Introduction
In the urban planetary boundary layer (PBL), ozone (O3) is formed as a
result of photochemical reactions involving volatile organic compounds
(VOCs) and nitrogen oxide (NOx) in the presence of sunlight (Brasseur
et al., 1999):
NO2+hυ→NO+O3P290nm<λ<420nmO3P+O2+M→O3+MO3+hυ→O2+O1D290nm<λ<329nmO1D+H2O→2OHOH+VOCs+O2→RO2+othersRO2+NO→RO+NO2
where hυ represents the energy of a photon; O3P and O1D represent the ground state and
electronically excited oxygen atoms, respectively; RO2, RO, and OH
denote peroxy, oxy-, and hydroxyl radicals, respectively. High O3
concentrations ([O3]) are of major environmental concern due to its
deleterious impacts on ecosystems (e.g., National Research Council, 1991) and
human health (Lippmann, 1993; Weinhold, 2008).
The emissions of O3 precursors, VOCs and NOx, have been
significantly increased recently in China due to rapid industrialization and
urbanization, and increasing transportation activity (e.g., Zhang et al.,
2009; Kurokawa et al., 2013; Yang et al., 2015). Satellite measurements have
demonstrated that NOx emissions have been increased by a factor of 2 in
central and east China from 2000 to 2006 (Richter et al., 2005). Zhang et
al. (2009) have also shown an increasing trend of NOx emissions with an
enhancement of 55 % in China from 2001 to 2006. NOx emissions have
still continued to increase since 2006, due to increasing power plants and
vehicles (S. W. Wang et al., 2012; Y. Wang et al., 2013; Yang et al., 2015).
In addition, the agriculture has been proposed to have a large potential to
produce NOx (Oikawa et al., 2015). VOC emissions have been estimated to
increase by 29 % during 2001–2006 in China (Zhang et al., 2009), and
predicted to increase by 49 % by 2020 relative to 2005 levels (Xing et
al., 2011). Additionally, modeling studies have been performed to investigate
the O3 pollution in eastern China (Wang et al., 2010; Liu et al., 2012;
Situ et al., 2013; Huang et al., 2015). For example, Tie et al. (2013) have
analyzed the characteristics of regional O3 formation to explain the
O3 pollution in Shanghai and its surrounding area using the
Weather Research and Forecasting model coupled to chemistry (WRF-CHEM). Using the observation-based chemical model, Xue et
al. (2014) have provided insights into the ozone pollution in Beijing,
Shanghai, and Guangzhou by analyzing the O3 precursors and the potential
impacts of heterogeneous chemistry.
Increasing O3 precursor emissions have caused O3 to be one of the
most serious air pollutants of concern during summertime, particularly in
eastern China, including the North China Plain (NCP), Yangtze River Delta
(YRD), and Pearl River Delta (PRD) (e.g., Xu et al., 2011; Tie et al., 2013;
Li et al., 2013; Feng et al., 2016). For example, a maximum O3
concentration of 286 ppb has been observed in urban plumes from Beijing
(Wang et al., 2006). Chen et al. (2015) have reported that the average
maximum daily [O3] exceed 150 µg m-3 in the summer of
2015 at most of monitoring sites in Beijing. Wu et al. (2017) have also shown
that, during summertime in 2015 in Beijing, the average O3 concentration
in the afternoon was 163.2 µg m-3, and the frequency of the
O3 exceedance with hourly [O3] exceeding 200 µg m-3
was 31.8 %. In addition, Cheng et al. (2016) have demonstrated an
increasing trend of daily maximum 1 h [O3] from 2004 to 2015 in
Beijing, and Ma et al. (2016) have reported a significant increase of surface
O3 at a rural site in NCP. In the PRD region, the annual average
near-surface O3 level has been reported to increase from 24 ppbv in
2006 to 29 ppbv in 2009, and the maximum 1 h [O3] can be up to
150–200 ppb in the summer and fall (Ou et al., 2016). Numerous studies have
been performed to examine the severe O3 pollution in China, but were
primarily confined to megacities or industrial complexes. Few studies have
been conducted for all of eastern China to investigate the O3 pollution
situation and formation.
WRF-CHEM simulation domain with topography. The filled circles
represent centers of cities with ambient monitoring sites and the size of
circles denotes the number of ambient monitoring sites of cities. The red and
blue filled circles show the cities with air pollutant observations since
2013 and 2015, respectively.
WRF-CHEM model configurations.
