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Aerosol and Air Quality Research, 17: 2491–2508, 2017
Copyright © Taiwan Association for Aerosol Research
ISSN: 1680-8584 print / 2071-1409 online
doi: 10.4209/aaqr.2017.01.0009
An Integrated WRF-CAMx Modeling Approach for Impact Analysis of
Implementing the Emergency PM
2.5
Control Measures during Red Alerts in
Beijing in December 2015
Jia Jia
1
, Shuiyuan Cheng
1,2*
, Lei Liu
3
, Jianlei Lang
1
, Gang Wang
1
, Guolei Chen
1
, Xiaoyu Liu
1
1 Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
2 Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, China
3 Department of Civil and Resource Engineering, Dalhousie University, Halifax, NS B3H 4RZ, Canada
ABSTRACT
In December 2015, the Beijing-Tianjin-Hebei (BTH) region experienced several episodes of heavy air pollution. Beijing
municipal government therefore issued 2 red alerts on December 7 and 19, respectively, and also implemented emergency
control measures to alleviate the negative effects of pollution. It is estimated that the heavy pollutions in 2 red alert periods
in Beijing were due mainly to the accumulation of air pollutants from local emission sources and the transboundary
transport of pollutants from surrounding areas. The collected meteorological and PM2.5 data indicate that the severity of air
pollutions were enlarged by the poor meteorological conditions along with lower mixing layer height. In this study, the
WRF-CAMx modeling system was utilized not only for analyzing the contributions of PM2.5 from different sources, but
also for quantitatively assessing the effects of implementing various emergency control measures on PM2.5 pollution
control during the red alert periods. The modeling results show that local emissions were the most dominant contributors
(64.8%–83.5%) among all emission sources, while the main external contributions came from the city of Baoding (3.4%–
9.3%). In addition, among 5 different emission source categories, coal and traffic were the two dominant contributors to
PM2.5 concentration in urban area of Beijing. Then four pollution control scenarios were designed particularly to
investigate the effectiveness of the emergency control measures, and the results show that, generally these emergency
control measures have positive effects on air pollution reduction. In particular, restrictive measures of traffic volume
control and industrial activity shutdown/suspension have been found as the most effective measures in comparison to other
emergency control measures. It is recommended that such effective measures should be considered to implement when
next time similar heavy air pollutions occur in the city of Beijing.
Keywords: Integrated WRF-CAMx modeling; Red alerts; Emergency control measures; Beijing.
INTRODUCTION
As the political and cultural center of China, Beijing has
made and implemented an ambitious urban development
plan over the past two decades to transform an old and
historical city to a new and modern metropolis (Dong et
al., 2013). Beijing has gradually achieved its goal in terms
of its stunning infrastructural construction and sweeping
architectural transformation. Examples of such progresses
and milestones include the successful holds of Beijing
Olympic Summer Games in 2008 and APEC (Asia-Pacific
Economic Cooperation) Beijing Summit in 2014, during
* Corresponding author.
Tel.: +86 10 67391656; Fax: +86 10 67391983
E-mail address: bjutpaper@gmail.com
which Beijing has acquired the moment and spotlight in
front of thousands visiting China and billions watching on
television.
However, associated with its rapid urban transformation
and economic expansion, air quality in Beijing’s urban areas
has been severely deteriorated by elevated levels of smog
and other air toxins. Among all the air pollutants, PM2.5
pollution was prompted and became the topmost pollution
related priority for Beijing municipal government (Zhang et
al., 2015b). The harmful consequences of high levels of PM2.5
in the air are obvious and substantial in terms of negative
impacts on urban environmental, economic development
and public health (Feng et al., 2014b; Guo et al., 2014;
Zhou et al., 2014b; Jiang et al., 2015). As a result, Beijing
government desires to take effective strategies and policies
to control the level of PM2.5 concentration in the air to
improve the environmental performance, and eventually
provide healthy and clean air for its own residents.
Jia et al., Aerosol and Air Quality Research, 17: 2491–2508, 2017
2492
The urban air quality system is a complex network of
multiple competing interactions within and among chemical
and physical atmospheric processes, which is also related
to various social, economic, environmental, technological,
managerial, regulatory, and political factors. Hence, the
decision making process of air quality management thus
requires an insightful knowledge of air pollution details
and a sound understanding of the significant drivers of
urban air pollution problems.
Since two decades ago, the Central Government of China
has made extensive efforts (including emergency control
measures for important events) to alleviate the emission in
both Beijing and its surrounding provinces for improving
the air quality and positive results have been achieved. For
example, during the aforementioned international events
(i.e., 2008 Beijing Olympic Games (Wang et al., 2009;
Wan g et al., 2014b), 2014 APEC Summit (Chen et al.,
2015b)), massive emission control measures were designed
collectively by the governments of Beijing and surrounding
provinces, and more importantly, were implemented a few
weeks prior to the events; as a result, the air quality of
Beijing was generally improved to good conditions and the
PM2.5 concentrations were at low levels. Although these
measures seemed to have positive effects on air quality
improvement, it has to be recognized that, both Olympics
and APEC Summit events were held in summer and autumn
seasons in Beijing, and the weather conditions were often
favorable for dispersing and pushing the polluted air out of
the Beijing airshed (Wang et al., 2010; Huang et al., 2015).
In December 2015, the Beijing-Tianjin-Hebei (BTH)
region experienced several episodes of heavy air pollution,
and each episodes lasted for a few days. The maximum
instantaneous PM2.5 concentration in Beijing reached over
600 µg m–3, which was the highest level since January 2013
(Bi et al., 2014). As a quick and emergency response to
pollution situations, Beijing municipal government therefore
issued 2 red alerts on December 7 and 19, respectively, and
emergency control measures were implemented immediately
to control the pollution and help alleviate the negative
effects of pollution. This was the first time that a red alert
for air pollution in Beijing was issued since the Emergency
Air Pollution Response System (Response System) was
announced in 2013 (Chen et al., 2015a). Previous studies
indicate that the effectiveness of emergency control measures
for improving air quality in Beijing was influenced by not
only air pollutant reductions at emission sources but also
the regional weather conditions (Yang et al., 2015b). Hence,
whether or not the emergency control measures are effective
or how much effects they could achieve in alleviating air
pollution without favorable meteorological conditions
became doubtful and are unknown to the governmental
authorities, and they needs to be investigated particularly
for Beijing in winter seasons when the heavy air pollutions
occur quite frequently and the weather conditions are often
not favorable in pollution dispersion.
