Fine particulate matter (PM2.5) is a severe air pollution
problem in China. Observations of PM2.5 have been available since 2013
from a large network operated by the China National Environmental Monitoring
Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean
PM2.5 across China over the 2013–2018 period, averaging at -5.2µg m-3 a-1. Trends in the five megacity cluster regions targeted by
the government for air quality control are -9.3±1.8µg m-3 a-1 (±95 % confidence interval) for Beijing–Tianjin–Hebei,
-6.1±1.1µg m-3 a-1 for the Yangtze River Delta, -2.7±0.8µg m-3 a-1 for the Pearl River Delta, -6.7±1.3µg m-3 a-1 for the Sichuan Basin, and -6.5±2.5µg m-3 a-1 for the Fenwei Plain (Xi'an). Concurrent 2013–2018
observations of sulfur dioxide (SO2) and carbon monoxide (CO) show that
the declines in PM2.5 are qualitatively consistent with drastic
controls of emissions from coal combustion. However, there is also a large
meteorologically driven interannual variability in PM2.5 that
complicates trend attribution. We used a stepwise multiple linear regression
(MLR) model to quantify this meteorological contribution to the PM2.5 trends across China. The MLR model correlates the 10 d PM2.5
anomalies to wind speed, precipitation, relative humidity, temperature, and
850 hPa meridional wind velocity (V850). The meteorology-corrected
PM2.5 trends after removal of the MLR meteorological contribution can
be viewed as being driven by trends in anthropogenic emissions. The mean
PM2.5 decrease across China is -4.6µg m-3 a-1 in the
meteorology-corrected data, 12 % weaker than in the original data, meaning
that 12 % of the PM2.5 decrease in the original data is
attributable to meteorology. The trends in the meteorology-corrected data
for the five megacity clusters are -8.0±1.1µg m-3 a-1 for Beijing–Tianjin–Hebei (14 % weaker than in the original
data), -6.3±0.9µg m-3 a-1 for the Yangtze River Delta
(3 % stronger), -2.2±0.5µg m-3 a-1 for the Pearl River
Delta (19 % weaker), -4.9±0.9µg m-3 a-1 for
the Sichuan Basin (27 % weaker), and -5.0±1.9µg m-3 a-1 for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of
flattening PM2.5 in the Pearl River Delta and increases in the Fenwei
Plain can be attributed to meteorology rather than to relaxation of emission
controls.
Introduction
PM2.5 (particulate matter with aerodynamic diameter less than 2.5 µm) is a severe air pollution problem in China, responsible for 1.1 million
excess deaths in 2015 (Cohen et al., 2017). The Chinese government
introduced, in 2013, the “Action Plan on the Prevention and Control of Air
Pollution” (Chinese State Council, 2013a), called Clean Air Action for short,
to aggressively control anthropogenic emissions. Starting that year,
PM2.5 data from a nationwide monitoring network of about 1000 sites
became available from the China National Environmental Monitoring Center
(CNEMC) of the Ministry of Ecology and Environment of China (MEEC). These
data show 30 %–40 % decreases in PM2.5 across eastern China over the
2013–2017 period (Chinese State Council, 2018a; Zhang et al., 2019).
However, interpretation of these trends in terms of emission controls may be
biased by interannual variability and trends in meteorology (Zhang et
al., 2014; Wang et al., 2014; Zhu et al., 2012; Jia et al., 2015; Li et
al., 2018; Yang et al., 2018, 2016; Liang et al., 2016; Cheng
et al., 2019; Chen et al., 2019; Silver et al., 2018). Here we use a
stepwise multilinear regression (MLR) model to separate the effects of
meteorological variability and emission controls on the 2013–2018 trends in
PM2.5 across China.
Meteorology drives large day-to-day, seasonal, and interannual variations in
PM2.5 in China by affecting transport, scavenging, emissions, and
chemical production (Wang et al., 2014; Leung et al., 2018; Tai et al.,
2012; Zou et al., 2017). The relationships between PM2.5 and
meteorological variables are complex and differ by region and time of year
(Shen et al., 2017). For example, wintertime PM2.5 pollution events in
central and eastern China are associated with low wind speed and high
relative humidity (RH; Wang et al., 2014; Zhang et al., 2014; Shen et al., 2018;
Pendergrass et al., 2019; Moch et al., 2018; Song et al., 2019). On the
other hand, high wind speeds in northern China in spring and summer promote
dust emission (Lyu et al., 2017; Wang et al., 2004). Precipitation
scavenging is a major factor driving PM2.5 variability in southern and
coastal China (Chen et al., 2018; Leung et al., 2018).
