2014, respectively (Fig. 2).
The regional mean PM2.5 concentration over the Jing-Jin-Ji area
decreased by 17.8 %. The two-year monthly mean PM2.5 concentrations (Fig. 2)
show that from January to October, the concentrations in 2015 are much
lower than those in the same months in 2014. The unusual increases in
PM2.5 concentration are found in the last two months and especially in
December, which is the focus of this study.
Regionally, the monthly mean PM2.5 concentrations in December 2015 saw a
large increase compared to the same month in 2014, ranging from 5 to
137 % in the Jing-Jin-Ji area with a mean increase of 36 % (Fig. 1).
Beijing had the largest increase at 137 %, jumping from approximately
61 µg m-3 in 2014 to 145 µg m-3 in 2015.
Qinhuangdao had the smallest increase at 5 %, jumping from
approximately 69 µg m-3 in 2014 to 72 µg m-3
in 2015.
Certain factors must have changed dramatically to cause this to happen. In
view of the steady decreases of air pollutants over the Jing-Jin-Ji area in the
first ten months of 2015, it can be inferred that the emission reduction
measures implemented in the region – including traffic restriction,
eliminating vehicles that fail to meet the European No. 1 standard for
exhaust emission, reducing coal consumption, forbidding straw burning, and
reducing volatile organic compounds emission
(http://bj.people.com.cn/n/2015/0526/c233088-25012933.html) – were
effective in lowering the average concentrations of major pollutants.
In next section, the meteorological conditions in December of 2014 and
2015 will be analyzed in detail to explain the reasons for this dramatic
increase in the Jing-Jin-Ji area.
Meteorology factor analysis
Closely related to air pollution variations, meteorological conditions
are important factors in determining day-to-day variations of pollutant
concentrations (He et al., 2016a). The correlation between daily average
PM2.5 concentrations and four meteorological parameters – 2 m
temperature (T2), 2 m relative humidity (RH2), 10 m wind speed
(WS10), and boundary layer height (BLH) – is shown in Fig. 3. The data
processing in the correlation calculation is the same as in He et al. (2017).
PM2.5 concentrations are positively correlated with T2 and RH2,
while negatively correlated with WS10 and BLH. All correlation
coefficients (Rs) are significant except T2 in Shijiazhuang.
The positive correlation between PM2.5 concentrations and RH2 reveals
the importance of hygroscopic growth for PM in the Jing-Jin-Ji area. The increase
of WS10 and BLH enhances the ventilation and diffusion capacity and
improves air quality. The comparison of the correlation coefficients in the three
cities reveals that the local meteorological condition has a more significant
effect in Beijing than in Tianjin and Shijiazhuang. Located in the
northern edge of the North China Plain, the regional transport of pollutants in
Beijing is less complex than in other cities, which may explain the high correlation
between PM2.5 concentration and meteorological parameters.
The correlation between daily average PM2.5 concentrations and
daily average meteorological parameters during 2014–2015. The dashed lines
represent the critical correlation coefficient that passes the t test at a
95 % confidence level.
Previous studies have shown that a major factor controlling the pollutant
accumulation is the atmospheric stability in association with the
convergence at lower levels, which leads to the accumulation of polluted
air from the surrounding areas and prevents pollutants from diffusing away
from the source regions (Liao et al., 2015; Wang et al., 2013; Zeng et al.,
2014). Therefore, the location of the convergence zone is critical in
identifying the meteorological conditions that are favorable or unfavorable for the
formation of heavy pollution.
Two weather analysis maps are constructed based on average surface
meteorological data for December of 2014 and 2015 from the China Meteorological
Administration (CMA) (Fig. 4). The mean wind speed for December of 2014
reveals a high wind speed in the Hebei province with a low wind speed
in the south of the Hebei province. Wind speed shear, i.e. an abrupt
decrease (increase) of wind speed, forms a convergence (divergence) zone that
is the WSCL. The WSCL is located at the boundary of the Hebei and Shandong provinces. A
serious pollution band near the WSCL will usually form due to the
adverse dispersion conditions and pollutant accumulation. Compared to 2014,
the WSCL shifted to the Beijing municipality and the center of the Hebei province in
2015. The relocation of the WSCL results in the pollution band moving
northward and more serious air pollution in Beijing and the surrounding cities.
The weather analysis maps in December 2014 (a) and
2015 (b). Red line represents WSCL.