To investigate the effectiveness of emission control
measures under different complex terrain and meteorological
conditions like Beijing, two different types of modeling
approaches were used in the past, including receptor
models and numerical models. Receptor models use the
observed data to compare the composition changes of
primary and secondary aerosols in the air before and after
the implementation of emergency control measures (Xing
et al., 2011; Wei et al., 2014; Wang et al., 2015b; Yan et
al., 2015); numerical models use emission inventories to
specify the source regions and the efficiency and effectiveness
of control measures can be assessed through simulating
different control scenarios. Previously, a Community Multi-
Scale Air Quality (CMAQ) numerical simulation model
has been applied to the BTH region and the modeling
results show that the emergency control measures have
relatively significant effects on reducing NOx and SO2
concentrations, but limited impacts on PM2.5 pollution
control (Chen et al., 2015a). It was found that the zero-out
method used in the CMAQ model couldn’t provide accurate
appointments of PM2.5 sources due to the complex non-
linearity of atmospheric chemistry. In order to overcome
this drawback, the Comprehensive Air Quality model with
Extensions (CAMx) has then been developed for better
PM2.5 pollution simulation. CAMx is an Eulerian (gridded)
regional photochemical dispersion model, which could
simulate the emission, dispersion, chemical reaction, and
removal of pollutants by marching the Eulerian continuity
equation forward in time for each chemical species on a
system of nested three-dimensional grids. In CAMx, the
Particulate Source Appointment Technology (PSAT), an
efficient source tagging method (Li et al., 2015b), is
employed as the source apportionment method to accurately
reflect the pollutant transmission and appointment of sources
at a regional scale, the effectiveness of emission control
measures can also be investigated (Shen et al., 2011; Wang
et al., 2014a; Qu et al., 2014; Koo et al., 2015; Li et al.,
2015b; Pirovano et al., 2015). For example, Megaritis et
al. (2014) applied CAMx model to Europe to study the
influence of emissions changes on fine PM levels, with the
result was that reduction of NH3 emissions seems to be the
most effective control strategy for reducing PM2.5 over
Europe. Akritidis et al. (2014) used the CAMx modeling
system to assess the impact of anthropogenic emission
changes on air quality improvement in Europe and pointed
out that any gain owning to emissions changes in short time
periods can be masked by meteorological and background
ozone variabilities. Li et al. (2013) applied the CAMx
model to a study on the contributions of various precursors to
air pollution formation, and found that regional collaborations
on pollution control are of particular importance in effectively
reducing the episodic ozone concentrations. Li et al. (2015b)
further employed the CAMx model to a study on the
contributions of different emission sources and regions to
PM2.5 level in the BTH region in 2006 and 2013, respectively,
and found that the emission control measures and
particularly regional joint emission control efforts could
help achieve much better pollution control results.
As an extension of previous efforts in this area, in this
study, the CAMx modeling system is used for the first time
to simulate the regional air quality system in BTH region
during 2 red alert periods and answer various questions
relating to PM2.5 pollution occurred in Beijing. This mainly
Jia et al., Aerosol and Air Quality Research, 17: 2491–2508, 2017 2493
entails: (1) how the heavy air pollution was formed in
December 2015, especially in 2 red alert periods in Beijing?
(2) how did the meteorological conditions influence the
formation of heavy air pollution in December 2015? (3)
what percentages did different emission sources and sub-
regions contribute to the formation of PM2.5 pollution in
Beijing in 2 red alert period? (4) how effective did the
emergency control measures perform in improving the air
quality in Beijing and which one is most effective and
preferred? The heavy air pollution episodes occurred in
December 2015 in Beijing provide a rare and valuable
opportunity not only for testing the capability of the
CAMx modeling system in simulating regional large-scale
air quality problem, but also for examining the effects of
various emergency control measures on air pollution control.
The modeling results would provide a scientific base for
Beijing’s government to make more sound air pollution
control policies and measures in future.
CASE STUDY
Two Red Alerts Issued in December 2015
According to the Heavy Air Pollution Emergency Response
Program stipulated by Beijing municipal government, four
colors (i.e., blue, yellow, orange and red) are used as a 4-
tier alert system to indicate the severity levels of air
pollution in the city, and the color of alert will be based on
the comprehensive forecasting of the degree and duration
of air pollution. Among four colors, blue, yellow, and
orange alerts are issued for the conditions that a heavy air
pollution is predicted to continue for 24 hours, 48 hours,
and 72 hours, respectively. Whereas, the red alert will be
issued for a forecasted heavy pollution that would last over
72 hours, and represents the highest level of alert, as
indicated in Table 1. Table 1 presents the issuing conditions
for the 4-tier alerts as well as their corresponding control
measures for protecting air and public health. The control
measures could be divided into three groups, i.e., public
health protection measures, recommended control measures,
and compulsory control measures. As long as a heavy
pollution occurs, these public health protection measures
will be brought to public notice for raising their awareness
and protecting themselves. Recommended measures are
designed to reduce personal emissions through, for example,
reducing the usage of private vehicles and instead using
more public transit. The yellow, orange and red alerts all
include a variety of compulsory measures which will be
implemented for reducing pollutant emissions from a
variety of major sources such as vehicles, industrial plants and
construction sites and thus possibly alleviating the PM2.5
pollution in the city.
On December 7, 2015, the PM2.5 concentrations observed
from almost all monitoring stations across Beijing were
more than 200 µg m–3, and Beijing Municipal Environmental
Monitoring Center predicted that a heavy air pollution with
a PM2.5 concentration of over 150 µg m–3 would continue
for more than three consecutive days, before the arrival of
the northwest cold winds which might be able to help
pollution disperse away. As a result, a red alert was issued
Tabl e 1 . The issuing conditions for the 4-Tier alerts and the corresponding control measures.
