Introduction
The North China Plain (NCP) is one of the most densely populated areas in
the world and it has been the Chinese center of culture and politics since
early times. Beijing, the capital of China, Tianjin, Shijiazhuang and other
big cities with active economic developments are located in the NCP. This
region is experiencing heavy haze pollution with record-breaking high
concentrations of particulate matters (L. T. Wang et al., 2014). Haze is
defined as an air pollution phenomenon where horizontal visibility is less
than 10 km caused by aerosol particles, such as dust and black carbon (BC),
suspended in the atmosphere (Tao et al., 2012). Its formation is highly
related to meteorological conditions, emissions of pollutants and
gas-to-particle conversion (Sun et al., 2006; Watson, 2002). Haze has
attracted much attention for its adverse impacts on visibility and human
health. During haze periods, reduced visibility affects land, sea and air
traffic safety and the fine particles can directly enter the human body and
adhere to lungs to cause respiratory and cardiovascular diseases (Liu et
al., 2013). Moreover, haze affects climate and ecosystems via
aerosol-cloud-radiation interactions (Sun, et al., 2006; Liu et al., 2013).
The temporal variations of observed and simulated 24 h average
temperature (a–d), relative humidity (e–h) and wind speed (i–l) in the
Beijing, Tianjin, Baoding, and Chengde stations.
Because haze influences visibility, human health and climate (Gao et al.,
2015), numerous studies have used multiple methods to investigate physical,
chemical and seasonal characteristics of aerosols during haze. The increase
of secondary inorganic aerosols is considered to be an attribute of the haze
pollution in east China (Tan et al., 2009; Zhao et al., 2013). Tan et al. (2009) studied the characteristics of aerosols in non-haze and haze days in
Guangzhou, China and found that secondary pollutants (OC, SO42-,
NO3- and NH4+) were the major components of haze aerosols and they
showed a remarkable increase from non-haze to haze days. Similar conclusions
were drawn by Zhao et al. (2013) after studying the chemical characteristics
of haze aerosols in the NCP. Secondary Organic Aerosol (SOA) formation can
also be significant during haze (Tan et al., 2009; Zhao et al., 2013).
Studies of aerosol optical properties show that fine-mode aerosols were
dominant during haze (Yu et al., 2011; Li et al., 2013). In addition,
contributions of diverse factors to haze formation, such as biomass burning
and regional transport, have been investigated. Chen et al. (2007) used
MM5-CMAQ to reproduce the haze pollution in September 2004 in the Pearl
Region Delta (PRD) region and discovered that sea-land breeze played an
important role. Wang et al. (2009) discovered that almost 30–90 % of
the organics during the haze happened in June 2007 in Nanjing were from
wheat straw burning. Cheng et al. (2014) concluded that biomass burning
could cause haze issues and they found biomass burning contributed 37 % of
PM2.5, 70 % of Organic Carbon (OC) and 61 % of Elemental Carbon
(EC) based upon both modeling and measurement results of case study in
summer 2011 in the Yangtze River Delta (YRD) region. These biomass burning
events mainly occurred in summer and autumn in east and south China (Cheng
et al., 2013, 2014; Li et al., 2010; Wang et al., 2007, 2009). To evaluate
regional contributors to the haze in southern Hebei, Wang et al. (2012)
simulated the time period from 2001 to 2010 and concluded that Shanxi
province and the northern Hebei were two major contributors, and winter was
the worst season, followed by autumn and summer.
Simulated and observed vertical temperature profiles at 08:00 and
20:00 (China Standard Time, CST) from 15 to 20 January.
X. Han et al. (2014) pointed out that the haze formation mechanism in winter
in Beijing was different from that in summer and mass concentrations of
PM2.5 in winter were relatively higher and the compositions were
different than in summer. The extreme winter haze in the NCP has attracted
enormous scientific interests. It has been found that stagnant meteorological
conditions (weak surface wind speed and low Planetary Boundary Layer (PBL)
height) and secondary aerosol formation are the main causes of winter haze
formation (S. Han et al., 2014; He et al., 2014b; K. Huang et al., 2014; Sun
et al., 2014; Wang et al., 2014a; Zhao et al., 2013; B. Zheng et al., 2015;
G. J. Zheng et al., 2015). Other causes proposed include high local emissions (He et al., 2014b;
G. J. Zheng et al., 2015), enhanced coal combustion in winter (K. Huang et al.,
2014; Sun et al., 2014), heterogeneous chemistry (He et al., 2014a; X. Huang
et al., 2014; Quan et al., 2014; Wang et al., 2014a, b; B. Zheng et al.,
2015; G. J. Zheng et al., 2015) and regional transport (Tao et al., 2014; Sun et al., 2014; L. T. Wang
et al., 2014; Z. Wang et al., 2014; G. J. Zheng et al., 2015). It was also pointed
out that fog processing (K. Huang et al., 2014), aerosol-radiation
interactions (J. Wang et al., 2014; Z. Wang et al., 2014; B. Zhang et al.,
2015) and nucleation events (Guo et al., 2014) may play important roles in
winter haze formation.
Simulated and observed hourly temperature, RH, wind speed,
PM2.5, NO2 and CO in the Shangdianzi (SDZ) station.
Temporal variations of the simulated and observed PM2.5,
NO2 and SO2 at Beijing (a–c), Tianjin (d–f) and Xianghe
(g–i) stations.
The complex haze formation mechanisms need further studies. Li et al. (2015)
emphasized that regional transport of PM2.5 is a major cause of
severe haze in Beijing, but R. Zhang et al. (2015) pointed out that the
evidence provided by Li et al. (2015) is insufficient and regional transport
should be evaluated using chemical transport models. Furthermore, the
contribution of aerosol feedbacks to PM2.5 levels remains
unquantified. Therefore, the roles of regional transport and
aerosol-radiation interactions in haze events need to be better understood.
