Ozone pollution in China is influenced by meteorological processes on
multiple scales. Using regression analysis and weather classification, we
statistically assess the impacts of local and synoptic meteorology on daily
variability in surface ozone in eastern China in summer during 2013–2018. In
this period, summertime surface ozone in eastern China (20–42∘ N, 110–130∘ E) is among the highest in the world, with regional means of 73.1
and 114.7 µg m-3, respectively, in daily mean and daily maximum
8 h average. Through developing a multiple linear regression (MLR) model
driven by local and synoptic weather factors, we establish a quantitative
linkage between the daily mean ozone concentrations and meteorology in the
study region. The meteorology described by the MLR can explain
∼43 % of the daily variability in summertime surface ozone
across eastern China. Among local meteorological factors, relative humidity
is the most influential variable in the center and south of eastern China,
including the Yangtze River Delta and the Pearl River Delta regions, while
temperature is the most influential variable in the north, covering the
Beijing–Tianjin–Hebei region. To further examine the synoptic influence of
weather conditions explicitly, six predominant synoptic weather patterns
(SWPs) over eastern China in summer are objectively identified using the
self-organizing map clustering technique. The six SWPs are formed under the
integral influence of the East Asian summer monsoon, the western Pacific
subtropical high, the Meiyu front, and the typhoon activities. On average,
regionally, two SWPs bring about positive ozone anomalies (1.1 µg m-3
or 1.7 % and 2.7 µg m-3 or 4.6 %), when eastern China is under a weak cyclone system or under the prevailing
southerly wind. The impact of SWPs on the daily variability in surface ozone
varies largely within eastern China. The maximum impact can reach ±8µg m-3 or ±16 % of the daily mean in some areas. A combination of the regression and the clustering approaches suggests a strong performance of the MLR in predicting the sensitivity of surface ozone in eastern China to the variation of synoptic weather. Our assessment highlights the importance of meteorology in modulating ozone pollution over China.
Introduction
Surface ozone is a major air pollutant detrimental to human health (Jerrett
et al., 2009) and vegetation growth (Yue et al., 2017). Ozone exposure is
estimated to be associated with over 0.2 million premature deaths globally
in 1 year (Cohen et al., 2017; Liang et al., 2018). The dominant source of
surface ozone is the photochemical oxidation of volatile organic compounds
(VOCs) and carbon monoxide (CO) in the presence of nitrogen oxides
(NOx; Monks et al., 2015). In recent decades, China has been suffering
from severe ozone pollution, causing worldwide concern (Verstraeten et
al., 2015). High ozone concentrations exceeding China's national air quality
standard (200 and 160 µg m-3, respectively, for hourly and
8-hourly maximum values) occur frequently in major Chinese cities in the
three most developed regions, the Beijing–Tianjin–Hebei (BTH) region (Wang et al., 2006a; Li et al., 2017), the Yangtze River Delta (YRD; Shu
et al., 2016, 2019), and the Pearl River Delta (PRD; Y. Wang et al., 2017;
Wang et al., 2018). An increase of 1 %–3 % per year in surface ozone
since 2000 is observed at urban and regional background sites in these three
city clusters (Wang et al., 2012; Zhang et al., 2014; Ma et al., 2016;
Sun et al., 2016; Gao et al., 2017) and at a global baseline station in
western China (Xu et al., 2016).
Surface ozone concentrations in China largely depend on emissions and
meteorology (Han et al., 2018a, 2019; Lu et al., 2019a). Anthropogenic and
natural emissions from both native and foreign sources provide precursors
for the formation of high ozone levels in China (Ni et al., 2018; Han et
al., 2019), while meteorology can influence the variability in surface ozone
from the instantaneous to decadal scale through its modulation of chemical and
physical processes involved (T. Wang et al., 2017). On a decadal scale, both
observations (Zhou et al., 2013) and simulations (S. Li et al., 2018) show
that surface ozone in southern China correlates positively to the strength
of the East Asian summer monsoon (EASM).
The daily variation of surface ozone in China is sensitive to synoptic
weather systems, as illustrated by studies for BTH (Zhang et al., 2012;
Huang et al., 2015), the YRD (Shu et al., 2016, 2019), the PRD (Zhang et al., 2013;
Jiang et al., 2015), and other regions of China (Tan et al., 2018). Frontal
systems can drive the transboundary transport of ozone in northern China
(Ding et al., 2015; Dufour et al., 2015). Downdrafts in the periphery
circulation of a typhoon system can strongly enhance surface ozone before
the typhoon landing in eastern or southern China (Jiang et al., 2015; Shu et
al., 2016). Zhao and Wang (2017) suggested that a stronger western Pacific
subtropical high (WPSH) can decrease and increase surface ozone
concentrations, respectively, over southern China and northern China in
summer. Moreover, surface ozone concentrations also vary with mesoscale
weather systems in hours (Hu et al., 2018), such as the mountain–valley
circulation (Wang et al., 2006b) and the land–sea breezes (Wang et
al., 2018). Despite these discussed mechanisms on how weather systems
influence ozone concentrations in China, there are a lack of quantitative
assessments on the influences of these weather systems on ozone pollution.
Weather systems on different scales bring about different changes in local
meteorological variables, which, in turn, impact chemical and physical
processes that modulate surface ozone concentrations. However, the relative
importance of local meteorological factors to surface ozone in China is
still unclear. Previous studies suggested the importance of temperature,
relative humidity, and winds to surface ozone in different regions of China
(Lou et al., 2015; Pu et al., 2017; Zhan et al., 2018). The key influential
meteorological factors vary from region to region (Gong et al., 2018; Chen
et al., 2019). In general, high ozone episodes commonly appear under weak
wind, high temperature, low humidity, and clear conditions (Bloomfield
al., 1996; Zanis et al., 2000, 2011; Ordóñez et al., 2005). These
weather conditions can enhance stagnation and production of ozone (Camalier
et al., 2007; Shen and Mickley, 2017). Variations in these local meteorological
variables depend on the dominant weather systems (Davis et al., 1998; Han et
al., 2018b; Leung et al., 2018).
To have a comprehensive and quantitative understanding of how weather
influences ozone pollution in China is the primary motivation of this study,
in which we aim to quantify the impacts of meteorology, specifically the
dominant synoptic weather systems and the key local meteorological
variables, on daily variations in surface ozone in eastern China, including
three representative megacity clusters, BTH, the YRD, and the PRD. Surface ozone in
China was not regularly and systematically monitored until 2012, and since
2013, real-time hourly ozone data have been available online from the China
Ministry of Ecology and Environment (MEE; http://www.mee.gov.cn/, last access: 10 December 2019; T. Wang
et al., 2017).
