ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-19-1455-2019Impacts of meteorology and emissions on summertime surface ozone increases
over central eastern China between 2003 and 2015Impacts of meteorology and emissions on summertime surface ozoneSunLeihttps://orcid.org/0000-0003-2503-7398XueLikunxuelikun@sdu.edu.cnWangYuhangyuhang.wang@eas.gatech.eduhttps://orcid.org/0000-0002-7290-2551LiLongleiLinJintaihttps://orcid.org/0000-0002-2362-2940NiRuijingYanYingyinghttps://orcid.org/0000-0001-6251-0899ChenLuluhttps://orcid.org/0000-0002-8929-3414LiJuanZhangQingzhuWangWenxingEnvironment Research Institute, Shandong University, Ji'nan, Shandong,
ChinaSchool of Earth and Atmospheric Sciences, Georgia Institute of
Technology, Atlanta, GA, USALaboratory for Climate and Ocean-Atmosphere Studies, Department of
Atmospheric and Oceanic Sciences, School of Physics, Peking University,
Beijing, ChinaDepartment of Atmospheric Sciences, School of Environmental Studies,
China University of Geosciences (Wuhan), 430074, Wuhan, ChinaLikun Xue (xuelikun@sdu.edu.cn) and Yuhang Wang (yuhang.wang@eas.gatech.edu)4February20191931455146918July201824July201828November201823January2019This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/19/1455/2019/acp-19-1455-2019.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/19/1455/2019/acp-19-1455-2019.pdf
Recent studies have shown that surface ozone (O3)
concentrations over central eastern China (CEC) have increased significantly
during the past decade. We quantified the effects of changes in
meteorological conditions and O3 precursor emissions on surface O3
levels over CEC between July 2003 and July 2015 using the GEOS-Chem model.
The simulated monthly mean maximum daily 8 h average O3 concentration
(MDA8 O3) in July increased by approximately 13.6 %, from 65.5±7.9 ppbv (2003) to 74.4±8.7 ppbv (2015), comparable to the observed
results. The change in meteorology led to an increase in MDA8 O3 of
5.8±3.9 ppbv over the central part of CEC, in contrast to a decrease
of about -0.8±3.5 ppbv over the eastern part of the region. In
comparison, the MDA8 O3 over the central and eastern parts of CEC
increased by 3.5±1.4 and 5.6±1.8 ppbv due to the increased
emissions. The increase in averaged O3 in the CEC region resulting from
the emission increase (4.0±1.9 ppbv) was higher than that caused by
meteorological changes (3.1±4.9 ppbv) relative to the 2003 standard
simulation, while the regions with larger O3 increases showed a higher
sensitivity to meteorological conditions than to emission changes.
Sensitivity tests indicate that increased levels of anthropogenic non-methane
volatile organic compounds (NMVOCs) dominate the O3 increase over the
eastern part of CEC, and anthropogenic nitrogen oxides (NOx) mainly increase
MDA8 O3 over the central and western parts and decrease O3 in a
few urban areas in the eastern part. Budget analysis showed that net
photochemical production and meteorological conditions (transport in
particular) are two important factors that influence O3 levels over the
CEC. The results of this study suggest a need to further assess the
effectiveness of control strategies for O3 pollution in the context of
regional meteorology and anthropogenic emission changes.
Introduction
Tropospheric ozone (O3) is a major atmospheric oxidant and the primary
source of hydroxyl radicals (OH), which control the atmospheric oxidizing
capacity (Seinfeld and Pandis, 2016). In the troposphere, O3 is
produced by the photochemical oxidation of hydrocarbons, carbon monoxide
(CO) and nitrogen oxides (NOx) in the presence of sunlight and can be
transported from the stratosphere (Crutzen, 1973; Danielsen, 1968). It is an
important greenhouse gas with a positive radiative forcing of 0.4 (0.2–0.6) W m-2 (IPCC, 2013), and it has adverse effects on human health and
ecosystem productivity (Monks et al., 2015).
Surface O3 concentrations increased globally during the 20th century.
Almost all available monitoring data from 1950–1979 until 2000–2010 for
the Northern Hemisphere indicate an increase of 1–5 ppbv per decade (Cooper
et al., 2014; Gaudel et al., 2018; Monks et al., 2015), although the trends
have varied regionally since the 1990s. The O3 concentrations in rural
and remote areas of Europe showed an increasing trend until 2000, but then
tended to level off or decline (Oltmans et al., 2013; Parrish et al., 2014;
Yan et al., 2018b). In the eastern US, summertime O3 has continued
declining since 1990, whereas springtime O3 in the western US shows
large interannual variability (Lin et al., 2015). At some remote sites in the
western US, only small increases (0.00–0.43 ppbv yr-1) have been
recorded (Cooper et al., 2012). In comparison with Europe and North America,
the O3 concentrations in China have shown significant increasing trends
since the 1990s (Ding et al., 2008; Ma et al., 2016, Sun et al., 2016; X. Xu
et al., 2008; W. Xu et al., 2016, 2018). Ding et al. (2008) reported an
increase of 3 ppbv yr-1 in the afternoon boundary-layer O3
concentrations in summer over Beijing using aircraft data obtained by the
Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft (MOZAIC)
program during 1995–2005. The maximum daily 8 h average O3
concentration (MDA8 O3) at Shangdianzi (SDZ), a rural site near Beijing,
showed a significant increase at a rate of about 1.1 ppbv yr-1 from
2003 to 2015 (Ma et al., 2016). Sun et al. (2016) reported an increase of
1.7–2.1 ppbv yr-1 at Mt. Tai during summertime from 2003 to 2015. In
recent years, high O3 concentrations have been widely observed in
China, especially in central eastern China (CEC: 103 to
120∘ E, 28 to 40∘ N) during the summertime
(Lu et al., 2018; Wang et al., 2006, 2017; Xue et al., 2014). All of these
results indicate that CEC might continue to experience worsening O3 air
pollution. In this study, we quantify the effects of several factors on
O3 changes and propose some suggestions to control surface O3 in
the future.
The level of O3 in the troposphere is mainly determined by the
abundance of its precursors, including both anthropogenic and natural
emissions, and the meteorological conditions (Logan, 1985). The
anthropogenic NOx emissions in China continued rising until the launch of
the Twelfth Five-Year Plan (2011–2015), which enforced a series of
stringent NOx emission control measures (China State Council, 2011).
However, anthropogenic emissions of non-methane volatile organic compounds
(NMVOCs) continue to increase unabated (Li et al., 2017a; Zheng et al.,
2018). Biomass burning also makes an important contribution to O3
formation (Real et al., 2007; Yamaji et al., 2010), and biogenic emissions
of isoprene and monoterpenes contribute to O3 levels, which are
influenced by meteorological variations (Fu and Liao, 2012). Meteorological
parameters, such as wind, temperature and humidity, can influence O3
concentrations via mechanisms related to transport, chemical production and
loss, and deposition (Monks, 2000; Zhao et al., 2010). Studies in the past
2 decades have shown that O3 and its precursors can be transported
across regions and even hemispheres, as it has a lifetime of several days to
weeks in the troposphere (Jacob et al., 1999; Lin et al., 2008; Verstraeten
et al., 2015). For example, Ni et al. (2018) showed significant foreign
contributions to springtime O3 over China. In addition, the
stratosphere–troposphere exchange (STE) is another important process
affecting the tropospheric O3 burden, especially in the midlatitudes
of the Northern Hemisphere during springtime (Hess and Zbinden, 2013).
However, currently there is still large variation in quantifying the
contribution of each factor to the O3 trends among different models and
study regions (Zhang et al., 2014a).
Previous studies have revealed the important effects of changing emission
levels and varying climate conditions on tropospheric O3 in different
regions. Lou et al. (2015) found that the effect of variations in
meteorological conditions on the interannual variability in surface O3
was larger than that of variations in anthropogenic emissions in eastern
China from 2004 to 2012. Using the GEOS-Chem model, Yan et al. (2018a) found
that interannual climate variability is the main driver of daytime O3
variability in the US, although the reduction of anthropogenic emissions of
NOx increased the nighttime O3 concentrations due to reduced O3
titration. The effects of the East Asian summer monsoon on surface O3 have
been analyzed by observational and modeling studies (He et al., 2008; Wang
et al., 2011; Zhao et al., 2010). Given the scarcity of previous research,
it is necessary to further quantify the contributions of emissions and
meteorological conditions to surface O3 levels to deepen our
understanding of the factors influencing O3 changes in China.
