The first OH and HO2 radical observation in Yangtze River Delta, one of
the four major urban agglomerations in China, was carried out at a suburban
site (Taizhou) in summer 2018 from May to June, aiming to elucidate the
atmospheric oxidation capacity in this region. The maximum diurnal averaged
OH and HO2 concentrations were 1.0×107 and
1.1×109 cm-3, respectively, which were the second
highest HOx (sum of OH and HO2) radical concentrations observed in
China. HONO photolysis was the dominant radical primary source, accounting
for 42 % of the total radical initiation rate. Other contributions were
from carbonyl photolysis (including HCHO, 24 %), O3 photolysis
(17 %), alkene ozonolysis (14 %), and NO3 oxidation (3 %). A
chemical box model based on the RACM2-LIM1 mechanism could generally reproduce
the observed HOx radicals, but systematic discrepancy remained in the
afternoon for the OH radical, when the NO mixing ratio was less than 0.3 ppb.
An additional recycling mechanism equivalent to 100 ppt NO was capable to fill
the gap. The sum of monoterpenes was on average up to 0.4 ppb during
daytime, which was all allocated to α-pinene in the base model.
A sensitivity test without monoterpene input showed the modeled OH and
HO2 concentrations would increase by 7 % and 4 %, respectively, but
modeled RO2 concentration would significantly decrease by 23 %,
indicating that monoterpene was an important precursor of RO2 radicals
in this study. Consequently, the daily integrated net ozone production would
reduce by 6.3 ppb without monoterpene input, proving the significant role
of monoterpene in the photochemical O3 production in this study.
In addition, the generally good agreement between observed and modeled HOx
concentrations suggested no significant HO2 heterogeneous uptake
process during this campaign. Incorporation of HO2 heterogeneous uptake
process would worsen the agreement between HOx radical observation and
simulation, and the discrepancy would be beyond the combined measurement–model
uncertainties using an effective uptake coefficient of 0.2.
Finally, the ozone production efficiency (OPE) was only 1.7 in this study, a
few folds lower than other studies in (sub)urban environments. The low OPE
indicated a slow radical propagation rate and short chain length. As a
consequence, ozone formation was suppressed by the low NO concentration in
this study.
Introduction
Stringent air quality regulations have been implemented in China for more
than a decade to combat severe air pollution problems, and dramatic
reduction of primary air pollutants such as sulfur dioxide (SO2),
nitrogen oxides (NOx), and coarse particulate matter (PM10) has been
achieved. Furthermore, a significant decrease in fine particulate matter
(PM2.5) has been found since 2013, when the Chinese government took the
strictest measures to reduce anthropogenic emissions in polluted
regions (Y. Wang et al., 2019, 2020). However, the surface
ozone (O3) showed a contrasting trend, with an increasing rate of 1–3 ppb yr-1 over the eastern Chinese megacity clusters, among which the North
China Plain and Yangtze River Delta regions show the most significant
increase of 3–12 ppb yr-1 (Y. Wang et al., 2020). The only known
formation pathway to O3 in the troposphere is the photolysis of
NO2 (Reactions R1 and R2). The increasing O3 despite the successful
reduction in NO2 demonstrates the nonlinearity of the photochemistry
caused by the dual role of NOx.
R1NO2+hν→NO+O(3P)(λ<398nm)R2O(3P)+O2+M→O3+M
The ozone formation nonlinearity can be described by investigating HOx
radical chemistry (Tan et al., 2018a, b). In low-NOx
conditions, the local ozone production rate P(O3) increases with
NOx due to the efficient NO-to-NO2 conversion by peroxy radicals
(Reactions R3–R4). In high-NOx conditions, P(O3) decreases with NOx
because the radical termination (Reaction R5) overwhelms the radical propagation
processes. The key is to find the optimized reduction strategy for both
NOx and VOCs to efficiently control the O3 production, which the
radical measurement could give insight to.