In this study, the online coupled model WRF-Chem, which is capable of
simulating aerosols' effects on meteorology and climate, is used to
reproduce the severe haze event that happened in the NCP from 16 to 19 January 2010. During this haze event, the highest hourly PM2.5 concentration reached 445.6 and 318.1 µg m-3 in Beijing and
Tianjin and the areas with low visibility covered most eastern China regions
(Zhao et al., 2013). In this study, we address the following important
questions: (1) what is the performance of the model configurations in
representing the meteorological variables, and the physical and chemical
characteristics of the aerosols during the selected study period; (2) how
does the haze build up and dissipate; (3) how do the chemical species of
PM2.5 change during haze period; (4) does regional transport play an
import role in the 2010 haze event in Beijing; (5) what is the contribution
of aerosol feedback mechanisms to PM2.5 levels during the haze
event; and (6) what is the role of BC absorption in the feedback mechanism?
In Sect. 2, we describe the model we use and model configuration, including
emissions and used parameterization schemes. In Sect. 3, surface
meteorological, chemical observations, atmospheric sounding products, as
well as remote-sensing products are used to evaluate the model performance.
In Sect. 4, questions from (2) to (6) are answered in detail. Conclusions
are provided in Sect. 5.
Observation data and variables used in this study.
Data setsa
Variablesb
Data
Number of
Data sources
frequency
sites used
CMDSSS
T2, RH2, WS10
Daily
4
http://cdc.cma.gov.cn/home.do
Atmospheric Sounding
T, RH
12 h
1
http://weather.uwyo.edu/upperair/sounding.html
CARE-China
PM2.5, NO2, SO2
Hourly
3
CSHNET
AOD
Hourly
4
SDZ
T1.5, RH1.5, WS10, PM2.5, NO2, CO
Hourly
1
Zhao et al. (2013)
CALIPSO
AOD
N/A
N/A
http://www-calipso.larc.nasa.gov/
MODIS
AOD
Daily
N/A
http://ladsweb.nascom.nasa.gov/data/search.html
a CMDSSS – China Meteorological Data Sharing Service System;
CARE-China – Campaign on the atmospheric Aerosol Research network of China;
CSHNET – Chinese Sun Hazemeter Network; SDZ – Observation data at
Shangdianzi site are extracted from paper Zhao et al. (2013); CALIPSO – The
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation;
MODIS – the Moderate Resolution Imaging Spectroradiometer.
b T2 –
temperature at 2 m; RH2 – relative humidity at 2 m; WS10 – wind speed at 10 m;
T1.5 – temperature at 1.5 m; RH1.5 – relative humidity at 1.5 m; AOD – Aerosol
Optical Depth.
Routes of CALIPSO satellite, simulated extinction coefficient and
observed plume top, and simulated AOD and CALIPSO retrieved AOD at 532 nm at
three moments: 14 January 12:00 (CST, a–c), 21 January 02:00 (CST, d–f), and
21 January 12:00 (CST, g–i).
Model description and configuration
The WRF-Chem model version 3.5.1 was employed to simulate the 2010 haze
event in the NCP region and aerosol-radiation interactions were included
(Chapman et al., 2009; Fast et al., 2006). Domain settings are the same as
those of Jing-Jin-Ji modeled area of Yu et al. (2012). Three domains with
two-way nesting were used and grid resolutions were 81 km × 81 km
(domain 1), 27 km × 27 km (domain 2) and 9 km × 9 km (domain
3; see Fig. S1 in the Supplement). The number of vertical grids used
was 27 and the number of horizontal grids was 81 × 57, 49 × 49, and 55 × 55, respectively. The first domain covers most areas of
the East Asia region, including China, Korea, Japan and Mongolia. Beijing
was set to be the center of the innermost nested domain. The chemical and
aerosol mechanism used was gas-phase chemical mechanism CBMZ (Zaveri and
Peters, 1999) coupled with the 8-bin sectional MOSAIC model with aqueous
chemistry (Zaveri et al., 2008). MOSAIC treats all the important aerosol
species, including sulfate, nitrate, chloride, ammonium, sodium, BC, primary
organic mass, liquid water and other inorganic mass (Zaveri et al., 2008).
Some of the physics configuration options include Lin cloud-microphysics
(Lin et al., 1983), RRTM long wave radiation (Mlawer et al., 1997), Goddard
short wave radiation (Chou et al., 1998), Noah land surface model, and the
Yonsei University planetary boundary layer parameterization (Hong et al.,
2006).
PM2.5 concentration from 14 January 00:00 to 21 January 00:00, plotted every 12 h.
Emissions are key factors in the accuracy of air quality modeling results.
The monthly 2010 Multi-resolution Emission Inventory for China (MEIC; http://www.meicmodel.org/) was used as the anthropogenic
emissions. This inventory includes emissions of sulfur dioxide (SO2),
nitrogen oxides (NOx), carbon monoxide (CO), non-methane volatile
organic compounds (NMVOC), NH3, BC, organic carbon (OC), PM2.5,
PM10, and carbon dioxide (CO2) by several sectors (power
generation, industry, residential, transportation, etc.). Biogenic emissions
were calculated on an online way by the MEGAN model (Guenther et al., 2006).
Meteorological initial and boundary conditions were obtained from the
National Centers for Environmental Prediction (NCEP) Final Analysis (FNL)
data set. Chemical initial and boundary conditions were taken from MOZART-4
forecasts (Emmons et al., 2010). The period from 11 to 24 January 2010 was
chosen as the modeling period, covering the 2010 NCP haze period (from 16 to
19 January 2010). To overcome the impacts of initial conditions, 3 days
were simulated and considered as spin-up time.