Anthropogenic emissions of PM2.5 and its precursors, including sulfur
dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), and
nonmethane volatile organic compounds (NMVOCs), have undergone large changes
in China over the past decades. Rapid growth in emissions from 1980 to 2006
led to a general increase in PM2.5 over China, as demonstrated by
visibility data (Che et al., 2007; Han et al., 2016; Wang and Chen, 2016; Fu
et al., 2014; Zhang et al., 2012) and, since 1999, by satellite aerosol
optical depth (AOD) data (Ma et al., 2016; Lin et al., 2018; Zhao et al.,
2017). SO2 emissions peaked in 2006–2007, NOx emissions peaked in
2011, and NH3 emissions peaked around 1996, as estimated from emission
inventories (Zhao et al., 2017; Wang et al., 2017; Xia et al., 2016; F. Liu et al., 2016; Lu et al., 2010; Xu et al., 2016; Kang et al., 2016) and
observed from satellites (Xia et al., 2016; F. Liu et al., 2016; de Foy et
al., 2016; van der A et al., 2017). SO2 and NOx emissions have
declined since their peaks, whereas emissions of NH3 have remained
relatively stable since its peak (Zhao et al., 2017). The onset of emission
controls led to slight decreases in PM2.5 over the 2006–2012 period,
as indicated by satellite AOD data (Ma et al., 2016, 2019; Lin et al., 2018; Zhao
et al., 2017) and surface observations (Tao et al., 2017;
Wang et al., 2017). The Clean Air Action greatly increased the scope of
emission controls. The Multi-resolution Emission Inventory for China (MEIC;
http://www.meicmodel.org, last access: 20 March 2019) estimates nationwide emission decreases over the
2013–2017 period of 59 % for SO2, 33 % for primary PM2.5,
21 % for NOx, and 3 % for NH3, with NMVOCs increasing by 2 %
(Zheng et al., 2018). Continued reductions in emissions are required and
implemented in 2018 (Chinese State Council, 2018b). Our goal in this work is
to quantify the response of PM2.5 to these rapid emission changes by
resolving the effect of meteorological variability, thus allowing improved
assessment of the success of the Clean Air Action.
Data and methodsObservations
We use 2013–2018 hourly data for surface air PM2.5 together with
SO2, nitrogen dioxide (NO2), and CO concentrations from the CNEMC
network (http://106.37.208.233:20035/, last access: 18 March 2019). The network started in January 2013,
with 496 sites in 74 major cities across the country (Chinese State Council,
2013b), growing to ∼1500 sites in 454 cities by 2018.
PM2.5 mass concentrations are measured using the micro-oscillating
balance method and/or the β-absorption method (MEE, 2012; Zhang and
Cao, 2015). SO2, NO2, and CO concentrations are measured at the
same sites as PM2.5. NO2 concentrations are measured by the
molybdenum converter method known to have positive interferences from
NO2 oxidation products (Dunlea et al., 2007). SO2 and CO are
respectively measured using ultraviolet fluorescence and infrared absorption
(MEE, 2012; Zhang and Cao, 2015). We applied quality control to the hourly
CNEMC data following Barrero et al. (2015) to exclude severe outliers (Lu et al., 2018). There
are also occasional consecutive repeats of data that may be caused by faulty
instruments or reporting (Rohde and Muller, 2015; Silver et al., 2018). Here
we removed values from the hourly time series when there are >24
consecutive repeats. These in whole removed 7.4 %, 7.0 %, 6.4 %, and
6.7 % of the PM2.5, SO2, NO2, and CO data respectively.
We correlated these air quality observations with meteorological
observations from 839 stations distributed across China (Fig. S1 in the Supplement). The meteorological observations are compiled in the
Surface Daily Climate Dataset (V3.0) released by the China
National Meteorological Information Center (CNMIC; http://data.cma.cn/, last access: 16 March 2019).
