Correct description of the boundary layer mixing process
of particle is an important prerequisite for understanding the formation
mechanism of pollutants, especially during heavy pollution episodes.
Turbulent vertical mixing determines the distribution of momentum, heat,
water vapor and pollutants within the planetary boundary layer (PBL).
However, what is questionable is that the turbulent mixing process of particles
is usually denoted by turbulent diffusion of heat in the
Weather Research and Forecasting model coupled with Chemistry (WRF-Chem).
With mixing-length theory, the turbulent diffusion relationship of particle
is established, embedded into the WRF-Chem and verified based on long-term
simulations from 2013 to 2017. The new turbulent diffusion coefficient is
used to represent the turbulent mixing process of pollutants separately,
without deteriorating the simulation results of meteorological parameters.
The new turbulent diffusion improves the simulation of pollutant
concentration to varying degrees, and the simulated results of PM2.5
concentration are improved by 8.3 % (2013), 17 % (2014), 11 % (2015)
and 11.7 % (2017) in eastern China, respectively. Furthermore, the
pollutant concentration is expected to increase due to the reduction of
turbulent diffusion in mountainous areas, but the pollutant concentration
did not change as expected. Therefore, under the influence of complex
topography, the turbulent diffusion process is insensitive to the simulation
of the pollutant concentration. For mountainous areas, the evolution of
pollutants is more susceptible to advection transport because of the
simulation of obvious wind speed gradient and pollutant concentration
gradient. In addition to the PM2.5 concentration, the concentration of
CO as a primary pollutant has also been improved, which shows that the
turbulent diffusion process is extremely critical for variation of the
various aerosol pollutants. Additional joint research on other processes
(e.g., dry deposition, chemical and emission processes) may be necessary to
promote the development of the model in the future.
Introduction
Along with intensive urbanization and tremendous economic development,
numerous incidents of aerosol pollution have frequently occurred in China
(An et al., 2019; Zhang et al., 2019). Aerosol pollution, characterized by
PM2.5, occurs primarily within the planetary boundary layer (PBL). The
horizontal transportation and vertical diffusion of pollutants are obviously
affected by the PBL mixing process, associated with intricate turbulent
eddies (Wang et al., 2018; Du et al., 2020). Turbulent diffusion, as a vital
process, controls the exchange of momentum, heat, water vapor and pollutants
through turbulent eddies within the PBL (Stull, 1988).
(a) Map of terrain height in the two nested model domains. (b) The
locations of surface meteorological stations, air quality monitoring
stations and sounding stations are marked by the gray crosses, red (black)
dots and yellow pluses, respectively. The turbulence data site is denoted by
the orange triangle. The dashed red circle indicates the areas of our
primary concern.
Moreover, PBL height (PBLH) directly determines the effective air volume of
pollutant diffusion and atmospheric environmental capacity. With the
continuous development of technology, there are numerous means (e.g.,
radiosonde, tethered balloon, meteorological tower, aircraft, ground-based
remote sensing and space-based remote sensing) and methods (e.g., based on
surface fluxes, Richardson number and others diagnostic methods) to
determine the PBLH. Of course, the results are also different (Zhang et al.,
2020). It is worth noting that the PBLH is not necessarily negatively correlated
with pollutant concentration (Miao et al., 2021). In particular, the turbulence
barrier effect (i.e., which means turbulence may disappear at certain heights
where it forms a laminar flow as if there is a barrier layer hindering the
transmission up and down during the heavy pollution episodes) can impose an
effect on the vertical distribution of pollutants (Ren et al., 2021), making
the relationship between pollutants and the PBLH more uncertain. The PBLH can
also be diagnosed by the boundary layer parameterization schemes in the
model, but the PBLH does not directly determine the effective diffusion of
pollutants (Jia and Zhang, 2020). Instead, vertical diffusion and mixing of
pollutants are directly controlled by the turbulent diffusion coefficient (TDC),
and the diagnosis of the PBLH may affect the calculation of the TDC. Previous
studies have analyzed a number of pollution cases with process analysis
methods (Gao et al., 2018; Chen et al., 2019). The results showed that, for
a pollution event, emissions and turbulent diffusion have the greatest
contribution to pollutant concentration. The evolution of pollutants is
mainly controlled by turbulent diffusion, when emissions remain unchanged
for a short period. Meanwhile, the contributions of dry deposition,
advection transport and chemistry cannot be ignored. Consequently, more
realistic turbulent diffusion characteristics are extremely important for
the simulation of pollutant concentration in the model.
