Haze pollution caused by PM2.5 is the largest air
quality concern in China in recent years. Long-term measurements of
PM2.5 and the precursors and chemical speciation are crucially important
for evaluating the efficiency of emission control, understanding formation
and transport of PM2.5 associated with the change of meteorology, and
accessing the impact of human activities on regional climate change.
Here we reported long-term continuous measurements of PM2.5, chemical
components, and their precursors at a regional background station, the
Station for Observing Regional Processes of the Earth System (SORPES), in
Nanjing, eastern China, since 2011. We found that PM2.5 at the station
has experienced a substantial decrease (-9.1 % yr-1), accompanied by even
a very significant reduction of SO2 (-16.7 % yr-1), since the national
“Ten Measures of Air” took action in 2013. Control of open biomass
burning and fossil-fuel combustion are the two dominant factors that
influence the PM2.5 reduction in early summer and winter, respectively.
In the cold season (November–January), the nitrate fraction was significantly
increased, especially when air masses were transported from the north. More NH3
available from a substantial reduction of SO2 and increased oxidization
capacity are the main factors for the enhanced nitrate formation. The
changes of year-to-year meteorology have contributed to 24 % of the PM2.5
decrease since 2013. This study highlights several important implications on
air pollution control policy in China.
Introduction
Fine particulate matter, with an aerodynamic diameter smaller than 2.5 µm (PM2.5), which impacts human health and visibility negatively (Cao
et al., 2012; Zhang et al., 2017; Malm et al., 2004), has been considered to be
one of the main air pollutants in China (He et al., 2001; Yao et al., 2002;
Sun et al., 2006; Pathak et al., 2009; Zhang et al., 2015; Wang et al.,
2017). To tackle this great challenge, China has been implementing the
“Action Plan for Air Pollution Prevention and Control” (i.e., the so-called
national “Ten Measures of air”) in several developed critical regions
since 2013 (Sheehan et al., 2014; Wang et al., 2017; Liu et al., 2018, 2019; Zheng
et al., 2018). Measurement data, mainly from the ambient
air quality monitoring network, showed some evidence of improved haze
pollution in many cities in recent years (Wang et al., 2017; Lang et al.,
2017; Zhang et al., 2019). However, to give a robust and quantitative
assessment of the change due to specific emission reduction, high-quality
long-term continuous measurements of PM2.5 and its chemical composition
and precursors together with comprehensive data analysis and model
simulations are needed because PM2.5 has complex chemical compositions,
sources and formation mechanisms (Pathak et al., 2009; G. Wang et al.,
2016; Cheng et al., 2016; Wen et al., 2018), and a strong dependence on
year-to-year meteorology (Zheng et al., 2015; Zhang et al., 2016, 2019).
The Yangtze River Delta (YRD) is one of the developed and highly populated
regions in China (Ding et al., 2013a; H. L. Wang et al., 2016). Under
unfavorable meteorological conditions, PM2.5 in this region could reach
very high concentrations in winter and early summer, contributed mainly by
fossil-fuel combustion and agricultural straw burning, respectively (Ding et
al., 2013a, b; Cheng et al., 2014; Huang et al., 2016; Ding et al., 2016a; H. L. Wang et al., 2016; J. Wang et al., 2018). Some recent studies reported the
change of PM2.5 and its chemical components in some YRD cities in a
short period, e.g., 2–3 years (H. L. Wang et al., 2016; Wang et al., 2017; Sun
et al., 2018); however, so far there has been a lack of long-term observational
study with comprehensive measurements that cover the entire national
“Ten Measures” period, i.e., 2013–2017, in this region.
In this study, we report the long-term continuous ground-based measurements
of PM2.5 and its chemical compositions as well as main precursors at
the Station for Observing Regional Processes of the Earth System (SORPES) in
Nanjing, in the western YRD, for the period 2011–2018. Based on Lagrangian
dispersion modeling and comprehensive analysis with other supporting data,
we investigate the impacts of emissions from fossil-fuel combustion and open
biomass burning (BB) and of year-to-year meteorology on the trend of primary
and secondary PM2.5 in this region.
