ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-14569-2018Ozone seasonal evolution and photochemical production regime in the polluted
troposphere in eastern China derived from high-resolution Fourier transform spectrometry (FTS) observationsOzone seasonal evolution and photochemical productionSunYouwenLiuChengchliu81@ustc.edu.cnhttps://orcid.org/0000-0002-3759-9219PalmMathiashttps://orcid.org/0000-0001-7191-6911VigourouxCorinneNotholtJustusHuQihouJonesNicholasWangWeiSuWenjingZhangWenqiangShanChangongTianYuanhttps://orcid.org/0000-0002-4249-9220XuXingweiDe MazièreMartineZhouMinqiangLiuJianguohttps://orcid.org/0000-0002-7051-4272Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, ChinaSchool of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, ChinaCenter for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, ChinaAnhui Province Key Laboratory of Polar Environment and Global Change, USTC, Hefei, 230026, ChinaUniversity of Bremen, Institute of Environmental Physics, P.O. Box 330440, 28334 Bremen, GermanyRoyal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, BelgiumSchool of Chemistry, University of Wollongong, Northfields Ave, Wollongong, NSW, 2522, Australia Cheng Liu (chliu81@ustc.edu.cn)11October20181819145691458314December201718December201724September201824September2018This 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/18/14569/2018/acp-18-14569-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/14569/2018/acp-18-14569-2018.pdf
The seasonal evolution of O3 and its photochemical
production regime in a polluted region of eastern China between 2014 and 2017
has been investigated using observations. We used tropospheric ozone
(O3), carbon monoxide (CO), and formaldehyde (HCHO, a marker of VOCs
(volatile organic compounds)) partial columns derived from high-resolution
Fourier transform spectrometry (FTS); tropospheric nitrogen dioxide
(NO2, a marker of NOx (nitrogen oxides)) partial
column deduced from the Ozone Monitoring Instrument (OMI); surface meteorological
data; and a back trajectory cluster analysis technique. A broad O3
maximum during both spring and summer (MAM/JJA) is observed; the day-to-day
variations in MAM/JJA are generally larger than those in autumn and winter
(SON/DJF). Tropospheric O3 columns in June are 1.55×1018 molecules cm-2 (56 DU (Dobson units)), and in December they are
1.05×1018 molecules cm-2 (39 DU). Tropospheric O3
columns in June were ∼50 % higher than those in December. Compared
with the SON/DJF season, the observed tropospheric O3 levels in MAM/JJA
are more influenced by the transport of air masses from densely populated and
industrialized areas, and the high O3 level and variability in
MAM/JJA is determined by the photochemical O3 production. The
tropospheric-column HCHO/NO2 ratio is used as a proxy to
investigate the photochemical O3 production rate (PO3).
The results show that the PO3 is mainly nitrogen oxide (NOx) limited in MAM/JJA, while it is mainly VOC or mixed VOC–NOx limited in SON/DJF. Statistics show that
NOx-limited, mixed VOC–NOx-limited, and VOC-limited PO3 accounts for 60.1 %, 28.7 %, and 11 % of days,
respectively. Considering most of PO3 is NOx
limited or mixed VOC–NOx limited, reductions in
NOx would reduce O3 pollution in eastern China.
Introduction
Human health, terrestrial ecosystems, and material degradation are impacted
by poor air quality resulting from high photochemical ozone (O3)
levels (Wennberg and Dabdub, 2008; Edwards et al., 2013; Schroeder et al.,
2017). In polluted areas, tropospheric O3 is generated from a series
of complex reactions in the presence of sunlight involving carbon monoxide
(CO), nitrogen oxides (NOx≡NO (nitric oxide)
+NO2 (nitrogen dioxide)), and volatile organic compounds (VOCs)
(Oltmans et al., 2006; Schroeder et al., 2017). Briefly, VOCs first react
with the hydroxyl radical (OH) to form a peroxy radical (HO2+RO2), which increases the rate of catalytic cycling of NO to
NO2. O3 is then produced by photolysis of NO2.
Subsequent reactions between HO2 or RO2 and NO lead to
radical propagation (via subsequent reformation of OH). Radical termination
proceeds via the reaction of OH with NOx to form nitric acid
(HNO3) (Reaction R1, referred to as
LNOx) or by radical–radical
reactions resulting in stable peroxide formation (Reactions R2–R4,
referred to as LROx, where
ROx≡RO2+HO2) (Schroeder et al., 2017):
OH+NO2→HNO3,2HO2→H2O2+O2,HO2+RO2→ROOH+O2,2RO2→ROOR+O2.
Typically, the relationship between these two competing radical termination
processes (referred to as the ratio LROx/LNOx) can be used to
evaluate the photochemical regime. In high-radical, low-NOx
environments, Reactions (R2)–(R4) remove radicals at a faster rate than
Reaction (R1) (i.e., LROx≫LNOx), and
the photochemical regime is regarded as “NOx limited”. In
low-radical, high-NOx environments the opposite is true
(i.e., LROx≪LNOx), and the regime is
regarded as “VOC limited”. When the rates of the two loss processes are
comparable (LNOx≈LROx), the
regime is said to be at the photochemical transition/ambiguous point, i.e.,
mixed VOC–NOx limited (Kleinman et al., 2005; Sillman et al.,
1995a; Schroeder et al., 2017).
