With the completion of the Beijing Five-year Clean Air Action Plan by the
end of 2017, the annual mean PM2.5 concentration in Beijing dropped
dramatically to 58.0 µg m-3 in 2017 from 89.5 µg m-3 in 2013. However, controversies exist to argue that favourable
meteorological conditions in 2017 were the major driver for such a rapid
decrease in PM2.5 concentrations. To comprehensively evaluate this
5-year plan, we employed a Kolmogorov–Zurbenko (KZ) filter and WRF-CMAQ (Weather Research and Forecasting and the Community Multi-scale Air Quality) to quantify the relative contribution of meteorological conditions and the
control of anthropogenic emissions to PM2.5 reduction in Beijing from
2013 to 2017. For these 5 years, the relative contribution of
emission reduction to the decrease in PM2.5 concentrations calculated
by KZ filtering and WRF-CMAQ was 80.6 % and 78.6 % respectively.
KZ filtering suggested that short-term variations in meteorological and
emission conditions contributed majorly to rapid changes in PM2.5
concentrations in Beijing. WRF-CMAQ revealed that the relative contribution
of local and regional emission reduction to the PM2.5 decrease in Beijing
was 53.7 % and 24.9 % respectively. For local emission-reduction
measures, the regulation of coal boilers, increasing use of clean fuels for
residential use and industrial restructuring contributed 20.1 %,
17.4 % and 10.8 % to PM2.5 reduction respectively. Both models
suggested that the control of anthropogenic emissions accounted for around
80 % of the PM2.5 reduction in Beijing, indicating that
emission reduction was crucial for air quality enhancement in Beijing from
2013 to 2017. Consequently, such a long-term air quality clean plan should be
continued in the following years to further reduce PM2.5 concentrations
in Beijing.
Introduction
In January 2013, persistent haze episodes occurred in Beijing, during which
the highest hourly PM2.5 concentration once reached 886 µg m-3, a historically high record. High-concentration PM2.5 led to
long-lasting black and thick fogs, which not only significantly influenced
people's daily life (low-visibility induced traffic jams), but also posed a
severe threat to public health (Brunekreef et al., 2002; Dominici et al.,
2014; Nel et al., 2005; Zhang et al., 2012; Qiao et al., 2014). Since then,
severe haze episodes have frequently been observed in Beijing and other
regions across China (Chan et al., 2008; Huang et al., 2014; Guo et
al.,2014; Zheng et al.,2015), and PM2.5 pollution has become one of the
most concerning environmental issues in China. Consequently, a national
network for monitoring hourly PM2.5 concentrations has been established
gradually, including 35 ground observation stations in Beijing, which
provide important support for better understanding and managing PM2.5
concentrations. To effectively mitigate PM2.5 pollution, the Beijing
Municipal Government released the Beijing Five-year
Clean Air Action Plan
(2013–2017) with a series of long-term emission-reduction measures,
including shutting down heavily polluting factories, restricting traffic
emissions and replacing coal fuels with clean energies; it also released a Heavy Air Pollution Contingency Plan with a series of contingent emission-reduction
measures during heavy pollution episodes. By the end of 2017, these
long-term and contingent emission-reduction measures worked jointly to
reduce the annual mean PM2.5 concentration in Beijing from 89.5 µg m-3 in 2013 to 58.0 µg m-3 in 2017, indicating a great
success of PM2.5 management during the past 5 years. The notable
decrease in PM2.5 concentrations attracted nationwide attention, and a growing number of studies has been conducted to understand spatio-temporal
characteristics (Shao et al., 2018; Sun et al., 2019; Wang et al., 2019),
sources (Chen et al., 2019; Xu et al., 2019; J. Cheng et al., 2019) and
health effects (Liang et al., 2019) of PM2.5 variations in Beijing from
2013 to 2017. These studies revealed that air quality in Beijing was
improved significantly in 2017 in terms of annual mean PM2.5
concentrations, polluted days and pollution durations. Furthermore, despite
different outputs, both source apportionment during pollution episodes based
on collected samples (Shao et al., 2019; Xu et al., 2019; Chen et al., 2019)
and long-term model simulation based on regional and local emission
inventories (J. Cheng et al., 2019) suggested that local and regional
anthropogenic emissions (e.g. coal combustion and vehicle emissions) were
the major influencing factors for long-term and short-term PM2.5 variations in Beijing.
In addition to anthropogenic emissions, the strong meteorological influence
on PM2.5 concentrations in Beijing have been widely acknowledged (Zhao
et al., 2013; Wang et al., 2014; UNEP, 2016; Chen et
al., 2017; Sun et al., 2019). For instance, for 2014, more than 180 d in
Beijing experienced a dramatic daily AQI (Air Quality Index) change
(AQI > 50) (Z. Y. Chen et al., 2016). Considering that anthropogenic
emissions for a megacity unlikely changed significantly on a daily basis,
rapid variations in meteorological conditions were one major driver for the
dramatic change in daily air quality in Beijing. In winter 2017, strong
northwest winds led to favourable meteorological conditions for PM2.5
diffusion and low PM2.5 concentrations in Beijing. This raised the
controversy that meteorological conditions, instead of
emission reduction, accounted for the remarkable PM2.5 reduction in
Beijing. In this case, with the completion of the 5-year plan, it is
highly necessary to quantify the relative contribution of meteorological
conditions and emission reduction to the notable decrease in PM2.5
concentrations in Beijing from 2013 to 2017.
In recent years, a growing number of studies has been conducted to investigate
meteorological and anthropogenic influence on long-term
PM2.5variations. Based on the Goddard Earth Observing System (GEOS)
chemical transport model (GEOS-Chem), Yang et al. (2016) revealed that the
relative contribution of meteorological conditions to PM2.5 variations
in eastern China from 1985 to 2005 was 12 %. Based on a multiple general
linear model (GLM), Gui et al. (2019) quantified that meteorological
conditions accounted for 48 % of PM2.5 variations in eastern China
from 1998 to 2016. Based on a stepwise multiple linear regression (MLR)
model, Zhai et al. (2019) quantified the relative contribution of
meteorology to PM2.5 variations from 2013 to 2018 in
the Beijing–Tianjin–Hebei region, the Yangtze River Delta, the Pearl River Delta, and the Sichuan Basin and Fenwei plain at 14 %, 3 %, 19 %, 27 % and 23 %
respectively. Through a two-stage hierarchical clustering method, Zhang et
al. (2018) calculated that the relative contribution of meteorological
conditions to heavy pollution episodes within the Beijing–Tianjin–Hebei
region was larger than 50 % from 2013 to 2017. These studies quantified
the overall meteorological influence on long-term PM2.5 variations
using different statistical models and chemical transport models (CTMs).
However, due to strong interactions between individual meteorological
factors, traditional statistical methods such as correlation analysis and
linear regression may be biased significantly when quantifying
meteorological influence on PM2.5 concentrations (Chen et al., 2017).
On the other hand, the accuracy of CTMs can be influenced largely by the
uncertainty in emission inventories (Xu et al., 2016) and the deficiency of
heterogeneous or aqueous processes (Li et al., 2011). Therefore, multiple
advanced models should be comprehensively considered to better quantify
meteorological influence on PM2.5 concentrations (Pearce et al.,
2011).