These include data for wind speed (WDS), precipitation (PRECIP), relative
humidity (RH), and temperature (TEM). We also used the 850 hPa meridional
wind velocity (V850) from the MERRA-2 reanalysis produced at
0.5∘× 0.625∘ horizontal resolution by the NASA Global Modeling and
Assimilation Office (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2, last access: 20 March 2019). We
choose these meteorological variables for their strong correlations with
PM2.5 identified in previous studies (Wang et al., 2014; Cai et al.,
2017; Shen et al., 2017; Leung et al., 2018; Song et al., 2019; Zou et al.,
2017). V850 in particular is a strong predictor of PM2.5 wintertime
pollution events in the North China Plain because northerly winds (negative
V850) ventilate the region with clean dry air (Cai et al., 2017; Pendergrass
et al., 2019).
All data in this work are averaged over 10 d (10 d time resolution).
Trend analyses use only those sites with at least 70 % data coverage for
each of the 6 years from 2013 to 2018. We did sensitivity tests with data
coverage thresholds changing from 70 % to 90 % and obtained similar
pollutant trends. To make the most use of available data, 70 % is chosen.
For the MLR model, we further average all data on a 2∘× 2.5∘ grid to increase statistical robustness, following Tai et al. (2012) and Shen et al. (2017).
The 2013 Clean Air Action (Chinese State Council, 2013a) identified three megacity
clusters as target regions for reducing air pollution: Beijing–Tianjin–Hebei
(BTH; 35–41∘ N, 113.75–118.75∘ E), the Yangtze River Delta
(YRD; 29–33∘ N, 118.75–123∘ E), and the Pearl River Delta
(PRD; 21–25∘ N, 111.25–116.25∘ E). The more recent plan
released in July 2018 (Chinese State Council, 2018b) removed PRD from the
list of target regions and added the Fenwei Plain (FWP; 33–35∘ N,
106.25–111.25∘ E; 35–37∘ N, 108.75–113.75∘ E). Previous studies (Zhang et al., 2012) also identified the Sichuan
Basin (SCB; 27–33∘ N, 103.75–108.75∘ E) as one of the
major haze regions in China. We present analyses for these five target
regions by averaging the data from all sites with more than 70 % data
coverage for each of the 6 years from 2013 to 2018. The only continuous
record for 2013–2018 in the FWP region is for Xi'an (13 sites). Additional
FWP sites outside Xi'an started operating in early 2015 and are consistent
with the Xi'an data, as will be shown below.
Multiple linear regression model
We construct a stepwise multiple linear regression (MLR) model to quantify
the effect of meteorology on PM2.5 variability. The model fits the
deseasonalized and detrended 10 d PM2.5 mean time series on the
2∘× 2.5∘ grid to the five deseasonalized and detrended
10 d mean meteorological variables (WDS, PRECIP, RH, TEM, and V850). The
deseasonalized and detrended time series are obtained by removing the 50 d
moving averages from the 10 d mean time series (Tai et al., 2010). This
focuses on synoptic scales of variability and avoids aliasing from common
seasonal variations and long-term trends between variables (Shen et al.,
2017).
Separate fits of PM2.5 to the meteorological variables are done for
each 2∘× 2.5∘ grid square and season (DJF, MAM, JJA,
and SON). The fit has the form
Yd,i(t)=∑k=15βi,kXd,i,k(t)+bi,
where Yd,i(t) is the deseasonalized and detrended PM2.5 time
series for grid square and season i, and Xd,i,k(t) is the corresponding
time series for the deseasonalized and detrended meteorological variable kϵ[1,5]. We fit the regression coefficients βi,k and the intercept
bi. The regression is done stepwise, adding and deleting terms based on
their independent statistical significance to obtain the best model fit
(Draper and Smith, 1998). The fits and the selected meteorological
variables differ by location and season, but with regional consistency (Table S1 in the Supplement). For meteorological variables not in the final MLR model, the regression
coefficients βi,k in Eq. (1) are zero.
Annual mean PM2.5 concentrations in China from the CNEMC
network. Panels (a) and (b) show values for 2013 and 2018 for sites with
more than 70 % data coverage for the corresponding year. Panel (c)
shows the ordinary linear regression trends on a 2∘× 2.5∘ grid for
sites with more than 70 % data coverage for each of the 6 years from
2013 to 2018. The trends are based on the time series of 10 d mean
anomalies, as described in the text. Polygons in (a) define the
four target regions of the Clean Air Action (Beijing–Tianjin–Hebei – BTH:
35–41∘ N, 113.75–118.75∘ E; Yangtze River Delta – YRD:
29–33∘ N, 118.75–123∘ E; Pearl River Delta – PRD:
21–25∘ N, 111.25–116.25∘ E; and Fenwei Plain – FWP:
33–35∘ N, 106.25–111.25∘ E, and 35–37∘ N,
108.75–113.75∘ E), to which we add Sichuan Basin (SCB;
27–33∘ N, 103.75–108.75∘ E). Number inset in panel (c) is the trend in mean PM2.5 over the study region
(21–41∘ N, 103.75–123∘ E). Dots in panel (c)
indicate grid squares with significant trends (p<0.05).
