Understanding
the effectiveness of air pollution control policies is
important for future policy making. China has implemented strict air pollution
control policies since the 11th Five-Year Plan (FYP). There is still a lack
of overall evaluation of the effects of air pollution control policies on
PM2.5 pollution improvement in China since the 11th FYP. In this
study, we aimed to assess the effects of air pollution control policies from
2005 to 2017 on PM2.5 using satellite remote sensing. We
used the satellite-derived PM2.5 of 2005–2013 from one of our previous
studies. For the data of 2014–2017, we developed a two-stage statistical
model to retrieve satellite PM2.5 data using the Moderate Resolution
Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD),
assimilated meteorology, and land use data. The first stage is a
day-specific linear mixed effects (LME) model and the second stage is a generalized
additive model (GAM). Results show that the Energy Conservation and
Emissions Reduction (ECER) policy, implemented in the 11th FYP period and
focused on SO2 emissions control, had co-benefits with PM2.5
reductions. The increasing trends of PM2.5 pollution (1.88 and
3.14 µg m-3 year-1 for all of China and the Jingjinji region in 2004–2007, p<0.005) were suppressed after 2007. The overall PM2.5 trend for all of China was -0.56µg m-3 year-1 with marginal significance
(p=0.053) and PM2.5 concentrations in the Pearl River Delta region had a
big drop (-4.81µg m-3 year-1, p<0.001) in 2007–2010. The
ECER policy during the 12th FYP period was basically an extension of
the 11th FYP policy. PM2.5 is a kind of composite pollutant which
comprises primary particles and secondary particles such as sulfate,
nitrate, ammonium, organic carbon, elemental carbon, etc. Since the ECER policy
focused on single-pollutant control, it had shown great limitation for
PM2.5 reductions. The PM2.5 concentrations did not decrease from
2010 to 2013 in polluted areas (p values of the trends were greater than
0.05). Therefore, China implemented two stricter policies: the 12th FYP on
Air Pollution Prevention and Control in Key Regions (APPC-KR) in 2012, and
the Action Plan of Air Pollution Prevention and Control (APPC-AP) in 2013. The
goal of air quality improvement (especially PM2.5 concentration
improvement) and measures for multi-pollutant control were proposed. These
policies led to dramatic decreases in PM2.5 after 2013 (-4.27µg m-3 year-1
for all of China in 2013–2017, p<0.001).
Introduction
Fine particulate matter (PM2.5, aerodynamic particulate matter with
a diameter less than 2.5 µm) is a major atmospheric pollutant, which
has been shown to be strongly associated with adverse health effects (e.g.,
cardiovascular and respiratory morbidity and mortality) in many
epidemiological studies (Crouse et al., 2012; Dominici et al., 2006; Pope
et al., 2002). With the rapid economic development and industrialization in
the past decades, PM2.5 pollution has gradually become a major
environmental issue in China (Liu et al., 2017a). However, the
Chinese government did not focus on the PM2.5 issues until 2012.
Therefore, air pollution control policies implemented before 2012 mainly
focus on SO2, industrial dust, and soot emission control. The air
pollution control policies of China started to pay attention to PM2.5
since late 2012.
Understanding the effectiveness of air pollution control policies is
important for future air pollution control in China. Several studies have
examined the historical air pollution control policies and their association
with the trends of SO2, NO2, and PM10 (Jin et al.,
2016; Chen et al., 2011; Hu et al., 2010). Since the national PM2.5
monitoring network was established in late 2012, few studies have evaluated
the effects of air pollution control policies on PM2.5 concentrations
before 2013 due to the lack of historical ground-monitoring data. Therefore,
it is difficult to understand whether the air pollution control policies had
synergistic effects on PM2.5 reductions before 2012.
In recent years, many studies have shown that satellite remote sensing
provides a powerful tool to assess the spatiotemporal trends of air
pollution for both global and regional scales (Miyazaki et al.,
2017; Itahashi et al., 2012; Krotkov et al., 2016). Estimating ground
PM2.5 using satellite aerosol optical depth (AOD) data was also an
effective way to fill the spatiotemporal PM2.5 gaps left by ground-monitoring networks (Liu, 2013, 2014; Hoff and Christopher, 2009). There
are two major methods to estimate ground-level PM2.5 concentrations using AOD
data, i.e., the scaling method and statistical approach (Liu, 2014). The
scaling method uses atmospheric chemistry models to simulate the association
between AOD and PM2.5, and then calculate the satellite-derived
PM2.5 using the equation Satellite-derivedPM2.5=SimulatedPM2.5SimulatedAOD×SatelliteAOD (Liu, 2014).
Boys et al. (2014) and van Donkelaar et al. (2015)
estimated the global satellite PM2.5 time series using the scaling
method. Compared to the scaling method, statistical models have greater
prediction accuracy but require large amount of ground-measured PM2.5 data
to develop the models (Liu, 2014). By taking advantage of the newly
established ground PM2.5 monitoring network, we developed a two-stage
statistical model to estimate historical monthly mean PM2.5
concentrations using Moderate Resolution Imaging Spectroradiometer
(MODIS) Collection 6 Aqua AOD data in one of our previous studies (Ma et
al., 2016). Validation results shows that this monthly PM2.5 dataset
has high prediction accuracy (R2=0.73). This accurate historical
PM2.5 dataset from 2004 to 2013 allowed us to examine the effects of
pollution control policies on PM2.5 concentrations. In this previous
study (Ma et al., 2016), we preliminarily analyzed the effects of
Energy Conservation and Emissions Reduction (ECER) policy in the 11th Five-Year Plan (FYP) (2006–2010). We found an inflection point around 2008, after which
PM2.5 concentration showed a slight decreasing trend, showing the
co-benefits of the ECER policy. From 2013 to 2017, China implemented the
Action Plan of Air Pollution Prevention and Control (APPC-AP), which focused
on PM2.5 pollution. Currently, there is still a lack of overall
evaluation of the effects of air pollution control policies on PM2.5
pollution improvement in China from 2005 to 2017.
