Surface ozone (O3) pollution during summer (June–August) over eastern
China has become more severe in recent years, resulting in a co-occurrence
of surface O3 and PM2.5 (particulate matter with aerodynamic
diameters ≤ 2.5 µm in the air) pollution. However, the mechanisms
regarding how the synoptic weather pattern (SWP) might influence this compound
pollution remain unclear. In this study, we applied the T-mode principal
component analysis (T-PCA) method to objectively classify the occurrence of
four SWPs over eastern China, based on the
geopotential heights at 500 hPa during summer (2015–2018). These four SWPs
over eastern China were closely related to the western Pacific subtropical
high (WPSH), exhibiting significant intra-seasonal and interannual
variations. Based on ground-level air quality observations, remarkable
spatial and temporal disparities of surface O3 and PM2.5 pollution
were also found under the four SWPs. In particular, there were two SWPs that
were sensitive to compound pollution (Type 1 and Type 2). Type 1 was
characterized by a stable WPSH ridge with its axis at about 22∘ N
and the rain belt located south of the Yangtze River Delta (YRD); Type 2 also exhibited WPSH dominance (ridge axis at ∼ 25∘ N) but with the rain belt (over the YRD) at a higher latitude
compared to Type 1. In general, SWPs have played an important role as
driving factors of surface O3–PM2.5 compound pollution in a
regional context. Our findings demonstrate the important role played by SWPs
in driving regional surface O3–PM2.5 compound pollution, in
addition to the large quantities of emissions, and may also provide insights
into the regional co-occurring high levels of both PM2.5 and O3
via the effects of certain meteorological factors.
Introduction
In recent years, China has been experiencing serious air pollution problems
owing to its enormous emissions of polluting gases (e.g., sulfur dioxide and
nitrogen dioxide (NO2)) and aerosol particulates (e.g.,
particulate matter with aerodynamic diameters ≤ 2.5 or 10 µm in
the air, abbreviated to PM2.5 and PM10, respectively) associated
with its rapid economic development, industrialization and urbanization,
together with certain unfavorable meteorological conditions
(Wang
and Chen, 2016; Zhang et al., 2014, 2016). In particular,
atmospheric compound pollution has become serious
(Li et al., 2019; Saikawa et al., 2017; C. Zhang et al., 2019), especially for the
economically developed and densely populated eastern urban agglomerations of
China, such as the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD)
and Pearl River Delta (PRD) regions
(Cai et al., 2017; Du et al., 2019; Ji et al., 2018; Li et al., 2020), exerting a
severe threat in terms of public health, economy and society
(Chen
et al., 2019; Cohen et al., 2017; Day et al., 2017; Yim et al., 2019).
In general, significant diurnal variation of PM2.5 pollution has been
observed, possibly due to obvious local emissions caused by industrial
production and human activities related to daily living
(Amil
et al., 2016; H. Liu et al., 2019). In particular, the pollution level tends
to be higher during the morning and evening of a normal weekday, with a
weakened effect found in the afternoon, possibly caused by the co-effects of
the boundary layer structure and anthropogenic emissions. There has also
been seasonal variation of PM2.5 pollution detected across China,
indicating a higher level of pollution in winter than summer
(Ye
et al., 2018; Zhang and Cao, 2015). The PM2.5 level in China showed a
steady increase from 2004 to 2007 and has since stabilized
(Ma et al.,
2016); however, there are still frequent PM2.5 pollution events in
autumn and winter
(Song
et al., 2017; Yang et al., 2018; Ye et al., 2018; Zhang et al., 2014). In
the past few years, the PM2.5 concentration in China has decreased
significantly as a result of measures introduced across the country that
have reduced multi-pollutant emissions, adjusted the energy structure and
increased the supply of clean energy
(Gui
et al., 2019; Yang et al., 2020; Q. Zhang et al., 2019; Zhang et al., 2020).
While PM2.5 is still one of the dominant air pollutants across China,
surface O3 pollution in summer has also gradually risen to prominence.
Several studies have even indicated that O3 might replace PM2.5
as the primary air pollutant during summer
(Li et al., 2019), which has
caught the attention of researchers in recent years. For instance,
Sun et al. (2016) showed that the
observed summertime O3 at Mt. Tai increased significantly by 1.7 ppbv yr-1 for the month of June and by 2.1 ppbv yr-1 for the months of
July–August during the period 2003–2015. Furthermore, an increase in the
maximum daily 8 h average concentration of O3 (MDA8 O3) at an
annual average rate of 4.6 % was reported by
Fan et al. (2020), albeit with a decrease
in the frequency of PM2.5 pollution.
The modulations of atmospheric circulation systems often lead to changes in
meteorological elements and thereby also affect the processes of pollutant
formation, transmission and diffusion. Furthermore, many studies have
indicated that PM2.5 and O3 pollution are strongly correlated with
local meteorological factors such as temperature, relative humidity (RH) and
wind speed (WS)
(Huang
et al., 2016; Miao et al., 2015; Shu et al., 2019; Tai et al., 2010).
Miao et al. (2015) suggested that a low boundary
layer height (BLH) and stable atmosphere would be an unfavorable condition
for the dispersion of winter aerosol pollution over the BTH region.
Zhang
et al. (2017) found that the majority of O3 extremes occurred with a
daily maximum temperature (Tmax) of between 300 and 320 K, a minimum RH
(RHmin) of less than 40 %, and a minimum WS of less than 3 m s-1,
through an analysis of extreme O3 and PM2.5 events from historical
data (30 years for O3 and 10 years for PM2.5) in the United
States. Furthermore, the number of annual extreme PM2.5 days was highly
positively correlated with extreme RHmin/Tmax days, and the correlation
coefficient between PM2.5 and RHmin (Tmax) was highest in urban and
suburban (rural) regions. Shi et al. (2020) studied the
sensitivity of O3-8 h (O3 8 h moving average) and PM2.5
associated with meteorological parameters. Their study focused on the air
pollution and meteorological conditions between January and July 2013, with
the results showing that temperature could have had the greatest impact on
the daily maximum O3-8 h, while the PM2.5 sensitivities were
negatively (positively) correlated with temperature, WS and BLH (absolute
humidity) in most regions of China. Miao et al. (2015) showed that RH was high when aerosol pollution occurred in the BTH
region. However, O3 pollution in China is more frequent in summer and
the warm and humid flow brought by the East Asian summer monsoon (EASM)
induces a hot and humid condition over the summer.