To date, plenty of issues remain to be addressed in the model; in particular,
turbulent diffusion processes of all scalars (including active and passive
scalars) are dealt with in a unified manner in the mesoscale model. Only a
few studies have proposed that the meteorological fields and pollutants can
be changed by adjusting the minimum value of the TDC (Savijärvi and
Kauhanen, 2002; Wang et al., 2018; Du et al., 2020), increasing turbulent
kinetic energy (TKE) (Foreman and Emeis, 2012) and modifying experiment
expressions (Sušelj and Sood, 2010; Huang and Peng, 2017). Recently, Jia
et al. (2021a) obtained the TDC of particles using high-resolution vertical
flux data of particles according to the mixing length theory. Additionally,
this TDC has been embedded in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to calculate the PBL
mixing process of pollutants separately. This work has initially improved
the overestimation of pollutant concentration at night in winter 2016 in
eastern China. However, a series of heavy pollution incidents have occurred
and attracted much attention since 2013. Therefore, we conducted a series of
simulations for the heavy pollution periods in winter from 2013 to 2017 in
this study. The difference between this study and previous work is that
previous work focused on the observational analysis, while this study mainly
explores the influence of turbulent diffusion on pollutant concentration in
the mesoscale model.
Data and methodsData
In this study, the aerosol pollution level is denoted by the hourly surface
PM2.5 concentration that is available from the official website of the
China National Environmental Monitoring Center from 1 January 2013 to 31
January 2017. PM2.5 concentration stations increased from 35 cities in
2013 (illustrated by red dots in Fig. 1b) to 78 cities in 2017 (illustrated by
black dots in Fig. 1b) in eastern China. Except for PM2.5 observations,
the hourly concentrations of CO were acquired from the National Air Quality
real-time publication platform (https://quotsoft.net/air/, last access: 5 November 2021). Meanwhile, hourly meteorological observation data (a total of
401 stations), including temperature, pressure, relative humidity, wind and
visibility, from the national automatic weather stations (AWSs) are provided by
the National Meteorological Information Center of China Meteorological
Administration (NMICMA) (illustrated by gray crosses in Fig. 1b). The time
period of the data selected is from 1 January 2013 to 31 January 2017. In
addition, the turbulent diffusion of particles is calculated based on the
high-frequency turbulence data, and the observational turbulence data are
obtained from the Pingyuan County Meteorological Bureau (37.15∘ N,
116.47∘ E), Shandong Province, China, from 27 December 2018 to 8
January 2019 (illustrated by orange triangle in Fig. 1b). The experiment
station is in the southern suburbs of the city of Dezhou, and there is flat farmland around
this station (Figs. S1 and 2 in Ren et al., 2020). Identical
eddy-covariance systems were operated, including a three-dimensional sonic
anemometer–thermometer (IRGASON, Campbell Scientific, USA) and a
CO2/H2O open-path gas analyzer (LI7500, LI-COR, USA). These
instruments measured three components of wind speed, potential temperature,
water vapor and CO2 concentrations with a frequency of 10 Hz. The
turbulence data finally were split into 30 min segments. A continuous
particle measuring instrument E-sampler (Met One) and a high-frequency
sampling visibility sensor (CS120A, Campbell Scientific, USA) were used to
obtain PM2.5 mass concentration every minute with a visibility of 1 Hz.
The calculation of 30 min vertical flux of PM2.5 is based on the
nonlinear relationship between PM2.5 concentration and visibility (Ren
et al., 2020). The PM2.5 concentration, temperature and wind speed at
approximately 60 and 10 m were used to compute the vertical gradient of each
variable. Because of the interference with the early time of the GPS
sounding balloons taking off, the PM2.5 concentration near the ground
would be uncertain. Thus, we selected 10 m as the lower height to avoid
that. Based on the constant flux layer hypothesis, the upper level should be
within the surface layer. To facilitate the calculation, we rounded the
height difference to 50 m. Finally, 60 m was selected to be the higher level to
compute the vertical gradient of each variable.
Flow chart of main program for turbulent diffusion coefficient in
the (a) original scheme and (b) new scheme.
A detailed background and the calculation principles of this method were presented
in Ren et al. (2020), so we only describe key steps here. Firstly, we separate
PM2.5 concentration (c) and visibility datasets (V) into mean and
turbulent deviations (i.e., c=c‾+c′ and V=V‾+V′ ). Secondly, we obtain the fitted coefficients using
exponential correlation (i.e., a and b) between the PM2.5 concentration
and visibility (i.e., c=a⋅Vb). Thirdly, combining the
first two steps, we can obtain the turbulent fluctuations of PM2.5
concentration (i.e., c′=a⋅V‾+V′b-c‾). Finally, we use fluctuations of
vertical velocity (i.e., w′) and of PM2.5 concentration (i.e., c′) to
calculate the vertical flux of PM2.5 (i.e., w′c′‾). The
observed particle flux is used to calculate the Richardson function of
particles later.