Regions
Eastern China
Simulation period
22–28 May 2015
Domain size
350×350
Domain center
35∘ N, 114∘ E
Horizontal resolution
10 km × 10 km
Vertical resolution
35 vertical levels with a stretched vertical grid with spacing ranging
from 30 m near the surface to 500 m at 2.5 and 1 km above 14 km
Microphysics scheme
WSM six-class graupel scheme (Hong and Lim, 2006)
Boundary layer scheme
MYJ TKE scheme (Janjić, 2002)
Surface layer scheme
MYJ surface scheme (Janjić, 2002)
Land-surface scheme
Unified Noah land-surface model (Chen and Dudhia, 2001)
Longwave radiation scheme
Goddard longwave scheme (Chou and Suarez, 2001)
Shortwave radiation scheme
Goddard shortwave scheme (Chou and Suarez, 1999)
Meteorological boundary and initial conditions
NCEP 1∘×1∘ reanalysis data
Chemical initial and boundary conditions
MOZART 6 h output (Horowitz et al., 2003)
Anthropogenic emission inventory
SAPRC-99 chemical mechanism emissions (Zhang et al., 2009)
Biogenic emission inventory
MEGAN model developed by Guenther et al. (2006)
Model spin-up time
28 h
The China Ministry of Environmental Protection (China MEP) has commenced to
release real-time hourly observations of pollutants, including O3,
NO2, CO, SO2, PM2.5, and PM10 (particulate matter with
aerodynamic diameter less than 2.5 and 10 µm, respectively) since
2013. In eastern China, there were 65 cities with air pollutant observations
in 2013 during summertime, mainly concentrated in Beijing–Tianjin–Hebei
(BTH), YRD, and PRD (Fig. 1). In 2015, a total of 223 cities had air
pollutant observations in eastern China, providing a good opportunity to
explore the O3 pollution distributions. Therefore, in the present study,
the O3 pollution situation in 2015 is first analyzed from April to
September when [O3] are high in eastern China. A high O3 episode
that occurred in eastern China in 2015 is simulated using the WRF-CHEM model
to evaluate the O3 formation from biogenic and various anthropogenic
sources. The WRF-CHEM model configuration and methodology are described in
Sect. 2. Data analysis and model results are presented in Sect. 3, and
conclusions and discussions are given in Sect. 4.
Model and methodology
WRF-CHEM model and configurations
In the present study, we use a specific version of the WRF-CHEM model (Grell
et al., 2005) to investigate the O3 formation in eastern China. The
model is developed by Li et al. (2010, 2011a, b, 2012) at the Molina Center
for Energy and the Environment, including a new, flexible gas-phase chemical
module and the Models-3 community multiscale air quality (CMAQ) aerosol
module developed by the US EPA (Binkowski and Roselle, 2003). The wet
deposition of chemical species is calculated using the method in the CMAQ
module and the dry deposition parameterization follows Wesely (1989). The
fast radiation transfer model (FTUV) is used to calculate photolysis rates (Tie et al., 2003; Li
et al., 2005), considering the impacts of aerosols and clouds on the
photochemistry (Li et al., 2011b). The ISORROPIA version 1.7 is used to
calculate the inorganic aerosols (Nenes et al., 1998). The secondary organic
aerosol (SOA) is predicted using a non-traditional SOA module, including the
volatility basis set (VBS) modeling approach and SOA contributions from
glyoxal and methylglyoxal. Detailed information about the WRF-CHEM model can
be found in Li et al. (2010, 2011a, b, 2012).
Observed hourly mass concentrations of pollutants averaged in the
afternoon from April to September 2013 and 2015 in 65 cities of eastern China.