Zhao et al. (2019)
investigated the RH of O3 pollution in Shijiazhuang between 15 June and
14 July 2016 and found that the O3 concentration was higher at
moderate humidity (average RH during daytime from 10:00 to 17:00 LT was
40 %–50 %). Recently, Han et
al. (2020) assessed the impacts of local and synoptic meteorological factors
on the daily variability of surface O3 over eastern China. Their study
revealed that meteorological factors could explain ∼ 46 % of
the daily variations in summer surface O3. In particular, synoptic
factors contributed ∼ 37 % to the overall effects associated
with meteorological factors. Furthermore, six predominant synoptic weather
patterns (SWPs) were identified by a self-organizing map, and related
results indicated a weak cyclonic system and southward prevailing wind
induced the positive O3 anomalies over eastern China. The
abovementioned studies indicate that the variations of meteorological
factors play a non-negligible role in air pollution. Therefore,
classification of air pollution according to meteorological circulation has
become particularly important, not least because of its worth when applied
to air quality monitoring, forecasting and evaluation
(N. Liu
et al., 2019; Ning et al., 2019; Yang et al., 2018; Zheng et al., 2015).
Since the 1990s, it has become possible to objectively classify atmospheric
circulation conditions using weather data such as geopotential height (GH),
sea level pressure, WS and temperature, thus allowing the physical mechanism
of extreme weather to be better understood and analyzed. Compared with
subjective weather classification, the objective approach has been widely
used in air pollution research
(Miao
et al., 2017, 2019; Ning et al., 2018).
Miao et al. (2019), based on the
daily 900 hPa GH fields during winter in Beijing, identified seven synoptic
patterns using an objective approach and found that the weak northwesterly
prevailing winds and strong elevated thermal inversion layer, along with the
local emissions of aerosols, play a decisive role in the formation of heavy
pollution in Beijing. The authors also noted that southerly prevailing winds
can transport pollutants emitted from southern cities to Beijing.
Zheng et al. (2015) studied the
relationship between regional pollution and the patterns of large-scale
atmospheric circulation over eastern China in October from 2001 to 2010 and
identified six pollution types and three clean types. Specifically, weather
patterns such as a uniform surface pressure field in eastern China or a
steady straight westerly in the middle troposphere, particularly when at the
rear of an anticyclone at 850 hPa, were found to be typically responsible
for heavy pollution events. Many studies have suggested a modulating effect
of the East Asian summer monsoon (EASM) and western Pacific subtropical high
(WPSH) on air quality over China
(Li
et al., 2018; Yin et al., 2019; Zhao et al., 2010). In particular,
Li et al. (2018) applied RegCM4-CHEM to
analyze the differences in O3 between three strong and weak monsoon
years and found that the concentrations of O3 over the central and
eastern part of China were higher in strong EASM years than in weak EASM
years. The anomalous high-pressure system at 500 hPa, associated with
downward dry, hot air and intense solar radiation, can enhance the
photochemical reactions to elevate the production of tropospheric O3
(Gong and Liao,
2019; Yin et al., 2019). Furthermore,
Zhao and Wang (2017) and
Yin et al. (2019) noted that the positive GH
anomalies at high latitudes tended to significantly weaken the cold-air
advection from the north, resulting in locally high temperatures near the
surface in northern China, while the WPSH could transport sufficient water
vapor to the YRD region, leading to a decrease in surface O3. In
addition, different subregions can exhibit various distributions of
pollutants, even with identical emission scenarios
(Li
et al., 2019; Saikawa et al., 2017; C. Zhang et al., 2019). Also, it is still
unclear how the distribution of pollution responds locally to large-scale
atmospheric circulation patterns. Due to the variability of local
meteorological conditions under various synoptic weather types and the
modulation of the large-scale movement of the WPSH
(Li
et al., 2018; Wang et al., 2019; Yang et al., 2021; Zhao and Wang, 2017),
the causes and consequences of meteorological factors for the formation of
compound O3–PM2.5 pollution could be complex. Overall, the
mechanism by which the SWP modulates the characteristics of
O3–PM2.5 compound pollution has yet to be comprehensively
described.
In this study, the SWPs corresponding to the co-occurrence of O3 and
PM2.5 pollution during summertime were analyzed, with a focus on
eastern China (104–135∘ E, 17–53∘ N). Then, the synoptic causes of O3–PM2.5
compound pollution, as well as O3-only pollution, from the perspective
of the objective classification of atmospheric circulation patterns were
revealed. The findings are expected to provide a scientific reference for
the monitoring, forecasting and evaluation of summertime air pollution in
eastern China.
