In the present study, the WRF-CHEM model is used to
evaluate the contributions of trans-boundary transport to the air quality in
Beijing during a persistent air pollution episode from 5 to 14 July 2015 in
Beijing–Tianjin–Hebei (BTH), China. Generally, the predicted temporal
variations and spatial distributions of PM
Beijing, the capital of China, has become an environmentally stressed city due to a growing population, increasing transportation activity, and city expansion (Parrish and Zhu, 2009). Beijing is situated in northeastern China, surrounded from the southwest to the northeast by the Taihang Mountains and the Yanshan Mountains and open to the North China Plain (NCP) in the south and east. Unfortunately, the NCP has become one of the most polluted areas in China due to rapid industrialization and urbanization (Zhang et al., 2013). When south or east winds are prevalent in the NCP, air pollutants originating in the NCP are transported to Beijing and surrounding areas and subject to accumulation due to the mountain blocking, causing heavy air pollution in Beijing (Long et al., 2016).
PM
In recent years, Beijing has implemented aggressive emission control
strategies to ameliorate the air quality (Parrish and Zhu, 2009). Both
NO
Several studies have been performed to investigate the role of
trans-boundary transport in the air quality of Beijing based on
observational analyses and model simulations. Using the US EPA (Environmental Protection Agency) Model-3/CMAQ (Community Multiscale Air Quality) model simulation in the Beijing area, Streets et al. (2007)
have pointed out that Hebei Province can contribute 50–70 % of Beijing's
PM
Since September 2013, the “Atmospheric Pollution Prevention and Control
Action Plan” (hereafter referred to as APPCAP) has been implemented, which
was released by the Chinese State Council to reduce PM
The purpose of the present study is to evaluate the contributions of trans-boundary transport of emissions outside of Beijing to the air quality in Beijing and interaction of emissions in and outside of Beijing after APPCAP using the WRF-CHEM model. The model configuration and methodology are described in Sect. 2. Model results and sensitivity studies are presented in Sect. 3, and conclusions and discussions are given in Sect. 4.
The WRF-CHEM model used in the study is developed by Li et al. (2010, 2011a,
b, 2012) at the Molina Center for Energy and the Environment, with a new
flexible gas-phase chemical module and the CMAQ aerosol module developed by
the US EPA. The aerosol component of the CMAQ
model is designed to be an efficient and economical depiction of aerosol
dynamics in the atmosphere (Binkowski and Roselle, 2003). The particle size
distribution in the study is represented as the superposition of three
lognormal subdistributions, called modes, which includes the processes of
coagulation, particle growth by the addition of mass, and new particle
formation. Following the work of Kulmala et al. (1998), the new particle
production rate presented here is calculated as a parameterized function of
temperature, relative humidity, and the vapor-phase H
The inorganic aerosols are predicted in the WRF-CHEM model using ISORROPIA
Version 1.7 (Nenes et al., 1998). The efficient and rapid secondary species
formation in Beijing has been found during the severe haze formation process
in the previous study (Guo et al., 2014). The secondary organic aerosol
(SOA) formation is calculated using a non-traditional SOA module. The
volatility basis set (VBS) modeling method is used in the module, assuming
that primary organic components are semi-volatile and photochemically
reactive and are distributed in logarithmically spaced volatility bins.
Detailed information about the VBS approach can be found in
Li et al. (2011b). Recent studies have shown that small di-carbonyls (glyoxal
and methylglyoxal) are important for the aerosol formation due to their
traffic origin (Zhao et al., 2006; Gomez et al., 2015). Li et al. (2011a)
have indicated that glyoxal and methylglyoxal can contribute about 10 % of
the SOA in the urban area of Mexico City. The SOA formation from glyoxal and
methylglyoxal in this study is parameterized as a first-order irreversible
uptake by aerosol particles and cloud droplets, with a reactive uptake
coefficient of
A persistent air pollution episode from 5 to 14 July 2015 in
Beijing–Tianjin–Hebei (BTH) is simulated using the WRF-CHEM model. During
the episode, the observed mean daily PM
WRF-CHEM simulation domain. The blue circles represent centers of cities with ambient monitoring sites and the red circle denotes the NCNST site. The size of the blue circle denotes the number of ambient monitoring sites of cities.