In this study, the ground ozone observations from MEE covering the 2013–2018
period are used. First, we characterize the seasonal variations in surface
ozone in eastern China and the interannual changes during 2013–2018 in
summer (June–August), which is the season of interest in this study. Second,
we search for a linkage between the daily variation of surface ozone and the
local and synoptic meteorological factors and develop a multiple linear
regression (MLR) model based on the linkage, using the data during
2013–2018. Third, we further examine the sensitivity of daily surface ozone
to the variation in synoptic weather systems. Considering the complexity of
the synoptic meteorology in eastern China (Ding et al., 2017; Han et al.,
2018b), we employ an objective clustering technique, the self-organizing map
(SOM), to identify the predominant synoptic weather patterns (SWPs). In the
following sections, we introduce the data and methods in Sect. 2. The
seasonal and interannual variations in surface ozone in eastern China are
characterized in Sect. 3. Section 4 illustrates the linkage between ozone
variability and meteorology on both local and synoptic scales, while Sect. 5 describes sensitivity of surface ozone to various typical SWPs over all of eastern China. Finally, we discuss our results and draw conclusions
in Sect. 6.
Data and methodsSurface ozone observations and meteorological data
Hourly surface ozone measurements from the MEE observation network averaged
over the stations in each city were used in the study. The measurements were
downloaded from http://beijingair.sinaapp.com/ (last access: 10 December 2019), which were previously
archived at http://pm25.in (last access: 10 December 2019), a mirror of data from the official MEE
publishing platform (http://106.37.208.233:20035/, last access: 10 December 2019). The network covers 63
cities in eastern China (20–42∘ N, 110–130∘ E) in 2013, increasing
to 118 in 2014 and 185 during 2015–2018. The locations of the 185 cities are
shown in Fig. 1, including 13, 26, and 9 cities, respectively, in BTH,
the YRD, and the PRD. The unit of ozone concentrations in the original records and
in this study is “µg m-3”, with a conversion factor of 1 µg m-3=0.47 ppbv at 273 K and 1013.25 hPa.
(a) Distribution of the cities (dots) with air quality monitoring
stations in eastern China (EC; 20–42∘ N, 110–130∘ E; the boxed area)
and the summer mean tropospheric column ozone (color shades; in Dobson
units – DU) from the OMI satellite measurements during 2013–2017. Summertime
mean surface ozone (in µg m-3) over eastern China during
2013–2018, shown (b) by city and (c) by grid (see Sect. 2.1). The red dots
in (a) indicate the cities in BTH, YRD, and PRD. The three boxed areas in
(c) indicate BTH (36–42∘ N, 114–120∘ E), YRD (28–34∘ N, 117–123∘ E), and PRD (21–25∘ N, 112–116∘ E). The unfilled grids in
(c) are due to the lack of monitoring stations nearby. The OMI tropospheric
column ozone monthly data at 1∘ latitude by 1.25∘ longitude were
obtained from NASA Goddard Space Flight Center
(https://acd-ext.gsfc.nasa.gov/Data_services/cloud_slice/, last access: 10 December 2019).
The National Centers for Environmental Prediction (NCEP) Final (FNL)
Operational Global Analysis data during the same period were acquired from
https://rda.ucar.edu/datasets/ds083.2/ (last access: 10 December 2019). The data are available on 1∘
latitude × 1∘ longitude grids every 6 h at the surface and
at 26 layers from 1000 to 10 hPa. We took daily averaged pollution and
meteorological data in summer from 2013 to 2018. The ozone–weather
relationship is examined using the daily mean data unless stated otherwise.
Using inverse-distance weighting (Tai et al., 2010), we interpolated the
pollution measurements from the cities onto the FNL grid (1∘× 1∘) to produce continuous gridded data. The ozone at each FNL
grid was calculated with a weighted average of the concentration in the
cities within a search distance (dmax) from that grid, following the
equation
zj=∑i=1nj1/di,jkzi∑i=1nj1/di,jk,
where zj is the calculated ozone concentrations at grid j, zi is the
observed ozone concentration in city i, di,j is the distance between city
i and the center of grid j, nj is the number of the cities within
dmax from grid j (di,j≤dmax), and k is a parameter measuring the
influence of distance on the target grid. We used 2 for k and a 1∘
distance in the latitude–longitude grid for dmax in the interpolation.
The generated gridded ozone data cover most of the mainland in eastern China
(Fig. 1c). The gridded data were used in this study unless stated
otherwise.
Development of a prediction model of surface ozone
MLR is an effective and widely used way to describe the relationship between
meteorology and air quality and thus to help prediction of air quality (Shen
et al., 2015; Otero et al., 2016; Li et al., 2019). MLR establishes a
linear function between a scalar response and the explanatory variables. In
this study, we applied stepwise MLR to quantitatively correlate daily
surface ozone and meteorology in summer. Considering the combined effect of
meteorology at various scales, we used both local meteorological variables
and synoptic circulation factors as predictors, following Shen et al. (2017), who showed that, compared with regression models only considering
local meteorology, adding the synoptic factors in MLR can significantly
improve the model performance. The MLR takes the following form:
Y^=b+∑i=1K1αiXi+∑j=1K2βjSj,
where Y^ is the predicted value of surface ozone, b is the intercept
term, Xi is the local meteorological variables with a total number of
K1, Sj is the synoptic meteorological factors with a total number of
K2, and αi and βj are the regression
coefficients. We used 10 local meteorological variables (K1=10),
including the relative humidity at 2 m (RH2m), cloud fraction (CF), temperature
at 2 m (T2m), planetary boundary layer height (PBLH), zonal wind at 850 hPa
(U850), meridional wind at 850 hPa (V850), vertical wind at 850 hPa (W850),
wind speed at 850 hPa (WS850), geopotential height at 850 hPa (HGT850), and
sea level pressure (SLP), all of which were identified as being significantly
(p<0.05) correlated to the daily variations in surface ozone in
part of eastern China, as shown in Fig. 2. Cloud fraction retrievals at
1∘× 1∘ grids were from the spaceborne atmospheric
infrared sounder (AIRS) instrument (AIRS3STD daily product;
https://disc.gsfc.nasa.gov/, last access: 10 December 2019). The other nine local meteorological variables were from FNL
data (Sect. 2.1). We computed the anomalies of meteorological variables
and ozone on a given day by taking the difference between the value of a
given meteorological variable (or ozone) on that day and the mean value of
the meteorological variable (or ozone) in that month. Thus, all the data
were detrended, and the influences of meteorology on the ozone variability on
longer timescales (trends and annual and seasonal variations) were
generally removed. Any anomaly of a variable (or ozone) divided by its
corresponding monthly mean is referred to as the relative anomaly of that variable
(or ozone), with a unit of percent.