Data and methods
The air quality data, including PM2.5, NO2, O3 and
O3-8 h, are from the national 24 h continuous air quality observations
published by the China Environmental Monitoring Station
(http://www.cnemc.cn/, last access: 7 June 2021). Summertime hourly data (2015–2018) for 1174
stations were retrieved from an observational network in eastern China
(104–135∘ E, 17–53∘ N), which
includes the more prominent pollution areas in the eastern urban
agglomeration, such as the BTH (113.5–119.8∘ E,
36–42.6∘ N), YRD (115.3–122.6∘ E, 27.2–34.5∘ N), PRD (112.5–113.7∘ E, 21.3–23.1∘ N), Guanzhong Plain
(GZP (104.6–112.2∘ E, 33.3–36.8∘ N)) and Northeast Megalopolis (NEM (121.2–131.0∘ E, 39.8–47.3∘ N)) regions (the
specific locations of stations and urban agglomerations are shown in Fig. 1a). Surface meteorological data, such as Tmax, precipitation, WS and RH
from 611 meteorological observation stations, along with 367 sounding
profiles at 14:00 Beijing time (BJT) from 64 stations and 368/368 sounding
profiles at 08:00/20:00 BJT from 77 stations, respectively, in eastern China
were obtained from the China National Meteorological Information Center of
the China Meteorological Administration
(http://data.cma.cn/site/index.html, last access: 7 June 2021). The BLH was calculated according to
the method given by Seidel et
al. (2012) and Guo et al. (2016, 2019) (the detailed method can be seen in Text S1 of the Supplement) and the FLWD (frequency of light wind (< 2 m s-1)
days, which can be defined as the ratio between the number of days with
average daily WS lower than 2 m s-1 and the total days of each
pattern), precipitation frequency (PF, which can be defined as the ratio of
the number of rainy days to total days under each pattern) and MDA8 O3
were also quantified.
Average concentration of MDA8 O3(a) and PM2.5(b) in
eastern China during the summers of 2015–2018. Stations and key urban
clusters (black boxes) are shown in (a).
Additionally, for synoptic analysis of particulate matter and O3
pollution in summer, we use the GH field at 500 hPa, wind and specific
humidity field at 850 hPa from the NCEP/NCAR (National Centers for
Environmental Prediction/National Center for Atmospheric Research) daily
reanalysis dataset (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html, last access: 7 June 2021) on a 2.5∘ latitude × 2.5∘-longitude grid during the study period. For further analysis of the
modulation of the co-occurrence of O3–PM2.5 pollution by the
boundary layer structure in some local areas, we also used the BLH, uv wind,
vertical velocity, RH and temperature fields of the fifth generation
European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), which
has a high spatiotemporal resolution (0.25∘ latitude × 0.25∘ longitude, hourly;
https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html, last access: 7 June 2021).
T-mode principal component analysis (T-PCA) is an objective mathematical
computer-based method that can be used to classify the synoptic circulation
patterns of regional gridded data in the troposphere at the lower level.
Indeed, it is commonly regarded as the most promising weather pattern
classification method at present (Huth et
al., 2008). Moreover, this approach has been widely used in studies of
aerosols and O3 pollution-related atmospheric circulation in China
(Miao
et al., 2017, 2019; Ning et al., 2018, 2019). The T-PCA analysis module of
the COST733 software (http://cost733.met.no/, last access: 7 June 2021) developed by the European
Scientific and Technical Research Cooperation was used to classify the
synoptic circulation pattern based on the 500 hPa GH field. The COST733class
program is a FORTRAN software package consisting of several modules for
classification, evaluation and comparison of weather and circulation
patterns. First, the weather data are spatially standardized and split into
10 subsets by T-PCA. Then, the principal components (PCs) of weather
information are estimated by applying singular value decomposition and the
PC score for each subset can be calculated after oblique rotation. Finally,
the resultant subset with the highest sum will be selected by comparing 10
subsets according to contingency tables and its type can be outputted as well
(Miao et al., 2017; Philipp et al.,
2014). To assess the performance of synoptic classification and determine
the number of classes, the explained cluster variance (ECV) was selected in
this study
(Hoffmann
and Schlünzen, 2013; Ning et al., 2019; Philipp et al., 2014). Detailed
information about the ECV method is provided in the Supplement.
Based on the Ambient Air Quality Standards (GB3095-2012) issued by the
Ministry of Ecology and Environment of the People's Republic of China,
O3 (PM2.5) pollution occurs when the MDA8 O3 (PM2.5
24-h) concentration exceeds 160 (75) µgm-3. For a particular
region, when haze occurs at more than 50 % of the observed sites, the day
can be defined as a haze day (Chen and Wang, 2015). In
this study, we characterized regional pollution days as occurring when the
average values of more than 50 % of sites in this region exceeded the
aforementioned thresholds. The specific standard limits of each pollution
level are set according to their concentration limits based on the Technical
Regulation on Ambient Air Quality Index (on trial) (HJ633-2012) issued by
the Ministry of Ecology and Environment of the People's Republic of China
(Table S1 in the Supplement).
Finally, in order to be clear regarding the changes in O3 and
PM2.5 concentrations in the analysis of different weather types, we
calculated the average distribution of O3 and PM2.5 as well as
the meteorological conditions for each type. Plus, the anomalous
distribution of these variables (i.e., the average of O3 and PM2.5
and the average of the meteorological conditions under the respective
patterns minus the average during the summertime of 2015–2018) were given as
well. The statistical significance was tested with a 0.05 confidence level
via analysis of variance, which enabled us to distinguish the significant
differences of spatial distribution characteristics between O3 and
PM2.5 pollution under four SWPs.
ResultsSpatial and temporal distribution of O3 and PM2.5 during summer
2015–2018
Figure 1 shows the summer-averaged MDA8 O3 and PM2.5
concentrations at 1174 stations in the eastern region of China for the
period 2015–2018. Among these stations, the MDA8 O3 concentration at
most stations (795/1174) exceeded 100 µgm-3, of which 45 sites
exceeded 160 µgm-3. The highest O3 pollution was found in
Zibo, Shandong, with a value of 181.5 µgm-3. The average
PM2.5 at most sites (844/1174) was below 35 µgm-3, while it
reached 62.6 µgm-3 in Handan, Hebei Province. On the whole, the
MDA8 O3 and PM2.5 in the BTH region and its surrounding areas was
significantly higher than in other regions. In addition, the level of
O3 in some urban clusters, such as the PRD, YRD, GZP and NEM regions,
was particularly higher than that of the surroundings, and thus we focus on
analyzing these key areas later in the paper.