Alert Level Issuing Conditions Control Measures
Public Health Protection Measures Recommended Measures Compulsory Measures
Blue Mean hourly PM
2.5
concentration
over 150 µg m
–3
lasting 24 hours
1. Reduce outdoor activities
2. Public warning and notification
1. Clean roads
2. Reduce usage of vehicles
Yellow Mean hourly PM
2.5
concentration
over 150 µg m
–3
lasting over 24
but less than 48 hours
1. Avoid outdoor activities
2. Public warning and notification
1. Reduce usage of vehicles
2. Cancel outdoor sports
activities for all schools
1. Ban partial activities of construction projects
2. More road cleaning
Orange Mean hourly PM
2.5
concentration
over 150 µg m
–3
lasting over 48
but less than 72 hours
1. Avoid outdoor activities
2. Expert interpretation and
information publicity
3. Wear masks
1. Reduce usage of vehicles
2. Cancel all outdoor
activities for all schools
1. More road cleaning
2. Enhanced dust control of construction sites
3. Suspend the operation of some industrial plants
4. Ban fireworks and outdoor barbecues
5. Ban heavy vehicles on road
Red Mean hourly PM
2.5
concentration
over 150 µg m
–3
lasting over 72
hours
1. Avoid outdoor activities
2. Expert interpretation and
information publicity
3. Wear masks
1. Cancel all large outdoor
activities
2. Close all schools
1. Implement even- and odd-numbered license
plates policy
2. Ban heavy-duty vehicles on road
3. Suspend all construction projects
4. Suspend the operation of more industrial plants
5. Ban fireworks and outdoor barbecues
Jia et al., Aerosol and Air Quality Research, 17: 2491–2508, 2017
2494
by Beijing municipal government at 7:00 AM on December
8; meanwhile the emergency control measures were put
into implementation immediately in Beijing. Furthermore,
according to the estimation made by Beijing Environmental
Protection Bureau, 28%–36% of total PM2.5 in Beijing’s
airshed came from the transboundary emissions from
neighboring provinces (Wang et al., 2015a), particularly
from Hebei Province and the City of Tianjin. In order to
help mitigate the severity of air pollution level in Beijing,
the surrounding provinces and cities responded quickly and
have taken collective responsibilities and actions through
issuing different levels of alerts and implementing various
control measures. For example, the city of Baoding issued
a red alert; the city of Tianjin issued an orange alert; and
the city of Qinhuangdao issued a blue alert on the same day of
December 7. This first red alert in Beijing was cancelled
till 12:00 PM on December 10 along with the improvement
of air quality in Beijing. The red alerts were issued at 7:00
AM on December 19, 2015 by the governments of Beijing
municipality, Tianjin city and Hebei province due to the
recurrence of high PM2.5 concentrations in this region, and
they were cancelled till 24:00 AM on December 22 where
the air quality was much improved in the region. Fig. 1
shows the different levels of alerts issued for different cities
and regions in December 2015. As mentioned earlier,
although the red alerts were issued in the region and
various emergency control measures were implemented,
the effectiveness of these measures on regional air quality
improvement remains to be unknown and deserves an
insightful research on this issue.
Data Collection
In this study, meteorological parameters and hourly
concentrations of different air pollutants (including PM2.5,
SO2, NO2, O3, and CO) were collected from the field
monitoring stations located in the study region.
Meteorological parameters are used to analyze the impacts
of meteorological conditions on the air quality in the study
region during the red alert periods. Air pollutant
concentrations are used not only for the validation of the
CAMx air quality simulation model but also for the trend
analysis of PM2.5 concentrations. The hourly concentrations
of air pollutants were collected by China National
Environmental Monitoring Centre (CNEMC) for the period
of December 2013 to December 2015. The mean values of
state-controlled monitoring stations in Beijing (12 sites in
Beijing-Urban), Tianjin, Shijiazhuang and Tangshan are
utilized to calculate the PM2.5 concentrations, respectively.
Meteorological data used in this study include temperature,
relative humidity, visibility, wind direction and wind speed,
and they are available on the China Weather Website which
are collected by China Meteorological Bureau and Weather
Underground. Surface meteorological maps are obtained
from the Korea Meteorological Administration (KMA),
and they are analyzed to identify the dominant synoptic
features of different weather patterns during the 2 red alert
periods. In addition, the daily maximum layer height (MLH)
is calculated by the dry adiabatic method through using the
daily maximum temperatures data drawn by Aircraft
Meteorological Data Relay (AMDAR) from Beijing Capital
International Airport (40°04′N, 116°35′E), which is located
25 km to the northeast of metro center of Beijing.
Fig. 1. Designed double nesting simulation domain and different alerts issued in December 2015.
Jia et al., Aerosol and Air Quality Research, 17: 2491–2508, 2017
2495
WRF-CAMx Modeling System
Methodology Design
In this study, an integrated WRF-CAMx modeling
system is used as the tool to investigate the effects of various
emergency control measures on air pollution control. The
WRF stands for the Weather Research & Forecasting Model
(Version 3.3), and it was developed by the National Center
for Atmospheric Research (NCAR). Its role is to generate
the meteorological background for air quality simulation. In
the WRF meteorological simulation, the Final Operational
Global Analyses data are used, which were produced by
National Centers for Environmental Prediction’s (NCEP)
Global Forecast System (GFS). In the WRF-CAMx modeling
system, the CAMx model (Version 5.1) is an advanced
photochemical diffusion model based on the framework of
‘one atmosphere’. In this study, the CAMx model is
employed to simulate the spatial and temporal variations of
PM2.5 concentrations within the study region. In the
process of simulation, the Particulate Source Appointment
Technology (PSAT) was used as a source apportionment
tool to estimate the contributions from respective emission
sources (Wu et al., 2013), through adding reactive tracers
into the CAMx model to apportion PM2.5 components from
different sub-regions and source categories. After each
chemical or physical process (e.g., chemical reaction,
deposition, diffusion and transport), the source species are
updated by apportioning the change of corresponding
species in the CAMx model to each source. As a result,
source information of each selected species on each grid at
each time step are delivered and evolved (Li et al., 2015b).
Detailed CAMx model and PSAT algorithms can be found
in the manual of the CAMx model.
Simulation Domain Design
In this study, a double nesting simulation domain was
designed for the integrated WRF-CAMx modeling system,
as shown in Fig. 1. Domain 1 (D1) refers to the outside
domain with a grid resolution of 27 km × 27 km. It covers
the BTH region and surrounding provinces including
Shandong, Shanxi, most of Inner Mongolia, Liaoning,
Shaanxi, Henan, Jiangsu, and parts of Jilin, Anhui, Hubei,
with a total area of almost 1.8 million km2. This design
makes all possible external PM2.5 emission sources (which
might affect the PM2.5 concentration in the BTH region) be
included in the simulation emission inventory. Domain 2
(D2) refers to the inside domain with a grid size of 9 km ×
9 km, and it covers mainly the BTH region. Presumably,
D2 provides a better PM2.5 simulation resolution.