Data and methodsBrief introduction to SORPES and
instrumentations
SORPES (32∘07′14′′ N, 118∘57′10′′ E; 62 m a.s.l.) is a cross-disciplinary research and
experiment platform that was established in 2011 to understand the impact of human
activities in the rapidly urbanized and industrialized eastern China region
(Ding et al., 2013a, 2016b). Because of the unique geographical location,
i.e., downwind of the North China Plain (NCP) and the YRD city cluster but
upwind of downtown Nanjing (with a distance of about 20 km), this site can be
considered to be a regional background station for air quality studies in
eastern China (Ding et al., 2016b). Regional anthropogenic plumes from the
NCP to YRD city clusters and the early summer open BB smoke in eastern China
(Fig. 1) can influence this site under complex multi-scale transport
associated with the Asian monsoon (Ding et al., 2013a, 2016b).
Spatial distributions of (a) anthropogenic
emission of primary PM2.5 in 2010 (data from the MIX inventory
available in MEIC database of Tsinghua University) and (b) averaged
carbon emission from open biomass burning in May–June during 2012–2017 (data
from GFEDv4 available at
https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1293).
Continuous measurement of PM2.5 mass concentration and its precursors,
e.g., sulfur dioxide (SO2), nitrogen oxides (NOx; nitric oxide
(NO) + nitrogen dioxide (NO2)), etc., started in August 2011. More
species, such as PM2.5 chemical compositions, including black carbon
(BC) and water-soluble ions (e.g., sulfate (SO42-), nitrate
(NO3-), ammonium (NH4+), potassium (K+), calcium
(Ca2+), etc.), have been measured since 2013 (Xie et al., 2015; Sun et
al., 2018; J. Wang et al., 2018; Shen et al., 2018). Details of the data used in
this study,
including instrumentation, the measurement period, and data coverage, are given in Table S1 in the Supplement.
Briefly, instruments and analyzers for chemical compositions measurement are
housed in a two-floor building on the top of a small hill at about 42 m a.g.l. PM2.5 mass concentration is measured by the online
analyzer based on the light scattering and beta-ray absorption method
(Thermo Fisher Scientific, Model 5030 SHARP, USA). The water-soluble inorganic
ions, including SO42-, NO3-, NH4+, K+,
etc., are detected by the Monitor for AeRosols and GAses in Ambient air
(MARGA; Metrohm, Switzerland; Xie et al., 2015; Sun et al., 2018). BC was
measured using a seven-wavelength aethalometer (AE-31, Magee Scientific), and the
data at a wavelength of 880 nm were used in this study (Shen et al., 2018;
Virkkula et al., 2015). SO2 and NOx are measured by the online
analyzers with a time resolution of 1–5 min (Thermo Fisher Scientific, 43i
and 42i). All the instruments are routinely calibrated for
different durations. During the continuous measurement period, most of the
instruments have data coverage of over 80 % (Table S1).
Lagrangian dispersion modeling and other data sources
In order to help quantitatively understand the influence of the year-to-year
change in meteorology on air pollutant transport and dispersion, we
conducted backward Lagrangian particle dispersion modeling (LPDM) using the
HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model
(Stein et al., 2015). We used the model to calculate single-particle
backward trajectories and to conduct cluster analysis. We also estimated the
hourly PM2.5 concentration based on the particle dispersion simulations
following a method developed by Ding et al. (2013a, c). Briefly, for
each hour during the study period, the model was run backwardly for 2 d and 7 d,
with 3000 particles being released every hour at the altitude of 100 m
over SORPES. The model calculated the position of particles by
mean wind and a turbulence transport component, and the spatiotemporal
distribution of these particles was further used to calculate the potential
source contribution by using the footprint “retroplume”, i.e., the residence
time at the altitude of 100 m a.g.l. and an emission
inventory (Ding et al., 2013a, c). Global Data Assimilation System
(GDAS) data were used to drive the model, and the MIX emission inventory
database for the year 2010 (Fig. 1a; M. Li et al., 2017) was used for a
quantitative estimation of PM2.5 concentration.