This is a follow-up study of Sun et al. (2016), who found a significant
increase in summertime O3 at a regional site in north China from 2003
and 2015. We integrate the global GEOS-Chem model and its Asian nested model
to investigate the spatial distributions of surface O3 over the whole
CEC region and to quantify the relative contributions from changes in
meteorological and anthropogenic emissions between 2003 and 2015. We
identify the key factors that affect O3 changes and make a policy
recommendation for O3 control in CEC in the future. Section 2 briefly
introduces the GEOS-Chem model and simulation scenarios. Comparisons of the
simulated and observed O3 concentrations are made in Sect. 3. We
quantify the individual effects of meteorological conditions and emissions
on O3 changes in Sects. 4 and 5, respectively. In Sect. 6,
we discuss important processes influencing O3 changes. Section 7
concludes the paper.
Model and simulationsModel description
A nested model coupled with the global chemical transport model GEOS-Chem
v11-01
(http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_v11-01\#v11-01_public_release, last access: 30 September 2018) is used to
simulate the surface O3 concentrations and distributions over CEC in
July of 2003 and 2015. The meteorological field is taken from MERRA-2 as
assimilated by the Goddard Earth Observing System (GEOS) at NASA's Global
Modeling and Assimilation Office. The global model and its nested model,
covering China and Southeast Asia (60 to 150∘ E,
11∘ S to 55∘ N), are configured to have horizontal
spatial resolutions of 2∘×2.5∘ and
0.5∘×0.625∘, respectively, by latitude and
longitude, and 47-layer reduced grids in the vertical direction with 10
layers (each ∼130 m in thickness) below 850 hPa. The models
are run with the full standard NOx–Ox–hydrocarbon–aerosol tropospheric
chemistry (Mao et al., 2013) for January to August of 2003 and 2015,
including the spin-up time of 6 months (January to June) for each
simulation, but only the results for July are discussed in this paper. The
results of August 2003 and 2015 are discussed in the Supplement to
confirm the result of this study. Since the crop residue burning usually
lasts from late May to late June over CEC and the emissions had varied
greatly over the past decade, which introduces large uncertainty in the
evaluation of impacts from anthropogenic emissions (Chen et al., 2017; Wu et
al., 2018), we do not focus on the O3 change simulations in June. For
comparison, we also conducted model simulations for July 2004 and July 2014,
and the results supported the major findings obtained from 2003 and 2015
(see results in the Supplement). We use the Linoz stratospheric ozone
chemistry mechanism for stratospheric O3 production (McLinden et al.,
2000) and the nonlocal planetary boundary layer (PBL) mixing scheme for
vertical mixing of air tracers in the PBL (Holtslag and Boville, 1993; Lin
and McElroy, 2010).
Global anthropogenic emissions of NOx and CO for 2003 and 2008 are taken
from EDGAR v4.2 (Emission Database for Global Atmospheric Research,
http://edgar.jrc.ec.europa.eu/overview.php?v=42, last access: 5 June 2018). NMVOC emissions are taken from the RETRO (REanalysis of TROpospheric
chemical composition) inventory for 2000, but the emissions of
C2H6 and C3H8 follow Xiao et al. (2008). For Europe, the
US, Asia, China, Canada and Mexico, the anthropogenic emissions
are taken from EMEP (from 2003 to 2012; Auvray et al., 2005), NEI2011 (base
year: 2011; annual scale factors: 2006–2013; ftp://aftp.fsl.noaa.gov/divisions/taq/, last access: 5 June 2018), MIX (from 2008 to 2010; Li et
al., 2017b), MEIC (2008 and 2014; http://meicmodel.org, last access: 6 June 2018), CAC
(NOx and CO: from 2003 to 2008 (scaled to 2010); http://www.ec.gc.ca/pdb/cac/cac_home_e.cfm, last access: 2 October 2018),
and BRAVO (1999; Kuhns et al., 2003), respectively. Over China, the CO, NOx
and NMVOC emissions from MEIC for 2008 are scaled to 2003 based on the
interannual variability in Regional Emission inventory in ASia (REAS-v2; Kurokawa et
al., 2013), but the anthropogenic emissions for 2014 are taken directly
without being scaled to 2015. According to Zheng et al. (2018), the
anthropogenic NOx and NMVOC emissions in China decreased by about 6 % and
2 % from 2014 to 2015, respectively, so here we may slightly overestimate
the NOx and NMVOC emissions. Daily biomass burning emissions are taken from
the Global Fire Emission Database v4 (GFED4) (Randerson et al., 2012).
Biogenic emissions in the GEOS-Chem model are calculated online from the
MEGAN v2.1 scheme (Guenther et al., 2012). Natural NOx emissions from
lightning are parameterized following Price and Rind (1992) and are further
constrained by the LIS/OTD satellite data (Murray et al., 2012). We obtain
the vertical profile of the lightning NOx based on Ott et al. (2010) and
calculate the soil NOx emissions online following Hudman et al. (2012).
Model simulations
Table 1 summarizes the six model scenarios we set to identify the
contributions from the changes in meteorological conditions and emissions
between 2003 and 2015. We refer to the scenario using the emissions
described in the previous section as the standard simulation and define the
standard simulations for 2003 and 2015 as 03E03M and 15E15M (2003 emissions
+ 2003 meteorology and 2015 emissions + 2015 meteorology). In this case,
the difference between O3 concentrations for 03E03M and 15E15M (denoted
as 15E15M - 03E03M) is due to the combined effect of changes in emissions and
meteorology between 2003 and 2015. Similarly, scenarios with 2003 emissions
+ 2015 meteorology and 2015 emissions + 2003 meteorology are defined as
03E15M and 15E03M, respectively. The contribution of the change in
meteorological conditions can thus be calculated by the difference between the simulated O3 concentrations in the 03E15M and 03E03M
scenarios (03E15M - 03E03M). Similarly, the contribution of emission changes
can be calculated by 15E03M - 03E03M (or 15E15M - 03E15M). The contribution of
the meteorological change based on the 2015 standard simulation is given by
15E15M - 15E03M. Since the amount of O3 formed responds nonlinearly to
the NOx and NMVOC emissions, the sum of (03E15M - 03E03M) and (15E03M - 03E03M)
does not equal (15E15M - 03E03M). However, we can still compare these two
scenarios to quantify the effects of meteorology and emission changes.
Model simulation scenarios in this study.
NameDescription1. 2003 standard(03E03M)The standard simulation of O3 concentrations over China based on 2003 emissions and 2003 meteorology2. 2015 standard (15E15M)The standard simulation of O3 concentrations over China based on 2015 emissions and 2015 meteorology3. 03E15MSame as 2 but with 2003 emissions4. 15E03MSame as 2 but with 2003 meteorology5. 03N15MSame as 2 but with 2003 anthropogenic NOx emissions in China6. 03V15MSame as 2 but with 2003 anthropogenic NMVOC emissions in China
We then investigate the effect of anthropogenic emissions (NOx and NMVOCs)
on surface O3 concentrations based on the 2015 simulations. We
replace the anthropogenic NOx or NMVOC emissions in the 2015 standard
simulation with corresponding emissions for 2003 and keep the meteorology
field, biomass burning and natural emissions (NOx from soil and lightning,
biogenic VOCs (BVOCs), etc.) unchanged (03N15M and 03V15M, respectively).
The contributions of anthropogenic NOx and NMVOC emission changes can be
calculated by the differences between 15E15M (the 2015 standard simulation)
and 03N15M (the 2003 NOx emission simulation) and between 15E15M and 03V15M
(the 2003 NMVOC emission simulation), respectively.
Simulated and observed O3 concentrationsModel evaluation
In this section, we evaluate the model's performance by comparing the
simulated surface O3 concentrations with observations from baseline
sites and the network of the Chinese National Environmental Monitoring
Center (http://datacenter.mee.gov.cn/aqiweb2/getAirQualityDailyEn, last access: 2 October 2018 (in English) and
http://datacenter.mee.gov.cn/websjzx/queryIndex.vm, last access: 2 October 2018 (in Chinese)).
For 2003 and 2004, only a few nonurban sites over CEC have surface O3
measurements available. We selected six rural/baseline sites for the model
evaluation: Mt. Tai (36.25∘ N, 117.10∘ E; 1534 m a.s.l.),
Mt. Hua (34.49∘ N, 110.09∘ E; 2064 m a.s.l.), Mt. Huang
(30.13∘ N, 118.15∘ E; 1840 m a.s.l.), SDZ
(40.65∘ N, 117.12∘ E; 293 m a.s.l.), Lin'an
(30.30∘ N, 119.73∘ E; 139 m a.s.l.), and Cape
D'Aguilar
(22.22∘ N, 114.25∘ E; 60 m a.s.l.) (see Fig. S1 for
the locations of these sites). The monthly mean O3 concentrations at
these six sites were taken from the literature (Li et al., 2007; Meng et
al., 2009; Wang et al., 2009; Fan et al., 2013; Sun et al., 2016). We
compare the simulated surface O3 concentrations with the 2003
observations for Mt. Tai and Cape D'Aguilar but with the 2004 observations for the
other four sites (Fig. 1a). The simulated surface O3 in 2004 was
also compared against these observations in Fig. S2.