Pollution Emission Sub-Areas Setup
In the air quality simulation, the simulation domain
needs to be divided into a series of sub-areas for evaluating
the pollutant transport between geographic boundaries of
sub-areas labeled by the CAMx model. In this study,
Beijing’s urban area and rural area were treated separately
due to their significant differences in population density
and traffic volume (Wu et al., 2013), and they represents
two different sub-areas as the inputs for the CAMx model.
In this study, a total of 15 sub-areas were delineated
within the D2 domain, representing 15 pollution emission
source inputs to the CAMx model, as indicated in Fig. 2.
The 15 sub-areas are Beijing-Urban (BJ-U), Beijing-Rural
(BJ-R), Tianjin (TJ), Zhangjiakou (ZJK), Chengde (CD),
Qinhuangdao (QHD), Tangshan (TS), Baoding (BD),
Langfang (LF), Cangzhou (CZ), Shijiazhuang (SJZ),
Hengshui (HS), Xingtai (XT), Handan (HD) and other
regions (OT). Receptor points were selected close to the state-
controlled monitoring stations within TJ, SJZ, TS and BJ-U
sub-area to calculate the local simulation PM2.5 concentrations
and verify the CAMx model, respectively. In addition, the
receptors in BJ-U were also used to investigate the pollutant
transboundary transport and sources apportionment.
Emission Inventory
The emission inventory was calculated based on raw
emissions data, emission coefficients and activity categories,
which were directly acquired from provincial or municipal
environmental protection bureaus and administration
departments. More detailed descriptions of the complete
emission inventory which were used in this study could be
found in previous works published by the researchers in
the Key Laboratory of Beijing on Regional Air Pollution
Control (Lang et al., 2013b; Zhou et al., 2014a). The
pollutants in the emission inventory include SO2, NOx, PM10,
Fig. 2. The 15 sub-areas delineated as the pollution source
inputs for the CAMx model.
Jia et al., Aerosol and Air Quality Research, 17: 2491–2508, 2017
2496
PM2.5, volatile organic compounds (VOCs), and various
components in PM2.5 and VOCs. In addition, the emission
inventory was divided into 5 emission categories, including
industrial sources (industry), transportation sources (traffic),
coal-burning sources (coal), dust sources (dust) and other
sources (others, including biomass burning sources, waste
discharge sources, agricultural ammonia emissions sources,
etc.). The calculated emission reduction ratios, which were
calculated base on the previous experiences (e.g., the
reduction ratios of the APEC Summit period which were
published on website of the Beijing Municipal Environmental
Protection Bureau) and the comparison of control measures in
different periods, resulted from the implementation of
emergency control measures during the 2 red alert periods
in December 2015 were also included to update the emission
inventory, as shown in Table 2.
Model Verification
In this study, the simulated PM2.5 concentrations were
compared to the observed data to validate the model. The
indicators for model verification include correlation
coefficient (R), normalized mean bias (NMB), normalized
mean error (NME), and the fraction (Mi/Oi) satisfying the
condition 0.5 ≤ Mi/Oi ≤ 2 (FAC), where Mi and Oi are the
simulated and observed PM2.5 concentration at time i. The
statistic approaches used in this study can be found in many
previous literatures (Cheng et al., 2012; Zhou et al., 2012).
Fig. 3 presents the comparison results of the simulated and
observed PM2.5 concentrations for the period of December
7 to 31, 2015 for the sub-areas of BJ, TJ, SJZ and TS. The
results indicate that high correlations between observed
and simulated concentrations are obtained and the simulation
errors are insignificant at an acceptable level. In particular,
the value of R is greater than 0.70, with the NMB ranges
of –15%–12%, the NME ranges of 33%–38%, and the
FAC ranges of 88%–91%. The model validation errors come
possibly from three sources: (1) missing or incomplete
activity data in the emission inventory; (2) improper or
imbalanced implementation of the emergency control
measures in different areas leading to errors when calculating
the emission reduction ratios; (3) errors from the WRF-
CAMx modeling process itself. However, in general, the
comparison results indicate that an acceptable agreement
between the simulated and observed concentrations has
been achieved (Lang et al., 2013a; Chen et al., 2014; He et
al., 2014) and the WRF-CAMx modeling system can be
utilized for PM2.5 concentration simulation and control
measure evaluation.
Simulation Scenario Design
In this study, four simulation scenarios were designed in
order to quantify the effects of implementing the emergency
control measures on PM2.5 concentration reduction in the
BTH region during the red alert periods. Scenario BASE
was designed to represent the actual PM2.5 pollution process
in Beijing for the 2 red alert periods in December 2015,
during which various emergency control measures were
implemented immediately after red alerts were issued. The
simulation results from Scenario BASE was used for
model verification and PM2.5 evolution trend analysis. The
BASE scenario was also utilized for ranking the relative
importance of different emission sub-areas and inventory
categories through calculating their specific contributions.
Scenario NC (No-Control scenario) was designed to represent
the situation which assumes no emergency control measures
being implemented in the study region during the 2 red
alert periods. The Scenario NC simulation results were used
to compare with Scenario BASE results and the difference
demonstrates the effectiveness of emergency control
measures. Scenario NBC (No-Beijing-Control scenario) was
designed to represent the situation that only Beijing areas
do not implement any emergency control measures. Scenario
NHTC (No-Hebei-Tianjin-Control scenario) represents the
situation that Hebei and Tianjin do not implement any
emergency control measures. Scenarios NBC and NHTC
were designed to evaluate the impacts of emission
contributions from Beijing areas and Hebei-Tianjin areas
on the level of PM2.5 concentration in Beijing urban area.
Tabl e 2 . The pollutant emission reduction ratios in the BTH region during red alert periods.
Emission Region Source Category SO2 NOx PM10 PM2.5 VOC
Beijing Traffic - 48% 59% 60% 50%
Industry 55% 46% 40% 42% 37%
Dust - - 46% 48% -
Othera 10% 10% 10% 10% 10%
Tianjin Traffic
b
- - - - -
Industry 30% 30% 30% 30% 30%
Dust - - 30% 28% -
Other 10% 10% 10% 10% 10%
Hebeic Traffic - 18%–55% 18%–55% 18%–55% 18%–55%
Industry 15%–30% 15%–30% 15%–30% 15%–30% 15%–30%
Dust - - 11%–29% 11%–29% -
Other 10% 10% 10% 10% 10%
a The emission reduction ratio for other sources refers to the pollutant reduction generated by the recommended measures.
b There were no control measures implemented for the traffic sources in Tianjin.
c The levels of alerts issued in the cities of Hebei province varies (as shown in Fig. 1), and the reduction ratios only
considered the cities with alerts issued.