To evaluate this 5-year clean-air plan, we employ an advanced statistical
model, Kolmogorov–Zurbenko (KZ) filtering, which is advantageous for filtering meteorological influence on long-term time series of airborne
pollutants, and a CTM model, WRF-CMAQ ( Weather Research and Forecasting and the Community Multi-scale Air Quality), which is advantageous for quantifying
the relative contribution of different emission sources, to comprehensively
investigate the relative contribution of meteorological conditions and
emission reduction to PM2.5 reduction in Beijing from 2013 to 2017
respectively. In this light, this research provides important insights for
better designing and implementing successive clean-air plans in the future
to further mitigate PM2.5 pollution in Beijing.
This paper is structured as follows. Firstly, the major data sources,
including PM2.5 and meteorological data and emission inventories,
employed for this research are briefly introduced. Secondly, the principle
and parameter setting of two models – KZ filtering and WRF-CMAQ – and model
verification are explained. In the Results section, the relative contribution
of meteorological conditions and anthropogenic emissions to PM2.5
variations in Beijing from 2013 to 2017 calculated using both models is
presented. In the “Discussion” and “Conclusion” parts, implementations of this
research and suggestions for further improving air quality in Beijing are
given.
Data sourcesPM<inline-formula><mml:math id="M49" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> and meteorological data
In this study, hourly PM2.5 concentration data were acquired from the
website PM25.in (http://www.PM25.in, last access: 18 August 2018), which collects official data
provided by the China National Environmental Monitoring Center (CNEMC). Beijing
has established an advanced air quality monitoring network with 35 ground
stations across the city. Considering the major contribution of industry and
traffic-induced emissions in urban areas, we selected all 12 urban
stations to analyse spatio-temporal variations in PM2.5 concentrations
and quantify their influencing factors. In addition to these urban stations,
we selected two background stations, the Dingling Station located in the
suburb and the Miyun Reservoir Station located in the outer suburb, one
transportation station (the Qianmen station) located close to a main road,
and one rural station (the Yufa Station) that is far away from central
Beijing for the following analysis. The Dingling and Miyun Reservoir Station
were chosen as background stations by the Ministry of Environmental
Protection of China. These two stations receive limited influence from
anthropogenic emissions due to their location in suburban and outer suburban
areas. The Qianmen transportation station received more influence from
vehicle emissions. Long-term variations in PM2.5 concentrations in
different types of stations provide a useful reference for comprehensively
understanding the effects of emission-reduction measures on the PM2.5
decrease in Beijing from 2013 to 2017. Meteorological data for this research
were collected from the Guanxiangtai Station (GXT; 54511; 39.80∘ N, 116.46∘ E), Beijing and downloaded from the Department of
Atmospheric Science, College of Engineering, University of Wyoming
(http://weather.uwyo.edu/upperair/sounding.html, last access: 18 August 2018). Both
PM2.5 and meteorological data were collected from 1 January 2013 to 31 December 2017. The locations of these selected stations
are shown in Fig. 1.
Locations of different ground monitoring stations.
Emission inventories
For this research, we employed both regional and local emission inventories
for running model simulations. The Multi-resolution Emission Inventory for China,
MEIC, (http://meicmodel.org/, last access: 16 February 2019) provided by Tsinghua University, was employed
as the regional emission inventory. MEIC has been widely employed and
verified as a reliable emission inventory by a diversity of studies (Hong et
al., 2017; Saikawa et al., 2017; Zhou et al., 2017; etc.). For simulating
5-year PM2.5 concentrations, MEIC from 2013 to 2017 is required.
Since the official MEIC 2017 has yet to become available, we employed a strategy from
previous studies (Chen et al., 2019; etc.) and updated MEIC 2016 for
simulating emission-reduction scenarios and PM2.5 concentrations in
2017 by considering official 2017 emission-reduction plans (e.g. the target
of coal combustion reduction) required by the local government.
Different from regional emission inventories, local emission inventories are
usually produced independently by local institutions. The Beijing
local emission inventory employed for this research was produced and updated
by the Beijing Municipal Research Institute of Environmental protection, fully
according to the requirement of MEP (Ministry of Ecology and Environment of the People's Republic of China) for the production of local emission
inventories within the Beijing–Tianjin–Hebei region. This Beijing local emission
inventory from 2013 to 2017 was produced by synthesising local environmental
statistical data and reported emission data, carrying out field
investigations, and conducting a series of estimations according to the Beijing
Five-year Clean Air Action Plan. As shown in Table 1, it is highly
consistent with other official statistical data, such as the annual report
from National Environmental Statistics Bulletin (http://www.mee.gov.cn/gzfw_13107/hjtj/qghjtjgb/, last access: 16 February 2019) and “2+26” Center for Air
Pollution Prevention and Control, and has been formally employed
for the implementation of recent 2017 Air Pollution Prevention and
Management Plan for the Beijing-Tianjin-Hebei Region and its Surrounding
Areas (MEP, 2017).
The comparison of local environmental statistical data used
for this research and other official statistical data in 2017 (unit: 10 kt). BC: black carbon; OC: organic carbon.
SO2NOxCOVOCNH3PM10PM2.5BCOCStatistical data for this research1.3810.1549.5413.473.2014.743.920.170.44National Environmental Statistics Bulletin1.3812.1652.0324.243.2614.683.910.220.41“2+26” Center for Air Pollution0.899.2448.9813.933.1613.823.720.190.46Prevention and ControlMethods
A key step for quantifying the relative contribution of anthropogenic
emissions to PM2.5 variations is to properly filter meteorological
influence on PM2.5 concentrations, which is highly challenging and
have rarely been investigated by previous studies. Therefore, we employed both a
statistical method and a CTM to comprehensively evaluate the role of
anthropogenic emissions and meteorological conditions in the decrease in
PM2.5 concentrations in Beijing from 2013 to 2017.
Kolmogorov–Zurbenko filtering
Since meteorological conditions exert a strong influence on PM2.5
concentrations in Beijing, the removal of seasonal signals from time series
of meteorological factors produces data sets suitable for understanding the
trend of PM2.5 concentrations mainly influenced by anthropogenic
factors (Eskridge et al., 1997). To better analyse the trend of time series
data without the disturbances from other major influencing variables, a
statistical method, KZ filtering, was proposed by Rao et
al. (1994). The KZ filter is advantageous for removing high-frequency
variations in data sets through an iterative moving average. Eskridge et al.
(1997) compared four major approaches for trend detection, including PEST (political, economic, technological and social) analysis,
anomalies, wavelet transform and the KZ filter, and suggested that KZ
achieved higher confidence in detecting long-term trends than other models.
Due to its reliable performance in trend detection in complicated
ecosystems, the KZ filter has been increasingly employed to remove seasonal
signals of meteorological conditions and extract a long-term trend of airborne
pollutants (Zurbenko, et al., 1996; Eskridge, et al., 1997; Kang, et al.,
2013; Ma et al., 2016; N. Cheng et al., 2019). One potential limitation of
the KZ filter is that an iterative moving average (m) may have an influence on
detecting abrupt variations. Therefore, Zurbenko et al (1996) proposed an
enhanced KZ filter that employed a dynamic variable m that decreased with the
increase in changing rate. For this research, we employed this dynamic m to
produce an adjusted time series of PM2.5 concentrations in Beijing by
removing large inter-annual and seasonal variations in meteorological
conditions. The principle of the KZ filter is briefly introduced as follows.