Besides the observations at SORPES and data used in the LPDM
simulations, various data are used to support the data analysis and
discussions. To identify the impact of agricultural straw burning, we used
the BB emission inventory from the Global Fire Emissions Database, Version 4.1
(with small fires; GFED4s; Giglio et al., 2013), and the MODIS (Moderate
Resolution Imaging Spectroradiometer) Thermal Anomalies/Fire Daily L3 Global
product (MOD/MYD14A1; Boschetti et al., 2009). The ERA5 reanalysis data
(https://cds.climate.copernicus.eu/cdsapp#!/home, last access: 1 February 2019)
from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Tropical Rainfall Measuring Mission (TRMM) satellite-observed
precipitation (Huffman et al., 2007) are also used to investigate the
year-to-year difference of meteorology that may influence the PM2.5
concentration.
Results and discussion
Based on continuous measurement at SORPES, Fig. 2 shows the
trends of PM2.5 mass concentration and key precursors (SO2 and
NOx (NO+NO2)) since 2011 and the main PM2.5 chemical
components (BC, SO42-, NO3-, and NH4+) since
2013. Considering the difference in the observation duration and the
specific emission control in eastern China associated with the national “Ten Measures of Air” since 2013 (Sheehan et al., 2014; Wang et al., 2017; Liu
et al., 2018), we conducted linear regression for the two periods: August
2011–July 2018 and August 2013–July 2018. It can be found
that PM2.5 concentration and the mixing ratio of two precursors have shown an
overall decreasing trend during the past 7 years (-6.4,
-12.1, -4.6, and -11.1 % yr-1 for PM2.5, SO2,
NO2, and NO, respectively) but more remarkable decreasing trends
(-9.1, -16.7, -5.2, and -14.1 % yr-1 for PM2.5,
SO2, NO2, and NO, respectively) since 2013. For
SO2, the 5-year reduction almost reached 70 %–80 % and showed
a significantly higher reduction rate in comparison with NOx. It demonstrates
that the YRD region, as one of the main industry bases with a huge
consumption of coal, achieved a very big success of air pollution prevention
from desulfurization in power plants and factories and from the replacement of
coal with natural gases and electricity in recent years. In fact, a nationwide
significant reduction of SO2 in the past few years has been also
reported by ground and satellite measurements and emission estimations (C. Li et al., 2017; Liu et al., 2018; Zheng et al., 2018).
Monthly statistics and trends for (a)
PM2.5, (b)SO2, (c) NO, (d)NO2,
(e) BC, (f)SO42-, (g)NH4+,
and (h)NO3- observed at SORPES. Note: for
BC, SO42-, NH4+, and NO3-, only data during
August 2013–July 2018 are shown here. The solid lines marked with open
circles represent the monthly medium value, and shaded areas mark the data
from 25th to 75th percentiles. Dashed and solid lines show the
linear regression fitting for data during 2011–2018 and 2013–2018,
respectively.
Figure 2d shows that NO2, another precursor of nitrate, has shown a
decreasing trend (-5.2 % yr-1) since 2013, which is much smaller than that
of SO2. Accordingly, the two secondary inorganic water-soluble ions,
SO42- and NO3-, showed different trends, with the former
showing a more significant reduction (-10.6 % yr-1 vs. -5.8 % yr-1). BC, an
important particle mainly from primary emission of incomplete combustion but
with a significant impact on climate and aerosol–boundary-layer feedback (Bond
et al., 2013; Ding et al., 2016a; Z. Wang et al., 2018; Huang et al., 2018),
showed a decreasing trend (about -8.4 % yr-1) between the trends of sulfate and
nitrate. Although BC is a short-lived climate forcer contributing to global
warming (Bond et al., 2013; IPCC, 2013; Ding et al., 2016a), so far there
has been no specific reduction-policy focus on it. Here the results show that the
efforts in reducing PM2.5 also caused BC reduction, which co-benefited
the mitigation of global warming (Bond et al., 2013; IPCC, 2013).
In eastern China, agricultural straw burning is particularly strong in early
summer, i.e., from the middle of May to the middle of June, after the harvest of wheat
(Ding et al., 2013a, b; Cheng, 2013; Huang et al., 2016; Chen et al., 2017).
