Fine-particle pollution associated with haze threatens human
health, especially in the North China Plain region, where extremely high PM2.5
concentrations are frequently observed during winter. In this study, the
Weather
Research and Forecasting with Chemistry (WRF-Chem) model coupled with an improved integrated process analysis scheme
was used to investigate the formation and evolution mechanisms of a haze
event over the Beijing–Tianjin–Hebei (BTH) region in December 2015; this included an examination of
the contributions of local emissions and regional transport to the PM2.5
concentration in the BTH area, and the contributions of each detailed physical or
chemical process to the variations in the PM2.5 concentration. The
mechanisms influencing aerosol radiative forcing (including aerosol direct
and indirect effects) were also examined by using process analysis.
During the aerosol accumulation stage (16–22 December, Stage 1), the near-surface PM2.5 concentration in the BTH region increased from 24.2 to 289.8 µgm-3, with the contributions of
regional transport increasing from 12 % to 40 %, while the contribution
of local emissions decreased from 59 % to 38 %. During the aerosol
dispersion stage (23–27 December, Stage 2), the average
concentration of PM2.5 was 107.9 µgm-3, which was
contributed by local emissions (51 %) and regional transport (24 %).
The 24 h change (23:00 minus 00:00 LST) in the near-surface PM2.5
concentration was +43.9µgm-3 during Stage 1
and -41.5µgm-3 during Stage 2. The contributions of
aerosol chemistry, advection, and vertical mixing to the 24 h change were
+29.6 (+17.9) µgm-3, -71.8 (-103.6) µgm-3, and
-177.3 (-221.6) µgm-3 during Stage 1
(Stage 2), respectively. Small differences in the contributions
of other processes were found between Stage 1 and
Stage 2. Therefore, the PM2.5 increase over the BTH region during the
haze formation stage was mainly attributed to strong production by the
aerosol chemistry process and weak removal by the advection and vertical mixing
processes. When aerosol radiative feedback was considered, the 24 h
PM2.5 increase was enhanced by 4.8 µgm-3 during
Stage 1, which could be mainly attributed to the
contributions of the vertical mixing process (+22.5µgm-3),
the advection process (-19.6µgm-3), and the aerosol chemistry process
(+1.2µgm-3). The restrained vertical mixing was the primary
reason for the enhancement in the near-surface PM2.5 increase when aerosol
radiative forcing was considered.
Introduction
Anthropogenic activities associated with rapid industrialization
and urbanization have been leading to a sustained increase in the amounts of
atmospheric pollutants, especially in quickly developing countries (IPCC,
2013). As one of the largest emission sources of aerosols and their
precursors, China has been suffering from serious air pollution for years
(Lei et al., 2011; Li et al., 2011; Z. Liu et al., 2018), with severe haze
events frequently occurring in winter, especially over large urban
agglomerations, such as the North China Plain (NCP) (Han et al., 2014; Gao
et al., 2015), the Yangtze River Delta (YRD) (Ding et al., 2016; H. Wang
et al., 2016), and the Sichuan Basin (SCB) (Zhao et al., 2018; Zhang et
al., 2019). During severe haze events, the observed maximum hourly
surface-layer PM2.5 (fine particulate matter with an aerodynamic diameter
of 2.5 µm or less) concentration can exceed 1000 µgm-3
(Z. Wang et al., 2013; Sun et al., 2016; J. Li et al., 2017), which can
significantly influence visibility (Li et al., 2014), the radiation budget
(Steiner et al., 2013), atmospheric circulation (Jiang et al., 2017), cloud
properties (Unger et al., 2009), and human health (Hu et al., 2014; Guo et
al., 2017).
Extensive studies have been carried out in recent years to analyze the
formation mechanisms of haze episodes in China. Y. Wang et al. (2013) used a
synergy of ground-based observations, satellite, and lidar measurements to
study a long-lasting, severe haze episode that occurred in eastern China in
January 2013, and concluded that stagnant meteorological conditions, which
can generally be characterized by a low wind speed, high relative humidity,
intense inversion, and a low mixing layer height, were tightly associated with
severe haze episodes. Based on National Center for Environmental Prediction
(NCEP) reanalysis data, Shu et al. (2017) identified five typical synoptic
patterns, and pointed out that each synoptic pattern exerted different
impacts on particle pollution over the YRD. By analyzing the simulation results
from a large ensemble climate model (MIROC5), K. Li et al. (2018) investigated
the contributions of the anthropogenic influence to severe haze events that occurred
over eastern China in January 2013 and December 2015, and found that
anthropogenic forcing (i.e., increased emissions of greenhouse gases) could
modify the atmospheric circulation pattern, and that these human-induced circulation
changes were conducive to the occurrence of severe haze events. B. Zhang et al. (2015) used a global 3-D chemical transport model (GEOS-Chem) to quantify
the local source contributions to wintertime surface-layer PM2.5
concentrations over North China from 2013 to 2015, and reported that
emissions from residential and industrial sources and transportation
contributed most to the high concentrations of atmospheric aerosols in
Beijing. Many studies have also reported that the regional transport of aerosols plays an important role in haze episodes (Z. Wang et al., 2013; Jiang et al.,
2015; N. Li et al., 2018). Z. Wang et al. (2013) reported that the “cross-city
clusters transport” outside BTH (Beijing–Tianjin–Hebei) and transport
among cities inside the BTH region contributed 20 %–35 % and 26 %–35 % of
PM2.5 concentrations over BTH, respectively. Secondary aerosol
formation and their hygroscopic growth were also confirmed to be a large
contributor to severe haze episodes (R. J. Huang et al., 2014; Han et al., 2015;
L. Chen et al., 2019). The conversion of SO2 to
SO42- was strongly associated with high
relative humidity, and NO3- was found
to be produced mainly by photochemical and heterogeneous reactions (Chen et
al., 2016; R. Zhang et al., 2018).
It is well known that aerosols can scatter and absorb solar radiation to
alter the radiative balance of the atmosphere and surface (direct radiative
effect), and can serve as cloud condensation nuclei or ice nuclei to affect
cloud properties (indirect radiative effect) (Twomey, 1974). These impacts
are coupled with atmospheric dynamics to produce a chain of interactions
with a large range of meteorological variables that influence both weather
and climate (Ramanathan et al., 2001; Huang et al., 2006; Li et al., 2017a;
Yang et al., 2017), which will further induce feedbacks on aerosol
production, accumulation, and even severe haze pollution (Petaja et al.,
2016; Li et al., 2017b; Zhao et al., 2017; Gao et al., 2018; Lou et al.,
2019). Based on multi-year measurements (from 2010 to 2016), Huang et al. (2018) found that aerosol radiative effects led to a significant heating in
the upper planetary boundary layer (PBL) and a substantial dimming at the
surface over North China. This is because high concentrations of
light-absorbing aerosols were observed, and the aerosol–meteorology
interactions depressed the development of the PBL, and, in turn, aggravated the
haze pollution (Su et al., 2018). The light-absorbing aerosols can also
amplify haze in the NCP region by decreasing East Asian winter monsoon wind speeds via ocean and cloud feedbacks (Lou et al., 2019). Using the WRF-Chem
model, Gao et al. (2015) analyzed the feedbacks between aerosols and
meteorological fields over the NCP in January 2013, and found that aerosols
caused a significant negative (positive) radiative forcing at the surface
(in the atmosphere), resulting in a lower surface-layer wind speed and
lower PBL height (PBLH). The average surface-layer PM2.5 concentration
increased by 10–50 µgm-3 as a result of the more stable
atmosphere. By analyzing the observations from a comprehensive field
experiment and simulation results from WRF-Chem model, Q. Liu et al. (2018)
concluded that the decreased PBLH associated with increased aerosol
concentrations could enhance surface-layer relative humidity by weakening
the vertical transport of water vapor, and that the increased relative humidity
at the surface accelerated the formation of secondary particulate matter
via heterogeneous reactions, leading to an increase of the PM2.5
concentration by 63 µgm-3 averaged over the NCP from 15 to 21 December 2016.