(a) Comparison of observed and simulated monthly mean
concentrations of surface O3 in July 2003. (Mt. Tai: July 2003; SDZ:
Shangdianzi station: July 2004; Mt. Huang: July 2004; Mt. Hua: July 2004;
Lin'an: July 2004; Cape D'Aguilar (or Hok Tsui in a): July 2003). (b) Correlation between observed
and modeled monthly mean MDA8 O3 in July 2015 at 115 stations in
eastern China.
Figure 1a compares the observed and simulated monthly mean O3
concentrations at the six sites. The simulated O3 concentrations match
the observations at Mt. Tai, SDZ, and Mt. Hua well, with only minor positive
biases (1–4 ppbv). In contrast, the model overestimates the O3
concentrations at Mt. Huang, Lin'an, and Cape D'Aguilar by approximately 10 ppbv.
These sites in the south sector are often rainy or cloudy during summer, so
the overestimation of O3 is likely to be due to the model's
underestimation of precipitation and cloud cover (Ni et al., 2018). The
overestimation at the Cape D'Aguilar coastal site of Hong Kong also reflects that
the model resolution is insufficient to capture the local terrains and
transport pathway (Ni et al., 2018). Similar results were obtained from the
comparison between observed and simulated monthly mean O3
concentrations at the six sites in July 2004 (see Fig. S2).
For 2015, the simulated O3 concentrations are compared with
observations by the network of the Chinese National Environmental Monitoring
Center over east China (Fig. 1b). To avoid the influence of local
emission and photochemical and deposition processes on small scales in urban
areas, we selected one nonurban site to represent the O3 concentrations
of each city over CEC. In general, the selected nonurban sites are
suburban or rural sites, which are far away from the urban and
industrialized areas. For cities where no nonurban sites are available, we
chose the stations that are least affected by local pollution (i.e., sites
relatively far away from traffic roads, factories, power plants). As a
result, 115 nonurban sites were selected to represent 115 cities in east
China. For MDA8 O3, the model results are highly correlated with the
observations at most sites (R2=0.79). The model only
overestimates the monthly MDA8 O3 by approximately 2.7±5.9 ppbv
over CEC.
Monthly mean spatial distributions of surface MDA8 O3 in July
over east China. (a) 03E03M: 2003 standard simulation; (b) 15E15M: 2015
standard simulation; (c) 03E15M: 2003 emission + 2015 meteorology; (d)
15E03M: 2015 emission + 2003 meteorology. Black contours in (a) and (b)
indicate the regions with MDA8 O3>75 ppbv. Filled circles
in (b) show the observed MDA8 O3 at 115 sites of the network of the Chinese
National Environmental Monitoring Center. The red rectangle represents the
central eastern China region (CEC: 103–120∘ E,
28–40∘ N).
The model also captures the spatial distribution of MDA8 O3 very well.
It ranges from 40–60 ppbv in the south to 80–100 ppbv in the north of CEC
(Fig. 2b), patterns similar to those reported by Lin et al. (2009) and Lou et al. (2015). Time series and diurnal variations in hourly O3
concentrations from the model and observations at Mt. Tai in 2003 and nine
representative sites in 2015 are compared in Figs. S3, S4 and S5. The nine observation sites are carefully selected to be far
away from urban areas in the capital cities of nine provinces and
municipalities, including Beijing, Tianjin, Ji'nan, Taiyuan, Zhengzhou,
Wuhan, Chongqing, Changsha and Nanjing. The model reproduces the time
series of O3 with a normalized mean bias of 4 % at Mt. Tai. The
overestimation of O3 concentrations in the afternoon is likely to be
due to the overestimated precursor emissions in the model. For the nine
sites, the model captures most day-to-day variability and diurnal variations
(Figs. S4 and S5). However, it produces larger biases during the night,
mostly due to the titration of NO and a lower inversion layer (Yan et al.,
2018a). We also compared the simulated diurnal variations in CO and NO2
in the nine cities against the observational data (see Figs. S6 and S7).
Overall, the model captures most diurnal variations in CO and NO2. The
underestimation of CO by the model may be due to the underestimation of
emissions and/or the excessive OH (Yan et al., 2014; Young et al., 2013).
The large bias in NO2 may be due to the effect of local emissions.
Another reason for the discrepancy between observed and modeled NO2 is
the overestimation by the measurements based on catalytic conversion of
other oxidized nitrogen species to NO (Xu et al., 2013).
The observed yearly average MDA8 O3 at SDZ station increased by about
10.9 ppbv from 2004 to 2014 (Ma et al., 2016), comparable to the simulated
result, which showed an increase of about 9.5 ppbv from July 2003 to July
2015. In addition, the observed results of Sun et al. (2016) reported the
MDA8 O3 at Mt. Tai increased from 75.9±15.9 to 102.1±28.1 ppbv in July–August from 2003 to 2015, which is higher than the simulated
result in this study (i.e., from 71.1±10.0 ppbv in July 2003 to
90.4±18.5 ppbv in July 2015). Nonetheless, the model captures the
significant increase in surface O3 levels over CEC between July 2003
and July 2015.
Spatial distribution and diurnal variation simulated in different model
scenarios
Figure 2 shows the simulated spatial distribution of monthly mean surface
MDA8 O3 over eastern China (100 to 125∘ E,
20 to 50∘ N) for July 2003 and July 2015. The model
simulates relatively high O3 concentrations over the North China Plain
and Sichuan Basin, where anthropogenic emissions of O3 precursors are
high. In July 2003, only a small area in CEC had an MDA8 O3 exceeding
the Level II National Ambient Air Quality Standard (75 ppbv) (Fig. 2a),
but in July 2015 it had expanded to nearly half of this region. Table 2
shows the monthly mean MDA8 O3 over CEC. The regional mean MDA8 O3
increased from 65.5±7.9 ppbv in July 2003 to 74.4±8.7 ppbv in
July 2015, showing an increase of about 8.9±3.9 ppbv in 12 years.
According to the limited reports of observed long-term (>10 years) changes of O3 concentrations, we find significant increases
in
summertime O3 (1–3 ppbv yr-1) in the north part (Beijing), east
part (Mt. Tai) and south part (Lin'an) of CEC over the past 2 decades (Ding
et al., 2008; Ma et al., 2016; Sun et al., 2016; Xu et al., 2008; Zhang et
al., 2014b). Our results show that both daily mean O3 concentration and
MDA8 O3 were significantly higher in July 2015 than in July 2003 over
most areas of CEC (Fig. 3). The spatial distributions of MDA8 O3 in
July 2004 and 2014 in Fig. S8 present patterns similar to in July 2003 and
2015. The regional mean MDA8 O3 increased from 67.8±6.2 ppbv in
July 2004 to 74.8±9.8 ppbv in July 2014. In addition, the regional
mean MDA8 O3 increased from 63.4±4.9 ppbv in August 2003 to
73.8±5.0 ppbv in August 2015 (Fig. S9). These results are
comparable to those derived from the comparison between July 2003 and July
2015. A detailed description is provided in the Supplement. As the MDA8
O3 over southwestern China did not exceed the Level II National Ambient
Air Quality Standard in July 2015, we do not focus our analysis on this area
in the following sections.
Monthly mean (standard deviation) MDA8 O3 over CEC based on
four model simulations. ΔMDA8 O3 represents the difference in
MDA8 O3 concentrations between the 2015 standard simulation and 2003
standard simulation: ΔMDA8 O3=MDA8O3 (2015) – MDA8
O3 (2003). MDA8 O3>75 ppbv indicates the region of
MDA8 O3 exceeding the Level II National Ambient Air Quality Standard
(75 ppbv) in July 2015.
Differences in monthly mean surface O3 in July of 2003 and
2015 (2015–2003) for daily mean O3(a) and MDA8 O3(b) simulated
by 2003 and 2015 standard simulations.
Averaged diurnal variations in surface O3 over CEC derived
from four simulation results.