In this study, we aimed to assess the effects of air pollution control
policies from 2005 to 2017 on PM2.5 using satellite remote
sensing. We used the satellite-derived PM2.5 dataset developed in our
previous study (Ma et al., 2016). Since this dataset was from 2004
to 2013 and data after 2014 have been lacking, we extended the dataset to
2017 in the present work. To keep consistent with our previous satellite
PM2.5 dataset, we used the same method as described in our previous
study (Ma et al., 2016).
Overview of air pollution control policies in China from 2005 to
2017
During 2005 to 2017, China implemented a series of air pollution prevention and
control policies, including the 11th FYP on Environmental
Protection (2006–2010), ECER policy during the 11th FYP period, the 12th
FYP on Environmental Protection (2011–2015), the 12th FYP on ECER,
the 12th FYP on Air Pollution Prevention and Control in Key Regions
(APPC-KR), and APPC-AP (2013–2017). The base year, implementation period,
major goals, and major measures are listed in Table 1.
Overview of major air pollution control policies in China from 2005
to 2017.
PolicyaBaseImplementationMajor goalsMajor measuresyearperiod(compared tobase year)11th FYP on Environmental Protection20052006–2010SO2 emission should be reduced by 10 %Implement desulfurization projects of coal-fired power plants; Prevent and control urban PM10 pollution, relocate pollution industrial plants in urban areas, control construction and road dust; Implement total emission control policy for key industrial pollution sources, control emission of sulfur dioxide and soot (dust); Strengthen vehicle pollution prevention and control, improve quality and efficiency of gasoline.ECER during 11th FYP20052006–2010Energy consumption per GDP capita should decrease by 20 % SO2 emission should be reduced by 10 %Promote industrial and energy structure adjustment, restrain the development of industries with high energy consumption and pollution, eliminate backward production capacity, promote production capacity with low energy consumption and low pollution; Implement 10 major energy conservation projects, implement desulfurization projects of coal-fired power plants.12th FYP on Environmental Protection20102011–2015SO2 emission should be reduced by 8 % NOx emission should be reduced by 10 %Implement desulfurization and denitration facilities for coal-fired power sector and major industrial sectors; Control NOx emissions of vehicles and ships; Deepen PM and VOC pollution control; Promote urban air pollution prevention and control, implement coordinated control of various pollutants in key areas, monitor PM2.5 and O3 in Jingjinji, Yangtze River Delta, and Pearl River Delta regions.ECER during 12th FYP20102011–2015Energy consumption per GDP capita should decrease by 16 % SO2 emission should be reduced by 8 % NOx emission should be reduced by 10 %Adjust and optimize industrial structure, control the development of industries with high energy consumption and pollution, eliminate backward production capacity; Adjust energy consumption structure, strengthen energy conservation for industrial, building, transportation, commercial and civil areas, etc; Strengthen emissions reduction in key industrial sectors, promote desulfurization and denitration, control emissions of vehicles, promote the control of PM2.5.The 12th FYP on APPC-KRb20102011–2015Emission of the SO2, NOx, and industrial PM should decrease by 12 %, 13 %, and 10 %, respectively The annual average concentration of PM10, SO2, NO2, and PM2.5 should decrease by 10 %, 10 %, 7 %, and 5 %, respectivelyIdentify the key regions and implement regional specific management Strictly control high-energy-consumption and high-pollution projects, control new pollutant emissions, implement strict emission standards, and enhance control requirements of VOCs in key regions; Strengthen elimination of backward production capacity, optimize industrial layout; Optimize energy consumption structure, develop clean energy, control total coal consumption, establish restricted zones for high polluting fuels, eliminate small coal boilers, promote clean and efficient utilization of coal; Comprehensively implement co-control of multiple pollutants (SO2, NOx, PM, VOCs), strengthen vehicle pollution prevention and control; Innovate regional management mechanisms, establish joint regional prevention and control coordination mechanisms, establish and perfect ground-monitoring networks.APPC-AP20122013–2017PM2.5 concentrations of Jingjinji, Yangtze River Delta, and Pearl River Delta regions should be reduced by 25 %, 20 %, and 15 % respectively PM2.5 concentrations of Beijing should be controlled at around 60 µg m-3Enhance comprehensive air pollution control on industrial enterprises, deepen non-point source control, strengthen vehicle pollution control; Adjust, optimize, and upgrade industrial structure, strictly control new capacity with high energy consumption and high pollution, accelerate elimination of backward production capacity, reduce excess capacity; Accelerate energy structure adjustment, accelerate utilization of clean energy, control total coal consumption, promote clean utilization of coal, improve energy efficiency; Optimize industrial layout; Utilize the market mechanisms, improve the pricing and tax policy, establish regional coordination mechanisms; Establish monitoring, early warning, and emergency system for heavy pollution episodes.
a Abbreviations – FYP: Five-Year Plan; ECER: Energy Conservation and
Emissions Reduction; APPC-KR: Air Pollution Prevention and Control in Key
Regions; APPC-AP: Action Plan of Air Pollution Prevention and Control. b The key regions are shown in Fig. S1 in the Supplement.
During the 11th FYP period, there was no specific air pollution control
policy. Air pollution prevention and control measures were incorporated into
the whole environmental protection plan or policy (i.e., 11th FYP on
Environmental Protection and ECER policy). From Table 1 we can see that the
air pollution policies during the 11th FYP mainly focused on total emission
reduction. In this period, environmental management in China was emission
control oriented; that is, the indicators for local governments'
environmental performance assessment were emission reduction rates, not the
environmental quality. The 12th FYP on Environmental Protection and
ECER policy were basically the extension of the 11th FYP policies,
which mainly focused on emission reduction.
The 12th FYP on APPC-KR is the first special plan for air pollution
prevention and control. This plan proposed the idea of unification of total
emission reduction and air quality improvement. And it proposed the goals of
air pollutant concentration control for the first time. PM2.5 pollution
control was also incorporated into this plan. Although the implementation
period of the 12th FYP on APPC-KR was 2011–2015, it was issued in 29 October 2012. After
that, China issued the APPC-AP (2013–2017) in 10 September 2013, which strengthened the air pollution control and the goals of air
quality improvement. These policies indicated that the focus of air
pollution control in China began to focus on PM2.5 concentrations
reductions.