Correlation coefficients (r) between daily surface ozone and each
of the 10 meteorological variables in summer during 2013–2018. The black
dot in a grid indicates that the r in that grid is significant (p<0.05). The regional mean r is shown in the bottom right corner of each panel
in purple if the r is significant (p<0.05) and in grey if the r is
insignificant. The abbreviations are for relative humidity at 2 m (RH2m),
cloud fraction (CF), temperature at 2 m (T2m), planetary boundary layer
height (PBLH), zonal wind at 850 hPa (U850), meridional wind at 850 hPa
(V850), vertical wind at 850 hPa (W850), wind speed at 850 hPa (WS850),
geopotential height at 850 hPa (HGT850), and sea level pressure (SLP).
For Sj in Eq. (), we also identified two synoptic factors through
the singular value decomposition (SVD) of the spatial correlations between
surface ozone and local meteorological variables in eastern China (Shen et
al., 2017). The SVD approach effectively extracted representative signals
from the spatial distribution of the correlation coefficients. The extracted
information was then used to characterize the spatial patterns of the
meteorological variables on a synoptic scale by making SVD inverse. For each of
the FNL grids in eastern China, we constructed the synoptic circulation
factors as follows. First, we calculated the correlation coefficients
between daily mean surface ozone at a given grid and each of the 10
meteorological variables at all the grids in eastern China in summer during
2013–2018. For example, the correlations for the grid of Nanjing are shown
in Fig. S1 in the Supplement, which indicates that surface ozone in Nanjing is correlated to
the meteorology in the surrounding regions. We made a matrix
A that consists of the correlation coefficients for that
grid with elements of 21 (numbers of grids in longitude) ×23
(numbers of grids in latitude) ×10 (numbers of the local
meteorological variables). Second, to fit the decomposition, we aligned the
dimension of longitude–latitude into one column and reshaped matrix
A into a 483 (longitude × latitude) ×10
two-dimensional matrix F. The SVD decomposed
F used the equation
F=ULVT,
where U is 483×10 matrix, L
is a 10×10 diagonal matrix with non-negative numbers on the
diagonal, and V is also a 10×10 matrix. The columns
of the three transformations together characterize SVD modes, with 10 modes
in total. Each column of U represents the spatial weights
of the SVD mode, and each column of V represents the
variable weights in the SVD mode. The spatial and variable weights of the
first two SVD modes for the Nanjing grid are shown in Fig. S2. The pattern
of the spatial weight of the first SVD mode for the Nanjing grid (Fig. S2a) is similar to the pattern of the correlations between surface ozone and
the relative humidity (Fig. S1a) and cloud fraction (Fig. S1b). The first
SVD mode is more correlated to the relative humidity and cloud fraction than
other variables (Fig. S2b). Therefore, the first SVD mode for the Nanjing
grid is related to chemical processes of ozone. In contrast, the second SVD
mode for the Nanjing grid is more related to transport than chemical
processes (Fig. S2d). Third, we assigned the anomalies of the daily mean
values of the 10 local meteorological variables in eastern China to a 552
(days in the summers of 2013–2018) ×21 (longitude) ×23
(latitude) ×10 (meteorology) four-dimensional matrix
M. At each grid, we normalized the time series of each
variable to the zero mean and unit standard deviation. Then, the magnitude of
each SVD mode for every day t was calculated by inverse SVD:
Sk,t=UkTMtVk,
where Uk and Vk are the
kth columns of U and V,
respectively. Sk,t is a scalar depicting the magnitude of the kth
SVD mode. Sk,t refers to a newly produced meteorological field that
represents the influence of synoptic meteorology on ozone variability. We
implemented the procedure at every grid in eastern China. The first two SVD
modes can generally explain 55 %–85 % of the total variance. They can,
respectively, reflect the dynamical or thermal characteristics of synoptic
meteorology (Shen et al., 2017). Therefore, we applied the primary two SVD
modes in the MLR (K2=2).
We used the leave-one-out cross validation to avoid overfitting of the MLR
for each grid. Data during the study period (summers of 2013–2018) included
552 d observations. Each time, one observation in the time series was
reserved as the test set, and the remaining ones were used as the training
set. The process was repeated until all observations had been predicted.
Every observation was to be a test set once and a training set 551 times.
We measured the relative importance of each of the meteorological variables
to ozone by its relative contribution to the total explained variance of
the MLR. The weight of each predictor (wi) was calculated from the
normalized MLR coefficient (zk):
wi=zk2∑k=112zk2,
where zk is
zk=sksyck,
and the number of all the predictors is 12, including 10 local and 2
synoptic meteorological factors (Sect. 2.2). ck is the regression
coefficient, referring to αi or βj in Eq. ().
sk is the standard deviation of a predictor, i.e. Xi or Sj in
Eq. (). sy is the standard deviation of the observed daily surface ozone.
Seasonal variations in (a) daily mean surface ozone
concentrations, (b) daily maximum 8 h average surface ozone
concentrations, (c) the regional exceedance probability of ozone, and (d) the probability of ozone being the primary pollutant. The values are
regional means over eastern China (EC) and the three subregions (BTH, YRD,
and PRD) over 2014–2017. The values were calculated from the observations in
the corresponding cities. The pink shaded area in each panel indicates the
range of the ±50 % standard deviation of the corresponding
variable for eastern China. The vertical shading over summer shows the
season of interest in this study. The means of the corresponding variables
for eastern China and the three subregions in summer are shown in the top
left corner of each panel.
Classification of the synoptic weather patterns
Weather classification is a well-established tool for characterizing
atmospheric processes on multiple scales and further for studying the air-pollution–weather relationship (Han et al., 2018b). The methods for weather
classification can be generally categorized into three groups: subjective,
mixed, and objective, depending on the automatic degree during the
classification process (Huth et al., 2008). The methods can also be
categorized in more detail according to the basic features of each
classification algorithm (Philipp et al., 2014). Depending on the study
domain and research objectives, different meteorological variables including
geopotential height, mean sea level pressure, and zonal and meridional winds
are used for the classification.
The SOM, an artificial neural network method with unsupervised learning
(Kohonen, 1990; Michaelides et al., 2007), is widely used in cluster
analysis in atmospheric sciences (Jiang et al., 2017; Liao et al., 2018;
Stauffer et al., 2018) because of its superiority over other algorithms
(Liu et al., 2006; Jensen et al., 2012). The SOM performs a nonlinear projection
from the input data space to a two-dimensional array of nodes objectively.
