Introduction
Over the past 3 decades, rapid industrialization and urbanization have caused
severe air pollution in China, particularly during wintertime heavy haze,
with extremely high levels of fine particles (PM2.5) frequently
engulfing the north of China (e.g., Chan and Yao, 2008; Fang et al., 2009;
Zhao et al., 2013; Huang et al., 2014; Guo et al., 2014; Wu et al., 2017; Li
et al., 2017a). Elevated atmospheric aerosols or PM2.5 not only
influence the Earth's climate system, but also remarkably impair visibility
and potentially cause severe health defects (e.g., Penner et al., 2001; Pope
and Dockery, 2006; Zhang et al., 2007).
Meteorological condition is critical for understanding the formation,
transformation, diffusion, transport, and removal of the pollutants in the
atmosphere. Dabberdt et al. (2004) have listed the meteorological research
needs for improving air quality forecasting, one of which is to provide the
model uncertainty information through ensemble prediction capabilities and
quantify uncertainties and feedbacks between meteorological and air quality
modeling components. Numerous studies have been performed in China to explore
the role of meteorological conditions in the air pollution formation (e.g.,
Gao et al., 2011; Zhang et al., 2012, 2015; Wu et al., 2013; Wang et al.,
2014; Bei et al., 2016a, b). Most recently, Liu et al. (2017) have
investigated the meteorological impacts on the PM2.5 concentrations over
Beijing–Tianjin–Hebei (BTH) in December 2015. Their results have
demonstrated that the unfavorable meteorological conditions are the main
reason for deterioration of the air quality in BTH, while the undertaken
emission control measures have only mitigated the air pollution slightly.
Previous studies on the air quality forecasting sensitivity to meteorological
uncertainties mainly include Monte Carlo simulations (e.g., Dabberdt and
Miller, 2000; Beekmann and Derognat, 2003) and adjoint sensitivity studies
(e.g., Menut, 2003). The ensemble approach has also been applied to
photochemical and secondary organic aerosol (SOA) simulations in various
numerical models (e.g., Galmarini et al., 2004; McKeen et al., 2005),
photo-chemical reactions (e.g., Delle Monache and Stull, 2003), emission
scenarios (e.g., Delle Monache et al., 2006), physical parameterizations
(e.g., Mallet and Sportisse, 2006), and meteorological initial conditions
(e.g., Zhang et al., 2007; Bei et al., 2012). The ensemble means have
generally performed better than most of the individual models. Uncertainties
in meteorological initial conditions have been shown to substantially
influence both ozone (O3) and SOA simulations, including the peak time
concentrations, the horizontal distributions, and the temporal variations
(Zhang et al., 2007; Bei et al., 2012). Recently, Sharma et al. (2016) have
evaluated uncertainties in surface O3 simulations over the South Asian
region during the pre-monsoon season due to different emission inventories
and different chemical mechanisms. They have suggested that the assessment of
the tropospheric O3 budget and its implications for public health and
agricultural output should be conducted prudently considering the huge
uncertainties caused by emission inventories and chemical mechanisms. Solazzo
et al. (2017) have emphasized the high interdependencies among meteorological
and chemical variables and the related errors, indicating that the evaluation
of the air quality model performance needs to be confirmed by more
complementary analysis of meteorological fields and chemical precursors.
The purpose of the present study is to explore impacts of the uncertainties
in meteorological conditions on the PM2.5 simulations or forecasts in
BTH through ensemble simulations using the WRF-CHEM model. The methodology
and model are presented in Sect. 2. The analyses, results, and discussions
are included in Sect. 3. The summary and conclusions are given in Sect. 4.
WRF-CHEM model configurations.