Data and methodsSatellite-based PM2.5 from 2004 to 2013
We estimated the monthly satellite-based PM2.5 data from 2004 to 2013
at 0.1∘ resolution in our previous work (Ma et al.,
2016). Briefly, we developed a two-stage statistical model using MODIS
Collection 6 AOD and assimilated meteorology, land use data, and ground-monitored PM2.5 concentrations in 2013. The overall model
cross-validation R2 (coefficient of determination) was 0.79 (daily
estimates) for the model year. Since ground-monitored data before 2013 have
been lacking and therefore it is not possible to develop statistical models before
2013 to estimate historical PM2.5 concentrations. Thus, the historical
PM2.5 concentrations (2004–2012) were then estimated using the model
developed based on the 2013 model. Two methods were used to validate the accuracy
of historical estimates. First, we compared the historical estimate
monitoring data from Hong Kong and Taiwan before 2013. Second, we estimated
PM2.5 concentrations in the first half of 2014 using the 2013 model and
compared them with the ground measurements to evaluate the accuracy of
PM2.5 estimates beyond the model year, which can represent the accuracy
of historical estimates. Validation results indicated that it accurately
predicted PM2.5 concentrations with little bias at the monthly level
(R2=0.73, slope =0.91).
For PM2.5 concentrations from 2004 to 2013, we used the
abovementioned satellite-based PM2.5 dataset, which was estimated
using the model developed in 2013. First, this dataset has shown high
accuracy and has been widely used in environmental epidemiological (Liu
et al., 2016a; Wang et al., 2018a), health impact (Liu et al., 2017b; Wang
et al., 2018b), and social economic impact (Chen and Jin, 2019; Yang
and Zhang, 2018) studies in China. Second, a recent study has shown that the
historical hindcast ability of the annual model decreased when hindcast year
was long before the model year (Xiao et al., 2018). Therefore, we
did not use the models of 2014 to 2017 to estimate the hindcast PM2.5.
Satellite-based PM2.5 from 2014 to 2017
Unlike historical estimates from 2004 to 2012, we have sufficient ground-monitored PM2.5 data to develop statistical models after 2013, which
allows us to estimate daily PM2.5 concentrations accurately.
Therefore, we developed a separate PM2.5–AOD statistical model for each
year of 2014–2017 to estimate the spatially resolved (0.1∘
resolution) PM2.5 concentrations. To keep satellite PM2.5
estimates of 2014–2017 consistent with our previous satellite PM2.5
dataset, we used the same method as described in our previous study
(Ma et al., 2016). The data, model development, and model
validation are briefly introduced as follows.
Spatial distributions of ground PM2.5 monitors involved in
model fitting and validation. Red circles denote the ground monitors in 2014.
Pink circles denote new ground monitors established in 2015.
The data used in this study include ground-monitored PM2.5
concentrations (µg m-3), Aqua MODIS Collection 6 Dark Target (DT)
AOD and Deep Blue (DB) AOD data, planetary boundary layer height (PBLH, 100 m),
wind speed (WS, m s-1) at 10 m above the ground, mean relative humidity in
PBL (RH_PBLH, %), surface pressure (PS, hPa),
precipitation of the previous day (Precip_Lag1; mm), MODIS
active fire spots, urban cover ( %), and forest cover ( %). ground-monitored PM2.5 data were collected from the China Environmental Monitoring
Center (CEMC), environmental protection agencies of Hong Kong and Taiwan.
Figure 1 shows the ground PM2.5 monitors used in this study. AOD data were
downloaded from the Level 1 and Atmospheric Archive and Distribution System
(https://ladsweb.modaps.eosdis.nasa.gov/, last access: 29 March 2019).
Meteorological data were extracted from Goddard Earth Observing System Data
Assimilation System GEOS-5 Forward Processing (GEOS 5-FP) meteorological
data (ftp://rain.ucis.dal.ca, last access: 29 March 2019). MODIS fire spots
were from the NASA Fire Information for Resource Management System
(https://earthdata.nasa.gov/earth-observation-data/near-real-time/firms,
last access: 29 March 2019). Land use information were downloaded from
Resource and Environment Data Cloud Platform of Chinese Academy of Science
(http://www.resdc.cn/data.aspx?DATAID=184, last access: 29 March 2019).
Previous studies have shown the data quality issue of ground PM2.5
measurements from the CEMC network (Liu et al., 2016b; Rohde and Muller,
2015). We performed the data screening procedure before model fitting.
Abnormal values (extreme high or extreme low values for a site compared with
its neighboring sites, repeated values for continuous hours, etc.) were
deleted before model fitting. We required at least 20 hourly records to
calculate the daily average PM2.5 concentrations. DT and DB AOD were
combined using an inverse variance weighting method to improve the spatial
coverage of AOD data (Ma et al., 2016). These combined AOD data have shown good consistency (R2=0.8, mean bias =0.07) with ground
AOD measurements from the Aerosol Robotic Network (AERONET) (Ma et al.,
2016). All data were assigned to a predefined 0.1∘ grid. Then all
of the variables were matched by grid cell and day of the year (DOY) for model
fitting.
A two-stage statistical model was developed for each year separately from
2014 to 2017. The first-stage linear mixed effects (LME) model included
day-specific random intercepts and slopes for AOD, season-specific random
slopes for meteorological variables, and fixed slope for precipitation and
fire spots. The model structure of first-stage model is shown as follows:
PM2.5,st=μ+μ′+β1+β1′AODst+β2+β2′WSst+β3+β3′PBLHst+β4+β4′PSst+β5+β5′RH_PBLHst+β6Precip_Lag1st+β7Fire_spotsst+ε1,stμ′β1′∼N0,0,Ψ11+ε2,sjβ2′β3′β4′β5′∼N0,0,0,0,Ψ2,
where PM2.5,st is ground PM2.5 measurements at grid cell s on
DOY t; AODst is DT-DB merged AOD; WSst, PBLHst, PSst,
RH_PBLHst, and Precip_Lag1st are meteorological variables; Fire_spotsst is the fire count;
μ and μ′ are the fixed and day-specific random intercepts,
respectively; β1–β7 are fixed slopes; β1′ is the day-specific random slope for AOD; β2′–β5′ are the season-specific random slopes for meteorological variables;
ε1,st is the error term at grid cell s on DOY t;ε2,sj is
the error term at grid cell s in season j; Ψ1
and Ψ2 are the variance–covariance matrices for the day- and
season-specific random effects, respectively. The first-stage model was
fitted for each province separately. We created a buffer zone for each
province to include data with at least 3000 data records and at least 300 d. We averaged overlapped predictions from neighboring provinces to
generate a smooth national PM2.5 concentration surface.