The WRF-CHEM model adopts one grid with horizontal resolution of 6 km and 35
sigma levels in the vertical direction, and the grid cells used for the
domain are
Emissions of major anthropogenic species in July 2013 (Unit:
10
Spatial distribution of anthropogenic
The anthropogenic emissions are developed by Zhang et al. (2009), whose work
is based on the 2013 emission inventory, including contributions from
agriculture, industry, power generation, residential, and transportation
sources. The SO
The formation of the secondary atmospheric pollutant, such as O
Suppose that field
The simulation including both factors
In the present study, the mean bias (MB), root mean square error
(RMSE), and index of agreement (IOA) are used as
indicators to evaluate the performance of WRF-CEHM model in simulation
against measurements. IOA describes the relative difference between
the model and observation, ranging from 0 to 1, with 1 indicating perfect
agreement.
Hourly mass concentrations of pollutants averaged in the afternoon at 12 monitoring sites in Beijing during summertime of 2013 and 2015.
The hourly measurements of O
The APPCAP has been implemented since 2013 September, so comparisons of
summertime pollutants between 2013 and 2015 can show the mitigation effects
on the air quality. Considering that high O
The hourly measurements of O
Figure 3 shows the temporal variations of observed and simulated
near-surface O
Comparison of measured (black dots) and predicted (blue line)
diurnal profiles of near-surface hourly
Figure 4 shows the temporal variations of simulated and observed aerosol
species at NCNST site in Beijing from 5 to 14 July 2015. The WRF-CHEM model
generally performs reasonably in simulating the aerosol species variations
compared with ACSM measurements. As a primary aerosol species, the POA in
Beijing is determined by direct emissions from various sources and transport
from outside of Beijing, so uncertainties from emissions and meteorological
fields have a remarkable effect on the model simulations (Bei et al., 2012, 2013).
Although the MB and RMSE for POA are 0.0
and 3.1
Comparison of measured (black dots) and simulated (black line)
diurnal profiles of submicron aerosol species of
Figure 5 shows the diurnal profiles of observed and simulated near-surface
O
Figure 6 presents the distributions of calculated and observed near-surface
PM
Comparison of measured (black dots) and predicted (blue line)
diurnal profiles of near-surface hourly
The O
The good agreements between predicted PM
Pattern comparison of simulated vs. observed near-surface
PM
Same as Fig. 6, but for O
Same as Fig. 6, but for NO
The analysis in Sect. 3.1.3 has shown the strong correlation between the
airflow and the high level of pollutants in Beijing during the study
episode. It is essential to confirm whether the continuous air pollution in
Beijing is directly related to the airflow transport from outside of
Beijing (An et al., 2007; Yang et al., 2010). In the present study, the
horizontal transport flux intensity is defined as the horizontal wind speed
on the grid border multiplied by the pollutant concentration of the
corresponding grid from which the airflows come (Jiang et al., 2008).