The raw time series of airborne pollutants can be decomposed as
1Xt=Et+St+W(t),2Xbt=Et+St,3Et=KZ365,3(X),4St=KZ15,5X-KZ365,3(X),5Wt=Xt-KZ15,5(X),
where Xb(t) is the original time series
of airborne pollutants, E(t) is the long-term
trend component, S(t) is the seasonal component and W(t) is the short-term (synoptic-scale) component
or residue. KZi,j(X) indicates KZ filtering on the
original data set X with a moving window size of i
and j iterations.
Xb(t) stands for the base component, the sum of the long-term and
seasonal component, presenting steady trend variation. E(t) is mainly affected
by long-term anthropogenic emission and climate change. S(t) is mainly
influenced by the seasonal variation in emission and meteorological
conditions. W(t) is caused by short-term and small-scale shifts in emissions
and meteorological conditions.
The long-term trend component E(t) processed by KZ filtering still contains
the influence of meteorological conditions, which can be removed by multiple
regression models. Multiple linear relationships are established for the
residue and baseline component respectively using meteorological factors
strongly correlated with airborne pollutants.
We examined correlations between seasonal PM2.5 concentrations in
Beijing and a set of meteorological factors, including temperature, wind
speed, wind direction, precipitation, relative humidity, solar radiation,
evaporation and air pressure. Due to limited space, detailed correlations
between PM2.5 concentrations and individual meteorological factors in
Beijing are not presented here and readers can refer to previous studies for
more information (Chen et al., 2017, 2018). The correlation analysis
revealed that wind speed, relative humidity, temperature and solar radiation
were strongly and significantly correlated with PM2.5 concentrations in
Beijing (as shown in Table 2), which was consistent with findings from other
studies (Sun et al., 2013; Wang et al., 2018).
Major meteorological factors strongly correlated with
seasonal PM2.5 concentrations in Beijing (Chen et
al., 2017).
* Correlation is significant at the 0.01 level (two-tailed). RHU: relative humidity; SSD: sunshine duration; TEM: temperature;
WIN: wind speed.
Therefore, we further established multiple linear regression equations
between PM2.5 concentrations and wind speed, relative humidity,
temperature and solar radiation as follows.
6Wt=α0+∑αiwit+εwt,7Xbt=b0+∑bixit+εb(t),8εt=εwt+εb(t),
where wi(t) and xi(t) stand for the
different short-term and baseline components of the ith meteorological factor. εw and εb are the regression residue of the short-term and
baseline component. ε(t) indicates
the total residue, including the short-term influence of local emission and
meteorological factors neglected during the regression process and other
noises.
Next, KZ filtering was conducted on the ε(t) for its
long-term component εE(t). After the influence of
meteorological variations was filtered, the reconstructed time series of
airborne pollutants XLT(t) was calculated as the sum of εE(t) and the average value of E(t) , E(t)‾.
XLTt=E(t)‾+εE(t)
After KZ filtering, the relative contribution of meteorological conditions
to PM2.5 variations can be calculated as follows:
Pcontrib=Korg-KKorg×100%,
where Pcontrib is the relative contribution of meteorological conditions to
PM2.5 variations in Beijing,
Korg is the variation slope of the
original PM2.5 time series and K is the variation slope
of adjusted PM2.5 time series with filtered
influence from meteorological variations.
WRF-CMAQ model
We employed WRF-CMAQ for simulating the effects of emission reduction on the
decrease in PM2.5 concentrations. WRF-CMAQ includes three models: the
middle-scale meteorology model (WRF), the source emission model (SMOKE)
(http://www.cmascenter.org/smoke/, last access: 16 February 2019) and the community multiscale air quality
modelling system (CMAQ) (CMAQ, http://www.cmascenter.org/, last access: 16 February 2019). The
centre of the CMAQ was set at coordinate 35∘ N, 110∘ E
and a bidirectional nested technology was employed, producing two layers of
grids with a horizontal resolution of 36 and 12 km. The
first layer of grids with a 36 km resolution and 200×160 cells
covered most areas in East Asia (including China, Japan, North Korea, South
Korea and other countries). The second layer of grids with a 12 km resolution
and 120×102 cells covered the North China Plain (including the
Beijing–Tianjin–Hebei region, Shandong and Henan provinces). The vertical
layer was divided into 20 unequal layers, eight of which were at less than 1 km distance to the ground to better feature the structure of the atmospheric boundary. The height of the ground layer was 35 m.
We employed ARW-WRF3.2 (Advanced Research Weather Research and Forecasting) to simulate the meteorological field. The setting of
the centre and the bidirectional nest for WRF and CMAQ was similar. There
were 35 vertical layers for WRF, and the outer layer provided boundary
conditions of the inner layer. The meteorological background field and
boundary information with an FNL (final) resolution of 1∘×1∘ and temporal resolution of 6 h were acquired from NCAR (National
Center for Atmospheric Research, https://ncar.ucar.edu/, last access: 16 February 2019) and NCEP (National
Centers for Environmental Prediction) respectively. The terrain and
underlying surface information was obtained from the USGS 30 s global DEM (digital elevation model) (https://earthquake.usgs.gov/, last access: 16 February 2019). The outputs from WRF were interpolated to
the region and grid of CMAQ using the Meteorology-Chemistry Interface
Processor (MCIP, https://www.cmascenter.org/mcip, last access: 16 February 2019). The meteorological
factors used for this model included temperature, air pressure, humidity,
geopotential height, zonal wind, meridional wind, precipitation, boundary
layer heights and so forth. An estimation model for terrestrial ecosystem, MEGAN (http://ab.inf.uni-tuebingen.de/software/megan/, last access: 16 February 2019), was employed to
process the natural emissions. MEIC 0.5∘×0.5∘ (http://www.meicmodel.org/, last access: 16 February 2019) and the Beijing emission inventory (http://www.cee.cn/, last access: 16 February 2019) provided anthropogenic emission data. We input the
processed natural and anthropogenic emission data into the SMOKE model and
acquired comprehensive emission source files.
Scenario simulation is employed to estimate the contribution of
emission reduction to the variation in PM2.5 concentrations.
Pcontrib=C-CbaseC×100%,
where Pcontrib, C and Cbase
are the contribution rate of emission reduction to
PM2.5 concentrations, simulated PM2.5 concentrations under the emission-reduction scenario, and
simulated PM2.5 concentrations under the baseline
scenario respectively.