The MSL and 10 m wind anomalies over the north China region.
(a) SSTA larger than zero; (b) SSTA less than zero;
(c) December 2015.
Observational evidence has shown a teleconnection between the central Pacific
and East Asia during the extreme phases of the ENSO cycles. This Pacific–East
Asian teleconnection is confined to the lower troposphere. The key system
that bridges the warm (cold) events in the eastern Pacific and the weak
(strong) East Asian winter monsoons (EAWMs) is an anomalous lower-tropospheric
anticyclone (cyclone) located in the western North Pacific (Wang et al.,
2000). Si et al. (2016) found that during the 2015 El Niño period,
the EAWM was weaker than normal in the winter, with a temperature
increase of 1.1 ∘C. The subtropical high was stronger and had a
larger area than in normal years (Li et al., 2016). As a consequence of the
weaker EAWM, the cold front in 2015 could not extend as far south as in
2014, leading to a northward shift of the WSCL.
Chang et al. (2016) found a close relationship between ENSO and air pollution
in north China in 2015. To more deeply investigate the relation between ENSO and
the air quality in north China, EAR-Interim reanalysis data from December
1979 to 2015 – including sea surface temperature (SST), mean sea level pressure
(MSL), 2 m temperature (T2), and 10 m U and V wind components (U10
and V10) – were used. Area averaged SST anomalies (SSTAs) over the Nino3
region (5∘ N–5∘ S, 150–90∘ W) provide an index
typically used to represent ENSO variability (Tang et al., 2016). Time series
of monthly averaged SSTAs over the Nino3 region are shown in Fig. S3.
Significant ENSO events were found in 1982, 1997, and 2015. The MSL and 10 m
wind anomalies over the north China region are shown in Fig. 5. It seems that
ENSO (SSTA > 0) results in weak cold air and northerly wind while
opposite is seen for La Niña (SSTA < 0).
These relations indicate that the worse
air quality seen in December 2015 over north China may be related to the
significant ENSO. Further study with longer-period data is needed to investigate this
correlation.
There are three consequences of the WSCL shift. The first consequence is the shifting of the stable atmosphere
zone to the central Hebei and Beijing areas in 2015, allowing the pollutants
to easily accumulate along the WSCL. The observed static wind frequency
distribution clearly supports this
observation. Figure 6a is the regional distribution of SWF in December 2015
showing a high frequency along the convergence line, with the SWF changes
from 2014 shown in Fig. 6b. Table 1 lists the SWF for the three typical cities and
the regional mean over the Jing-Jin-Ji area. Except for Shijiazhuang, which had an
unusually high SWF in 2014 and a decreasing SWF in 2015, the cities
experienced an increasing trend for stable weather. Impacted heavily by the
WSCL shift, Beijing and Tianjin had a 16–19 % increase in SWF in 2015 compared to
2014. Even with a decreasing trend for SWF, Shijiazhuang had a similar SWF
to the other cities, with more than half of the days (> 50 %) under
static stable environment.
In Beijing, the WSCL shift in December 2015 not only increased the SWF but
also changed the wind directions. Figure 7 shows that the
northwest winds that usually diffuse air pollution away from Beijing were reduced
by about 20 % while
the southerly wind frequencies that brought air pollution to Beijing were increased by 8 % in December 2015 compared to the same period in 2014.
Compared to Beijing, the city of Shijiazhuang did not
see such a large change (Fig. 7). The SWF in Shijiazhuang was reduced and
the northerly wind frequency increased by 6 % in December 2015.
Comparison of SWF (%), WS10 (m s-1), and RH2 (%)
for December of 2015 and 2014.
The observed SWF and RH2 distribution average for December
2015 (a, c); Changes from 2014 (b, d).
The second consequence of the WSCL shift is the northerly movement of
moisture from the south. Figure 6c and d show the average RH2 for
December of 2015 and the changes from 2014, respectively. It is obvious that as the
WSCL shifts to the north, the RH increases are primarily on the northern side of
the WSCL with an increase of 30 % in Beijing (other cities in Table 1).