Jia et al., Aerosol and Air Quality Research, 17: 2491–2508, 2017 2497
20151208
20151209
20151210
20151211
20151212
20151213
20151214
20151215
20151216
20151217
20151218
20151219
20151220
20151221
20151222
20151223
20151224
20151225
20151226
20151227
20151228
20151229
20151230
20151231
0
180
360
540
0
180
360
540
0
180
360
540
0
180
360
540
Obs-BJ
Sim-BJ
Obs-TJ
Sim-TJ
PM
2.5
(μg/m
3
)PM
2.5
(μg/m
3
)PM
2.5
(μg/m
3
)PM
2.5
(μg/m
3
)
Obs-SJZ
Sim-SJZ
R=0.72 NMB=-7%
NME=36% FAC=86%
R=0.71 NMB=12%
NME=37% FAC=89%
Obs-TS
Sim-TS
R=0.75 NMB=-13%
NME=36% FAC=90%
R=0.74 NMB=-14%
NME=40% FAC=87%
Tangshan
Shijiazhuang
Tianjin
Beijing
Fig. 3. Comparisons of the simulated and observed PM2.5 concentrations for the period of December 7 to 31, 2015 for the
sub-areas of BJ, TJ, SJZ and TS.
RESULTS AND DISCUSSION
Meteorological Conditions and PM2.5 Evolution
Characteristics in December 2015
It is evident from the previous studies that PM2.5
concentration is influenced by meteorological conditions,
and the MLH is considered to be an important factor
causing the variance of PM2.5 concentrations (Yang et al.,
2015a). Fig. 4 shows the daily PM2.5 concentrations and
MLH in Beijing in Decembers of 2013, 2014 and 2015.
The MLH shows a generally negative correlation with the
PM2.5 concentrations, and higher PM2.5 load near the ground
surface was always coupled with a much-lower-than 1000
m MLH for Beijing (Schafer et al., 2009). The horizontal
breakpoint lines in Fig. 4 represent a PM2.5 concentration
of 150 µg m–3, which is the threshold between light
pollution and heavy pollution according to the China’s
National Ambient Air Quality Standard (CNAAQA); they
also represent a MLH height of 1000m. Although the total
emissions have continued to decline over the past years due to
the implementation of the National Total Emission Control
Program (NTECP), heavy air pollutions occurred more
frequently in December 2015 as compared to Decembers of
2013 and 2014 (Xue et al., 2013). The distribution probability
of MLH being below 1000m in Decembers of 2013, 2014
and 2015 were 32.3%, 9.7%, and 48.4%, respectively,
indicating that December 2015 had a superior atmospheric
stability and the meteorological conditions were thus not
favorable for air pollutant diffusion and movement. The
meteorological conditions were possibly caused by the strong
El Nino effect occurred in the winter of 2015 (Chang et al.,
2016; Feng et al., 2016; Wie and Moon, 2016).
Fig. 5 shows the monitored meteorological parameters
in Beijing in December 2015, including temperature, air
pressure, humidity, and visibility. The bottom graph in Fig. 5
presents the temporal variation and evolution of hourly
PM2.5 concentrations in the urban area of Beijing. The red
horizontal breakpoint line in the bottom graph represents a
PM2.5 concentration of 150 µg m–3. It is obvious that there
were five peak PM2.5 concentration sections as labelled in
the bottom graph of Fig. 5, and they represent five PM2.5
pollution episodes occurred during the 30-day stretch of
December 2015. Each heavy pollution episode lasted for
different number of days. The highest PM2.5 concentration
occurred in the fourth episode with a transient concentration
of 567 µg m–3. The first red alerts were issued for the first
episode, and the second red alert was issued for the third
episode, since both episodes lasted for more than 72 hours,
while the durations of episodes 2, 4 and 5 were all less
than 72 hours.
Jia et al., Aerosol and Air Quality Research, 17: 2491–2508, 2017
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1203 1206 1209 1212 1215 1218 1221 1224 1227 1230
0
1000
2000
0
1000
2000
0
1000
2000
2015
2014
MLH (m) MLH (m)
MLH
MLH (m)
0
100
200
300
400
PM
2.5
(μg/m
3
)
0
100
200
300
400
PM
2.5
(μg/m
3
)
0
100
200
300
400
PM
2.5
PM
2.5
(μg/m
3
)
2013
Fig. 4. Daily PM2.5 concentrations and MLH in Beijing in Decembers of 2013, 2014 and 2015.
Fig. 5. Hourly PM2.5 concentration and meteorological parameters in Beijing in December 2015.
Jia et al., Aerosol and Air Quality Research, 17: 2491–2508, 2017 2499
The data in Fig. 5 also demonstrate that PM2.5
concentrations in the urban areas of Beijing were negatively
correlated with the wind speeds and air pressure, but
positively correlated with the relative humidity. Wind can
enhance the dispersion of air pollutants out of the region
whereas high air pressure constrains the air pollutants in
Beijing’s airshed. Humidity is associated with the
hygroscopicity and scattering of particles (Zhang et al.,
2015a), which makes the precursors of PM2.5 have the
tendency to form PM2.5 through complex chemical reactions.
During the heavy pollution periods in December 2015,
south wind with a speed lower than 2 m s–1 was dominant
and carried the air pollutants (Feng et al., 2014a) generated
from heavily industrialized Hebei province to Beijing; at
the same time, the regional air pressure in Beijing continued
to drop and the atmospheric conditions were very stable
and stagnant so that the pollutant diffusion and movement
in both vertical and horizontal directions were significantly
restricted. In addition, high humidity facilitated the
hygroscopic growth of aerosols which leads to the increased
aerosol mass concentrations and thus low-visibility of air
(when the humidity is greater than 80%, the aerosol
particulate scattering coefficient is nearly 2 times of that in
dry conditions (Yang et al., 2015a)).
In other periods of December 2015, the strong northwest
winds prevailed so that the meteorological conditions were
much improved with increased regional air pressure and
decreased humidity. The strong winds also accelerated the
horizontal dispersion and vertical diffusion of air pollutants,
pushing PM2.5 to its downwind directions and thus removing
them from Beijing’s airshed.