Performance statistics for meteorological variables.
Beijing
Tianjin
Baoding
Chengde
Variables
Obs.
Mod.
MB
ME
RMSE
Obs.
Mod.
MB
ME
RMSE
Obs.
Mod.
MB
ME
RMSE
Obs.
Mod.
MB
ME
RMSE
T2 (K)
269.5
267.6
-1.9
2.0
2.5
269.3
268.1
-1.1
1.2
1.5
270.4
268.5
-2.0
2.0
2.3
262.5
264.5
2.0
2.4
3.2
RH2 (%)
46.9
53.4
6.6
7.2
11.1
61.5
58.4
-3.1
5.9
6.4
44.4
52.5
8.1
8.1
10.4
59.4
55.0
-4.4
8.0
8.8
WS10 (m s-1)
2.1
3.4
1.3
1.3
1.6
2.8
3.2
0.4
1.0
1.1
1.4
2.8
1.4
1.4
2.1
1.4
2.9
1.5
1.5
1.8
Cross section plots of PM2.5 concentration and clouds from
14 January 00:00 to 21 January 00:00 every 12 h.
Model evaluation
Observation data sets and evaluation metrics
Model evaluation was conducted in terms of both temporal variation and
spatial distribution. Table 1 gives a summary of the observation data and
variables used in the model evaluation. The meteorological variables,
including 2 m temperature (T2), 2 m relative humidity (RH2) and 10 m wind speed (WS10), at four stations (Beijing, Tianjin, Baoding and
Chengde) were used. Surface concentrations of PM2.5, NO2, SO2
at three sites (Beijing, Tianjin and Xianghe, shown in Fig. S1), and
Aerosol Optical Depth (AOD) at four sites (Beijing city, Beijing forest,
Baoding city, Cangzhou city) were also used in the evaluation against
measurements. PM2.5 and AOD are typical variables to represent
severity of haze pollution. To evaluate how model performs in simulating
horizontal and vertical distributions of meteorological and chemical
variables, soundings of temperature and RH at Beijing, and AODs derived from
CALIPSO were used in this study. The statistical metrics calculated include
correlation coefficient R, mean bias (MB), mean error (ME), the root mean
square error (RMSE), the normalized mean bias (NMB), the normalized mean
error (NME), the mean fractional bias (MFB) and the mean fractional error
(MFE). The definitions of these metrics can be found in Morris et al. (2005)
and Willmott and Matsuura (2005).
Meteorology simulations
Figure 1 shows the temporal variations of simulated and observed 24 h
average temperature (a–d), relative humidity (e–h) and wind speed (i–l) at
Beijing, Tianjin, Baoding and Chengde stations. These observations were
collected from the China Meteorological Data Sharing Service System (CMDSSS)
data set. From normal days to haze days (gray shaded), temperature and
relative humidity increased and wind speeds decreased. Generally, the
variations of surface temperature, RH and wind speeds are captured by model,
although overestimations of wind speed occur at the Chengde station
throughout the whole period. Model mean, observation mean, MB, ME and RMSE
were calculated and summarized in Table 2. The MB and RMSE for surface
temperature vary from -2.0 to 2.0 K and from 1.5 to 3.2 K, respectively. The
model underestimates temperature at Beijing, Tianjin and Baoding stations,
and overestimates temperature at the Chengde station. RH agrees well with
observations, with MB varying from -4.4 to 8.1 % and RMSE varying from
6.4 to 11.1 %. The magnitudes of MB and RMSE are comparable with those
of Wang et al. (2014b). The model shows good performance in simulating wind
speed, with RMSE ranging from 1.1 to 1.6 m s-1 at Beijing, Tianjin and Baoding
stations, below the level of “good” model performance criteria for wind
speed prediction proposed by Emery et al. (2001). Wind speeds at the Chengde
station were overestimated, with RMSE larger than the proposed criteria
(2 m s-1).
Performance statistics of PM2.5.
Obs.
Model
R
MB
ME
NMB
NME
MFB
MFE
(µg m-3)
(µg m-3)
(µg m-3)
(µg m-3)
(%)
(%)
(%)
(%)
Beijing
111.7
122.1
0.77
-10.4
30.4
-8.5
24.9
0.4
26.3
Tianjin
103.3
141.2
0.75
-37.9
56.1
-26.9
39.7
-7.8
49.6
Xianghe
93.0
152.6
0.69
-59.7
68.0
-39.1
44.5
-21.8
50.7
Figure 2 compares simulated and observed vertical temperature profiles at
08:00 and 20:00 (CST) from 15 to 20 January at Beijing city. These
atmospheric sounding data are from the NCAR Earth observing laboratory
atmospheric sounding data set. The model captures the vertical profiles of
temperature well. Obvious strong temperature inversions existed during the
haze period (from 16 January 08:00 CST to 19 January 20:00 CST) and the lapse rate during this
period was about 5–15 ∘C km-1, indicating unfavorable conditions for
diffusion of pollutants. The model captures the general vertical profiles of
RH, although the performance is not as good as for temperature (see Fig. S2).