Regions
Beijing–Tianjin–Hebei (BTH)
Simulation period
13 to 21 January 2014
Domain size
200 × 200
Domain center
39∘ N, 117∘ E
Horizontal resolution
6 km × 6 km
Vertical resolution
35 vertical levels with a stretched vertical grid with spacing ranging from 30 m near the surface, to 500 m at 2.5 km and 1 km above 14 km
Microphysics scheme
WSM six-class graupel scheme (Hong and Lim, 2006)
Boundary layer scheme
MYJ TKE scheme (Janjić, 2002)
Surface layer scheme
MYJ surface scheme (Janjić, 2002)
Land-surface scheme
Unified Noah land-surface model (Chen and Dudhia, 2001)
Longwave radiation scheme
Goddard longwave scheme (Chou and Suarez, 2001)
Shortwave radiation scheme
Goddard shortwave scheme (Chou and Suarez, 1999)
Meteorological boundary and initial conditions
NCEP 1∘ × 1∘ reanalysis data
Chemical initial and boundary conditions
MOZART 6-hourly output (Horowitz et al., 2003)
Anthropogenic emission inventory
Developed by Zhang et al. (2009)
Biogenic emission inventory
MEGAN model developed by Guenther et al. (2006)
Model and methodology
WRF-CHEM model
A specific version of the WRF-CHEM model is used to examine impacts of the
uncertainties in meteorological conditions on the PM2.5 simulations or
the haze formation in BTH, which is developed by Li et al. (2010, 2011a, b,
2012) at the Molina Center for Energy and the Environment. The model includes
a new flexible gas-phase chemical module and the CMAQ/Models-3 aerosol module
developed by the US EPA (Binkowski and Roselle, 2003). The inorganic aerosols
are predicted using ISORROPIA version 1.7 (Nenes et al., 1998). The SOA
formation is simulated using a non-traditional SOA module, including the
volatility basis set (VBS) modeling method and the SOA contributions from
glyoxal and methylglyoxal. A detailed description of the WRF-CHEM model can
be found in Li et al. (2010, 2011a, b, 2012). A persistent heavy haze
pollution episode from 13 to 20 January 2014 in BTH is simulated. The model
simulation domain is shown in Fig. 1, and detailed model configurations can
be found in Table 1.
WRF-CHEM simulation domain. The filled red (in BTH) and blue
(outside of BTH) circles represent centers of cities with ambient monitoring
sites. The size of the circle denotes the number of ambient monitoring sites
of cities. The filled black triangle and rectangle denote the deployment
location of the HR-ToF-AMS and the surface meteorological site in Beijing,
respectively.
Ensemble initialization method
The ensemble initialization method used in the present study is called the
“climatological ensemble initialization method” (Zhang et al., 2007; Bei et
al., 2012). In the approach, dynamically consistent initial and boundary
conditions are statistically sampled from a seasonal meteorological data set.
In order to represent the wintertime climatological statistics, a data set
during the period from 1 November 2013 to 28 February 2014 is generated using
NCEP-FNL 1∘ × 1∘ reanalysis data. The perturbed
variables include the horizontal wind components, potential temperature,
perturbation pressure, and mixing ratio of water vapor. Other prognostic
variables such as vertical velocity and mixing ratios of hydrometeors are not
perturbed. In general, the perturbation in horizontal wind components
constitutes the most important uncertainty in those variables (Bei et al.,
2008, 2010). Thirty ensemble members are randomly chosen from this
climatological data set. Similarly, boundary conditions for each ensemble
member are generated from the same data set beginning at the randomly
selected initial time of the given member, and extended for the same length
of time as the simulated episode. Deviations of the initial and boundary
condition data for each member from the climatological mean for the entire
period are then scaled down to 20 % to reduce the ensemble spread to less
than typical observation error magnitudes (Nielsen-Gammon et al., 2007) and
added to the unperturbed initial and boundary conditions derived directly
from the NCEP-FNL analyses valid at 12:00 UTC on 12 January 2014, which are
used for the 6 km domain ensemble simulation. Figures 2a–d show the
vertical distribution of the average initial ensemble spread which is
calculated as the standard deviation of the perturbations imposed on each
ensemble member's initial field. The average spread is 0.5–3.0 m s-1
for horizontal winds (U and V components), 0.5–1.1 K for temperature,
0.02–0.48 hPa for pressure, and 0–0.15 g kg-1 for the water vapor
mass mixing ratio. The initial ensemble spreads of meteorological variables
are generally less than their typical observation error magnitudes. It is
worth noting that all the ensemble simulations used the same initial and
boundary conditions for chemical fields, as well as the same anthropogenic
emission inventory.