The diurnal variation in O3 over CEC illustrated in Fig. 4 shows that
O3 increases by 4.9–6.7 ppbv before dawn (02:00–07:00 LT) and by
8.5–9.0 ppbv in the afternoon (13:00–18:00 LT). The much more significant
increase in O3 in the afternoon in July 2015 is likely to be due to the
stronger photochemical production, which is affected by both meteorological
conditions and O3 precursor emissions. The slight increase in
nighttime O3 reflects the residual effect of the daytime increase,
despite strong nighttime titration by NO. This result is very different
from the trends over the US, where summertime daytime O3 declined over
the past decades is contrasted with the nighttime growth in all seasons (Yan et
al., 2018a). Considering that the nighttime O3 is easily titrated by NO
and the MDA8 O3 is a good indicator for the overall O3 pollution
condition, we focus on the MDA8 O3 changes over CEC between July 2003
and July 2015 instead of daily mean O3.
Impacts of meteorology on surface O3
We performed sensitivity tests to investigate the effects of meteorology and
emissions on the MDA8 O3 over CEC. The contributions of meteorological
change to the change in MDA8 O3 are defined by the 03E15M - 03E03M and
15E15M - 15E03M simulations. Here we discuss only 03E15M - 03E03M in detail, as
the results of 15E15M - 15E03M are similar. The spatial distributions of
O3 precursors (NO2 and NMVOCs) for the different simulation
scenarios and their differences are shown in Figs. S10 and S11, which can
better explain these results. A detailed description is given in the
Supplement.
(a) Contributions of meteorological changes to surface MDA8
O3, comparing 03E15M and 03E03M (2003 standard) simulations. (b)
Contributions of emission changes to surface MDA8 O3, comparing 15E03M
and 03E03M (2003 standard) simulations. (c) Contributions of meteorological
changes to surface MDA8 O3, comparing 15E15M (2015 standard) and 15E03M
simulations. (d) Contributions of emission changes to surface MDA8 O3,
comparing 15E15M (2015 standard) and 03E15M simulations.
The regional averaged MDA8 O3 simulated by 03E15M is 68.7±7.1 ppbv, comparable to that simulated by 15E03M (69.6±8.9 ppbv),
indicating the comparable contributions made by the changes in meteorology
and in emissions. Figure 5 shows the spatial distribution of MDA8 O3
changes among different simulation scenarios. The regional mean MDA8
O3 of CEC is approximately 5.8±3.9 ppbv (5 %–95 % interval:
-0.1–12.4 ppbv) higher in scenario 03E15M than in 03E03M (Fig. 5a) over
the central part of CEC (106 to 115∘ E, 28 to 40∘ N). Over the eastern coastal areas (115 to
120∘ E, 28 to 40∘ N), however, the MDA8
O3 in the former scenario is less than in the latter by approximately
-0.8±3.5 ppbv (5 %–95 % interval: -6.8–3.8 ppbv), indicating
great spatial variation in the influence of meteorological changes.
Atmospheric circulation patterns complicate the prediction of O3
concentrations in a specific region (He et al., 2012). The geopotential
height map in Fig. S12 shows a high-pressure system over CEC at 850 hPa in
July 2015. It is well known that high-O3-pollution events
preferentially occur under high-pressure conditions (Wild et al., 2004; Zhao
et al., 2009; Xu et al., 2011). This is because the relatively high
geopotential height induces a stable weather condition. Neither horizontal
nor vertical transport is strong, which favors the accumulation of
atmospheric pollutants such as surface O3. We found that in July 2015
the wind speeds over the southern and eastern boundaries of CEC were much lower
than those in July 2003 (Fig. S13), leading to much lower O3 flux
across these two boundaries. The low O3 over southern CEC in July 2003
was mainly due to the strong southwesterly wind, decreasing O3 levels
in this area. However, a large amount of O3 and its precursors from the
central part of CEC was transported to the eastern coastal area, which
increased O3 concentrations there (refer to Table 4: about 1343 Gg month-1 of O3 transported out across the east boundary). Conversely, in
July 2015, only a small amount of O3 (refer to Table 4: -61 Gg month-1) and its precursors was transported away from the ocean by the
weak southeasterly winds, which only decreased the O3 levels in the
coastal area. However, in the central part of CEC, the wind was weak,
leading to accumulating O3 pollution in this area. As a result, the
O3 concentrations increased in the central part of CEC and decreased in
the eastern coastal area in July 2015 compared to July 2003. More detailed
and quantitative results on O3 transport flux will be discussed in
Sect. 6.
In addition to the wind, air temperature and relative humidity are two other
important meteorological parameters that can affect atmospheric O3
concentrations. High temperatures tend to accelerate the rate of
ozone-related photochemical reactions, promoting O3 production
(Ramsey et al., 2014). Cloud indirectly affects O3 pollution by
blocking solar radiation, thus affecting the emission of BVOCs and the
photochemical production of O3 (Lin et al., 2009). Figure S14 shows the
simulated monthly mean spatial distributions of air temperature and relative
humidity in July 2003 and July 2015. The simulated air temperatures in 2003
and 2015 were 300.6±3.2 and 300.5±3.2 K, respectively, almost
at the same level. The simulated relative humidity in 2003 was 82±10 %, a little higher than in 2015 (77±12 %). The average net
O3 production over CEC simulated by 03E03M (11.7 ppbv day-1) is
very close to that simulated by 03E15M (11.9 ppbv day-1) (Table 4),
suggesting that meteorological factors in 2003 and 2015 did not greatly
change O3 photochemical reactions. Therefore, neither air temperature
nor relative humidity plays an important role in explaining the difference
in surface O3 between 2003 and 2015.
We summarize the regional mean O3 over CEC and the regions with MDA8
O3>75 ppbv in Table 2. To avoid the influence of uneven
spatial distributions of O3 concentration changes, we performed a
gradient analysis, which selected different levels for the difference of
MDA8 O3 (ΔMDA8 O3) between 2003 standard and 2015 standard
simulation (15E15M - 03E03M). The differences in MDA8 O3 were analyzed in
four ways: regional mean, ΔMDA8 O3≥0 ppbv, ΔMDA8
O3≥5 ppbv and ΔMDA8 O3≥10 ppbv. For the
regional mean over CEC, the increase in MDA8 O3 driven by meteorology
is approximately 3.1±4.9 ppbv, from 65.5±7.9 ppbv (03E03M) to
68.7±7.1 ppbv (03E15M). Where ΔMDA8 O3≥10 ppbv,
mostly over the central part of CEC, the MDA8 O3 increases by
6.7±3.4 ppbv from 64.3±9.7 ppbv to 71.0±7.4 ppbv due to
the meteorological change. Thus, the meteorological conditions have a
greater impact on the O3 change when the difference between 2003 and
2015 is higher than 10 ppbv. Similar results are also found in regions with
MDA8 O3>75 ppbv, where the increase in the O3
concentration is approximately 3.6±3.2 and 5.1±2.5 ppbv
for the regional mean and for the ΔMDA8 O3≥10 case,
respectively. This indicates that surface O3 levels are more sensitive
to meteorological conditions in regions with larger O3 increase.
Impact of emission changes on surface O3
As described above, the impact of emission changes on MDA8 O3 between
2003 and 2015 can be estimated by 15E03M - 03E03M or 15E15M - 03E15M. Here we
discuss 15E03M - 03E03M in detail. Similar results were found from
15E15M - 03E15M.
Figure 5b shows the contributions of emission changes to surface O3
levels. The emission change leads to an increase in MDA8 O3 over most
areas of CEC, and it has a much smaller spatial variability than the
meteorological change does (Fig. 5a). Compared to the influence of the
meteorological change (03E15M - 03E03M: 3.1±4.9 ppbv), the increase in
emissions leads to a higher regional mean O3 increase (15E03M - 03E03M:
4.0±1.9 ppbv) over CEC (Table 2). The changes in NO2 and NMVOCs
also indicate the impact of emission changes is larger than that of
meteorological change (Figs. S10 and S11). In contrast, for the case of
ΔMDA8 O3≥10 ppbv, the influence of emission change on
O3 (15E03M - 03E03M: 4.5±2.1 ppbv) is smaller than that of the
meteorological field change (03E15M - 03E03M: 6.7±3.4 ppbv). The
increases in MDA8 O3 due to emission change are about 3.5±1.4 ppbv (5 %–95 % interval: 1.6–6.0 ppbv) and 5.6±1.8 ppbv
(5 %–95 % interval: 2.2–8.4 ppbv) over the central and eastern parts
of CEC, which are different from the spatial pattern caused by
meteorological change. It is worth noting that in the polluted regions where
MDA8 O3>75 ppbv, the contribution of emission change
increases from 5.0±1.8 ppbv for the ΔMDA8 O3≥0 ppbv
case to 5.2±1.7 ppbv for the ΔMDA8 O3≥10 ppbv case,
whilst the contribution of meteorology change increases from 3.7±3.2 to 5.0±2.5 ppbv. Even if the ΔMDA8 O3 is greater
than 10 ppbv, the O3 increase caused by emission change is still a
little higher than that caused by meteorological change, indicating the
dominant effect of emissions on O3 pollution in the highly polluted
regions.
