In the summer of 2017, measurements of ozone (O3) and its precursors were
carried out at an urban site in Jinan, a central city in the North China
Plain (NCP). A continuous O3 pollution event was captured during
4–11 August, with the maximum hourly O3 mixing ratio reaching 154.1 ppbv.
Model simulation indicated that local photochemical formation and regional
transport contributed 14.0±2.3 and 18.7±4.0 ppbv h-1, respectively, to the
increase in O3 during 09:00–15:00 LT (local time) in this event.
For local O3 formation, the calculated OH
reactivities of volatile organic compounds (VOCs) and carbon monoxide (CO)
were comparable between O3 episodes and non-episodes (p>0.05), so
was the OH reactivity of nitrogen oxides (NOx). However, the
ratio of OH reactivity of VOCs and CO to that of NOx
increased from 2.0±0.4 s-1 s1 during non-episodes to
3.7±0.7 s-1 s1 during O3 episodes, which resulted in the change in
the O3 formation mechanism from the VOC-limited regime before the
O3 pollution event to the transitional regime during the event.
Correspondingly, the simulated local O3 production rate during the
event (maximum: 21.3 ppbv h-1) was markedly higher than that before
the event (p<0.05) (maximum: 16.9 ppbv h-1). Given that gasoline and
diesel exhaust made large contributions to the abundance of O3
precursors and the O3 production rate, constraint on vehicular
emissions is the most effective strategy to control O3 pollution in
Jinan. The NCP has been confirmed as a source region of tropospheric
O3, where the shift in regimes controlling O3 formation
like the case presented in this study can be expected across the entire
region, due to the substantial reductions of NOx emissions
in recent years.
Introduction
Air pollution in the North China Plain (NCP), the largest alluvial plain of
China consisting of Beijing, Tianjin and many cities in Hebei, Shandong and
Henan provinces, has attracted much attention in recent years. While the
annual average concentration of PM2.5 (particulate matter with
aerodynamic diameter less than or equal to 2.5 µm) has been reduced
under concerted efforts on emission restrictions (Zhang et al., 2015; Lang
et al., 2017), the tropospheric ozone (O3) pollution, which is less
visible than haze but may be equally harmful to human health, is still
severe. At a regional receptor site of the NCP in a mountainous area to the
north of Beijing, Wang et al. (2006) reported the maximum hourly O3 mixing ratio
of 286 ppbv. A year-round observation of O3 at 10 urban sites in Beijing
indicated frequent O3 non-attainments (hourly O3> 100 ppbv)
through May to August 2013 (Z. Wang et al., 2015). An hourly O3
mixing ratio of up to 120 ppbv was reported on Mt. Tai, the highest mountain
in the NCP (1534 m a.s.l.) (Gao et al., 2005). All these studies revealed
the significant photochemical O3 pollution over the entire NCP.
Moreover, O3 has been increasing in the NCP during the last decades
(Zhang et al., 2014, 2015). The increase rate of O3 at an
urban site in Beijing from 2005 to 2011 was quantified as 2.6 ppbv yr-1
(Zhang et al., 2014), comparable to that (1.7–2.1 ppbv yr-1) at Mt. Tai in
the summer between 2003 and 2015 (Sun et al., 2016). Overall, the NCP
suffers from severe O3 pollution, which is aggravating.
Apart from the intrusion of stratospheric O3 in some places with high
elevations (Cooper et al., 2005; Lin et al., 2015), photochemical formation
is the main source of the ground-level O3. Volatile organic compounds (VOCs),
carbon monoxide (CO) and nitrogen oxides (NOx) are key
precursors of tropospheric O3 (Crutzen, 1973; Chameides and Walker,
1973; Carter, 1994; Carter et al., 1995). The general chemical Reactions R1–R5
show the production of O3 from the OH-initiated oxidation
of hydrocarbons (RH) (Jenkin et al., 1997; Atkinson, 2000; Jenkin and Clemitshaw, 2000).
RH+OH+O2→RO2+H2O,RO2+NO→RO+NO2,RO+O2→Carbonyls+HO2,HO2+NO→OH+NO2,NO2+O2+hv→O3+NO,
The production of O3 is generally limited by VOCs or NOx or
co-limited by both VOCs and NOx, depending upon the chemical
compositions of the air, particularly the ratio between OH reactivity of
VOCs and NOx (OH reactivity is the sum of the products of O3
precursor concentrations and the reaction rate constants between O3
precursors and OH). Xue et al. (2014) indicated that the formation of
O3 was limited by NOx in Lanzhou in summer, consistent with the
findings of Liu et al. (2010), who proved that the NOx-limited regime
dominated O3 formation in most areas of northwestern China. In
southwestern China, O3 formation was diagnosed as VOC-limited in
Chengdu but NOx-limited in Pengzhou due to the large amount of
emissions from petrochemical industry (Tan et al., 2018a). Lyu et al. (2016)
reported the VOC-limited regime in Wuhan, a city in central China.
The VOC-limited regime has also been repeatedly confirmed for O3 formation in Shanghai
(Xue et al., 2014; Xing et al., 2017) and Nanjing (Ding et al., 2013),
eastern China. In the Pearl River Delta of southern China, it was found that
O3 formation was generally limited by VOCs in the southwest, while
it was limited by NOx in the northeast (Ye et al., 2016). In the NCP, both Han
et al. (2018) and Xing et al. (2018) summarized that VOCs limited the
production of O3 in most urban areas. However, in the suburban and
rural areas, O3 formation was generally in the transitional regime,
e.g., Yucheng (Zong et al., 2018), or limited by NOx, e.g., Wangdu (Tan et al.,
2018b). From a historical perspective, Jin et al. (2017) pointed out that
the sensitivity of O3 formation to VOCs increased in most Chinese
cities but decreased in some megacities (such as Beijing and Shanghai)
due to the stringent control of NOx emissions in recent years.
Different VOCs play non-equivalent roles in O3 formation. Alkenes,
aromatics and carbonyls can be readily oxidized by oxidative radicals
(e.g., OH) or photolyzed (applicable for carbonyls), leading to O3 formation
(Cheng et al., 2010; Guo et al., 2013). Therefore, the sources with large
quantities of emissions of these VOCs generally make considerable
contributions to the photochemical production of ground-level O3. For
example, Cheng et al. (2010) pointed out that carbonyls increased the peak
O3 production rates at a rural site and at a suburban site in
southern China by 64 % and 47 %, respectively. Solvent-based industry and paint
solvent usage with intensive emissions of aromatics were responsible for
more than half of the O3 formation potential in Shanghai (Cai et al.,
2010). Carbonyls and alkenes accounted for 71 %–85 % of the total OH
reactivity of VOCs in Beijing (Shao et al., 2009).
In addition to the chemical processes, meteorological conditions also play
significant roles in the formation, transport and accumulation of O3.
Studies (Chan and Chan, 2000; Huang et al., 2005) indicated that tropical
cyclone (typhoon as the mature form) and continental anticyclone are the
most common synoptic systems conducive to O3 pollution in coastal
cities of southern China. Many O3 episodes in eastern China occurred under
the control of the Western Pacific Subtropical High (WPSH) (He et al., 2012;
Shu et al., 2016). In the NCP (northern China), the summertime O3
pollution is generally accompanied by weak high-pressure systems (Wang et
al., 2010). Furthermore, O3 pollution is also related to the
topography. For example, the mountains to the north and west of Beijing lead
to upslope winds (valley breeze) in daytime, which transport the polluted
air masses laden with O3 and/or O3 precursors from the NCP to
Beijing (Lin et al., 2008). Overall, the causes of O3 pollution are
complicated and need to be analyzed case by case.
The NCP is the region with the largest emissions of many air pollutants,
such as VOCs and NOx, in China (Gu et al., 2014; Li et al., 2017),
partially accounting for the severe O3 pollution there. In addition,
O3 pollution in the NCP is closely related to the synoptic systems and
topographic features (Chen et al., 2009; Zhang et al., 2016). For example,
the strong photochemical production of O3 in urban plumes of Beijing
was found by Wang et al. (2006), while the contribution of regional
transport was revealed by the enhanced O3 production at a rural site in
the NCP under southerly winds (Lin et al., 2008). Through the review of
synoptic systems in the NCP from 1980 to 2013, Zhang et al. (2016) concluded
that the air quality was generally unhealthy under weak East Asian monsoons.
Moreover, a decadal statistical analysis indicated that meteorological
factors explained ∼50 % of the O3 variations in
Beijing (Zhang et al., 2015). Despite many previous studies, the evolutions
of the synoptic and photochemical processes in O3 pollution events and
their contributions to the non-attainment of O3 have been seldom looked
into in the NCP. Besides, the local and regional contributions to the
elevated O3 in the NCP are not unambiguously quantified and are limited by the
deficiencies in the model representation of either physical or local chemical
processes. The situation was even much worse for Jinan, the capital of
Shandong province. As early as the 2000s, studies (Shan et al., 2008; Yin et
al., 2009) reported the maximum hourly O3 mixing ratios of 143.8 and 147.8 ppbv
in June 2004 and 2005, respectively. Even higher O3 (198 ppbv) was
observed at a rural site downwind of Jinan in June 2013 (Zong et al.,
2018). However, almost no study was carried out to explore the mechanisms
responsible for high O3 there, though it has been confirmed that air
pollution in the cities like Jinan in the NCP influenced air quality in
Beijing (Lin et al., 2008; Wang et al., 2010). To better understand O3
pollution in the NCP, this study investigated the causes of an O3
episode event lasting for 8 days in Jinan in summer 2017. The analyses
presented here focused on the synoptic systems dominating the Shandong Peninsula
during this event, the chemical profiles of O3 and O3
precursors, and the simulation of factors contributing to O3 in Jinan with the aid
of a chemical transport model and a photochemical box model. In addition, we
proposed feasible O3 control measures based on the source-resolved OH
reactivity of VOCs and NOx.