Vertical distribution of the mean of initial ensemble spreads and
the standard deviation for (a) horizontal winds (U and V
components), (b) temperature, (c) pressure,
and (d) water vapor mixing ratio.
Pollutant measurements
The hourly near-surface CO, SO2, NO2, O3, and PM2.5 mass
concentrations in BTH are released by the China Ministry of Environmental
Protection (China MEP) and can be downloaded from the website at
http://www.aqistudy.cn/. The Aerodyne High Resolution Time-of-Flight
Aerosol Mass Spectrometer (HR-ToF-AMS) with a novel PM2.5 lens is used
to measure the sulfate, nitrate, ammonium, and organic aerosols (OA) from 9
to 26 January 2014 at the Institute of Remote Sensing and Digital Earth
(IRSDE), Chinese Academy of Sciences (40.00∘ N, 116.38∘ E)
in Beijing (Fig. 1) (Williams et al., 2013). The positive matrix
factorization (PMF) technique is utilized with constraints implemented in
SoFi (Canonaco et al., 2013) to analyze the sources of OA and five components
are separated by their mass spectra and time series. The components include
hydrocarbon-like OA (HOA), cooking OA (COA), biomass burning OA (BBOA), coal
combustion OA (CCOA), and oxygenated OA (OOA). HOA, COA, BBOA, and CCOA are
interpreted for surrogates of primary OA (POA), and OOA is a surrogate for
SOA. Detailed information about the HR-ToF-AMS measurements and data analysis
can be found in Elser et al. (2016). A lidar has also been deployed at IRSDE
and the aerosol backscatter signal is used to retrieve the planetary boundary
layer (PBL) height.
Results and discussions
Synoptic overview
Figure 3 shows temporal evolutions of the observed PM2.5 mass
concentrations averaged over 13 cities (see Fig. 1) in BTH during the severe
haze episode from 13 to 21 January 2014. The observed PM2.5 mass
concentrations are frequently higher than 250 µg m-3 in the
13 cities during the episode, exceeding the standard of severe pollution
(hourly PM2.5 mass concentration exceeding 250 µg m-3,
Feng et al., 2016). The haze in BTH was in the stage of development from 13
to 15 January, with the gradual increase in the PM2.5 concentration. BTH
was most polluted when the haze was in the maturity stage on 16 January, with
the PM2.5 concentration exceeding 400 µg m-3 in most of
the cities. From 17 to 19 January, the PM2.5 concentrations fluctuated
considerably, which was primarily caused by the transition between different
synoptic situations. During nighttime on 19 January, the haze in BTH rapidly
dissipated, with the PM2.5 concentration decrease of several hundreds of
µg m-3 in 2 or 3 h. In addition, the diurnal cycles of the
observed PM2.5 mass concentrations were not clear, demonstrating the
obvious regional pollution characteristics in BTH. For the four mega-cities
in BTH, the PM2.5 levels in Shijiazhuang and Baoding were much higher
than Beijing and Tianjin, which is caused by the massive local emissions in
Shijiazhuang and Baoding.
Observed hourly PM2.5 concentrations averaged
in (a) four mega-cities (Beijing,
Tianjin, Baoding, and Shijiazhuang) and (b) nine non-mega-cities of
BTH during the period from 13 to 20 January 2014.