Intensive emission from these activities could cause a second maximum of
PM2.5 in early summer (Ding et al., 2013a) and also severe haze events
with high concentrations of PM2.5 and other pollutants. For example, SORPES recorded an hourly concentration of PM2.5 of over 400 µg m-3 on 10 June 2012 (Ding et al., 2013a; Xie et al., 2015;
Nie et al., 2015). To examine the change before and after the “Ten Measures” took action, Fig. 3a presents the seasonal variation in
PM2.5 mass concentration averaged for the periods of 2011–2014 and
2015–2018. It clearly shows that the secondary maximum of
PM2.5 mass concentration in early summer was missing in recent years,
instead of having a relatively flat change (Fig. 3a).
(a) Seasonal variation in PM2.5 mass concentration
measured at SORPES during 2011–2014 and 2015–2018.
(b) Scatter plots of K+ as a function of PM2.5
concentration measured at SORPES and the sum of MODIS fire
counts and of TRMM precipitation in the BB domain (square in Fig. 1b) during
15 May–20 June, 2012–2018.
Since the “Ten Measures” took action in 2013, the Chinese government has
conducted very strict emission control of agricultural straw burning by
using a real-time satellite as a tool to monitor these activities (Chen et
al., 2017; Wang et al., 2017). The MODIS satellite fire count data have
demonstrated the significant reduction in BB activities in this region since
2013 (Fig. 3b). To further investigate the potential impacts of other
factors, i.e., the change of air pollutant transport associated with
circulation patterns, we conducted LPDM simulations for the BB seasons (15
May–20 June) during the two 3-year periods. Figure 4 shows that the
averaged transport pathways of air masses did not change much for the two
periods, but the MODIS satellite fire counts showed a distinct difference in
both total amount and spatial distribution. This BB-emitted smoke could be
transported to SORPES in 2 d when the north wind prevailed.
Averaged retroplume from 2 d backward Lagrangian
dispersion modeling and MODIS fire counts for the period of 15 May–20 June
during (a) 2012–2014 and (b) 2016–2018.
Figure 3b also shows the averaged TRMM precipitation in the same area as the
fire count data (i.e., dashed square in Fig. 1b) during 15 May–20 June in
2011–2018. It is indicative of a certain reverse correlation of
precipitation with the total amount fire counts. However, the relationship
between precipitation and BB is complicated. On one hand, the harvest season
is generally before the meiyu season in this region (Kitoh and Takao,
2006). Farmers usually choose continuous sunny days to harvest and to dry the
wheat, while the straw also burns easily in dry conditions (Feng
et al., 2019). On the other hand, precipitation can influence the wet deposition
of smoke (Uematsu et al., 2010). Meanwhile, BB smoke has been demonstrated
to modify rainfall. For example, Ding et al. (2013b) and Huang et al. (2016)
reported a case of suppressed rainfall and a changed rainfall pattern by BB
smoke in this region based on the SORPES observations and numerical
modeling. Although more quantitative studies are still needed, here the
year-to-year variation in precipitation should not be the dominant factor
influencing the substantial decreasing the PM2.5, especially for the
period between 2013 and 2018, when the precipitation did not show a significant
trend.
To confirm the impact of BB on the PM2.5 reduction, we further
investigate the measured BB tracer, fine particulate K+, as previous
studies (Ding et al., 2013b; Xie et al., 2015; Nie et al., 2015; Zhou et
al., 2017) suggested that it is a good tracer for agricultural straw burning
in this region. The scatter plot of K+ as a function of PM2.5 mass
concentration, color-coded with time and given in Fig. 3b, clearly shows a
dramatic decrease in the K+/PM2.5 proportion at SORPES, especially after 2015. Using the concentration of K+ as a
threshold, we identified BB (K+ higher than the 75 % percentile) and
non_BB cases (K+ lower than 25 % percentile) and
showed the scatter plots of sulfate and nitrate as a function of
PM2.5 in Fig. 5. It confirms that very efficient control of open BB
emission is the dominant factor that influenced the early summer PM2.5
reduction in this region.
Scatter plots of (a) sulfate and (b)
nitrate as a function of PM2.5 for biomass burning (with K+ higher
than 75 % percentile) and non-biomass burning (with K+ lower than
25 % percentile) cases for the period of 15 May–20 June during 2012–2018.