Figures 2 and 3 show the daily variations in pollution levels
of O3 and PM2.5, respectively. In recent years, the reduced visibility of haze
days has weakened the solar radiation reaching the ground and inhibited
photochemical reactions from generating O3 (Li
et al., 2019; Zhang et al., 2015). As a result, the concentration of O3
has continued to increase with the mitigation of PM2.5 pollution.
During the study period, the number of days of O3 pollution in the BTH,
YRD, PRD, GZP and NEM regions was 254, 133, 84, 165 and 96, respectively,
while the number of days of PM2.5 pollution was only 93, 8, 0, 2 and 1,
of which compound pollution occurred on 76, 7, 0, 2 and 0 d according to
Chinese standards (the asterisks in Fig. 3 indicate the compound pollution
events). China has implemented strict policies for emission control, and the
effects of these policies have been remarkable. However, despite a decrease
in PM2.5 in the last five years, there has also been an increase in
O3 pollution over China
(Fan et al.,
2020; Sun et al., 2016); “double-high” pollution reported on the weather
scale has reduced. As the limit of PM2.5 concentration for pollution
control is relatively relaxed in China, previous studies have tended to
refer to the interim target 1 (IT-1) of the World Health Organization (WHO)
as the standard threshold. Our study pushed forward to the next stage, i.e.,
we used the WHO's IT-2 threshold (24 h average concentration of PM2.5
of 35 µgm-3) as our target limit to count the number of compound
pollution days across each region. Based on this target, the number of
pollution days for the five urban clusters was 194, 52, 16, 47 and 20,
respectively (Fig. 3). These results indicate that despite PM2.5
reductions, compound pollution events remain serious and deserving of public
attention. Overall, the O3 and PM2.5 concentrations in eastern
China exhibit distinct intra-seasonal and interannual variations, indicating
that aside from the changes in emission sources (because it is considered
that inter-seasonal and short-term changes in emission sources are not
significant), they may also be regulated by meteorological conditions, which
is further analyzed below.
Time series of MDA8 O3 pollution levels in key urban clusters.
Time series of PM2.5 pollution levels in key urban clusters.
Black dots indicate the compound events. Asterisks indicate the compound
events under the Chinese standard (WHO interim target 1; IT-1) and the
circles indicate the compound events under WHO IT-2.
Objective classification of large-scale SWPs in
summer
To analyze the effect of meteorological conditions on the changes in O3
and PM2.5 concentrations, it is necessary to statistically analyze the
large-scale weather circulation situation in summer. Existing studies have
shown that the WPSH (500 hPa GH field with obvious anticyclonic
characteristics and downward flow around the center) in summer prominently
regulates the weather and climate of East Asia
(Lu, 2002), owing to its varying
location, shape and intensity (Ding, 1994). Low-level
southerly monsoonal flow forming at the periphery of an anomalously enhanced
WPSH, along with the transportation of warm and humid air from the ocean to
East Asia, might also be responsible for the asymmetric spatial distribution
of ground-level O3 (i.e., a decrease in southern China but an increase
over northern China,
Zhao and Wang, 2017).
Therefore, we used the T-PCA method to objectively classify the weather
circulation of the 500 hPa GH field in the summers of 2015–2018, from which
we ultimately obtained four SWPs related to the movement and development of
the WPSH (Wang et al., 2019; Yang et al., 2021). The western ridge point and
northern boundary of the WPSH at 500 hPa in Type 1 were located at around
120∘ E and 30∘ N, respectively (Fig. 4a and Table S2 in the Supplement).
The southwestern flow of this WPSH was able to transport water vapor to the
YRD region, resulting in a southwestward prevailing wind across the YRD
region and westward flow from the north of the WPSH, forming a convergence
area at 850 hPa. These conditions were also associated with high temperature
and humidity during the summer in the Meiyu season, which is a climate
phenomenon characterized by continuous cloudy and rainy days that generally
occurs during June and July every year across the middle and lower reaches
of the Yangtze river, Taiwan in China, central and southern Japan, and southern
Korea. For Type 2, the westerly trough was able to deepen as the WPSH
shifted northwards slightly from Type 1 or retreated southeast from Type 3
(Fig. 4b). The southwest wind from the South China Sea might have combined
with the southerly wind in the eastern periphery of the WPSH. As a result,
southerly winds prevailed across southeastern China, while northern China
was mainly controlled by the westerly trough. In comparison to Type 2, Type 3 was characterized by the boundary of the WPSH being at a higher latitude
with a westward extension (Fig. 4c), disintegrating a closed high-pressure
monomer along the eastern coast of China, and the main body of the WPSH
remained over the ocean (Figs. 4c and S4 in the Supplement). This led to a condition that was
completely controlled by the monomer of the WPSH over the YRD region,
resulting in hot and dry weather at the end the rainy season at the
beginning of mid-summer. Figure 4d indicates that the location of the WPSH
monomer was more to the west and north compared with under the other SWPs,
thus controlling northern China for a long time; the western ridge point was
located at around 95∘ E and the northern boundary at around
40∘ N.
850 hPa water vapor flux (WVF=V⋅q/g, where q is specific humidity, g is gravitational
acceleration and V is horizonal wind; vectors indicate WVF; see scale arrow in the bottom
right in units of 5 g cm-1 hPa-1 s-1) and 500 hPa GH (contours indicate GH;
see scale bar at bottom in units of gpm) patterns based on objective
classification (see text for details). The white framed area is the area of
eastern China, the number in the upper-right corner of each panel indicates
the frequency of occurrence of each pattern type and the black line in each
panel presents the ridge axis of the WPSH.