MethodologySite description
The air quality monitoring and sample collection were carried out on the
rooftop of a seven-story building on the campus of Shandong University from 15 July
to 14 August 2017. The campus is located in the urban area of Jinan,
and the site is about 50 m from a main road (Shanda South Road) outside the
campus. Figure 1 shows the locations of the sampling site (36.68∘ N,
117.07∘ E; 22 m a.g.l.) and the surrounding air quality
monitoring stations (AQMSs) set up by the China National Environmental
Monitoring Center (CNEMC). Also shown are the observed O3 and rainfall
averaged over 4–11 August 2017 when the O3 episode event occurred in
Jinan. It is noteworthy that the days with maximum hourly O3 mixing ratios exceeding
100 ppbv (Grade II of National Ambient Air Quality Standard) were defined as
O3 episode days. The hourly O3 values at the AQMSs were obtained
from the website of CNEMC (http://www.cnemc.cn/, last access: 12 September 2018). The high
O3 levels at almost all the AQMSs in the NCP (Fig. 1a) indicated a
regional O3 pollution event in this period. In view of the comparable
O3 mixing ratios observed at our sampling site to those at the
surrounding AQMSs, we believe that the observations at our sampling site to
some extent represented the characteristics of this regional pollution
event. This was confirmed by the strong influences of regional transport on
O3 variations at the site, as discussed in Sect. 3.3.
(a) Locations of the sampling site and the CNEMC AQMSs, and
the average observed O3 at 14:00 LT on 4–11 August 2017 (colored
circles). The sampling site is overlapped with the nearest AQMS in Jinan.
(b) Rainfall distribution, in millimeters (mm), averaged over
4–11 August 2017.
Air quality monitoring and sample collectionContinuous monitoring of air pollutants and meteorological parameters
O3, NO and NO2 were continuously monitored at the sampling site
between 15 July and 14 August 2017. The air was drawn through a 4 m Teflon
tube by the built-in pumps of the trace gas analyzers at the total flow rate
of 2 L min-1 (1.4 L min-1 for O3 analyzer and 0.6 L min-1 for NOx
analyzer). The inlet was located ∼1 m above the rooftop of
the building. O3 and NO/NOx were detected with a UV-based photometric analyzer
and a chemiluminescence NO–NO2–NOx analyzer,
respectively (see Table S1 in the Supplement for the specifications). The lowest NO observed
during the sampling period was 2.4 ppbv, which is 6 times the detection limit (DL) of
the NOx analyzer (0.4 ppbv). Since the measurement accuracy of the
analyzer was <15 %, the DL was low enough to not influence the
accurate measurements of NO in this study. NO2 was calculated from the
difference between NO and NOx. Studies indicated that
NO2 monitored with chemiluminescence was generally overestimated due to the
conversion of the total odd nitrogen (NOy) to NO by molybdenum oxide
catalysts (McClenny et al., 2002; Dunlea et al., 2007; Xu et al., 2013). The
positive bias was more significant in more aged air masses, resulting from
higher levels of NOz (NOz=NOy-NOx) (Dunlea et al.,
2007). The average overestimation of NO2 was 22 % in Mexico City,
which even increased to 50 % in the afternoon (Dunlea et al., 2007). Xu et
al. (2013) suggested that the chemiluminescence monitors overestimated
NO2 by less than 10 % in urban areas with fresh emission of NOx,
but the positive bias went up to 30 %–50 % at the suburban sites. As
described in Sect. 2.1, our sampling site was located in the urban area of
Jinan and was only ∼50 m from a main road. Therefore, we infer
that NO2 might not be significantly overestimated in this study.
However, larger overestimation could be expected during O3 episodes,
when the stronger photochemical reactions caused higher production of
NOz. According to Xu et al. (2013), we adopted 30 % (minimum bias in
suburban area) and 10 % (maximum bias in urban area) as the maximum
fraction of NO2 overestimation during episodes and non-episodes at this
urban site, respectively. The influences of the NO2 measurement
interferences on the results are discussed where necessary.
The hourly concentrations of sulfur dioxide (SO2) and CO were acquired
from a nearest AQMS, which is ∼1 km from our sampling site.
Year-round monitoring of inorganic trace gases was conducted at this AQMS.
The air was drawn into the analyzers at a flow of 3 L min-1 through an inlet,
∼1 m above the rooftop of a five-story building (∼16 m a.g.l.).
The specifications of the analyzers deployed at the AQMS are
also provided in Table S1. The hourly concentrations of O3 and NO2
measured at the AQMS (NO data were not available at the CNEMC website) agreed
well with those observed at our sampling site, with the slope of 1.04
(R2=0.82) and 1.13 (R2=0.71) for O3 and NO2 in
the linear least squares regressions, respectively (Fig. S1 in the Supplement). Due to the
differences in analyzers and/or in sources and sinks of air pollutants
between the two sites, the agreements were worse at low mixing ratios for
both O3 and NO2. Therefore, we only used SO2 and CO monitored
at the AQMS in this study, which had lower photochemical reactivity than
O3 and NO2 and were more homogeneous at a larger scale.
In addition, the meteorological parameters, including wind speed, wind
direction, pressure, temperature and relative humidity, were monitored at
the sampling site by a widely used weather station (China Huayun Group,
model CAWS600-B). The daily total solar radiation was obtained from the
observations at a meteorological station in Jinan (36.6∘ N,
117.05∘ E; 170.3 m a.s.l.), 9 km from our sampling site.
Sample collection and chemical analysis
The VOC and oxygenated VOC (OVOC) samples were collected on 9 selective days
(i.e., 20 and 30 July and 1, 4–7 and 10–11 August), referred to as VOC sampling
days hereafter. The days were selected to cover the periods with relatively
high and normal levels of O3. The high-O3 days were forecasted
prior to sampling based on the numerical simulations of meteorological
conditions and air quality. In total, 6 out of 9 VOC sampling days were
O3 episode days with the maximum hourly O3 mixing ratio values ranging from
100.4 to 154.1 ppbv. On each day (regardless of episode or non-episode),
six VOC and OVOC samples were collected between 08:00 and 18:00 LT every 2 h
with the duration of 1 h for VOC and 2 h for OVOC samples. VOC
samples were collected with 2 L stainless steel canisters which were cleaned
and evacuated before sampling. A flow restrictor was connected to the inlet
of the canister to guarantee 1 h sampling. OVOCs were sampled with the
2,4-dinitrophenylhydrazine (DNPH) cartridge, in front of which an O3
scrubber was interfaced to remove O3 in the air. A pump was used to
draw the air through the DNPH cartridge at a flow of 500 mL min-1. After
sampling, all the DNPH cartridges were stored in a refrigerator at
4 ∘C until chemical analysis.
VOC samples were analyzed using a gas chromatograph with mass selective
detector, flame ion detector and electron capture detector system (Colman et al.,
2001). In total, 85 VOCs, including 59 hydrocarbons, 19 halocarbons and
7 alkyl nitrates, were quantified. The overall ranges of the DL, accuracy and
precision for VOC analysis were 1–154 pptv, 1.2 %–19.8 % and 0.1 %–17.9 %,
respectively. The analysis results given by this system have been compared
with those analyzed by the University of California, Irvine, and good agreements were achieved (Fig. S2).
OVOC samples were eluted with 5 mL acetonitrile, followed by analysis with
high-performance liquid chromatography. The DL, accuracy and precision
for the detected OVOC species were within the range of 3–11 pptv,
0.32 %–0.98 % and 0.01 %–1.03 %, respectively.
Model configurationChemical transport model
To analyze the processes contributing to high O3 in Jinan, a chemical
transport model, i.e., the Weather Research and Forecasting–Community Multiscale
Air Quality (WRF-CMAQ) model, was utilized to simulate O3 in this study.
WRF v3.6.1 was run to provide the offline meteorological field for CMAQ v5.0.2.
A two-nested domain was adopted with the resolution of 36 km (outer domain)
and 12 km (inner domain). As shown in Fig. S3, the outer
domain covered the entire continental area of China, aiming to provide
sufficient boundary conditions for the inner domain, which specifically
focused on eastern China.
We used the 2012-based Multi-resolution Emission Inventory for China (MEIC)
to provide anthropogenic emissions of air pollutants, which was developed by
Tsinghua University specifically for China, with the grid resolution of
0.25∘× 0.25∘ (Zhang et al., 2007; He, 2012).
Five emission sectors, namely transportation, agriculture, power plant,
industry and residence, were included in MEIC. The emission inventory was
linearly interpolated to the domains with consideration of the earth
curvature effect. For grids outside China, the air pollutant emissions were
derived from the INTEX-B (Intercontinental Chemical Transport Experiment Phase
B) Asian emission inventory (Zhang et al., 2009). Consistent with many
previous studies (Jiang et al., 2010; N. Wang et al., 2015), the Model of
Emissions of Gases and Aerosols from Nature (MEGAN) was used to calculate
the biogenic emissions. The physical and chemical parameterizations for
WRF-CMAQ were generally identical to those described in N. Wang et al. (2015),
with the following improvements. Firstly, the carbon bond v5 with updated
toluene chemistry (CB05-TU) was chosen as the gas-phase chemical mechanism
(Whitten et al., 2010). Secondly, a single-layer urban canopy model (Kusaka
and Kimura, 2004) was used to model the urban surface–atmosphere
interactions. Thirdly, the default 1990s US Geological Survey data in WRF
were replaced by adopting the 2012-based Moderate Resolution Imaging
Spectroradiometer (MODIS) land cover data for eastern China. The
substitution was performed to update the simulation of boundary
meteorological conditions (Wang et al., 2007).