NCEP-FNL reanalysis data are used to examine the effect of synoptic
conditions on the air pollution during the haze episode in BTH.
Figures S1–S3 in the Supplement show the synoptic conditions at the surface
level, 850 and 500 hPa, respectively. On 13 January, BTH is to the north of
a high pressure at the surface level, causing the southerly wind in the east
of BTH, and sandwiched between the trough in the northeast of BTH and the
high pressure in the southwest of BTH at 850 hPa, inducing the westerly
surface wind in the west of BTH. At 500 hPa, BTH is situated in the rear of
the trough, and the westerly airflow is dominant. The air pollutants in BTH
are subject to transport to the east but hindered by the southerly wind,
causing accumulation of air pollutants. On 14 January, the high-pressure
system begins to control BTH at the surface level and 850 hPa, and the wind
is varied and weak, favorable for the accumulation of air pollutants in BTH.
On 15 January, BTH is still controlled by the high pressure at the surface
level and 850 hPa, and the westerly wind prevails at 500 hPa. The weak
surface wind, together with the stable stratification, further facilitates
accumulation of air pollutants in BTH. On 16 January, a trough develops over
BTH at 850 and 500 hPa, and BTH is situated near the trough line, in which
the northerly and southerly winds occur at the same time. At the surface
level, the northerly wind prevails in the north of BTH and the southerly wind
prevails in the south of BTH, leading to evacuation of air pollutants in the
north of BTH and the high level of air pollutants in the south of BTH.
On 17 January, the trough at 850 hPa commences weakening and the controlling
region of the trough at 500 hPa becomes narrow. The northwesterly wind is
dominant over BTH, leading to divergence of the air pollutants in BTH.
On 18 January, BTH is located near the ridgeline at 850 hPa and at the verge
of the high pressure at the surface level. The controlling scope of the
high-pressure system at the surface level is wide, inducing the varied wind
over BTH, and is not conducive to the evacuation of air pollutants in BTH.
On 19 January, the prevailing southerly wind in the south of BTH and the
strong westerly wind in the west of BTH lead to the convergence of air
pollutants at the surface level. At 850 and 500 hPa, BTH is situated in the
southeast of the trough and southwesterly wind is prevalent. On 20 January,
BTH is located in the southwest of the trough at 500 and 850 hPa, and the
strong northwesterly wind prevails over BTH. At the surface level, BTH is
situated between the high pressure in the west and the low pressure in the
east, inducing the strong northwesterly wind over BTH. The cold clean air
sweeps BTH and efficiently decreases the air pollutant concentrations in BTH.
Uncertainties in meteorological simulations
Figures 4a–d provide the temporal profiles of the ensemble simulations of
the surface meteorological fields and the corresponding observations at the
meteorological site in Beijing from 13 to 20 January 2014. The U component
exhibits larger ensemble spread than the V component (Fig. S4), but the
ensemble mean (ENSM) of the U component generally yields the observed
diurnal variations. The ensemble prediction of the V component fails to
reproduce the observed intensified southerly or northerly winds. The
meteorological site is located in the north of the Yanshan Mountains,
substantially influenced by the mountain–valley circulation (MVC).
Apparently, the WRF-CHEM model lacks the ability to simulate the occurrence
and development of MVC well, causing the considerable biases of the ensemble
prediction of the V component. The ensemble prediction performs well in
producing the diurnal variation of the surface temperature, but the
underestimation or overestimation is still large when the V component
prediction is biased. The relative humidity (RH) shows a rather large
ensemble spread (Fig. S4d), and the ENSM reasonably tracks the observed
diurnal variation, with high nighttime and low afternoon simulated RH. The RH
simulation is sensitive to the simulated surface temperature. Generally, the
overestimation of the surface temperature corresponds well to the
underestimation of the RH, or vice versa. The ENSM considerably overestimates
the PBL height during daytime on 13 and 14 January, and underestimates it
on 15 January (Fig. 4e). In addition, most of the ensemble members frequently
underestimate the observed PBL height during nighttime, and all ensemble
members fail to produce the peak PBL height on 17 and 20 January. The PBL
height is principally determined by the vertical shear of horizontal winds
and the ground thermal condition. Therefore, uncertainties of wind and
temperature field simulations cause large biases of the PBL height
simulation.