Emissions of NOx, CO and NMVOCs over CEC for July 2003 and July
2015, including anthropogenic emissions and biogenic emissions. Units: NO,
CO and CH2O: Gg month-1; others: Gg C month-1.
a ALK4: alkanes and other nonaromatic compounds that react only with
OH and have kOH between 5×103 and 1×104 ppm-1 min-1.
b PRPE: OLE1+OLE2, OLE1: alkenes (other than ethene) with kOH<7×104 ppm-1 min-1; OLE2: alkenes with
kOH>7×104 ppm-1 min-1.
We summarize the emissions of NOx, CO and NMVOCs over CEC for July 2003 and
July 2015 in Table 3. The anthropogenic NOx emissions increased from 397 Gg month-1 in July 2003 to 683 Gg month-1 in July 2015. The anthropogenic
NMVOCs also increased significantly, with the NMVOC emissions increasing
from 190 Gg C month-1 in July 2003 to 365 Gg C month-1 in July 2015.
The spatial distributions of anthropogenic NOx and NMVOC emissions in
Figs. S15 and S16 also indicate significant increases from 2003 to 2015.
Anthropogenic CO emissions increased from 4619 Gg month-1 in July 2003 to
6011 Gg month-1 in July 2015. The natural BVOCs, which are greatly
affected by meteorological conditions, remained unchanged between 2003 and
2015. Biomass burning often occurs sequentially from south to north in CEC
in the spring harvest season and lasts from late May to late June (Chen et
al., 2017). In July, the biomass burning emissions generally decrease to
approximately 1 % of the anthropogenic emissions (not shown). Therefore,
the effect of the emission change on O3 is primarily due to
anthropogenic emissions of NOx and NMVOCs.
Effects of anthropogenic NMVOCs (a) and NOx(b) emission changes
on surface MDA8 O3 concentrations between 2003 and 2015 when other
emissions and meteorological parameters are fixed at 2015 levels.
To separate the effect of anthropogenic emissions from the effect of natural
emission on O3 variability, we conducted two further simulations,
03N15M and 03V15M (see Sect. 2.2). Figure 6 shows the spatial distribution
of the MDA8 O3 differences between the 2015 standard simulation and
these two simulations. Anthropogenic NMVOCs (Fig. 6a) have a great
impact on MDA8 O3 over the eastern part of CEC, increasing MDA8 O3
by approximately 2.5±0.8 ppbv (5 %–95 % interval: 1.1–3.7 ppbv). The emissions of NMVOCs increased greatly over the eastern part of
CEC (see Fig. S16). The change in MDA8 O3 due to anthropogenic NMVOCs
varies from -0.5 to 5.1 ppbv over different subregions of CEC, with a
regional mean of 1.4±1.1 ppbv. The effect of anthropogenic NOx
(Fig. 6b), in comparison, is more complicated. From 2003 to 2015, MDA8
O3 declined in some cities such as Tianjin, Ji'nan, Taiyuan and
Nanjing in the eastern part of CEC, but increased in the central and western
parts (regional mean: 2.8±0.9 ppbv, 5 %–95 % interval:
1.4–4.1 ppbv). The change in MDA8 O3 due to anthropogenic NOx varies from
-3.1
to 6.7 ppbv, with a regional mean of 2.5±1.1 ppbv over CEC
(5 %–95 % interval: -0.2–3.3 ppbv). The reduction of O3 in the
urban area is likely to be due to the abundant NOx from industrial and
traffic sources. Beijing shows a slight decrease in NOx emissions, leading
to a slight change in O3 levels. In most rural areas of CEC, O3
formation tends to be limited by the concentrations of NOx (the so-called
NOx-limited regime). Thus, O3 is increased significantly as we increase
the anthropogenic emissions of NOx. A VOC-limited regime in a few urban
areas and a NOx-limited or transition regime in regional rural areas of CEC
have been reported in some observational and model simulation studies (Wang
et al., 2017, and references therein). The change in BVOC emissions only
leads to a small change in MDA8 O3 over CEC, resulting in an increase
in the O3 level of only 0.3 ppbv (not shown), mostly due to the change
in meteorological conditions. Therefore, if the meteorological conditions
are fixed as the 2015 conditions, the increase in anthropogenic NMVOCs is
the most important factor responsible for the O3 increase over the
eastern part of CEC, whereas NOx emissions tend to increase MDA8 O3
over central and western parts but decrease it in a few urban areas over
eastern parts of CEC.
Budget analyses
Ozone concentrations are determined by chemical and dynamic processes
including transport, chemical production and loss, and deposition. In this
section, we discuss the effects of these processes on the surface O3
over CEC.
Horizontal and vertical flux (Gg month-1), photochemical
production and loss (Gg month-1; the numbers in the parentheses are in
ppbv day-1), and dry deposition (Gg month-1) of O3 over CEC
from the surface to 850 hPa based on four types of simulations. For horizontal
flux, positive values indicate eastward or northward transport. For vertical
fluxes, positive values indicate upward transport. “Total” refers to the
sum of horizontal and vertical transport. Net photochemical O3
production is the difference between production and loss of O3.
Table 4 documents the horizontal and vertical mass fluxes of O3 over
CEC at four boundaries (north, east, south and west). The flux at each
boundary was calculated from the surface to 850 hPa. In July 2003, the air flows
into CEC through the south boundary, and then out across the other three
boundaries. In contrast, the air masses flow into this area across the east
boundary in July 2015, and then out across the left three boundaries. The
larger O3 flux from each boundary in July 2003 is due to stronger
winds. Compared to the 03E03M simulation (-897 Gg month-1; negative value
means export of O3 from this region), 03E15M shows a much lower O3
flux (-401 Gg month-1), indicating that weather conditions in 2015 play a
more important role in pollutant accumulation, which is consistent with our
analysis in Sect. 4. The larger O3 flux in 15E03M (-1100 Gg month-1) in comparison to the 03E03M simulation, however, is mostly due
to the increased precursor emissions in 2015.
Table 4 also shows the chemical production and loss of O3 over CEC
from the
surface to 850 hPa. The net photochemical production of O3 in July 2015
(2158 Gg month-1 or 15.5 ppbv day-1) is higher than that in July
2003 (1629 Gg month-1 or 11.7 ppbv day-1). By comparing the 03E03M
simulation with the 03E15M simulation, we find that the weather conditions in
2015 do not promote excessive net O3 production (03E15M: 1657 Gg month-1 or 11.9 ppbv day-1), almost the same level as 03E03M
simulation. In comparison, due to more O3 precursor emissions in 2015,
the net O3 production by 15E03M (2166 Gg month-1 or 15.6 ppbv day-1) is much higher than the 03E03M simulation. The net photochemical
O3 production in this study is similar to the result of Li et al. (2007), who reported a net production of 10–32 ppbv day-1 at three
mountain sites over CEC in 2004. Deposition (mainly dry deposition) is
another factor that affects O3 concentrations. The 03E15M simulation
shows an increase in O3 dry deposition by only 10 Gg month-1,
compared to the 03E03M simulation (156 Gg month-1). Thus dry deposition is
less affected by changes in weather conditions.
As shown in Table 4, the O3 budget analysis indicates CEC is a strong
photochemical source region in both 2003 and 2015. The photochemically
produced O3 is mostly exported by transport and to a lesser extent
removed by dry deposition. In July 2003, about half of the net
photochemically formed O3 in the CEC region was removed by transport
(897 out of 1629 Gg month-1). In comparison, only one-fourth of the net
photochemically produced O3 (502 out of 2158 Gg month-1) was
transported out of CEC in July 2015. Comparing the results of the 2003 and
2015 standard simulations (15E15M - 03E03M), we find less O3 export from
CEC in 2015 than in 2003, which means about 395 Gg month-1
(2015–2003) of
O3 was accumulated in this region. In addition, net O3 production
increased by 529 Gg month-1 and O3 dry deposition only increased by
24 Gg month-1 from the 2003 standard simulation to 2015 standard simulation.
As a result, the increase in O3 concentrations from July 2003 to July
2015 should be due to the enhanced photochemical production (mainly due to
the increased emissions) and the weakened export (due to the meteorological
conditions).
Conclusions
In this study, we used the global GEOS-Chem model and its Asian nested model
to simulate surface O3 over central eastern China between July 2003 and
July 2015. We found that the regional averaged concentration of MDA8 O3
increased from 65.5±7.9 ppbv in 2003 to 74.4±8.7 ppbv in 2015.