Each node is representative of the input data. The SOM allows missing values in
the input data and can effectively visualize the relationships between
different output nodes (Hewitson and Crane, 2002).
The FNL geopotential height fields (Sect. 2.1) at 850 hPa can capture
the synoptic circulation variations over eastern China well (Han et al., 2018b).
In this study, we used the geopotential height at 850 hPa in 2013–2018 as the
input for the SOM. Each of the SOM output nodes corresponds to a cluster of
SWPs. Finally, we identified six predominant SWPs over eastern China in
summer. All days in the summers of 2013–2018 were included in the clustering
results.
Seasonal and interannual variations in surface ozone in eastern China
Figures 3 and 4, respectively, show the seasonal and interannual
variations in the regional mean surface ozone concentrations in eastern
China and the three subregions (BTH, the YRD, and the PRD) during 2013–2018. Among
n cities with air quality monitoring in a given region, if ozone levels
exceed the national air quality standard in m cities, we defined the ratio of
m to n as the regional exceedance probability of ozone (Fig. 3c). Higher
regional exceedance probability implies ozone pollution over a wider surface
area in that region. The primary pollutant (Fig. 3d) is defined in the air
quality index (AQI) system, in which the AQI for an individual air pollutant is
calculated based on the concentrations of that pollutant. When the
individual AQI of a pollutant on a day is both above 50 and the largest
among all the pollutants, that pollutant is defined as the primary pollutant
on that day.
Interannual variations in regional daily mean (a–d),
daytime mean (e–h), and nighttime mean (i–l) surface ozone
concentrations over eastern China and the three subregions in summer from
2013 to 2018. The values were calculated from the observations in the
corresponding cities. The error bar indicates values that are 2 times the standard
deviation. The red numbers are the increasing rates of the regional mean
ozone in summer (in µg m-3 per year and in % per year) as
well as the corresponding significant level.
On average, regionally, the seasonality of daily mean ozone is similar to that
of daily maximum 8 h average (MDA8) ozone in eastern China as well as in
the three subregions (BTH, the YRD, and the PRD; Fig. 3a and b). In BTH, both
the daily mean and MDA8 have a unimodal seasonal pattern and peak in June, being
99.5 and 158.4 µg m-3, respectively. The extremely high ozone in
June leads to a simultaneous seasonal maximum in both the probability of the
regional exceedance (46.9 % of the cities with ozone measurements in BTH)
and primary pollutant (68.7 % of the days in June; Fig. 3c and d).
The seasonal peak of surface ozone in BTH mainly results from enhanced
photochemistry due to stronger solar radiation and lower humidity (Hou et
al., 2014). Surface ozone over the YRD reaches a seasonal maximum in May (82.6
and 127.7 µg m-3, respectively, for daily mean and MDA8 ozone),
earlier than that over BTH. In contrast, the seasonal peak over the PRD occurs at the
latest in October (71.5 and 118.1 µg m-3, respectively, for
daily mean and MDA8 ozone). Although temperature is higher in summer than in
the other seasons, the EASM brings more cloudy weather, stronger convection,
and clearer air from the oceans, weakening the production and accumulation
of surface ozone levels over the YRD and PRD (Hou et al., 2015; S. Li et al., 2018).
The pre-monsoon and post-monsoon peaks of surface ozone, respectively, in the YRD
and PRD were also reported in previous studies (He et al., 2008; Wang et
al., 2009).
On average, regionally and seasonally, daily mean and MDA8 ozone levels over eastern
China in summer are 73.1 and 114.7 µg m-3, respectively. Among
the three subregions, summertime surface ozone is highest in BTH (88.3 and
143.7 µg m-3, respectively, for daily mean and MDA8 ozone),
second highest in the YRD (72.9 and 114.7 µg m-3), and lowest in the PRD
(51.0 and 91.9 µg m-3; Fig. 3a and b). These regional
differences among the three subregions appear similar to those in the ozone
monitoring instrument (OMI) tropospheric column ozone (Fig. 1). The
regional exceedance probability of ozone over eastern China reaches 17.7 %
in the summer, accompanied by a high percentage (45.6 %) of ozone, which is the
primary pollutant. Among the three subregions, BTH has the highest regional
exceedance probability of ozone (35.1 %) and probability of ozone being
the primary pollutant (55.8 %).
A rapid increase in summertime surface ozone over China after 2012 was
observed in recent studies (Lu et al., 2018; Silver et al., 2018; Shen et
al., 2019a; Li et al., 2019). We examine the regional changes over
eastern China in daily, daytime (07:00–18:00 CST), and nighttime (19:00–06:00 CST)
means (Fig. 4). The increases of ∼3–6 µg m-3 or
4 %–8 % per year are found significantly (p<0.05) over eastern
China, BTH, and the YRD during 2013–2018, while the increase over the PRD during the
same period is insignificant. Silver et al. (2018) found that the annual
mean MDA8 ozone increased significantly (p<0.05) at ∼50 % of the over 1000 stations across China from 2015 to 2017, with a
median rate of 4.6 µg m-3 yr-1. The increase in
surface ozone over eastern China was also captured by the OMI satellite
records, reported by Shen et al. (2019a). The absolute increasing rate (in
µg m-3) in daytime is higher than that in nighttime, whereas the
relative increasing rate (in %) in daytime is lower than that in
nighttime (Fig. 4e–h vs. Fig. 4i–l). The increase in ozone over
China may result from both meteorology and anthropogenic emissions. During
2013–2017, the anthropogenic emissions of NOx in China declined (Zheng
et al., 2018), but the anthropogenic emissions of VOCs changed little (Zheng
et al., 2018; Shen et al., 2019b). Li et al. (2019) suggested that the
∼40 % decrease in fine particulate matter (PM2.5) is
the primary reason for the rising surface ozone in summer during
2013–2017, as the aerosol sink of hydroperoxy radicals was weakened and thus
ozone production was enhanced. Figure 4b demonstrates a strong increase in
summertime surface ozone over BTH from 2016 to 2017, which is probably
related to the extremely high temperature in 2017 (Herring et al., 2019).
The sudden decline in summertime surface ozone over the PRD from 2016 to 2017
(Fig. 4d) is likely associated with the extremely heavy precipitation in
2017 (Herring et al., 2019).