All of the studies discussed above revealed that the formation of haze
episodes was caused by the synergy impacts of local emissions, regional
transport, meteorological conditions, and chemical production. Nevertheless,
only the net combined effects on the concentrations of pollutants were
provided, without the capability to understand and isolate the
atmospheric physical and chemical processes involved. The quantitative
assessment of the contributions from each detailed physical/chemical process
(e.g., vertical mixing process, advection process, emission source process,
aerosol chemistry process and cloud chemistry process) is necessary to fully
understand the formation and evolution mechanisms of haze episodes
(Gonçalves et al., 2009; Xing et al., 2017; Kang et al., 2019). Furthermore, although many previous studies have identified the positive feedback effects
of aerosol radiative forcing on particulate accumulation, the detailed
influence mechanisms of the forcing–response relationship at each process
chain remain largely elusive (i.e., the prominent physical or chemical
processes responsible for the aerosol radiative impacts on haze episodes).
Since 2013, substantial efforts have been made to improve air quality in
China, including emission reduction and energy transition. However, haze
events have continued to frequently occur all over the country. For example, a
severe, long-lasting, and wide-ranging haze episode was observed in December
2015 over central and eastern China, with the regional average
PM2.5 concentration exceeding 150 µgm-3. In the BTH region, a red
alert for haze (the most serious level) was issued for the period from 20 to
22 December 2015, with the maximum hourly PM2.5 concentration exceeding
1000 µgm-3. The formation and evolution mechanisms, and the
aerosol radiative feedbacks of this severe haze episode have not yet been fully estimated.
In this study, we develop an improved online integrated process rate (IPR)
analysis scheme (i.e., process analysis) in the fully coupled online Weather
Research and Forecasting with Chemistry (WRF-Chem) model, to investigate the
formation and evolution mechanisms of the severe haze episode that occurred over the
NCP from 16 to 29 December 2015. Sensitivity experiments are conducted to
examine the contributions of local emissions and regional transport to the
PM2.5 concentrations during the haze episode, while IPR analysis is
used to quantify the contributions of each detailed physical/chemical
process to the variations in the PM2.5 concentrations. The effects of
aerosol radiative forcing, including the direct and indirect effects, on
meteorological parameters and PM2.5 levels during the haze episode are
also quantified, with a special focus on the detailed influence mechanism.
We hope that the results from this study may provide a better
understanding of the formation mechanisms for severe haze events, and help
policy makers design and carry out targeted measures to improve air quality over North
China.
This paper is arranged as follows. The model configuration, integrated
process rate (IPR) analysis (i.e., process analysis), numerical experiments,
and observations are presented in Sect. 2. Model evaluation is conducted
in Sect. 3. The formation and evolution mechanisms of the haze episode are
investigated in Sect. 4. Section 5 provides the impacts of aerosol
radiative forcing. Summaries and discussions are presented in Sect. 6.
MethodsModel configuration
A fully coupled online Weather Research and Forecasting with Chemistry model
(WRF-Chem v3.7) is used to simulate meteorological fields and concentrations
of gases and aerosols simultaneously (Skamarock et al., 2008; Grell et al.,
2005). The WRF-Chem model is designed with two domains using 219 (west–east)
×159 (south–north) and 150 (west–east) ×111
(south–north) grid points at the horizontal resolutions of 27 and 9 km,
respectively (Fig. 1). The outer domain covers nearly the whole of East Asia,
and the inner domain is located in the NCP. In order to minimize the impacts
from LBCs (lateral boundary conditions), we only analyze the simulation
results from the inner region of the second domain (i.e., BTH), following
Chen et al. (2018) and Wu et al. (2012). The vertical dimension is resolved
by 29 full sigma levels, with 15 layers located in the bottom 2 km for finer
resolution in the PBL; the height of the first
layer averaged in BTH is about 30 m.
(a) Map of the two nested model domains. (b) Locations of the
observations used for model evaluation.
Time series of the observed (black dots) and simulated (red dots) hourly 2 m temperature (T2, K), 2 m relative humidity
(RH2, %), 10 m wind speed (WS10, m s-1), and 10 m wind
direction (WD10, ∘) averaged over the 12 stations from
16 to 29 December 2015.
Meteorological initial and lateral boundary conditions used in the WRF-Chem
model are taken from the NCEP (National Center for Environmental Prediction)
(Final) Operational Global Analysis data with a spatial resolution of
1∘×1∘. Four-dimensional data assimilation
(FDDA) with the nudging coefficient of 3.0×10-4 for wind (in
and above the PBL), temperature (above the PBL), and water vapor mixing ratio (above
the PBL) is adopted to improve the accuracy of simulation results (no analysis
nudging is included for the inner domain) (Lo et al., 2008; Otte, 2008; L. Wang
et al., 2016; Werner et al., 2016). The forecasts from the MOZART-4 global chemical
transport model are processed to provide the chemical initial and
boundary conditions for the WRF-Chem model (Emmons et al., 2010).
Anthropogenic emission data are obtained from the MIX Asian emission
inventory (http://www.meicmodel.org/dataset-mix.html, last access: 12 August 2019), with a horizontal
resolution of 0.25∘ (M. Li et al., 2017). MIX is developed to support the
MICS-Asia III (Model Inter-Comparison Study for Asia Phase III) and the TF
HTAP (Task Force on Hemispheric Transport of Air Pollution) projects. This
inventory includes SO2 (sulfur dioxide), NOx (nitrogen oxides), CO
(carbon monoxide), CO2 (carbon dioxide), NMVOC (non-methane volatile
organic compounds), NH3 (ammonia), BC (black carbon), OC (organic
carbon), PM2.5, and PM10. All of these species are from several
sectors, such as agriculture, industry, power, transportation, and
residential, and the emission rate of each species for each hour is based on
Gao et al. (2015). The biogenic emissions are calculated online using the
MEGANv2.04 (Model of Emission of Gases and Aerosol from Nature v2.04) model
(Guenther et al., 2006). Biomass-burning emissions are obtained from the GFEDv3
(Global Fire Emissions Database v3) (Randerson et al., 2005). Dust emissions
and sea salt emissions are calculated online using algorithms proposed by
Shao (2004) and Gong et al. (1997), respectively.