Chemical simulations
Figure 3d–f shows variations of simulated and observed hourly PM2.5,
NO2 and CO at the SDZ station. The haze event started from 16 January
with rapid increase of PM2.5, NO2, and CO concentrations and ended
on 20 January. The relationships between meteorological condition and
pollution levels are clearly shown. Both the observation and the model show
that temperature and relative humidity increase, wind speeds are low, and
pollution levels build up (Fig. 3). The magnitudes and trends over time of
the simulated PM2.5, NO2 and CO are generally consistent with
measurements, although overestimation of PM2.5 and underestimations of
NO2 and CO exist during the haze days. Figure 4 shows the temporal
variations of the simulated and observed PM2.5, NO2 and SO2
at Beijing (a–c), Tianjin (d–f) and Xianghe (g–i) stations. The observations
and the model predictions show that the buildups of pollution during the
haze event were similar at these three sites, occurring over a large
geographical region at the same time. SO2 was overestimated in
Beijing, but other simulations agree well with observations, especially for
PM2.5. Observation mean, model mean, MB, ME, NMB, NME, MFB, and MFE
were calculated for 24 h average simulated and observed PM2.5 at
these three stations and summarized in Table 3. As shown in Table 3, the
model underestimates PM2.5 concentrations at all stations. NMBs for
PM2.5 are -8.5, -26.9 and -39.1 % at Beijing, Tianjin and
Xianghe, respectively. MFBs at these three stations range from -21.8 to
0.4 % and MFEs range from 26.3 to 50.7 %. They are all within the
criteria proposed by Boylan and Russel (2006) that model performance is
“satisfactory” when MFB is within ±60 % and MFE is below 75 %.
Although the model performance for PM2.5 is satisfactory, biases
still exist, especially during severe haze days. Reasons for the biases
might be errors in meteorological variables, large uncertainties of emission
inventory, effects of horizontal and vertical resolutions, and incomplete
treatments of atmospheric chemistry. Many atmospheric chemistry reactions
have been and are being proposed for PM formation in winter haze. For
example, He et al. (2014a) proposed that mineral dust and NOx could
promote the formation of sulfate in heavy pollution days. The sensitivity of
the simulations to some of these factors will be discussed in future
studies.
Temporal variations of simulated PM2.5 at Shijiazhuang,
Beijing and Chengde.
Simulated temporal variations of meteorological and chemical
variables in Beijing.
Simulations of optical properties
In WRF-Chem, aerosol optical properties are calculated at four specific
wavelengths, 300, 400, 600, and 1000 nm, while AOD observations from
CSHNET, CALIPSO are not at these four wavelengths. To evaluate model
performance of simulating AOD, we derived AOD at observation wavelengths
based on Angstrom exponent relation (Schuster et al., 2006). In severe haze
days, AOD could not be retrieved , so the observed AOD data in some days
are missing. Model agrees very well with the CSHNET AOD observations at all
four stations (Fig. S3).
CALIPSO retrievals provide vertical curtains of aerosol and clouds. Figure 5
shows paths of the CALIPSO satellite, simulated extinction coefficient and
observed plume top, and simulated AOD and CALIPSO retrieved AOD at 532nm at
three moments: 14 January 12:00 CST (a–c), 21 January 02:00 CST (d–f), and
21 January 12:00 CST (g–i), respectively. There were no retrievals in the
NCP during haze days. Figure 5a, d and g show that the CALIPSO
satellite passed over the NCP region at these three moments. Simulated
extinction coefficient matches observed plume top (Fig. 5b, e and h), indicating that the model captures the vertical distributions of
aerosols. The model also has good performance in simulating AOD at 532 nm,
although underestimations happen around latitude 36∘ N (Fig. 5c, f and i).
The model is shown to be capable of simulating the major meteorological and
chemical evolution of this haze event. As spatial and vertical profiles of
the haze period are incomplete or missing in the satellite retrievals and
ground stations only provide point estimates, we can use the model to
understand the haze spatial, vertical and temporal evolution, as discussed
in the following sections.
Results and discussions
Meteorological conditions and evolution of air pollutants
The evolution of the spatial distributions of the haze event is shown in
Fig. 6, where the horizontal distributions of PM2.5 and wind
vectors are plotted every 12 h from 14 January 00:00 CST to 21 January
00:00 CST. In the second plot (14 January 12:00 CST), air flows converged at the NCP
surface areas, resulting in a small increase of PM2.5 concentration.
From 14 January 00:00 CST to 16 January 00:00 CST, PM2.5 concentration over
the NCP was generally below 120 µg m-3. From 16 to 18 January
Beijing and surrounding areas were controlled by a weak high pressure
system (Zhao et al., 2013). During this period, large amounts of emissions
in the NCP accumulated and the persistent southerly winds brought some air
pollutants northward to Beijing and southern Hebei areas. The weak high
pressure system was replaced by a low pressure system that lasted until
20 January and this weather condition was not conductive for dispersion of
air pollutants (Zhao et al., 2013). On 19 January the NCP haze was in the
worst state, with PM2.5 concentrations above 350 µg m-3 in
south NCP. From 20 January strong northerly winds dispersed the accumulated
air pollutants and the haze ended.
Temporal variations of vertical profiles of simulated (a) PM2.5 (unit: µg m-3, b) temperature
(unit: ∘C, c) RH (unit: %, d) wind speeds (unit: m s-1) in
Beijing.
Observed (a) and simulated (b) chemical
species of PM2.5 and simulated SOA (c) in the Beijing site.
To illustrate the vertical structure of the haze, vertical cross sections of
PM2.5 concentration and clouds are presented in Fig. 7. The cross
section diagonally cuts the region with the lower left corner of 34∘ N, 110∘ E
to the upper corner at 44∘ N, 122∘ E (see Fig. S4).
There were two highly polluted points (around latitudes 35 and 39) and they
started merging as one from 18 January 12:00 (Fig. 7). At that time,
southerly winds blew air pollutants northwards (Fig. 6) and the polluted
region was expanded. On 19 January there were fog and/or clouds near the
surface and the impacts of fog and/or clouds will be discussed in Sect. 4.2.