Pollutants
CO (mg m-3)
SO2 (µg m-3)
NO2 (µg m-3)
O3 (µg m-3)
PM2.5 (µg m-3)
2013
1.05
24.8
27.7
100.5
46.9
2015
0.77
15.4
23.9
110.5
38.2
Change (%)
-26.7
-37.8
-13.5
+9.9
-18.5
A high O3 pollution episode from 22 to 28 May 2015 in eastern China is
simulated using the WRF-CHEM model. The WRF-CHEM model adopts one grid with
horizontal resolution of 10 km and 35 sigma levels in the vertical
direction, and the grid cells used for the domain are 350×350
(Fig. 1). The physical parameterizations include the microphysics scheme of
Hong and Lim (2006), the Mellor–Yamada–Janjic (MYJ) turbulent kinetic
energy (TKE) planetary boundary layer scheme (Janjić, 2002), the unified
Noah land-surface model (Chen and Dudhia, 2001), the Goddard longwave (Chou
and Suarez, 2001) and shortwave parameterization (Chou and Suarez, 1999). The
National Centers for Environmental Prediction (NCEP) 1∘×1∘ reanalysis data are used to
obtain the meteorological initial and boundary conditions, and the
meteorological simulations are not nudged in the study. The chemical initial
and boundary conditions are interpolated from the 6 h output of MOZART
(Horowitz et al., 2003). The spin-up time of the WRF-CHEM model is 28 h,
which is generally long enough for simulations considering that the initial
and boundary conditions are adopted from MOZART, a global chemical transport
model. The SAPRC-99 chemical mechanism is used in the present study. The
anthropogenic emissions are developed by Zhang et al. (2009) and Li et
al. (2017), including contributions from agriculture, industry, power
generation, residential, and transportation sources. The biogenic emissions
are calculated online using the MEGAN (Model of Emissions of Gases and
Aerosol from Nature) model developed by Guenther et al. (2006). Detailed
model configurations are given in Table 1. The simulation domain is shown in
Fig. 1.
For discussion convenience, eastern China is divided into four sections:
(1) northeast China (including Heilongjiang, Jilin, Liaoning, and the
east part of Inner Mongolia, hereafter referred to as NEC), (2) the North
China Plain and surrounding areas (including Beijing, Tianjin, Hebei,
Shandong, Henan, Shanxi, and the north part of Jiangsu and Anhui, hereafter
referred to as NCPs), (3) the YRD and surrounding areas (including the south
part of Jiangsu and Anhui, Shanghai, Zhejiang, and Hubei, hereafter referred
to as YRDs), and (4) the PRD and surrounding areas (including Fujian,
Jiangxi, Hunan, Guangxi, and Guangdong, hereafter referred to as PRDs) (shown
in the Supplement, Fig. S1).
Statistical methods for comparisons
We use the mean bias (MB) and the index of agreement (IOA) to assess the
WRF-CHEM model performance in simulating air pollutants against measurements.
MB=1N∑i=1NPi-OiIOA=1-∑i=1NPi-Oi2∑i=1NPi-O‾+Oi-O‾2,
where Pi and Oi are the calculated and observed pollutant
concentrations, respectively. N is the total number of the predictions used
for comparisons, and O‾ represents the average of the prediction
and observation, respectively. The IOA ranges from 0 to 1, with 1 showing
perfect agreement of the prediction with the observation.
Air pollutants measurements
The hourly near-surface CO, NO2, SO2, and PM2.5 mass
concentrations from April to September 2015 in eastern China are released by
China MEP and can be downloaded from the website
http://www.aqistudy.cn/. China MEP releases the pollutant observations
using the mass concentration (µg m-3 or mg m-3) as the
unit. Therefore, in order to keep consistent with the observations, the mass
concentration is used in the paper, although the mixing ratio (such as
ppbv) is a more common unit used in the literature for air pollutants.
Results and discussions
O3 pollution in eastern China
Continuous deterioration of air quality in China has engendered the
implementation of the Atmospheric Pollution Prevention and Control Action
Plan (hereafter referred to as APPCAP), released by Chinese State Council
in September 2013 to reduce PM2.5 by up to 25 % by 2017 relative to
2012 levels. Therefore, variations of air pollutants from 2013 to 2015
demonstrate the mitigation effects of implementation of the APPCAP on the air
quality to a considerable degree. A total of 65 cities, with 427 monitoring
sites, have air pollutant observations from 2013 to 2015 during April to
September in eastern China (Fig. 1). Considering the occurrence of high
[O3] in the afternoon (12:00–18:00 Beijing time (BJT)), Table 2
provides the average concentrations of air pollutants in the afternoon from
April to September in the 65 cites of eastern China in 2013 and 2015.
Apparently, implementation of the APPCAP has decreased the mass
concentrations of CO, SO2, NO2, and PM2.5 in eastern China,
particularly with regard to SO2, with a reduction of close to 40 %
from 2013 to 2015. The [O3] however exhibit an increasing trend,
enhanced by 9.9 % from 2013 to 2015. Additionally, if the O3
exceedance is defined as hourly [O3] exceeding
200 µg m-3 (the second grade of National Ambient Air Quality
Standards in China), the O3 exceedance frequency in the afternoon had
increased from 5.2 % in 2013 to 6.8 % in 2015, enhanced by about
31.5 %. The ozone monitoring instrument (OMI) satellite observations have
also shown that the annual O3 concentration has increased by 1.6 %
per year over central and eastern China from 2005 to 2014 (Shan et al.,
2016).