The Carbon-Bond Mechanism version Z (CBMZ) (Zaveri and Peters, 1999) is
selected to simulate the gas-phase chemistry, and the eight-bin sectional
aerosol module, MOSAIC (Model for Simulating Aerosol Interactions and
Chemistry) (Zaveri et al., 2008), with some aqueous chemistry, is used to
simulate aerosol evolution. All major aerosol species are considered in the
MOSAIC scheme, including sulfate
(SO42-), nitrate
(NO3-), ammonium
(NH4+), chloride (Cl), sodium (Na), BC,
primary organic mass, liquid water, and other inorganic mass (Zaveri et al.,
2008). The aerosol size distribution is divided into discrete size bins
defined by their lower and upper dry particle diameters (Zhao et al., 2010).
In the current CBMZ/MOSAIC scheme, the formation of SOA (secondary organic
aerosol) is not included (Zhang et al., 2012; Gao et al., 2016). Aerosol
optical properties, including the extinction efficiency, the single scatter albedo,
and the asymmetry factor are computed using Mie theory, based on aerosol
composition, mixing state, and size distribution (Barnard et al., 2010). The
impacts of aerosols on photolysis rates are calculated using the Fast-J
photolysis scheme (Wild et al., 2010). Aerosol radiation is simulated using
RRTMG (Rapid Radiative Transfer Model for GCMs) for both shortwave (SW) and
longwave (LW) radiation (Zhao et al., 2011). More information regarding the
parameterizations used in this study can be found in Table 1.
Most air quality models are configured to output only the pollutant
concentrations that reflect the combined effects of all physical and
chemical processes. Quantitative information on the impacts of individual
process is usually unavailable. Process analysis techniques, i.e.,
integrated process rate (IPR) analysis, can be used in grid-based Eulerian
models (e.g., WRF-Chem) to obtain contributions of each physical/chemical
process to variations in pollutant concentrations. Eulerian models utilize
the numerical technique of operator splitting to solve continuity equations
for each species into several simple ordinary differential equations or
partial differential equations that only contain the influence of one or two
processes (Gipson, 1999).
The IPR analysis method has been fully implemented in Community Multi-scale
Air Quality (CMAQ) model, and has been widely applied to study regional
photochemical ozone (O3) pollution (Gonçalves et al., 2009; Khiem et
al., 2010; Xing et al., 2017; Tang et al., 2017). Several WRF-Chem model
studies have used the IPR analysis to investigate the impacts of
physical/chemical process on variations in O3 concentrations. Gao et
al. (2018) investigated the impacts of BC–PBL interactions on O3
concentrations by analyzing the contributions from photochemistry, vertical
mixing, and advection processes. Jiang et al. (2012) calculated the
contributions of photochemical reactions and physical processes to O3 formation using a simplified IPR analysis scheme.
Applying the IPR analysis to diagnose the contributions of each physical or
chemical process to variations in aerosol concentrations in the WRF-Chem model
is more complex technically; therefore, few studies have utilized the IPR
analysis for aerosols. In this study, we developed an improved IPR analysis
scheme in the WRF-Chem model to isolate the processes impacting variations
in aerosol concentrations into nine different processes, namely advection
(TRAN), emission source (EMIS), dry deposition (DYRD), turbulent diffusion
(DIFF), sub-grid convection (SGCV), gas-phase chemistry (GASC), cloud
chemistry (CLDC), aerosol chemistry (AERC), and wet scavenging (WETP). TRAN
includes horizontal and vertical advection, which is highly related to wind
and aerosol concentration gradients from upwind regions to downwind areas
(Gao et al., 2018). DRYD is based on resistance models for trace gases
(Wesely, 1989) and aerosol particles (Ackermann et al., 1998). SGCV refers
to the scavenging within the sub-grid wet convective updrafts. CLDC refers
to aqueous-phase photolytic and radical chemistry reactions in clouds,
including the activation processes. AERC refers to microphysical nucleation,
condensation, and coagulation, as well as the mass transfer between the gas
phase and condensed phase. WETP contains in-cloud rainout and below-cloud
washout during grid-scale precipitation. The contribution of individual
processes can be calculated as the difference of aerosol concentrations before
and after the corresponding operator.
Based on the principle of mass balance, IPR can be verified by comparing the
variations in aerosol concentrations (the concentration at the current time
minus the concentration at the previous time) with the sum of the
contributions from the nine processes during each time step. As shown in
Fig. S1 in the Supplement, the net contributions of all processes match the variations in
aerosol concentrations quite well.
Numerical experiments
Table 2 summarizes the experimental designs. To investigate the
contributions of regional transport and local emissions to the PM2.5
concentrations in the BTH region, four simulations with different anthropogenic
emission categories were conducted: (1) CTL – the control simulation with all
anthropogenic emissions considered; (2) NoAnth – no anthropogenic emissions are
considered in the whole domain; (3) NoBTH_Anth – the same as the CTL,
but anthropogenic emissions in the BTH area are excluded; and (4) OnlyBTH_Anth – contrary to the NoBTH_Anth case, anthropogenic
emissions are only considered in the BTH region. All the physical and chemical schemes
used in these cases are identical. The contributions of regional transport
and local emissions to the PM2.5 concentration in the BTH region can be identified
by comparing the simulation results of NoBTH_Anth and NoAnth
(i.e., NoBTH_Anth minus NoAnth) and OnlyBTH_Anth and NoAnth (i.e., OnlyBTH_Anth minus NoAnth),
respectively.
Experimental design. “Y” represents yes, and “N” represents no.
CaseAnthropogenicAerosol directAerosol indirectdescriptionemissioneffecteffectCTLYYYNoAnthWithout emission in the whole domainYYNoBTH_AnthWithout emission in BTHYYOnlyBTH_AnthOnly emission in BTHYYNoAREYNN
To quantify the aerosol radiative effects (ARE) on haze pollution, another
sensitivity experiment (referred to as the NoARE case) was designed by turning the feedbacks between aerosols and meteorological variables off, including
eliminating the aerosol direct effect (ADE) and the aerosol indirect effect
(AIE) in the model. The ADE is turned off by removing the mass of aerosol
species from the calculation of aerosol optical properties, following Qiu et
al. (2017). The AIE is turned off using a prescribed vertically uniform
cloud droplet number, which is calculated from the CTL case during the whole
simulation period, following Gao et al. (2015) and B. Zhang et al. (2015).
The differences between CTL and NoARE (i.e., CTL minus NoARE) represent the
impacts of aerosol radiative forcing.
The IPR analysis method is applied to all of the experiments designed.
Comparing the contributions of each detailed process between the pollution
accumulation stage and the dissipation stage in the CTL case can quantitatively explain
the reason for the variation in the PM2.5 concentrations in the BTH region.
Meanwhile, the prominent physical or chemical process responsible for the
aerosol radiative impacts on the haze episode can also be investigated by
analyzing the IPR analysis method used in the CTL and NoARE cases.