Application to 2013–2018 PM2.5 trends
We use the MLR model to remove the effect of meteorological variability from
the 2013–2018 PM2.5 trends, including not only the 10 d
synoptic-scale variability but also any interannual variability and 6-year
trends. This makes the standard assumption that the same factors that drive
synoptic-scale variability also drive interannual variability (Jacob and
Winner, 2009; Tai et al., 2012). We thus apply Eq. (1) to the
meteorological anomalies Xa,i,k, obtained by removing the 6-year means
of the 50 d moving averages from the 10 d mean time series. The
anomalies calculated in this manner are deseasonalized but not detrended.
This yields the meteorology-driven PM2.5 anomalies Ym,i:
Ym,i(t)=∑k=15βi,kXa,i,k(t)+bi.
Consider now the PM2.5 anomaly Ya,i for grid square and season i obtained
by deseasonalizing but not detrending the PM2.5 data (by removing the
6-year means of the 50 d moving averages) in the same way as for the
meteorological variables. The residual anomaly Yr,i, after removing
meteorological influence from the MLR model, is given by
Yr,it=Ya,i(t)-Ym,i(t).
The residual is the component of the anomaly that cannot be explained by the
MLR meteorological model, and we will refer to it as the
meteorology-corrected data. It includes noise due to limitations of the MLR
model and other factors but also a long-term trend over the 6-year period
that we can attribute to changes in anthropogenic emissions. The same
approach was recently applied by Li et al. (2019) to separate anthropogenic
and meteorological drivers of ozone trends in China.
Results and discussionPM2.5 trends in China, 2013–2018
Figure 1 shows annual mean observed PM2.5 concentrations from the CNEMC
over China for 2013 and 2018 and the linear regression trends on the
2∘× 2.5∘ grid based on the PM2.5 anomalies
Ya,i(t), including effects of both changing emissions and meteorology. In
2013, PM2.5 across most of China was well above the Chinese national
air quality standard (annual mean of 35 µg m-3). BTH and FWP
(Xi'an) had the highest PM2.5 among the five target regions, with
annual average concentrations of 108±34µg m-3 (standard
deviation describes variability in the annual average across sites in the
region) and 108±11µg m-3 respectively, followed by SCB
(71±17µg m-3), YRD (67±12µg m-3), and
PRD (47±7µg m-3). PM2.5 decreased dramatically from
2013 to 2018, by 34 %–49 % for the five target regions. Mean 2018
concentrations were 55±13µg m-3 in BTH, 62±4µg m-3 in FWP (Xi'an), 40±6µg m-3 in SCB, 40±7µg m-3 in YRD, and 31±5µg m-3 in PRD.
Figure 2 shows the 2013–2018 relative trends of annual mean PM2.5 for
the five target regions along with the corresponding trends of SO2,
NO2, and CO concentrations measured at the same sites. Also shown in
the bottom panels are the MEIC trends in emissions of primary
PM2.5, SO2, NOx, NH3, and CO for 2013–2017. The
PM2.5 observations show steady decreases for BTH, YRD, and SCB. PRD
flattens out in 2015–2017 before decreasing again in 2018. FWP (Xi'an)
decreases sharply by 47 % from 2013 to 2015 but rebounds in 2015–2017
before decreasing again in 2018. Trends at other FWP sites that became
operational in early 2015 are similar to Xi'an. We argue in Sect. 3.3 that
the 2015–2017 flattening at PRD and the anomalous 2013–2015 sharp decrease
and 2015–2017 rebound at FWP are driven by meteorology.
Relative trends of 2013–2018 observed concentrations and 2013–2017
MEIC emission estimates for the five target regions of Fig. 1. The
observed PM2.5 trends are shown as thick lines. Values are annual means
referenced to 2013. The observed concentrations are averaged over all sites
in each region with at least 70 % data coverage for each year. The number
of sites for each region is indicated. Fenwei Plain trends are for Xi'an, as
other sites did not start operating until early 2015. Post-2015 relative
PM2.5 trends at these other sites are shown as the dashed line.