An integrated process rate (IPR) module incorporated in CMAQ was used to
analyze the processes influencing the variations of O3. Through solving
the mass continuity equation established between the overall change in
O3 concentration across time and the change in O3 concentration
caused by individual processes, including horizontal diffusion (HDIF),
horizontal advection (HADV), vertical diffusion (VDIF), vertical advection (VADV),
dry deposition, net effect of chemistry (CHEM) and cloud
processes, the O3 variation rates induced by individual processes
were determined. Note that since the estimate of CHEM is influenced by the
estimate of O3 precursor emissions, the simulation of meteorological
conditions and the chemical mechanism, all three aspects should be taken
into account wherever CHEM is discussed. The IPR analysis has been widely
applied in the diagnosis of processes influencing O3 pollution (Huang et
al., 2005; N. Wang et al., 2015). Since the field observations were conducted
near the surface (∼22 m a.g.l.), and the box model (Sect. 2.3.2)
was constrained by the observations, the modeling results on the
ground-level layer were extracted from WRF-CMAQ for analyses in this study.
Photochemical box model
We also utilized a photochemical box model incorporating the Master Chemical
Mechanism (PBM-MCM) to study the in situ O3 chemistry, thanks to the
detailed (species-based) descriptions of VOC degradations in the MCM
(Saunders et al., 2003; Lam et al., 2013). The PBM model was localized to be
applicable in Jinan, with the settings of geographic coordinates, sunlight
duration and photolysis rates. The photolysis rates were calculated by the
TUV (Tropospheric Ultraviolet and Visible) model (Madronich and Flocke, 1997). Specifically, the geographical
coordinates, date and time were input into the TUV model to initialize the
calculation of solar radiation with the default aerosol optical depth (AOD),
cloud optical depth (COD), surface albedo and other parameters. Then, COD
was adjusted to make the calculated daily total solar radiation
progressively approach the observed value. When the difference between the
calculated and observed solar radiation was less than 1 %, the input
parameters with the adjusted COD were accepted. Based on the settings, the
hourly solar radiations and the photolysis rates of O3 (J(O1D))
and NO2 (JNO2) were calculated by the TUV model and applied to
the PBM-MCM for O3 chemistry modeling. Table S2 shows the daily
maximum J(O1D) and JNO2 on the VOC sampling days. The MCM v3.2
(http://mcm.leeds.ac.uk/MCM/, last access: 12 September 2018) consists of 17 242 reactions
among 5836 species. The mixing ratios of O3 and its precursors at
00:00 LT on each day were used as the initial conditions for each day's
modeling. The initial O3 therefore represented O3 left over from
the days before the modeling day and partially accounted for the primary
OH production. Hourly concentrations of 46 VOCs, 4 OVOCs and 4 trace gases
(SO2, CO, NO and NO2), as well as hourly meteorological parameters
(temperature and relative humidity), were taken as inputs to constrain the
model. O3, as the species to be modeled, was not input except for the
setting of initial conditions. The Freon, cycloalkanes and methyl
cycloalkanes with low O3 formation potentials were not included in
model inputs either. Also excluded were the species whose concentrations
were lower than the DLs in more than 20 % of samples,
such as the methyl hexane and methyl heptane isomers. For the hours when measurement data were
not available, the concentrations were obtained with linear interpolation.
Some secondary species, such as formaldehyde (HCHO), acetaldehyde and
acetone, were input into the model to constrain the simulation. Since other
secondary species, e.g., PAN and HNO3, were not observed in this study,
their concentrations were calculated by the model. The model simulated dry
depositions of all the chemicals, and the deposition velocities were set
identical to those in Lam et al. (2013). Since NO and NO2 were
separately measured, they were not treated as a whole (i.e., NOx) in the
model. Instead, both NO and NO2 data were input into the model so that
the partitioning between them was constrained to observations.
The simulations were separately performed on all the VOC sampling days. For
the spin-up, the model was run 72 h prior to the simulation on the day of
interest, with the same inputs. The model treated the air pollutants to be
well-mixed within the boundary layer, while dilution and transport were not
considered. O3 in the free troposphere was not considered either, due
to the lack of O3 observations above the boundary layer over Jinan.
This might hinder the accurate reproduction of the observed O3,
particularly on the days when advection and diffusion were strong. Since the
model mainly described the in situ photochemistry, it was validated through
comparison with the CHEM process simulated by WRF-CMAQ. The simulated
O3 production rates were output every hour, which were integrated
values over every 3600 s in 1 h (model resolution: 1 s). More details
about the model configuration can be found in Lam et al. (2013) and Lyu et al. (2017).
Results and discussionOverall characteristics of O3 pollution in Jinan
Figure 2 shows the time series of trace gases; OH reactivity of VOCs, CO and
NOx; and meteorological conditions on the VOC sampling days in Jinan
(trace gases in the whole sampling period are shown in Fig. S4). All the
OH reactivity values discussed in this study were calculated rather than
observed ones. The OH reactivity of VOCs was categorized into carbonyls,
biogenic VOCs (BVOCs), aromatics, alkenes and alkanes (Table S3 lists the
VOCs included in each group). The reaction rate constants between O3
precursors and OH in the calculation of OH reactivity were adopted from the MCM v3.2.
The average total OH reactivity on all the VOC sampling days
(19.4±2.1 s-1) was comparable to that reported in New York
(19±3 s-1, Ren et al., 2003), Houston (9–22 s-1, Mao et
al., 2010) and Beijing (15–27 s-1, Williams et al., 2016). Consistent
with previous studies in urban areas (Ren et al., 2003; Yang et al., 2016
and references therein), NOx was the largest contributor
(28.9±1.9 %) to the total OH reactivity. Noticeably, 20.5±4.1 % of the
total OH reactivity was attributable to BVOCs, which were much higher than
the contributions in urban areas (<10 %) reviewed by Yang et al. (2016).
The elevated isoprene levels (2.2±0.6 ppbv during episodes
and 0.9±0.3 ppbv during non-episodes) under high temperature (mean:
31 ∘C) explained the considerable contribution of BVOCs to the
total OH reactivity in this study.
The total OH reactivity of VOCs and CO (OH reactivityVOCs+CO) was
comparable between O3 episodes (14.8±2.0 s-1) and
non-episodes (12.2±3.0 s-1), so was the OH reactivity of
NOx (4.7±0.8 and 6.9±1.9 s-1 during
episodes and non-episodes, respectively). Taking the positive biases of
NO2 measurements into account (Sect. 2.2.1), we found that the OH
reactivity of NOx was overestimated by up to 17.5±1.1 %
and 5.4±0.7 % during O3 episodes and non-episodes,
respectively. In the case of maximum overestimation, the actual OH
reactivity of NOx during episodes (4.0±0.7 s-1) might be
lower (p<0.05) than that during non-episodes (6.6±1.9 s-1).
The high OH reactivity during non-episodes mainly occurred on
30 July and 1 August, due to the unfavorable meteorological conditions,
which are discussed later. Despite the comparable OH reactivity, we found
that the ratio of OHreactivityVOCs+COOHreactivityNOx
during O3 episodes (3.7±0.7 s-1 s1) was higher than during non-episodes
(2.0±0.4 s-1 s1) (p<0.05). The difference was likely even larger,
due to the more significant overestimation of NO2 during episodes. This
indicated that O3 formation was more limited by VOCs during
non-episodes than during episodes. Indeed, O3 formation in Jinan
switched from the VOC-limited regime during non-episodes to the transitional
regime during episodes (see Sect. 3.4.2). This partially explained the
build-up of O3 on episode days, because the transitional regime
features the highest O3 production rates.
From the aspect of meteorological conditions, O3 episodes had
relatively stronger solar radiation, higher temperature, lower relative
humidity and weaker winds (p<0.05). This is reasonable as O3
formation and accumulation are generally enhanced under these weather
conditions. In particular, the solar radiation on 30 July was much weaker
than that during O3 episodes, primarily accounting for the low O3
on this day. Figure S5 shows the COD retrieved from the Terra MODIS
(https://ladsweb.modaps.eosdis.nasa.gov/search/imageViewer/1/MOD06_L2–61/2017-08-06/DB/Site:142/2873994172–3, last access: 12 September 2018)
at 10:00–12:00 LT on all the
VOC sampling days. The Terra MODIS image revealed thick cloud cover with
high COD over Jinan on 30 July, which caused the weak solar radiation. The
influences of cloud cover/COD and solar radiation on O3 pollution
are further discussed in Sect. 3.2. Unlike our previous understanding that
O3 pollution is aggravated under high pressure (Chan and Chan, 2000;
Zhao et al., 2009), the sea-level pressure during O3 episodes
(993.4±0.2 hPa) was significantly lower than during non-episodes
(996.1±0.4 hPa) in this study (p<0.05). When O3 reached
its hourly maximum on 10 August (154.1 ppbv), the pressure was at its lowest
value (990.2 hPa). This discrepancy inspired us to look into the synoptic
and chemical processes in this continuous O3 pollution event.
Time series of trace gases, OH reactivity of O3 precursors
and meteorological parameters. Wind speed and wind direction were not monitored
from 17:00 LT on 5 August to 23:00 LT on 7 August due to malfunction of the
weather station. RH in the top panel denotes the relative humidity. RX in the bottom panel is
the OH reactivity of species/group X.