All five simulations are conducted for the period from 13 to 29 December 2015, and the initial 3 days are discarded as the model spin-up to
minimize the impacts of initial conditions. Simulation results from the CTL
case from 16 to 29 December 2015 are used to evaluate the model
performance.
Observational data
Simulated meteorological parameters in CTL case, including 2 m temperature
(T2), 2 m relative humidity (RH2), 10 m wind speed (WS10), and
10 m wind direction (WD10), are compared with hourly observations at
12 stations, which are collected from NOAA's National Climatic Data
Center (https://gis.ncdc.noaa.gov/maps/ncei/cdo/hourly, last access: 12 August 2019). Due to limited
observations of the PBL height in the BTH area, the retrieved PBLH observations at 3 h intervals
obtained from the GDAS (Global Data Assimilation System)
(https://ready.arl.noaa.gov/READYamet.php, last access: 12 August 2019) in Beijing (39.93∘ N,
116.28∘ E) are also used to evaluate the model performance. More
detailed information about the GDAS meteorological dataset (1∘×1∘) can be found in Rolph (2013), Kong et al. (2015), and at https://www.ready.noaa.gov/gdas1.php (last access: 12 August 2019). The hourly shortwave downward
radiation flux (SWDOWN) at the Xianghe station (39.75∘ N,
116.96∘ E) is taken from WRMC-BSRN (World Radiation Monitoring
Center-Baseline Surface Radiation Network, http://bsrn.awi.de, last access: 12 August 2019) for the
energy budget evaluation. The hourly observed surface-layer PM2.5 concentrations at the 59 stations are obtained from the CNEMC (China
National Environmental Monitoring Center, http://www.cnemc.cn/, last access: 12 August 2019). The daily
measurements of the mass concentrations of
SO42-,
NO3-,
NH4+, BC, and OC are collected at the
Beijing (39.97∘ N, 116.37∘ E) and
Shijiazhuang (38.03∘ N, 114.53∘ E) sites (Huang et al.,
2017; Z. Liu et al., 2018). Detailed locations of these observations are shown
in Fig. 1b.
Model evaluation
Accurate representations of observed meteorological fields and pollutant
concentrations provide foundations for haze analysis with the WRF-Chem
model. Detailed comparisons between observed and simulated meteorological
parameters (T2, RH2, WS10, WD10, PBLH, and SWDOWN) and
pollutant concentrations (PM2.5, BC, OC,
SO42-,
NO3-, and
NH4+) are presented in this section.
Meteorological parameters
Figure 2 shows the time series of observed and simulated hourly
meteorological variables averaged over the 12 stations from 16 to 29 December 2015. Corresponding statistical metrics, including the mean value, the normalized
mean bias (NMB), the mean fractional bias (MFB), the mean fractional error (MFE),
the index of agreement (IOA), and the correlation coefficient (R) are presented in
Table 3. As shown in Fig. 2, simulated T2, RH2, WS10, and
WD10 agree well with the observational data. For temperature, the
WRF-Chem model can perfectly depict its diurnal and daily variations with R
and IOA values of 0.90 and 0.94, respectively, but slightly overestimates the low
values at night, with a NMB of 1 %. Observed relative humidity can be
reasonably reproduced by the model with R and IOA values of 0.73 and 0.82,
respectively, but a persistent underestimation is found with a NMB of
-12 %. Different surface layer and boundary layer parameterizations may
influence the simulated near-surface moisture fluxes, and the
settings of these schemes can partially explain the biases of RH2
between the observations and simulations (Qian et al., 2016). This negative bias
of RH2 can also be simulated by other studies (Zhang et al., 2009; Gao
et al., 2015). WRF-Chem can capture the observed low wind speed values
from 19 to 23 December and high wind speed values from 16 to 17 and 25 to 27 December. The positive NMB of 28 % probably results from unresolved
topographical features in the surface drag parameterization and the coarse
resolution used in the nested domain (Yahya et al., 2015; Zheng et al.,
2015). For wind direction, the calculated NMB is 1 % and the IOA is 0.65,
indicating that the WRF-Chem model can generally reproduce the varied wind
direction during the simulation period.
Statistical metrics between observations and simulations.
a,bOBS‾ and SIM‾
represent the average observations and simulations, respectively. OBS‾=1n×∑i=1nOBSi,
SIM‾=1n×∑i=1nSIMi.c NMB is the normalized mean bias,
NMB=1n×∑i=1nSIMi-OBSiOBSi×100%.d MFB is the mean fractional bias,
MFB=2n×∑i=1nSIMi-OBSiSIMi+OBSi×100%.e MFE is the mean fractional error,
MFE=2n×∑i=1nSIMi-OBSiSIMi+OBSi×100%.f IOA is the index of agreement,
IOA=1-∑i=1nSIMi-OBSi2∑i=1n|OBSi-OBS‾|+|SIMi-SIM‾)|2.gR is the correlation coefficient, R=∑in|(OBSi-OBS‾)×(SIMi-SIM‾)|∑in(OBSi-OBS‾)2+∑in(SIMi-SIM‾)2.In the above OBSi and SIMi refer to
observations and model predictions, respectively, i refers to a
given station, and n is the total number of stations.hT2: temperature at 2 m (K); RH2: relative humidity at 2 m
(%); WS10: wind speed at 10 m (m s-1); WD10: wind
direction at 10 m (∘).
Simulated hourly PBLH and SWDOWN are also compared with observations in Fig. 3. It is noted that the PBLH measurements provided by GDAS of NOAA are in 3 h intervals.
The simulations in the CTL case agree well with the observations, including
capturing the daily maximum at daytime and the low values at night. The
correlation coefficients are 0.68 and 0.91 for PBLH and SWDOWN,
respectively.
Time series of the observed (black dots) and simulated (red lines) hourly planetary boundary layer height (PBLH, m) at the site
in Beijing (39.93∘ N, 116.28∘ E), and shortwave
downward radiation flux (SWDOWN, W m-2) at the Xianghe station
(39.75∘ N, 116.96∘ E) from 16 to 29 December 2015.
Notably, PBLH measurements provided by Global Data Assimilation System (GDAS) are in
3 h intervals. All times are converted to China standard time (Beijing
time).
PM2.5 and its components
Observed hourly surface-layer PM2.5 concentrations from 16 to 29 December 2015 in the nine cities (Shengyang, Beijing, Xingtai, Hengshui,
Baoding, Langfang, Yangquan, Anyang, and Jinan) are compared with the model
results from the CTL case (Fig. 4). The statistical metrics are shown in Table 3. Generally, the WRF-Chem model can reasonably reproduce the evolutional
characteristics of the observed PM2.5 concentrations in the nine cities
(Rs =0.57–0.90). Both the observed and simulated PM2.5 concentrations
exhibit a growth trend from 16 to 22 and 28 to 29 December, and a decreasing
tendency from 23 to 27 December. However, an obvious underestimation is found
in Beijing from 25 to 26 December when a maximum hourly concentration of 600 µgm-3 was observed. This negative bias is also simulated by
previous studies (Chen et al., 2018; Z. Zhang et al., 2018). The possible
reasons for the underestimation are as follows: (1) the bias in simulated meteorological
conditions (e.g., underestimated RH2 and overestimated WS10); (2) the missing mechanisms of some gas–aerosol phase partitioning and
heterogeneous reactions which may produce secondary inorganic aerosol (X. Huang
et al., 2014; Wang et al., 2014); and (3) the lack of SOA simulation in the MOSAIC
mechanism (Gao et al., 2016). Generally, the performance statistics of
PM2.5 in almost all cities meet the model performance goal (MFB within
±30 % and MFE ≤50 %) proposed by Boylan and Russel (2006).