The second-stage generalized additive model (GAM) established the
relationship between the residuals of the first-stage model and smooth terms
of geographical coordinates, forest and urban cover.
PM2.5_residst=μ0+s(X,Y)s+s(ForestCover)s2+s(UrbanCover)s+εst,
where PM2.5_residst is the residual of the first-stage model at grid cell s on
DOY t, μ0 is the intercept, s(X,Y)s is the smooth term of the
coordinates of the centroid of grid cell s, s(ForestCover)s and
s(UrbanCover)s are the smooth functions of forest cover and urban area for grid
cell s, and εst is the error term.
To evaluate the model over-fitting, 10-fold cross-validation (CV) was used;
that is, the model could have better prediction performance in the model
fitting dataset than the data, which are not included model fitting. In
10-fold CV, all samples in the model dataset are randomly and equally
divided into 10 subsets. One subset was used as testing samples and the
rest of the subsets are used to fit the model. This process was repeated for 10 rounds until each subset was used for testing for once. Statistical
indicators of coefficient of determination (R2), mean prediction error
(MPE), and root mean squared prediction error (RMSE) were calculated and
compared between model fitting and CV to assess model performance and
over-fitting.
Time series analysis
Monthly mean PM2.5 concentrations for each grid cell were calculated
to perform the time series analysis. Following our previous study
(Ma et al., 2016), we required at least six daily PM2.5 predictions in each month to calculate the monthly mean PM2.5. We
deseasonalized the monthly PM2.5 time series by calculating the monthly
PM2.5 anomaly time series for each grid cell to remove the seasonal
effect. The PM2.5 trend for each grid cell was calculated using least
squares regression (Weatherhead et al., 1998):
(PM2.5)anomaly,s,m=(PM2.5)s,m-(PM2.5)s,j‾3m=1,2,3,…,M;j=1,2,3,…,12,(PM2.5)anomaly,s,m=μ+β×m+ε;4m=1,2,3,…,M,
where (PM2.5)anomaly,s,m is the
PM2.5 anomaly at grid cell s for month m during the
calculating period; (PM2.5)s,m is the estimated
PM2.5 concentration at grid cell s for month m;
m is the month index and M is the total number of months during
the calculating period (2004–2017, M=168);
(PM2.5)s,j‾ is the 14-year average PM2.5
concentration of the month to which month m belongs (j=1 for
January, j=2 for February, … , etc.); μ is the intercept;
β is the slope, which is also the trend of PM2.5
(µg m-3 month-1); and ε is the error term.
The annual PM2.5 trend (µg m-3 year-1) is
12×β. A t test was used to obtain the statistical significance
of the trends. This method has been successfully applied to trend analyses of
monthly mean PM2.5 and AOD anomaly time-series data (Hsu et al.,
2012; Boys et al., 2014; Zhang and Reid, 2010; Xue et al., 2019). We analyzed
the PM2.5 trend for different periods to examine the effects of air
pollution control policies on PM2.5 pollution improvement.
Results and discussionValidation of satellite-based PM2.5 concentrations from 2014 to
2017
Table S1 in the Supplement summarized the statistics of all variables for the
modeling dataset from 2014 to 2017. Overall, there are 95 649, 110 805,
113 490, and 123 652 matchups for the model fitting
datasets for years of 2014, 2015, 2016, and 2017, respectively. The average
PM2.5 concentration decreases year by year, from
65.66 µg m-3 in 2014 to 48.32 µg m-3 in 2017.
Correspondingly, the average AOD also shows a decreasing trend from 0.67 in
2014 to 0.50 in 2017.
Model fitting (upper row) and
cross-validation (CV, lower row) results for satellite PM2.5
prediction models from 2014 to 2017.
Figure 2 shows the model fitting and cross-validation results for each year's
model. The model fitting R2 ranges from 0.75 (2015) to 0.80 (2017) and
CV R2 ranges from 0.72 (2015) to 0.77 (2017), which is similar to the
2013 model (0.82 for model fitting and 0.79 for CV) developed in our previous
study (Ma et al., 2016). The model prediction accuracy is different among
years, which is consistent with previous studies. Hu et al. (2014) studied
the 10-year spatial and temporal trends of PM2.5 concentrations in
the southeastern US from 2001 to 2010. They developed a separate two-stage
statistical model for each year and found the CV R2 ranged from 0.62 in
2009 to 0.78 in 2005 and 2006. Kloog et al. (2011, 2012) conducted two
studies in the northeastern US and also found that the validation R2
varied among years. Compared to the model fitting R2, the CV R2
only decreases by 0.02 in 2016
and 0.03 in 2014, 2015, and 2017, showing that our models were not
substantially over-fitted. For the monthly mean concentrations calculated
from at least six daily PM2.5 predictions, the validation R2
values range from 0.75 to 0.81 (Fig. 3). The results show that the overall
prediction accuracy of the models from 2014 to 2017 is satisfying.
Validation of monthly mean PM2.5 predictions from 2014 to
2017.
The fixed effects, model fitting, and CV results of the first-stage LME
model for each province are shown in Tables S2–S5. AOD is the only
variable that was statistically significant in all provincial models for all
years (p<0.05). Wind speed, relative humidity, precipitation, and
fire spots were significant in most provincial models. The CV R2 varies
for different provinces and different years. The CV R2 values range from
0.61 in Xinjiang to 0.77 in Heilongjiang for 2014, from 0.34 in Xinjiang to
0.76 in Hebei for 2015, from 0.44 in Tibet to 0.77 in Jiangsu for 2016, and
from 0.38 in Xinjiang to 0.79 in Sichuan for 2017. We also fitted a
first-stage LME model for all of China. Results show that the overall CV
R2 values for the first-stage LME model dropped to 0.57, 0.52, 0.56, and
0.54, for 2014, 2015, 2016, and 2017, respectively. Therefore, fitting the
first-stage model for each province separately can greatly improve the
prediction accuracy.