Synoptic processes and relationship with O3 pollution
Figure 3 displays the average weather charts at 14:00 LT during O3
episodes and non-episodes (weather charts on individual VOC sampling days
are shown in Fig. S6). Clearly, the temperature in Shandong province was
much higher during O3 episodes than non-episodes, which favored O3
formation on episode days. Additionally, southerly and southwesterly winds
originating from the inland areas (Hubei, Henan and Anhui provinces)
prevailed in central and western Shandong during O3 episodes. In
contrast, the winds were generally from the sea or coastal regions in
Jiangsu province during non-episodes. It is more likely that O3 and
O3 precursors were transported to Jinan during episodes. The high
concentrations of O3 precursors on 30 July and 1 August (non-episode
days) were mainly caused by the weather conditions (high pressure, low
temperature and low solar radiation), as discussed in Sect. 3.1. Further,
we also noticed that the winds changed direction from the southwest to the
northwest around Jinan during O3 episodes. This meant that there might
be a local circulation, hampering the dispersion of air pollutants during
episodes. It seemed that the change in wind direction was caused by the
convergence of continental air and sea breeze from Bohai Bay, similar to the
convergence zone formed over the Pearl River estuary in southern China (Fung et
al., 2005; Lo et al., 2006). Overall, the surface winds were more favorable
for regional transport and accumulation of air pollutants during O3
episodes. In addition, Shandong province was under the control of a uniform
pressure system with the sea-level pressure of 1000–1001 hPa during O3
episodes, implying the relatively stagnant weather.
Weather chart at 14:00 LT averaged over (a)O3
episodes and (b) non-episodes. The red star represents Jinan. The
dark black line is the boundary of Shandong province. Bohai Bay is located to
the northeast of Shandong province. Numbers in the figure are sea-level
pressures in units of hectopascal (hPa).
To better understand the relationship between O3 pollution and the
synoptic systems, Table 1 summarizes the synoptic systems, weather
conditions and air mass origins on all the VOC sampling days. The weather
charts at surface level and 500 hPa on 1, 4, 7, 10 and 13 August are
presented in Figs. S7 and S8, showing the evolution of the synoptic systems.
To identify the origins of air masses, the backward trajectories of air
masses are shown in Fig. 4. The trajectories were computed using the
Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model v4.9.
Each trajectory was calculated for 48 h and the calculation was
done every 6 h (four trajectories each day). Our sampling site
(36.68∘ N, 117.07∘ E) was set as the end point of the
trajectories with the height of 500 m a.s.l. The discrepancy between the
wind direction and origin of air masses, e.g., on 1 and 11 August, was likely due
to the air recirculation at the ground level.
It was found that Jinan was under the control of the WPSH on 20 July (weather chart on 500 hPa is not shown
here), and the air masses arriving in Jinan originated from southern China
(Fig. 4). As anticipated, the WPSH caused high temperatures and the
intensive solar radiation (Fig. 2), which was conducive to O3
formation. However, the winds on 20 July were the strongest in the entire
VOC sampling period, with the highest hourly wind speed of 3.9 m s-1. The
strong winds facilitated the dispersion of O3 and its precursors,
leading to low O3 levels on this day. The WPSH moved southward on the
following days and Jinan was controlled by a uniform pressure system, which
was formed in the peripheries of two low-pressure systems (two rain belts as
shown in Fig. 1), i.e., one over central China and another over
northern China
(Fig. S7). Thus, the pressure in Jinan was relatively high
(997.1±0.3 hPa), compared to the south and north regions. This synoptic system
lasted for several days until 7 August, covering 2 non-episode days and
4 O3 episode days. The low O3 on 2 non-episode days (30 July and
1 August) was mainly attributable to the weak solar radiation and low
temperature as discussed above.
In contrast, continuously strong solar radiations with low COD (Figs. 2
and S5), high temperature and continental air masses (Fig. 4) were
observed on 4–7 August. This, in addition to the shift in the O3 formation
mechanism (see Sects. 3.1 and 3.4.2), explained the prolonged O3
pollution event. On 10 August, the rain belt over northern China moved
southward, forming a deep low-pressure trough over the NCP, and Jinan was
behind the trough (Fig. S8d). The low-pressure trough is a typical
synoptic system conducive to O3 pollution, resulting from the intrusion
of O3 in the stratosphere and/or the upper troposphere (Chan and Chan,
2000). Moreover, there was nearly no cloud cover over the entire NCP on
10 August (Fig. S5). Consequently, the highest O3 (154.1 ppbv) in
this sampling campaign was observed. On 11 August, the low-pressure system
continued to extend to the Yellow Sea. O3 decreased substantially on
this day with the disappearance of the low-pressure trough and the weakening
of solar radiation, though the hourly maximum O3 mixing ratio still reached
100.4 ppbv. On the following days, the precipitations relieved the O3
pollution in Jinan.
Summary of the synoptic systems, weather conditions and air mass
origins on VOC sampling days.
DateMaximumEpisode/non-Synoptic systemAir mass originhourly O3episodeweather condition(ppbv)20 July71.0Non-episodeWPSH, strongContinental airsouthwesterly windsmasses from southernChina30 July57.6Uniform pressure fieldMarine air masses(weak high pressure),rain, fog, calm winds1 August90.6Uniform pressure field(weak high pressure),northeasterly winds4 August107.5EpisodeUniform pressure field(weak high pressure),northeasterly winds5 August128.2Uniform pressure fieldContinental air(weak high pressure),masses fromcalm windsShandong province6 August116.9Uniform pressure field(weak high pressure),southwesterly winds7 August126.9Uniform pressure fieldContinental air(weak high pressure),masses from thecalm windsnorth10 August154.1Low-pressure trough,Continental aircalm windsmasses from thewest11 August100.4Subtropical high,Continental airsoutheasterly windsmasses from thesouthwest
The 48 h backward trajectories calculated every 6 h, with
Jinan (36.68∘ N, 117.07∘ E; 500 m a.g.l.) as the ending
point. The trajectories are simulated by HYSPLIT v4.9. The water areas are
highlighted in blue.
O3 simulation and process analysis
The observations indicated the likely different regimes controlling local
O3 formation and the potential impacts of regional transport. To
understand the atmospheric chemistry and dynamics, as well as their roles in
this O3 pollution event, the WRF-CMAQ was applied. Figure 5 shows the
hourly average simulated and observed O3 on the VOC sampling days in
Jinan. Overall, the model reproduced the magnitudes and diurnal
patterns of the observed O3 well, except for the higher simulated O3 on
20 July and the under-prediction of O3 on 1, 7 and 10 August.
Discussions on the discrepancies and the model validation were provided in
Text S1, Figs. S9–S11 and Table S4.
Hourly average mixing ratios of the WRF-CMAQ simulated and observed
O3 in Jinan. The grey area shows the minimum and maximum simulated
O3 at the sampling site and eight adjoining grids (12km×12km
for each grid).
The IPR analysis quantified the O3 variation rates induced by different
processes, as shown in Fig. 6. HDIF and HADV were summed as horizontal
transport (HTRA), and the vertical transport (VTRA) was a total
representative of VDIF and VADV. It was found that chemical reactions
generally led to the decrease in the O3 mixing ratio during non-episodes.
The negative contributions of chemical reactions on 20 July coincided with
the very low concentrations of O3 precursors and the flat diurnal cycle
of O3 (Fig. 2). The chemical destruction to O3 on 30 July and
1 August was most likely related to the weak solar radiation and low
temperature, which inhibited the photochemical reactions. In fact, the
negative chemical effect should be considered as the titration of NO to the
regionally transported and/or background O3 and the depletion of
O3 by the freshly emitted NO near the sources (Beck and Grennfelt,
1994; Sillman, 1999). Conversely, the combined effect of horizontal and
vertical transport was to increase O3 levels during non-episodes.
Time series of O3 variation rate in Jinan induced by
individual processes calculated based on the change in O3 per hour.
Total transport is the sum of HTRA and VTRA, and the sum of O3
variation rates attributable to all the processes is represented by the total
O3 variation rate. The nighttime (18:00–06:00 LT) has been
highlighted in grey.
During O3 episodes, chemical reactions made positive contributions to
O3 production rates between 09:00 and 15:00 LT, with the average
hourly O3 production rate of 14.0±2.3 ppbv h-1. At the same time,
O3 was also elevated by transport at an average rate of
18.7±4.0 ppbv h-1, as a combined effect of vertical transport
(-40.8±20.2 ppbv h-1) and horizontal transport (59.5±19.8 ppbv h-1). The negative
contribution of vertical transport to O3 in these hours might be caused
by the updraft with the increase in temperature in the city. The positive
contributions of horizontal transport could be explained by the air masses
laden with O3 originating from the west and the north (Figs. 4 and S10).
The much-higher O3 over the NCP than in the surrounding
regions indicated that the NCP was an O3 source in this case. In fact,
the transport of O3 from the lower troposphere over the NCP to the free
troposphere and further to northeast China was also presented by Ding et al. (2009).
During 16:00–08:00 LT on O3 episode days, O3 was titrated and
chemically consumed at the rate of 49.4±6.3 ppbv h-1. This was
reasonable, because the fresh vehicular emissions in the morning and evening
rush hours consumed O3, particularly the irreversible titration of NO
to O3 in the absence of sunlight. The NO2 produced from the titration
reaction was carried over to the other places by air circulation and/or
oxidized to NO3 and N2O5, which could further react with
aerosol to form HNO3 and ClNO2 in the evening. Horizontal and
vertical transport dominated O3 sources, with the average positive
contribution of 5.7±7.0 and 54.5±9.6 ppbv h-1 during
16:00–08:00 LT on 4–11 August, respectively. The strong vertical transport
coincided with the downward winds in the evening, which brought the
high-altitude O3 to the ground, as indicated in Fig. S9. However, the
sources of O3 in the upper atmosphere were beyond the scope of this study.
Pathway contributions to O3 production and destruction rate
during episodes (a) and non-episodes (b). Contributions of
O3 precursor sources to the net O3 production rate during
episodes (c) and non-episodes (d).