Temporal evolution of the surface (a) U
component, (b) V component, (c) temperature,
and (d) relative humidity at the meteorological site,
and (e) the PBL height at IRSDE in Beijing from each ensemble member
(thin green lines), the ensemble mean (bold black line), and observations
(black dots) from 13 to 20 January 2014.
Temporal evolution of
the (a) POA, (b) SOA, (c) sulfate, (d) nitrate,
and (e) ammonium mass concentrations at IRSDE in Beijing from each
ensemble member (thin green lines), the ensemble mean (bold black line), and
observations (black dots) from 13 to 20 January 2014.
Uncertainties in aerosol species simulations
Figure 5 shows the temporal profiles of the ensemble simulations of the
aerosol species and the observations at IRSDE in Beijing. The ENSM reasonably
produces the observed variations of the POA concentrations. However, all
ensemble members fail to capture the peaks in the morning on 16 January and
in the evening on 17 January, indicating that the underestimation might not
be caused by the meteorological uncertainties, but by emission biases. The
POA in the atmosphere is from multiple sources, including the direct
emissions from vehicles, cooking, biomass, and coal combustion. Diurnal
variations of those sources might constitute one of the major reasons for the
biases of the POA simulations. The ENSM generally performs reasonably well in
simulating the SOA concentration against the measured OOA. The ratio of the
ensemble spread to the ensemble mean (RESM) for the SOA prediction is large
compared to that of POA (Fig. S5a, b). Four SOA formation pathways are
included in simulations: oxidations of anthropogenic and biogenic volatile
organic compounds (VOCs), oxidation HOA semi-volatile vapors, and
irreversible uptake of glyoxal and methylglyoxal on aerosol surfaces.
Therefore, uncertainties in meteorological fields influence not only the
transport of the SOA precursors, but also the SOA formation processes in the
atmosphere, causing the rather large RESM of SOA simulations. The ENSM
generally reproduces the observed variations of sulfate, nitrate, and
ammonium (SNA), but the RESM of SNA is also considerably large (Fig. S5c–d).
During haze days, sulfate is primarily formed through heterogeneous reactions
of SO2 on aerosol surfaces, which is highly dependent on the relative
humidity (Li et al., 2017b). Nitrate formation is determined by the HNO3
and N2O5 that originated from the NO2 oxidation, is sensitive
to the temperature and relative humidity, and is also influenced by the level
of sulfate in the particle phase and ammonia in the atmosphere. The ammonium
aerosol is formed through neutralization of sulfate and nitrate aerosols by
NH3. Additionally, in the present study, ISORROPIA (version 1.7) is used
to calculate the thermodynamic equilibrium between the
sulfate–nitrate–ammonium–water aerosols and their gas-phase precursors
H2SO4–HNO3–NH3–water vapor. Therefore, uncertainties
in meteorological fields propagate to the transport, atmospheric oxidation,
and thermal dynamic processes, which all have contributions to the large RESM
of the SNA simulations. Apparently, uncertainties in meteorological
conditions substantially affect the aerosol species simulations at a single
observation site, which is consistent with the previous studies (Bei et al.,
2012).