A potential source of uncertainties in statistical models is the uneven
spatial distribution of ground PM2.5 monitors. The CEMC air quality
network mainly covers large urban centers with very limited site coverage in
rural areas, especially in western part of the country. Since it requires a
large amount of ground-measured PM2.5 data to develop
satellite-based statistical model, this bias cannot be avoided. Despite this
limitation, high model performances, which are much better than those using
the scaling method, have been achieved in this study and previous similar
studies (Zheng et al., 2016; Huang et al.,
2018; Xue et al., 2019). For example,
Geng et al. (2015) estimated long-term PM2.5
concentrations in China using a scaling method and found the validation
R2 of PM2.5 predictions was 0.72 compared to the 5-month
averaged ground PM2.5 concentrations for January–May 2013. A global study
of PM2.5 estimates combining scaling and statistical methods shows that
their validation R2 of long-term average PM2.5 was 0.67 for their
first-stage scaling method (van Donkelaar et al., 2016).
Overall spatial and temporal trend of PM2.5 concentrations in
China from 2004 to 2017
Figure 4 shows that spatial distribution characteristics of annual mean
PM2.5 concentrations are similar among the years from 2004 to 2017. The
most polluted area was the North China Plain (including the south of the
Jingjinji region, Henan, and Shandong Provinces), which was also the largest polluted
area. The Sichuan Basin (including eastern Sichuan and western Chongqing) is
another polluted area. The cleanest areas were mainly located in Tibet,
Hainan, Taiwan, Yunnan, and the north of Inner Mongolia. The spatial
distributions of satellite-derived PM2.5 concentrations from 2013 to
2017 are consistent with the spatial characteristics of ground-monitored
PM2.5 (Fig. S2).
Spatial distributions of annual mean satellite-derived PM2.5
concentrations from 2004 to 2017.
Spatial distributions of PM2.5 trends and significance levels
in China from 2004 to 2017.
Trends and 95 % confidence intervals (CIs) of PM2.5
concentrations for all of China and the Jingjinji, Yangtze River Delta, and Pearl
River Delta regions from 2004 to 2017.
PeriodTrendAll of ChinaJingjinji regionYangtze River DeltaPearl River Delta2004–2017Trend (µg m-3 year-1)-1.27-1.55-1.60-1.2795 % CI (µg m-3 year-1)(-1.50, -1.04)(-2.06, -1.03)(-2.02, -1.18)(-1.66, -0.88)Significancep<0.001p<0.001p<0.001p<0.0012005–2010Trend (µg m-3 year-1)0.410.260.61-1.2695 % CI (µg m-3 year-1)(-0.01, 0.82)(-0.83, 1.36)(-0.31, 1.54)(-2.73, 0.21)Significancep=0.055p=0.633p=0.191p=0.0912004–2007Trend (µg m-3 year-1)1.883.141.121.7295 % CI (µg m-3 year-1)(1.12, 2.64)(1.07, 5.22)(-0.51, 2.74)(-0.79, 4.23)Significancep<0.001p<0.005p=0.174p=0.1742007–2010Trend (µg m-3 year-1)-0.56-0.08-0.37-4.8195 % CI (µg m-3 year-1)(-1.12, 0.01)(-1.80, 1.64)(-2.10, 1.35)(-7.06, -2.55)Significancep=0.053p=0.927p=0.664p<0.0012010–2013Trend (µg m-3 year-1)-1.03-0.45-0.040.8995 % CI (µg m-3 year-1)(-1.84, -0.21)(-3.73, 2.83)(-2.16, 2.08)(-1.34, 3.13)Significancep<0.050p=0.783p=0.970p=0.4252010–2015Trend (µg m-3 year-1)-2.89-3.63-3.33-0.9095 % CI (µg m-3 year-1)(-3.50, -2.28)(-5.59, -1.68)(-4.76, -1.89)(-2.34, 0.54)Significancep<0.001p<0.001p<0.001p=0.2192013–2017Trend (µg m-3 year-1)-4.27-6.77-6.36-2.1195 % CI (µg m-3 year-1)(-5.20, -3.34)(-9.46, -4.07)(-8.38, -4.34)(-4.14, -0.09)Significancep<0.001p<0.001p<0.001p<0.050
Figure 5 shows the spatial distributions of PM2.5 trends and
significance levels in China from 2004 to 2017. Overall, the PM2.5
pollution level of most areas in China showed a decreasing trend
(p<0.05). Figure 6 and Table 2 show that the overall trends of
2004–2017 for all of China and the Jingjinji, Yangtze River Delta (YRD), and Pearl
River Delta (PRD) regions were -1.27, -1.55, -1.60, and -1.27µg m-3 year-1
(all p<0.001), respectively. Back to Fig. 4, we can
see that the decrease in PM2.5 mainly happened after 2013. PM2.5
concentrations showed an obvious increase from 2004 to 2007. The area with
PM2.5 concentrations higher than 100 µg m-3 continuously
expanded during this period. From 2008 to 2013, the pollution levels
plateaued in most areas. After 2013, the PM2.5 concentrations obviously
decreased.
PM2.5 trends for all of China and the Jingjinji, Yangtze River Delta
(YRD), and Pearl River Delta (PRD) regions from 2004 to 2017, and
corresponding air pollution control policies.
Effect of ECER policy during the 11th Five-Year Plan period
To assess the effect of ECER policy during the 11th FYP, we calculated the
trends of PM2.5 for 2005–2010, 2004–2007, and 2007–2010 for each grid
cell (Fig. 7).
Spatial distributions of PM2.5 trends and significance levels
in China from 2005 to 2010.
Compared to the base year (2005) of the 11th FYP period, the overall
PM2.5 pollution of 2010 did not show obvious change. Some of the area
showed decreasing trends (Fig. 7a) but the trends were insignificant
(Fig. 7b). Some regions (Shandong, Henan, and Jiangsu provinces and
northeastern China) showed a slight increasing trend (∼1–2 µg m-3 year-1, p<0.001).