There are several possible reasons for the O3 pollution deterioration
in eastern China since implementation of the APPCAP. Firstly, if the O3
production regime in eastern China is VOC sensitive, the decrease of
NOx due to implementation of the APPCAP likely enhances the O3
formation. Secondly, mitigation of PM2.5 or aerosols directly or
indirectly increases the photolysis rates and expedites the O3
formation. Thirdly, increasing transportation activities enhances the
emissions of VOCs and semi-VOCs, facilitating the O3 formation. In
addition, variability of meteorological situations also leads to the
[O3] fluctuation (Calkins et al., 2016). Hence, implementation of the
APPCAP does not help mitigate [O3], and unfortunately, severe O3
pollution has been looming in eastern China.
In 2015, O3 observations had been performed in 223 cities with 1064
monitoring sites in eastern China, which were used to analyze the O3
pollution situation from April to September. For comparisons, Fig. 2 shows
the distribution of observed maximum 1 h [O3] in mainland China from
April to September in 2015. The cities with the maximum 1 h [O3]
exceeding 300 µg m-3 are mainly concentrated in NCPs, YRDs,
and PRD. In eastern China, there are only two cities with the maximum 1 h
[O3] less than 200 µg m-3. About 28 % of cities have
observed more than 400 µg m-3 [O3] (about 200 ppb),
showing widespread O3 pollution in eastern China. Furthermore, it is
worth to note that the observed maximum 1 h [O3] in six cites exceed
800 µg m-3 (about 400 ppb) at a very dangerous level.
Distribution of observed maximum 1 h [O3] in mainland China
from April to September 2015.
Distribution of average daily maximum 1 h [O3] in mainland China from April to September 2015.
Figure 3 presents the distribution of average daily maximum 1 h [O3] in
mainland China from April to September 2015. The average daily maximum 1 h
[O3] are more than 120 µg m-3 in more than 95 % of
the cities, and 160 µg m-3 in 46 % of the cities in
eastern China. Particularly, there are seven cities with the average daily
maximum 1 h [O3] exceeding 200 µg m-3 during 6 months.
Figures 4 and 5 show the distributions of exceedance days with the
maximum 1 h [O3] exceeding 160 and 200 µg m-3 in
mainland China from April to September 2015, respectively. There are more
than 60 days with the maximum 1 h [O3] exceeding
160 µg m-3 in 114 cities, and even more than 90 days in 62
cites in eastern China from April to September. The 1 h [O3] of
200 µg m-3 have been exceeded on over 10 % of days in 129
cities, and on 30 % of days in 38 cities (Fig. 5). Hence, persistent
O3 pollution has occurred in eastern China from April to September in
2015.
Distribution of days with the maximum 1 h [O3] exceeding
160 µg m-3 in mainland China from April to September 2015.
Distribution of days with the maximum 1 h [O3] exceeding
200 µg m-3 in mainland China from April to September 2015.
Furthermore, in the urban PBL, high [O3] generally take place under calm
or stable circumstances with strong solar radiation. From April to September,
the east Asian summer monsoon influences eastern China, causing intensified
precipitation which inhibits the high O3 formation by washing out
O3 precursors and decreasing photolysis rates. Thus, if excluding rainy
days in the analysis, the O3 pollution becomes more severe in eastern China. For example, in Beijing, there are 54 rainy days and 65 days with the
maximum 1 h [O3] exceeding 200 µg m-3 from May to
August in 2015. If it does not rain in Beijing, the occurrence possibility of
the maximum 1 h [O3] exceeding 200 µg m-3 is around
94 %, showing severe and persistent O3 pollution.