To illustrate the influence of the PBL height (PBLH) on the PM2.5
pollution, soundings collected at the Fuyang site (32.54∘ N, 115.5∘ E) and the Anqing site (30.37∘ N, 116.58∘ E) (illustrated by yellow pluses in Fig. 1b) for the
period 2013–2017 were analyzed. These two stations are equipped with L-band
radiosonde systems (Jia et al., 2021b), which provide fine-resolution (1 Hz, and the rise rate is ∼ 6 m s-1) profiles of
temperature, relative humidity and wind speed two times (08:00 and 20:00 BJT)
a day during winter. The Richardson number method is used to calculate the
PBLH (Miao et al., 2018). The height at which the Richardson number equals
0.25 is defined as the PBLH, which is consistent with the definition of
simulation.
The average value of (a–d) simulated and (e–h) observed PM2.5
concentration (µg m-3) at night and (i–l) the relative bias (RB,
%) between simulation and observation. The calculation formula of
relative bias is
RB =X‾sim-X‾obs/X‾obs×100 %, where X‾sim
and
X‾obs represent the average value of
simulation and observation, respectively. The locations of three rivers
(i.e., Yellow River, Yangtze–Huaihe and Yangtze River) are marked by blue
lines. The dashed red and green circles represent the whole simulation area
and eastern China, respectively. The solid purple irregular circle indicates
mountainous areas, and “TJ” in red indicates Tianjin.
Numerical simulation
Long-term three-dimensional simulation experiments are conducted using the
Weather Research and Forecasting model coupled with Chemistry (WRF-Chem
version 3.9.1) (Grell et al., 2005) in this study from the winter of 2013 to
2017, when eastern China frequently experienced severe and persistent
aerosol pollution events. For each winter from 2013 to 2017, 1 month is
selected, and a total of 4 months are confirmed, which are January 2013,
December 2014, December 2015 and January 2017, respectively. The
anthropogenic emissions of BC, OC, CO, NH3, NOx, PM2.5,
PM10 and volatile organic compounds (VOCs) are set based on the latest
monthly Multi-resolution Emission Inventory for China (MEIC) from 2013 to
2017, provided by Tsinghua University, with a resolution of 0.25∘×0.25∘ (http://meicmodel.org/, last access: 20 May
2021). The model domain was centered over eastern China with a horizontal
resolution of 33 and 6.6 km (Fig. 1a). The model top was set to the 50 hPa
level, and 48 vertical layers were configured below the top. To resolve the
PBL structure, 21 vertical layers were set below 2 km (i.e., the specific
setting of vertical levels is σ=1.000, 0.997, 0.994, 0.991,
0.988, 0.985, 0.980, 0.975, 0.970, 0.960, 0.950, 0.940, 0.930, 0.920, 0.910,
0.895, 0.880, 0.865, 0.850, 0.825, 0.800). The physics parameterization
schemes selected for this study included the Morrison double-moment
microphysics scheme (Morrison et al., 2009), RRTMG longwave/shortwave
radiation schemes (Iacono et al., 2008), the MM5 similarity surface layer scheme
(Jiménez et al., 2012), the Noah land surface scheme (Chen and Dudhia,
2001), the single-layer UCM scheme (Kusaka et al., 2001), the CLM4.5 lake physics
scheme (Gu et al., 2015), the ACM2 planetary boundary layer scheme (Pleim,
2007) and the Grell-3D cumulus scheme (Grell and Devenyi, 2002). And the chemical
mechanism is the RADM2-MADE/SORGM scheme (Ackermann et al., 1998; Schell et
al., 2001). The initial and boundary conditions of meteorological fields
were set up using the National Centers for Environmental Prediction (NCEP)
global final (FNL) reanalysis data, with a resolution of 1∘×1∘ (https://rda.ucar.edu/datasets/ds083.2/, last
access: 20 May 2021). And the initial and boundary conditions of chemical
fields were configured using the global model output of the Model for Ozone
and Related Chemical Tracers (MOZART)
(http://www/acom.ucar.edu/wrf-chem/mozart.shtml, last access: 20 May 2021).
Figure 2 shows the flow chart of the main program related to the turbulent
diffusion coefficient. Simulations using the abovementioned configurations are
referred to as the original runs. In the original PBL parameterization
scheme, TDCs of heat and momentum are different (i.e., Kh≠Km). The turbulent mixing process of pollutants is considered to be
similar to that of heat, which supposes the turbulent diffusion of
particles and heat is identical (i.e., Kh=Kc) (Fig. 2a), while in
the new scheme, the turbulent mixing process of pollutants is calculated by
the TDC of particles (i.e., Kc), which is different from the TDC of heat
(i.e., Kh≠Kc). These improved experiments are regarded as the
new runs hereafter (Fig. 2b). All simulations included a total of 8
months. The 91 h simulation is conducted beginning from 00:00 UTC of 3 d ago for each day (i.e., 248 simulation experiments), the first 64 h of
each simulation is considered as the spin-up period, the next 24 h is used
for further analysis and the remaining 3 h is discarded (e.g., run one
simulation from 00:00 UTC (08:00 BJT) on 29 December to 18:00 UTC
(12 January, 02:00 BJT) on 1 January and 91 h in total. We need the results from 00:00 to 23:00 BJT on 1 January. The period from 08:00 BJT on 29 December to
23:00 BJT on 31 December is considered as the spin-up period (in total 64 h), and the
results from 00:00 to 02:00 BJT on 2 January are discarded).