Local O3 formation and controlPathway and source contributions to O3 production
The IPR analyses showed that chemical reactions served as an important
source of O3 on episode days in Jinan, particularly during
09:00–15:00 LT when O3 was at high levels. This process was further studied through
the simulation of the in situ photochemistry by PBM-MCM. It should be noted
that the simulations were based on the observed concentrations of O3
precursors, which could be influenced by both local and regional air. Caution
was required to extend the results to all the situations in Jinan,
because the regional effect was not always consistent. Table S5 lists the
production and destruction pathways of O3 (Thornton et al., 2002;
Monks, 2005; Kanaya et al., 2009). Briefly, the oxidation of NO by HO2
and RO2 produced NO2, which led to O3 formation following
NO2 photolysis (Reactions R2 and R4–R5 in Sect. 1). Therefore, the reactions
between NO and HO2/RO2 were considered as the production pathways
of O3. To account for O3 destruction, the reaction between O1(D)
and H2O denoted the photolysis of O3, and reactions of O3
with OH, HO2 and alkenes were also included. Furthermore, since
HNO3 was an important sink of NO2, the reaction between OH and
NO2 was treated to be destructive to O3. The titration of O3
by NO was not included in O3 destruction, because NO2 produced in
this reaction was not considered as a source of O3.
Contributions to VOCs, CO, NO, NO2 and the O3
production rate by the sources of O3 precursors averaged on the VOC
sampling days in Jinan (Unit: % unless otherwise specified).
SourceVOCs*CONONO2O3 production rate (ppbv h-1) O3Non-episodesepisodesGE125.7±3.629.9±2.130.9±2.422.2±2.41.8±0.61.0±0.3DE217.6±2.457.3±5.252.0±5.854.4±5.81.7±0.41.0±0.3BVOC6.1±2.60.0±1.70.0±2.80.0±2.31.2±0.50.2±0.1LPG314.7±2.02.2±1.19.1±1.64.7±0.90.8±0.50.1±0.1Solvent417.1±3.93.1±1.85.1±3.87.8±3.10.8±0.50.7±0.3PI518.8±3.17.4±1.92.9±1.810.9±2.51.0±0.3-0.1±0.1
VOCs*: VOCs applied in source apportionment (see Text S2).
1 Gasoline exhaust. 2 Diesel exhaust. 3 Liquefied petroleum gas (LPG) usage. 4 Solvent
usage. 5 Petrochemical industry.
Figure 7a and b show the average diurnal cycles of the simulated
contributions to O3 production rates of different pathways. Also shown
are the net O3 production rates simulated by PBM-MCM (O3 productionPBM-MCM),
those simulated by WRF-CMAQ (O3 productionCHEM) and those
calculated from the observed hourly O3 (O3 productionobs.). Overall,
O3 productionPBM-MCM and O3 productionobs. were on the same magnitudes,
especially during O3 episodes with more stagnant weather conditions.
This indicated that the PBM-MCM model reasonably reproduced the in situ
O3 photochemistry. Though obvious discrepancies existed between
O3 productionCHEM and O3 productionPBM-MCM, they agreed well with each other
during 10:00–15:00 LT on episode days, consistent with the finding that
chemical reactions made great contributions to O3 in these hours
(Fig. 6). The lower or even negative O3 productionCHEM resulted from the
titration of the regionally transported and/or local background O3 by
NO and the following depletion of NO2 through reaction with OH and/or
dispersion. Differently, PBM-MCM did not consider the transport of O3,
though the transport effect was partially represented by constraining the
model to the observed concentrations of O3 precursors. In addition, the
PBM-MCM was constructed by the observed air pollutants, which were already
subject to chemical reactions before being detected by the analytical
instruments. This meant that the reaction between NO and O3 from the
emission to the detection of NOx was not considered in PBM-MCM.
However, as an emission-based model, WRF-CMAQ performed better in describing
the reactions immediately after the emissions of air pollutants. Therefore,
the chemical destruction of O3 in the vicinity of NOx sources
also accounted for the aforementioned discrepancy. The obviously higher
reaction rates between NO and O3 simulated by WRF-CMAQ (Fig. S12)
confirmed our inferences.
During both O3 episodes and non-episodes, the reaction between
HO2 and NO dominated over RO2+ NO in O3 production, while the
O3 destruction was mainly attributable to the formation of HNO3,
the reaction between O3 and HO2, and photolysis of O3. The net
O3 production rate during O3 episodes (maximum: 21.3 ppbv h-1) was
much (p<0.05) higher than during non-episodes (maximum:
16.9 ppbv h-1), which partially explained the higher O3 on episode days. In
general, OH +NO2 serves as the chain-terminating reaction in
the VOC-limited regime of O3 formation, while the radical–radical reactions
take over the role in the NOx-limited regime (Finlayson-Pitts and Pitts Jr.,
1993; Kleinman, 2005). Here, we found that the ratio of total reaction rates
between HO2+RO2 and OH +NO2 substantially
increased from 0.2±0.1 during non-episodes to 1.0±0.3 during
O3 episodes (p<0.05). This suggested that O3 formation
during non-episodes was limited by VOCs, while it switched to being co-limited
by VOCs and NOx during O3 episodes in view of the equivalent role
of HO2+RO2 and OH +NO2 in
terminating the chain reactions.
Further, the contributions to the net O3 production rates of different
sources of O3 precursors were identified, as presented in Fig. 7c
and d. Text S2 and Fig. S13 illustrate the source apportionment of
O3 precursors and the simulations of the source-specific contributions
to O3 production rates. The results are presented in Table 2. Since the
source apportionment was performed for the ambient O3 precursors which
were already subject to atmospheric processes, such as dispersion,
deposition and chemical reactions, the results represented the source
contributions to the steady-state concentrations of O3 precursors
and the corresponding O3 production rates. It was found that gasoline
exhaust and diesel exhaust were the largest contributors to O3
production rates regardless of O3 episodes or non-episodes. Further,
the net O3 production rates attributable to gasoline exhaust (diesel
exhaust) increased from 1.0±0.3 ppbv h-1 (1.0±0.3 ppbv h-1)
during non-episodes to 1.8±0.6 ppbv h-1 (1.7±0.4 ppbv h-1)
during O3 episodes. This suggested that vehicular emissions played
critical roles in building up ground-level O3 in the O3 pollution
event. If carbonyls were taken into account, the contributions of vehicular
emissions to O3 production rates were even higher than the currently
simulated values, due to the abundances of carbonyls in vehicle exhausts
(Grosjean et al., 1990; Granby et al., 1997). In addition, the contributions
of the other sources to O3 production rates all increased during
O3 episodes except for solvent usage (p>0.05), as listed in
Table 2. It is not surprising to see the synchronous increases, because of
the stronger solar radiation and higher temperature during episodes.
Further insight into the percentage contributions (not shown here) found
that the contributions of BVOC, liquefied petroleum gas (LPG) usage and petrochemical industry to
O3 production rates increased substantially from 9.9±4.2 %,
4.3±1.4 % and -2.8±1.9 % during non-episodes to
19.2±4.3 %, 9.1±3.4 % and 12.1±3.1 % during
O3 episodes, respectively. The increased O3 production rates by
BVOCs could be explained by the increase in isoprene (episodes:
2.2±0.6 ppbv; non-episodes: 0.9±0.3 ppbv) under higher temperature and
stronger solar radiation during O3 episodes. The enhancement of O3
production rates driven by petrochemical industry on episode days was likely
associated with the dominance of continental air (Fig. 4) and the
extensive petrochemical industries in the NCP. For example, the mixing ratio
of styrene increased from 54.7±22.0 pptv during non-episodes to
162.3±44.7 pptv during O3 episodes. The reason for elevated
O3 production rates resulting from LPG usage during episodes was
unknown. It is worth noting that the source contributions to O3
production rates might have some uncertainties due to the limited number of
samples (54 samples) and O3 precursors (31 VOCs, CO, NO and NO2)
applied for source apportionment.
Isopleths of the net O3 production rate (ppbv h-1) at
12:00 LT as a function of OH reactivityVOCs# and OH
reactivityNOx. The red blocks and orange circles denote
the calculated OH reactivityVOCs# and OH reactivityNOx
values at 12:00 LT on O3 episode and non-episode days, respectively.
Each orange cross represents the OH reactivityVOCs# and
OH reactivityNOx at 12:00 LT in the scenario with the highest
O3 production rate at a given OH reactivityVOCs#.
The dashed orange line and dashed blue line divide O3 formation into
the VOC-limited regime, transitional regime and NOx-limited regime.
Line 1 (solid straight blue line): gasoline exhaust; line 2 (straight red line):
diesel exhaust; line 3 (straight green line): BVOCs; line 4 (straight pink line):
LPG usage; line 5 (solid straight orange line): solvent usage; line 6 (straight
light-blue line): petrochemical industry.
O3 control measures
Both WRF-CMAQ and PBM-MCM revealed the significant local O3 formation
in the O3 pollution event. The relationships between O3 and its
precursors needed to be clarified so that the science-based control
measures could be taken. Throughout the VOC sampling period, the OH
reactivity values of VOCs (OH reactivityVOCs) were within the range
of 33 %–123 % of the average OH reactivityVOCs during O3
episodes. For OH reactivity of NOx (OH reactivityNOx), the
range was 61 %–242 %. The O3 production rates were simulated in a set
of assumed scenarios with different OH reactivityVOCs and OH
reactivityNOx values. To include the OH reactivity of VOCs and NOx
on all the VOC sampling days, factors from 10 % to 140 % with the step
of 10 % were applied to the average diurnal profiles of VOCs and CO during
O3 episodes, while the factors ranged from 10 % to 300 % with the
step of 10 % for NOx. The initial concentrations of all the air
pollutants were also scaled by the factors, and the model was constrained to
these scaled concentrations every hour, except for O3. It should be
noted that the factors applied to CO were exactly the same as those applied
to VOCs; therefore we use VOCs# to represent the sum of VOCs and CO
hereafter. The 14 gradients of OH reactivityVOCs# values and
30 gradients of OH reactivityNOx values made up 420 scenarios.
Meteorological conditions were exactly the same for all the scenarios and
the clear sky was hypothesized. According to the simulations, the maximum
O3 production rates occurred at 12:00 LT. Thus, the simulated O3
production rates at 12:00 LT, as a function of percentages of OH
reactivityVOCs and OH reactivityNOx, are plotted in Fig. 8.