Cross-validated coefficient of determination (R2) between the
observed and predicted daily surface ozone in summer during 2013–2018 for
the MLR (a) with both local and synoptic meteorological factors and (b) with
only local meteorological factors. (c) The most important variable among the
local meteorology for ozone in the MLR. The boxed areas indicate BTH, YRD,
and PRD, respectively, in the north, center, and south of the study domain.
The regional mean R2 in (a) and (b) and meteorological variable in (c) are shown in the bottom right corner of each panel. The abbreviations are for cloud fraction (CF), geopotential height at 850 hPa (HGT850), planetary boundary layer height (PBLH), relative humidity at 2 m (RH2m), sea level pressure (SLP), temperature at 2 m (T2m), zonal wind at 850 hPa (U850), meridional wind at 850 hPa (V850), vertical wind at 850 hPa (W850), and wind speed at 850 hPa (WS850).
Meteorological drivers for summertime surface ozone in eastern China
Meteorological factors can individually or integrally modulate surface ozone
concentration through their impacts on relevant chemical, dynamical, and
thermal processes in the atmosphere. Figure 2 shows a simple way to examine
the overall effect of each of the meteorological variables statistically by
correlating surface ozone with a selected set of local meteorological
variables during 2013–2018 summers. Among all the meteorological variables,
relative humidity shows the highest correlation with surface ozone in
eastern China on average regionally (r=-0.39). Relative humidity can influence
ozone through various processes. Atmospheric water vapor can directly
influence ozone concentrations by HOx (HOx = OH + H + peroxy
radicals) chemistry in complicated ways (Zanis et al., 2002; Jacob and Winner,
2009; Lu et al., 2019b). Moreover, a higher relative humidity is usually
associated with higher fractions of clouds, which can slow the photochemical
production of surface ozone. Higher relative humidity may also somewhat be
linked with larger atmospheric instability, favoring the dispersion of
surface ozone (Camalier et al., 2007). The correlation map of the cloud fraction
is similar to that of relative humidity (Fig. 2a and b). The correlation
of temperature with ozone is higher in the north than in the south over
eastern China (Fig. 2c), which is similar to the pattern found in the
eastern United States (Camalier et al., 2007; Shen et al., 2016). Meridional
wind at 850 hPa is correlated to surface ozone positively in the north but
negatively in the most areas of the south (Fig. 2f). In summer, the
south-westerly monsoon wind prevails over eastern China (Fig. S3). Higher
meridional wind brings clean and humid marine air to the south, while it
transports ozone and its precursors from the south to the north. All the
meteorological variables are not independent of each other. Overall, the
meteorological variables that are related to photochemistry processes
(relative humidity, cloud fraction, and temperature) have a more significant
correlation than transport-related variables (zonal, meridional, and
vertical winds and wind speed; Fig. 2), implying greater effects of
the chemical process than physical transport. S. Li et al. (2018) also suggested
that the chemical process is the uppermost factor controlling surface ozone
levels over eastern China in summer.
Combining the effects of different meteorological variables, we applied the
MLR (Sect. 2.2) using predictors of both local and synoptic factors to
simulate summertime daily surface ozone in eastern China. The MLR was
evaluated using the leave-one-out cross validation to avoid overfitting. The
MLR performs strongly, as it can explain 14 %–65 % of variations in the observed
surface ozone concentrations, yielding a regional mean coefficient of
determination (R2) of 43 % (Fig. 5a). The mean absolute error (MAE)
and the root-mean-square error (RMSE) of regional mean ozone anomalies in
eastern China between observations and predictions by the MLR are 12.0 and
7.1 µg m-3, respectively (Fig. 6a). Geographically, the model
performs better in the south (R2=0.51 in the YRD and R2=0.49 in
the PRD) than in the north (R2=0.42 in BTH; Fig. 5a). Compared with
the simulation that only considers the local meteorological variables in the
MLR, the model performance is improved overall in eastern China when both
local and synoptic meteorological factors are considered (Fig. 5a vs. b). Shen et al. (2017) found that, compared with the MLR that describes
monthly PM2.5 in the United States only using local meteorological
factors, the inclusion of synoptic meteorological factors in the MLR
increases R2 from 34 % to 43 %. We also conducted the stepwise MLR
using local and synoptic meteorology without detrending the input data. The
results show that meteorology can explain 18 % of the enhancement in the
regional mean of summertime surface ozone over eastern China from 2013 to
2018, and the explained variance is 16 %, 41 %, and 44 % for BTH, the YRD,
and the PRD, respectively (Fig. S4).
We applied the MLR to identify the dominant meteorological drivers for ozone
variability (Sect. 2.2). Among the local meteorology, relative humidity is
dominant over ∼51 % of the areas of eastern China, mainly in the
central and the southern regions including the YRD and PRD (Fig. 5c), although
on a city scale in the PRD, Zhao et al. (2016) suggested that sea level pressure
is the most significant variable for MDA8 ozone in Hong Kong. Air
temperature is the most important local meteorological variable in
∼17 % of the areas of eastern China, specifically in the north
including BTH (Fig. 5c). The importance of temperature to surface ozone
over BTH was also suggested by Chen et al. (2019). Previous studies found
that temperature and relative humidity showed pronounced impact on ozone in
the north and south of the eastern United States, respectively (Camalier et
al., 2007; Porter et al., 2015). The difference of the most influential
variables between the south and north in eastern China is similar to that in
the eastern United States. In Europe, Otero et al. (2016) suggested that
temperature is the most important local meteorological driver over a major
part of Europe. On average, regionally, the second most important
meteorological variable for the daily surface ozone variation in eastern
China, BTH, the YRD, and the PRD is temperature, relative humidity, sea level
pressure, and meridional wind at 850 hPa, respectively (Fig. 6).
Comparison of daily surface ozone anomalies in summer between the
predictions using MLR and observations averaged over eastern China and the
three subregions. The linearly fitted line in blue is shown in comparison
with a 1:1 line in black. Cross-validated coefficient of determination
(R2), mean absolute error (MAE), root-mean-square error (RMSE), and
the three most dominant meteorological variables in the MLR for ozone in
each region are shown in the inset. The abbreviations are for relative humidity at
2 m (RH2m), temperature at 2 m (T2m), meridional wind at 850 hPa (V850),
cloud fraction (CF), sea level pressure (SLP), geopotential height at 850 hPa (HGT850), and wind speed at 850 hPa (WS850).