The average value of (a–d) simulated PM2.5 concentration
(µg m-3) by new schemes, (e–h) the relative bias (RB, %) of
PM2.5 concentration between simulation of new scheme and observation and
(i–l) the absolute bias (AB, %) between the new and original schemes. The
calculation formula of absolute bias is AB=RBnew-RBoriginal , where RBnew and RBoriginal represent the relative bias of
new and original schemes, respectively. The dashed red and green circles
represent the whole simulation area and eastern China, respectively.
Calculation principle of turbulent diffusion of particles
Considering that the pollution is usually accompanied by the stable boundary
layer (SBL), and the simulation results of pollutant concentration are poor
in the SBL at night, we mainly modify the program of the stable boundary
layer, while for the unstable boundary layer, we still use the default
program of the original scheme (Fig. 2). Although the turbulent vertical
mixing and dry deposition are calculated in the same program in WRF-Chem, we
only modified the turbulent diffusion in the new scheme. Here, we briefly
describe the information about dry deposition. We adopt the MADE/SORGAM
aerosol scheme, in which the dry deposition is calculated based on
Binkowski's method (Binkowski and Shankar, 1995). The dry deposition
velocity (Vd) can be expressed as Vd=Vg+1/Ra+Rs+Ra⋅Rs⋅Vg, where Vg is
the gravitational settling velocity, Ra is the aerodynamic resistance
and Rs is the canopy resistance.
The TDC is parameterized by the mixing length (l) and the function of
Richardson number (f(Ri)) based on mixing length theory; that is
K=0.01+ss⋅l2⋅fRi,
where ss is the wind shear
(i.e., ss =∂u‾/∂z2+∂v‾/∂z2), and 0.01
refers to the minimum value of the TDC in the model. The minimum value of the TDC
remains unchanged in the new scheme. The mixing length formula (i.e.,
l=kz/(1+kz/λ), λ=80) is proposed by Blackadar (1962), and it is widely
used in the model. Ri is the gradient Richardson number (i.e., Ri=g/θv‾∂θv‾/∂z(∂u‾/∂z)2+∂v‾/∂z2),
where z is the observation height, g is the gravity, θv is the
virtual potential temperature, and u and v are the component of wind), which is
approximated in finite difference form, and the resulting parameter is
sometimes referred to as the bulk Richardson number (Garratt, 1992). For
example, Louis et al. (1982) suggest that Ri is the bulk Richardson number, but
the expression is in the form of the gradient Richardson number (Eq. 5 in Louis
et al., 1982). Many previous studies have shown various functions of
the Richardson number, which represent the different situations of turbulence.
Here, we mainly compare the similarities and differences between the
turbulent diffusion of momentum, heat and particles in the model.
Taylor diagram of simulation by original scheme and modified
scheme. XY axes and arcs represent the normalized standard deviations
(NSDs; NSD=1N-1∑i=1nXsim,i-X‾sim21N-1∑i=1nXobs,i-X‾obs2, where X‾sim and
X‾obs represent the average
value of simulation and observation, respectively) and index of agreement
(IOA, IOA=1-∑i=1nXsim,i-Xobs,i2∑i=1nXsim,i-X‾obs+Xobs,i-X‾obs2, where Xsim,i and
Xobs,i represent the value of simulated and observed,
respectively; i refers to time, and n is the total number of time series),
respectively. All cities (a total of 35 cities in 2013 and 78 cities in
2014, 2015 and 2017) are shown by dots, and black (red) represents
the original (new) scheme. The root mean square (rms) is denoted by the dashed blue
line, and the arrow indicates the change of the new scheme compared to the
original scheme at the same station.
For the stable conditions (i.e., Ri≥0), Esau and Byrkjedal (2007)
suggested
2fh=1+10Ri+50Ri2+5000Ri4-1+0.0012,3fm=0.8fh+0.00104,
where fh and fm denote the functions of heat and momentum,
respectively, and these functions existed in the original model.
We added an additional function of particles into the model; that is
fc=1+66.6Ri-1,
which is used to denote the turbulent mixing process of particles within the
PBL. When Ri is greater than ∼ 0.2, the TDC of particles is
greater than that of heat, which may reduce pollutant concentration. With
the increase of instability, the TDC of particles is gradually smaller than
that of heat, theoretically leading to the increase of pollutant
concentration. For detailed analysis and comparison of functions, please
refer to Jia et al. (2021a).