PM2.5 Concentration Trend during 2 Red Alert Periods
With the hourly PM2.5 concentrations collected from all the
monitoring stations in BTH region, the Kriging interpolation
method built into Geographic Information System (GIS)
was used as a spatial interpolation technique to analyze the
entire heavy pollutions in December 2015, particularly for
the two red alert periods in the BTH region. The semi-
variogram method was used to quantity the spatial variation
of regionalized variables (Yang et al., 2008).
The First Red Alert Period
Fig. 6 presents four graphs of the regional air pressure
patterns during the first red alert period at 8:00AM for 4
consecutive days from December 7 to 10. It is observed
that the BTH study region was under control of a high-
pressure center and system on December 7, a sparse-isobar
pressure system on December 8, a low-pressure center and
system on December 9, and an intensive-isobar pressure
system on December 10, respectively. During the first red
alert period, the emergency control measures were implement
on December 8, and the BTH region was then under control
by a sparse-isobar pressure system with widely spaced
isobars, and the horizontal composition and movement of
the atmosphere were relatively stagnant. These conditions
made the air pollutants be easily trapped and accumulated
in Beijing’s airshed. On December 9, a low-pressure center
was formed around the BTH region, and the air masses
(containing air pollutants) were driven into the BTH region
from the surrounding provinces; as a result, PM2.5
concentrations in Beijing increased dramatically. The
PM2.5 concentrations have remained high till December 10
when an intensive-isobar pressure system moved into the
region and brought high-speed winds to help push the air
pollutants out of the region.
Fig. 7 shows the evolution of PM2.5 concentration in the
BTH region over the first alert period. At 8:00AM on
December 7, the entire BTH region had a relatively fair air
quality with the PM2.5 concentration being lower than 150
µg m–3. 24 hours later at 8:00AM on December 7, PM2.5
concentrations have increased dramatically to a level of
over 250 µg m–3 in the southern part of the BTH region,
and heavy pollutions were formed in the southern part and
started to spread and moved towards the northern parts of
the region. The air movement was obstructed by the
Yanshan and Taihang Mountains in the west and north of
the BTH region, and as a result, the pollutants stopped
moving forward and got accumulated in Beijing’s airshed.
This explains why Beijing and most area of other BTH region
have remained under heavy air pollutions on December 8
and 9. On December 10, an intensive-isobar pressure system
brought high-speed winds to disperse the air pollutants out
of the region, and the severity of pollution was significantly
reduced so that the first red alert was cancelled on that day.
It is obvious that the heavy pollution during the first red
alert period was severed not only by transboundary emissions
from outside sources but also by unfavorable meteorological
conditions. It should be pointed out that, the heavy pollution
has been formed in Beijing since December 7 while the
first red alert was issued till December 8, and therefore the red
alert was issued too late considering a general requirement
of 24-hour ahead prior to the occurrence of a heavy
pollution. Furthermore, the late red alert has delayed the
actions of implementing the emergency control measures
so that their effects could be negatively jeopardized.
The Second Red Alert Period
It was observed that the regional air pressure systems
over the BTH region during the second red alert period
were similar to that in the first red alert period, and this
makes the PM2.5 pollution evolve similarly as well.
Fig. 8 shows the evolution of PM2.5 concentrations in the
BTH region over the second red alert period. On December
18, there was no heavy pollution formed in the BTH
region. From December 19, the pollutants began to gradually
accumulate in Beijing and the middle parts of the BTH
region and tended to spread to the surrounding areas. One day
later on December 20, Beijing and its surrounding areas
experienced heavy air pollutions. This situation continued
till December 23 when the strong northwest winds blew
through the region. It is obvious that the heavy pollution
occurred in Beijing during the second red alert period was
mainly caused by local emissions and accumulation of
pollutants in the study region. The second red alert was
issued at 7:00AM on December 19 and was regarded as a
imely issuing in comparison to the issuing time of the first
red alert. The timely action could possibly enhance the
Jia et al.
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2500
Fig. 6. Air pressure patterns over the BTH region at 8:00AM from December 7 to 10 in 2015 (Note: blue lines represent
isobars; air pressure unit: hpa; H and L represent high and low pressure centers).
effects of implementing emergency control measures on
air pollution control (Liu et al., 2013).
PM2.5 Source Apportionment and Contribution Analysis
Fig. 9 compares the percentages of PM2.5 contributions
from different sub-areas and different emission categories
to the urban area of Beijing among no-heavy-pollution
period, the first red alert period, and the second red alert
period in December 2015. The PM2.5 concentrations were
obtained by the WRF-CAMx modeling system.
Fig. 9 shows that, in the no-heavy-pollution period, PM2.5
in Beijing originated mainly from local emissions with a
total percentage of 76.0% (57.7% from BJ-U and 18.3% from
BJ-R). This was due to relatively stable meteorological
conditions occurred during these no-heavy-pollution days
in December 2015 (such as low-speed wind and stagnant
weather). The contribution percentages from surrounding sub-
areas were pretty low, and main external sources including
BD, CD and TS contributed only 3.4%, 2.9%, 2.9%,
respectively, to the total. Among various emission categories
in the BTH region, the sequence of contribution percentage is
traffic (27.9%), coal (21.4%), dust (16.3%), industry (14.9%),
and others (13.1%). For the BJ-U area, traffic (19.7%) and
coal (15.0%) were the main contributors among all the
local emission categories. This is not surprising at all
considering the facts that December is the heating season
in Beijing for more coal consumption and the number of
vehicles on road and thus traffic volume in Beijing have
increased significantly in the past two decades. In addition,
the coal and vehicles were the main sources for emitting
SO2 and NO2 into the atmosphere as well. As shown in
Fig. 5, in December 2015, the observed SO2 concentration
(with an hourly mean concentration of 19.7 µg m–3) was
much lower than the observed NO2 concentration (with an
hourly mean concentration of 78.1 µg m–3) in Beijing. The
emitted SO2 could be transformed and converted into
sulfate by a liquid-phase oxidation process (Li et al., 2015a),
which might be further facilitated by the high humidity in
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2501
(a) (b)
(c) (d)
(e) (f)
Fig. 7. Evolution of PM2.5 concentration (unit: µg m–3) in BTH region over the first red alert period: (a) 8:00 AM on
December 7, (b) 8:00 PM on December 7, (c) 8:00AM on December 8, (d) 8:00 AM on December 9, (e) 8:00 AM on
December 10, (f) 8:00 PM on December 10.