We see from Fig. 2 that only SO2 has a decrease steeper than
PM2.5, indicating that SO2 emission controls have been a major
driver of the PM2.5 trend (Lang et al., 2017; Shao et al., 2018). The
overall SO2 decrease for the five regions is 57 %–76 % from 2013 to
2018. The SO2 decrease is quantitatively consistent with the decrease
in SO2 emissions estimated by MEIC (Zheng et al., 2018). This drastic
cut of China SO2 emissions is due to installation of scrubbers at
coal-fired power plants (Siwen et al., 2015; Karplus et al., 2018; Silver et
al., 2018), elimination of small coal boilers, improvement of coal quality
(Zheng et al., 2018), and switch from residential coal to cleaner fuels
(Zhao et al., 2018). We also see a significant decrease in CO of 18 %–43 %
for the five regions from 2013 to 2018, again consistent with the MEIC
and suggesting a reduction in organic PM2.5
emissions. Primary PM2.5 emissions in the MEIC decreased at a
rate comparable to or steeper than CO. Trends in China PM2.5, SO2,
and NO2 presented here are consistent with previous studies (Silver et
al., 2018; Ma et al., 2019) that cover a shorter time period than 2013–2018.
Figure 3 shows the time series of monthly mean PM2.5 for the five
target regions, illustrating the seasonal and interannual variability. All
regions show winter maxima that can be mostly attributed to meteorology,
including shallower mixing depth, lower precipitation, and increased
stagnation in winter (Wang et al., 2018). Residential heating emissions
in winter also contribute to the seasonality in China, north of about
33∘ N (covering BTH and FWP in this study; J. Liu et al., 2016;
Xiao et al., 2015). There is a large interannual variability, particularly
in winter, that must be largely driven by meteorology. Studies for BTH have
shown that high PM2.5 in winter months is associated with weak
southerly winds, low mixing depths, and high relative humidity (Zhang et
al., 2014; Chang et al., 2016; Li et al., 2018; Shao et al., 2018). The
relatively clean 2017–2018 winter was due in part to a higher frequency of
northerly flow and associated ventilation (CMA, 2018; Yi et al.,
2019). In addition, particularly aggressive actions by the government to
restrict coal use that winter may have played a role in reducing PM2.5
levels (Zhang et al., 2019).
Time series in 2013–2018 of monthly mean PM2.5 concentrations over the five target regions. Values are averages from all sites in the region with over 70 % data coverage for each of the 6 years.
Meteorological influence on PM2.5
Figure 4 shows the correlations of 10 d PM2.5 concentrations with the
individual meteorological variables used in the MLR model. Correlation
coefficients r as low as 0.3 are statistically significant, more so when
consistent across a region. Wind speed is negatively correlated with
PM2.5, as would be expected from ventilation, except in areas of the
north where wind promotes dust formation (Lyu et al., 2017; Wang et al.,
2004). Precipitation is also generally negatively correlated with
PM2.5, as one would expect from scavenging (Chen et al., 2018). The
positive correlation between precipitation and PM2.5 over northern China
in spring is likely a result of high RH associated with precipitation in
adjacent days.
Correlation coefficients (r) of PM2.5 concentration with the
individual meteorological variables used in the MLR model: surface wind
speed (m s-1), precipitation (mm d-1), relative humidity (RH;
%), surface air temperature (∘C), and 850 hPa meridional wind
velocity (m s-1) for different seasons in China. The correlations are
based on 10 d average observations on a 2∘× 2.5∘ grid.
Dots indicate statistically significant correlations (p<0.05).
Correlation between RH and PM2.5 is positive over northern China,
especially in winter, and negative over southern China, especially in
summer. The positive correlation between PM2.5 and RH over northern
China in winter has been reported by previous studies and attributed in part
to the role of aqueous-phase aerosol chemistry in driving secondary
PM2.5 formation (Zheng et al., 2015; He et al., 2018; Song et al.,
2019; Pendergrass et al., 2019; Tie et al., 2017). The negative correlation
of PM2.5 with RH over southern China likely reflects the association of
high RH with precipitation and onshore wind, which facilitate PM2.5 wet
removal and ventilation (Zhu et al., 2012; Leung et al., 2018).