For the unstable conditions (Ri<0),
5fh=fc=1-25Ri1/2,6fm=Pr⋅fh,
where the TDC of particles is still equal to that of heat (i.e.,
Kc=Kh), while the TDC of momentum is calculated by the turbulent
Prandtl number (i.e., Pr, Pr=0.8).
Time–height cross sections for the difference of PM2.5
concentration between original and new schemes (i.e., the new scheme minus
the original scheme) within the PBL in (a–d) Anqing and (e–h) Fuyang from
2013 to 2017. The gray line indicates the PBLH.
There is much important information about the TDC of particles that needs
to be illustrated. (1) Turbulent diffusion of particles calculated by the
explicit local gradient represents the PBL mixing process of particles,
which is more suitable in the stable boundary layer (SBL) (Mahrt and
Vickers, 2003). (2) The uncertainty of turbulent diffusion coefficient
calculated by the PBLH and the Monin–Obukhov similarity theory (MOST) at night
is large, which has been avoided in the new scheme. Meanwhile, the
computational efficiency based on mixing length is higher (Li et al., 2010),
and it is easier to apply to forecasting models in the future. (3) Turbulent diffusion of particles is used to evaluate the PBL mixing process
of pollutants separately, which can affect the simulation results of
pollutants and not influence the simulation results of meteorological
parameters.
Time series of the observed (black) and simulated (red) PBLH at
08:00 and 20:00 (BJT) in (a–d) Anqing and (e–h) Fuyang from 2013 to 2017.
Evaluation of PM2.5 concentration simulation
Based on the TDC relationship of particles in the previous study (Jia et
al., 2021a), this study applies this relationship to a long-term scale
simulation for verification. Figure 3 shows the average value of simulated
and observed PM2.5 concentration at night (i.e., from 18:00 on the
first day to 07:00 on the second day) from 2013 to 2017, and the simulation
results can better reproduce the distribution of pollutant concentration
(i.e., represented by the dashed red circle). However, the PM2.5
concentration was overestimated to varying degrees in eastern China (i.e.,
indicated by the dashed green circle), and the mean value of relative bias
(RB) of the region is as high as 11.8 % (2013), 48 % (2014), 23.8 %
(2015) and 20.9 % (2017), respectively (Fig. 3i–l). In addition, we also
found that the pollutant concentrations are underestimated in Beijing (BJ)
and along the Taihang Mountains (Mt. Taihang) (i.e., indicated by the purple
irregular circle) but overestimated in Tianjin (TJ) (Fig. 3i–l). Why is the
pollutant concentration simulated by the same model different in each
region? What are the different effects of turbulent diffusion in different
regions? These issues will be further explained later, and this section
primarily evaluates the simulation results of the pollutant concentration.
Compared to the original scheme, the new scheme improves the situation where
the pollutant concentration is overestimated at night in eastern China (Fig. 4a–d). The degree of overestimation of the pollutant concentration is
reduced, and the mean value of relative bias of the new scheme is 3.5 %
(2013), 31 % (2014), 12.8 % (2015) and 9.2 % (2017), respectively
(Fig. 4e–h). In addition, the mean value of the absolute bias is decreased by
8.3 % (2013), 17 % (2014), 11 % (2015) and 11.7 % (2017),
respectively (Fig. 4i–l). In summary, compared with the original scheme, the
new scheme can generally improve the overestimation of pollutant
concentration in eastern China, due to the changes in turbulent diffusion.
For the above stations where the pollutant concentration was underestimated
in the original scheme, the pollutant concentration will be further
underestimated with the increase in turbulent diffusion. However, this
underestimation cannot be avoided because there is an opposite phenomenon in
the pollutant concentration in the two regions. We can only look at the
differences in the two regions from other perspectives (see Sect. 4 for
details), as the model is fraught with uncertainties.
The relative bias (%) between simulation and observation at all
environment monitoring stations and terrain height in Beijing–Tianjin–Hebei
in (a) 2013, (b) 2014, (c) 2015 and (d) 2017. Taihang Mountain (Mt. Taihang)
and Yan Shan (Mt. Yan) are indicated by the red text, Beijing (BJ),
Tianjin (TJ) and Hebei (HB) are represented by purple abbreviations and the
dividing line between overestimated and underestimated areas is indicated by
a dashed white line.