Understanding the photochemical regime at local scales is a crucial piece of
information for enacting effective policies to mitigate O3
pollution (Jin et al., 2017; Schroeder et al., 2017). In order to determine
the regime, the total reactivity with OH of the myriad of VOCs in the
polluted area has to be estimated (Sillman, 1995a; Xing et al., 2017). In the
absence of such information, the formaldehyde (HCHO) concentration can be
used as a proxy for VOC reactivity because it is a short-lived oxidation
product of many VOCs and is positively correlated with peroxy radicals
(Schroeder et al., 2017). Sillman (1995a) and Tonnesen and Dennis (2000)
found that in situ measurements of the ratio of HCHO (a marker of VOCs) to
NO2 (a marker of NOx) could be used to diagnose
local photochemical regimes. Over polluted areas, both HCHO and tropospheric
NO2 have vertical distributions that are heavily weighted toward
the lower troposphere, indicating that tropospheric-column measurements of
these gases are fairly representative of near-surface conditions. Many
studies have taken advantage of these favorable vertical distributions to
investigate surface emissions of NOx and VOCs from space
(Boersma et al., 2009; Martin et al., 2004a; Millet et al., 2008; Streets et
al., 2013). Martin et al. (2004a) and Duncan et al. (2010) used satellite
measurements of the column HCHO/NO2 ratio to explore tropospheric
O3 sensitivities from space and disclosed that this diagnosis of
O3 production rate (PO3) is consistent with previous
findings of surface photochemistry. Witte et al. (2011) used a similar
technique to estimate changes in PO3 from the strict emission control
measures (ECMs) during the Beijing Summer Olympic Games period in 2008. Recent
papers have applied the findings of Duncan et al. (2010) to observe
O3 sensitivity in other parts of the world (Choi et al., 2012;
Witte et al., 2011; Jin and Holloway, 2015; Mahajan et al., 2015; Jin et al.,
2017).
With in situ measurements, Tonnesen and Dennis (2000) observed a
radical-limited environment with HCHO/NO2 ratios <0.8, an NOx-limited environment with HCHO/NO2 ratios
>1.8, and a transition environment with HCHO/NO2 ratios
between 0.8 and 1.8. With 3-D chemical model simulations, Sillman (1995a) and
Martin et al. (2004b) estimated that the transition between the VOC- and
NOx-limited regimes occurs when the HCHO/NO2 ratio
is ∼1.0. With a combination of regional chemical model simulations and
the Ozone Monitoring Instrument (OMI) measurements, Duncan et al. (2010)
concluded that O3 production decreases with reductions in VOCs at a column HCHO/NO2 ratio <1.0 and NOx at
column HCHO/NO2 ratio >2.0; both NOx
and VOC reductions decrease O3 production when the column
HCHO/NO2 ratio lies in between 1.0 and 2.0. With a 0-D
photochemical box model and airborne measurements, Schroeder et al. (2017)
presented a thorough analysis of the utility of column HCHO/NO2
ratios to indicate surface O3 sensitivity and found that the
transition/ambiguous range estimated via column data is much larger than that
indicated by in situ data alone. Furthermore, Schroeder et al. (2017)
concluded that many additional sources of uncertainty (regional variability,
seasonal variability, variable free-tropospheric contributions, retrieval
uncertainty, air pollution levels and meteorological conditions) may cause
the transition threshold vary both geographically and temporally, and thus the
results from one region are not likely to be applicable globally.
With the rapid increase in fossil fuel consumption in China over the past
3 decades, the emission of chemical precursors of O3
(NOx and VOCs) has increased dramatically, surpassing that
of North America and Europe and raising concerns about worsening O3
pollution in China (Tang et al., 2012; Wang et al., 2017; Xing et al., 2017).
Tropospheric O3 was already included in the new air quality
standard as a routine monitoring component (http://www.mep.gov.cn; last
access: 23 May 2018), where the limit for the maximum daily 8 h average
(MDA8) O3 in urban and industrial areas is 160 µg m-3
(∼75 ppbv at 273 K, 101.3 kPa). According to air quality data released by
the Chinese Ministry of Environmental Protection, tropospheric O3
has replaced PM2.5 as the primary pollutant in many cities during summer
(http://www.mep.gov.cn/; last access: 23 May 2018). A precise knowledge of
O3 evolution and photochemical production regime in the polluted
troposphere in China has important policy implications for O3
pollution controls (Tang et al., 2011; Xing et al., 2017; Wang et al., 2017).