Uncertainties in PM2.5 simulations in BTH
Heavy haze with high levels of PM2.5 frequently constitutes a regional
pollution event. Figure 6 shows the temporal profiles of the ensemble
simulations and observations of air pollutants averaged at the monitoring
sites in BTH from 13 to 20 January 2014. The RESM of the average air
pollutants is much less than those of aerosol species at the single
observation site (Fig. S6). For the primary air pollutants, SO2 and CO,
the ENSM generally tracks reasonably the observed variations. However,
sometimes all the ensemble members underestimate or overestimate the
observation. There are two possible reasons for the biases of ensemble
simulations of SO2 and CO: uncertainties of emissions and systematic
errors of meteorological fields. In the evening on 15 January, the ensemble
prediction substantially overestimates the observed SO2 concentration,
but CO overestimation is not large. In contrast, in the morning on
16 January, the ensemble prediction slightly underestimates the SO2
observation but noticeably
underestimates the CO concentration. Therefore, the overestimation of
SO2 on 15 January and underestimation of CO on 16 January might be
primarily contributed by the emission uncertainties. In the morning
on 18 January, the ensemble prediction significantly underestimates both
SO2 and CO observations, indicating the plausible uncertainties caused
by the systematic errors of meteorological fields.
Temporal evolution of the (a) PM2.5,
(b) O3, (c) NO2, (d) SO2, and
(e) CO mass concentrations averaged over monitoring sites in BTH
from each ensemble member (thin green lines), the ensemble mean (bold black
line), and observations (black dots) from 13 to 20 January 2014.
ENSM of the daily average surface PM2.5 concentration
distributions (color contour) along with the ENSM of the daily average
surface winds (black arrows) from 13 to 20 January 2014. The colored circles
denote the PM2.5 measurements in cities.
The ENSM of the average surface O3 and NO2 over the monitoring
sites in BTH is in good agreement with observations. The ensemble prediction
is prone to underestimating the O3 observation during nighttime, but is
very consistent with the NO2 observation. Considering the massive
NOx emission and the titration of NO, the nighttime O3
concentrations are generally very low, particularly during wintertime when
the daytime O3 concentrations are not high. Hence, the underestimation
of nighttime O3 concentrations is perhaps caused by the observation
uncertainties, such as the setting of a lower detection limit. In addition,
the ENSM does not reproduce the high O3 level during nighttime on
19 January when the northwesterly wind is intensified to evacuate the air
pollutants in BTH. Rapid increase in the observed O3 concentrations
during nighttime shows the substantial contribution of the background O3
transport. Therefore, the background O3 uncertainties constitute the
major reason for the O3 underestimation on 19 January.
The ENSM also performs well in replicating the observed PM2.5
observation, except for the underestimation on 16 and 18 January. However,
the RESM of the PM2.5 simulations is larger than those of O3,
NO2, SO2, and CO (Fig. S6). The average ENSM of the PM2.5
concentration over the monitoring sites during the simulation period is
189.5 µg m-3, close to the observed
197.6 µg m-3. In addition, the ensemble member of 16 and 30
(EN-16 and EN-30, respectively) produces the highest and lowest PM2.5
levels, with average PM2.5 concentrations of 231.5 and
167.3 µg m-3, respectively. The PM2.5 mainly includes
the primary aerosols which are determined by direct emissions, and the
secondary aerosols which are determined by their precursor emissions and the
homogeneous and heterogeneous oxidation process in the atmosphere. Therefore,
the large RESM of SOA and SNA simulations enhances the ensemble spread of the
PM2.5 simulations.
Figure 7 presents the spatial distributions of ENSM and observations of the
daily average near-surface PM2.5 mass concentrations during the haze
episode, along with the simulated wind fields. The ENSM predicted PM2.5
spatial patterns are generally in good agreement with the observations at the
ambient monitoring sites in BTH. The ENSM successfully reproduces the haze
development and maturity stages from 13 to 16 January 2014. From 17 to
18 January, the northeasterly wind develops and decreases the PM2.5
level in BTH, but not strongly enough to evacuate the air pollutants. The
PM2.5 pattern of ENSM is very consistent with observations, but
on 18 January, the PM2.5 concentrations are remarkably underestimated in
four cities in BTH. On 19 January, the westerly wind prevails in BTH, causing
the divergence of the PM2.5. On 20 January, the intensified
northwesterly wind begins to empty the PM2.5 in BTH. However,
apparently, the occurrence of the intensification of the northwesterly wind
is early, causing considerable underestimation of the PM2.5
concentration in the ENSM.