December 2015; instead, the photochemical conversion of
NOx to nitrate needs sunlight and high temperature, and
the weather conditions in December 2015 did not favor the
conversion process (Zhao et al., 2013).
As shown in Fig. 9, in the first red alert period, the
contribution percentage from local emission sources in
Beijing decreased to 64.8% (including a percentage of
45.3% from BJ-U and a percentage of 19.5% from BJ-R)
in comparison to 76% in no-heavy-pollution periods. This
indicates increased transboundary contributions of pollutants
from surrounding areas. The modeling results show that
the contribution percentages from sub-areas or cities in the
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(a) (b)
(c) (d)
(e) (f)
Fig. 8. Evolution of PM2.5 concentration (unit: µg m–3) in BTH region over the second red alert period: (a) 4:00 PM on
December 18, (b) 4:00 PM on December 19, (c) 4:00 AM on December 20, (d) 6:00 PM on December 20, (e) 10:00 AM
on December 22, (f) 4:00 PM on December 23. *The legend in Fig. 8 is same as Fig. 7.
southern part of the region increased significantly. For
example, the contribution percentage from BD was
increased to 9.3% which was doubled more than that in no-
heavy-pollution periods; similar observations can be found
for LF (3.4%) and SJZ (2.0%). However, the contribution
percentages from the northern sub-areas remained nearly
unchanged. Among all the 5 emission categories, the coal
became the largest source with a contribution percentage
of 31.2% in the first red alert period, while the contribution
percentages from the traffic, industry, and construction
dust were 22.6%, 16.8%, and 10.4%, respectively. During
the first red alert period, the implemented emergency control
Jia et al., Aerosol and Air Quality Research, 17: 2491–2508, 2017 2503
BJ-
U
BJ-
R
T
J
ZJ
K
CD
Q
HD TS BD LF CZ SJZ HS XT HD
0%
20%
40%
60%
The second red alert period
Contibution Rates
BJ-U BJ-R TJ ZJK CD QHD TS BD LF CZ SJZ HS XT HD
0%
20%
40%
60%
The first red alert period
Contibution Rates
BJ-U BJ-R TJ ZJK CD QHD TS BD LF CZ SJZ HS XT HD
0%
20%
40%
60%
Industry
Traffic
Coal
Dust
Others
Contibution Rates
No heavy pollution periods
Fig. 9. The percentages of PM2.5 contributions from different sub-areas and different emission categories to the urban area
of Beijing among no-heavy-pollution period, the first red alert period, and the second red alert period in December 2015.
measures consisted mainly of traffic volume reduction,
industrial plants shutdown, and construction project
suspension, and no actions were taken for reducing the
coal combustion and domestic boiler usage, and as a result.
In addition, local coal consumption and traffics were top two
largest sources contributing percentages of 16.1% and 11.4%,
respectively, to the PM2.5 concentration in BJ-U among all
the sources in the BTH region. It is apparent that the
emergency control measures played a vital role in changing
the emission contribution percentages among different
emission categories during the first red alert period.
During the second red alert period, the contribution
percentage from local emission sources in Beijing increased
to 83.5% with 63.4% from BJ-U and 20.1% from BJ-R.
This indicates the heavy pollution occurred in the second
red alert period was mainly caused by local pollutant
accumulation. The contribution percentages from
transboundary sources in the surrounding sub-areas and
cities were generally less than that in no-heavy-pollution
periods or the first red alert period, and this was due
mainly to the relatively stable meteorological conditions in
the second red alert period. As for the emission categories,
the coal (32.4%) was the highest contributor in the second
red alert period, followed by the traffic (24.7%), others
(14.7%), dust (12.4%) and industry (10.8%). Similarly, the
coal consumption (23.6%) and traffics (18.1%) were top
two contributors to the PM2.5 pollutions in BJ-U with a
percentage of 23.6% and 18.1%, respectively. The ranking
of contribution percentages of different emission categories
could help the governmental authorities to make and select
appropriate emergency control measure when facing heavy
pollution conditions.
Impact Analysis of the Emergency Control Measures on
PM2.5 Concentration
In this study, four simulation scenarios (i.e., BASE, NC,
NBC, and NHTC) were designed and simulated through
the WRF-CAMx modeling system for assessing the effects
of the emergency control measures on PM2.5 concentration
changes.
Overall Effects Analysis
Fig. 10 shows the simulated mean PM2.5 concentrations in
BJ-U under four designed simulation scenarios for the first
and second red alert periods, respectively. The simulation
results were used to examine the effects of implementing
emergency control measures on PM2.5 concentrations.
As shown in Fig. 10, during the first red alert period, the
mean PM2.5 concentrations of BASE, NC, NBC and NHTC
scenarios were 239.5 µg m–3, 289.0 µg m–3, 261.5 µg m–3
and 265.5 µg m–3, respectively. The observations from the
simulation results entail: (1) the mean PM2.5 concentration
under BASE scenario was the lowest one among all four
scenario, implying that the emergency control measures
implemented in the BTH region during the first red alert
period were effective in reducing the PM2.5 concentration
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2504
Fig. 10. The simulated mean PM2.5 concentrations in BJ-U under four designed simulation scenarios for both red alert
periods.
in the urban area of Beijing; (2) the NC scenario refers to
the situation that no any emergency control measures were
implemented in the study region during the first red alert
period. Under this scenario, the simulated mean PM2.5
concentration in BJ-U was the highest one, which indicates
again the emergency control measures have positive effects
in mitigating the PM2.5 pollution; (3) the NBC and NHTC
scenarios refer to the situations that the emergency control
measures were implemented only in the areas outside Beijing
or in the areas only inside Beijing. The simulated mean PM2.5
concentrations under the NHTC scenario was slightly
higher that under the NBC scenario, but both were higher
than the BASE scenario and lower than the NC scenario. It
is indicated that not only the emergency control measures
were generally effective, but also the areas where they were
implemented played a vital role in PM2.5 pollution control.
During the second red alert period, the mean PM2.5
concentration of BASE, NC, NBC and NHTC scenarios
were 171.7 µg m–3, 220.7 µg m–3, 203.5 µg m–3 and 187.4
µg m–3, respectively. Similar observations could be obtained.
For example, the mean PM2.5 concentration under BASE
scenario was lowest and that under NC scenario were
highest, which means implementing the emergency control
measure had positive effects in reducing PM2.5 pollution in
Beijing. In the second red alert period, the mean PM2.5
concentration under NHTC scenario is a little lower than that
under NBC scenario, which means implementing emergency
control measures in the areas of Beijing had better effects
in PM2.5 pollution control.