Temperature has a positive correlation with PM2.5 year-round over most
of China (Wang et al., 2014; Leung et al., 2018), even though there is no
strong direct dependence of PM2.5 on temperature (Jacob and Winner,
2009). The correlation likely reflects the covariation of temperature with
other meteorological variables, including wind speed, precipitation, and RH
(Tai et al., 2012; Zhu et al., 2012). A possible explanation for the
negative correlation with temperature in summer over the North China Plain could
be the volatilization of ammonium nitrate at high temperatures (Kleeman,
2008). V850 shows strong positive correlations with winter PM2.5 over
most of China, and strong negative correlations with summer PM2.5 over southern China, especially for the Pearl River Delta.
Resolving meteorological influences on PM2.5 2013–2018 trends
in China. Panel (a) shows the fraction of detrended and deseasonalized
variance in 10 d PM2.5 means explained by the stepwise multilinear
regression (MLR) meteorological model. Panel (b) shows the
meteorology-corrected trends to be compared to the trends in the original
data shown in Fig. 1. Number inset in (b) is the trend in mean
PM2.5 over the study region (same definition as in Fig. 1). Dots
indicate significant correlations (p<0.05) in (a) and
significant trends (p<0.05) in (b).
Time series of 2013–2018 PM2.5 10 d mean anomalies for the
five target regions of Fig. 1. The anomalies are relative to the 2013–2018
means. The data are averaged over all measurement sites in each region with
at least 70 % of data coverage for each year (same as for Fig. 2). The
meteorological contribution to the anomalies as determined from the MLR model
is shown in red. The long-term trend in the meteorology-corrected residual
in blue (Eq. 3) is interpreted as being driven by changes in anthropogenic
emissions. Values inset in each panel are the ordinary linear regression trends,
with 95 % confidence intervals obtained by the bootstrap method.
Figure 5a describes the ability of the MLR model to account for
PM2.5 variability in relation to wind speed, precipitation, RH,
temperature, and V850 as potential predictor variables. Results are
presented as the coefficients of determination R2 (fraction of variance
explained) between observed and model PM2.5 in the detrended
deseasonalized time series. The R2 values have been adjusted to account
for different numbers of significant explanatory terms (predictor
variables). R2 values for the five target regions are 0.59 (BTH), 0.46
(YRD), 0.65 (PRD), 0.65 (SCD), and 0.41 (FWP). Figure 5b
shows the meteorology-corrected PM2.5 trends after removal of
meteorological variability predicted by the MLR model, i.e., the trends in
the residuals Yr,i(t) in Eq. (3). The meteorology-corrected
decreasing trend averaged across China is -4.6µg m-3 a-1,
12 % weaker than in the original data, meaning that 12 % of the
PM2.5 decrease in the original data is attributable to meteorology.
We elaborate on this below for the five target regions.
Meteorology-corrected PM2.5 trends for the five target regions
Figure 6 shows the 10 d mean PM2.5 anomalies in the deseasonalized
(but not detrended) data for the five target regions (Ya(t) in Sect. 2.3). Also shown is the meteorological component Ym(t) derived from the
MLR meteorological model and the residual Yr(t) (meteorology-corrected;
Eq. 3) whose long-term trend can be interpreted as being due to changes in
anthropogenic emissions. The PM2.5 anomalies show large features on
10 d basis that can be mostly captured by the MLR model. The residual
meteorology-corrected time series is much smoother, as depicted by the
narrower 95 % confidence intervals in the anthropogenic residual trends
than in the original observed trends. The meteorology-corrected trends
differ by 3 % (YRD) to 27 % (SCB) from the observed trends. The YRD
trend reflects a significant contribution from the December 2013 outlier,
which reflects unfavorable meteorological conditions (Fig. S2) that are
not adequately captured by the MLR model. If we exclude this outlier month
from the time series, the observed YRD trend becomes -5.7±0.9µg m-3 a-1 and the meteorology-corrected trend becomes -5.9±0.7µg m-3 a-1.
Most remarkably, it appears that the 2015–2017 flattening in the PRD and
2015–2017 increase in the FWP (see Fig. 2) can be mostly attributed to
meteorological variability as resolved by the MLR model rather than to
emissions. The trend in the residual is more consistent with a steady
2013–2018 anthropogenic decrease in both regions. The MLR model shows that
meteorology accelerated the PM2.5 decline in PRD and FWP from 2013 to
2015 and contributed partly to the 2015–2017 PM2.5 rebound over FWP.