In this study, we investigate the O3 seasonal evolution and
photochemical production regime in the polluted troposphere in eastern China
with tropospheric O3, CO, and HCHO derived from ground-based high-resolution Fourier transform spectrometry (FTS) in Hefei, China, tropospheric
NO2 deduced from the OMI satellite (https://aura.gsfc.nasa.gov/omi.html; last access: 23 May 2018), surface meteorological data, and a
back trajectory cluster analysis technique. Considering the fact that most
NDACC (Network for Detection of Atmospheric Composition Change) FTS sites are
located in Europe and Northern America, whereas the number of sites in Asia,
Africa, and South America is very sparse, and there is still no official
NDACC FTS station that covers China (http://www.ndacc.org/; last access: 23
May 2018), this study can not only improve our understanding of regional
photochemical O3 production regime but also contributes to the
evaluation of O3 pollution controls.
This study concentrates on measurements recorded during midday, when the
mixing layer has largely been dissolved. All FTS retrievals are selected
within ±30 min of OMI overpass time (13:30 local time (LT)). While
the FTS instrument can measure throughout the whole day, unless cloudy, OMI
measures only during midday. For Hefei, this coincidence criterion is a
balance between the accuracy and the number of data points.
Site description and instrumentation
The FTS observation site (117∘10′ E, 31∘54′ N; 30 m a.s.l. (above sea level)) is located in the western
suburbs of Hefei city (the capital of Anhui Province, population of 8 million)
in central-eastern China (Fig. S1 in the Supplement). A detailed description of this site and
its typical observation scenario can be found in Tian et al. (2018). Similar
to other Chinese megacities, serious air pollution is common in Hefei
throughout the whole year (http://mep.gov.cn/; last access: 23 May 2018).
Our observation system consists of a high-resolution FTS spectrometer
(IFS125HR, Bruker GmbH, Germany), a solar tracker (Tracker-A Solar 547,
Bruker GmbH, Germany), and a weather station (ZENO-3200, Coastal
Environmental Systems, Inc., USA). The near-infrared (NIR) and middle
infrared (MIR) solar spectra were alternately acquired in routine
observations (Wang et al., 2017). The MIR spectra used in this study are
recorded over a wide spectral range (about 600–4500 cm-1) with a
spectral resolution of 0.005 cm-1. The instrument is equipped with a KBr
beam splitter and MCT detector for O3 measurements and a KBr beam
splitter and InSb detector for other gases. The weather station includes
sensors for air pressure (±0.1 hpa), air temperature (±0.3∘C), relative humidity (±3 %), solar radiation
(±5 %), wind speed (±0.2 m s-1), wind direction (±5∘), and the presence of rain.
FTS retrievals of O3, CO, and HCHORetrieval strategy
The SFIT4 (version 0.9.4.4) algorithm is used in the profile retrieval
(Supplement Sect. S1; https://www2.acom.ucar.edu/irwg/links; last
access:
23 May 2018). The retrieval settings for O3, CO, and HCHO are
listed in Table 1. All spectroscopic line parameters are adopted from HITRAN
2008 (Rothman et al., 2009). A priori profiles of all gases except H2O
are from a dedicated WACCM (Whole Atmosphere Community Climate Model) run. A
priori profiles of pressure, temperature, and H2O are interpolated from
the National Centers for Environmental Protection and National Center for
Atmospheric Research (NCEP/NCAR) reanalysis (Kalnay et al., 1996). For
O3 and CO, we follow the NDACC standard convention with respect to
micro-window (MW) selection and interfering gas consideration
(https://www2.acom. ucar.edu/irwg/links; last access: 23 May 2018). HCHO is
not yet an official NDACC species but has been retrieved at a few stations
with different retrieval settings (Albrecht et al., 2002; Vigouroux et al.,
2009; Jones et al., 2009; Viatte et al., 2014; Franco et al., 2015). The four
MWs used in the current study are chosen from a harmonization project taking
place in view of future satellite validation (Vigouroux et al., 2018). They
are centered at around 2770 cm-1, and the interfering gases are CH4,
O3, N2O, and HDO.
Summary of the retrieval parameters used for O3, CO, and
HCHO. All micro-windows (MWs) are given cm-1.
Gases O3COHCHORetrieval code SFIT4 v 0.9.4.4SFIT4 v 0.9.4.4SFIT4 v 0.9.4.4Spectroscopy HITRAN2008HITRAN2008HITRAN2008P, T, H2O profiles NCEPNCEPNCEPA priori profiles for target/interfering gases except H2OWACCMWACCMWACCMMW for profile retrievals 1000–1004.52057.7–2058 2069.56–2069.76 2157.5–2159.152763.42–2764.17 2765.65–2766.01 2778.15–2779.1 2780.65–2782.0Retrieved interfering gases H2O, CO2, C2H4, 668O3, 686O3O3, N2O, CO2, OCS, H2OCH4, O3, N2O, HDOSNR for de-weighting None500600SaStandard deviation of WACCMStandard deviation of WACCMStandard deviation of WACCMSεReal SNRReal SNRReal SNRILS LINEFIT145LINEFIT145LINEFIT145Error analysis Systematic error – smoothing error (smoothing) – errors from other parameters: background curvature (curvature), optical path difference (max_opd), field of view (omega), solar line strength (solstrnth), background slope (slope), solar line shift (solshft), phase (phase), solar zenith angle(sza), line temperature broadening (linetair_gas), line pressure broadening (linepair_gas), line intensity(lineint_gas)Random error – interference errors: retrieval parameters (retrieval_parameters), interfering species (interfering_species) – Measurement error (measurement) – errors from other parameters: temperature (temperature), zero level (zshift)
We assume measurement noise covariance matrices Sε to be
diagonal and set their diagonal elements to the inverse square of the signal-to-noise ratio (SNR) of the fitted spectra and the non-diagonal
elements of Sε to
zero. For all gases, the diagonal elements of a priori profile covariance matrices
Sa are set to the standard deviation of a dedicated WACCM run from 1980 to
2020, and its non-diagonal elements are set to zero.