To evaluate the relative contribution of meteorological conditions and
different emission-reduction measures to the decrease in PM2.5
concentrations, we designed two baseline experiments and four sensitivity
experiments. For the first baseline experiment, we employed the actual
meteorological data in 2013. For the second baseline experiment, we employed
the actual meteorological data in 2017 and the emission inventory in 2017. Since
no emission-reduction measures were conducted in 2013, the first baseline
experiment was used to estimate the relative contribution of meteorological
conditions to the variation in PM2.5 concentrations. By comparing the
first and second baseline experiment, the relative contribution of all
emission-reduction measures to the variation in PM2.5 concentrations
can be quantified. For the first sensitivity experiment, we employed the
actual meteorological conditions in 2013 and the emission inventory in 2017 and
compared the simulation result with the baseline experiment, which
demonstrated the relative contribution of meteorological concentrations to
PM2.5 reduction in Beijing from 2013 to 2017. Since the WRF-CMAQ
simulation simply considers PM2.5 concentrations and meteorological
conditions in 2013 and 2017 without considering their variation process from
2013 to 2017, KZ filtering may perform better in quantifying the relative
contribution of meteorological variations to PM2.5 reduction in
Beijing. However, the output from this sensitivity experiment served as a
useful reference for cross-verifying the output from the KZ filtering. For
the remaining three sensitivity-simulation experiments, we added the reduced
emission amount induced by one specific emission-reduction measure to the
actual emission amount in 2017 and kept other parameters unchanged, and thus
we quantified the relative contribution of one specific emission-reduction
measure to PM2.5 reduction in Beijing from 2013 to 2017. Consequently,
we quantified the relative contribution of three major emission-reduction
measures to PM2.5 reduction in Beijing (Table 3).
The design and materials for two baseline and four
sensitivity experiments using WRF-CMAQ.
IDMeteorologicalEmission-reductionSimulationMajor purposesdatameasuresyearBaseline experiment 12013No emission-reductionmeasures20132013 baseline scenarioBaseline experiment 22017All emission-reductionmeasures20172017 baseline scenarioSensitivity experiment 12013All emission-reductionmeasures2017The relative contribution ofmeteorological variations to thedecrease in PM2.5 concentrationsin Beijing from 2013 to 2017.Sensitivity experiment 22017All emission-reductionmeasures except for industrialrestructuring2017The relative contribution of industrial restructuring to the decrease in PM2.5 concentrations in Beijing from 2013 to 2017.Sensitivity experiment 32017All emission-reductionmeasures except for theregulation of coal boilers2017The relative contribution of theregulation of coal boilers to the decrease in PM2.5 concentrationsin Beijing from 2013 to 2017.Sensitivity experiment 42017All emission-reductionmeasures except for increasingclean fuels for civil use2017The relative contribution of increasing clean fuels for civil use to the decrease in PM2.5 concentrationsin Beijing from 2013 to 2017.
For emission data, all experiments employed the Beijing local emissions
inventory in 2017 for Beijing and the regional emission inventory in 2017 for
other regions.
MEIC 2017 was acquired based on our update of MEIC 2016 according to
official 2017 emission-reduction targets required by the local government.
Model verificationVerification of KZ filtering
For each station, the original time series of PM2.5 data was processed
by the KZ filter and the relative contribution of the long-term, seasonal
and short-term component to the total variance is shown as Table 4. The sum
of the long-term, seasonal and short-term component contributed
93.6%∼95.3 % to the total variance at different stations
respectively. The larger the total variance, the more independent the three components are of each other. The total variance close to 100 % suggests that
a majority of meteorological influence has been considered and effectively
removed. As shown in Table 4, the large value of the total variation in all
stations indicated a satisfactory output from the KZ filtering.
Specifically, the relative contribution of the seasonal component (ranging
from 9 % to 23.8 %) and short-term component (ranging from
66.8 % to 83.8 %) was much larger than that of the long-term component
(ranging from 1.2 % to 3.5 %), suggesting that seasonal and short-term
variations in meteorological and emission factors exerted a major influence
on the rapid change in PM2.5 concentrations in Beijing. The decomposed
long-term, seasonal and short-term component from the original time series
of mean urban PM2.5 concentrations in Beijing from 2013 to 2017 are
demonstrated as Fig. 2. According to Fig. 2, the notable peaks of decomposed
seasonal and short-term components were highly consistent with the peaks of
PM2.5 concentrations in the original time series, which further proved
the dominant influence of seasonal and short-term variations in
meteorological and anthropogenic factors on the temporal changes in
PM2.5 concentrations in Beijing.
The long-term, seasonal and short-term components extracted
from the original time series of mean urban PM2.5
concentrations in Beijing from 2013 to 2017.
Verification of WRF-CMAQ
We employed the emission inventory and meteorological data for 2017 to
verify the accuracy of WRF-CMAQ simulation. For six stations of different
types (Dingling background station, Yufa rural station, Olympic centre urban
station, Guanyuan urban station, Dongsi urban station and Agricultural
museum urban station), we compared the observed and estimated PM2.5
concentrations and presented the comparison result as Fig. 3. According to
Fig. 3, the general trend of the simulated PM2.5 concentrations was
consistent with that of the observed PM2.5 concentrations. For six
stations, the correlation coefficient R, normalised mean bias (NMB),
normalised mean error (NME), mean fractional bias (MFB) and mean fractional
error (MFE) between observed and simulated data were 0.63%∼0.91%, -6%∼6%, 26%∼40%,
-5%∼7% and 27%∼46% respectively, indicating a satisfactory simulation output (EPA, 2005; Boylan et al.,
2006). However, as shown in Fig. 3, WRF-CMAQ may notably underestimate
PM2.5 concentrations during heavy pollution episodes due to unified
parameter setting for long-term simulation, the uncertainty in emission
inventories and especially insufficient chemical reaction mechanisms, which
is a common challenge for CTM-based PM2.5 simulation (Li et al., 2011).
For instance, without considering heterogeneous or aqueous reactions between
multiple precursors, CTMs failed to approach the maximum PM2.5
concentrations during severe haze episodes and the simulation accuracy was
dramatically improved by including proper descriptions of
heterogeneous or aqueous reactions in CTMs (D. Chen et al., 2016). With more
finer-scale emission inventories and better descriptions of reaction
mechanisms between precursors, the accuracy of PM2.5 simulation can be
improved significantly.
The relative contribution of different components to the
total variance of the original time series of PM2.5
concentrations from 2013 to 2017 at different stations.
The comparison between observed and WRF-CMAQ simulated
PM2.5 concentrations in 2017 at six stations across
Beijing.
ResultsThe relative contribution of emission reduction and meteorological
variations to the decrease in PM<inline-formula><mml:math id="M197" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations in Beijing from 2013
to 2017Estimation based on KZ filtering
Through KZ filtering, the adjusted time series of PM2.5 concentrations
with filtered meteorological variations was acquired. Next, for each
station, the actual PM2.5 variations and adjusted PM2.5 variations without the disturbance of meteorological variations from 2013
to 2017 were calculated respectively (as shown in Table 5). Based on this,
the relative contribution of emission reduction and meteorological
conditions to PM2.5 reduction in Beijing from 2013 to 2017 can be
quantified.
The original and KZ-processed time series of PM2.5 concentrations were
illustrated using one background station, one rural station and four urban
stations (Fig. 4). As shown in Fig 4, most abrupt variations in the original
time series of PM2.5 concentrations have been smoothed through KZ
filtering, and the generally decreasing trend of PM2.5 variations from
2013 to 2017 caused by anthropogenic emissions can be clearly presented.