Model performance
The hourly measurements of O3 and NO2 in eastern China are used to
validate the WRF-CHEM model simulations. Figure 6 presents the distributions
of calculated and observed near-surface [O3] along with the simulated
wind fields at 15:00 BJT from 22 to 27 May 2015. In order to interpret the
effect of meteorological and synoptic conditions on the air quality in
eastern China, Fig. S2 presents the average geopotential height wind filed at
500 hPa from 22 to 27 May 2015. During the study episode, the NCPs and NEC
are generally located behind the trough whose center is located between 120
and 130∘ E. At the end of May, the main part of subtropical high at
500 hPa was located in the western Pacific, with the ridgeline moving around the
10–15∘ N. With the onset of the summer monsoon, the subtropical high
gradually moves northwards and affects southern China, with more
precipitation occurrence over YRDs and PRDs. Figure 6 presents the
distributions of calculated and observed near-surface [O3] along with
the simulated wind fields at 15:00 BJT from 22 to 27 May 2015. On 22 May,
eastern China is influenced by the high pressure whose center, located over
the Yellow Sea, was induced by the high-level trough. The east winds in
the south of the high transport humid air into PRDs, causing rainfall weather
that substantially decreases [O3]. The WRF-CHEM model well reproduces
the observed low [O3] in the south of PRDs. In NCPs and YRDs, calm
winds, clear sky, and high temperature, induced by the high, facilitate the
O3 formation, and the simulated [O3] generally exceed
160 µg m-3, which is consistent with the observations. On
23 May, the subtropical high moves northward, also causing the rainfall belt
in the south of PRDs to extend northward. The simulated O3 pollution in
NCPs is deteriorated and also extended to NEC, in good agreement with the
measurements. From 24 to 25 May, the high pressure located at the Yellow Sea
continuously deteriorates the O3 pollution in eastern China. The
simulated and observed O3 pollution on 25 May is almost widespread in
eastern China, and northwest China also experiences high O3
pollution. On 26 and 27 May, the simulated and observed [O3] in the
north of NCPs and NEC are still high, but in PRDs and YRDs, the [O3]
have been significantly decreased due to the precipitation caused by the
subtropical high and summer monsoon.
Pattern comparison of simulated versus observed near-surface O3 at
15:00 BJT from 22 to 27 May 2015. Colored circles: O3 observations;
color contour: O3 simulations; black arrows: simulated surface winds.
Generally, the simulated O3 spatial patterns are consistent with
observations, but the model underestimation or overestimation still exists.
For example, the model remarkably overestimates the observed [O3] on 24
May, and also cannot well reproduce the high [O3] on 25 May in PRD.
There are several reasons for the model biases in simulating [O3]
distribution. Firstly, the meteorological situations play a key role in air
pollution simulations (Bei et al., 2010, 2012), determining the formation,
transformation, diffusion, transport, and removal of the air pollutants.
Therefore, uncertainties in meteorological field simulations significantly
influence the air pollutant simulations. On 24 May, the model fails to
predict the rainy or overcast weather, leading to remarkable overestimation of
[O3] in PRD. Secondly, the 10 km horizontal resolution is used in
simulations, which cannot resolve cumulus clouds well. The model
overestimates the [O3] observed in some cities with [O3] much lower
than their surrounding cities, which is primarily caused by the model failure
in resolving convections. Thirdly, the fast changes in emissions are not
reflected in the emission inventories used in the present study.
Figure 7 provides the diurnal profiles of calculated and observed
near-surface [O3] averaged over the ambient monitoring sites in
provinces and municipalities in eastern China during the episode. The model
reasonably well reproduces the temporal variations of surface [O3]
compared to observations, e.g., peak [O3] in the afternoon due to active
photochemistry and low [O3] during nighttime caused by the NOx
titration. Three provinces in NEC (Jilin, Liaoning, and Inner Mongolia) are
apparently impacted by the transboundary transport from NCPs when the south
winds are prevailing (Fig. 6). Thus, the uncertainties of wind field
simulations constitute one of the most important reasons for the model biases
in modeling [O3] in these three provinces. The model underestimates
considerably the observed [O3] in the three provinces (Fig. 7a, c, d),
with MBs exceeding 19 µg m-3. The model generally exhibits
good performance in simulating [O3] variations in the provinces of NCPs
(Fig. 7e–l) with IOAs exceeding 0.90, but is subject to underestimating
the observations, particularly in Beijing which is also significantly
influenced by the transboundary transport (Wu et al., 2017). In YRDs, the
model cannot well predict the observed [O3] in Shanghai, which is
affected by the sea breeze when the large-scale wind fields are weak. In
general, however, current numerical weather prediction models, even in
research mode, still have difficulties in producing the location, timing,
depth, and intensity of the sea-breeze front (Banta et al., 2005; J. Wang et
al., 2013). The model reasonably predicts the [O3] variations compared
to measurements in PRDs (Fig. 7p–t) with IOAs more than 0.7, but
overestimates the observed [O3] with MBs varying from 3.8 to
16.7 µg m-3, showing model biases in modeling precipitation
processes.