Besides the early summer, months in the cold season, e.g., November, December,
and January (NDJ), experienced strong PM2.5 reduction in the past few
years (Fig. 3a). In order to further explore the inter-relationship among
precursors and secondary and primary particles, we show the scatter plots of
different species color-coded with time in Fig. 6. For the SO2–NOx
plot, it changed from a bifurcation pattern in the earlier years to the latest linear
one, which is monotonous (Fig. 6a). The bifurcated pattern of SO2 versus NOx
indicates the impact of emissions from elevated coal-burning point sources
(with high SO2/NOx ratio) and scattered vehicle sources (Wang et
al., 2002). The pattern change in recent years further confirms that the
reduction of SO2 was mainly due to efficient control and coal replacement with natural gas or electricity from large
elevated coal-burning sources, such as power plants (Wang et al., 2002; Liu et al., 2018).
Scatter plots of (a)SO2 versus NOx,
(b) SNA (i.e., the sum of sulfate, nitrate, and ammonium) versus BC,
(c)SO42- versus PM2.5, and (d)NO3- versus PM2.5 in November, December, and January during
2013–2018.
Averaged retroplume for air masses from (a) NCP
(C3 in Fig. S1), (b) central–eastern China plain (C2 in Fig. S1),
and (c) YRD (C1 and C5 in Fig. S1), and (d–f) a comparison
of pie charts of SNA and BC in PM2.5 for the three types of air masses
in NDJ of 2013 (November and December 2013 and January 2014) and 2017
(November and December 2017 and January 2018), corresponding to the three
transport pathways of different air masses given in (a–c). Note: averaged PM2.5 concentrations for the corresponding air masses and periods are
shown in the top left of each pie chart and also indicated by the size of
the pie charts. The reduction rates between the two periods are shown in red
in the top right of right panel (in percentage).
For the scatter plot of SNA (the sum of sulfate, nitrate, and ammonium) versus BC (Fig. 6b), a less-changed SNA/BC slope
from 2014 to 2018 somehow suggests the co-benefited reduction of short-lived
climate forcers from the mitigation of haze pollution in China. Since SNA is
mainly secondary products due to oxidation of SO2 and NOx and
neutralization of NH3 (Pinder et al., 2007) and BC is a tracer of
primary pollutants from combustion sources (Bond et al., 2013), the similar
slope here also indicates that the overall proportion of secondary particles
in PM2.5 was less changed. However, the sulfate / PM2.5 ratio was
significantly reduced from 2014 to 2018 (Fig. 6c), linked to the remarkable
reduction of its precursor SO2, as shown in Fig. 2b. However,
for nitrate, an overall increased nitrate / PM2.5 ratio could be clearly
seen in recent years, especially for 2018, despite of a moderate decreasing
of NO2 (Fig. 2d). It is well-known that PM2.5 has a
nonlinear response to the reduction of sulfate because decreases in sulfate
may increase aerosols when more nitric acid may enter the aerosol phase
(West et al., 1999; Pinder et al., 2007; Liu et al., 2019). Here the
regional-scale sulfate reduction should have increased the availability of
NH3 for the formation of nitrate (Pathak et al., 2009; Huang et al.,
2012; Wang et al., 2011; Liu et al., 2018, 2019). In addition,
the increased atmospheric oxidization capacity will also enhance the
formation of nitrate (Wen et al., 2018; Liu et al., 2019).
To further investigate the change in SNA and BC associated with air masses
from main source regions in eastern China, we carried out back-trajectory
cluster analysis for the months of NDJ during 2013–2018 (Fig. S1). In Fig. 7, we show the pie charts of SNA and BC in PM2.5 at SORPES between 2013
and 2017, i.e., the beginning and end of the first 5-year “Ten Measures”,
for air masses that originated from the NCP (Fig. 7a, d), the central–eastern China plain
(Fig. 7b and e), and the YRD (Fig. 7c and f). The results show a
significantly higher reduction (-52.3 %) in the air masses from the NCP than
the other two areas (∼ 32 %), indicating more strict and efficient
control in northern China (Liu et al., 2019; Li et al., 2019). For the fraction
of chemical composition, sulfate shows a significant decrease in the NCP (from
23 % to 19 %) and YRD (from 18 % to 14 %), but nitrate shows
a significant increase (from 24 % to 29 % for YRD air masses and from 24 % to 32 %
for air masses from the two regions in the north), while ammonium shows
a 1 % increase in all three regions. These results confirmed the
increased availability of NH3 for the formation of SNA (Pathak et al.,
2009; Liu et al., 2018, 2019; Li et al., 2019) and suggest a
stricter NH3 emission reduction as a potentially efficient means for
further PM2.5 mitigation in eastern China.