Estimated relative contribution of emission reduction and
meteorological variations to PM2.5 reduction in
Beijing from 2013 to 2017 using KZ filter.
1 PM2.5 decrease rate: the fitted variation slope of original
monthly average PM2.5 time series.
2 Adjusted PM2.5 decrease rate: the fitted variation slope of
adjusted monthly average PM2.5 time series.
3 Contribution of emission reduction = 1 – Contribution of
meteorological variations.
4 Contribution of meteorological variations = (PM2.5 decrease
rate – Adjusted PM2.5 decrease rate) / PM2.5 decrease rate.
The comparison of original and KZ processed time series of
PM2.5 concentrations at six stations from 2013 to
2017.
According to Table 5, the annual mean PM2.5 concentration in Beijing in
2017 was 35.6 % lower than that in 2013. By filtering the influence of
meteorological variations, the adjusted annual mean PM2.5 concentration
in Beijing in 2017 decreased by 31.7 % when compared to that in 2013,
indicating that the variation in meteorological conditions exerted a
moderate influence on PM2.5 reduction from 2013 to 2017.
Meteorological conditions in Beijing were generally favourable for PM2.5
dispersion during the 5-year period, especially in the latter half of 2017,
when there was a high frequency of strong northerly winds and much lower
wintertime PM2.5 concentrations than in previous years.
For the winter of 2017, frequent windy weather and successive clean sky had
a strong influence on the reduction of PM2.5 concentrations in Beijing.
This led to a hot debate concerning whether the notable decrease in
PM2.5 concentrations was mainly attributed to the favourable
meteorological conditions or emission reduction. Table 5 suggests that the
control of anthropogenic emissions contributed 75.2%∼85.0% to the PM2.5 decrease in the 5-year period, indicating that
emission reduction worked effectively in all rural, urban and background
stations. On average, the relative contribution of emission reduction and
meteorological variations to PM2.5 reduction in Beijing from 2013 to
2017 was 80.6 % and 19.4 % respectively. Therefore, in spite of more
favourable meteorological conditions, properly designed and implemented
emission-reduction measures were the dominant driver for the remarkable
decrease in PM2.5 concentrations in Beijing from 2013 to 2017.
Estimation based on WRF-CMAQ
In addition to the KZ filter, we also employed WRF-CMAQ to estimate the
relative contribution of emission reduction and meteorological conditions to
the decrease in PM2.5 concentrations in Beijing. The result is shown in
Table 6.
Estimated relative contribution of emission reduction and
meteorological variations to PM2.5 reduction in
Beijing from 2013 to 2017 using WRF-CMAQ.
Based on WRF-CMAQ, the relative contribution of meteorological variations to
the decrease in PM2.5 concentrations in Beijing ranged from 20.3 % to
22.2 % in different stations, whilst emission reduction accounted for
about four-fifths of PM2.5 reduction from 2013 to 2017. It is worth
mentioning that WRF-CMAQ is a grid-based model and thus the calculated
contribution of meteorological variations for some stations located in the
same grid was the same. Instead, station-based KZ filtering led to a more
reliable analysis for each station and can better distinguish the
differences between multiple stations. Furthermore, WRF-CMAQ simply
considered the differences between meteorological conditions in 2013 and
2017 without considering their variations during the 5-year period while
the KZ filtering analysed the entire time series of PM2.5 and
meteorological data from 2013 to 2017. The averaged relative contribution of
meteorological variations to PM2.5 reduction in Beijing calculated
using WRF-CMAQ was 21.4 %, very similar to the 19.4 % calculated using
KZ filtering. The slightly larger meteorological contribution calculated
using WRF-CMAQ might be attributed to the fact that WRF-CMAQ simply considered the
favourable meteorological conditions in 2017 whilst KZ fully considered the
long-term meteorological variations from 2013 to 2017.
Since KZ filtering is fully based on observed data and simply considers the
influence of time series meteorology data on PM2.5 variations, less
uncertainty is involved. The accuracy of KZ filtering is influenced mainly
by the variations in PM2.5–meteorology interactions in different areas
and seasons. On the other hand, CTMs (e.g. WRF-CMAQ or WRF-CAMx, Weather Research and Forecasting-Comprehensive air quality Model with Extensions) consider
both meteorological conditions (mainly large-scale meteorological data for
model simulation, not as accurate as local observed meteorological data) and
anthropogenic emissions for estimating PM2.5 concentrations under
different emission scenarios. The accuracy of these models is not only
decided by a proper understanding of PM2.5–meteorology interactions, but
also the reliability of emission inventories and proper descriptions of
reaction mechanisms for PM2.5 production, especially during heavy
pollution episodes, which is a major challenge for the current model simulation.
Consequently, KZ filtering provides a more reliable method for researchers
and decision makers to understand the relative importance of
emission reduction and meteorological conditions in recent PM2.5
reduction in Beijing. Meanwhile, similar outputs from the WRF-CMAQ simulation
provide complementary evidence for the fact that anthropogenic emissions
exerted a much stronger influence on PM2.5 concentrations than
meteorological conditions. In addition to the combined effects of all
emission-reduction measures, we further employed WRF-CMAQ to quantify the
relative contribution of different emission-reduction measures to the
decrease in PM2.5 concentrations in Beijing from 2013 to 2017.
The relative contribution of different emission-reduction measures to
the decrease in PM<inline-formula><mml:math id="M299" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations in Beijing
The observed annual average PM2.5 concentration in Beijing in 2017 was
58 mg m-3, compared with 89.5 µg m-3 in 2013. Based on WRF-CMAQ
simulation, meteorological conditions contributed 6.7 µg m-3, whilst
the control of anthropogenic emissions contributed 24.7 µg m-3 to the total PM2.5 reduction of 31.5 µg m-3 in
Beijing from 2013 to 2017. Specifically, local and regional
emission reduction accounted for 16.9 and 7.8 µg m-3 of PM2.5 reduction. Local emissions and regional transport
took up 68.4 % and 31.6 % of total anthropogenic emissions in Beijing.
This result is consistent with our recent study (Chen et al., 2019). Chen et
al. (2019) investigated four pollution episodes in Beijing in 2013, 2016,
2017 and 2018 respectively and found that local emissions accounted for
69.3 %, 76.8 %, 49.5 % and 88.4 % of total emissions in Beijing
respectively. Except for the moderate pollution episode in 2017, local
emissions caused more than two-thirds of anthropogenic emissions in Beijing.
Therefore, local emissions played a dominant role for PM2.5 variations
in Beijing in both the long-term run and heavy pollution episodes. According to
three emission-reduction scenarios designed, the regulation of coal boilers
had the most significant effect on PM2.5 reduction in Beijing and
resulted in a decrease of 6.3 µg m-3. Meanwhile, increasing clean
fuels for residential use and industrial restructuring also exerted a strong
influence on PM2.5 reduction and contributed to a decrease of 5.5 and 3.4 µg m-3 respectively. The three major
strategies accounted for around half of the total effects of
emission reduction on PM2.5 variations in Beijing.