We regularly used a low-pressure HBr cell to monitor the instrument line
shape (ILS) and included the measured ILS in the retrieval
(Hase, 2012; Sun et al., 2018).
Profile information in the FTS retrievals
The sensitive range for CO and HCHO is mainly tropospheric, and for
O3 it is both tropospheric and stratospheric (Fig. S2). The typical
degrees of freedom (DOFS) over the total atmosphere obtained at Hefei for
each gas are included in Table 2: they are about 4.8, 3.5, and 1.2 for
O3, CO, and HCHO, respectively. In order to separate independent
partial column amounts in the retrieved profiles, we have chosen the altitude
limit for each independent layer such that the DOFS in each associated
partial column is not less than 1.0. The retrieved profiles of O3,
CO, and HCHO can be divided into four, three, and one independent layers,
respectively (Fig. S3). The troposphere is well resolved by O3,
CO, and HCHO, where CO exhibits the best vertical resolution with more than
two independent layers in the troposphere.
Typical degrees of freedom for signal (DOFs) and sensitive range of
the retrieved O3, CO, and HCHO profiles at Hefei site.
In this study, we have chosen the same upper limit (12 km) for the
tropospheric columns for all gases (Table 2), which is about 3 km lower than
the mean value of the tropopause (∼15.1 km). In this way we ensured the
accuracies for the tropospheric O3, CO, and HCHO retrievals and
minimized the influence of transport from the stratosphere, i.e., the so-called
STE process (stratosphere–troposphere exchange).
Error analysis
The results of the error analysis presented here are based on the average of all
measurements that fulfill the screening scheme, which is used to minimize the
impacts of significant weather events or instrument problems (Supplement
Sect. S2). In the troposphere, the dominant systematic error for
O3 and CO is the smoothing error, and for HCHO it is the line
intensity error (Fig. S4). The dominant random error for O3 and
HCHO is the measurement error, and for CO it is the zero baseline level error
(Fig. S5). Taking all error items into account, the summarized errors in
O3, CO, and HCHO for the 0–12 km tropospheric partial column and for
the total column are listed in Table 3. The total errors in the tropospheric
partial columns for O3, CO, and HCHO have been evaluated to be
8.7 %, 6.8 %, and 10.2 %, respectively.
Errors in % of the column amount of O3, CO, and HCHO
for the 0–12 km tropospheric partial column and for the total column.
Figure 1a shows the tropospheric O3 column time series recorded
by the FTS from 2014 to 2017, where we followed Gardiner's method and used a
second-order Fourier series plus a linear component to determine the annual
variability (Gardiner et al., 2008). The analysis did not indicate a
significant secular trend of tropospheric O3 column probably
because the time series is much shorter than those in Gardiner et al. (2008);
the observed seasonal cycle of tropospheric O3 variations is well
captured by the bootstrap resampling method (Gardiner et al., 2008). As
commonly observed, high levels of tropospheric O3 occur in spring
and summer (hereafter MAM/JJA). Low levels of tropospheric O3 occur
in autumn and winter (hereafter SON/DJF). Day-to-day variations in MAM/JJA
are generally larger than those in SON/DJF (Fig. 1b). At the same time,
the tropospheric O3 column roughly increases over time in the first
half of the year and reaches the maximum in June and then decreases during
the second half of the year. Tropospheric O3 columns in June are
1.55×1018 molecules cm-2 (56 DU (Dobson units)) and in
December are 1.05×1018 molecules cm-2 (39 DU).
Tropospheric O3 columns in June were ∼50 % higher than those
in December.
(a) FTS measured and bootstrap resampled tropospheric
O3 columns at Hefei site. The linear trend and the residual are
also shown. Detailed description of the bootstrap method can be found in
Gardiner et al. (2008). (b) Tropospheric O3 column monthly
means derived from (a).