Comparison of measured (black dots) and predicted (blue line)
diurnal profiles of near-surface O3 averaged over all ambient monitoring
stations in provinces of eastern China from 22 to 28 May 2015.
The comparisons of simulated versus observed distributions and temporal
variations of NO2 mass concentrations ([NO2]) are shown in
the Supplement (Figs. S3 and S4). The simulated high near-surface [NO2] are
mainly concentrated in NCP, YRD, and PRD, which is generally consistent with
the measurements. The model also reasonably yields temporal variations of
[NO2] compared to measurements, but the simulations of [NO2] are
not as good as those of [O3], and the IOAs in Liaoning, Tianjin, and
Shanghai are lower than 0.5. The difference between simulations and
observations is frequently rather large during nighttime, which is perhaps
caused by the model biases in modeling nighttime PBL or the complexity of
nighttime chemistry. Another possible reason for NOx biases in
simulations is lack of consideration of the NOx emissions in the
agricultural region, which has been proposed to generate high NOx
emissions under high-temperature conditions (Oikawa et al., 2015). In
general, the calculated distributions and variations of [O3] and
[NO2] are consistent with the corresponding observations, showing that
the simulations of meteorological fields and emission inventories are
reasonable, providing the base for sensitivity studies.
Sensitivity studies
O3 formation in the PBL is a complicated nonlinear process, depending on
its precursors of NOx and VOCs from biogenic and various anthropogenic
sources. It is imperative to evaluate the O3 contribution from various
sources for devising the O3 control strategy. Rapid growth of
industries, transportation, and urbanization has caused increasing emissions
of NOx and VOCs in eastern China (e.g., Zhang et al., 2009; Huang et
al., 2011; Z. Wang et al., 2012; Y. Wang et al., 2013; Yang et al., 2015).
Numerous studies have also demonstrated that biogenic VOCs, such as isoprene
and monoterpenes, play a considerable role in the O3 formation in the
PBL (e.g., Chameides et al., 1988; Tao et al., 2003; Li et al., 2007, 2014).
Therefore, sensitivity studies are used to evaluate the O3 contributions
of biogenic, industry, residential, and transportation sources in eastern
China, respectively. It is worth to note that emissions of power plants are
directly associated with residential living and industrial activities. Thus,
in the study, 75 % of emissions from power plants are assigned to the
industry source and the rest are assigned to the residential source according
to the ratio of the power consumption used in industrial activities to
residential living (Wang et al., 2012).
Distributions of the contribution to near-surface [O3] averaged
in the afternoon during the whole episode from (a) industry, (b) residential,
(c) transportation, and (d) biogenic emissions.
O3 contributions of industry (red line), residential (brown
line), transportation (blue line), and biogenic emissions (green line) in
NEC, NCPs, YRDs, and PRDs, as a function of simulated [O3] in the
control case.
The factor separation approach (FSA) is used to evaluate the contribution of
some emission source to the O3 concentration by differentiating two
model simulations: one with all emission sources and the other without some
emission source. Therefore, except the control simulations with all
emissions, an additional four sensitivity simulations are performed in which
the biogenic, industry, residential, and transportation emissions are
excluded, respectively, to assess their corresponding contributions to the
O3 formation in eastern China.
Figure 8 shows the contribution of near-surface [O3] averaged in the
afternoon during the whole episode from industry, residential,
transportation, and biogenic emissions. The industry source plays a more
important role in the O3 formation than the other three sources, with the
O3 contribution of 10–50 µg m-3 in the afternoon in
eastern China. In highly industrialized areas, such as Hebei, Tianjin,
Shandong, Zhejiang, etc., the O3 contribution of the industry source
exceeds 30 µg m-3. The residential source is not important in
the O3 formation and contributes about 2–15 µg m-3
O3 generally. The transportation source plays a considerable role in the
O3 formation, accounting for about 5–30 µg m-3 O3
in eastern China. The O3 enhancement due to biogenic emissions is mainly
concentrated in NCPs and PRDs, particularly in PRDs, with the O3
contribution of around 5–50 µg m-3.
In order to further evaluate the contribution of various sources to the
[O3], the hourly near-surface [O3] in the control simulation are
first subdivided into 16 bins with the interval of 20 µg m-3.