Discussion
By the end of 2017, the Beijing Five-year Clean Air Action Plan (2013–2017)
was completed and achieved its primary goal of reducing the annual average
PM2.5 concentration to less than 60 µg m-3. Meanwhile, in
November 2017, strong northerly winds in Beijing resulted in the cleanest
winter in the past 5 years, raising arguments of whether the favourable
meteorological conditions were primarily responsible for PM2.5
reduction or whether the significant improvement in air quality in Beijing
was mainly attributed to the control of anthropogenic emissions. In this
case, a quantitative comparison between the influence of meteorological
conditions and emission reduction on PM2.5 reduction is necessary for
comprehensively evaluating the Five-year Clean Air Action Plan. Based on two
different approaches, this research revealed that the control of
anthropogenic emissions contributed around 80 % to PM2.5
reductions in Beijing from 2013 to 2017, indicating that the Five-Year Clean
Air Action Plan exerted a dominant influence on air quality enhancement in Beijing.
The large contribution of some specific emission-reduction measures may be
obscured in the presence of favourable meteorological conditions. For
instance, many residents may attribute the clean winter of 2017 to the
notable strong winds without noticing some of major emission-reduction
strategies implemented during this period. A large-scale replacement of coal
boilers with gas boilers has been conducted in Beijing and its neighbouring areas
since 2013. As quantified by WRF-CMAQ, the regulation of coal boilers and
increasing use of clean fuels for residential use jointly contributed to an
11.8 µg m-3 decrease in PM2.5 concentrations, much (almost
twice) larger than the 6.7 µg m-3 decrease caused by favourable
meteorological conditions. In general, although favourable meteorological
conditions (e.g. strong winds) may lead to an instant improvement of air
quality, regular emission-reduction measures exert a reliable and consistent
influence on the long-term reduction of PM2.5 concentrations in
Beijing. Given the satisfactory performance of the Five-year Clean Air
Action Plan in PM2.5 reduction, such a long-term clean-air plan should be
further designed and implemented in Beijing and other megacities with heavy
PM2.5 pollution.
The relative contribution of different influencing factors to
the decrease in PM2.5 concentrations in Beijing from
2013 to 2017.
Recently, with growing attention to the completion of the Five-year Clean
Air Action Plan, some other studies have also been conducted to evaluate
this 5-year plan. J. Cheng et al. (2019) employed a finer-scale and more
detailed local emission inventory and quantified the relative contribution
of multiple emission-reduction strategies, including the control of
coal-fired boilers, increasing use of clean fuels, optimisation of
industrial structure, fugitive dust control, vehicle emission control,
improved end-of-pipe control and integrated treatment of VOCs (volatile organic compounds). The relative
contribution of these emission-reduction measures to PM2.5 reduction in
Beijing from 2013 to 2017 was 18.7 %, 16.8 %, 10.2 %, 7.3 %,
6.0 %, 5.7 % and 0.6 % respectively. By contrast, our research
revealed that three major emission-reduction measures (the regulation of
coal-fired boiler, increasing use of clean fuels and industrial
restructuring) contributed 20.1 %, 17.4 % and 10.8 % of total
PM2.5 reduction in Beijing from 2013 to 2017, which was very close to
J. Cheng et al.'s (2019) findings. Based on finer-scale local
emission-inventories with more field-collected emission data, J. Cheng et
al. (2019) provided a comprehensive and reliable understanding of the
effects of multiple emission-reduction measures on PM2.5 reduction in
Beijing. The similar outputs from the two studies further proved the
reliability of WRF-CMAQ simulation. Meanwhile, J. Cheng et al. (2019) and
UNEP (2019) jointly quantified that the total amount of reduction in
SO2, NOx, VOCs and direct PM2.5 induced by the control of
anthropogenic emissions was 79 420, 93 522, 115 752 and 44 307 t respectively,
which was the major driver for the notable PM2.5 reduction in Beijing
from 2013 to 2017.
Although the “2+26” regional strategy for air quality improvement in
Beijing has become a hotly debated issue and growing emphasis has been
placed on the proper design and implementation of regional
emission-reduction strategies in Beijing and its surrounding
cities, previous studies (Chen et al., 2019; J. Cheng et al., 2019) and
this research proved that local emissions played a dominant role in
affecting PM2.5 concentrations in Beijing. Specifically, Chen et al. (2019) pointed out that with the intensive reduction of coal-fired boilers in the Beijing–Tianjin–Hebei region, the relative contribution of vehicle emissions
to PM2.5 concentrations in Beijing, especially during heavy pollution
episodes, could be up to 50 %. To further improve air quality in Beijing,
stricter regulations on local vehicle emissions, including contingent
strategies during pollution episodes (e.g. odd-even license plate policy)
and long-term policies (e.g. increasing availability of public transit
systems and electric cars) should be a major priority for the next stage
clean-air actions.
Based on KZ filtering, N. Cheng et al. (2019) and Ma et al. (2016) suggested
the seasonal component contributed dominantly to O3 variations in
Beijing. By comparison, this research revealed that the short-term component
contributed dominantly to PM2.5 variations in Beijing. These findings explained the phenomenon well that ground ozone pollution in Beijing,
controlled by seasonal variations in emission and meteorological conditions
(especially high temperature and low humidity), simply occurred in summer,
whilst PM2.5 pollution in Beijing, controlled by short-term variations in meteorological and emission factors might occur in all seasons.
Consequently, contingent emission-reduction measures during heavy pollution
episodes are an effective approach to offset the short-term deterioration of
meteorological conditions and improve local air quality.
Despite the major contribution of emission-reduction measures to PM2.5 reduction in Beijing, meteorological influence, which contributed 20 % to PM2.5 reduction, should also be considered in a balanced way. In
addition to the control of anthropogenic emissions, PM2.5 reduction may
be realised through meteorological means. For the winter of 2017, strong
northwesterly winds led to instant improvement in air quality, suggesting
wind was a dominant meteorological factor for the accumulation or dispersion
of PM2.5 in Beijing. Meanwhile, previous studies (Chen et al., 2017)
suggested that increasing wind speeds led to increased evaporation,
increased sunshine duration (SSD) and reduced humidity, which further
reduced local PM2.5 concentrations. In other words, strong winds help
reduce PM2.5 concentrations through direct and indirect measures. In
this light, the forthcoming Beijing Wind-corridor Project, which includes
five 500 m width corridors and more than ten 80 m width corridors to bring in
stronger wintertime northwesterly winds, can be a promising approach for
promoting a long-term favourable meteorological influence on PM2.5
reduction in Beijing.
Conclusions
To comprehensively evaluate the effect of the Beijing Five-year Clean Air
Action Plan (2013–2017), we quantified the relative contribution of
meteorological conditions and the control of anthropogenic emissions to the
notable decrease in PM2.5 concentrations in Beijing from 2013 to 2017.
Based on KZ filtering, we found that meteorological conditions and
emission reduction accounted for 19.4 % and 80.6 % of the PM2.5 reduction in Beijing respectively. The large short-term component
suggested that short-term variations in meteorological and emission factors
exerted a dominant influence on the rapid variation in PM2.5
concentrations in Beijing. Meanwhile, WRF-CMAQ revealed that meteorological
conditions and emission reduction contributed 21.4 % and 78.6 % to
PM2.5 variations. Specifically, local and regional emission-reduction
measures contributed 53.7 % and 24.9 % to PM2.5 reduction.