PM2.5 concentration is positively correlated with RH2 (Fig. 3). The
increasing RH has an adverse influence on the visibility under the
same loading of particulate matter and promotes the
secondary formation of particulate matter from gaseous species. Because of
the WSCL shift, the increase in RH in Shijiazhuang was slightly
larger than in Beijing, at about 32 %. Researchers (Chang et al.,
2009) have shown that the extent of SO2 oxidation to sulfate and
NO2 oxidation to nitrate increased with the increase of relative
humidity during two episodes of daytime and nighttime pollution in
Taiwan. Gund et al. (1991) found that the oxidation rate of SO2 to
sulfate would increase by about 10 times if the relative humidity increased
from 40 to 80 % in sea-salt aerosols. If NO2 (SO2 : NO2= 1 : 1) were added to the gas phase, the rate of conversion of SO2 to PM2.5 at a relative
humidity of 40 % would be increased by about 24 times (Gund et al., 1991).
Though the detailed mechanism of
this enhanced oxidation in northern China needs further study, the increased
relative humidity may partially be attributed to the decrease of SO2
(from 86 to 61 µg m-3 in the Jing-Jin-Ji area) during the heavy
pollution months in the winter of 2015 as compared to the same period of 2014.
The third consequence of the WSCL shift is the decrease of the BLH. Previous
research (Liu et al., 2010) has revealed that the invasion of cold air
increases the turbulence flux and the BLH over the Beijing area. Compared to
2014, the weaker cold air in December 2015 resulted in a decrease of BLH in
the range of 50 to 300 m over the Jing-Jin-Ji area (Fig. 8), which was one of
the main reasons for heavy haze pollution in December 2015.
(a) The observed wind frequency and direction averaged
in December of 2014 and 2015 in Beijing (a, b) and
Shijiazhuang (c, d) respectively.
(a) The monthly mean BLH in December 2014 and
(b) the change of monthly average BLH in December between 2015 and
2014 over north China.
Modeling analysis
In order to further explore and quantify the meteorological impacts on the
changes of the air pollution situation between December of 2014 and 2015, a
comparison of the two years' simulations with the same emission data was
performed for December. The difference in the simulated pollutants
concentrations can be attributed to the difference impacted by the meteorological
conditions.
(a) Simulated PM2.5 concentrations difference between
December of 2015 and 2014. (b) PM2.5 fractional difference.
The CUACE is an atmospheric chemistry module that includes an emission modeling
system, gaseous and aerosol processes, chemistry processes, and related
thermodynamic equilibrium modules for processing the transformation between
gas and particulate matter (Gong et al., 2003; Wang et al., 2010; Zhou et al.,
2012). The meteorological model coupled to CUACE is the fifth-generation Penn
State/NCAR mesoscale model (MM5). The MM5/CUACE model system was run with
three nested domains (a horizontal resolution of 27, 9, and 3 km) to reduce
spurious inner domain boundary effects (Fig. S1). In the vertical, there are
a total of 35 full sigma levels extending to the model top at 10 hPa with 16
levels below 2 km. One month (December) was simulated for 2014 and
2015 with the result differences presented for the analysis.
The comparison of the CUACE emission inventory (representing the emission in
2013) to other inventories and the details of the integration scheme,
initial condition, and boundary conditions were presented in He et al. (2016b).
Six statistical indices – index of agreement (IOA), correlation
coefficient (R), standard deviation (SD), root mean square error (RMSE),
mean bias (MB), and mean error (ME) – were employed to investigate the
performance of MM5/CUACE with the routine meteorological data from the CMA and
hourly average PM2.5 concentrations from the Ministry of Environmental
Protection. The statistical performance based on hourly observed data were
provided in Tables S1 and S2 for MM5 and CUACE, respectively. Direct
comparisons between the observed and simulated daily average PM2.5
concentrations are shown in Fig. S2. The error in December 2015 is larger
than in December 2014, which may relate to the uncertainty of the emission
inventory that represents the emission in 2013. The MB of PM2.5 reached
25–30 µg m-3 in the simulation for July and December of 2013
(He et al., 2016b), while
it decreases to 19 and 17 µg m-3 for December of 2014 and 2015
respectively (Table S2). This indicates that the emission in the CUACE model might be
overestimated considering the gradual emission reduction in recent years.
The error of simulated meteorological fields is another important source for
the error of simulated PM2.5 concentrations. Generally, the MM5/CUACE
model can reproduce the variation characteristics of meteorological
parameters and air pollution well, and it is comparable to previous studies (He et
al., 2016b; Kioutsioukis et al., 2016).