The increase in the regional average MDA8 O3 due to emission changes
(4.0±1.9 ppbv) is higher than that caused by meteorological changes
(3.1±4.9 ppbv) compared with the 2003 standard simulation. The
effects of meteorological changes have a larger spatial variability than
those of emission changes. The increase in anthropogenic NMVOC emissions
increased O3 over the eastern part of CEC, whereas the increased
anthropogenic NOx emissions dominated the increase in O3 over the
central and western parts of CEC but decreased O3 levels in a few urban
areas over eastern CEC. The O3 formation over most areas is in a
NOx-limited or transition regime, whereas a few urban areas tend to be
in a
VOC-limited regime. The increase in surface O3 is mainly via
photochemical production and transport processes. The meteorological
conditions (mostly due to wind patterns) in July 2015 tended to accumulate
pollution and reduced O3 export over the central part of CEC and thus
enhanced O3 levels there. Air temperature and relative humidity do not
promote the O3 production in July 2015. The increased net O3
photochemical production is mostly due to increased precursor emissions.
Our results have implications for the formulation of effective control
strategies for O3 air pollution in CEC. Although the simulated average
effect of emission changes is larger than the effect of meteorological
changes, the regions with larger O3 increases (e.g., ΔMDA8
O3≥10 ppbv) show a higher sensitivity to meteorology than to
emission changes. The results imply that assessment of the effectiveness of
regional and urban O3 control strategies needs to be placed in the
context of meteorology. The O3 transport flux analysis further suggests
that large-scale regional transport is an important contributor to the
surface O3 increases from 2003 to 2015. Transport issues in local
O3 control strategies should go beyond transport from neighboring
areas (e.g., cities) and account for the long-distance transport (e.g.,
across provinces).
The underlying research data can be accessed upon contact with the corresponding author
(Likun Xue; xuelikun@sdu.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-1455-2019-supplement.
LS processed the data, conducted the GEOS-Chem simulation
and wrote the paper. LX designed the research, supervised the data analysis
and revised the paper. YW supervised the data analysis. LL, JL, RN, YY, LC and JL
assisted with the model simulation and helped in the data analysis and paper correction.
QZ and WW helped in paper correction.
The authors declare that they have no conflict of
interest.
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
This research was supported by the National Key Research and Development
Program of China (2016YFC0200500), the National Natural Science Foundation
of China (41675118, 91544213, 41775115), the Qilu Youth Talent Program of
Shandong University, the Jiangsu Collaborative Innovation Center for Climate
Change and the Taishan Scholars (ts201712003). The model simulations were
performed at the Supercomputing Center of Shandong University in Weihai. We thank
the Chinese National Environmental Monitoring Center for providing the
observation data. Lei Sun acknowledges the support of the China Scholarship
Council. We also appreciate the three anonymous reviewers for their helpful
comments to improve the original submission.
Edited by: David Parrish
Reviewed by: three anonymous referees
ReferencesAuvray, M. and Bey, I.: Long-range transport to Europe: Seasonal variations
and implications for the European ozone budget, J. Geophys. Res.-Atmos, 110,
D11303, 10.1029/2004JD005503, 2005.Chen, J., Li, C., Ristovski, Z., Milic, A., Gu, Y., Islam, M. S., Wang, S.,
Hao, J., Zhang, H., He, C., Guo, H., Fu, H., Miljevic, B., Morawska, L.,
Thai, P., Fat, L., Pereira, G., Ding, A., Huang, X., and Dumka, U.: A review
of biomass burning: Emissions and impacts on air quality, health and climate
in China, Sci. Total Environ., 579, 1000–1034,
10.1016/j.scitotenv.2016.11.025, 2017.China State Council: Twelfth Five-Year Plan on National Economy and Social
Development of the People's Republic of China, available at:
http://www.gov.cn/2011lh/content_1825838.htm, 2011 (in
Chinese).Cooper, O. R., Gao, R. S., Tarasick, D., Leblanc, T., and Sweeney, C.:
Long-term ozone trends at rural ozone monitoring sites across the United
States, 1990–2010, J. Geophys. Res.-Atmos., 117, D22307,
10.1029/2012JD018261, 2012.Cooper, O. R., Parrish, D., Ziemke, J., Balashov, N., Cupeiro, M., Galbally,
I., Gilge, S., Horowitz, L., Jensen, N., Lamarque, J.-F., Naik, V., Oltmans,
S., Schwab, J., Shindell, D., Thompson, A., Thouret, V., Wang, Y., and
Zbinden, R.: Global distribution and trends of tropospheric ozone: An
observation-based review, Elem. Sci. Anth., 2, 000029,
10.12952/journal.elementa.000029, 2014.Crutzen, P.: A discussion of the chemistry of some minor constituents in the
stratosphere and troposphere, Pure Appl. Geophys., 106, 1385–1399,
10.1007/BF00881092, 1973.Danielsen, E. F.: Stratospheric-tropospheric exchange based on
radioactivity, ozone and potential vorticity, J. Atmos. Sci., 25, 502–518,
10.1175/1520-0469(1968)025<0502:STEBOR>2.0.CO;2, 1968.Ding, A. J., Wang, T., Thouret, V., Cammas, J.-P., and Nédélec, P.:
Tropospheric ozone climatology over Beijing: analysis of aircraft data from
the MOZAIC program, Atmos. Chem. Phys., 8, 1–13,
10.5194/acp-8-1-2008, 2008.Fan, Y., Fan, S. Zhang, H, Zu, F., Meng, Q., and He, J.: Characteristics of
SO2, NO2, O3 volume fractions and their relationship
with weather conditions at Linan in summer and winter, Trans. Atmos. Sci., 36
121–128, 10.13878/j.cnki.dqkxxb.2013.01.013, 2013 (in Chinese).Fu, Y. and Liao, H.: Simulation of the interannual variations of biogenic
emissions of volatile organic compounds in China: Impacts on tropospheric
ozone and secondary organic aerosol, Atmos. Environ., 59, 170–185,
10.1016/j.atmosenv.2012.05.053, 2012.Gaudel, A., Cooper, O. R., Ancellet, G., Barret, B., Boynard, A., Burrows, J.
P., Clerbaux, C., Coheur, P. F., Cuesta, J., Cuevas Agulló, E., and
Doniki, S.: Tropospheric Ozone Assessment Report: Present-day distribution
and trends of tropospheric ozone relevant to climate and global atmospheric
chemistry model evaluation, Elem. Sci. Anth., 6, 39,
10.1525/elementa.291, 2018.Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T.,
Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols
from Nature version 2.1 (MEGAN2.1): an extended and updated framework for
modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492,
10.5194/gmd-5-1471-2012, 2012.He, J., Wang, Y., Hao, J., Shen, L., and Wang, L.: Variations of surface
O3 in August at a rural site near Shanghai: Influences from the
West Pacific subtropical high and anthropogenic emissions, Environ. Sci.
Pollut. Res., 19, 4016–4029, 10.1007/s11356-012-0970-5,
2012.He, Y. J., Uno, I., Wang, Z. F., Pochanart, P., Li, J., and Akimoto, H.:
Significant impact of the East Asia monsoon on ozone seasonal behavior in the
boundary layer of Eastern China and the west Pacific region, Atmos. Chem.
Phys., 8, 7543–7555, 10.5194/acp-8-7543-2008, 2008.Hess, P. G. and Zbinden, R.: Stratospheric impact on tropospheric ozone
variability and trends: 1990–2009, Atmos. Chem. Phys., 13, 649–674,
10.5194/acp-13-649-2013, 2013.Holtslag, A. A. M. and Boville, B. A.: Local versus nonlocal boundary-layer
diffusion in a global climate model, J. Clim., 6, 1825–1842,
10.1175/1520-0442(1993)006<1825:LVNBLD>2.0.CO;2,
1993.Hudman, R. C., Moore, N. E., Mebust, A. K., Martin, R. V., Russell, A. R.,
Valin, L. C., and Cohen, R. C.: Steps towards a mechanistic model of global
soil nitric oxide emissions: implementation and space based-constraints,
Atmos. Chem. Phys., 12, 7779–7795, 10.5194/acp-12-7779-2012,
2012.
IPCC: Climate change 2013: The physical science basis, in: Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Mildgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New
York, 1–1535, 2013.Jacob, D. J., Logan, J. A., and Murti, P. P.: Effect of rising Asian
emissions on surface ozone in the United States, Geophys. Res. Lett., 26,
2175–2178, 10.1029/1999GL900450, 1999.