Further details of the evolution of the haze are shown in the temporal
variations of PM2.5 concentrations in Shijiazhuang, Tianjin and
Chengde (marked in Fig. S1) in Fig. 8. All three sites show similar
temporal variations. Around noon of 15 January PM2.5 concentrations
in Shijiazhuang, Chengde and Beijing increased at nearly the same time,
labeled by red arrow in Fig. 8. Air pollutants started accumulating when
the NCP was controlled by the weak and stable weather conditions. Compared
to Shijiazhuang and Beijing, the capital city of Hebei province and the
capital of China, PM2.5 concentrations in Chengde were lower (Fig. 8). It was estimated that there are more than 8100 coal-fired boilers and
industrial kilns in Shijiazhuang city (Peng et al., 2002), resulting in high
intensity of emissions in Shijiazhuang. On 20 January Chengde was the first
to show a sharp decrease of PM2.5 concentrations, followed by Beijing
and Shijiazhuang, corresponding to the northerly wind impacts discussed
above.
To better understand the relationships between meteorological factors and
pollution levels, time series of different pairs of variables are shown in
Fig. 9. CO shows very high correlation with PM2.5 (Fig. 9a),
which is consistent with the observation and modeling results in Santiago,
Chile (Perez et al., 2004; Saide et al., 2011), and shows the large
contribution of primary sources (including gaseous precursors) to
PM2.5. Secondary aerosol formation also plays a role as PM2.5 peaks on the 19th while CO peaks on the 18th. RH and wind speed
are two important factors affecting the concentrations of aerosols. RH has
similar variations as PM2.5 concentration (shown in Figs. 9a and 5b). The NCP is close to the sea and under the slow southerly flows,
temperature and RH increase along with PM2.5. During the haze event, RH
values were generally above 40 % and wind speeds were below 2 m s-1
(Fig. 9b). Low wind speed is unfavorable for the dilution of air pollutants and
high RH would accelerate the formation of secondary species, such as sulfate
and nitrate, to aggravate the pollution level (Sun et al., 2006). NOX
concentrations show similar variations as PM2.5, indicating the buildup
of concentrations during the wind speed stagnation. Ozone shows lower
concentrations during haze event (Fig. 9c) because high aerosol loadings
produce low photochemical activity due to decrease in UV radiation. The
concentrations have an inverse relationship with PBL Height (PBLH) as shown
in Fig. 9d. Diurnal maximums of PBLHs were mostly below 400 m and PBL
collapsed at night during the haze event, indicating aerosols were trapped
near the surface. On 21 and 22 January PBLHs were between 800 and 1000 m, which helped diffuse and dilute the air pollutants, resulting in a
decrease in concentration. The relationships between these variables are
further discussed with respect to the influences of aerosol feedback
mechanism in Sect. 4.4.
Primary aerosol, SIA and SOA (µg m-3) during haze days and
non-haze days in Beijing.
Primary
SIA
SOA
Haze days
56.4
81.9
1.1
Non-haze days
14.2
10.8
0.3
Ratio
4.0
7.6
3.7
Backward dispersion of particles released on 19 January 00:00,
plotted 6, 12, 24, and 48 h before being released (unit: number/grid
cell).
Figure 10 shows the temporal variations of vertical profiles of simulated
PM2.5 concentration (a), temperature (b), RH (c) and wind speeds (d)
at the Beijing site. PM2.5 was accumulated below 500 m and
concentrations reached peak values around 18 January 00:00 (Fig. 10a),
when a strong temperature inversion happened over Beijing (Fig. 10b),
which inhibited vertical atmospheric mixing. A strong temperature inversion
also happened on 19 January (Fig. 10b). From 16 to 19 January RH was
mostly higher than 50 % and reached a peak on the night of 19 January
(Fig. 10c). As a result, air pollutants released into the atmosphere
were trapped in the moist atmosphere and accumulated as near surface
horizontal winds were very weak (below 1.5 m s-1) during the haze period
(Fig. 10d). As mentioned above, the high RH enhances the formation of
secondary species, which will be discussed in the following section.
Observed daily maximum surface solar radiation and simulated
surface shortwave radiation for the with feedback (WF) and without feedback
(NF) scenarios in Beijing (a), simulated PBLH (b) in WF and NF scenarios at
Shijiazhuang, and simulated PM2.5 concentration (c) in WF and NF
scenarios at Shijiazhuang.
Temporal variations of vertical profiles of (a) PM2.5 (unit: µg m-3, c) RH (unit: %, e) temperature
(unit: ∘C, g) wind speeds (unit: m s-1) differences in Beijing between WF
and NF scenarios; (b), (d), (f) and (h) are PM2.5 , RH, temperature
and wind speeds differences in Beijing between WF and NBCA (BC absorptions
are teased out) scenarios.
Evolution of aerosol composition during haze
As shown above, during haze events, aerosols build up due to low mixing
heights and low wind speeds. An important question is what is the role of
secondary aerosol formation during such events? Previous measurement studies
have found that the increase of secondary inorganic pollutants could be
considered as a common property of haze pollution in East China (Zhao et
al., 2013). The observed and simulated chemical species of PM2.5 in
Beijing are shown in Fig. 11a and b, respectively. Observed
secondary inorganic aerosols (SIA; NH4+, SO42-,
NO3-) increased significantly during the haze episode and accounted for
37.7 % of PM2.5 mass concentration (Zhao et al., 2013). Primary OC,
BC, sulfate, nitrate and ammonium accounted for the major parts of the
simulated PM2.5 during haze. Table 4 summarizes the mean
concentrations of primary aerosols (primary OC and BC) and SIA
(NH4+, SO42-, NO3-) in non-haze days, and in the
most serious haze day. The primary aerosols increased by a factor of 4.0
from non-haze days to haze days. The SIA also increased from non-haze days
to haze days, which agrees with the observation (Tan et al., 2009; Zhao et
al., 2013). The SIA increased by a factor of 7.6 from non-haze days to haze
days. The increasing factors for observed primary aerosols and SIA are 2.9
and 6.9, which are close to those factors from simulations. However, the
amounts of sulfate are underestimated by WRF-Chem, compared with the
observation in Fig. 11a from Zhao et al. (2013). Tuccella et al. (2012)
pointed out that the underestimation of simulated sulfate could be due to
the underestimation of SO2 gas phase oxidation, errors in nighttime
boundary layer height predicted by WRF-Chem, and/or the uncertainties in
aqueous-phase chemistry. It could also be caused by the missing
heterogeneous sulfate formation in current model (He et al., 2014a; Wang et
al., 2014; B. Zheng et al., 2015). As discussed earlier, the SO2 gas
phase concentrations at this site were overestimated. Adding reaction
pathways to produce sulfate aerosol would improve both the predictions of
sulfate (increase) and SO2 (decrease; He et al., 2014a; Wang et al.,
2014; B. Zheng et al., 2015).