Figure 9a shows the December PM2.5 concentration difference between 2015
and 2014. It is clear that the meteorological conditions alone have
contributed to the worsening air quality (PM2.5) in northern China, with
a high degradation of about 30–60 µg m-3 in the southern
Beijing and southern Hebei regions in December 2015. This corresponds well with
the WSCL from the surface meteorological data analysis (Fig. 4), which shows
the more stable zone moving to closer to southern Beijing.
From the modeling results, it can be seen that the PM2.5
difference (i.e., the concentration difference between
December 2015 and 2014 divided by the average concentration in December 2014)
due to meteorological difference between December 2014 and 2015 for the major
cities in the Jing-Jin-Ji area is in the range of 10–150 % (Fig. 9b). This shows a
system-wide negative impact on air quality in the region in 2015. This
simulated difference is a comprehensive consequence of the meteorological
impacts including circulation, dispersing ability, deposition,
transports, and chemical reactions.
It is well known that the PM2.5 concentrations are determined by
three major factors: emissions, meteorology, and atmospheric processes. Given
that the degree of meteorological impacts was simulated by the model and
the observed differences between December 2014 and 2015 were known, the
impact from emission changes can be inferred from the observed differences
and the simulated meteorological impacts.
Comparison of observed and simulated PM2.5 in December 2015 and
2014.
Table 2 is a summary of the differences for the major cities in the Jing-Jin-Ji area
between December 2014 and 2015. The observed percentage changes are all
lower than the simulated ones except for Beijing. This indicates that if no
emission control measures were taken during this period, the observed
difference would be much larger. Therefore, it can be
deduced that despite the unfavorable weather conditions that worsened the
air quality in December 2015, the control measures have greatly
contributed to the reduction in the ambient concentrations by about 9 % in
the Jing-Jin-Ji area. The increase of the relative variation of the simulated PM2.5
concentration for December between 2015 and 2014 in Beijing is larger than
the observed value, which might be related to the bias of the local wind field. In
fact, it is very difficult for the mesoscale meteorological model to accurately capture the
local wind fields. The comparison of the wind rose map between
observation and simulation in December 2015 over Beijing (Fig. S4) reveals
that MM5 overestimated the frequency of northwesterly winds, which results in
the underestimation of regional transport and PM2.5 concentration in the
model. This can explain why there is a difference in the relative change of
PM2.5 in Beijing and other cities (Table 2). Another factor that needs
further investigation is the observational data quality itself, which may
have a large uncertainty associated from different stations and may influence
the accurate assessment of the impact.
Conclusions
The meteorological data analysis and modeling study of heavy haze
pollution episodes in the winter of 2015 were carried out to explore the causes of the unusual
increase of haze (PM2.5) in December. It was discovered that the monthly
mean PM2.5 concentrations in December 2015 saw a large increase compared
to the same month in 2014, ranging from 5 to 137 % in the Jing-Jin-Ji
area with a mean increase of 36 %. Due to the unusual atmospheric circulation in
the winter of 2015 (El Niño event), the warm and wet flow was enhanced in
north China and the WSCL shifted northerly compared to 2014. The
SWF and RH2 increased by 12 and 25 % in the Jing-Jin-Ji area, respectively.
These changes of meteorology brought more static stable weather, which was
primarily responsible for the degradation of air pollution in the winter of 2015.
The modeling analysis further confirmed that the meteorological conditions
contributed to the worsening air quality in the Jing-Jin-Ji area in the winter
of 2015, with the PM2.5 concentration for the major cities in December 2015
increasing by 45 % compared to the same period in 2014. In the modeling study with the same
emission data for 2014 and 2015, the relative changes
in pollution level between the two years were larger than those from the
observation. This indicates that the control measures to compensate for the negative meteorological
impacts have effectively brought the PM2.5 concentration down (∼ 9 %).
The air quality dataset used in this study is from the Ministry of Environmental
Protection of China (http://www.cnemc.cn/) and can be accessed via
https://www.zq12369.com/. The meteorological observation dataset can be acquired
from the China Meteorological Data Service Center (http://data.cma.cn/en). The European Centre
for Medium-Range Weather Forecasts' ERA
data is available from the ECMWF Public Datasets web interface
(http://apps.ecmwf.int/datasets/).