In particular, the high PM2.5 anomalies in PRD in 2013 and early 2014
are driven by anomalously low V850, and the low PM2.5 in winter
2015–2016 is associated with anomalously high southerly flow and
precipitation (Fig. S4). The low PM2.5 in FWP in the winter
2014–2015 is associated with anomalously high wind speed, low RH, and
low temperature, while the high anomalies in the winter 2016–2017 are
associated with anomalously low wind speed, high RH, and high temperature
(Fig. S5).
Conclusions
Observations of fine particulate matter (PM2.5) pollution in China from
the extensive CNEMC network established in 2013 show large 2013–2018
decreases driven by emission controls with complicating influences from
meteorology. Here we used a stepwise multiple linear regression (MLR)
meteorological model to investigate and separate contributions from
anthropogenic emissions and meteorology to these 6-year trends.
The CNEMC observations show 34 %–49 % decreases in PM2.5 in the five
megacity clusters targeted by the Chinese government's Clean Air Action to
reduce anthropogenic emissions. Concurrent observations of SO2, CO, and
NO2 are qualitatively consistent with these PM2.5 decreases being
driven by drastic cuts in emissions from coal combustion. At the same time,
there is large interannual variability driven by meteorology particularly in
winter when PM2.5 is highest.
We used the stepwise MLR meteorological model to relate PM2.5 anomalies
across China to wind speed, precipitation, relative humidity (RH),
temperature, and meridional velocity at 850 hPa (V850) as potential
predictors. The model accounts for ∼50 % of the variance in
the deseasonalized detrended PM2.5 data, including 41 %–65 % for the
five megacity clusters. Application to the PM2.5 time series shows
that meteorological variability contributed significantly to the 6-year
trends across China and in the megacity clusters. Removing meteorological
variability as given by the MLR model also reduces the uncertainty in the
trend that can be attributed to emission controls. We refer to the data
series after removal of meteorological variability as the
meteorology-corrected data. Thus the 2013–2018 PM2.5 decrease for
Beijing–Tianjin–Hebei is -9.3±1.8µg m-3 a-1 in the
original data and is 14 % weaker in the meteorology-corrected data (-8.0±1.1µg m-3 a-1). For the Sichuan Basin where the
meteorological correction is particularly large, the PM2.5 decrease is
-6.7±1.3µg m-3 a-1 in the original data and is
reduced by 27 % to -4.9±0.9µg m-3 a-1 in the
meteorology-corrected data. The average 2013–2018 PM2.5 decrease over
our study domain is -5.2µg m-3 a-1 in the original data
(Fig. 1c) and is reduced by 12 % to -4.6µg m-3 a-1 in the meteorology-corrected data (Fig. 5b).
Observations for the 2015–2017 period indicate a flattening of the
PM2.5 trend in the Pearl River Delta and an increase in the Fenwei
Plain. We find from the MLR model that these 3-year trends can be explained
by meteorological variability (including particularly steep 2013–2015
decreases) rather than by relaxation of emission controls.
Data availability
All of the measurements and reanalysis data are openly
available for download from the websites given in the main text. The
anthropogenic emission inventory is available from http://www.meicmodel.org (last access: 20 January 2019), and
for more information, please contact Qiang Zhang
(qiangzhang@tsinghua.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-11031-2019-supplement.
Author contributions
SZ, DJJ, and HL designed the study. SXZ developed the
model, performed the simulations and analyses. XW, LS, KL, YZ, and TZ
helped with scientific interpretation and discussion. KG helped with
pollutants data processing. SZ and DJJ wrote the paper, and all authors
provided input on the paper for revision before submission.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work is a contribution from the Harvard-NUIST Joint Laboratory for Air Quality and Climate (JLAQC). We thank the MEIC (Multi-resolution Emission Inventory for China) team at Tsinghua University for providing the MEIC. We acknowledge Loretta J. Mickley (Harvard University) and Jonathan Moch (Harvard University) for helpful discussions.
Financial support
This research has been supported by the Harvard-NUIST Joint Laboratory for Air Quality and Climate (JLAQC), the National Natural Science Foundation of China (grant nos. 41830965 and 91744209), and the National Key R&D Program Pilot Projects (grant no. 2016YFC0203304). Shixian Zhai acknowledges support from the China Scholarship Council (201708320314). Hong Liao is supported by the National Natural Science Foundation of China (91744311).
Review statement
This paper was edited by Toshihiko Takemura and reviewed by Hyung-Min Lee and one anonymous referee.
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