We investigated the role of aqueous phase chemistry during the haze event.
The aqueous phase pathway can reach a level of over 50 µg m-3 around the Beijing area, accounting for a significant part (about
14.3 %) of total PM2.5 concentration (see Fig. S5). As shown in Fig. 7, fog/clouds existed near the surface on
19 January and this corresponds to the PM2.5 difference on that day
due to aqueous phase pathway. The sulfate production in aqueous phase may be
higher than shown in this study after adding missing aqueous-phase
reactions. The impacts of heterogeneous reactions on sulfate production will
be investigated in future studies.
As shown in Figs. 1a and 7b, the model underestimates OC. To evaluate
the formation of Secondary Organic Aerosol (SOA) during the haze event, the
RADM2/MADE-SORGAM model was used. The CBMZ/MOSAIC version used is not
capable of simulating SOA formation because CBMZ was hard-wired with a
numerical solver in WRF-Chem and thus SOA condensable precursors could not
be directly added into it (Zhang et al., 2012). RADM2 is an upgrade of RADM1
and it gives more realistic predictions of H2O2 (Stockwell et al.,
1990), and Schell et al. (2001) incorporated SOA into the Modal Aerosol
Dynamics Model for Europe (MADE; Ackermann et al., 1998) by means of the
Secondary Organic Aerosol Model (SORGAM). SORGAM treats anthropogenic and
biogenic aerosol precursors separately and eight SOA compounds are
considered, of which four are anthropogenic and the other four are biogenic
(Schell et al., 2001). Predicted Anthropogenic SOA (ASOA), biogenic SOA
(BSOA) and Primary Organic Aerosol (POA) in Beijing are shown in Fig. 11c. SOA indeed shows a marked increase from non-haze days to haze days,
but the amount of SOA is very small compared with POA. The highest SOA
concentrations in China are usually found in summer and in Central China
(Jiang et al., 2012). In addition, almost all of the simulated SOA are ASOA.
Jiang et al. (2012) also concluded that in winter, the fractions of ASOA are
larger than 90 % in north China. Biogenic emissions are usually controlled
by solar radiation and temperature, and solar radiation is weaker and
temperature is lower in winter compared with summer. Moreover, the high
isoprene, API (a-pinene and other cyclic terpenes with one double bond) and
LIM (limonene and other cyclic diene terpenes) emissions are located below
30∘ N and in Northeast China (Jiang et al., 2012), not in the
NCP, so the SOA concentrations are not high in this winter haze event period
in the NCP. As shown in Table 4, the mean SOA concentration in non-haze days
is 0.15 µg m-3 and in the most serious haze day is 8.2 µg m-3. The factor increase of SOA from non-haze days to haze day is
8.2, which is lower than that of primary aerosols and much lower than that
of SIA. The SOA formation in winter has not been well studied and it might
be underestimated by the model as it could have missing pathways to SOA
formation. Further work is needed to improve the underestimation of SOA
formation in the winter.
Differences of PM2.5 concentration (unit: µg m-3), temperature (unit: ∘C), PBLH (unit: m) and
horizontal wind (unit: m s-1) at 02:00 p.m. (a, c, e, g) and 02:00 a.m. (b, d, f, h) between WF and NF scenarios.
Impacts of surrounding areas on haze in Beijing
Previous studies found that both local emissions and regional transport have
significant contributions to the high fine particle levels in Beijing (Yang
et al., 2011). A sensitivity simulation was conducted to quantify the
contributions of surrounding areas to haze in Beijing, when Beijing local
emissions were turned off. The ratio of PM2.5 in Beijing when Beijing
emissions are turned off to PM2.5 in Beijing when Beijing emissions
are on represents the non-local contributions. It can reach above 80 %
during haze (see Fig. S6) and the average
contribution is about 65 % from 16 to 19 January.
To figure out the dominant transport paths, FLEXPART-WRF (Stohl et al.,
1998; Fast and Easter, 2006) was used to generate 72 h backward
dispersions around the Beijing area. 50 000 particles were released backwards
from a box (1∘ × 1∘ × 400 m), the center of which
is Beijing urban area, from 19 January 00:00. The number concentrations of
particles were plotted at 6 h before, 12 h before, 24 h before
and 48 h before the released time (Fig. 12). For 12 h, Beijing was
influenced by sources to the south, including sources from south Hebei,
Tianjin and Shandong. For 2 days, more sources contributed to the haze
buildup in Beijing, including sources from Henan and Inner Mongolia. A
number of coal mines are located in Hebei, Shandong and Henan provinces and
Inner Mongolia areas have high emissions of primary aerosols.