For three major emission-reduction measures, the regulation of coal boilers,
increasing use of clean fuels for residential use and industrial
restructuring contributed 20.1 %, 17.4 % and 10.8 % to PM2.5
reduction respectively. Similar outputs from two models suggested that the
control of anthropogenic emissions contributed around 80 % to the total
decrease in PM2.5 concentrations in Beijing from 2013 to 2017,
indicating that the Five-year Clean Air Action Plan worked effectively and that such a long-term clean-air plan should be continued in the following years to
further reduce PM2.5 concentrations in Beijing.
Data availability
The PM2.5 data used for this research are available at http://pm25.in/ (China National Environmental Monitoring Center, 2017, last access: 18 August 2018), whilst meteorological data are available at http://www.cma.gov.cn/2011qxfw/2011qsjgx/ (China Meteorological Data Sharing Service System, 2017, last access: 18 August 2018).
Author contributions
ZC, BG and BX designed this research. ZC wrote this
paper. DC, YZ, BG and RL conducted data analysis.
DC and YZ produced the figures. MK and BC helped
revise this paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Sincere gratitude goes to Tsinghua University, which produced the
Multi-resolution Emission Inventory for China (http://meicmodel.org/, last access: 16 February 2019) and the Research Center for Air Quality Simulation and Forecast, Chinese Research
Academy of Environmental Sciences (http://106.38.83.6/, last access: 16 February 2019), which supported the
model simulation in this research. This research is supported by the
National Key Research and Development Program of China (no. 2016YFA0600104)
and National Natural Science Foundation of China (grant no. 41601447).
Financial support
This research has been supported by the National Key Research and Development Program of China (no. 2016YFA0600104), and the State Key Laboratory of Earth Surface Processes and Resource Ecology (grant no. 2017-KF-22).
Review statement
This paper was edited by Yves Balkanski and reviewed by two anonymous referees.
References
Boylan, J. W. and Russell, A. G.: PM and light extinction model performance metrics,
goals and criteria for three-dimensional air quality models, Atmos.
Environ., 40, 4946–4959, 2006.
Brunekreef, B. and Holgate, S.: Air pollution and health Lancet, 360,
1233–1242, 2002.
Chan, C. K. and Yao, X.: Air pollution in mega cities in China, Atmos. Environ.,
42, 1–42, 2008.Chen, D., Liu, Z., Fast, J., and Ban, J.: Simulations of sulfate–nitrate–ammonium (SNA) aerosols during the extreme haze events over northern China in October 2014, Atmos. Chem. Phys., 16, 10707–10724, 10.5194/acp-16-10707-2016, 2016.Chen, Z., Xie, X., Cai, J., Chen, D., Gao, B., He, B., Cheng, N., and Xu, B.: Understanding meteorological influences on PM2.5 concentrations across China: a temporal and spatial perspective, Atmos. Chem. Phys., 18, 5343–5358, 10.5194/acp-18-5343-2018, 2018.Chen, Z. Chen, D., Wen, W., Zhuang, Y., Kwan, M., Chen, B., Zhao, B., Yang,
L., Gao, B., Li, R., and Xu, B.: Evaluating the “2+26” Regional Strategy for
Air Quality Improvement During Two Air Pollution Alerts in Beijing:
variations of PM2.5 concentrations, source apportionment, and the
relative contribution of local emission and regional transport, 19,
6879–6891, 2019.
Chen, Z. Y., Xu, B., Cai, J., and Gao, B. B.: Understanding temporal patterns and
characteristics of air quality in Beijing: A local and regional perspective,
Atmos. Environ., 127, 303–315, 2016.Chen, Z. Y., Cai, J., Gao, B. B., Xu, B., Dai, S., He, B., and Xie, X. M.:
Detecting the causality influence of individual meteorological factors on
local PM2.5 concentrations in the Jing-Jin-Ji region, Sci. Rep., 7, 40735, 10.1038/srep40735, 2017.Cheng, J., Su, J., Cui, T., Li, X., Dong, X., Sun, F., Yang, Y., Tong, D., Zheng, Y., Li, Y., Li, J., Zhang, Q., and He, K.: Dominant role of emission reduction in PM2.5 air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis, Atmos. Chem. Phys., 19, 6125–6146, 10.5194/acp-19-6125-2019, 2019.
Cheng, N., Li, R., Xu, C., Chen, Z., Chen, D., Meng, F., Cheng, B., Ma, Z.,
Zhuang, Y., He, B., and Gao, B.: Ground ozone variations at an urban and a rural
station in Beijing from 2006 to 2017: Trend, meteorological influences and
formation regimes, J. Clean. Prod., 235, 11–20, 2019.Cheng, N., Zhang, D., Li, Y., Xie, X., Chen, Z., Meng, F., Gao, B., and He, B.:
Spatio-temporal variations of PM2.5 concentrations and the evaluation
of emission reduction measures during two red air pollution alerts in
Beijing, Sci. Rep., 7, 8220, 10.1038/s41598-017-08895-x, 2017
Dominici, F., Greenstone, M., and Sunstein, C.: Particulate matter matters,
Science, 344, 257–259, 2014.
Eskridge, R. E., Ku, J. Y., and Rao, S. T.: Separating Different Scales of Motion in
Time Series of Meteorological Variables, B. Am. Meteorol. Soc., 78,
1473–1483, 1997.Gui, K., Che, H., Wang, Y., Wang, H., Zhang, L., Zhao, H., Zheng, Y., Sun,
T., and Zhang, X.: Satellite-derived PM2.5 concentration trends over Eastern
China from 1998 to 2016: Relationships to emissions and meteorological
parameters, Environ. Pollut., 247, 1125–1133, 2019.
Guo, S., Hu, M., and Zamora, M. L.: Elucidating severe urban haze formation in China,
P. Natl. Acad. Sci. USA, 1, 17373–17378, 2014.Hong, C., Zhang, Q., He, K., Guan, D., Li, M., Liu, F., and Zheng, B.: Variations of China's emission estimates: response to uncertainties in energy statistics, Atmos. Chem. Phys., 17, 1227–1239, 10.5194/acp-17-1227-2017, 2017.
Huang, R., Zhang, Y., Bozzetti, C., Ho, K. F., Cao, J. J., and Han, Y.: High secondary
aerosol contribution to particulate pollution during haze events in China,
Nature, 514, 218–222, 2014.
Kang, D., Hogrefe, C., Foley, K. L., Napelenok, S. L., Mathur, R., and Rao, S.
T.: Application of the kolmogorov–zurbenko filter and the decoupled direct
3d method for the dynamic evaluation of a regional air quality model,
Atmos. Environ., 80, 58–69, 2013.Li, G., Zavala, M., Lei, W., Tsimpidi, A. P., Karydis, V. A., Pandis, S. N., Canagaratna, M. R., and Molina, L. T.: Simulations of organic aerosol concentrations in Mexico City using the WRF-CHEM model during the MCMA-2006/MILAGRO campaign, Atmos. Chem. Phys., 11, 3789–3809, 10.5194/acp-11-3789-2011, 2011.