[O3] are assembled in the control and sensitivity simulations as the bin
[O3], respectively, and an average
of [O3] in each bin are calculated. Figure 9 shows the contributions of
various emission sources to [O3] in the four sections of eastern China
during the episode. The industry emission plays the most important role in
the O3 formation, and is the culprit of the high O3 pollution. When
the [O3] in the control simulation are less than
100 µg m-3, the industry source generally decreases
[O3]. However, when the simulated [O3] are more than around
200 µg m-3, the O3 contribution from the industry
emissions generally exceeds 50 µg m-3, and when the simulated
[O3] are more than 300 µg m-3, the industrial O3
contribution can be up to 100 µg m-3, constituting one-third
of the [O3]. The O3 contribution from the residential source is not
significant (generally less than 20 µg m-3). The
transportation source plays the second most important role in the O3
formation in NEC, NCPs, and YRDs, but its O3 contribution is much less
than that from the industry source when the simulated [O3] are more than
150 µg m-3. VOCs from the biogenic source generally enhance
the O3 formation, providing a background O3 source. The biogenic
source contributes about 10–50 µg m-3 O3 when simulated
[O3] are more than 150 µg m-3 in NEC, NCPs, and YRDs.
However, in PRDs, the biogenic emissions constitute the second most important
O3 source, with the O3 contribution exceeding
50 µg m-3 when simulated [O3] are more than
250 µg m-3. Apparently, controlling the industry emissions
can substantially mitigate the more severe O3 pollution in eastern
China. If the industry emissions are not considered in model simulations, on
average, the [O3] are generally not more than 200 µg m-3
in NEC, YRDs, and PRDs, but still can exceed 160 µg m-3. In
addition, excluding the industry source in NCPs does not mitigate [O3]
as remarkably as in the other regions, indicating that other emission sources
also play an important role in the O3 formation. Although the
transportation emission is the second most important O3 source in NEC,
NCPs, and PRDs, its O3 contribution is much less than that from the
industry source. Table S1 in the Supplement further presents the emission
rates of major O3 precursors from different emission sources in the
model domain during the study episode. The industrial source dominates the
VOC and NOx emissions, playing a key role in the O3 formation. The
transportation source emits more NOx and active VOCs, such as olefins and
aromatics, than the residential source, contributing considerably to the
O3 formation.
O3 contributions when only the industry (red line), residential
(brown line), and transportation emissions (blue line) are considered in NEC,
NCPs, YRDs, and PRDs, as a function of simulated [O3] in the control
case.
Distributions of the average O3 concentration during peak time
with (a) all anthropogenic emissions, (b) industry emissions alone,
(c) residential emissions alone, and (d) transportation emissions alone on May
2015.
Another three sensitivity studies are conducted to further explore the high
O3 formation in eastern China, in which only the industry, residential,
and transportation sources are considered, respectively. It is worth to note
that biogenic emissions are included in all the three sensitivity simulations
considering that the biogenic emissions provide natural O3 precursors
and cannot be anthropogenically controlled. Figure 10 presents the O3
contributions from individual anthropogenic sources averaged in the afternoon
during the whole episode in the four sections of eastern China. If only the
industry source is considered or the residential and transportation sources
are excluded in the simulation, eastern China still experiences high O3
pollution. The O3 contribution of the residential and transportation
sources is less than 60 µg m-3 on average, further showing
the important role of the industry source in the O3 pollution. When the
industry and residential sources are not considered in the simulation, the
transportation source still causes the simulated [O3] to exceed
160 µg m-3, particularly in NCPs. Taking into consideration
the very fast increase of vehicles in China recently (X. Wu et al., 2016),
the transportation source increasingly constitutes a more important O3
source, particularly when the industry source is under control. Apparently,
when the industry and transportation sources are excluded or only the residential
source is included, the high O3 pollution is significantly mitigated and
the simulated [O3] are less than 160 µg m-3 on average.
Figure 11 provides the distribution of the [O3] averaged during the peak
time on 25 May when the most serous O3 pollution occurs during the
simulated episode. When only the industry emissions are considered, the
O3 pollution is mitigated considerably in eastern China, but still
widespread in NCPs and PRDs. If only considering the transportation source,
the O3 pollution still occurs in NCPs, with the [O3] exceeding
160 µg m-3. When the industry and transportation sources are
excluded, the O3 pollution is generally under control. Hence, reducing
the emissions from industry and transportation is key to mitigating O3
pollution in eastern China.