Text S3 describes the methods to define the regimes of O3 formation.
Overall, O3 formation was mainly limited by VOCs# during
non-episodes. However, it switched to being co-limited by VOCs# and
NOx (transitional regime) on episode days with the net O3
production rates being among the highest, except for 5 August when the strong sea
breeze diluted air pollutants in Jinan and/or intercepted the transport of
air pollutants from central China to Jinan (Fig. S6). In fact, the
sensitivity of O3 formation to NOx might be underemphasized due to
the positive biases of NO2 measurements (Lu et al., 2010). This effect
was expected to be more significant during episodes when the overestimation
of NO2 was more obvious. However, O3 formation was not likely only
limited by NOx even during O3 episodes because NO2 could not
be overestimated by more than 30 % according to our inferences (see
Sect. 2.2.1). Therefore, O3 formation was treated to be in the
transitional regime during episodes. This partially explained the increased
O3 during episodes in Jinan, given the higher O3 production rates
in the transitional regime (Fig. 8). Noticeably, the change in regimes
controlling O3 formation is consistent with that predicted by the
OHreactivityVOCs#OHreactivityNOx ratio and
the ratio of the reaction rates between HO2+RO2 and
OH +NO2.
The source apportionment of O3 precursors enabled us to calculate the
source-specific OH reactivityVOCs# and OH
reactivityNOx values. Accordingly, the variations in O3 production
rates induced by the reductions in source emissions are presented in
Fig. 8 (straight solid lines 1–6). The start point of the straight lines
corresponded to 100% of the total average OH reactivityVOCs#
and OH reactivityNOx during O3 episodes. The end points,
however, represented the OH reactivityVOCs# and OH
reactivityNOx with the complete removal of emissions from the
individual sources. Therefore, the differences of the O3 production
rates between the start point and end points were the source contributions
to the O3 production rates, while the lengths of the lines reflected
the contributions to the OH reactivity of the sources. Further, the
simulated O3 production rates on the lines 1–6, as a response
of reductions in source emissions, are extracted and plotted in Fig. S14.
Obviously, the highest efficiencies of O3 reduction could be achieved
by cutting diesel exhaust (0.58 ppbv h-1/10 % emission
reduction) and gasoline exhaust (0.47 ppbv h-1/10 %
emission reduction). In fact, the sensitivities of O3 production rates
to the vehicle exhausts might be somewhat underestimated, due to the
exclusion of carbonyls in the source apportionment. However, the reductions
of O3 production rates by cutting 10 % of vehicle exhausts were still
insignificant, compared to the overall maximum O3 production rate of
21.3 ppbv h-1 during O3 episodes. This indicated that, by only
restraining emissions from one to two sources, high percentages of emission
reductions were required to sufficiently reduce the overall O3
production rate. Otherwise, a combined effort should be made to control
the emissions of O3 precursors from the diverse sources. In particular,
it is essential to get rid of the transitional regime featuring high O3
production rates.
Implications
This study investigates the causes of a severe O3 pollution event
lasting for 8 consecutive days in the NCP, one of the most densely
populated regions in the world. Photochemical formation in the lower
troposphere of the NCP is demonstrated as the main source of O3, under
the control of weak high-pressure or low-pressure trough. Though the
emissions of NOx, an important precursor of O3, have been
significantly reduced in China since 2013 (Duncan et al., 2016; Liu et al.,
2017), O3 pollution is still severe or even worsening in the NCP, as
revealed in the present and also previous studies (Zhang et al., 2014; Sun
et al., 2016). The finding that O3 formation shifted from the VOC-limited
regime on relatively low O3 days to the transitional regime on O3
non-attainment days may elucidate the increase in O3, because O3
production rates in the transitional regime are the highest. It is
unrealistic to expect the continuously linear reduction in NOx
emissions in the NCP after the substantial decreases in emissions from
power plants and industries in recent years. In other words, restraining
VOC emissions is urgent for O3 abatement in the NCP. Another important
finding in this study is that the NCP served as an O3 source. This was
proposed by Ding et al. (2009), based on the aircraft measurement and
simulation of atmospheric dynamics. We confirm it through the ground-level
observation and the simulation of in situ photochemistry. It can be expected
that organic nitrates are also intensively formed in the NCP as byproducts
of O3 formation. In view of the fact that the NCP is located within the
midlatitude band of Northern Hemisphere under the dominance of westerlies,
we believe that O3 and organic nitrates formed in this region may be
transported over a long distance following the uplifting of air masses,
which has been confirmed to partially account for the enhancement of
background O3 in North America and even Europe (Derwent et al., 2015;
Lin et al., 2017). Therefore, the recent air pollution control measures
taken in China (including China's Clean Air Action Plan in force in 2013) (Zheng et al., 2018)
are still inadequate to ease the burden of global tropospheric O3 in a
short period. More effective action plans should be implemented for O3
abatement, with comprehensive thinking of atmospheric dynamics and chemistry.
Data availability
The data are accessible at https://drive.google.com/open?id=1_KeOxOuVsLY83xL74RtcRORsiiyIR 8FZ (Lyu, 2018).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-3025-2019-supplement.
Author contributions
The study was designed by the corresponding author, HG,
with the help of LH and YZ. FJ and HC provided valuable input on the characteristics
of meteorological conditions and emissions of air pollutants in the NCP.
XL collected the samples and managed the logistics in the field campaign, and
LX provided sufficient assistance in field sampling campaign. The solar
radiation data in Jinan were obtained from ZC. XL performed data analyses and wrote
the paper, while the chemical transport modeling was done by NW and YZ.
HG revised and finalized the paper for submission.
Acknowledgements
This study was supported by the National Key R & D Program of
China (2017YFC0212001); the Research Grants Council of the Hong Kong Special Administrative Region via
grants PolyU5154/13E, PolyU152052/14E, PolyU152052/16E, CRF/C5004-15E
and CRF/C5022-14G; the Collaborative Research program between the Beijing
University of Technology and the Hong Kong Polytechnic
University (PolyU) (4-ZZFW); the Hong Kong Polytechnic University PhD scholarships (project RTUP); and the
National Natural Science Foundation of China (no. 41675118). This study was
partly supported by the Hong Kong PolyU internal grant (G-YBUQ, 1-ZVJT and
1-BBW4). The valuable comments of the anonymous reviewers were highly appreciated.
Edited by: Steven Brown
Reviewed by: two anonymous referees
ReferencesAtkinson, R.: Atmospheric chemistry of VOCs and NOx, Atmos.
Environ., 34, 2063–2101, 2000.
Beck, J. P. and Grennfelt, P.: Estimate of ozone production and destruction
over northwestern Europe, Atmos. Environ., 28, 129–140, 1994.
Cai, C., Geng, F., Tie, X., Yu, Q., and An, J.: Characteristics and source
apportionment of VOCs measured in Shanghai, China, Atmos. Environ., 44, 5005–5014, 2010.
Carter, W. P.: Development of ozone reactivity scales for volatile organic
compounds, Air Waste Manage. Assoc., 44, 881–899, 1994.
Carter, W. P., Pierce, J. A., Luo, D., and Malkina, I. L.: Environmental chamber
study of maximum incremental reactivities of volatile organic compounds, Atmos.
Environ., 29, 2499–2511, 1995.
Chameides, W. and Walker, J. C.: A photochemical theory of tropospheric ozone,
J. Geophys. Res., 78, 8751–8760, 1973.
Chan, C. Y. and Chan, L. Y.: Effect of meteorology and air pollutant transport
on ozone episodes at a subtropical coastal Asian city, Hong Kong, J. Geophys.
Res.-Atmos., 105, 20707–20724, 2000.Chen, Y., Zhao, C., Zhang, Q., Deng, Z., Huang, M., and Ma, X.: Aircraft study
of mountain chimney effect of Beijing, China, J. Geophys. Res.-Atmos., 114,
D08306, 10.1029/2008JD010610, 2009.
Cheng, H., Guo, H., Wang, X., Saunders, S. M., Lam, S. H. M., Jiang, F., Wang,
T., Ding, A., Lee, S., and Ho, K. F.: On the relationship between ozone and
its precursors in the Pearl River Delta: application of an observation-based
model (OBM), Environ. Sci. Pollut. Res., 17, 547–560, 2010.
Colman, J. J., Swanson, A. L., Meinardi, S., Sive, B. C., Blake, D. R., and
Rowland, F. S.: Description of the analysis of a wide range of volatile organic
compounds in whole air samples collected during PEM-Tropics A and B, Anal. Chem.,
73, 3723–3731, 2001.Cooper, O. R., Stohl, A., Hübler, G., Hsie, E. Y., Parrish, D. D., Tuck,
A. F., Kiladis, G. N., Oltmans, S. J., Johnson, B. J., Shapiro, M., and Moody,
J. L.: Direct transport of midlatitude stratospheric ozone into the lower
troposphere and marine boundary layer of the tropical Pacific Ocean, J. Geophys.
Res.-Atmos., 110, D23310, 10.1029/2005JD005783, 2005.
Crutzen, P.: A discussion of the chemistry of some minor constituents in the
stratosphere and troposphere, Pure Appl. Geophys., 106, 1385–1399, 1973.
Derwent, R. G., Utembe, S. R., Jenkin, M. E., and Shallcross, D. E.: Tropospheric
ozone production regions and the intercontinental origins of surface ozone over
Europe, Atmos. Environ., 112, 216–224, 2015.Ding, A., Wang, T., Xue, L., Gao, J., Stohl, A., Lei, H., Jin, D., Ren, Y.,
Wang, X., Wei, X., and Qi, Y.: Transport of north China air pollution by
midlatitude cyclones: Case study of aircraft measurements in summer 2007, J.