Time series of the observed (black dots) and simulated (red dots) hourly PM2.5 concentrations (µgm-3) in the
nine cities (Shengyang, Beijing, Xingtai, Hengshui, Baoding, Langfang,
Yangquan, Anyang, and Jinan) from 16 to 29 December 2015. The “n” in each
panel represents the number of observation sites in each city. Beijing time
is used for these hourly time series.
Figure 5 compares the simulated and observed surface-layer concentrations of
BC, OC, SO42-,
NO3-, and
NH4+ in Beijing and Shijiazhuang
averaged from 16 to 29 December 2015. The WRF-Chem model underestimates the
concentrations of SO42-,
NH4+, and OC in Beijing (Shijiazhuang)
by 19 % (40 %), 14 % (9 %), and 21 % (41 %), respectively, but
overestimates the NO3- concentration by
29 % (44 %). Due to the low reactivity of BC in the atmosphere, the
uncertainty in the BC emissions may cause the biases in Beijing (NMB =+10 %)
and Shijiazhuang (NMB =-24 %). For OC, the underestimation may result
from the lack of SOA in the MOSAIC aerosol module (Qiu et al., 2017).
Missing some SO2 gas-phase and aqueous-phase oxidation mechanisms,
as well as heterogeneous chemistry may explain the underestimation of
SO42-. It is noted that similar biases
of aerosol components were also reported by other WRF-Chem studies (B. Zhang et
al., 2015; Qiu et al., 2017).
Comparison of observed and simulated surface-layer mass
concentrations (µgm-3) of
SO42- (red),
NO3- (blue),
NH4+ (purple), OC
(green), and BC (gray) at sites (a) in Beijing (39.97∘ N,
116.37∘ E) and (b) Shijiazhuang (38.03∘ N,
114.53∘ E) averaged over the 16–29 December 2015 period. Normalized mean biases (NMBs) are also listed for each
species (colored numbers).
Formation and evolution mechanisms of the haze episode
In this section, we first reproduce the evolution of the severe haze
episode, and then investigate the formation and evolution mechanisms,
including examining contributions of local emissions and regional transport
to the PM2.5 concentration in the BTH region, and the contributions of each
detailed physical/chemical process to the variations in the PM2.5
concentration.
Spatial–temporal evolutions of surface-layer PM2.5 concentrations
Figure 6a–k show the spatial distributions of the simulated daily mean
surface-layer PM2.5 concentrations from 17 to 28 December 2015. From
17 December, aerosol particles started to accumulate in the near-surface
layer in the BTH region under a prevailing southerly air flow. On 20 December, the BTH
area was under a uniform pressure field (Fig. S2a). The regional average
wind speed was less than 3 m s-1, and the boundary layer became stable,
which constrained aerosols within a low mixing layer. Meanwhile, a
low-pressure center was situated to the north of the BTH region, where air pollutants from
south, southwest, and southeast converged. Consequently, the daily mean
PM2.5 concentration averaged over the BTH area was over 200 µgm-3.
On 21 December, a weak low-pressure center formed near Bohai Bay and
a weak high-pressure center moved to the Shandong Peninsula (Fig. S2b). The
synoptic conditions brought more air masses from south to north, and
worsened air quality in the BTH region. On 22 December, a weak high-pressure system
moved within Inner Mongolia (Fig. S2c), which carried cold air to the
BTH region. Meanwhile, the polluted air was also transported back to
the BTH, leading to a continuous increase in the PM2.5 concentration,
with the maximum daily mean value exceeding 600 µgm-3
(Fig. 6e). Due to the enhanced anticyclone originating from Siberia (Fig. S2d), the accumulation of aerosol particles in the BTH region was terminated by the
incursion of a strong cold front from 23 to 27 December. However, frequent
transitions between high- and low-pressure systems over the BTH area accompanied by shifting wind directions resulted in a rapid PM2.5 variation,
especially on 24 and 25 December, when a low-pressure system developed
northeast of BTH (Fig. S2e). The air mass over BTH was influenced by the
pollutants from the south, resulting in a temporary increase in the
concentration of PM2.5 on 25 December. After 27 December, another haze
episode gradually formed.
(a–k) Spatial distributions of simulated daily PM2.5
concentrations (shaded, µgm-3) and wind vectors (arrows, m s-1). Time series of simulated daily PM2.5 concentrations averaged
over the Beijing–Tianjin–Hebei region are also shown in panel (l).
According to the trends in simulated PM2.5 concentrations averaged over
the BTH region (Fig. 6l), we divide the whole simulation period into three
stages: (1) the aerosol accumulation stage (16–22 December, Stage 1), (2) the aerosol dispersion stage (23–27 December, Stage 2), and
(3) the formation stage for another haze event (28–29 December,
Stage 3). In this paper, we mainly focus on the first
two stages to reveal important factors that cause the accumulation and
dispersion of particulate matter.
In Stage 1, the daily mean PM2.5 concentrations averaged
over the BTH region increased from 24.2 to 289.8 µgm-3,
and the average PM2.5 concentration was 145.6 µgm-3
(Fig. 7a), which is close to the “heavily
polluted” air quality threshold value (PM2.5 24 h average concentration >150µgm-3). The WS10 was low (Fig. 7b), especially during the heavily
pollution period (20–22 December), and the mean wind speed was 2.3 m s-1, which is less than 3.2 m s-1 (one of the indicators used to define air
stagnation by NOAA,
https://www.ncdc.noaa.gov/societal-impacts/air-stagnation/overview, last access: 12 August 2019),
indicating that the near-surface circulation was insufficient to disperse
accumulated air pollutants. The decreased PBLH (from 701.6 to 109.9 m)
could compress air pollutants into a shallow layer, resulting in an elevated
pollution level. During Stage 2, the PM2.5 concentration
decreased gradually with the increased wind speed and PBLH. The average PM2.5 concentration during Stage 2 was 107.9 µgm-3, which still exceeded the Grade II standard (75 µgm-3)
defined by the National Ambient Air Quality Standards of China.
(a) Contributions of local emissions (red) and regional
transport (blue) to the near-surface PM2.5 concentrations
averaged over the Beijing–Tianjin–Hebei region from 16 to 29 December 2015.