Meteorological conditions, especially transport and dispersion, could
significantly influence PM2.5 concentration (Ding et al., 2013a, b; Zhang
et al., 2016). From the air quality management perspectives, it is very
important to quantify the influence of emission reduction and year-to-year
change in meteorology based on the observed trend of air pollutants. Because
NDJ is the 3-month period with the highest PM2.5 concentration at SORPES
(Fig. 3a), the trend during these months should also dominate the overall
trend in the annual average. In addition, the total precipitation during this
period was relatively low (upper panel of Fig. 8), indicating the limited
influence of wet deposition. Considering the limitation of LPDM in
characterizing wet deposition and also secondary PM formation, we only chose
NDJ to conduct the LPDM simulations based on the fixed MIX emission
inventory for 2010 (M. Li et al., 2017) to quantify the impacts from
emission reduction and from the change in meteorology. To better
characterize the influence of year-to-year differences in meteorology alone
based on LPDM, we scaled the simulation results to make the modeled median
values equal to observation for the first year, i.e., November 2011–January
2012. This procedure removes other systematic differences between the LPDM
simulation and the observation, e.g., uncertainty of the emission inventory,
parameterization of the boundary layer, etc., in the model. In fact, for the
results with all years scaled using this method, the LPDM simulations could
reproduce the day-to-day variation in PM2.5 well (Fig. S2). The good
agreement implies that the primary and secondary PM2.5 had a consistent
change with day-to-day weather, which controls the transport and dispersion
of pollutants.
Figure 8 shows the trends of observed PM2.5 concentration at SORPES and the scaled LPDM simulation results to the first-year observation
(i.e., 2011–2012) in NDJ from 2012 to 2018. The observations in the 3
months show a consistent trend, with the annual average given in Fig. 1a.
From the LPDM simulation results based on the fixed emission inventory, we can
find that the meteorology-influenced PM2.5 had strong year-to-year
variation, with a minimum medium value of 84.8 µg m-3 in
2011–2012 to 108.4 µg m-3 in 2013–2014, i.e., with a year-to-year
difference of up to 28 %. The differences in the 2 years were mainly due to
different transport and dispersion patterns related to different large-scale
circulations (Fig. 9). In NDJ of 2013–2014, eastern China was
dominated by a stagnant high isolated from the continental high, causing
more subregional influence of air masses in eastern China (Fig. 9d)
than NDJ of 2011–2012. For the LPDM simulations with fixed emission
inventory, an increasing trend (about 1.1 % yr-1) existed for the entire 7 years, but a moderate decreasing trend (-2.6 % yr-1) existed in the last 5 years
(Fig. 8). Here the distinctly different trends for the 5-year and 7-year
periods indicate that the interannual variation could substantially
influence the understanding of the trend in air quality for a period of less than
1 decade. For the period since 2013, i.e., when the “Ten Measures” took
action, the observed decrease in PM2.5 (-10.9 % yr-1) are mainly
induced by emission reduction (76 %) and the year-to-year change in
meteorology contributed to the remaining 24 %, i.e., about a quarter of the
overall decrease. Here our estimation is consistent with that by Zhang et al. (2019), in which meteorology was estimated to contribute 20 %–30 % of
the deceased trend in the YRD region during the period 2013–2017.
Statistics of PM2.5 concentrations from the SORPES
observation and the scaled LPDM simulations to the first-year observation
(lower panel) and a sum of 3-month TRMM precipitation averaged in a
2∘×2∘ grid (31–33∘ N, 118–120∘ E) around SORPES (upper panel)
for November–December–January during 2011–2018.