The impact of aerosol feedback
Aerosols affect weather and climate through many pathways, including
reducing downward solar radiation through absorption and scattering (direct
effect), changing temperature, wind speed, RH and atmospheric stability due
to absorption by absorbing aerosols (semi-direct effect), serving as cloud
condensation nuclei (CCN) and thus impacting optical properties of clouds
(first indirect effect), and affecting cloud coverage, lifetime of clouds
and precipitation (second indirect effect; Zhang et al., 2010; Forkel et
al., 2012). The feedback mechanisms are complex and many aspects of them are
not well understood. Although previous studies have investigated
aerosol-radiation-meteorology interactions (Zhang et al., 2010; Forkel
et al., 2012), the studies on short timescale events with high aerosol
loadings, such as haze events, are limited. This section focuses on
evaluating the impacts of aerosol feedback mechanism on meteorology and air
quality. The feedback discussed in this paper only includes aerosols' direct
and semi-direct effects.
Impact of feedback on meteorology and PM2.5 distribution
Figure 13a shows the observed daily maximum surface solar radiation and
simulated surface solar radiation for the with feedback (WF) and without
feedback (NF) scenarios in Beijing. Simulated daily maximum surface
shortwave radiation values for the NF scenario are higher than observations
and the overestimations are reduced by implementing aerosol feedback (Fig. 13a). For the NF case, the correlation coefficient R between simulated and
observed daily maximum surface shortwave radiation is 0.84 in Beijing; for
the WF scenario, the correlation coefficient increased to R= 0.93, and the
haze reduced the shortwave radiation values by 30 to 80 %.
The changes in radiation have impacts on the environment. Simulated PBLH and
PM2.5 concentration at Shijiazhuang for the WF and NF scenarios are
shown in Fig. 13b and c. In non-haze days, PBLH differences between
the two scenarios are negligible due to low aerosol loadings. In haze days,
PBLHs in the WF scenario are generally lower (by up to 60 %) than in the
NF scenario. As shown in Fig. 13c, PM2.5 concentration at
Shijiazhuang in WF scenario is higher than it is in the NF scenario and the
difference reaches about 50 µg m-3 on 19 January. Aerosols affect
PBLHs in two ways: (1) radiation is scattered back to sky and absorbed, and
as a result, radiation reaching the surface is reduced (Fig. 13a) and
temperature is lowered; and (2) suspended aerosols like BC absorb radiation
to heat the upper PBL (Ding et al., 2013). Both of these ways increase
temperature inversion and atmospheric stability, and thus exacerbate
PM2.5 pollution.
Differences of PM2.5 concentration (unit: µg m-3), temperature (unit: ∘C), PBLH (unit: m) and
horizontal wind (unit: m s-1) at 02:00 p.m. (a, c, e, g) and 02:00 a.m. (b, d, f, h) between WF and NBCA scenarios.
Figure 14 shows temporal variations of vertical profiles of (a) PM2.5 (c) RH (e) temperature (g) wind speeds differences in Beijing between WF
and NF scenarios. When aerosol feedback is included, PM2.5 concentrations near Beijing surface are mostly increased, except on
the morning of 17 January on the afternoon of 18 and on 19 January
(Fig. 14a). The increases of PM2.5 are caused by the above
mentioned decrease of temperature gradient from surface to aloft (shown in
Fig. 14e) and atmospheric stability. Apart from these, PM2.5 concentrations are also affected by RH and wind speeds. In WF
scenario, RH is generally increased near the surface, especially on 19 January
(Fig. 14c), while horizontal wind speeds are also increased on
19 January which is the main cause of decreases of PM2.5 concentrations in Beijing.
To evaluate the impact of aerosol feedback on horizontal meteorological
fields and PM2.5 distributions, averaged differences of PM2.5 concentrations, temperature, PBLHs and horizontal winds between WF and NF
scenarios at 02:00 p.m. and 02:00 a.m. in haze days (from 16 to 19 January) were
calculated and are shown in Fig. 15. Figure 15c shows that PBLHs are
reduced in almost all NCP areas when aerosol feedbacks are considered at
02:00 p.m. At 02:00 p.m. PM2.5 concentrations increase about 21.9 µg m-3 at Shijiazhuang (114.53∘ E, 38.03∘ N). In a
few locations (the areas to the south of Beijing (Fig. 15a), PM levels
decrease although PBLHs are suppressed in those areas. The decreases of
PM2.5 concentrations in the areas south of Beijing are due to big
horizontal wind changes, shown in Fig. 15g. When aerosol feedback is
included, surface temperature decreases in areas where there are high
aerosol loadings (Fig. 15e). Figure 15d shows that PBLHs are enhanced
in east and southwest NCP areas at 02:00 a.m. with aerosol feedback. Aerosol
feedback mechanism at night time is more complex compared to it at day time.
At night, there is no incoming shortwave radiation from the sun and major
radiation is the long wave radiation emitted from the earth. The presence of
clouds and some kinds of aerosols can trap outgoing long wave radiation, and
as a result, the surface atmosphere is warmed. Different aerosols show
different effects on long wave radiation. Greenhouse gases (GHGs) absorb
long wave radiation, while large particles like dust scatter long wave
radiation. As a result, the upper atmosphere temperature is likely to be
warmer or cooler than surface atmosphere temperature. If the upper
atmosphere is warmer than the surface, a stable PBL will form. This can
explain why aerosol feedbacks increase PBL heights in some regions and
decrease in some other regions of NCP. Changes of PM2.5 concentrations at 2a.m. are mainly caused by changed PBLHs (Fig. 15b),
showing decreasing trends in areas where PBLHs are enhanced, because changes
of winds are relatively small (Fig. 15h). Temperature changes at 02:00 a.m.
are similar to it at 02:00 p.m., but the magnitudes are smaller.