Same as Fig. 7 but for the ensemble member of 16 with the highest
simulated PM2.5 concentration.
Same as Fig. 7 but for the ensemble member of 30 with the lowest
simulated PM2.5 concentration.
Temporal evolution of the PM2.5 mass concentrations averaged
in (a) Beijing, (b) Tianjin, (c) Baoding,
and (d) Shijiazhuang from each ensemble member (thin green lines),
the ensemble mean (bold black line), and observations (black dots) during the
period from 13 to 20 January 2014. The red and blue lines represent the
simulations in the members with the highest and lowest PM2.5
concentrations, respectively.
The uncertainties in meteorological fields are generally less than
observational and analysis errors, but the ensemble simulations still exhibit
considerable spreads. In order to contrast the PM2.5 simulations of
different ensemble members, we have selected two members: EN-16 and EN-30,
representing the highest and lowest PM2.5 simulations in BTH,
respectively. Figures 8 and 9 provide the horizontal distributions of the daily average surface
PM2.5 concentrations along with surface winds during the episode in
EN-16 and EN-30, respectively. Similar PM2.5 distribution patterns are
simulated in EN-16 and EN-30, showing that the meteorological uncertainties
do not dominate the haze formation and development principally. The
PM2.5 level in EN-16 is much higher than that in EN-30 in BTH, which is
mainly caused by the considerable discrepancies in the surface winds between
the two members. The simulated southerly wind in EN-16 is generally more
intense than that in EN-30, but the northerly wind in EN-16 is weak compared
to EN-30, which is more favorable for the air pollutant accumulation in EN-16
than in EN-30. On 13 and 14 January, the winds in EN-30 are weak or calm in
BTH and the PM2.5 is mainly attributed to the local production. However,
in EN-16, the prevailing southerly winds also deliver the air pollutants from
the southern areas to BTH, substantially enhancing the PM2.5 level.
On 15 January, although EN-16 and EN-30 both produce the prevailing southerly
wind in BTH, the westerly wind in EN-30 is intense compared to EN-16,
considerably decreasing the PM2.5 level in EN-30. On 16 January, the
northeasterly wind in EN-30 is intensified and evacuates the PM2.5 in
the north of BTH. However, in EN-16, the simulated northeasterly wind is weak
and the PM2.5 level in the north of BTH still remains high.
On 17 January, the simulated northerly wind in EN-16 is weak compared to that
in EN-30, causing a higher PM2.5 concentration in EN-16 than EN-30 in
BTH. On 18 January, the intensified southerly wind in EN-16 considerably
increases the PM2.5 level in BTH compared to EN-30. On 19 January, the
westerly wind is prevalent in EN-30 and the PM2.5 level begins to
decrease, but in EN-16, the southwesterly wind still causes high PM2.5
concentrations in BTH. On 20 January, the stronger northeasterly wind in
EN-30 more efficiently evacuates the PM2.5 than that in EN-16.
Uncertainties in PM2.5 simulations in mega-cities
EN-16 and EN-30 both predict the haze occurrence and development in BTH
during the episode, although the difference in the PM2.5 level between
those two members is considerable, showing that the meteorological
uncertainties do not dominate the regional haze formation. Previous studies
have shown that the meteorological uncertainties substantially impact the air
quality simulations at the city scale (Bei et al., 2012). Figure 10 presents
the temporal variation of the ensemble simulations and observations averaged
at four mega-cities in BTH during the episode. The ENSM of the PM2.5
concentrations in Beijing, Tianjin, and Baoding is in good agreement with the
observation. However, the ENSM remarkably underestimates the observed
PM2.5 concentration in Shijiazhuang from 16 to 19 January, which is
hardly interpreted by the emission biases. The ENSM performs well in
simulating the PM2.5 variations from 13 to 15 January, and overestimates
the observation on 20 January in Shijiazhuang. One of the possible reasons
for the underestimation in Shijiazhuang is that the westerly wind is
systematically overestimated from 16 to 19 January along the foothills of the
Taihang Mountains, causing the haze plume to move eastward.