Vigouroux et al. (2015) studied the O3 trends and variabilities at
eight NDACC FTS stations that have a long-term time series of O3
measurements, namely, Ny-Ålesund (79∘ N), Thule (77∘ N),
Kiruna (68∘ N), Harestua (60∘ N), Jungfraujoch
(47∘ N), Izaña (28∘ N), Wollongong (34∘ S), and Lauder (45∘ S). All these stations were located in non-polluted
or relatively clean areas. The tropospheric columns at these stations are of
the order of 0.7×1018 to 1.1×1018 molecules cm-2. The results showed a maximum tropospheric
O3 column in spring at all these stations except at the high-altitude stations Jungfraujoch and Izaña, where it extended into early
summer. This is because the STE process is most effective during late winter
and spring (Vigouroux et al., 2015). In contrast, we observed a broader
maximum at Hefei which extends over the MAM/JJA season, and the values are
∼35 % higher than those studied in Vigouroux et al. (2015). This is
because the observed tropospheric O3 levels in MAM/JJA are more
influenced by air masses originating from densely populated and industrialized
areas (see Sect. 4.2), and the MAM/JJA meteorological conditions are more
favorable to photochemical O3 production (see Sect. 5.1). The
selection of tropospheric limits 3 km below the tropopause minimized but
cannot avoid the influence of transport from the stratosphere; the STE process
may also contribute to high level of tropospheric O3 column in
spring.
Regional contribution to tropospheric O3 levels
In order to determine where the air masses came from and thus contributed to
the observed tropospheric O3 levels, we have used the HYSPLIT
(Hybrid Single-Particle Lagrangian Integrated Trajectory) model to calculate
the three-dimensional kinematic back trajectories that coincide with the FTS
measurements from 2014 to 2017 (Draxler et al., 2009). In the calculation, the
GDAS (University of Alaska Fairbanks GDAS Archive) meteorological fields were
used with a spatial resolution of 0.25∘×0.25∘, a time resolution of 6 h, and 22 vertical levels from the surface to 250 mbar. All daily back trajectories at 12:00 UTC, with a 24 h pathway arriving
at Hefei site at 1500 m a.s.l., have been grouped into clusters and divided
into MAM/JJA and SON/DJF seasons (Stunder, 1996). The results showed that air
masses in Jiangsu and Anhui provinces in eastern China; Hebei and Shandong
provinces in northern China; Shaanxi, Henan, and Shanxi provinces in
northwestern China; and Hunan and Hubei provinces in central China contributed to
the observed tropospheric O3 levels.
In the MAM/JJA season (Fig. 2a), 28.8 % of air masses are of eastern origin and
arrived at Hefei through the southeast of Jiangsu Province and east of Anhui
Province; 41.0 % are of southwestern origin and arrived at Hefei through the
northeast of Hunan and Hubei provinces, and southwest of Anhui Province;
10.1 % are of northwestern origin and arrived at Hefei through the southeast of
Shanxi and Henan provinces, and northwest of Anhui Province; 10.1 % are of northern origin and arrived at Hefei through the south of Shandong Province and
north of Anhui Province; 10.1 % are of local origin generated in the south of
Anhui Province. As a result, air pollution from megacities such as Shanghai,
Nanjing, Hangzhou, and Hefei in eastern China; Changsha and Wuhan in
central-southern China; Zhenzhou and Taiyuan in northwest China; and Jinan in
north China could contribute to the observed tropospheric O3
levels.
One-day HYSPLIT back trajectory clusters arriving at Hefei at
1500 m a.s.l that are coincident with the FTS measurements from 2014 to 2017.
(a) Spring and summer (MAM/JJA) and (b) Autumn and winter
(SON/DJF) season. The base map was generated using the TrajStat 1.2.2
software (http://www.meteothinker.com, last access: 23 May 2018).
In the SON/DJF season, trajectories are generally longer and originated in the
northwest of the MAM/JJA ones (Fig. 2b). The direction of air masses
originating in the eastern sector shifts from the southeast to the northeast of
Jiangsu Province, and that of local air masses shifts from the south to the
northwest of Anhui province. Trajectories of eastern-origin, western-origin, and
northern-origin air masses in SON/DJF are 6.5 %, 13.1 %, and 0.7 % less
frequent than the MAM/JJA ones, respectively. As a result, the air masses
outside Anhui province have a 20.2 % smaller contribution to the observed
tropospheric O3 levels in SON/DJF than in MAM/JJA. In contrast,
trajectories of local-origin air masses in SON/DJF are 20.2 % more frequent
than the MAM/JJA ones, indicating a more significant contribution of air
masses in Anhui province in SON/DJF.
Minutely averaged time series of temperature, pressure, humidity,
and solar radiation recorded by the surface weather station.
The majority of the Chinese population lives in the eastern part of China,
especially in the three most developed regions, the Jing–Jin–Ji
(Beijing–Tianjin–Hebei), the Yangtze River Delta (YRD; including
Shanghai–Jiangsu–Zhejiang–Anhui), and the Pearl River Delta (PRD; including
Guangzhou, Shenzhen, and Hong Kong). These regions consistently have the
highest emissions of anthropogenic precursors (Fig. S6), which have led to
severe region-wide air pollution. This is particularly the case for the Hefei site, located in the
central-western corner of the YRD, where the population in the southeastern
area is typically denser than the northwestern area. Specifically, the
southeast of Jiangsu province and the south of Anhui province are two of the
most developed areas in YRD, and human activities therein are very intense.