Impact of BC absorption on meteorology and PM2.5 distribution
To investigate BC's influence on meteorology and air quality, sensitivity
tests were conducted by removing BC absorption in WRF-Chem (i.e., imaginary
refractive index set to zero). Figure 14 shows temporal variations of
vertical profiles of (b) PM2.5 (d) RH (f) temperature and (h) wind
speeds differences in Beijing between WF and NBCA scenarios. The differences
between WF and NBCA can be used to represent impacts of BC absorption since
in WF scenario both scattering and absorbing are considered while in the
NBCA scenario only scattering is considered. It is obvious from Fig. 14f
that the upper atmosphere is heated by BC, especially at 1.5 km, which
increases temperature inversion and atmospheric stability. BC absorption's
impacts on PM2.5, RH and wind speeds are similar to the impacts of both
scattering and absorption, but the magnitudes are smaller (Fig. 14b, d and g).
Differences of PBLH (unit: m) and PM2.5 concentration
(unit: µg m-3) at 02:00 p.m. between WF and NF scenarios (a, c) when BC
emissions were reduced by half; differences of PBLH (unit: m) and PM2.5 concentration (unit: µg m-3) at 02:00 p.m.
between WF and NF scenarios (b, d) when BC emissions were reduced by
half.
Figure 16 is similar to Fig. 15 except that the differences are between WF
and NBCA scenarios. At 02:00 p.m., PM2.5 concentration is increased by about
14.4 µg m-3 in Shijiazhuang (114.53∘ E, 38.03∘ N), accounting for about 65.7 % of PM2.5 changes due to the total
aerosol feedback (Fig. 16a). At 02:00 p.m., the maximum decrease in PBLH is
about 166.6 m (Fig. 16c), accounting for about 59.9 % of the maximum
decrease in PBLH in Fig. 15c. At 02:00 p.m., surface temperature in high
aerosol loading areas are decreased by about 0–2 ∘C (Fig. 16e),
while the temperature decreases in the same areas are above 2 ∘C
in Fig. 16e. At 02:00 a.m., changes of PM2.5, PBLHs, surface
temperature and wind speeds are similar to Fig. 15, with smaller
magnitudes.
The contribution of BC absorption in aerosol feedbacks depends on the model
performance in simulating BC and scattering aerosols (sulfate, OC). As shown
in Fig. 11, BC was overestimated, and sulfate and OC were underestimated
in Beijing. The overestimation could be as large as a factor of 2 in some
days. As a result, the relative contributions of BC absorption in aerosol
feedbacks are uncertain. To explore the uncertainties of the BC absorption
contribution, we conducted a simulation by reducing BC emissions by 50 %.
The changes of PBLH and PM2.5 concentrations at 02:00 p.m. due to aerosol
feedbacks and BC absorption after BC emission changes are shown in Fig. 17. The domain maximum increases of PM2.5 concentrations because of
aerosol feedbacks and BC absorption are 19.1 and 10.2 µg m-3, respectively for the base and 50 % BC emission cases. The
domain maximum decreases of PBLH due to aerosol feedbacks and BC absorption
are 235.7 and 114.2 m, respectively. These numbers are smaller than before
because BC emissions were reduced by 50 %. Due to 50 % perturbation in
BC emissions, the contribution of BC absorption in aerosol feedbacks
decreased from about 60 to 50 %. This number can be additionally
reduced if OC and sulfate concentrations are simulated well. These
calculations suggest that the contributions of BC absorption to the aerosol
feedbacks are significant, but there remain large uncertainties in the
absolute magnitude. In the future, we can get more accurate estimations of
BC absorption in aerosol feedbacks after the performances of simulating BC,
OC and sulfate are improved.
Conclusions
In this study, the online coupled WRF-Chem model was used to reproduce the
haze event which happened in January 2010 in the NCP. The model was evaluated
against multiple observations, including surface observations of
meteorological variables and air pollutants, atmospheric sounding products,
surface AOD measurements, and satellite AOD measurements. The correlation
coefficients between simulated and observed PM2.5 concentrations in
Beijing, Tianjin and Xianghe stations are 0.77, 0.75 and 0.69, indicating
that WRF-Chem provides reliable representation for the 2010 haze event in
the NCP.
This haze event is mainly caused by high emissions of air pollutants in the
NCP region and stable weather conditions in winter. The haze built up almost
simultaneously in major cities in the NCP and dissipated from north to
south. During haze days, horizontal wind speeds and mixing heights were low,
temperature inversion happened above surface and RH values were above
40 %. Photochemistry was not significant during haze days due to weak UV
radiation. In addition, secondary inorganic aerosols played an important
role in the haze event. The role of cloud chemistry in this haze event
cannot be ignored.
The contribution of non-local sources to PM2.5 in Beijing was also
studied. The average contribution was about 64.5 % in haze days. The
FLEXPART model was implemented to investigate the sources of the non-local
contributions and results show that air pollutants from south Hebei, Tianjin
city, Shandong and Henan provinces are the major contributors to the
PM2.5 in Beijing.
Impacts of high aerosols in haze days on radiation, boundary layer heights
and PM2.5 have been demonstrated. When aerosol feedback is
considered, simulated surface radiation agrees well with observations. In
haze days, aerosol feedback has important impacts on surface temperature, RH
and wind speeds, and these meteorological variables affect aerosol
distribution and formation in turn. The role of BC in aerosol feedback loop
has also been investigated. The model sensitivity studies showed that BC
absorption has significant impacts on meteorology and air quality.
Therefore, more attention should be paid to BC from both air pollution
control and climate change perspectives. However the uncertainties remain
large and further studies are needed to better quantify the role of
absorption in the feedbacks.