Although the ENSM produces reasonably well the PM2.5 variations in the
four mega-cities against the measurement, the meteorological uncertainties
still cause large uncertainties in the PM2.5 concentration (Fig. S7).
During the first 3 days of the episode, the ENSM is very consistent with the
observations in the four mega-cities, but the PM2.5 level discrepancy
between the members with the highest and lowest PM2.5 concentrations is
rather large, causing troubles for the assessment of the control strategies.
For example, in Shijiazhuang, the average PM2.5 concentrations during
the first 3 days in the members with the highest and lowest PM2.5
concentrations are 403.5 and 213.8 µg m-3, respectively, and
the difference is about 190 µg m-3. In Beijing, the average
PM2.5 concentrations in the two members are 103.9 and
196.3 µg m-3. It is worth noting that, according to the
Chinese air quality standard released in 2012, the PM2.5 concentration
of 103.9 µg m-3 is defined as a “lightly polluted
condition”, but that 196.3 µg m-3 is defined as a “heavily
polluted condition”. If the heavy air pollution occurs, the control
strategies will be implemented. Therefore, it is necessary to use the
ensemble simulation to avoid the impact of the meteorological uncertainties
on the haze prediction.
Summary and conclusions
In the present study, the uncertainties in simulating haze formation due to
meteorological uncertainties are investigated using the WRF-CHEM model
through ensemble simulations. A persistent heavy haze episode that occurred
in BTH from 13 to 20 January 2014 is simulated. A
climatological ensemble initialization approach is used to produce initial
and boundary conditions for each ensemble member.
The ENSMs of the aerosol constituents are generally in good agreement with
the observations at an observation site in Beijing, including the sharp
buildup of the aerosol constituents in the evening on 15 January and rapid
falloff in the morning on 20 January. However, the ENSM considerably
underestimates the observed primary aerosols in the evening on 17 January.
The ensemble spread is rather large for the aerosol constituent simulations,
and the RESM exceeds 50 %, respectively.
The ENSM performs well in simulating the temporal variations of the average
surface CO, SO2, NO2, O3, and PM2.5 mass
concentrations over the monitoring sites in BTH, and the RESM of the air
pollutants is generally less than 30 %. The RESM of PM2.5
simulations is larger than the other air pollutants, which is due to the
complicated composition of PM2.5, including the contributions of primary
and secondary aerosols. The meteorological uncertainties do not principally
dominate the haze formation and development, but considerably alter the
simulated PM2.5 level. The average PM2.5 difference during the
episode exceeds 60 µg m-3 between the two members with the
highest and lowest PM2.5 simulations.
Although the meteorological uncertainties do not dominate the regional haze
formation, they still substantially influence the PM2.5 simulations at
city scale. The ENSM predicts the PM2.5 variations in the four
mega-cities against the measurements reasonably well, including Beijing,
Tianjin, Baoding, and Shijiazhuang, but the RESM of the PM2.5
simulations is rather large, causing troubles for the evaluation of the
control strategies. Therefore, the ensemble simulation is needed to take into
consideration the impact of the meteorological uncertainties on the haze
prediction. It is worth noting that aside from meteorological fields,
uncertainties in emissions or various chemistry/aerosol schemes also
considerably influence the WRF-CHEM model simulations. The extended response
surface modeling (ERSM) technique can be used to quantify the relative
importance of each uncertainty source in the WRF-CHEM model (Zhao et al.,
2017).