To better evaluate the model performance, Fig. 5 shows the Taylor diagram
of hourly PM2.5 concentration, and the black (red) dots indicate
original (new) simulation results at all stations from 2013 to 2017. The
statistical results have a consistent feature; that is, the worse the
simulation results of the original scheme are, the more obvious the
improvement of the new scheme becomes (arrows indicate improved stations in
the Fig. 5). The results indicate that the pollutant concentrations are not
improved to the same degree at all stations. When the simulation of
pollutant concentration is overestimated in the original scheme, the new
scheme improves the degree of overestimation. While the simulation of
pollutant concentration is underestimated in the original scheme, the new
scheme cannot further underestimate, and the degree of “re-underestimate” is
not obvious (Figs. 5 and S2). And the mean value of standard deviation
(normalized) is decreased by 0.2 (2013), 0.28 (2014), 0.14 (2015) and 0.16
(2017) (Fig. 5). As a whole, the new scheme can improve the common
phenomenon of overestimated pollutant concentration at night in eastern
China (Fig. 5).
As turbulent diffusion increases, the pollutant concentration gradually
decreases. Where do the reduced pollutants go? Do they spread to the
surrounding area in the horizontal direction or diffuse to the upper level
in the vertical direction? This question warrants a more in-depth
discussion. It can be seen from Fig. 4 that the reduction in pollutant
concentration is a regional synchronous change, and there is no regular
concentration gradient in the horizontal direction. Therefore, we should
also pay more attention to the changes in the vertical direction.
Theoretically, increasing turbulent diffusion can reduce the pollutant
concentrations near the surface layer, and the pollutants can be more fully
mixed in the vertical direction, leading to lower pollutant concentration
in the near-surface layer and higher in the upper layer. As we expect, the
pollutant concentration is reduced in the surface layer and increased in the
upper layer at night in eastern China (Figs. 6, S3–S5), which is
consistent with the theory.
Turbulent diffusion coefficient of (a–d) heat and (e–h) particles
and (i–l) the difference between two turbulent diffusion coefficients. The
dashed red and solid green irregular circles represent eastern China and
mountainous areas, respectively.
Uncertainty analysisMeteorological parameters
Depending on the high-frequency particle flux, the TDC of particles has been
added into the model to compute the turbulent mixing process of particles
separately. Compared with previous studies about the improvement of
parameterization scheme, the greatest strengths of the new scheme are that
it not only improves the simulation results of pollutant concentration, but
also does not deteriorate the simulation results of other parameters. To
verify that the new scheme does not affect the simulation results of the
meteorological parameters, the simulation results of the near-surface
meteorological elements (i.e., 2 m temperature, 2 m relative humidity and
10 m wind speed) between the original and new schemes have been compared and
analyzed. It can be seen from Figs. S6–S8 that the correlation coefficients
of meteorological parameters by the two schemes are greater than 0.99. Noting
that the new scheme does not alter the performance of meteorological fields,
it is an advantage of the new scheme. As mentioned earlier, modifying the
turbulent diffusion coefficient of heat not only affects the simulation of
temperature (Savijärvi and Kauhanen, 2002), but also affects the results of
pollutants (Du et al., 2020).
Improving the parameterization scheme is a long and tough process, making it
difficult to improve the simulation results of all parameters at the same
time. When the simulation results of one parameter are improved, we should
first ensure that the simulation results of other parameters are not
deteriorated. Then, we will work on improving other parameters. Although the
aerosol–radiation two-way feedback process is considered in the WRF-Chem
model, the change in PM2.5 concentration caused by radiation feedback
is only by a few percent (Li et al., 2017; Wu et al., 2019). We should focus
more on the impact of turbulence on aerosol pollution, and we need to pay more
attention to some turbulent characteristics (e.g., turbulence barrier effect
and turbulent intermittency) during heavy pollution episodes (HPEs), which
can reflect a more realistic evolution process of pollutant concentration.
We will further clarify the relationship between particles, momentum and
heat transport through observational data, so as to lay the foundation for
model improvement.
(a–d) Simulated and (e–h) observed wind speed at 10 m a.g.l. and (i–l) the difference between simulated and observed wind speed. The
purple rectangle indicates the area where the observed wind speed is
significantly overestimated.
PBL height
Although the PBL height (PBLH) is widely used to determine the effective air
volume and atmospheric environmental capacity for pollutant diffusion,
various methods diagnose different PBLHs, which reinforces uncertainty about
the PBLH as a criterion. When there is a transport stage with a high wind
speed during HPEs, the mechanical turbulence is strong, and the pollutant
concentration and PBLH increase simultaneously (Jia et al., 2021b; Miao et
al., 2021). As a result, the relationship between PBLH and PM2.5
pollution is intricate. The impact of the PBLH is ultimately represented through
the TDC in the model, as the PBLH is used to calculate the TDC (Jia et al.,
2021a). And artificially changing the PBLH can also affect the simulation of
pollutant concentration. If the simulation error in pollutant concentration
is caused by the PBLH, then the pollutant concentration is overestimated, and
the PBLH should be underestimated. However, the PBLH is well reproduced by the
model, while the model does not underestimate PBLH (Fig. 7). Anqing is
located in the mountain corridor, where the simulation results of PBLH
(index of agreement, IOA = 0.49–0.81) are slightly worse than
in Fuyang (IOA = 0.63–0.85). That is to say, various
factors can influence the calculation of PBLH.