The absolute contributions (µgm-3) are shown using bars, and the
percentage contributions (%) are shown using lines. The PM2.5
concentration and the percentage contributions averaged over each stage are
listed at the top of panel (a). Simulated daily 10 m wind speed (WS10; m s-1; black dotted line), specific humidity (g kg-1;
green dotted line), and PBLH (m; magenta dotted line) averaged over
Beijing–Tianjin–Hebei are also shown in panel (b).
Contributions of local emissions and regional transport to PM2.5
concentrations
Previous studies have reported that anthropogenic emissions are the dominant
cause of haze events in China (Jiang et al., 2013; Sun et al., 2014; Gu and
Liao, 2016; Y. Yang et al., 2016). Emission control measures have been taken
to ensure good air quality for major events (e.g., APEC) or to mitigate the
severity of coming pollution episodes (Zhou et al., 2018). Other studies,
such as Sun et al. (2017) and Wang et al. (2017), have pointed out that regional
transport contributed more than 50 % of the particulate concentrations in
the BTH region during haze events. This section discusses the contributions of local
anthropogenic emission and regional transport to the PM2.5
concentration in the BTH area, aiming to reveal their relative importance during this
haze episode.
As shown in Fig. 7a, the PM2.5 concentration in BTH during
Stage 1 was mainly contributed by the combined effects of
local emissions and regional transport. The contributions of local emissions
and regional transport to the PM2.5 concentration were comparable
(49 % and 32 %, respectively), especially during the heavy pollution
period (20–22 December, 43 % vs. 37 %). During Stage 2,
the contributions of regional transport decreased from 30 % to 16 %. The
relative high PM2.5 concentration (107.9 µgm-3) was
principally caused by the local emissions. On average, the contributions of
local emissions and regional transport to the PM2.5 concentration in
Stage 2 were 51 % and 24 %, respectively. The impact of
regional transport could be qualitatively expressed by specific humidity,
which was treated as an indicator of the origin of air masses (Jia et al.,
2008). Air masses from the south were usually warmer and wetter than those
from the north; thus, the specific humidity averaged over the BTH region was higher in
Stage 1 (1.7 g kg-1) than in Stage 2 (1.4 g kg-1) (Fig. 7b). The evolution of PM2.5 followed the trend
of specific humidity well, with a high correlation coefficient of 0.86.
Contributions of each physical/chemical process to variations in
PM2.5 concentrations
Figure 8a1–a2 show the diurnal variations of PM2.5 concentrations
averaged over the BTH region during Stage 1 and
Stage 2, respectively. The PM2.5 concentration increased
by 43.9 µgm-3 (from 136.5 µgm-3 at 00:00 LST to
180.4 µgm-3 at 23:00 LST) during the period of particulate
accumulation (Stage 1), but it decreased by 41.5 µgm-3 during the period of particulate elimination (Stage 2).
The hourly PM2.5 changes induced by each and all physical/chemical
processes during Stage 1 and Stage 2 established using
the IPR analysis method are shown in Fig. 8b1–b2. During both stages, the
dominant sources of surface-layer PM2.5 were EMIS and AERC, whereas the
main sinks were TRAN, DIFF, and DRYD. The maximum positive contribution of
EMIS could be found during the rush hours (07:00–08:00 and
16:00–19:00 LST; Fig. S3). The maximum negative contributions of TRAN and
DIFF appeared at late night (01:00–05:00 LST) and at noon (11:00–14:00 LST),
respectively.
(a1–a2) Diurnal variations of PM2.5 concentrations averaged
over Beijing–Tianjin–Hebei during Stage 1 and
Stage 2 (purple dotted lines). The colored bars
represent different components. The 24 h change in PM2.5 concentration (23:00 minus
00:00 LST) is also shown in the top-left corner of each
panel. (b1–b2) The hourly PM2.5 changes induced by each
physical/chemical process using the IPR analysis method (colored
bars). The purple dotted lines represent hourly PM2.5 changes induced by
all processes, also indicating the differences between current and
previous-hour PM2.5 concentrations. (c1–c2) Contributions of each
physical/chemical process to 24 h PM2.5 changes.
To explain the reason for the 24 h PM2.5 increase during
Stage 1 and the 24 h PM2.5 decrease during
Stage 2 (Fig. 8a1–a2), we quantify the contributions of
each physical/chemical process to 24 h PM2.5 changes for both stages
(Fig. 8c1–c2), which are calculated by integrating the hourly PM2.5 changes induced by each process from 00:00 to 23:00 LST (Fig. 8b1–b2). In WRF-Chem, DRYD is intermingled with vertical diffusion, meaning that
changes in the column burden during vertical mixing can be attributed to
DRYD (Tao et al., 2015). Following Tao et al. (2015), we define vertical
mixing (VMIX) as the sum of DIFF and DRYD. As shown in Fig. 8c1–c2,
contributions of the AERC, TRAN, and VMIX processes to the 24 h PM2.5 changes
were +29.6 (+17.9) µgm-3, -71.8 (-103.6) µgm-3,
and -177.3 (-221.6) µgm-3 for Stage 1
(Stage 2), respectively. Small differences were found for
contributions from other processes between Stage 1 and
Stage 2 (differences smaller than 5 µgm-3).
Therefore, the PM2.5 increase over the BTH region during the haze formation
stage was mainly attributed to strong production by the aerosol chemistry
process and weak removal by the advection and vertical mixing processes. On the
contrary, during haze elimination stage (Stage 2), more
aerosols in the BTH area were transported out of the BTH region, dispersed to the upper
atmosphere or subsided to the ground. Furthermore, the dry cold air from the
north decreased the specific humidity (as shown in Fig. 7b) in the BTH area,
leading to weaker production of secondary aerosols by aerosol chemistry
process.
Aerosol radiative effects (ARE) on the haze episode
Previous studies have demonstrated that aerosol radiative forcing could
increase the near-surface PM2.5 concentrations by about 12 %–29 %
(Gao et al., 2015, 2016; Qiu et al., 2017; Zhou et al., 2018).
However, the detailed influence mechanisms (i.e., the prominent physical or
chemical process responsible for the aerosol radiative impacts on PM2.5
concentrations) are still unclear. In this section, we examine the effects
of aerosol radiative forcing on meteorological parameters and PM2.5
levels during the haze episode, with a special focus on the detailed
influence mechanism using IPR analysis.
Effects of aerosol radiative forcing on meteorological parameters and
PM2.5 concentrations
Figure 9 illustrates the impacts of aerosols on the downward shortwave
radiative flux (SW) at the surface (BOT_SW) and in the
atmosphere (ATM_SW), calculated by subtracting the model
results of NoARE from those of CTL, during Stage 1,
Stage 2, and the whole simulation period. Downward SW at the
surface decreased strongly when ARE was considered, especially over high
aerosol-loading regions during heavily polluted periods. Generally, the
shortwave radiation fluxes at the surface averaged over BTH were reduced by
28 % (23.9 W m-2) in Stage 1, 18 % (16.6 W m-2)
in Stage 2, and 23 % (19.9 W m-2) during the whole
simulation period. Contrary to the significant negative
effects at the surface, as a result of ARE, the downward SW fluxes in the
atmosphere averaged over the BTH region were increased by 65 % (19.1 W m-2) in
Stage 1, 37 % (10.8 W m-2) in Stage 2,
and 51 % (14.7 W m-2) during the whole period.