R3HO2+NO→OH+NO2R4RO2+NO→RO+NO2R5OH+NO2→HNO3
Numerous field campaigns focusing on the hydroxyl (OH) and hydroperoxy
radical (HO2) measurements have been performed worldwide for the past
decades, covering forest, marine, remote,
polar, rural, suburban, and urban environments (Stone et al., 2012). The measured
OH concentrations varied in an order of magnitude (in the range of
106–107 cm-3) among different types of environments, and the
OH daily maximum concentrations showed a tendency of higher values in urban
areas. Six field campaigns have been implemented in China during summer
periods, namely the Backgarden (2006), Heshan (2014), and Shenzhen (2018)
campaigns in the Pearl River Delta (PRD) (Lu et al., 2012; Tan et al.,
2019a; F. Y. Wang et al., 2019) and Yufa (2006), Wangdu (2014), and Beijing
(2017) campaigns in the North China Plain (NCP) (Lu et al., 2013; Tan et al.,
2017; Whalley et al., 2021) to investigate the atmospheric oxidation
capacities and photochemistry characteristics of two of the most polluted
regions in China, in which the Backgarden campaign reported the highest OH
concentration (15×106 cm-3) ever observed (Lu et
al., 2019). Chemical box model simulation based on conventional mechanisms
could generally reproduce the OH radical concentrations in these Chinese
campaigns at NO concentrations above 1 ppb, but a tendency to underestimate
OH radicals is continuously observed at NO concentrations less than 1 ppb,
which is a common feature in isoprene-rich forest environments, and OH
concentration could be underestimated by a factor of up to 10 (Rohrer et
al., 2014; Tan et al., 2001; Lelieveld et al., 2008). A novel recycling
mechanism related to isoprene and its degradation products without the
involvement of NO has been considered as a possible reason for the OH
measurement–model discrepancy in isoprene-rich environments (Peeters et
al., 2009, 2014; Lelieveld et al., 2008), but it is not
sufficient to explain the large discrepancy for campaigns in urban and
suburban environments. Moreover, even in isoprene-rich environments, the
inclusion of the novel recycling mechanism of isoprene is still not
sufficient to reproduce the observed OH concentrations (Stone et al.,
2011b). It is worth noting that the high OH concentration might be caused by
an unknown interference in OH measurements by laser-induced fluorescence
(LIF) (Mao et al., 2012; Novelli et al., 2014; Hens et al., 2014; Feiner et
al., 2016). Mao et al. (2012) reported
that up to 80 % of OH measurement is interference in a pine forest.
However, the interference was minimal and within the instrumental detection
limit in other campaigns under urban and suburban environments by different
LIF instruments (Griffith et al., 2016; Tan et al., 2017; Woodward-Massey
et al., 2020). Therefore, the OH measurement accuracy needs to be addressed
prior to critical discussion about defects in our knowledge of the radical
chemistry.
The Yangtze River Delta (YRD) region is one of the four major polluted regions
in China, and O3 has become the most critical pollutant in this region
(Li et al., 2019). A 4-year continuous observation campaign showed the ozone
pollution days have more than doubled from 2014 to 2017 (28 to 76 d) in
the YRD region (Y. Liu et al., 2020). Lu et al. (2018)
reported that the monthly averaged daily maximum 8 h concentrations of
O3 were even higher in the YRD than in the NCP. Plenty of studies have been
performed to investigate the ozone pollution characteristics and diagnose
the sensitivity of ozone formation to its precursors over this region
(Zhang et al., 2020; Ding et al., 2013; Tie et al., 2013; Geng et al.,
2015; Xing et al., 2017), but none of the studies were deployed with HOx
radical observations. In the present study, we report a new radical
observation in the YRD region during the campaign EXPLORE-YRD (EXPeriment on the
eLucidation of the atmospheric Oxidation capacity and aerosol foRmation, and
their Effects in Yangtze River Delta) together with a comprehensive set of
trace gas measurements. It provides a unique chance to investigate the
photochemistry with the support of HOx radical observation in this
region. In addition, the in situ HOx radical observation also allows us to
investigate the impact of potential mechanisms such as HO2
heterogeneous uptake on the photochemistry.
MethodologyMeasurement site
The EXPLORE-YRD campaign was conducted in the summer of 2018 (14 May to 20
June) in the park of the meteorological radar station in suburban Taizhou
(32.56∘ N, 119.99∘ E), Jiangsu Province, which is
approximately 200 km northwest and 100 km northeast of the two major
megacities Shanghai and Nanjing in the Yangtze River Delta region (Fig. S1 in the Supplement).
The site was surrounded by fishponds and grasslands, characterized by strong
biogenic emissions and occasionally biomass burning. No major industrial
emissions were found within 500 m. The closest road with slight traffic
was about 100 m to the south, and to the north and east of the
measurement site were the highways S28 and S35 with moderate traffic. For
most of the campaign, southerly and easterly winds prevailed and brought
air from the megacities and sea upwind to this site during the daytime.
Thus, the sampled air mass during this campaign could generally embody the
atmospheric chemical characteristics in this region.
OH and HO2 radical measurements
OH and HO2 radicals were measured by the Peking University Laser
Induced Fluorescence system (called PKU-LIF), which was successfully
deployed several times in previous campaigns in the Pearl River Delta and North
China Plain regions in China (Tan et al., 2017, 2018c, 2019a; Ma et al., 2019). The OH radical is detected by laser-induced
fluorescence at a low-pressure cell (4 hPa) after a sampling nozzle
(Hofzumahaus et al., 1998; Holland et al., 2003). The OH signal is
determined by tuning the laser wavelength (308 nm) on- and off-line,
so-called wavelength modulation. Specific description of the instrument
configuration could be found in Tan et al. (2017) and references
therein.