Therefore, when the air masses originated from these two areas, the O3
level is usually very high. Overall, compared with the SON/DJF season, the more
southeastern air masses transportation in MAM/JJA indicated that the observed
tropospheric O3 levels could be more influenced by the densely
populated and industrialized areas, broadly accounting for the higher
O3 level and variability in MAM/JJA.
Tropospheric O3 production regimeMeteorological dependency
Photochemistry in polluted atmospheres, particularly the formation of
O3, depends not only on pollutant emissions but also on
meteorological conditions (Lei et al., 2008; Wang et al., 2017; Coates et
al., 2016). In order to investigate the meteorological dependency of the O3
production regime in the observed area, we analyzed the correlation of the
tropospheric O3 with the coincident surface meteorological data.
Figure 3 shows time series of temperature, pressure, humidity, and solar
radiation recorded by the surface weather station. The seasonal dependencies
of all these coincident meteorological elements show no clear dependencies
except for the temperature and pressure, which show clear reverse seasonal
cycles. Generally, the temperatures are higher and the pressures are lower in
MAM/JJA than those in SON/DJF. The correlation plots between the FTS tropospheric
O3 column and each meteorological element are shown in Fig. 4.
The tropospheric O3 column shows positive correlations with solar
radiation, temperature, and humidity, and negative correlations with
pressure.
Correlation plot between the FTS tropospheric O3 column
and the coincident surface meteorological data. Black dots are data pairs
within the MAM/JJA season and green dots are data pairs within the SON/DJF season.
High temperature and strong sunlight primarily affects O3
production in Hefei in two ways: by speeding up the rates of many chemical
reactions and by increasing emissions of VOCs from biogenic sources (BVOCs)
(Sillman and Samson, 1995b). While emissions of anthropogenic VOCs (AVOCs)
are generally not dependent on temperature, evaporative emissions of some
AVOCs do increase with temperature (Rubin et al., 2006; Coates et al., 2016).
Elevated O3 concentration generally occurs on days with wet
conditions and low pressure in Hefei, probably because these conditions favor
the accumulation of O3 and its precursors. Overall, MAM/JJA
meteorological conditions are more favorable to O3 production
(higher sun intensity, higher temperature, wetter condition, and lower
pressure) than SON/DJF, which supports the fact that tropospheric
O3 in MAM/JJA is larger than that in SON/DJF.
PO3 relative to CO, HCHO, and NO2 changes
In order to determine the relationship between tropospheric O3
production and its precursors, the chemical sensitivity of PO3
relative to tropospheric CO, HCHO, and NO2 changes was
investigated. Figure 5 shows time series of tropospheric CO, HCHO, and
NO2 columns that are coincident with O3 counterparts. The
tropospheric NO2 was deduced from the OMI product selected within the
±0.7∘ latitude/longitude rectangular area around the Hefei site.
The retrieval uncertainty for the tropospheric column is less than 30 %
(https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/ last access: 23 May 2018).
Tropospheric HCHO and NO2 show clear reverse seasonal cycles.
Generally, tropospheric HCHO is higher and tropospheric NO2 is
lower in MAM/JJA than in SON/DJF. Pronounced tropospheric CO was
observed, but the seasonal cycle is not evident, probably because CO emission
is not constant over the season or season dependent.
Time series of tropospheric CO, HCHO, and NO2.
Tropospheric CO and HCHO were derived from FTS observations, which is the same
as tropospheric O3, and tropospheric NO2 is derived from
OMI data.
Correlation plot between the FTS tropospheric O3 column
and coincident tropospheric CO (a), HCHO (b), and
NO2(c) columns. The CO and HCHO data are retrieved from
FTS observations, and the NO2 data were deduced from the OMI product.
Figure 6 shows the correlation plot between the FTS tropospheric O3
column and the coincident tropospheric CO, HCHO, and NO2 columns.
The tropospheric O3 column shows positive correlations with
tropospheric CO, HCHO, and NO2 columns. Generally, the higher the
tropospheric CO concentration, the higher the tropospheric O3, and
both VOCs and NOx reductions decrease O3
production. As an indicator of regional air pollution, the good correlation
between O3 and CO (Fig. 6a) indicates that the enhancement of
tropospheric O3 is highly associated with the photochemical
reactions which occurred in polluted conditions rather than due to the STE
process. The relatively weaker overall correlations of O3 with HCHO
(Fig. 6b) and NO2 (Fig. 6c) are partly explained by
different lifetimes of these gases, i.e., several hours to 1 day in summer
for NO2 and HCHO and several days to weeks for O3. So older
O3-enhanced air masses easily loose traces of NO2 or HCHO.