Synoptic impacts on summertime surface ozone in eastern China
In the last section, we have shown that both local and synoptic
meteorological factors are important to surface ozone variations in eastern
China. The synoptic factors used there were extracted via an inverse SVD
process and do not stand for specific weather systems. In this section, we
further investigate how different synoptic weather systems influence surface
ozone in eastern China by looking into the typical SWPs. Atmospheric
circulations over eastern China in summer are largely regulated by the
evolution of the components of EASM, for instance, the WPSH, the subtropical
westerly jet, the Meiyu front, and the southwest vortex (Ding and Chan,
2005). Among these systems, the WPSH can largely modulate the seasonal
migration of the rain belt over eastern China. Typhoons are also an
influential weather system, especially on the southeastern coastal regions. The
main features of the synoptic circulations over eastern China during
2013–2018 can be represented by six predominant SWPs (Figs. 7–12), which
were identified by an objective approach, the SOM (Sect. 2.3). The occurrence
frequency of these SWPs is shown in Figs. 7–12. We name the six SWPs by
their dominant weather systems or prevailing wind: Pattern 1
featured southwesterly wind (P1 or PSW), Pattern 2 featured southerly wind
(P2 or PS), Pattern 3 featured the northeast cold vortex (P3 or PNECV), Pattern
4 featured a weak cyclone (P4 or PWC), Pattern 5 featured strong WPSH (P5 or
PSWPSH), and Pattern 6 featured typhoon systems (P6 or PTC; Table 1).
PSW (P1). The mean (a) geopotential height at 850 hPa (HGT850),
(b) occurrence frequency of this weather pattern, (c) observed daily surface
ozone anomaly, and (d) predicted daily surface ozone anomaly using MLR.
Anomalies of (e) HGT850, (f) relative humidity at 2 m (RH2m), (g) temperature at 2 m (T2m), (h) cloud fraction (CF), (i) planetary boundary
layer height (PBLH), (j) meridional wind at 850 hPa (V850), (k) wind speed
at 850 hPa (WS850), and (l) air stagnation (AS) under this SWP in summer
during 2013–2018. Anomalies of normalized EASMI and WPSHI shown in (a) represent the strength of EASM and WPSH, respectively. The red numbers in (b) are the total occurrence (in the number of days and in %) of this SWP
during the study period. The purple dots in the boxed area in (a) indicate
the cities in BTH, YRD, and PRD, respectively, in the north, center, and
south of the study domain: eastern China. The three boxed areas in (c) and
(d) indicate BTH, YRD, and PRD accordingly. The regional mean anomalies of
observed and predicted ozone (± 2 times the
standard error of the
mean) are shown in the bottom right corners of (c) and (d), respectively.
The same as Fig. 7 but for PS (P2).
The same as Fig. 7 but for PNECV (P3).
The same as Fig. 7 but for PWC (P4).
The same as Fig. 7 but for PSWPSH (P5).
The same as Fig. 7 but for PTC (P6).
To compare the differences of meteorological conditions among the six SWPs,
we calculated the daily EASM index (EASMI) and WPSH index (WPSHI),
representing the strength of EASM and WPSH, respectively. The two indices
were normalized to the zero mean and unit standard deviation. The averaged
anomalies of the normalized indices under each SWP are shown in Figs. 7–12
and Table 1. The EASMI is a shear vorticity index defined as the difference
of the regional mean zonal wind at 850 hPa between 5 and 15∘ N,
22.5 and 32.5∘ N, 90 and 130∘ E, and 110 and 140∘ E in Wang and Fan (1999),
recommended by Wang et al. (2008). The WPSHI is defined by the
accumulative enhancement of geopotential height above the WPSH
characteristic isoline (5880 gpm at 500 hPa) averaged over the area north to
10∘ N. The WPSHI is adopted by the National Climate Center in China
(https://cmdp.ncc-cma.net, last access: 10 December 2019) in the monitoring and diagnosis of the
atmospheric circulation. Using the WPSHI, Zhao and Wang (2017) found a
significant correlation between the WPSH and the first empirical orthogonal
function (EOF) pattern of summertime surface ozone in eastern China.
Moreover, we used the averaged anomalies of the meteorological variables in
a SWP to describe that SWP. We used the averaged ozone anomaly (in µg m-3) (Figs. 7–12) and the averaged relative ozone anomaly (the ozone
anomaly divided by the monthly ozone mean; in %; Table 1 and Fig. S5)
under a SWP to assess the influence of that SWP on ozone (Han et al.,
2018b). Furthermore, a common index for air stagnation (Horton et al., 2012)
is used to assess the impact of air stagnation on surface ozone. For each
FNL grid, when the daily average wind speed at 10 m, daily average wind
speed at 500 hPa, and the daily total precipitation on a day are,
respectively, less than 3.2 m s-1, 13 m s-1, and 1 mm, the day is
considered to be a stagnant day at that grid. The National Oceanic and
Atmospheric Administration (NOAA) Climate Prediction Center (CPC)
precipitation data
(https://www.esrl.noaa.gov/psd/data/gridded/data.cpc.globalprecip.html, last access: 10 December 2019) were
used in the calculation of the air stagnation index.
Characteristics of the six predominant SWPs. Ozone anomalies
observed in each SWP are shown in regional mean ± 2 times the
standard error of the mean. Anomalies of normalized EASMI and WPSHI in each
SWP are shown to represent the strength of EASM and WPSH, respectively. A
higher EASMI (WPSHI) indicates a stronger EASM (WPSH).
The characteristics of the six SWPs and their impacts on surface ozone are
briefly summarized in Table 1. PSW (P1) is the most common circulation
pattern occurring on 25 % of the days of summer during 2013–2018 (Fig. 7b).
Characterized by weak EASM conditions, PSW is dominated by an anomalous
anticyclone located in the southeast of eastern China (Fig. 7e). In PSW,
the enhanced meridional wind brings clear marine air to the south of eastern
China (Fig. 7j), where the meridional wind is significantly correlated to
surface ozone (Fig. 2f). The enhanced zonal wind from the anomalous
anticyclonic circulation (Fig. 7e) increases the ozone export from the
south of eastern China (Yang et al., 2014). The negative anomalies of
temperature (Fig. 7g), and positive anomalies of relative humidity (Fig. 7f) and cloud fraction (Fig. 7h) in the south, are all unfavorable for
photochemical processes. In consequence, PSW reduces ozone levels in the
south (Fig. 7c) by enhancing the dispersion and suppressing the production
of ozone. Negative anomalies of -1.5 (-2.4 %) and -6.6µg m-3
(-13 %) in the regional mean ozone are observed over the YRD and PRD,
respectively (Fig. 7c and Table 1). In contrast, the lower cloud fraction
(Fig. 7h) and higher temperature (Fig. 7g) in the north stimulate ozone
production. Surface ozone over BTH increases by 3.4 µg m-3
(3.6 %) from the regional mean in PSW (Fig. 7c and Table 1).