Geophys. Res.-Atmos., 114, D08304, 10.1029/2008JD011023, 2009.Ding, A. J., Fu, C. B., Yang, X. Q., Sun, J. N., Zheng, L. F., Xie, Y. N.,
Herrmann, E., Nie, W., Petäjä, T., Kerminen, V.-M., and Kulmala, M.:
Ozone and fine particle in the western Yangtze River Delta: an overview of
1 yr data at the SORPES station, Atmos. Chem. Phys., 13, 5813–5830,
10.5194/acp-13-5813-2013, 2013.Duncan, B. N., Lamsal, L. N., Thompson, A. M., Yoshida, Y., Lu, Z., Streets, D.
G., Hurwitz, M. M., and Pickering, K.E.: A space-based, high-resolution view of
notable changes in urban NOx pollution around the world (2005–2014),
J. Geophys. Res.-Atmos., 121, 976–996, 2016.Dunlea, E. J., Herndon, S. C., Nelson, D. D., Volkamer, R. M., San Martini, F.,
Sheehy, P. M., Zahniser, M. S., Shorter, J. H., Wormhoudt, J. C., Lamb, B. K.,
Allwine, E. J., Gaffney, J. S., Marley, N. A., Grutter, M., Marquez, C., Blanco,
S., Cardenas, B., Retama, A., Ramos Villegas, C. R., Kolb, C. E., Molina, L. T.,
and Molina, M. J.: Evaluation of nitrogen dioxide chemiluminescence monitors in
a polluted urban environment, Atmos. Chem. Phys., 7, 2691–2704, 10.5194/acp-7-2691-2007, 2007.
Finlayson-Pitts, B. J. and Pitts Jr., J. N.: Atmospheric chemistry of
tropospheric ozone formation: scientific and regulatory implications, Air
Waste Manage. Assoc., 43, 1091–1100, 1993.Fung, J. C. H., Lau, A. K. H., Lam, J. S. L., and Yuan, Z.: Observational and
modeling analysis of a severe air pollution episode in western Hong Kong, J.
Geophys. Res.-Atmos., 110, D09105, 10.1029/2004JD005105, 2005.
Gao, J., Wang, T., Ding, A., and Liu, C.: Observational study of ozone and
carbon monoxide at the summit of mount Tai (1534 m asl) in central-eastern
China, Atmos. Environ., 39, 4779–4791, 2005.
Granby, K., Christensen, C. S., and Lohse, C.: Urban and semi-rural observations
of carboxylic acids and carbonyls, Atmos. Environ., 31, 1403–1415, 1997.
Grosjean, D., Miguel, A. H., and Tavares, T. M.: Urban air pollution in Brazil:
Acetaldehyde and other carbonyls, Atmos. Environ., 24, 101–106, 1990.Gu, D., Wang, Y., Smeltzer, C., and Boersma, K. F.: Anthropogenic emissions of
NOx over China: Reconciling the difference of inverse modeling
results using GOME-2 and OMI measurements, J. Geophys. Res.-Atmos., 119, 7732–7740, 2014.Guo, H., Ling, Z. H., Cheung, K., Jiang, F., Wang, D. W., Simpson, I. J.,
Barletta, B., Meinardi, S., Wang, T. J., Wang, X. M., Saunders, S. M., and
Blake, D. R.: Characterization of photochemical pollution at different elevations
in mountainous areas in Hong Kong, Atmos. Chem. Phys., 13, 3881–3898,
10.5194/acp-13-3881-2013, 2013.Han, X., Zhu, L., Wang, S., Meng, X., Zhang, M., and Hu, J.: Modeling study of
impacts on surface ozone of regional transport and emissions reductions over
North China Plain in summer 2015, Atmos. Chem. Phys., 18, 12207–12221,
10.5194/acp-18-12207-2018, 2018.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, 2012.
He, K.: Multi-resolution Emission Inventory for China (MEIC): model framework
and 1990–2010 anthropogenic emissions, in: AGU Fall Meeting Abstracts,
3–7 December 2012, San Francisco, 2012.Huang, J. P., Fung, J. C., Lau, A. K., and Qin, Y.: Numerical simulation and
process analysis of typhoon-related ozone episodes in Hong Kong, J. Geophys.
Res.-Atmos., 110, D05301, 10.1029/2004JD004914, 2005.
Jenkin, M. E. and Clemitshaw, K. C.: Ozone and other secondary photochemical
pollutants: chemical processes governing their formation in the planetary
boundary layer, Atmos. Environ., 34, 2499–2527, 2000.
Jenkin, M. E., Saunders, S. M., and Pilling, M. J.: The tropospheric degradation
of volatile organic compounds: a protocol for mechanism development, Atmos.
Environ., 31, 81–104, 1997.Jiang, F., Guo, H., Wang, T. J., Cheng, H. R., Wang, X. M., Simpson, I. J.,
Ding, A. J., Saunders, S. M., Lam, S. H. M., and Blake, D. R.: An ozone episode
in the Pearl River Delta: Field observation and model simulation, J. Geophys.
Res.-Atmos., 115, D22305, 10.1029/2009JD013583, 2010.Jin, X., Fiore, A. M., Murray, L. T., Valin, L. C., Lamsal, L. N., Duncan, B.,
Folkert Boersma, K., De Smedt, I., Abad, G. G., Chance, K., and Tonnesen, G. S.:
Evaluating a Space-Based Indicator of Surface Ozone-NOx-VOC
Sensitivity Over Midlatitude Source Regions and Application to Decadal Trends,
J. Geophys. Res.-Atmos., 122, 10439–10461, 2017.Kanaya, Y., Pochanart, P., Liu, Y., Li, J., Tanimoto, H., Kato, S., Suthawaree,
J., Inomata, S., Taketani, F., Okuzawa, K., Kawamura, K., Akimoto, H., and Wang,
Z. F.: Rates and regimes of photochemical ozone production over Central East
China in June 2006: a box model analysis using comprehensive measurements of
ozone precursors, Atmos. Chem. Phys., 9, 7711–7723, 10.5194/acp-9-7711-2009, 2009.
Kleinman, L. I.: The dependence of tropospheric ozone production rate on ozone
precursors, Atmos. Environ., 3, 575–586, 2005.
Kusaka, H. and Kimura, F.: Coupling a single-layer urban canopy model with a
simple atmospheric model: Impact on urban heat island simulation for an
idealized case, J. Meteorol. Soc. Jpn. Ser. II, 82, 67–80, 2004.
Lam, S. H. M., Saunders, S. M., Guo, H., Ling, Z. H., Jiang, F., Wang, X. M.,
and Wang, T. J.: Modelling VOC source impacts on high ozone episode days
observed at a mountain summit in Hong Kong under the influence of mountain-valley
breezes, Atmos. Environ., 81, 166–176, 2013.Lang, J., Zhang, Y., Zhou, Y., Cheng, S., Chen, D., Guo, X., Chen, S., Li, X.,
Xing, X., and Wang, H.: Trends of PM2.5 and chemical composition in Beijing,
2000–2015, Aerosol Air Qual. Res., 17, 412–425, 2017.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, 2017.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.Lin, M., Horowitz, L. W., Payton, R., Fiore, A. M., and Tonnesen, G.: US surface
ozone trends and extremes from 1980 to 2014: quantifying the roles of rising
Asian emissions, domestic controls, wildfires, and climate, Atmos. Chem. Phys.,
17, 2943–2970, 10.5194/acp-17-2943-2017, 2017.Lin, W., Xu, X., Zhang, X., and Tang, J.: Contributions of pollutants from
North China Plain to surface ozone at the Shangdianzi GAW Station, Atmos. Chem.
Phys., 8, 5889–5898, 10.5194/acp-8-5889-2008, 2008.Liu, F., Beirle, S., Zhang, Q., van der A, R. J., Zheng, B., Tong, D., and He,
K.: NOx emission trends over Chinese cities estimated from OMI
observations during 2005 to 2015, Atmos. Chem. Phys., 17, 9261–9275,
10.5194/acp-17-9261-2017, 2017.
Liu, X. H., Zhang, Y., Xing, J., Zhang, Q., Wang, K., Streets, D. G., Jang, C.,
Wang, W. X., and Hao, J. M.: Understanding of regional air pollution over China
using CMAQ, part II. Process analysis and sensitivity of ozone and particulate
matter to precursor emissions, Atmos. Environ., 44, 3719–3727, 2010.Lo, J. C., Lau, A. K., Fung, J. C., and Chen, F.: Investigation of enhanced
cross-city transport and trapping of air pollutants by coastal and urban
land-sea breeze circulations, J. Geophys. Res.-Atmos., 111, D14104,
10.1029/2005JD006837, 2006.Lu, K., Zhang, Y., Su, H., Brauers, T., Chou, C. C., Hofzumahaus, A., Liu, S.
C., Kita, K., Kondo, Y., Shao, M., and Wahner, A.: Oxidant (O3+NO2)
production processes and formation regimes in Beijing, J. Geophys. Res.-Atmos.,
115, D07303, 10.1029/2009JD012714, 2010.Lyu, X.: Data for the paper “Causes of a continuous summertime O3
pollution event in Jinan, a central city in the North China Plain”, Google Drive,
available at: https://drive.google.com/open?id=1_KeOxOuVsLY83xL74RtcRORsiiyIR8FZ, 2018.
Lyu, X. P., Chen, N., Guo, H., Zhang, W. H., Wang, N., Wang, Y., and Liu, M.:
Ambient volatile organic compounds and their effect on ozone production in
Wuhan, central China, Sci. Total Environ., 541, 200–209, 2016.Lyu, X. P., Guo, H., Wang, N., Simpson, I. J., Cheng, H. R., Zeng, L. W.,
Saunders, S. M., Lam, S. H. M., Meinardi, S., and Blake, D. R.: Modeling
C1–C4 alkyl nitrate photochemistry and their impacts on O3
production in urban and suburban environments of Hong Kong, J. Geophys.