Overall, the trends for all of China and the
Jingjinji, YRD, and PRD regions were all insignificant (0.41, 0.26, 0.61,
and -1.26µg m-3 year-1, and all p>0.1) during the 11th
FYP period.
However, when separating this period into two periods, we can see that
before 2007, the PM2.5 concentrations generally had significant
increasing trends (Fig. 7c, d), especially in the south of the Jingjinji
region and in Henan, Shandong, and Hubei provinces. The overall trends for all of China and
the Jingjinji region are 1.88 (p<0.001) and 3.14 µg m-3 year-1 (p<0.005)
(Table 2). The trends for YRD and PRD regions are
insignificant. During the 10th FYP period, China missed the emission
control goals. The emission of sulfur dioxide increased by ∼28 % (Xue et al., 2014; Schreifels et al., 2012). The 11th FYP for
National Economic and Social Development of China released in 2006 proposed
the ECER goals. However, China did not achieve the annual goal in 2006.
These could explain the increasing trend of PM2.5 during 2004–2007.
After that, China released the Comprehensive Working Plan on ECER
(http://www.gov.cn/zwgk/2007-06/03/content_634545.htm,
last access: 29 March 2019) in 2007 to strengthen the ECER measures. Major
control measures included (Schreifels et al., 2012)
implementing flue gas desulfurization for coal-fired power plants, closing
inefficient and backward production centers, implementing energy
conservation projects, increasing the pollution levy for SO2 emission,
recommending baghouse dust filters for industrial soot and dust emission
control, etc. As a result, great achievements had been made at the end of
11th FYP (Schreifels et al., 2012; Zhou et al., 2015): total emission
of SO2 decreased by ∼14 % compared to the level of
1995; approximately 86 % of the power plants were installed with
desulfurization facilities in 2010 compared to 14 % in 2005; nearly 80 GW of small coal-fired power units were closed during 2006–2010; soot emission
of coal-fired power plants in 2010 was reduced by 55.6 % compared with
that in 2005, etc.
Due to these control measures, the increasing trend of PM2.5 pollution
was suppressed after 2007. PM2.5 concentrations of central and
southern China decreased significantly, with highest trend of around -9µg m-3 year-1
(Fig. 7e, f), p<0.01). The south of Jingjinji
region and Henan, Shandong, and Hubei provinces, which had significantly
increased before 2007, showed insignificant trends (Fig. 7f,
p>0.05). Table 2 shows that the overall PM2.5 trend for
all of China was -0.56µg m-3 year-1 with marginal significance
(p=0.053). Overall trends for the Jingjinji and YRD regions were not significant
during the latter half of the 11th FYP period. And PM2.5
concentrations in the PRD region had a big drop (-4.81µg m-3 year-1,
p<0.001). Results show that although air pollution control policies
of the 11th FYP were not designed for PM2.5 prevention and control,
they still had co-benefits on PM2.5 pollution control. There were two
main reasons. First, SO2 is the precursor gas of sulfate. Previous
studies have shown that sulfate was the major component of PM2.5 during the 11th FYP period (Li et al., 2009, 2010; Pathak et al.,
2009). The reduction of SO2 could therefore contribute to the
suppression of increasing PM2.5 pollution. Second, the control of
industrial dust and soot, which include a portion of primary PM2.5
(Yao et al., 2009), also contributed to the PM2.5
pollution reduction.
Effect of air pollution control policies in the 12th Five-Year Plan
period (2011–2015)
Figure 8a and b show that most of the areas of China show a significant
decreasing trend during the 12th FYP period. PM2.5 concentrations of
all of China, Jingjinji, and YRD dropped by 2.89, 3.63,
and 3.33 µg m-3 year-1 (p<0.001). When considering the years from 2010 to
2013, although the overall trend of all of China was -1.03µg m-3 year-1 (p<0.05, Table 2), the decreasing trend mainly
happened in Xinjiang and central Inner Mongolia. The deserts in Xinjiang and
Inner Mongolia are the major sources of dust pollution in China. A recent
study showed that dust is the largest contributor to PM2.5 over this
region (Philip et al., 2014). The change in natural dust in desert areas may be
the major contributor to the decreasing trend of
PM2.5 during 2010–2013. Most of the polluted area in China did not
show obvious change (Fig. 8c and d). As we mentioned above, The ECER policy
during the 12th FYP period was basically the extension of the policy in the 11th FYP, which mainly focused on emissions reduction. Due to the development of the social economy, the ECER policy has shown limitations
in PM2.5 reduction. PM2.5 is a kind of composite pollutant and its
constituents includes primary particles and secondary particles such as
sulfate, nitrate, ammonium, organic carbon, elemental carbon, etc. With the
deepening of SO2 and industrial dust and soot emission reduction, their
contributions to PM2.5 pollution control would be reduced, although
the 12th FYP on Environmental Protection also proposed a 10 % reduction of
NOx from 2010 to 2015. However, along with economic growth in China,
the benefits of emission control for a single pollutant could be offset by
increased energy usage. Considering the complicated PM2.5 compositions,
comprehensive and coordinated control measures for multiple pollutants are
urgently needed.
Spatial distributions of PM2.5 trends and significance levels
in China from 2010 to 2017.
Therefore, China issued the 12th FYP on APPC-KR in late 2012, which is
the first special plan for air pollution prevention and control and focused
on air quality improvement. APPC-KR proposed a series of key projects which
included 477 SO2 treatment projects, 755 NOx treatment projects,
10 073 industrial soot and dust treatment projects, 1311 VOC treatment
projects in key industrial sectors, 281 vapor recovery projects for oil and
gas, 188 yellow-sticker vehicle elimination projects, 192 fugitive dust
comprehensive treatment projects, and 122 capacity building projects. An
English translation of APPC-KR and its key projects has been
prepared by the Clean Air Alliance of China (CAAC) and can be found elsewhere
(http://www.cleanairchina.org/product/6347.html, last access: 29 March 2019)
(CAAC, 2013c, a).
In addition, in 2012, China issued a new air quality standard, i.e., the
National Ambient Air Quality Standard of China (NAAQS) (GB 3095-2012).