The differences in simulated all-sky radiative forcing (W m-2) between the CTL and NoARE cases (CTL minus NoARE) averaged over
Stage 1, Stage 2, and the whole simulation
period. “BOT_SW” and “ATM_SW” denote the
downward shortwave radiative flux at the surface and in the atmosphere,
respectively. The calculated differences in the simulated radiative forcing
averaged over Beijing–Tianjin–Hebei for each stage are also shown at the
bottom of each panel.
The impacts of ARE (including aerosol direct and indirect effects) on
meteorological parameters and PM2.5 concentrations are analyzed in Fig. 10. Because less SW could reach the ground, near-surface temperature
decreased over BTH (Fig. 10a), especially during heavy pollution periods,
and the largest decrease was up to 2 K. Meanwhile, the increased SW in the
atmosphere warmed the upper air. As a result, a more stable atmosphere
was expected. It is known that the atmospheric stability can be exactly
characterized by the profile of equivalent potential temperature (EPT)
(Bolton, 1980; Zhao et al., 2013; J. Yang et al., 2016). If the EPT rises with
height, the atmosphere is stable. As shown in Fig. 10b, the EPT
decreased in the lower atmosphere (below ∼1000 m) with the
largest decrease of 3 K on 22 December, but it increased in the upper
atmosphere (above ∼1200 m). The change in the EPT profile
indicated that ARE could lead to a more stable atmosphere, which further
weakened vertical movement in the BTH (Fig. 10c). As a result of ARE, the PBLH decreased and the relative humidity in the lower atmosphere
increased (Fig. 10d). All of the changes in the meteorological variables were
beneficial for PM2.5 accumulation in the lower atmosphere (Fig. 10e). The daily maximum increase in the PM2.5 concentration was 43.2 µgm-3 due to ARE. It was noticed that ARE had a negative impact
on the near-surface PM2.5 concentrations from 23 to 24 December, which
could be explained by the fact that absorbing aerosols (i.e., BC) induced anomalous northeasterlies, and then the relatively clean air transported from the northeastern regions to the BTH region (Fig. S4).
Time series of differences in (a) temperature (K), (b) equivalent
potential temperature (K), (c) vertical wind speed (cm s-1), (d) relative humidity (%), and (e) PM2.5 concentration (µgm-3) between the CTL and NoARE cases (CTL minus NoARE) averaged over the
Beijing–Tianjin–Hebei region. The purple and green lines denote the
simulated PBLH in the CTL and NoARE cases, respectively. The black line
represents the zero contour line.
Influence mechanism of aerosol radiative effects
As variations in PM2.5 concentrations are directly caused by
physical and chemical processes (Zhu et al., 2015), the IPR method is then
used to investigate the detailed influence mechanisms (i.e., the prominent
physical or chemical processes responsible for the aerosol radiative impacts
on haze episodes). Figure 11a–b show the diurnal variations of PM2.5
concentrations in the NoARE and CTL cases averaged over the BTH region in
Stage 1. A 24 h increase of 39.1 µgm-3 was
simulated in the NoARE case. When aerosol radiative forcing was considered, the
24 h increase of PM2.5 concentration was 43.9 µgm-3. The
enhancement of 4.8 µgm-3 (12 %) induced by ARE could be
mainly attributed to the contributions of the VMIX, TRAN, and AERC processes, as
shown in Fig. 11c. The vertical mixing was strongly restrained by ARE;
therefore, fewer particles diffused from the surface to the upper layer,
resulting in the accumulation of PM2.5 in a lower atmospheric
boundary layer. The changes induced by ARE in contributions of the VMIX process
exhibited positive values in the lower layers and negative values in the
upper layers (Fig. S5a). Generally, the VMIX process contributed +22.5µgm-3 to the enhancement in the 24 h PM2.5 increase (+4.8µgm-3) for Stage 1. The TRAN process, however,
contributed -19.6µgm-3. Constrained vertical mixing due to ARE
could increase aerosol precursors and water vapor in the thin boundary layer
to enhance the formation of secondary particles. Generally, the AERC process
contributed +1.2µgm-3. The positive contribution of AERC was mainly distributed over the highly polluted regions in the BTH area (Fig. S5b).
Specifically, the average changes in the concentrations of
SO42-,
NO3-, and
NH4+ during the daytime from 11:00 to
17:00 LST in Stage 1 were -0.5, +1.3, and +0.8µgm-3, respectively. The
decreased near-surface temperature caused by ARE may suppress the chemical
formation of SO42-. Generally, the
total contribution of the VMIX, TRAN, and AERC processes to the change in the 24 h
PM2.5 increase caused by ARE was +4.1 µgm-3, and the
restrained vertical mixing could be the primary reason for the near-surface
PM2.5 increase when aerosol radiative forcing was considered.
Diurnal variations of the near-surface PM2.5 concentrations
in the (a) NoARE and (b) CTL simulations averaged over the Beijing–Tianjin–Hebei
region during Stage 1 (purple dotted lines). The
colored bars represent different components. The 24 h increase in PM2.5 concentration
(23:00 minus 00:00 LST) is also shown in the top-left
corner of each panel. (c) Differences in hourly IPRs caused by aerosol
radiative forcing (CTL minus NoARE). The numbers listed in panel (c) represent the
contributions of each process to the change in the 24 h PM2.5 increase
caused by aerosol radiative forcing.
Figure 12a shows the vertical profiles of the 24 h increases in the PM2.5
concentrations (23:00 minus 00:00 LST) averaged over the BTH region during
Stage 1 in the CTL and NoARE cases. Below ∼300 m
(between L01 and L04), the 24 h increase simulated by CTL was larger than
that in NoARE, which could be mainly explained by the fact that the positive
contributions of VMIX exceeded the negative contributions of TRAN in the
lower atmosphere when aerosol radiative effect was considered (Fig. 12b).
However, in the upper layers (from 300 to 2000 m), aerosol radiative forcing
weakened the 24 h PM2.5 increase during Stage 1. When the
aerosol radiative effect was considered, less particulate matter,
precursors and water vapor were diffused from the surface to the upper
layers; therefore, fewer particles were formed in the upper layers.
Despite the positive contributions of TRAN, the net contributions of
VMIX, TRAN, and AERC to PM2.5 changes caused by ARE in the upper
atmosphere were negative.
(a) Vertical profiles of the 24 h increases in PM2.5
concentrations (23:00 minus 00:00 LST) averaged over Beijing–Tianjin–Hebei
during Stage 1 in the CTL and NoARE cases. (b) Vertical profiles
of the differences in the 24 h PM2.5 increases caused by the aerosol
radiative effect (CTL minus NoARE, purple dotted line), and the
contributions of each physical/chemical process (colored bars).