Figure 5 presents the daily and annual variations of the SWPs in the summers
of 2015–2018. Usually, the advancement of the WPSH in eastern China occurs
in June and July, while its gradual withdrawal occurs mainly in August; in this respect, Type 1 and Type 2 represented normal WPSH characteristics
during early and late summer. Type 3 and Type 4, however, reflected a split
of the WPSH, which mainly occurred in late summer. Consequently, there were
167, 117, 52 and 32 d for Type 1, Type 2, Type 3 and Type 4 over the
study period, respectively. Since the WPSH's movement is generally affected
by the weather phenomena of its surrounding climatic systems (such as
typhoons and the Tibetan high)
(Ge
et al., 2019; Liu and You, 2020; Shu et al., 2016; Wang et al., 2019), it
may have resulted in a short-term southward retreat during the advancement
of the WPSH (e.g., around 10 August 2018) and a short-term northward
advancement during its process of retreat (e.g., 21 and 29 August 2016). For
instance, tropical storm NEPARTAK generated at 00:00 UTC (08:00 BJT) 3 July
2016 over the western North Pacific and upgraded to a super typhoon at 12:00 UTC (20:00 BJT) 5 July 2016
(Fig. S5 in the Supplement; see also Su et
al., 2017). Due to the rapid movement of NEPARTAK to the northwest, the WPSH
quickly decomposed to a monomer and moved north. With the strengthening and
landing of the typhoon, the monomer gradually collapsed. The SWP also
underwent a transition from Type 2 to Type 4 and then to Type 1 (Figs. 4
and S5 in the Supplement). In general, the WPSH is able to provide evidence of intra-seasonal
and interannual changes over China, which will inevitably modulate the
weather as well as climatic and environmental changes in eastern China.
Time series of synoptic circulation patterns.
O3 and PM2.5 pollution characteristics under the four SWPsSpatial characteristics
We calculated the averaged (Fig. S6 in the Supplement) and anomalous (Fig. 6) spatial
distributions of the MDA8 of O3 and PM2.5 under the four SWPs. The
O3 concentration was relatively high in the area north of the Yangtze
River under Type 1 and the high values of MDA8 O3 were mainly
concentrated in the North China Plain (NCP) region, with a total of 100
stations surpassing 160 µgm-3. Type 2 O3 pollution was
slightly weaker than that for Type 1, and the MDA8 O3 at the 72 sites
exceeded 160 µgm-3. The O3 high-value areas lay mainly in
the NCP, GZP and YRD regions under Type 4, and there were 37 stations with
concentrations larger than 160 µgm-3. Of the four SWPs, the
lowest overall MDA8 O3 occurred under Type 3, with only one site
exceeding 160 µgm-3 (Fig. S6a–d in the Supplement). It was also found that the
regions that experienced significant positive deviations of MDA8 O3
from the summer mean were as follows: the BTH, YRD and NEM regions under
Type 1; the BTH and GZP regions under Type 2; the central of the YRD and PRD
regions under Type 3; and the YRD, GZP and PRD regions under Type 4 (Fig. 6a–d).
The MDA8 O3(a–d) and PM2.5(e–h) anomaly under the four
SWPs, where the sites marked with a “+” indicate that the analysis of variance
passed the significance level of 0.05.
Analogously, Fig. 6e–h shows the anomaly and significance of difference in
PM2.5 under the four weather types, presented as positive anomalies in
the south of the BTH and YRD regions under Type 1, in the BTH, GZP and PRD
regions under Type 2, and in the GZP and PRD regions under Type 4. Due to
the obvious seasonal variations of PM2.5 concentration (higher in
winter and lower in summer)
(H. Liu et
al., 2019; Miao et al., 2015), no site exceeded 75 µgm-3 for the
averaged PM2.5 concentration. Even so, the level of PM2.5 in the
BTH region was still significantly higher under the four types than that for
other urban agglomerations (Fig. S6e–h in the Supplement).
Pollution pattern differences in key areas
Air pollution is principally found in dense urban areas such as the BTH and
YRD regions
(Gui
et al., 2019; Han et al., 2019), so we took the BTH, PRD, YRD, GZP and NEM
regions in the eastern region as key areas, counted the daily anomalies and
average variation of O3 and PM2.5 in each key region under the
different weather patterns (Figs. 7 and S7 in the Supplement) and calculated the over-limit
ratio in those key regions via the “stations × days” statistics
(see Table 1). The diurnal variation of O3 was more obvious, peaking at
about 15:00 BJT, while contrasting diurnal variations of PM2.5 were
found for different regions. According to Fig. 7 and Table 1, the following
characteristics could be identified for different urban clusters: (1) in the
BTH region, the O3 concentrations of Type 1 and Type 2 were relatively
high with their over-limit rates reaching 47.1 % and 54.2 % and the
PM2.5 pollution rates reaching 18.8 % and 16.3 %, respectively; (2) in the PRD region, the over-limit rates and concentrations of O3 and
PM2.5 were similar under the four SWPs; (3) in the YRD region, the
O3 pollution over-limit rate presents as Type 1 > Type 4 > Type 2 > Type 3, PM2.5 pollution largely
appeared under Type 1, and both O3 and PM2.5 under Type 1 were
higher than those for the other types; (4) in the GZP region, the O3
pollution frequency was higher under Type 2 and Type 4, and PM2.5
pollution occurred more frequently under Type 2; and (5) in the NEM region,
O3 pollution was always found under Type 1, Type 2 and Type 4, but the
over-limit rate was no more than 15 % and PM2.5 pollution under Type 1 was more than under Type 2.
Over-limit ratio and concentration of MDA8 O3 and PM2.5
calculated via “stations × days” statistics in key urban clusters
under four SWPs.
Stas × days = stations × days. OLR = over-limit
ratio. Con = concentration (µgm-3).
Daily variations of O3 and PM2.5 anomalies under the four
SWPs in key urban clusters.