Compared with the former NAAQS (GB 3095-1996) issued
in 1996, this new standard incorporated PM2.5 as a major control
pollutant. According to GB 3095-2012, the Level 1 annual mean standard of
PM2.5 is 15 µg m-3, which is assigned for protecting the air
quality of natural reserves and scenic areas and is equivalent to the World
Health Organization (WHO) Air Quality Interim Target-3 (IT-3). The
Level 2 standard of 35 µg m-3 is designated for residential,
cultural, industrial, and commercial areas, which is equivalent to WHO Air
Quality Interim Target-1 (IT-1). Meanwhile, a comprehensive real-time
air quality monitoring network covering 74 major Chinese cities was
established in late 2012.
Goals and accomplishments for key regions of the 12th FYP on APPC-KR.
The implementation of APPC-KR, together with the implementation of APPC-AP
starting from 2013 (shown in the following section), led to dramatic
drops in PM2.5 concentrations in China after 2013. Table 3 shows
PM2.5 concentration improvement goals and final accomplishments for key
regions (see Fig. S1) of the 12th FYP on APPC-KR calculated from
satellite PM2.5. Results show that all key regions accomplished the
goals except for Yinchuan. The changes in population-weighted averages also
show similar results. Overall, the 12th FYP on APPC-KR accomplished its
air pollution control goals. And the decrease in PM2.5 concentrations
was mainly attributable to the decrease after 2013.
Effect of Action Plan for Air Pollution Prevention and Control
(2013–2017)
China issued the APPC-AP (2013–2017) in late 2013, which further
strengthened the air pollution control measures and air quality improvement
goals. The air pollution control measures included 10 categories:
increase effort for comprehensive pollution control, reduce emissions of
multi-pollutants;
optimize industrial structure and promote industrial restructuring;
accelerate technology transformation and improve innovation capability;
adjust energy structure and increase clean energy supply;
strengthen environmental thresholds and optimize industrial layout;
promote the role of market mechanisms and improve environmental economic
policies;
improve law and regulation system and carry on supervision and management based
on law;
establish regional coordination mechanism and integrated regional
environmental management;
establish monitoring and warning system and cope with heavy pollution episodes;
clarify responsibilities of government, enterprise, and society and mobilize
public participation
Detailed measures of the APPC-AP can be found in English translation
at http://www.cleanairchina.org/product/6349.html (last access: 29 March 2019)
(CAAC, 2013b). To ensure that APPC-AP goals could be accomplished,
China adopted a temporary measure in 2017, i.e., the intensified supervision
for air pollution control in Jingjinji and the surrounding area
(http://www.gov.cn/hudong/2017-07/14/content_5210588.htm,
last access: 29 March 2019). There had been great achievements at the end of
2017. For example (Zheng et al., 2018),
71 % of the power plants met the ultralow emission levels; the average
efficiency of coal-fired power units decreased from 321 g.c.e. kWh-1 in 2013 to 309 g.c.e. kWh-1 in 2017;
non-methane volatile organic compound (NMVOC) emissions
were cut down by 30 % through the implementation of a leak detection and
repair (LDAR) program for the petrochemical industry; all coal boilers smaller
than 7 MW in urban areas were shut down; and all “yellow label” vehicles
(referring to gasoline and diesel vehicles that fail to meet Euro 1
and Euro 3 standards, respectively) were eliminated by the end of 2017, to name a few.
∗ See http://www.mee.gov.cn/gkml/sthjbgw/stbgth/201806/t20180601_442262.htm (last access:
29 March 2019).
The implementation of APPC-AP, together with the 12th FYP on APPC-KR, had
led to a dramatic drop in PM2.5 concentrations from 2013 to 2017 (Fig. 8e and f).
PM2.5 trends of 2013–2017 for all of China, Jingjinji,
YRD, and PRD regions were -4.27, -6.77, -6.36, and -2.11µg m-3 year-1 (all p<0.05),
respectively (Table 2). This is
comparable to a recent study (Silver et al., 2018), which
found that the median trend in annual mean PM2.5 concentration across all
ground air pollution monitoring stations is -3.4µg m-3 year-1 from
2015 to 2017. Table 4 shows PM2.5 concentration improvement goals and
final accomplishments for APPC-AP. The goals required that PM2.5
concentrations in Jingjinji, YRD, and PRD regions in 2017 should decrease
by 25 %, 20 %, and 15 % compared to 2012, and the annual mean
PM2.5 of Beijing should reach around 60 µg m-3. Since
there were no ground measurements in 2012, the Ministry of Ecology and
Environment (MEE) of China used 2013 as the base year to assess the
performance of APPC-AP
(http://www.mee.gov.cn/gkml/sthjbgw/stbgth/201806/t20180601_442262.htm, last access: 29 March 2019). To maintain consistency with the
official performance assessment, we also used 2013 as the base year. Results
show that the arithmetic average of satellite PM2.5 concentrations for
Jingjinji, YRD, and PRD regions decreased by 36.9 %, 37.1 %, and
14.0 %, respectively, and annual mean PM2.5 of Beijing was 44.67 µg m-3 in 2017. From the view of satellite, Jingjinji, YRD, and
Beijing accomplished their goals, and PRD was very close to the goal.
However, the pollution level was still higher than WHO Air Quality IT-1
level and NAAQS (GB 3095-2012) Level 2 annual PM2.5 standards (both 35 µg m-3).
According to the official results of APPC-AP performance assessment (Table 4), PM2.5 of Jingjinji, YRD, and PRD regions decreased by
39.6 %, 34.3 %, and 27.7 %, respectively. And annual mean PM2.5
of Beijing was 58 µg m-3 in 2017. Compared to the arithmetic
average satellite PM2.5, the populations weighted average results
(Table 4) are closer to the official results. The main reason is that
official performance assessment used ground measurements. However, the
spatial distribution of ground monitors is uneven. Most of the sites are
distributed in populated urban areas and only a few are located in rural
areas. Compared to ground monitors, satellite remote sensing has more
comprehensive spatial coverage. Figure S3 shows the spatial distribution of
satellite and ground PM2.5 concentrations of 2017 in Beijing. It can be
seen that the ground monitors are clustered in polluted urban centers. The
cleaner north and northwest of Beijing have few sites. Thus the population-weighted results of satellite PM2.5 are closer to the official results,
but still have differences. Since satellites have better spatial
coverage than ground monitors, satellite PM2.5 can better represent the
spatial variation of PM2.5 pollution. The population-weighted average
satellite PM2.5 can better represent the health impact of PM2.5
pollution. When using ground monitors to calculate the regional mean
concentrations, the weights of area and population for each site should be
considered.