According to the simulated mean PM2.5 concentrations
under BASE and NC scenarios, the reduction ratios of
PM2.5 concentration in BJ-U can be calculated. The
calculated reduction ratios for the first and second red alert
periods were 17.1% and 22.2%, respectively. It is indicated
that, although same emergency control measures were
implemented during two red alert periods, their effects on
PM2.5 pollution control in the first red alert period was
obviously lower than that in the second red alert period.
The reasons behind this difference might remain unknown
exactly, however, one possible explanation is that the
implementation of emergency control measures was delayed
almost 2 days due the late issuing of the first red alert. If
the first red alert could be issued earlier, a large portion of
the pollutants emitted from various sources in the study
region could be reduced before the heavy pollution was
formed in the region, and this would lead to a higher PM2.5
concentration reduction ratio. It is suggested that the 24-
hour rule for issuing the red alert should be reinforced by
the government in order to maximizing the effects of the
emergency control measures.
Effect Analysis for Individual Emergency Control
Measure
The effects of individual emergency control measure on
PM2.5 reduction were also examined through the WRF-
CAMx simulation modeling system. The emergency control
measures were ranked in terms of their effectiveness in
PM2.5 pollution control.
Fig. 11 shows the reduced PM2.5 concentrations from the
implementation of individual emergency control measures
in BJ-U during two red alert periods. The individual
emergency control measure under examination consists of
control actions taken for four different emission categories
(i.e., industry, traffic, dust and the others). During the first
red alert period, the average reduced PM2.5 concentrations
in BJ-U from implementing four individual control measure
in the BTH region were 14.1 µg m–3 for industry, 21.5 µg m–3
for traffic, 6.6 µg m–3 for dust, and 3.9 µg m–3 for the
others, respectively. It is obvious that the reduction from
the traffic volume control measures (such as the odd-and-even
license plate rule) was highest among all four emission
control categories. In terms of the reduction contributions
from all the emission sub-areas, the largest PM2.5
concentration reduction in BJ-U came from the control
Jia et al., Aerosol and Air Quality Research, 17: 2491–2508, 2017 2505
BJ-U BJ-R TJ ZJK CD QHD TS BD LF CZ SJZ HS XT HD
0
5
10
15
20
25
Industry
Traffic
Dust
Others
PM
2.5
(μg/m
3
)
The first red alert period
BJ-U BJ-R TJ ZJK CD QHD TS BD LF CZ SJZ HS XT HD
0
5
10
15
20
25
The second red alert period
PM
2.5
(μg/m
3
)
Fig. 11. The PM2.5 concentration reductions from the implementation of individual emergency control measure in BJ-U
during two red alert periods.
measures implemented in the sub-areas outside Beijing
with an amount of 26.0 µg m–3 in the first red alert period.
It is obvious that the implementation of emergency control
measures in the southern part of the study region played a
critical role in improving the air quality in Beijing.
During the second red alert period, the average reduced
PM2.5 concentrations in BJ-U from implementing four
individual control measure in the BTH region were
15.7 µg m–3 for industry category, 22.3 µg m–3 for traffic
category, 6.0 µg m–3 for dust category, and 3.4 µg m–3 for
other categories. Again the reduction from implementing
traffic volume control measures was highest among all
four emission control categories.
The effects of implementing control measures on four
emission categories in the sub-areas of BJ-U and BJ-R were
examined due to their significant differences in industrial
settings and traffic volume. During the second red alert
period, the average reduced PM2.5 concentrations in BJ-U
from implementing four individual control measure in the
urban area of Beijing were 2.7 µg m–3 for industrial category,
15.0 µg m–3 for traffic category, 2.3 µg m–3 for dust category,
and 1.1 µg m–3 for other categories. In contrast, the average
reduced PM2.5 concentrations in BJ-U from implementing
four individual control measure in the rural area of Beijing
were 8.4 µg m–3 for industrial category, 2.0 µg m–3 for traffic
category, 0.3 µg m–3 for dust category, and 0.7 µg m–3 for
other categories. It is apparent that implementing control
measures for traffic category in the urban area of Beijing
was most effective in PM2.5 concentration reduction while
implementing control measures for industrial category in
the rural area of Beijing was most effective. This is in
accordance with the functions for different sub-areas of
Beijing municipality, which should be put into consideration
when implementing emergency control measures for
alleviating or preventing the occurrence of heavy air
pollutions.
CONCLUSIONS
In December 2015, 2 red alerts were issued by Beijing
municipal government and various emergency control
measures were also implemented in the BTH region to
alleviate the negative effects of severe air pollutions occurred
in Beijing. In order to assess the effects of these emergency
control measures on pollution control in Beijing, in this
study, the WRF-CAMx modeling system was implemented
over the 2-level-nested grid BTH domain to simulate spatial
and temporal variations of the PM2.5 concentration in Beijing
during 2 red alert periods. The model was verified through
4 statistical approaches, and an acceptable agreement between
the simulated and observed concentrations has been achieved.
The simulation results show that the heavy pollution in
Beijing was formed by transboundary emissions from outside
sources in the first red alert period, while the heavy pollution
was mainly caused by local emissions and accumulation of
pollutants in the second red alert period due to stable
atmospheric conditions. Four emission-reduction-control
scenarios were then designed and simulated by the
modeling system for quantitatively assessing the effects of
the emergency control measures on PM2.5 concentration
Jia et al., Aerosol and Air Quality Research, 17: 2491–2508, 2017
2506
evolution. It is evident from the results that the control
measures implemented in Beijing and its surrounding areas
during the 2 red alert periods could generally improve the
air quality in urban area of Beijing, and the control
measures in the categories of traffic volume control and
industrial operation suspension were the most effective one
among all the individual emergency control measures for
both red alert period. It is recommended that joint and
collective control efforts in the BTH region be developed
for improving environmental performance and achieving
better results in the long-term.
ACKNOWLEDGEMENT
This work was supported by the National Natural
Science Foundation of China (No. 91544232 & 51638001)
and the Ministry of Environmental Protection Special
Funds for Scientific Research on Public Causes (No.
201409006). In addition, we greatly appreciated the fund
support from Beijing Municipal Commission of Science
and Technology (No. D16110900440000 &
D161100004416001). The authors are grateful to the
anonymous reviewers for their insightful comments.
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Received for review, January 4, 2017
Revised, March 9, 2017
Accepted, March 23, 2017