PS (P2) is the second frequent SWP (Fig. 8b), characterized by a strong
EASM and weak WPSH (Fig. 8a). Under PS, the FNL meteorological data show
frequent stagnation events (Fig. 8l), low humidity (Fig. 8f), and a low
cloud fraction (Fig. 8h) over most of eastern China. In contrast to PSW, the
zonal wind has negative anomalies (Fig. 8e) in PS, reducing ozone export
from the south of eastern China. Overall, an increase of 1.1 µg m-3 (1.7 %) in the regional mean ozone concentrations is seen in
eastern China under PS (Fig. 8c and Table 1).
PNECV (P3) is a typical pattern for Meiyu, an important climate phenomenon
over the middle and lower reaches of the Yangtze River from early June to
mid-July (Fig. 9a). PNECV is characterized by persistent rainfall (Ding
and Chan, 2005). Under a combined effect of the northeast cold vortex and
the WPSH, the Meiyu front forms and stays over the YRD (He et al., 2007). Meiyu
in PNECV increases relative humidity (Fig. 9f) and decreases air
stagnation (Fig. 9l) over the YRD. Consequently, PNECV reduces surface ozone
concentrations by 1.3 µg m-3 (1.7 %) over the YRD (Fig. 9c and
Table 1). In the meantime, more sunny days with high temperature (Fig. 9g) and
low moisture (Fig. 9f) occur in the north to the YRD, affected by the
northwesterly and downward airflows from the northeast cold vortex (Fig. 9a). As a result, positive ozone anomalies are observed in the regions north
of the YRD (Fig. 9c).
PWC (P4) features the weakest WPSH, when a weak extratropical cyclone
is located over the east of the mainland China (Fig. 10a). The extratropical
cyclone is probably formed by an eastward movement of the southwest vortex
or a transition from a typhoon. Pushed by the cyclone, the WPSH retreats (Y. Li et al., 2018). The weak pressure gradient over the mainland of eastern
China (Fig. 10a) in PWC results in more-stable weather conditions. The
anomalies of the meteorological variables in PWC show opposite spatial
patterns to those in PSW (Fig. 7 vs. Fig. 10). With the favorable
meteorological conditions except temperature, PWC enhances ozone over the
south. PWC statistically increased regional mean ozone by 5.2 µg m-3 (7.5 %) over the YRD and 6.7 µg m-3 (11.8 %) over the PRD
(Fig. 10c and Table 1). Mean negative ozone anomalies of -4.8µg m-3 (-5.1 %) are observed over BTH in PWC (Fig. 10c and Table 1).
PSWPSH (P5) occurs in late summer (Fig. 11b), when the Meiyu breaks in the
Yangtze River and the rain belt shifts to northern China (Ding and Chan, 2005).
In PSWPSH, the WPSH is the strongest and extends mostly westward (Fig. 11a). Thus, relative humidity is lower than the seasonal mean over the YRD and
higher than the seasonal mean over BTH (Fig. 11f). In the meantime, stable
weather conditions occur more frequently over the YRD (Fig. 11l). Therefore,
ozone accumulates over the YRD in PSWPSH with a regional mean enhancement of 1.8 µg m-3 (2.5 %; Fig. 11c and Table 1). Surface ozone
decreases by 0.8 (1.4 %) and 5.0 µg m-3 (8.9 %),
respectively, over BTH and the PRD under this SWP (Fig. 11c and Table 1).
PTC (P6) is typical typhoon weather that is over the southeastern coast of
the mainland China (Fig. 12a). Forced by a typhoon system, the WPSH in PTC
migrates further north than under the other SWPs. The typhoon brings clear
and moist marine air to coastal areas in eastern China, reducing surface
ozone by 6.8 µg m-3 (9.2 %) over the YRD (Fig. 12c and Table 1).
Shu et al. (2017) identified that SWPs, like PTC, can lead to clean PM2.5
episodes in the YRD. However, the cyclonic circulation enhances ozone transport
from the central part of eastern China to the downwind regions in the south,
including the PRD. Through the collective effect of higher temperature, lower
humidity, and heavier downdrafts, PTC increases surface ozone in the PRD by 7.9 µg m-3 (15.5 %; Fig. 12c and Table 1). Lam et al. (2018)
found that ozone increases by 16.8 µg m-3 at urban stations in Hong
Kong of the PRD, when the synoptic circulation controlling the PRD is featured
as a typhoon in the vicinity of Taiwan, similar to PTC. They also suggested that
this SWP is associated with the interannual variations in ozone pollution in
Hong Kong.
The most important variable among the local meteorology for ozone
in the MLR under each of the SWPs. The regional mean meteorological
variables are shown in the bottom right corner of each panel. The boxed
areas indicate BTH, YRD, and PRD, respectively, to the north, center, and
south of the study domain. The abbreviations are for cloud fraction (CF),
geopotential height at 850 hPa (HGT850), planetary boundary layer height
(PBLH), relative humidity at 2 m (RH2m), sea level pressure (SLP),
temperature at 2 m (T2m), zonal wind at 850 hPa (U850), meridional wind at
850 hPa (V850), vertical wind at 850 hPa (W850), and wind speed at 850 hPa
(WS850).
We further compared the SWP analysis with that from the MLR discussed in
Sect. 4. We evaluate the performance of the MLR under the six SWPs based
on the predicted (Figs. 7d, 8d, 9d, 10d, 12d, and 13d) and observed
(Figs. 7c, 8c, 9c, 10c, 11c, and 12c) ozone anomalies. The comparison
shows that the ozone anomalies predicted by the MLR have spatial variations
and magnitudes similar to those in the observations under each of the SWPs.
The MAE of averaged ozone anomalies under each of the SWPs ranges from 1.0 to 2.2 µg m-3, and the RMSE ranges from 1.4 to 2.8 µg m-3 (Table S1). The MLR can capture the ozone anomalies under the six predominant
SWPs well (Figs. 7–12). For example, the negative ozone anomaly over the PRD under
PSW featured a weak EASM (Fig. 7c vs. d), the negative ozone anomaly over
the YRD under PNECV featured Meiyu (Fig. 9c vs. d), and the positive ozone
anomaly over the PRD under PTC featured a typhoon (Fig. 12c vs. d). Since the
MLR only considers the meteorological influence on surface ozone, the
consistency between the regression and the clustering results suggests that
the mean observed ozone anomalies under a SWP can adequately reflect the
response of daily ozone variation to meteorology. The noise of day-to-day
variations in chemistry and emissions in the surface ozone data can be
largely removed by the long-term average of ozone anomalies under a SWP from the
big dataset of surface ozone (Han et al., 2018b).