The Supplement related to this article is available online at doi:10.5194/acp-17-2971-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This research was financially supported by the Science and Technology support
program (2014BAC16B03) and by the National Natural Science Foundation of
China (No. 51305112 and 91544232). Edited by:
T. Zhu Reviewed by: three anonymous referees
References
Chang, L. P., Yao, Y. C., Liao, C. F., Chiang, S. W., and Tsai J. H.:
Influence of ozone and humidity on the formation of sulfate and nitrate in
airborne fine particles, J. Environ. Sci. Heal. A, 44, 767–777, 2009.Chang, L., Xu, J., Tie, X., and Wu, J.: Impact of the 2015 El Nino event on
winter air quality in China, Scientific Reports, 6, 34275,
10.1038/srep34275, 2016.Gong, S. L. and Zhang, X. Y.: CUACE/Dust – an integrated system of
observation and modeling systems for operational dust forecasting in Asia,
Atmos. Chem. Phys., 8, 2333–2340, 10.5194/acp-8-2333-2008, 2008.Gong, S. L., Barrie, L. A., Blanchet, J. P., von Salzen K., Lohmann, U.,
Lesins, G., Spacek, L., Zhang, L. M., Girard, E., Lin, H., Leaitch, R.,
Leighton, H., Chylek, P., and Huang, P.: Canadian aerosol module: a
size-segregated simulation of atmospheric aerosol processes for climate and
air quality models – 1. Model development, J. Geophys. Res., 108, 4007,
10.1029/2001JD002002, 2003.
Gund, G., Wien, F., and Weisweiler, W.: Oxidation of S02 to sulfate in sea
salt aerosols, Fresenius J. Anal. Chem., 340, 616–620, 1991.Gurjar, B. R., Jain, A., Sharma, A., Agarwal, A., Gupta, P., Nagpure, A. S.,
and Lelieveld, J.: Human health risks in megacities due to air pollution,
Atmos. Environ., 44, 4606–4613, 10.1016/j.atmosenv.2010.08.011, 2010.Han, X., Zhang, M., Tao, J., Wang, L., Gao, J., Wang, S., and Chai, F.:
Modeling aerosol impacts on atmospheric visibility in Beijing with RAMS-CMAQ,
Atmos. Environ., 72, 177–191, 10.1016/j.atmosenv.2013.02.030, 2013.He, J., Yu, Y., Xie, Y., Mao, H., Wu, L., Liu, N., and Zhao, S.: Numerical
model-based artificial neural network model and its application for
quantifying impact factors of urban air quality, Water Air Soil Poll., 227,
235, 10.1007/s11270-016-2930-z, 2016a.He, J., Wu, L., Mao, H., Liu, H., Jing, B., Yu, Y., Ren, P., Feng, C., and
Liu, X.: Development of a vehicle emission inventory with high
temporal-spatial resolution based on NRT traffic data and its impact on air
pollution in Beijing – Part 2: Impact of vehicle emission on urban air
quality, Atmos. Chem. Phys., 16, 3171–3184, 10.5194/acp-16-3171-2016,
2016b.He, J., Gong, S., Yu, Y., Yu, L., Wu, L., Mao, H., Song, C., Zhao, S., Liu,
H., Li, X., and Li, R.: Air pollution characteristics and their relation to
meteorological conditions during 2014–2015 in major Chinese Cities, Environ.
Pollut., 10.1016/j.envpol.2017.01.050, online first,
2017.Jia, B., Wang, Y., Yao, Y., and Xie, Y.: A new indicator on the impact of
large-scale circulation on wintertime particulate matter pollution over
China, Atmos. Chem. Phys., 15, 11919–11929, 10.5194/acp-15-11919-2015,
2015.Kan, H., Chen, R., and Tong, S.: Ambient air pollution, climate change, and
population health in China, Environ. Int., 42, 10–19,
10.1016/j.envint.2011.03.003, 2012.Kioutsioukis, I., de Meij, A., Jakobs, H., Katragkou, E., Vinuesa, J., and
Kazantzidis, A.: High resolution WRF ensemble forecasting for irrigation:
Multi-variable evaluation, Atmos. Res., 167, 156–174,
10.1016/j.atmosres.2015.07.015, 2016.