Averaged sea-level pressure and wind flows for (a) November 2011–January 2012 and (b) November 2013–January 2014,
and in (c) and (d) averaged retroplume from 2 d backward
Lagrangian dispersion modeling for the corresponding period in (a)
and (b), respectively, is shown.
Conclusions
In this study, we make a comprehensive analysis based on long-term
continuous high-quality measurements of PM2.5, relevant chemical
compositions, and precursors at a regional background station, SORPES, in
Nanjing, eastern China, since 2011. By utilizing Lagrangian dispersion
modeling and various data, we quantified the impact of changes in different
emission sources and in year-to-year meteorology on the observed trends,
especially after the implementation of “Action Plan for Air Pollution
Prevention and Control” (i.e., the national “Ten Measures of Air”) in 2013.
The main conclusions are given below:
We found a substantial reduction of PM2.5 together with a stronger reduction of SO2 at SORPES during 2011–2018 and much
faster decreasing trends (-9.1 % yr-1 in PM2.5 and -16.7 % yr-1 in
SO2) associated with a concurrent reduction in sulfate (-10.6 % yr-1),
nitrate (-5.8 % yr-1), and BC (-8.4 % yr-1) during 2013–2018, i.e., after the
“Ten Measures” were implemented.
The early summer agricultural straw burning has been found to have
significantly reduced since 2013 in eastern China. It significantly
reduced PM2.5 concentration at SORPES during May–June,
resulting in a change in the seasonal pattern of PM2.5. In the cold season
(November–January), the fraction of nitrate in PM2.5 was significantly
increased, especially when air masses came from the north. Besides an increased
oxidization capacity, more NH3 being available for nitrate formation under
the condition of reduced sulfate associated with a substantial reduction of
SO2 is the main reason causing the enhanced nitrate formation. A
stricter NH3 emission reduction is suggested for further PM2.5
mitigation in eastern China.
The year-to-year difference in meteorological conditions could cause a strong
change in wintertime PM2.5 concentrations by mainly influencing the
transport and dispersion of air pollutants. The change in meteorology
contributed to 24 % of the observed decrease in PM2.5 at the SORPES
station in November–January during 2013–2018, with the remaining 76 % caused
mainly by emission reduction.
This study demonstrates the unique role of long-term high-quality continuous
measurement at a regional background station in understanding the
impact of emission sources, chemical mechanisms, and meteorology processes on
the change of atmospheric components. Based on comprehensive and in-depth
analysis of the data, the long-term measurements can provide a quantitative
understanding about the large-scale air quality measures and can also raise
some insights on policy-making for future air pollution prevention and
control.
Data availability
The ERA-5 global reanalysis
data are available at https://cds.climate.copernicus.eu/cdsapp\#!/home (Copernicus Climate Change Service, 2019). MIX
emission data are available at http://www.meicmodel.org/dataset-mix.html (last access: 15 March 2019). Biomass burning emission data are available at https://www.geo.vu.nl/~gwerf/GFED/GFED4/ (van der Werf et al., 2017). MODIS satellite fire count data are available at
https://e4ftl01.cr.usgs.gov/MOTA/ (NASA, 2019), and the TRMM precipitation
data are available at https://disc.gsfc.nasa.gov/datasets/TRMM_3B42_Daily_7/summary?keywords=TRMM (last access: 15 March 2019). The SORPES data in
this paper will be publicly available when the paper is published.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-11791-2019-supplement.
Author contributions
AD designed this study, carried out the data analysis,
and wrote the paper, with contributions from all co-authors. XH participated
the data analysis and modeling and plotted some figures. WN, XC, ZX, LZ, ZNX,
YX, XQ, YS, PS, JW, LW, WQ, and XZ conducted the measurements at SORPES.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank colleagues and students in the School of Atmospheric
Sciences at Nanjing University and Markku Kulmala's group at the
University of Helsinki for their contributions to the development of SORPES
and the maintenance of the measurements.
Financial support
This research has been supported by the Ministry of Science and Technology, China (grant nos. 2016YFC0200500, 2018YFC0213800, and 2016YFC0202000), and the National Natural Science Foundation of China (grant nos. 41725020 and 41621005).
Review statement
This paper was edited by Qiang Zhang and reviewed by three anonymous referees.
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