Considering that trans-boundary transport mainly occurs within the PBL, the
study also focuses on the contribution of trans-boundary transport of
pollutants within the PBL over Beijing and its surrounding areas. Previous
studies have shown that the average mixing layer height is approximately
between 600 and 800 m during summertime, with the maximum during noontime
higher than 1000 m (H. Wang et al., 2015; Tang et al., 2016). Figure 9 shows the
temporal variations of net horizontal transport flux of PM
Temporal variations of total net horizontal transport flux of
PM
Temporal variations of the average near-surface O
The FSA is used in the present study to evaluate the contributions and
interactions of emissions from Beijing and outside of Beijing to the
near-surface concentrations of O
Average O
Figure 10 provides the temporal variations of the average near-surface
O
Average PM
Previous studies have proposed that the regional transport of O
When the Beijing local emissions are not considered in simulations, Beijing
still experiences high PM
Figure 11 shows the temporal variation of the averaged contributions to the
near-surface aerosol constituents from total emissions
(
Table 5 presents the average aerosol constituent contributions from Beijing
local emissions, non-Beijing emissions, emission interactions, and the
background, and mass fractions in the total PM
The local emissions contribute more than 20 % of the mass concentrations for the primary aerosol species, but less than 15 % for the secondary aerosol species in Beijing (Table 5). The trans-boundary transport of non-Beijing emissions dominates all the aerosol species levels in Beijing, with contributions exceeding 50 %, particularly for SOA and nitrate. In addition, the POA and sulfate background contributions are also high, more than 20 %. Although the primary aerosol species of EC and unspecified constituents are not involved in the chemical process and also do not participate in the gas-particle partitioning, the emission interactions still enhance EC and unspecified constituents concentrations, with contributions of around 1.5 %, which is caused by the PBL–pollution interaction. It is clear that the PBL–pollution interaction plays an important role in the pollutant accumulation in Beijing (Y. Wang et al., 2013; Peng et al., 2016). Mixing of Beijing local emissions with those outside of Beijing increases the aerosol concentrations in the PBL and decreases the incoming solar radiation down to the surface, cooling the temperature of the low-level atmosphere to suppress the development of the PBL and hinder the aerosol dispersion in the vertical direction.
Aerosol species' contributions (%) from local emissions,
non-Beijing emissions, interactions of both emissions, and background, as
well as mass fraction in the total PM
Temporal variations of the average contributions to the
near-surface aerosol species concentrations from total emissions (black
line, defined as
The emission interactions increase the POA and SOA concentrations, with a POA contribution of 5.3 % and a SOA contribution of 5.9 %. In the VBS modeling approach, primary organic components are assumed to be semi-volatile and photochemically reactive. Mixing of Beijing local emissions with non-Beijing emissions enhances the organic condensable gases, and considering that the saturation concentrations of the organic condensable gases do not change, more organic condensable gases partition into the particle phase, increasing the POA and SOA concentrations.
The contributions of emission interactions to inorganic aerosols, including
sulfate, nitrate, and ammonium, are more complicated, depending on their
particle phase and precursor concentrations. In the present study,
ISORROPIA (Version 1.7) is used to calculate the thermodynamic equilibrium
between the sulfate–nitrate–ammonium–water aerosols and their gas phase
precursors H
In the present study, a persistent air pollution episode with high
concentrations of O
In general, the predicted temporal variations of PM
The FSA is used to investigate the contribution of trans-boundary transport
of non-Beijing emissions to the air quality in Beijing. If the Beijing local
emissions are not included in model simulations, the O
However, there is still controversy over whether local or non-local emissions
play a dominant role in the air quality in Beijing (Guo et al., 2010, 2014;
P. Li et al., 2015; R. Zhang et al., 2015). When only considering the local
emissions, the summertime PM
It is worth noting that, although the WRF-CHEM model captures the
spatial distributions and temporal variations of pollutants well, the model
biases still exist. The discrepancies between the predictions and
observations are possibly caused by the uncertainties in the emission
inventory and the meteorological field simulations (R. Zhang et al., 2015).
BTH has been considered as a polluted air basin (Zhao et al., 2009;
Parrish and Stockwell, 2015), which frequently experiences O
The real-time O
The authors declare that they have no conflict of interest.
This work was supported by the National Natural Science Foundation of China (no. 41275153) and the “Strategic Priority Research Program” of the Chinese Academy of Science, grant no. XDB05060500. Guohui Li is also supported by the “Hundred Talents Program” of the Chinese Academy of Sciences. Naifang Bei is supported by the National Natural Science Foundation of China (No. 41275101). Edited by: D. Parrish Reviewed by: three anonymous referees