In summary, Type 1 was prone to the formation of O3–PM2.5
compound pollution (that is, when the ground MDA8 O3 concentration
exceeded 160 µgm-3, the PM2.5 concentration also exceeded 75 µgm-3) in the area from the BTH to northern YRD regions (Fig. S6 in the Supplement), which can be denoted as “BTH–NYRD O3–PM2.5 compound
pollution”. In detail, Fig. S8 in the Supplement shows the number and probability of
occurrence of compound pollution days at each site in summer during
2015–2018, indicating that a high occurrence probability (maximum values
approaching 46.7 %) of compound pollution appeared over the NCP (to the
north of 32∘ N) and that approximately 55.6 % of compound
pollution occurrence days at all sites occurred under Type 1. Similarly,
Type 2 can also be denoted as “BTH O3–PM2.5 compound
pollution”, with compound pollution occurrence days accounting for
33.8 %, Type 3 as “BTH–YRD–PRD O3-only pollution” and Type 4 as
“BTH–GZP–YRD–PRD O3-only pollution” (Fig. 12).
Analysis of local meteorological factors
To explore the meteorological causes of O3 and PM2.5 pollution, we
analyzed the distribution of the average and anomalies for Tmax, RH, PF, BLH
and FLWD under the four SWPs (Figs. 8 and 9; Figs. S9 and S10 in the Supplement). Under the influence
of the EASM, over 80 % of the stations experienced high temperatures (Tmax>27∘) under each SWP, although the anomaly of Tmax
under Type 1 (early summer) presented as negative (Fig. 8a). Type 1 was
characterized by humid conditions in the southern area and dry conditions in
the northern region owing to an extensive southwestern flow of the WPSH,
resulting in a rain belt found in southeastern coastal areas such as the PRD
and YRD regions. Type 2 was associated with meridional flow and dry and wet
anomalies in northern China, resulting in a rain band located over the
central areas between the BTH and YRD regions owing to the northern
advancement of the WPSH compared with Type 1. Furthermore, there was higher
RH for most of the study sites under Type 3 and Type 4, possibly because of
the shifted rain belt in the BTH and NEM regions under Type 3 once the
northern boundary of the WPSH reached 37.5∘ N, and an occurrence
of heavy precipitation across the western PRD region as well as central
areas between the BTH and YRD regions under Type 4 (Fig. S9 in the Supplement).
As in Fig. 6 but for Tmax(a–d), RH (e–h) and PF (i–l). The
black solid line presents the rain belt of each SWP.
In terms of their anomalous spatial distributions, the positive anomalies of
Tmax were located in the southern region under Type 3 and most of the
eastern region under Type 4; since Type 1 always appeared in early
summer, most areas were negative (Fig. 8a–d). For RH, Types 2, 3 and 4
were negative for the south and positive in the north, while the opposite
was true under Type 1 (Fig. 8e–h). PF was characterized by positive
anomalies in the area south of the Yangtze River under Type 1, in the YRD
region under Type 2, in the BTH and NEM regions under Type 3, and in the
area between the BTH and YRD regions under Type 4 (Fig. 8i–l). As can be
seen from Fig. 9, when the BLH at 14:00 BJT had a positive anomaly, the
contrary FLWD had a negative anomaly (e.g., BTH in Type 1), which indicates
that the higher the BLH and lower the FLWD, the more conducive it was to the
diffusion of pollutants; conversely, a lower BLH and higher FLWD (such
as BTH under Type 2) did not support the diffusion of pollutants. After
further inspection of Fig. S10 in the Supplement, we found that the YRD region under Type 1,
the YRD under Type 2, and the BTH and PRD regions under Type 3 and 4 had
shallow BLHs and high FLWDs, which was detrimental to the transportation of
pollution in these areas, thus corresponding to high levels of pollution
under these weather patterns. However, there was also more serious pollution
in some higher BLH areas, such as in the BTH region under Type 1, which we
discuss next.
As in Fig. 6 but for the BLH at 14:00 BJT (a–d) and FLWD (e–h).
Potential implications of NO2
The photochemical production of O3 mainly involves emissions of
volatile organic compounds and NOx from anthropogenic, biogenic and
biomass burning sources
(Deng
et al., 2019; Gvozdić et al., 2011; Sillman, 2002). The photochemical
reaction of NO, NO2 and O3 in the troposphere forms a closed
system (Yu et al., 2020), and this
photochemical cycle of NOx and O3 is the basis of photochemical
processes in the troposphere. Oxidant (OX, OX = O3+ NO2), a
conservative quantity over short timescales, is defined as a parameter to
evaluate the photochemical processes and, due to the unstable nature of NO,
it can quickly react with the equivalent amount of O3 to generate
NO2 (Kley et al., 1994). In order to
compare the photochemical reaction efficiency of the five urban clusters
under the different SWPs, Fig. 10 presents the daily variations of NO2
and OX. As we can see, the daily variations of NO2 showed two peaks
during a day, including a first peak in the morning and a second peak
associated with traffic emissions in the evening
(Xie et
al., 2016; Yu et al., 2020). As we found the lowest point of NO2 at
15:00 BJT and since NO2 can be photolyzed to produce O3 during the day,
we assumed that this particular time was the peak time for O3 formation
across the study areas. As NO2 is consumed through a photochemical
reaction with the involvement of other precursors to produce a large amount
of O3, OX can form a peak in the afternoon. In particular, abundant
sunlight in summer is beneficial to the photochemical reaction process, but
since most parts of eastern China are under a subtropical climate with the
same period of rain and heat, the existence of the rainy season will
inevitably inhibit the summertime photochemical process. Under the different
SWPs, the photochemical reaction over each area bore an obvious relationship
with the rain belt. For example, the rainy season in the BTH and NEM areas
mainly occurred under Type 3 and the OX of Type 3 in this area was
significantly lower than under the other SWPs.
Daily variations of NO2(a–e) and OX (f–j) under four SWPs
in key urban clusters.
Daily variations of horizonal wind, potential temperature and BLH
in the BTH area during clean and compound pollution periods under Type 1 and
Type 2 (a, b, e, f). The vertical cross section of u wind, w wind and
potential temperature for the same situation in the BTH region (c, d, g, h).