Li, M., Hua, C., and Ma, X.: Analysis of the December 2015 Atmospheric
Circulation and Weather, Meteorol. Mon., 42, 382–388, 2016 (in Chinese).Liao, X., Sun, Z., Tang, Y., Pu, W., Li, Z., and Lu, B.: Meteorological
Mechanism for the Formation of a Serious Pollution Case in Beijing in the
Background of Northerly Flow at Upper Levels, Environ. Sci., 36, 801–808,
10.13227/j.hjkx.2015.03.007, 2015 (in Chinese).
Liu, X., Hu, F., Zou, H., Cao, X., and Dou, J.: Analysis on characteristic of
atmospheric boundary layer during a typical heavy fog process in Beijing
area, Plateau Meteorol., 29, 1174–1182, 2010 (in Chinese).
Si, D., Liu, Y., Shao, X., and Wang, Y.: Anomalies of Oceanic and Atmospheric
Circulation in 2015 and Their Impacts on Climate in China, Meteorol. Mon.,
42, 481–488, 2016 (in Chinese).Sun, Y., Chen, C., Zhang, Y., Xu, W., Zhou L., Cheng, X., Zheng, H., Ji, D.,
Li, J., Tang, X., Fu, P., and Wang, Z.: Rapid formation and evolution of an
extreme haze episode in Northern China during winter 2015, Scientific
Reports, 6, 27151, 10.1038/srep27151, 2016.
Tang, Y., Li, L., Dong, W., and Wang, B.: Tracing the source of ENSO
simulation differences to the atmospheric component of two CGCMs, Atmos. Sci.
Lett., 17, 155–161, 2016.Varotsos, C. A., Tzanis, C. G., and Sarlis, N. V.: On the progress of the
2015–2016 El Niño event, Atmos. Chem. Phys., 16, 2007–2011,
10.5194/acp-16-2007-2016, 2016.
Wang, B., Wu, R., and Fu, X.: Pacific-East Asian teleconnection: how does
ENSO affect East Asian climate?, J. Climate, 13, 1517–1536, 2000.Wang, C., Yang, Y., Li, Y., and Fan, Y.: Analysis on the meteorological
condition and formation mechanism of serious pollution in south Hebei
Province in January 2013, Res. Environ. Sci., 26, 695–702,
10.13198/j.res.2013.07.4.wangcm.006, 2013 (in Chinese).
Wang, H., Gong, S., Zhang, H., Chen, Y., Shen, X., Chen, D., Xue, J., Shen,
Y., Wu, X., and Jin, Z.: A new-generation sand and dust storm forecasting
system GRAPES_CUACE/Dust: Model development, verification and numerical
simulation, Sci. Bull., 55, 635–649, 10.1007/s11434-009-0481-z, 2010.Wang, L., Hao, J., He, K., Wang, S., Li, J., Zhang, Q., Streets, D., Fu, J.,
Jang, C., Takekawa, H., and Chatani, S.: A Modeling Study of Coarse
Particulate Matter Pollution in Beijing: Regional Source Contributions and
Control Implications for the 2008 Summer Olympics, J. Air Waste Manage., 58,
1057–1069, 10.3155/1047-3289.58.8.1057, 2008.Wang, L., Wei, Z., Wei, W., Fu, J. S., Meng, C., and Ma, S.: Source
apportionment of PM2.5 in top polluted cities in Hebei, China using the
CMAQ model, Atmos. Environ., 122, 723–736,
10.1016/j.atmosenv.2015.10.041, 2015.Zeng, J., Wang, M., and Zhang, H.: Correlation between atmospheric PM2.5
concentration and meteorological factors during summer and autumn in Beijing,
China, Chinese J. Appl. Ecol., 25, 2695–2699,
10.13287/j.1001-9332.20140616.002, 2014 (in Chinese).Zhang, S., Wu, Y., Wu, X., Li, M., Ge, Y., Liang, B., Xu, Y., Zhou, Y., Liu,
H., Fu, L., and Hao, J.: Historic and future trends of vehicle emissions in
Beijing, 1998–2020: A policy assessment for the most stringent vehicle
emission control program in China, Atmos. Environ., 89, 216–229,
10.1016/j.atmosenv.2013.12.002, 2014.Zhou, C., Gong, S., Zhang, X., Liu, H., Xue, M., Cao, G., An, X., Che, H.,
Zhang, Y., and Niu, T.: Towards the improvements of simulating the chemical
and optical properties of Chinese aerosols using an online coupled
model–CUACE/Aero, Tellus B, 64, 18965, 10.3402/tellusb.v64i0.18965,
2012.