In addition, we applied the MLR to reveal the most important local
meteorological factor for daily ozone variability under each of the six SWPs
(Fig. 13). The MLR was conducted under each of the SWPs with the same
procedure in the full summer. The most important meteorological variable for
ozone over some areas in eastern China may vary with the prevailing SWP
(Fig. 13). The dominant driver in the PRD is meridional wind at 850 hPa under
PSW, PS, and PSWPSH, demonstrating the significant influence of marine air
inflow. Controlled by the typhoon system, the most important factor over
some coastal areas is zonal wind at 850 hPa under PTC.
Summary
Meteorology can influence surface ozone variability on different timescales, from long-term trends to sub-daily variation. Based on surface ozone
observations during 2013–2018, we characterized the seasonal and interannual
variations in surface ozone in eastern China. The measurements show that
surface ozone pollution in the study region is severest in summer, and the
severity is rapidly exacerbated during 2013–2018. We focused on the
meteorological influence on the daily variability in summertime surface
ozone in eastern China. We took daily anomalies of meteorological and ozone
values to remove the variability on scales longer than daily variations in
these datasets. We estimated the local and synoptic meteorological impacts
on daily variability in surface ozone using a MLR and a SOM clustering
technique. The MLR is driven by local meteorological variables and synoptic
weather factors identified by the SVD analysis.
The MLR suggests that on average, regionally, meteorology can explain 43 % of
variations in the summertime daily surface ozone in eastern China, with an
explained variance of up to 65 % at some locations (Fig. 5a). The
regression model shows that meteorology contributes to 18 % of the
increase in the regional mean of summertime surface ozone over eastern China
from 2013 to 2018. Exploiting the MLR, we also identified the key
meteorological variables that are mostly responsible for daily variations in
summertime surface ozone in eastern China during 2013–2018. Among the local
meteorological variables, relative humidity is the foremost factor over most
areas in the center and south of eastern China including the YRD and PRD, while
temperature is the foremost factor in the north including BTH (Fig. 5c).
We assessed the impacts of the dominant synoptic weather systems on surface
ozone using cluster analysis. Employing the SOM, the summer synoptic
circulations over eastern China during 2013–2018 were objectively classified
into six predominant SWPs (Figs. 7–12). The six SWPs control the
variations in the key meteorological variables and thus impact the transport
and production of ozone regionally. Among the six SWPs, the SWP (PS),
featuring southerly wind, strong EASM, and weak WPSH (Fig. 8), and the SWP
(PWC), featuring a weak extratropical cyclone and the weakest WPSH (Fig. 10), tend to increase the regional mean surface ozone in eastern China. In
contrast, the other four SWPs (namely, PSW, PNECV, PSWPSH, and PTC) tend to
reduce regional mean surface ozone in eastern China (Figs. 7, 9, 11, and
12). As the predominant meteorological variables vary largely in space
(Figs. 2 and 5), surface ozone concentrations under every SWP vary largely
between the northern and southern parts of eastern China or between eastern and
western parts of eastern China (Figs. 7–12). Influenced by the dominant
SWP, daily mean surface ozone in some areas of eastern China can increase or
decrease maximally by 8 µg m-3 or 16 % of the daily mean
(Table 1).
This study provides some new insights on the relationship between
meteorology and air pollution by untangling the complex response of surface
ozone to different SWPs and local meteorological variables. The most
significant meteorological variables for surface ozone in eastern China were
identified regionally, which was rarely investigated by previous studies
(Gong et al., 2018; Zhan et al., 2018; Chen et al., 2019). Extending from
previous studies, we quantified ozone anomalies in eastern China resulting
from the prominent synoptic weather systems such as the WPSH (Shu et al.,
2016; Zhao and Wang, 2017), the extratropical cyclones (Zhang et al., 2013;
Liao et al., 2017), the Meiyu front, and typhoons (Jiang et al., 2015; Lam et
al., 2018). These systems are important drivers for variations in air
pollutants over eastern China (Ding et al., 2017). The mean ozone anomalies
under a SWP over 2013–2018 were used to describe ozone sensitivity to that
SWP. This method can remove the seasonal differences in the pollutant
concentrations and the frequency of SWPs (Han et al., 2018b). No
consideration of seasonal differences in pollutant concentrations and
meteorology can lead to biases in addressing daily variation of a pollutant
(e.g. Zhang et al., 2013, 2016; Liao et al., 2017).
In this study, the developed MLR and cluster techniques can describe
the meteorological impacts on the surface ozone variation in eastern China well.
Both regression and clustering analyses show strong performance, so they can
be effective tools for air quality forecast. Here, we emphasize the
importance of synoptic meteorology to the daily variation of surface ozone.
The constructed synoptic factors by the SVD analysis can be a useful
predictor for forecasting such daily variation. As ozone responds
nonlinearly to variation in meteorology, emissions, and chemistry (Wu et
al., 2009), the developed MLR cannot fully predict daily ozone variation
yet. Therefore, the nonlinearity issue needs to be addressed in the future.
Future work can also be focused on the sensitivity of the diurnal ozone
variation to meteorology and on the impact of climate change on future
surface ozone levels regionally and globally (Shen et al., 2017).
Data availability
Surface ozone measurements were obtained from the public website of MEE
(http://beijingair.sinaapp.com/, last access: 10 December 2019). The FNL meteorological data were acquired
from NCEP (10.5065/D6M043C6). The OMI tropospheric
column ozone monthly data were from the NASA Goddard Space Flight Center
(https://acd-ext.gsfc.nasa.gov/Data_services/cloud_slice/, last access: 10 December 2019; Ziemke et al., 2006).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-203-2020-supplement.
Author contributions
HH designed the study and performed the research. HH and LS
analyzed the data and developed the model. HH and JL wrote the
paper, with input from LS, TW, and HY.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
Acknowledgements
We are grateful to MEE for the available air pollution data, to NCEP for the
FNL meteorological data, and to the NASA Goddard Space Flight Center for the OMI
tropospheric column ozone data. We thank the constructive comments and
suggestions from the anonymous reviewers.
Financial support
This research is supported by the National Key Basic Research Development Program (grant no. 2016YFA0600204) and by the Natural Science Foundation of China (grant nos. 41621005, 91744209, 91544230, 41375140).
Review statement
This paper was edited by Tong Zhu and reviewed by two anonymous referees.
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