Since the sensitivity of PO3 to VOCs and NOx is
different under different limitation regimes, the relatively flat overall
slopes indicate that the O3 pollution in Hefei can be
fully attributed neither to NOx pollution nor to VOC pollution.
O3–NOx–VOC sensitivitiesTransition/ambiguous range estimation
Referring to previous studies, the chemical sensitivity of PO3 in
Hefei was investigated using the column HCHO/NO2 ratio (Martin et al.,
2004; Duncan et al., 2010; Witte et al., 2011; Choi et al., 2012; Jin and
Holloway, 2015; Mahajan et al., 2015; Schroeder et al., 2017; Jin et al.,
2017). The methods have been adapted to the particular conditions in Hefei.
In particular the findings of Schroeder et al. (2017) have been taken into
account.
Since the measurement tools for O3 and HCHO, the pollution
characteristic, and the meteorological condition in this study were not the
same as those of previous studies, the transition thresholds estimated in previous studies were not applied here (Martin et al.,
2004a; Duncan et al., 2010; Witte et al., 2011; Choi et al., 2012; Jin and
Holloway, 2015; Mahajan et al., 2015; Schroeder et al., 2017; Jin et al.,
2017). In order to determine transition thresholds applicable in Hefei,
China, we iteratively altered the column HCHO/NO2 ratio threshold
and judged whether the sensitivities of tropospheric O3 to HCHO or
NO2 changed abruptly. For example, in order to estimate the
VOC-limited threshold, we first fitted tropospheric O3 to HCHO that
lies within column HCHO/NO2 ratios <2 (an empirical starting point) to obtain the corresponding slope and then we decreased the threshold
by 0.1 (an empirical step size) and repeated the fit, i.e., only fitted the
data pairs with column HCHO/NO2 ratios <1.9. This was done iteratively. Finally, we sorted out the transition ratio which shows an
abrupt change in slope, and regarded this as the VOC-limited threshold.
Similarly, the NOx-limited threshold was determined by
iteratively increasing the column HCHO/NO2 ratio threshold until the
sensitivity of tropospheric O3 to NO2 changed abruptly.
The transition threshold estimation with this scheme exploits the fact that
O3 production is more sensitive to VOCs if it is VOC-limited and
is more sensitive to NOx if it is NOx
limited, and there exists a transition point near the threshold (Martin et al.,
2004a). Su et al. (2017) used this scheme to investigate the
O3–NOx–VOC sensitivities during the 2016 G20
conference in Hangzhou, China, and argued that this diagnosis of PO3
could reflect the overall O3 production conditions.
PO3 limitations in Hefei
Through the above empirical iterative calculation, we observed a VOC-limited
regime with column HCHO/NO2 ratios <1.3, an NOx-limited regime with column HCHO/NO2 ratios >2.8,
and a mixed VOC–NOx-limited regime with
column HCHO/NO2 ratios between 1.3 and 2.8. Column measurements sample
a larger portion of the atmosphere, and thus their spatial coverage is larger than in situ measurements. So the photochemical scene disclosed by
column measurements is larger than the in situ measurement. Specifically, this
study reflects the mean photochemical condition of the troposphere.
Chemical sensitivities of PO3 for the selected 106 days of
observations that have coincident O3, HCHO, and NO2
counterparts.
ItemsProportion Autumn and winter Spring and summer dayspercentagedayspercentagedayspercentageNOx limited6460.3 %1929.7 %4570.3 %Mixed VOC–NOx limited3028.3 %2170 %930 %VOC limited1211.4 %975 %325 %Sum106100 %4946.2 %5753.8 %
Schroeder et al. (2017) argued that the column measurements
from space have to be used with care because of the high uncertainty and the
inhomogeneity of the satellite measurements. This has been mitigated in this
study by the following.
The FTS measurements have a much smaller footprint than the satellite
measurements. Also, we concentrate on measurements recorded during midday,
when the mixing layer has largely been dissolved.
The measurements are more sensitive to the lower parts of the troposphere,
which can be inferred from the normalized averaging kernels (AVKs). The reason is simply that
the AVKs show the sensitivity to the column, but the column per altitude
decreases with altitude.
Time series of column HCHO/NO2 ratios.
Figure 7 shows time series of column HCHO/NO2 ratios which varied
over a wide range from 1.0 to 9.0. The column HCHO/NO2 ratios in
summer are typically larger than those in winter, indicating that the
PO3 is mainly NOx limited in summer and mainly VOC
limited or mixed VOC–NOx limited in winter. Based on the
calculated transition criteria, 106 days of observations that have coincident
O3, HCHO, and NO2 counterparts in the reported period are
classified, where 57 days (53.8 %) are in the MAM/JJA season and 49 days
(46.2 %) are in the SON/DJF season. Table 4 lists the statistics for the 106 days of observations, which shows that NOx-limited, mixed VOC–NOx-limited, and VOC-limited PO3 accounts for
60.3 % (64 days), 28.3 % (30 days), and 11.4 % (12 days), respectively.