The w wind is multiplied by 100 when used. The data are from ERA5
reanalysis.
Precipitation, WS and WD during clean and compound pollution
periods under Type 1 over BTH.
Discussion
In the last section, we discussed how the SWPs and local meteorological
factors modified the summertime O3 and PM2.5 pollution. However,
how did the boundary layer structure interact with the co-occurrence of
O3 and PM2.5 pollution? To address this question, we conducted
some further analysis as follows. As mentioned, the co-occurrence of O3
and PM2.5 pollution mainly took place in the BTH–NYRD areas under Type 1 and in the BTH area under Type 2. Lower WS and its negative anomalies at a
lower boundary layer over the BTH–NYRD under Type 1 and over the BTH area
under Type 2 may not have enhanced the diffusion of air pollutants (Fig. S11 in the Supplement). In contrast, the moderate RH and its negative anomalies might have
favored the formation of compound pollution. Downward vertical motion and
negative anomalies might also have stabilized the atmospheric characteristic
of the boundary layer (Fig. S12 in the Supplement). Furthermore, we summarized the boundary
layer structure, precipitation and ground-level wind flow across the BTH
region. Based on their characteristics, we separately defined Type 1 and Type 2 into clean (the concentrations of both O3 and PM2.5 were less
than the level of pollution) and compound pollution periods (Figs. 11–13).
It can be clearly seen that various precipitation events primarily caused
differences in concentrations of both O3 and PM2.5 between clean
and pollution days under Type 1/Type 2 (see Figs. 12–13). In particular,
Type 1 had significantly warmer temperatures over the boundary layer during
the compound pollution periods of the BTH region, as compared with the clean
periods. The daytime BLH under the compound pollution condition was also
higher than that under the clean condition. In addition, there were
different directions of prevailing winds during the two periods. The
prevailing southerly winds during the compound pollution period may have
driven the transportation of air pollutants from the southern NCP, resulting
in more serious pollution (Fig. 11), which is consistent with the results of
Miao et al. (2017, 2019). Miao et al. (2020) also proposed another
mechanism – that is, the synoptic southerly warm advections at the top of
planetary boundary layer (PBL) can strengthen the elevated thermal inversion layer and suppress the
development of the PBL, causing worse pollution. Co-influenced by the
topographical effect of the northern mountainous areas and the boundary
layer structure, air pollutants could be trapped in the BTH region. In
comparison, although there was a southerly prevailing wind in the BTH region
(Figs. 11 and 13), the rain belt under Type 2 being located in the southern
area of the BTH might have led to the potential removal of PM2.5 over
this area (Fig. 9j), so the pollutants transported from the southern NCP would
be partially reduced. Therefore, we can conclude that the emissions of local
pollutants accompanied by unfavorable meteorological conditions will
continuously accumulate pollutants (Figs. 12–13; Gui et al., 2019; Zhang et
al., 2020), which should be main cause of the BTH compound pollution. In
summary, the different SWPs modulated the regional variability of summertime
O3 and PM2.5 via changes to the local meteorological conditions as
follows:
Type 1: Under the conditions of high temperatures (Tmax> 27∘), moderate humidity (RH ∼ 60 %) and low PF,
photochemical reactions were greatly promoted to cause severe O3
pollution. Meanwhile, the BTH–NYRD areas were located in front of the
westerly trough, under the influence of the warm and humid air of the WPSH,
and so the hygroscopic growth of fine particulates potentially caused a
certain amount of PM2.5 pollution
(Li
et al., 2017; Zhang et al., 2016), eventually becoming becoming O3–PM2.5 compound
pollution (Fig. 14). In addition, the prevailing southerly winds in the
boundary layer were able to transport the pollutants emitted from southern
cities to the BTH, atmospheric stratification was stable when the air mass
was sinking (Miao et al., 2019;
Figs. 11 and S12 in the Supplement) and compound pollution may have been especially severe.
Although a relatively high BLH occurred in the BTH region, the prevailing
southerly winds in the boundary layer served to further increase the
pollution.
Type 2: O3 pollution was severe under the meteorological conditions of
high temperatures, moderate humidity and weak precipitation. The PM2.5
in the BTH region, which was located in front of the westerly trough, was
high since the shallow boundary layer and low wind frequency were
unfavorable for the diffusion of pollutants. Therefore, O3–PM2.5
compound pollution was also rather frequent (Fig. 14).
Type 3: High temperatures, low humidity and weak precipitation over the YRD
region tended to generate a large amount of O3, while the positive BLH
and negative FLWD anomalies were unfavorable to O3 accumulation. On the
other hand, summer typhoon activities might have weakened the WPSH intensity
over the YRD region, leading to the eastward retreat and northward shift of
the WPSH. As a result, the high WS across coastal areas was able to ease the
ground-level O3 pollution
(Shu et al., 2016). For the BTH
and PRD regions, the high PF tended to suppress the production of O3.
Type 4: High temperatures, medium-high humidity and weak precipitation in
the GZP and PRD regions were able to cause O3–PM2.5 compound
pollution, but the PM2.5 pollution in both regions was not heavy,
possibly in relation to local lower emissions of pollutants. Under the
control of the WPSH, there were strong photochemical reactions at high
temperatures and little rainfall in some eastern regions (such as the
northern BTH, YRD), which was also conducive to O3 generation (Fig. 14). Meanwhile, relative to Type 1, O3 pollution was lighter in the
BTH, due to the differences of RH, BLH and FLWD.