Res.-Atmos., 122, 10539–10556, 2017.
Madronich, S. and Flocke, S.: Theoretical estimation of biologically effective
UV radiation at the Earth's surface, in: Solar Ultraviolet Radiation, Springer,
Berlin, Heidelberg, 23–48, 1997.
Mao, J., Ren, X., Chen, S., Brune, W. H., Chen, Z., Martinez, M., Harder, H.,
Lefer, B., Rappenglueck, B., Flynn, J., and Leuchner, M.: Atmospheric oxidation
capacity in the summer of Houston 2006: Comparison with summer measurements in
other metropolitan studies, Atmos. Environ., 44, 4107–4115, 2010.McClenny, W. A., Williams, E. J., Cohen, R. C., and Stutz, J.: Preparing to
measure the effects of the NOx SIP Call – methods for ambient
air monitoring of NO, NO2, NOy, and individual
NOz species, Air Waste Manage. Assoc., 52, 542–562, 2002.
Monks, P. S.: Gas-phase radical chemistry in the troposphere, Chem. Soc. Rev.,
34, 376–395, 2005.Ren, X., Harder, H., Martinez, M., Lesher, R. L., Oliger, A., Simpas, J. B.,
Brune, W. H., Schwab, J. J., Demerjian, K. L., He, Y., and Zhou, X.: OH and
HO2 chemistry in the urban atmosphere of New York City, Atmos. Environ.,
37, 3639–3651, 2003.Saunders, S. M., Jenkin, M. E., Derwent, R. G., and Pilling, M. J.: Protocol
for the development of the Master Chemical Mechanism, MCM v3 (Part A):
tropospheric degradation of non-aromatic volatile organic compounds, Atmos.
Chem. Phys., 3, 161–180, 10.5194/acp-3-161-2003, 2003.
Shan, W., Yin, Y., Zhang, J., and Ding, Y.: Observational study of surface
ozone at an urban site in East China, Atmos. Res., 89, 252–261, 2008.Shao, M., Lu, S., Liu, Y., Xie, X., Chang, C., Huang, S., and Chen, Z.: Volatile
organic compounds measured in summer in Beijing and their role in ground-level
ozone formation, J. Geophys. Res.-Atmos., 114, D00G06, 10.1029/2008JD010863, 2009.Shu, L., Xie, M., Wang, T., Gao, D., Chen, P., Han, Y., Li, S., Zhuang, B., and
Li, M.: Integrated studies of a regional ozone pollution synthetically affected
by subtropical high and typhoon system in the Yangtze River Delta region, China,
Atmos. Chem. Phys., 16, 15801–15819, 10.5194/acp-16-15801-2016, 2016.Sillman, S.: The relation between ozone, NOx and hydrocarbons
in urban and polluted rural environments, Atmos. Environ., 33, 1821–1845, 1999.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.Tan, Z., Lu, K., Dong, H., Hu, M., Li, X., Liu, Y., Lu, S., Shao, M., Su, R.,
Wang, H., and Wu, Y.: Explicit diagnosis of the local ozone production rate and
the ozone-NOx-VOC sensitivities, Sci. Bull., 63, 1067–1076, 2018a.Tan, Z., Lu, K., Jiang, M., Su, R., Dong, H., Zeng, L., Xie, S., Tan, Q., and
Zhang, Y.: Exploring ozone pollution in Chengdu, southwestern China: A case
study from radical chemistry to O3-VOC-NOx sensitivity,
Sci. Total Environ., 636, 775–786, 2018b.Thornton, J. A., Wooldridge, P. J., Cohen, R. C., Martinez, M., Harder, H.,
Brune, W. H., Williams, E. J., Roberts, J. M., Fehsenfeld, F. C., Hall, S. R.,
and Shetter, R. E.: Ozone production rates as a function of NOx
abundances and HOx production rates in the Nashville urban plume, J.
Geophys. Res.-Atmos., 107, 4146, 10.1029/2001JD000932, 2002.
Wang, N., Guo, H., Jiang, F., Ling, Z. H., and Wang, T.: Simulation of ozone
formation at different elevations in mountainous area of Hong Kong using
WRF-CMAQ model, Sci. Total Environ., 505, 939–951, 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., Nie, W., Gao, J., Xue, L. K., Gao, X. M., Wang, X. F., Qiu, J., Poon,
C. N., Meinardi, S., Blake, D., Wang, S. L., Ding, A. J., Chai, F. H., Zhang,
Q. Z., and Wang, W. X.: Air quality during the 2008 Beijing Olympics: secondary
pollutants and regional impact, Atmos. Chem. Phys., 10, 7603–7615,
10.5194/acp-10-7603-2010, 2010.
Wang, X. M., Lin, W. S., Yang, L. M., Deng, R. R., and Lin, H.: A numerical
study of influences of urban land-use change on ozone distribution over the
Pearl River Delta region, China, Tellus B, 59, 633–641, 2007.
Wang, Z., Li, Y., Chen, T., Zhang, D., Sun, F., Wei, Q., Dong, X., Sun, R.,
Huan, N., and Pan, L.: Ground-level ozone in urban Beijing over a 1-year
period: Temporal variations and relationship to atmospheric oxidation, Atmos.
Res., 164, 110–117, 2015.
Whitten, G. Z., Heo, G., Kimura, Y., McDonald-Buller, E., Allen, D. T., Carter,
W. P., and Yarwood, G.: A new condensed toluene mechanism for Carbon Bond:
CB05-TU, Atmos. Environ., 44, 5346–5355, 2010.
Williams, J., Keßel, S. U., Nölscher, A. C., Yang, Y., Lee, Y.,
Yáñez-Serrano, A. M., Wolff, S., Kesselmeier, J., Klüpfel, T.,
Lelieveld, J., and Shao, M.: Opposite OH reactivity and ozone cycles in the
Amazon rainforest and megacity Beijing: Subversion of biospheric oxidant control
by anthropogenic emissions, Atmos. Environ., 125, 112–118, 2016.Xing, C., Liu, C., Wang, S., Chan, K. L., Gao, Y., Huang, X., Su, W., Zhang, C.,
Dong, Y., Fan, G., Zhang, T., Chen, Z., Hu, Q., Su, H., Xie, Z., and Liu, J.:
Observations of the vertical distributions of summertime atmospheric pollutants
and the corresponding ozone production in Shanghai, China, Atmos. Chem. Phys.,
17, 14275–14289, 10.5194/acp-17-14275-2017, 2017.Xing, J., Ding, D., Wang, S., Zhao, B., Jang, C., Wu, W., Zhang, F., Zhu, Y.,
and Hao, J.: Quantification of the enhanced effectiveness of NOx
control from simultaneous reductions of VOC and NH3 for reducing air
pollution in the Beijing–Tianjin–Hebei region, China, Atmos. Chem. Phys., 18,
7799–7814, 10.5194/acp-18-7799-2018, 2018.
Xu, Z., Wang, T., Xue, L. K., Louie, P. K., Luk, C. W., 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, 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.
Yang, Y., Shao, M., Wang, X., Nolscher, A. C., Kessel, S., Guenther, A., and
Williams, J.: Towards a quantitative understanding of total OH reactivity: A
review, Atmos. Environ., 134, 147–161, 2016.Ye, L., Wang, X., Fan, S., Chen, W., Chang, M., Zhou, S., Wu, Z., and Fan, Q.:
Photochemical indicators of ozone sensitivity: application in the Pearl River
Delta, China, Front. Environ. Sci. Eng., 10, 15, 10.1007/s11783-016-0887-1, 2016.
Yin, Y., Lu, H., Shan, W., and Zheng, Y.: Analysis of observed ozone episode
in urban Jinan, China, Bulletin Environ, Contam. Toxicol., 83, 159–163, 2009.Zhang, Q., Streets, D. G., He, K., Wang, Y., Richter, A., Burrows, J. P., Uno,
I., Jang, C. J., Chen, D., Yao, Z., and Lei, Y.: NOx emission
trends for China, 1995–2004: The view from the ground and the view from space,
J. Geophys. Res.-Atmos., 112, D22306, 10.1029/2007JD008684, 2007.Zhang, Q., Streets, D. G., Carmichael, G. R., He, K. B., Huo, H., Kannari, A.,
limont, Z., Park, I. S., Reddy, S., Fu, J. S., Chen, D., Duan, L., Lei, Y.,
Wang, L. T., and Yao, Z. L.: Asian emissions in 2006 for the NASA INTEX-B
mission, Atmos. Chem. Phys., 9, 5131–5153, 10.5194/acp-9-5131-2009, 2009.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, 2014.
Zhang, Y., Ding, A., Mao, H., Nie, W., Zhou, D., Liu, L., Huang, X., and Fu, C.:
Impact of synoptic weather patterns and inter-decadal climate variability on
air quality in the North China Plain during 1980–2013, Atmos. Environ.,
124, 119–128, 2016.Zhang, Z., Zhang, X., Gong, D., Quan, W., Zhao, X., Ma, Z., and Kim, S. J.:
Evolution of surface O3 and PM2.5 concentrations and their
relationships with meteorological conditions over the last decade in Beijing,
Atmos. Environ., 108, 67–75, 2015.
Zhao, C., Wang, Y., and Zeng, T.: East China plains: A “basin” of ozone
pollution, Environ. Sci. Technol., 43, 1911–1915, 2009.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.Zong, R., Yang, X., Wen, L., Xu, C., Zhu, Y., Chen, T., Yao, L., Wang, L.,
Zhang, J., Yang, L., Wang, X., Shao, M., Zhu, T., Xue, L., and Wang, W.: Strong
ozone production at a rural site in the North China Plain: Mixed effects of
urban plumes and biogenic emissions, J. Environ. Sci., 71, 261–270,
10.1016/j.jes.2018.05.003, 2018.