Kuhns, H., Green, M., Etyemezian, V., Watson, J., and Pitchford, M.: Big
Bend Regional Aerosol and Visibility Observational (BRAVO) Study Emissions
Inventory, Report prepared for BRAVO Steering Committee, Desert Research
Institute, Las Vegas, Nevada, 2003.Kurokawa, J., Ohara, T., Morikawa, T., Hanayama, S., Janssens-Maenhout, G.,
Fukui, T., Kawashima, K., and Akimoto, H.: Emissions of air pollutants and
greenhouse gases over Asian regions during 2000–2008: Regional Emission
inventory in ASia (REAS) version 2, Atmos. Chem. Phys., 13, 11019–11058,
10.5194/acp-13-11019-2013, 2013.Li, J., Wang, Z., Akimoto, H., Gao, C., Pochanart, P., and Wang, X.:
Modeling study of ozone seasonal cycle in lower troposphere over east Asia,
J. Geophys. Res.-Atmos., 112, D22S25, 10.1029/2006JD008209,
2007.Li, M., Liu, H., Geng, G., Hong, C., Liu, F., Song, Y., Tong, D., Zheng, B.,
Cui, H., Man, H., Zhang, Q., and He, K.: Anthropogenic emission inventories
in China: A review, Natl. Sci. Rev., 4, 834–866, 10.1093/nsr/nwx150,
2017a.Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T.,
Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H.,
Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian
anthropogenic emission inventory under the international collaboration
framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963,
10.5194/acp-17-935-2017, 2017b.Lin, J. T. and McElroy, M. B.: Impacts of boundary layer mixing on pollutant vertical profiles
in the lower troposphere: Implications to satellite remote sensing, Atmos. Environ., 44, 1726–1739, 10.1016/j.atmosenv.2010.02.009, 2010.Lin, J. T., Wuebbles, D. J., and Liang, X. Z.: Effects of intercontinental
transport on surface ozone over the United States: Present and future
assessment with a global model, Geophys. Res. Lett., 35, L02805,
10.1029/2007GL031415, 2008.Lin, M., Holloway, T., Oki, T., Streets, D. G., and Richter, A.: Multi-scale
model analysis of boundary layer ozone over East Asia, Atmos. Chem. Phys., 9,
3277–3301, 10.5194/acp-9-3277-2009, 2009.Lin, M., Fiore, A. M., Horowitz, L. W., Langford, A. O., Oltmans, S. J.,
Tarasick, D., and Rieder, H. E.: Climate variability modulates western US
ozone air quality in spring via deep stratospheric intrusions, Nat. Commun.,
6, 7105, 10.1038/ncomms8105, 2015.Logan, J. A.: Tropospheric ozone: Seasonal behavior, trends, and
anthropogenic influence, J. Geophys. Res.-Atmos., 90, 10463–10482,
10.1029/JD090iD06p10463, 1985.Lou, S., Liao, H., Yang, Y., and Mu, Q.: Simulation of the interannual
variations of tropospheric ozone over China: Roles of variations in
meteorological parameters and anthropogenic emissions, Atmos. Environ., 122,
839–851, 10.1016/j.atmosenv.2015.08.081, 2015.
Lu X., Hong J., Zhang L., Cooper OR.,Schultz MG., Xu X., Wang T., Gao
M., Zhao Y., and Zhang Y., Environ. Sci. Technol. Lett., 5, 487–494, 2018.Ma, Z., Xu, J., Quan, W., Zhang, Z., Lin, W., and Xu, X.: Significant
increase of surface ozone at a rural site, north of eastern China, Atmos.
Chem. Phys., 16, 3969–3977, 10.5194/acp-16-3969-2016, 2016.Mao, J., Paulot, F., Jacob, D. J., Cohen, R. C., Crounse, J. D., Wennberg, P.
O., Keller, C. A., Hudman, R. C., Barkley, M. P., and Horowitz, L. W.: Ozone
and organic nitrates over the eastern United States: Sensitivity to isoprene
chemistry, J. Geophys. Res.-Atmos., 118, 11256–11268,
10.1002/jgrd.50817, 2013.McLinden, C. A., Olsen, S. C., Hannegan, B., Wild, O., Prather, M. J., and
Sundet, J.: Stratospheric ozone in 3-D models: A simple chemistry and the
cross-tropopause flux, J. Geophys. Res.-Atmos., 105, 14653–14665,
10.1029/2000JD900124, 2000.Meng, Z. Y., Xu, X. B., Yan, P., Ding, G. A., Tang, J., Lin, W. L., Xu, X.
D., and Wang, S. F.: Characteristics of trace gaseous pollutants at a
regional background station in Northern China, Atmos. Chem. Phys., 9,
927–936, 10.5194/acp-9-927-2009, 2009.Monks, P. S.: A review of the observations and origins of the spring ozone
maximum, Atmos. Environ., 34, 3545–3561,
10.1016/S1352-2310(00)00129-1, 2000.Monks, P. S., Archibald, A. T., Colette, A., Cooper, O., Coyle, M., Derwent,
R., Fowler, D., Granier, C., Law, K. S., Mills, G. E., Stevenson, D. S.,
Tarasova, O., Thouret, V., von Schneidemesser, E., Sommariva, R., Wild, O.,
and Williams, M. L.: Tropospheric ozone and its precursors from the urban to
the global scale from air quality to short-lived climate forcer, Atmos. Chem.
Phys., 15, 8889–8973, 10.5194/acp-15-8889-2015, 2015.Murray, L. T., Jacob, D. J., Logan, J. A., Hudman, R. C., and Koshak, W. J.:
Optimized regional and interannual variability of lightning in a global
chemical transport model constrained by LIS/OTD satellite data, J. Geophys.
Res.-Atmos., 117, D20307 10.1029/2012JD017934, 2012.Ni, R., Lin, J., Yan, Y., and Lin, W.: Foreign and domestic contributions to
springtime ozone over China, Atmos. Chem. Phys., 18, 11447–11469,
10.5194/acp-18-11447-2018, 2018.Oltmans, S. J., Lefohn, A. S., Shadwick, D., Harris, J. M., Scheel, H. E.,
Galbally, I., Tarasick, D. W., Johnson, B. J., Brunke, E. G., Claude, H.,
Zeng, G., Nichol, S., Schmidlin, F., Davies, J., Cuevas, E., Redondas, A.,
Naoe, H., Nakano, T., and Kawasato, T.: Recent tropospheric ozone changes: A
pattern dominated by slow or no growth, Atmos. Environ., 67, 331–351,
10.1016/j.atmosenv.2012.10.057, 2013.Ott, L. E., Pickering, K. E., Stenchikov, G. L., Allen, D. J., DeCaria, A.
J., Ridley, B., Lin, R. F., Lang, S., and Tao, W. K.: Production of
lightning NOx and its vertical distribution calculated from
three-dimensional cloud-scale chemical transport model simulations, J.
Geophys. Res.-Atmos., 115, D04301, 10.1029/2009JD011880,
2010.Parrish, D., Lamarque, J. F., Naik, V., Horowitz, L., Shindell, D.,
Staehelin, J., Derwent, R., Cooper, O., Tanimoto, H., Volz-Thomas, A., Gilge,
S., Scheel, H.-E., Steinbacher, M., and Fröhlich, M.: Long-term changes
in lower tropospheric baseline ozone concentrations: Comparing
chemistry-climate models and observations at northern midlatitudes, J.
Geophys. Res.-Atmos., 119, 5719–5736, 10.1002/2013JD021435,
2014.Price, C. and Rind, D.: A simple lightning parameterization for calculating
global lightning distributions, J. Geophys. Res.-Atmos., 97, 9919–9933,
10.1029/92JD00719, 1992.Ramsey, N. R., Klein, P. M., and Moore, B.: The impact of meteorological
parameters on urban air quality, Atmos. Environ., 86, 58–67,
10.1016/j.atmosenv.2013.12.006, 2014.Randerson, J., Chen, Y., Werf, G., Rogers, B., and Morton, D.: Global burned
area and biomass burning emissions from small fires, J. Geophys. Res., 117,
G04012, 10.1029/2012JG002128, 2012.Real, E., Law, K. S., Weinzierl, B., Fiebig, M., Petzold, A., Wild, O.,
Methven, J., Arnold, S., Stohl, A., Huntrieser, H., Roiger, A., Schlager,
H., Stewart, D., Avery, M., Sachse, G., Browell, E., Ferrare, R., and Blake,
D.: Processes influencing ozone levels in Alaskan forest fire plumes during
long-range transport over the North Atlantic, J. Geophys. Res.-Atmos., 112,
D10S41, 10.1029/2006JD007576, 2007.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From
Air Pollution to Climate Change, John Wiley & Sons, 2016.Sun, L., Xue, L., Wang, T., Gao, J., Ding, A., Cooper, O. R., Lin, M., Xu,
P., Wang, Z., Wang, X., Wen, L., Zhu, Y., Chen, T., Yang, L., Wang, Y., Chen,
J., and Wang, W.: Significant increase of summertime ozone at Mount Tai in
Central Eastern China, Atmos. Chem. Phys., 16, 10637–10650,
10.5194/acp-16-10637-2016, 2016.Verstraeten, W. W., Neu, J. L., Williams, J. E., Bowman, K. W., Worden, J.