Conclusions and discussions
In this study, an online coupled mesoscale meteorology–chemistry model
(WRF-Chem) with an improved integrated process rate (IPR) analysis (i.e.,
process analysis) scheme was applied to investigate the formation and
evolution mechanisms of a severe haze episode that occurred in the BTH region
from 16 to 29 December 2015. Sensitivity experiments were conducted to examine
the contributions of local emissions and regional transport to the PM2.5
concentrations during the haze event, while IPR analysis was used to quantify
the contributions of each physical/chemical process to the variation in
PM2.5 concentration. The impacts of aerosol radiative forcing
(including direct and indirect effects) were also quantified, with a special
focus on the detailed influence mechanism (i.e., prominent process
responsible for the aerosol radiative impacts on the haze event). An
integrated comparison between observations and simulations demonstrated good
performance for both meteorological and chemical variables, indicating that
the WRF-Chem model has the capability to reproduce the haze episode.
Spatial–temporal evolutions of the near-surface PM2.5 concentration,
and the contributions of local emissions and regional transport to the severe
haze event in BTH, were first analyzed. During the aerosol accumulation
stage (16–22 December, Stage 1), the daily PM2.5
concentration in the BTH region experienced a consistent increase, with the mean value
of 145.6 µgm-3. The contributions of local emissions and
regional transport to the PM2.5 concentration were comparable (49 %
and 32 %, respectively), meaning that the combined effect resulted in the high
PM2.5 concentration in the BTH area. During the aerosol dispersion stage
(23–27 December, Stage 2), the average PM2.5
concentration in BTH was 107.9 µgm-3. The contributions of
local emissions and regional transport were 51 % and 24 %, respectively.
Therefore, the relatively high PM2.5 concentration during
Stage 2 was principally caused by local emissions. Over the period from
28 to 29 December (Stage 3), another haze event was formed and
developed.
IPR analysis was then used to explain the reason for the PM2.5 increase
during Stage 1 and the decrease during Stage 2, by
quantifying the contributions of each physical/chemical process to
variations in PM2.5 concentration. During both stages, the dominant
sources were emissions (EMIS) and aerosol chemistry (AERC), whereas the main
sinks were turbulent diffusion (DIFF), advection (TRAN), and dry deposition
(DRYD). The PM2.5 concentration increased by 43.9 µgm-3
(23:00 minus 00:00 LST) during Stage 1, but it decreased by
41.5 µgm-3 during Stage 2. The contributions of
AERC, TRAN, and VMIX (vertical mixing, the sum of DRYD and DIFF) to the 24 h
PM2.5 changes were +29.6 (+17.9) µgm-3, -71.8 (-103.6) µgm-3 and -177.3 (-221.6) µgm-3 for
Stage 1 (Stage 2), respectively. Small
differences in the contributions from other processes were found between
Stage 1 and Stage 2. Therefore, the PM2.5
increase over the BTH region during the haze formation stage was attributed to strong
production by aerosol chemistry process and weak removal by advection and
vertical mixing processes.
When aerosol radiative forcing was considered, the equivalent potential
temperature decreased in the lower layers but increased in the upper
layers, leading to a more stable atmosphere. Meanwhile, the decreased PBLH
and increased relative humidity were also beneficial for PM2.5 accumulation. The daily maximum increase of the near-surface PM2.5
concentration in the BTH region was 43.2 µgm-3. The IPR method was also
used to investigate the detailed influence mechanism of aerosol radiative
effects. When aerosol radiative feedback was considered, the 24 h PM2.5 increase was enhanced by 4.8 µgm-3 (12 %) during
Stage 1, which could be mainly attributed to the
contributions of VMIX (+22.5µgm-3), TRAN (-19.6µgm-3), and AERC (+1.2µgm-3). The restrained vertical
mixing could be the primary reason for near-surface PM2.5 increase when
aerosol radiative forcing was considered.
There are some limitations to this work. The uncertainty of the MIX
anthropogenic emission inventory, the lack of secondary organic aerosols,
and the missing mechanisms of some heterogeneous reactions may result in
large uncertainties in the final simulation results, especially the
predicted aerosol chemical compositions, such as
SO42-,
NO3-, and
NH4+. The biases in simulated
concentrations of SO42-,
NO3-, and
NH4+ may have impacts on the
contributions of the AERC and CLDC processes to the air pollution variation.
Uncertainties should be quantitatively analyzed in future studies. Furthermore,
conclusions draw from a case study in the BTH region cannot represent a full view of
the underlying mechanisms of haze formation and elimination. A better
understanding will be attained by conducting multiple-case simulations in the
future. Furthermore, an anomalous northeasterly induced by absorbing
aerosols was observed, leading to a decrease in the near-surface PM2.5
concentrations from 23 to 24 December 2015 in the BTH area, which was different from
previous studies that reported that light-absorbing aerosols could worsen air
quality (Li et al., 2016; Huang et al., 2018; Gao et al., 2018). More
experiments should be designed in the future to examine the changes in
atmospheric thermal and atmospheric dynamic caused by absorbing aerosol
radiative forcing and their impacts on haze episodes.
As Zheng et al. (2018) pointed out, the PM2.5 concentration in
China has been decreasing in recent years; however, this decrease in fine
particulate matter could stimulate ozone production (K. Li et al., 2019; Zhu
et al., 2019). Multi-pollutant mixture may be a hot topic in the future, and
IPR analysis could be a useful method to provide a quantitative analysis
of the formation mechanism of complex air pollution, including
figuring out the major physical/chemical process behind these events.
Meanwhile, significant differences between model predictions (e.g., O3
and PM2.5) are found among current multi-scale air quality models (L. Chen
et al., 2019; J. Li et al., 2019), even though the same inputs are used.
These different performances can be associated with the differences in model
formulations, including parameterizations and numerical methods (Carmichael
et al., 2008). In order to acquire a quantitative attribution of the cause
of differences between simulation results, a process analysis method should be
developed and implemented in these models, and the use of IPR analysis would make it
easier to draw conclusions about the fundamental problems that cause the
differences between model predictions.
Data availability
Observational datasets and simulation results are available upon request from
the corresponding author (hongliao@nuist.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-10845-2019-supplement.
Author contributions
HL and LC conceived the study and designed the experiments. LC and JZ
performed the simulations and carried out the data analysis. YG, MZ, YQ, ZL,
NL, and YW provided useful comments on the paper. LC prepared the paper
with contributions from all the co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
Acknowledgements
The authors thank the Campaign on Atmospheric Aerosol Research
network of China (CARE-China) for providing measurements on aerosol chemical
compositions to evaluate the model performance. Many thanks to the anonymous
reviewers for their helpful comments that improved our paper.
Financial support
This study was supported by the National Natural Science Foundation of China (grant nos. 91544219 and 91744311), the University Natural Science Research Foundation of Jiangsu Province (grant no. 18KJB170012), the China Postdoctoral Science Foundation
(grant no. 2019M650117), and the Startup Foundation for Introducing Talent of NUIST (grant no. 2018r007).
Review statement
This paper was edited by Zhanqing Li and reviewed by three anonymous referees.
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