Discussion and conclusions
Xue et al. (2019) developed a machine learning method to estimate
PM2.5 concentrations in China from 2000 to 2016. They reported that
overall trends of PM2.5 in China were 2.097 µg m-3 year-1
(p<0.001), 0.299 µg m-3 year-1 (p>0.05),
and -4.511µg m-3 year-1 (p<0.001) in 2000–2007, 2008–2013, and 2013–2016,
respectively. Lin et al. (2018) estimated high-resolution PM2.5
in annual scale in China from 2001 to 2015, and found national-scale trends of
0.04, -0.65, and -2.33µg m-3 year-1 in 2001–2005, 2005–2010, and 2011–2015, respectively.
Overall, our satellite-based PM2.5 trends are consistent with these two
recent studies, except that we found no significant trend from 2005 to 2010
(0.41 µg m-3 year-1 but p>0.05), which is different from the study of
Lin et al. (2018). The main reason could be that they did not include
western China in their study area, and statistical significance levels were
not reported in their study, which means that it is not known whether the
trend was significant or not.
Although there have been several studies on the historical trends
of PM2.5 in China, few have looked at the relations between the trends and
air pollution control policies. This paper reviewed the air pollution
control policies from 2005 to 2017. And for the first time we gave an
overall evaluation of the effects of these policies on PM2.5 pollution
improvement in China from the perspective of satellite remote sensing.
Results show that our satellite PM2.5 dataset is a good source to
evaluate the performance of air pollution policies. The trends of
satellite-derived PM2.5 concentrations are consistent with the
implementation of air pollution control policies in different periods.
The ECER policy implemented in the 11th FYP period (see Table 1 and Sect. 4.3)
had co-benefits with PM2.5 pollution control. The overall PM2.5
pollution decreased to a certain extent (-0.56µg m-3 year-1 for all of China, p=0.053) after 2007, but the effects were limited. The Environmental
Protection Plan and ECER policy during the 12th FYP period were basically
the extension of the 11th FYP policy, with additional total emission
control on NOx. However, the total emission control oriented policy had
shown its limitation. The PM2.5 concentrations of polluted areas did
not decrease from 2010 to 2013 (e.g., -0.45µg m-3 year-1 for
the Jingjinji region, p=0.783).
To address the PM2.5 pollution issue, China implemented two strict
policies: the 12th FYP on APPC-KR in 2012 and APPC-AP in 2013. The goal
of air quality improvement was proposed for the first time. Besides, China
incorporated PM2.5 as a major control pollutant into the National
Ambient Air Quality Standard (GB 3095-2012). All these policies (details can
be found in Table 1 and Sect. 4.4 and 4.5) led to dramatic decreases in PM2.5 after 2013 (-4.27µg m-3 year-1 for all of China,
p<0.001). And the implementation of these policies was also an
important mark that environmental management in China began to change from
total emission control oriented mode to environmental quality improvement
oriented mode.
It should be noted that interannual variation in meteorology has also
contributed to the changes in PM2.5. A recent study shows that
meteorological conditions contributed approximately 20 % of the PM2.5
reduction in Beijing from 2013 to 2017, while the control of anthropogenic
emissions contributed 80 % (Chen et al., 2019). In
addition, the slowdown of economic development after the financial crisis in
2008 might contribute to the PM2.5 emissions reduction. According to
the China Statistical Yearbook (NBS, 2018), the gross domestic product (GDP)
growth rate decreased from 14.2 % in 2007 to 6.9 % in 2017. However, the
GDP growth rates are still relatively high at the current stage
(6 %–7 %). Contrarily, the PM2.5 concentrations have
decreased dramatically. Without effective air pollution control policies,
the PM2.5 pollution level would not decrease rapidly. Therefore,
the effective air pollution control policy was the main reason for PM2.5
pollution reduction after 2013. Meteorological conditions also contributed a
small portion of PM2.5 reductions.
The trends in PM2.5 concentrations in China also showed spatial
heterogeneity. Multiple reasons may explain the regional differences, e.g.,
the pollution levels of base year, the regional differences of industrial
structures, the spatial heterogeneity of anthropogenic and natural
emissions, economic and industry development differences, variations of
regional policies, and variations of meteorological conditions.
Currently, China has achieved great success in PM2.5 pollution control.
However, PM2.5 concentrations in many areas are still much higher than
the Level 2 annual PM2.5 standard of 35 µg m-3 of GB 3095-2012,
which corresponds to WHO Air Quality IT-1. China has implemented
a new air pollution control policy from 2018, i.e., the Three-year Action
Plan to Win the Battle for Blue Skies (2018–2020). China's air quality is
expected to be further improved in the next 3 years.
This study extended the satellite PM2.5 dataset in our previous study
(Ma et al., 2016) to the year of 2017 and obtained longer time
series of satellite PM2.5 data, which can provide more
spatially resolved and highly accurate PM2.5 data for epidemiological,
health impact assessment, and social economic impact studies in China.
Data availability
The satellite-derived PM2.5 data
used in this study can be requested from the corresponding author (jbi@nju.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-6861-2019-supplement.
Author contributions
JB conceived and designed the study. RL collected and processed the
data. ZM and YL performed statistical modeling for satellite
PM2.5 predictions. ZM analyzed the spatiotemporal trends of
PM2.5 concentrations. JB prepared and analyzed the air pollution
control policies. ZM prepared the paper with contributions from all
coauthors.
Competing interests
The authors declare that they have no conflict of
interest.
Financial support
This research has
been supported by the National Natural Science Foundation of China (grant
nos. 91644220, 71433007, and 41601546) and the Fundamental Research Funds
for the Central Universities of China (grant no. 0211-14380078).
Review statement
This paper was edited by Dominick Spracklen and reviewed by two anonymous referees.
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