Summary and conclusions
In the present study, air pollutant observations, released by China MEP,
have been analyzed to explore the O3 pollution situation in eastern China. Analyses of air pollutant observations in 66 cities from 2013 to 2015
have shown that, although implementation of the APPCAP has considerably
decreased the CO, SO2, NO2, and PM2.5 mass concentrations from
April to September in eastern China, the [O3] have increased by
9.2 % and the frequency of O3 exceedance with hourly [O3]
exceeding 200 µg m-3 has increased by about 25 % in the
afternoon. Mitigation of NOx and PM2.5 due to implementation of the
APPCAP, increasing transportation activities, or variability of
meteorological situations perhaps contributes to the deterioration of the
O3 pollution in eastern China.
O3 observations from April to September in 2015 have shown that eastern China has experienced widespread and persistent O3 pollution. Only two
cities in eastern China have observed the maximum 1 h [O3] of less than
200 µg m-3. Over 25 % of cities have observed the maximum
1 h [O3] exceeding 400 µg m-3; particularly, more than
800 µg m-3 [O3] have been observed in six cities in
eastern China. The average daily maximum 1 h [O3] from April to
September exceed 160 µg m-3 in 45 % of cities in eastern China, and the 1 h [O3] of 200 µg m-3 have been
exceeded on over 10 % of days from April to September in 129 cities, and
on 40 % of days in 10 cities.
A widespread and severe O3 pollution episode from 22 to 28 May 2015 in
eastern China has been simulated using the WRF-CHEM model. The model
generally simulates reasonably well the temporal variations and spatial
distributions of near-surface [O3], but the uncertainties of
meteorological fields or emission inventories still cause model
overestimation or underestimation. The model performs reasonably in
simulating NO2, but the model biases are rather large during nighttime.
FSA is utilized to assess the O3 contribution of biogenic and various
anthropogenic sources. Sensitivity studies have shown that the industry
source plays the most important role in the O3 pollution formation. When
the simulated [O3] are more than around 200 µg m-3, the
O3 contribution from the industry emissions generally exceeds
50 µg m-3 in eastern China, particularly when the simulated
[O3] exceed 300 µg m-3, the industrial O3
contribution constitutes one-third of the [O3]. The transportation
emission is the second most important O3 source in NEC, YRDs, and PRDs,
but its O3 contribution is much less than that from the industry source
when the simulated [O3] exceed 150 µg m-3. The biogenic
source plays a more important role in O3 formation than the
transportation source in PRDs, with the O3 contribution exceeding
50 µg m-3 when simulated [O3] are more than
250 µg m-3. In general, the O3 contribution from the
residential source is not significant. Further sensitivity studies have also
indicated that if only considering the residential source or excluding the
industry and transportation sources in simulations, the O3 pollution in
eastern China could be significantly improved. Only the industry or
transportation sources still cause O3 pollution, particularly with
regard to the industry source.
Widespread and persistent O3 pollution poses adverse impacts on
ecosystems and human health. Considering the key role of the industry source
in the high O3 formation, mitigation of the industry source becomes the
top choice to improve the O3 pollution in eastern China, particularly
with regard to the VOC emissions that are still not fully considered in the
current air pollutant control strategy. Rapid increase of vehicles also
enhances the VOC and NOx emissions and the transportation source plays
an increasingly important role in the O3 pollution. In addition, the
rapid decrease of PM2.5 due to implementation of the APPCAP reduces
the aerosol and cloud optical depth, which is subject to enhance the O3
formation by increasing the photolysis. Hence, stringent control strategies
of VOCs and NOx need to be designed comprehensively and implemented to
avoid the looming severe O3 pollution in eastern China.
Although the model performs generally well in simulating O3 and
NO2 during a 7-day O3 pollution episode in eastern China,
uncertainties from meteorological field simulations and emission inventory
still cause model biases. Meteorological conditions play a key role in the
formation of air pollution, determining the formation, transformation,
diffusion, transport, and removal of the air pollutants in the atmosphere
(Bei et al., 2010, 2012). A nudging of wind and temperature fields using
observations generally improves the simulation of meteorological fields,
reducing the model biases in reproducing the O3 temporal variation and
spatial distribution. Thus, future studies are needed to improve the
meteorological fields using the data assimilation, such as the
four-dimension data assimilation (FDDA). Taking into consideration the
complexity of the O3 formation and rapid changes of emission
inventories, further model studies need to be performed to investigate the
O3 formation for supporting the design and implementation of emission
control strategies, based on the improved meteorological field simulations.