The majority of NOx-limited (70.3 %) PO3 lies in
the MAM/JJA season, while the majority of mixed VOC–NOx-limited
(70 %) and VOC-limited (75 %) PO3 lies in the SON/DJF season. As a
result, reductions in NOx and VOC could be more effective to
mitigate O3 pollution in the MAM/JJA and SON/DJF seasons, respectively.
Furthermore, considering most of PO3 is NOx
limited or mixed VOC–NOx limited, reductions in
NOx would reduce O3 pollution in eastern China.
Conclusions
We investigated the seasonal evolution and photochemical production regime of
tropospheric O3 in eastern China from 2014 to 2017 by using
tropospheric O3, CO, and HCHO columns derived from Fourier transform
infrared spectrometry (FTS), the tropospheric NO2 column deduced from
the Ozone Monitoring Instrument (OMI), the surface meteorological data, and a
back trajectory cluster analysis technique. A pronounced seasonal cycle for
tropospheric O3 is captured by the FTS, which roughly increases
over time in the first half year and reaches the maximum in June, and then it
decreases over time in the second half year. Tropospheric O3
columns in June are 1.55×1018 molecules cm-2 (56 DU
(Dobson units)), and in December they are 1.05×1018 molecules cm-2 (39 DU). Tropospheric O3 columns in June
were ∼50 % higher than those in December. A broad maximum within both
spring and summer (MAM/JJA) is observed, and the day-to-day variations in
MAM/JJA are generally larger than those in autumn and winter (SON/DJF). This
differs from tropospheric O3 measurements in Vigouroux et al. (2015). However, Vigouroux et al. (2015) used measurements at relatively
clean sites.
Back trajectory analysis showed that air pollution in Jiangsu and Anhui
provinces in eastern China; Hebei and Shandong provinces in northern China; Shaanxi, Henan, and Shanxi provinces in northwest China; and Hunan and Hubei
provinces in central China contributed to the observed tropospheric
O3 levels. Compared with the SON/DJF season, the observed tropospheric
O3 levels in MAM/JJA are more influenced by the transport of air masses
from densely populated and industrialized areas, and the high O3
level and variability in MAM/JJA is determined by the photochemical
O3 production. The tropospheric-column HCHO/NO2 ratio is
used as a proxy to investigate the chemical sensitivity of the O3
production rate (PO3). The results show that PO3 is
mainly nitrogen oxide (NOx) limited in MAM/JJA, while it is
mainly VOC or mixed VOC–NOx limited in SON/DJF. Reductions in
NOx and VOC could be more effective to mitigate O3
pollution in the MAM/JJA and SON/DJF seasons, respectively. Considering most of
PO3 is NOx limited or mixed VOC–NOx limited, reductions in NOx would
reduce O3 pollution in eastern China.
The SFIT4 software can be found via https://www2.acom.ucar.edu/irwg/links
(last access: 23 May 2018). The data used in this paper are available on
request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-14569-2018-supplement.
The first two authors contributed equally to this work. YS and CL
prepared the paper with inputs from all coauthors. MP, CV, JN, NJ, and MDM
designed the retrieval and optimized the content. QH and WS conceived ozone
production regime study. WZ provided the OMI NO2 product. WW, CS, YT,
XX, MZ, and JL carried out the experiments and performed back trajectory
cluster analysis.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Quadrennial Ozone Symposium 2016 – Status and trends of atmospheric ozone (ACP/AMT inter-journal SI)”.
It is a result of the Quadrennial Ozone Symposium 2016,
Edinburgh, United Kingdom, 4–9 September 2016.
Acknowledgements
This work is jointly supported by the National High Technology Research and
Development Program of China (no. 2016YFC0200800, no.2018YFC0213104, no.
2017YFC0210002, no. 2016YFC0203302), the National Science Foundation of
China (no. 41605018, no.41877309, no. 41405134, no.41775025, no. 41575021,
no. 51778596, no. 91544212, no. 41722501, no. 51778596), the Anhui Province
Natural Science Foundation of China (no. 1608085MD79), the Outstanding Youth
Science Foundation (no. 41722501), and the German Federal Ministry of
Education and Research (BMBF) (grant no. 01LG1214A). The processing and post-processing environments for SFIT4 are provided by the National Center for
Atmospheric Research (NCAR), Boulder, Colorado, USA. The NDACC networks are
acknowledged for supplying the SFIT software and advice. The HCHO
micro-windows were obtained at BIRA-IASB during the ESA PRODEX project TROVA
(2016–2018) funded by the Belgian Science Policy Office. The LINEFIT code is
provided by Frank Hase, Karlsruhe Institute of Technology (KIT), Institute
for Meteorology and Climate Research (IMK-ASF), Germany. The authors
acknowledge the NOAA Air Resources Laboratory (ARL) for making the HYSPLIT
transport and dispersion model available on the Internet. The authors would
also like to thank Jason R. Schroeder and three anonymous referees for
useful comments that improved the quality of this
paper.Edited by: Stefan Reis
Reviewed by: five anonymous referees
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