A more accurate PBLH can reduce some uncertainty in the model, but how to
apply the accurate PBLH through observation to the model is a thorny
problem. For example, the turbulence barrier effect modifies the mixing
height of pollutants (Ren et al., 2021), which cannot be reflected in the
model, and it can lead to deviation in the simulation of pollutant
concentration. The new scheme does not disturb the simulation results of
meteorological fields and, thus, does not affect the simulation results of PBLH
(Fig. S9). The simulation results of pollutant concentrations are improved
under a similar PBLH, further demonstrating that the simulation of
pollutant concentration is not only controlled by the PBLH, but also influenced
by turbulent diffusion. Finally, turbulent diffusion controls the mixing of
pollutant concentration and the evolution of meteorological parameters.
Influence of other processes
Overestimation of pollutant concentrations has been improved in eastern
China, but there are also some stations in northern China where pollutant
concentrations are underestimated (Fig. 3i–l). Therefore, the stratification
is more stable in most of the nighttime in eastern China (Ri is greater
than ∼ 0.2, based on the fitting function in Jia et al.,
2021a), while the stratification tends to be weakly stable/unstable at the
same time in the mountainous area with complex terrain. These stations
(i.e., Hebei and Beijing) are mostly located in the east of the Taihang
Mountains and the south of the Yan Mountains (Fig. 8). For example, in
December 2016, the pollutant concentrations of all stations in Beijing were
not underestimated. Jia et al. (2021a) found that the pollutant
concentrations of two stations located in the south of Beijing (i.e., blue
dots in Fig. S2 in Jia et al., 2021a) are well reproduced by the model. This
phenomenon of pollutant concentrations being significantly underestimated at
some stations near the mountainous area also occurred in 2013–2017 (Fig. 8).
The boundaries of overestimated and underestimated sites are pronounced in
the Beijing–Tianjin–Hebei region (dashed white line in Fig. 8), and the pollutant
concentration is overestimated at some stations away from the mountainous
area (i.e., Tianjin and southeast of Hebei). Meanwhile, the TDC of particles
in the new scheme is greater than that of heat in the original scheme in
eastern China (i.e., dashed red circle in Fig. 9i–l); that is, the increased
turbulent diffusion improves the overestimation of pollutant concentration
in this area. Compared to the original scheme, the increase in the TDC in the
new scheme is attributed to the change in f(Ri) when other physical quantities
remain unchanged. What is more, we find that the TDC in the new scheme is
much smaller than that in the original scheme in the mountainous area (i.e.,
green irregular circle in Fig. 9). Theoretically, the reduced TDC is
expected to increase the pollutant concentration and improve the
underestimation of pollutant concentration in the original scheme.
Disappointingly, the change in the TDC does not improve the underestimation of
pollutant concentration in the mountainous area (Figs. 8, 9i–l), indicating
that the change in turbulent diffusion is not sensitive to the pollutant
concentration in the mountainous area.
In addition to the main influencing factors of emission and turbulent
diffusion, advection transport, chemistry processes and dry deposition can
also affect the simulation of pollutant concentration. Given that we are
using the latest emissions source inventory, it is impossible to use other
more elaborate inventories to quantify the uncertainty caused by emissions.
The advection process is strongly related to wind and PM2.5 concentration
gradients from upwind areas to downwind areas (Gao et al., 2018). Figure 10
shows the simulation results of wind speed, and we find that the wind
speed is overestimated in the whole simulation area. The model often overestimates the wind speed, which is the reason for the model itself (Jia and Zhang, 2020). Nevertheless, there are regional differences in the overestimation of wind speed, which is more
obvious in areas with complex terrain (framed by purple lines in Fig. 10).
Jiménez and Dudhia (2012) indicated that the overestimation of wind speed
may be caused by the incorrect description of sub-grid surface roughness. For the
purple rectangle region, although the wind speed is overestimated, there is
no evident wind speed gradient and pollutant concentration gradient (Figs. 3a–d, 10a–d). Thus, the effect of advection is insignificant, while for the
irregular purple region, we can see that the wind speed gradient and
pollutant concentration gradient are obvious (Figs. 3a–d, 10a–d). In the
northwest of the irregular purple area, clean air will pass through this
area under the control of stronger northwesterly wind. Consequently, this
area is extremely susceptible to advection transport; therefore, the pollutant
concentration has been underestimated here. We should pay more attention to
the improvement of wind field simulation in complex terrain. It is expected
that the simulation of the wind field will be improved, leading to an
improvement in pollutant concentration in this area.