It is important to note that our work contains a few limitations and
uncertainties. Although T-PCA, an objective classification method, was
chosen in this study, there were still some subjective decisions made, e.g.,
the number of SWPs (Huth et al., 2008). In the present work, we selected
four SWPs based on both the larger ECV and greater ΔECV to further
reduce the subjective impact. Nevertheless, at a large scale, the present
four SWPs were closely associated with intra-seasonal movements of the WPSH,
because the WPSH is one of the most important components of the present
large-scale SWPs in summertime (Zhao and Wang, 2017). In addition, note that
short-term disturbances induced by typhoons with a specific pattern were not
excluded. The quick passage of a typhoon in summer could lead to various
atmospheric processes (e.g., precipitation, large-scale subsidence) and
pollution levels (Deng et al., 2019), which should be explored in future
work. In addition, although this study emphasizes the important impacts of
large-scale synoptic drivers of co-occurring summertime O3 and
PM2.5 pollution in eastern China, the presence of PM2.5 may play a
role in radiation forcing to reduce O3. Indeed, the interaction between
O3 and PM2.5 is deserving of further exploration in future work to
better comprehend the mechanism of O3–PM2.5 compound pollution.
As in Fig. 12 but for Type 2.
Schematic diagrams describing the relationships between the WPSH,
four SWPs and summertime O3 and PM2.5 pollution in various
regions.
Conclusions
In this study, T-PCA, an objective classification method, was applied to
classify the 500 hPa weather circulation pattern into four SWPs in the
summers of 2015–2018. It was found that these four SWPs were closely
related to the development of the WPSH. The spatial and temporal
distribution characteristics of O3 and PM2.5 pollution in eastern
China under the four SWPs were analyzed to regulate and differentiate
O3 and PM2.5 pollution in key areas. We found two synoptic
patterns were prone to leading to the co-occurrence of O3 and
PM2.5 pollution: in BTH–NYRD areas under Type 1 and in the BTH area
under Type 2, which were associated with double-high levels of O3 and
PM2.5. The probabilities of compound pollution at all sites under Type 1, 2, 3 and 4 were 54.3 %, 33.8 %, 6.8 % and 5.1 %, respectively.
The Type 1 weather pattern appeared frequently in early summer with a
stable WPSH ridge axis at about 22∘ N, and the warm and humid air
brought by the WPSH reached the area south of the Yangtze River, where a hot
and humid Meiyu season was formed, with the high humidity suppressing the
photochemical reaction of O3 generation. Meanwhile, the north of China
was controlled by a low-pressure trough at 500 hPa with high temperatures
and little rain. The hygroscopic growth of PM2.5 occurred in the
corresponding area in front of the trough, with a small amount of water
vapor transported by the WPSH, causing compound pollution of O3 and
PM2.5 in the BTH–NYRD regions. In addition, the prevailing southerly
winds in the boundary layer were able to transport the pollutants emitted
from southern cities to the BTH region, and the atmospheric stratification
was stable when the air mass was sinking. Thus, the compound pollution was
potentially severe. In general, the synoptic circulation in the boundary
layer might be responsible for the concentration of pollutants under this
SWP.
Under Type 2, the WPSH shifted northwards from Type 1 or retreated
southwards from Type 3 or Type 4 to 32.5∘ N with the meridional
deepening of the East Asian major trough at 500 hPa, and thus warm and humid
airstreams were brought to northern China (e.g., the BTH region), gradually
elevating temperatures and humidity. Although the positive RH anomaly
promoted the hygroscopic growth of PM2.5, water vapor absorbed solar
radiation, leading by contrast to reduced O3 formation. As a result,
the probability of double-high levels of O3 and PM2.5 under Type 2
was less than under Type 1, and the extent of compound pollution under Type 2 was also narrowed, which was mainly located in the BTH area. On the other
hand, weak precipitation, a shallow boundary layer and low WS in the BTH
area tended to create favorable conditions for the maintenance of pollution.
In spite of the southerly winds over the BTH area, the precipitation in
southern cities reduced pollutant concentrations and horizontal
transportation. Meteorological factors might have been responsible for the
accumulation of compound pollution.
In general, the location of the WPSH was found to be tightly associated with
O3 pollution in eastern China, and the changes in meteorological
conditions in different regions affected by the WPSH induced significant
regional differences in O3 and PM2.5 pollution. On one hand,
appropriate warm and moist flow brought by the WPSH promoted hygroscopic
growth of the fine particulate matter in some local areas (i.e., the
BTH–NYRD areas under Type 1 and the BTH area under Type 2), resulting in
increased of PM2.5 concentrations. On the other hand,
transboundary O3 and PM2.5 were simultaneously transported to
these local areas, which may have contributed to the formation of
co-occurring surface O3 and PM2.5 pollution. More importantly, the
effects of various large-scale weather circulation patterns on
O3–PM2.5 compound pollution and their corresponding physical and
chemical processes have been clarified, which has important scientific
reference value in terms of summertime air-quality forecasts as well as
assessment and policymaking services.
Data availability
Hourly PM2.5, NO2, O3 and O3-8 h data are published by
the China National Environmental Monitoring Station (2021, http://www.cnemc.cn/). Surface
meteorological data, such as Tmax, precipitation, WS and RH, and radiosonde
data can be obtained from the China National Meteorological Information
Center of the China Meteorological Administration (2021, http://data.cma.cn/site/index.html). The NCEP/NCAR daily reanalysis dataset
can be downloaded from
https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html (Physical Sciences Laboratory, 2021). The ERA5 hourly
reanalysis dataset can be derived from
https://cds.climate.copernicus.eu/cdsapp#!/home (European Centre for Medium-Range Weather Forecasts, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-9105-2021-supplement.
Author contributions
YY designed the research. LZ and YY developed and wrote the manuscript. LZ and YY collected and analyzed the data. MG, HW, PW, HZ, LW, GN, CL, YL and ZG provided useful comments. All the authors contributed to the revision of the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank editor, Stefan Rahimi-Esfarjani and two anonymous reviewers for their constructive comments.
Financial support
This research has been supported by the National Natural Science
Foundation of China (grant nos. 42061134009, 42075072, and 41575010)
and the National Key Research and Development Program of China (grant no. 2018YFC1506502).
Review statement
This paper was edited by Xiaohong Liu and reviewed by Stefan Rahimi-Esfarjani and two anonymous referees.
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