R., and Boersma, K. F.: Rapid increases in tropospheric ozone production and
export from China, Nat. Geosci., 8, 690, 10.1038/ngeo2493,
2015.Wang, T., Ding, A., Gao, J., and Wu, W. S.: Strong ozone production in urban
plumes from Beijing, China, Geophys. Res. Lett., 33, L21806,
10.1029/2006GL027689, 2006.Wang, T., Wei, X. L., Ding, A. J., Poon, C. N., Lam, K. S., Li, Y. S., Chan,
L. Y., and Anson, M.: Increasing surface ozone concentrations in the
background atmosphere of Southern China, 1994–2007, Atmos. Chem. Phys., 9,
6217–6227, 10.5194/acp-9-6217-2009, 2009.Wang, T., Xue, L., Brimblecombe, P., Lam, Y. F., Li, L., and Zhang, L.:
Ozone pollution in China: A review of concentrations, meteorological
influences, chemical precursors, and effects, Sci. Total Environ., 575,
1582–1596, 10.1016/j.scitotenv.2016.10.081, 2017.Wang, Y., Zhang, Y., Hao, J., and Luo, M.: Seasonal and spatial variability
of surface ozone over China: contributions from background and domestic
pollution, Atmos. Chem. Phys., 11, 3511–3525,
10.5194/acp-11-3511-2011, 2011.Wild, O., Pochanart, P., and Akimoto, H.: Trans-Eurasian transport of ozone
and its precursors, J. Geophys. Res.-Atmos., 109, D11302,
10.1029/2003JD004501, 2004.Wu, J., Kong, S., Wu, F., Cheng, Y., Zheng, S., Yan, Q., Zheng, H., Yang, G.,
Zheng, M., Liu, D., Zhao, D., and Qi, S.: Estimating the open biomass burning
emissions in central and eastern China from 2003 to 2015 based on satellite
observation, Atmos. Chem. Phys., 18, 11623–11646,
10.5194/acp-18-11623-2018, 2018.Xiao, Y., Logan, J. A., Jacob, D. J., Hudman, R. C., Yantosca, R., and
Blake, D. R.: Global budget of ethane and regional constraints on US
sources, J. Geophys. Res.-Atmos., 113, D21306,
10.1029/2007JD009415, 2008.Xu, W., Lin, W., Xu, X., Tang, J., Huang, J., Wu, H., and Zhang, X.:
Long-term trends of surface ozone and its influencing factors at the Mt
Waliguan GAW station, China – Part 1: Overall trends and characteristics,
Atmos. Chem. Phys., 16, 6191–6205, 10.5194/acp-16-6191-2016,
2016.Xu, W., Xu, X., Lin, M., Lin, W., Tarasick, D., Tang, J., Ma, J., and Zheng,
X.: Long-term trends of surface ozone and its influencing factors at the Mt
Waliguan GAW station, China – Part 2: The roles of anthropogenic emissions
and climate variability, Atmos. Chem. Phys., 18, 773–798,
10.5194/acp-18-773-2018, 2018.Xu, W. Y., Zhao, C. S., Ran, L., Deng, Z. Z., Liu, P. F., Ma, N., Lin, W. L.,
Xu, X. B., Yan, P., He, X., Yu, J., Liang, W. D., and Chen, L. L.:
Characteristics of pollutants and their correlation to meteorological
conditions at a suburban site in the North China Plain, Atmos. Chem. Phys.,
11, 4353–4369, 10.5194/acp-11-4353-2011, 2011.Xu, X., Lin, W., Wang, T., Yan, P., Tang, J., Meng, Z., and Wang, Y.:
Long-term trend of surface ozone at a regional background station in eastern
China 1991–2006: enhanced variability, Atmos. Chem. Phys., 8, 2595–2607,
10.5194/acp-8-2595-2008, 2008.Xu, Z., Wang, T., Xue, L. K., Louie, P. K. K., Luk, C. W. Y., Gao, J., Wang,
S. L., Chai, F. H., and Wang, W. X.: Evaluating the uncertainties of thermal
catalytic conversion in measuring atmospheric nitrogen dioxide at four
differently polluted sites in China, Atmos. Environ., 76, 221–226,
10.1016/j.atmosenv.2012.09.043, 2013.Xue, L. K., Wang, T., Gao, J., Ding, A. J., Zhou, X. H., Blake, D. R., Wang,
X. F., Saunders, S. M., Fan, S. J., Zuo, H. C., Zhang, Q. Z., and Wang, W.
X.: Ground-level ozone in four Chinese cities: precursors, regional transport
and heterogeneous processes, Atmos. Chem. Phys., 14, 13175–13188,
10.5194/acp-14-13175-2014, 2014.Yamaji, K., Li, J., Uno, I., Kanaya, Y., Irie, H., Takigawa, M., Komazaki,
Y., Pochanart, P., Liu, Y., Tanimoto, H., Ohara, T., Yan, X., Wang, Z., and
Akimoto, H.: Impact of open crop residual burning on air quality over Central
Eastern China during the Mount Tai Experiment 2006 (MTX2006), Atmos. Chem.
Phys., 10, 7353–7368, 10.5194/acp-10-7353-2010, 2010.Yan, Y., Lin, J., and He, C.: Ozone trends over the United States at
different times of day, Atmos. Chem. Phys., 18, 1185–1202,
10.5194/acp-18-1185-2018, 2018a.Yan, Y., Pozzer, A., Ojha, N., Lin, J., and Lelieveld, J.: Analysis of
European ozone trends in the period 1995–2014, Atmos. Chem. Phys., 18,
5589–5605, 10.5194/acp-18-5589-2018, 2018b.Yan, Y.-Y., Lin, J.-T., Kuang, Y., Yang, D., and Zhang, L.: Tropospheric
carbon monoxide over the Pacific during HIPPO: two-way coupled simulation of
GEOS-Chem and its multiple nested models, Atmos. Chem. Phys., 14,
12649–12663, 10.5194/acp-14-12649-2014, 2014.
Young, P. J., Archibald, A. T., Bowman, K. W., Lamarque, J.-F., Naik, V.,
Stevenson, D. S., Tilmes, S., Voulgarakis, A., Wild, O., Bergmann, D.,
Cameron-Smith, P., Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty,
R. M., Eyring, V., Faluvegi, G., Horowitz, L. W., Josse, B., Lee, Y. H.,
MacKenzie, I. A., Nagashima, T., Plummer, D. A., Righi, M., Rumbold, S. T.,
Skeie, R. B., Shindell, D. T., Strode, S. A., Sudo, K., Szopa, S., and Zeng,
G.: Pre-industrial to end 21st century projections of tropospheric ozone from
the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP),
Atmos. Chem. Phys., 13, 2063–2090, 10.5194/acp-13-2063-2013,
2013.Zhang, L., Jacob, D. J., Yue, X., Downey, N. V., Wood, D. A., and Blewitt,
D.: Sources contributing to background surface ozone in the US Intermountain
West, Atmos. Chem. Phys., 14, 5295–5309,
10.5194/acp-14-5295-2014, 2014a.Zhang, Q., Yuan, B., Shao, M., Wang, X., Lu, S., Lu, K., Wang, M., Chen, L.,
Chang, C.-C., and Liu, S. C.: Variations of ground-level O3 and its
precursors in Beijing in summertime between 2005 and 2011, Atmos. Chem.
Phys., 14, 6089–6101, 10.5194/acp-14-6089-2014, 2014b.Zhao, C., Wang, Y., and Zeng, T.: East China Plains: A `basin' of ozone
pollution, Environ. Sci. Technol., 43, 1911–1915, 10.1021/es8027764,
2009.Zhao, C., Wang, Y., Yang, Q., Fu, R., Cunnold, D., and Choi, Y.: Impact of
East Asian summer monsoon on the air quality over China: View from space, J.
Geophys. Res.-Atmos., 115, D09301, 10.1029/2009JD012745,
2010.Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X.,
Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang,
Q.: Trends in China's anthropogenic emissions since 2010 as the consequence
of clean air actions, Atmos. Chem. Phys., 18, 14095–14111,
10.5194/acp-18-14095-2018, 2018.