The chemistry process, i.e., the PM2.5 concentration contribution caused by
secondary transformation, was negligible in this study and is not mentioned
further in this paper. Whether the simulation of chemical components has
been improved cannot be properly verified because of the lack of
observational data. Although the simulation results of PM2.5 components
cannot be evaluated, CO, as a representative of primary pollutants, can be
compared with the observations. Results from the new scheme with the TDC of
particles are more consistent with the observations than the original scheme
(Fig. S10), giving support to the improvement of PM2.5 concentration
(Figs. 5 and S10). Moreover, the dry deposition process of particles is also
extremely important (Zhang et al., 2001; Farmer et al., 2021). The turbulent
mixing and dry deposition processes belong to the same main program in the
mesoscale model. However, as particle size increases, particle inertia and
gravity cannot be neglected, but these inertia and gravity effects are
neglected for particles smaller than 10 µm in diameter (Fratini et
al., 2007). In this sense, we did not include the influence of gravity on
pollutant concentration in this study. Petroff and Zhang (2010) showed that
according to the method in Zhang et al. (2001), the dry deposition can be
overestimated, especially for fine particles. Special attention must be paid
to the fact that the overestimation of dry deposition affects the
distribution of pollutant concentration. Therefore, the choice of dry
deposition scheme also needs to be carefully considered, in that this
process is also very important for the evolution of pollutants. In the
future, long-term simulation results should be used to verify the difference
in aerosol process decomposition in detail.
Conclusions and prospects
At present, the mesoscale model is facing numerous challenges, especially
the accurate simulation of pollutant concentration during heavy pollution
episodes. One of these challenges is to correctly describe the turbulent
mixing process of pollutants. Though the model can reproduce the evolution
of pollutants, the simulation of diurnal variation of pollutants is
fundamentally flawed, especially at night. Errors in the estimation of
pollutant concentration are primarily caused by defects in the turbulent
mixing of pollutants in the model. Actually, a difference exists between the
turbulent transport of heat and particles, which inspires us to deal with
the turbulent diffusion of heat and particles separately. Therefore, based
on the turbulent diffusion expression of particles proposed by Jia et al. (2021a), we demonstrate the improvement of pollutant concentration in winter
from 2013 to 2017, and the uncertainty factors are also analyzed in the
model.
The original scheme overestimates the surface PM2.5 concentration by
11.8 % (2013), 48 % (2014), 23.8 % (2015) and 20.9 % (2017) at
night, respectively. The new scheme has improved the overestimation of the
surface PM2.5 concentration in eastern China at night, and the mean
value of absolute bias of the region can be reduced by 8.3 % (2013),
17 % (2014), 11 % (2015) and 11.7 % (2017), respectively. In the
horizontal direction, the pollutant concentration shows regional synchronous
changes. Consequently, the pollutant concentration is reduced near the
surface layer and better mixed in the entire layer, increasing the pollutant
concentration in the upper level. Moreover, the new scheme not only improves
the simulation of pollutant concentration, but also does not deteriorate the
simulation of other meteorological parameters. Although the PBLH affects the
diffusion of pollutants, the simulation of pollutant concentration is not
specifically controlled by the PBLH. It should be noted that the TDC has a
negligible impact on the simulation of pollutant concentration at some
stations with complex topography. Meanwhile, advection transport may
dominate the evolution of pollutant concentration in mountainous area. The
simulation results of PM2.5 components cannot be evaluated, owing to
the lack of observational data. CO, however, as a representative of primary
pollutants, can be compared with observations. Results from the new scheme are
more consistent with the observations than the original scheme, which
supports the improvement of the PM2.5 concentration.
The coefficient of function in this study should be discussed combined with
the sample size of data, but we hope the new scheme can provide promising
guidance during heavy pollution episodes. The turbulent transport mechanism
and turbulence parameterization constitute a complex topic (Couvreux et al.,
2020; Edwards et al., 2020), and beyond this, other processes (or other
parameters) require in-depth understanding and exploration (Zhang et al.,
2001; Shao et al., 2019; Emerson et al., 2020). Hence, more research may
shed more light on the turbulent mixing process and transport mechanisms of
pollutants during heavy pollution episodes, especially on the
experimental side (e.g., by carrying out extensive measurement campaigns).
Data availability
The surface PM2.5 concentration data, meteorological data, turbulent
datasets and turbulent flux PM2.5 data are available upon request
(xiaoye@cma.gov.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-16827-2021-supplement.
Author contributions
All of the authors contributed to the development of the ideas and concepts behind this work. Model execution, data analysis and paper preparation were performed by WJ, and XZ gave feedback and advice. All authors read and approved the final paper.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The work was carried out at the National Supercomputer Center in Tianjin, and the calculations were performed on TianHe-1 (A). The authors would like to acknowledge Tsinghua University for the support with the emission data.
Financial support
This research was supported by the NSFC Major Project (grant nos. 42090030 and 42090031) and NSFC Project (grant no. U19A2044).
Review statement
This paper was edited by Leiming Zhang and reviewed by two anonymous referees.
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