Liang, L., Cai, Y., Barratt, B., Lyu, B., Chan, Q., Hansell, A. L., Xie, W.,
Zhang, D., Kelly, F. J., and Tong, Z.: Associations between daily air quality and
hospitalisations for acute exacerbation of chronic obstructive pulmonary
disease in Beijing, 2013–17: an ecological analysis, Lancet Planet.
Health, 3, 270–279, 2019.Ma, Z., Xu, J., Quan, W., Zhang, Z., Lin, W., and Xu, X.: Significant increase of surface ozone at a rural site, north of eastern China, Atmos. Chem. Phys., 16, 3969–3977, 10.5194/acp-16-3969-2016, 2016.MEP: 2017 air pollution prevention and management plan for the
Beijing-Tianjin-Hebei region and its surrounding areas, available at: http://dqhj.mee.gov.cn/dtxx/201703/t20170323_408663.shtml (last access: 18 August 2018), 2017.
Nel, A.: Air pollution-related illness effects of particles Science, 308,
804–806, 2015.
Pearce, J. L., Beringer, J., Nicholls, N., Hyndman, R. J., and Tapper, N. J.:
Quantifying the influence of local meteorology on air quality using
generalized additive models, Atmos. Environ., 45, 1328–1336,
2011.Qiao, L. P., Cai, J., Wang, H. L., Wang, W. L., Zhou, M., Lou, S. R., Chen,
R. J., Dai, H. X., Chen, C. H., and Kan, H. D.: PM2.5 Constituents and Hospital
Emergency-Room Visits in Shanghai, China, Environ. Sci.
Technol., 48, 10406–10414, 2014.Rao, S. T. and Zurbenko, I. G.: Detecting and Tracking Changes in Ozone Air
Quality, Air Waste, 44, 1089–1092, 10.1080/10473289.1994.10467303, 1994.Saikawa, E., Kim, H., Zhong, M., Avramov, A., Zhao, Y., Janssens-Maenhout, G., Kurokawa, J.-I., Klimont, Z., Wagner, F., Naik, V., Horowitz, L. W., and Zhang, Q.: Comparison of emissions inventories of anthropogenic air pollutants and greenhouse gases in China, Atmos. Chem. Phys., 17, 6393–6421, 10.5194/acp-17-6393-2017, 2017.Shao, P., Tian, H., Sun, Y., Liu, H., Wu, B., Liu, S., Liu, X., Wu, Y.,
Liang, W., Wang, Y., Gao, J., Xue, Y., Bai, X., Liu, W., Lin, S., and Hu, G.:
Characterizing remarkable changes of severe haze events and chemical
compositions in multi-size airborne particles (PM1, PM2.5 and
PM10) from January 2013 to 2016–2017 winter in Beijing, China,
Atmos. Environ., 189, 133–144, 2018.Sun, J., Gong, J., Zhou, J., Liu, J., and Liang, J.: Analysis of PM2.5
pollution episodes in Beijing from 2014 to 2017: Classification, interannual
variations and associations with meteorological features, Atmos.
Environ., 213, 384–394, 2019.Sun, Y., Song, T., and Tang, G.: The vertical distribution of PM2.5, and
boundary-layer structure during summer haze in Beijing, Atmos.
Environ., 74, 413–421, 2013.UNEP: A Review of Air Pollution Control in Beijing: 1998–2013, United
Nations Environment Programme (UNEP), Nairobi, Kenya, 9–23, available at: http://www.unep.org/publications (last access: 16 February 2019), 2016.UNEP: A Review of Air Pollution Control in Beijing: 1998–2017, United
Nations Environment Programme (UNEP), Nairobi, Kenya, 9–23, available at: http://www.unep.org/publications (last access: 16 February 2019), 2019.
Wang, S., Xing, J., Zhao, B., Jang, C., and Hao, J.: Effectiveness of national
air pollution control policies on the air quality in metropolitan areas of
China, J. Environ. Sci., 26, 13–22, 2014.
Wang, T., Du, Z., Tan, T., Xu, N., Hu, M., Hu, J., and Guo, S.: Measurement of aerosol
optical properties and their potential source origin in urban Beijing from
2013–2017, Atmos. Environ., 206, 293–302, 2019.Wang, X., Wei, W., and Cheng, S.: Characteristics and classification of PM2.5
pollution episodes in Beijing from 2013 to 2015, Sci. Total
Environ., 612, 170–179, 2018.
Xu, H., Xiao, Z., Chen, K., Tang, M., Zheng, N., Li, P., Yang, N., Yang, W., and Deng, X.:
Spatial and temporal distribution, chemical characteristics, and sources of
ambient particulate matter in the Beijing-Tianjin-Hebei region, Sci.
Total Environ., 658, 280–293, 2019.Xu, J., Chang, L., Qu, Y., Yan, F., Wang, F., and Fu, Q.: The meteorological
modulation on PM2.5 interannual oscillation during 2013 to 2015 in
Shanghai, China, Sci. Total Environ., 572, 1138–1149, 2016.
Yang, Y., Liao, H., and Lou, S.: Increase in winter haze over eastern china in
recent decades: roles of variations in meteorological parameters and
anthropogenic emissions, J. Geophys. Res.-Atmos.,
121, 13050–13065, 2016.Zhai, S., Jacob, D. J., Wang, X., Shen, L., Li, K., Zhang, Y., Gui, K., Zhao, T., and Liao, H.: Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology, Atmos. Chem. Phys., 19, 11031–11041, 10.5194/acp-19-11031-2019, 2019.Zhang, H., Yuan, H., Liu, X., Yu, J., and Jiao, Y.: Impact of synoptic weather
patterns on 24 h-average PM2.5 concentrations in the North China Plain
during 2013–2017, Sci. Total Environ., 627, 200–210, 2018.
Zhang, Q., He, K., and Huo, H.: Cleaning China's air, Nature, 484, 161–162, 2012.Zhao, B., Wang, S. X., Wang, J. D., Fu, J., Liu, T. H., Xu, J. Y., Fu, X., and Hao,
J. M.: Impact of national NOX and SO2 control policies on
particulate matter pollution in China, Atmos. Environ., 77, 453–463, 2013.Zheng, G. J., Duan, F. K., Su, H., Ma, Y. L., Cheng, Y., and Zheng, B.:
Exploring the severe winter haze in Beijing: the impact of synoptic weather,
regional transport and heterogeneous reactions, Atmos. Chem. Phys., 15,
2969–2983, 10.5194/acp-15-2969-2015, 2015.
Zhou, Y., Zhao, Y., Mao, P., Zhang, Q., Zhang, J., Qiu, L., and Yang, Y.: Development of a high-resolution emission inventory and its evaluation and application through air quality modeling for Jiangsu Province, China, Atmos. Chem. Phys., 17, 211–233, 10.5194/acp-17-211-2017, 2017.
Zurbenko, I., Chen, J., and Rao, S. T.: Detecting discontinuities in time series of
upper air data: Demonstration of an adaptive filter technique, J. Clim.,
9, 3548–3560, 1996.