The HO2 radical is chemically converted to OH by reaction with NO that is
injected into the flow through a ring-shaped injector installed below the
sampling nozzle and then is detected in the form of OH in the second
detection cell. Previous studies indicated that part of the RO2 species
derived from longer chain alkanes (> C3), alkenes, and aromatic
compounds (namely complex-RO2) have the potential to rapidly convert to
OH on the same timescale as HO2 inside the fluorescence cell and
thus might cause interference for HO2 measurement (Fuchs et al.,
2011; Whalley et al., 2013). To minimize the potential interference from
RO2, the added NO mixing ratio was switched between 2.5 and 5 ppm
every 2 min, corresponding to the HO2 conversion efficiencies of
10 % and 20 %, respectively. The expected RO2 conversion efficiency
for both modes was below 10 % for this experimental setup for isoprene-derived RO2 from laboratory tests (Fuchs et al., 2011). The extent of
the RO2 interference was also proportional to the
complex-RO2-to-HO2 ratio. Unfortunately, RO2 was not measured
during this campaign, but one would expect a strong correlation between
RO2 (or complex-RO2) and HO2 (Tan et al., 2017; Whalley et
al., 2021). Previous field summer campaigns in China showed that the ratio
of complex RO2 to HO2 varies from 0.6 at a rural site in Wangdu
(Tan et al., 2017) to 2 at an urban site in Beijing (Whalley et
al., 2021). As the chemical condition encountered in the YRD was more similar to
that of Wangdu (the Beijing campaign was conducted at an urban site), it was
reasonable to assume the complex-RO2-to-HO2 ratio in this study was
closer to 0.6. Therefore, by applying the RO2 conversion efficiency of
0.1 as an upper limit, the maximum HO2 interference from RO2
radicals should be closer to 6 % of the HO2 measurement in this study
assuming the complex-RO2-to-HO2 ratio to be 0.6.
The PKU-LIF instrument was calibrated every 2 d during the campaign using
a radical calibration source (Hofzumahaus et al., 1996; Holland et al.,
1998). Stable sensitivities were found over the whole campaign with
reproducibility of 1.2 % and 8.0 % for OH and HO2, respectively
(1σ standard deviation). Thus, averaged sensitivity was applied for the
radical concentration determination. Considering the combined uncertainty of
calibration source (10 %, 1σ) with reproducibility of calibrated
sensitivities, the accuracies of OH and HO2 measurement were 10 % and
13 %, respectively. The detection limits of OH and HO2 measurements
using the LIF technique depend on the sensitivity, the laser power, the
background signal, and the integration time (Holland et al.,
1995) and were 6.0×105 cm-3 for OH and 1.0×107 cm-3 for HO2 at a typical laser power of 12 mW for a data
acquisition time of 30 s (for signal-to-noise ratio of 2).
Several studies conducted in forested environments indicated that OH
measurements by laser-induced fluorescence technique using the wavelength
modulation method might suffer from unknown internally produced interference
(Mao et al., 2012; Novelli et al., 2017), and the magnitude of
interference is highly dependent on the specific design of the instrument,
the operating parameters, and the type of environment in which the
instrument is deployed (Fuchs et al., 2016; Novelli et al.,
2014; Woodward-Massey et al., 2020; Cho et al., 2021). To investigate the
possible OH interference in this campaign, we performed an extended chemical
modulation experiment on 7 June. During the experiment, a chemical
modulation device consisting of a Teflon tube with an inner diameter of 1.0 cm and a length of 10 cm was placed on top of the OH sampling nozzle.
About 17 slpm (standard liters per minute) of ambient air was drawn through
the tube by a blower, 1 slpm of which entered the fluorescence cell. Tests
on the transmission efficiency of OH through the chemical modulation device
showed that the signals differed by less than 7 % with or without a chemical
modulation device, indicating the losses of ambient OH to the chemical
modulation device were insignificant. For ambient measurement application,
either propane (a 12 % mixture in nitrogen, 6 sccm) diluted in a carrier
flow of pure nitrogen (200 sccm) or pure nitrogen (200 sccm) was injected
into the center of the tube alternatively every 5 min via two oppositely
posited needles at the entrance of the Teflon tube. The ambient OH signal can then be deduced by differentiating the signals from adjacent measurement modes
with and without propane injection. The amount of the scavenger added is
typically selected to be sufficiently high for reacting with ambient OH but
not in excess in case of reaction with internally produced OH, and thus, the
scavenging efficiency is usually kept around 90 %. Calibrations of OH
sensitivity with and without propane injection showed the scavenging
efficiency of OH was around 93 % in this experiment, and the kinetic
calculation indicated the added propane removed less than 0.7 % of the
internal-produced OH. Therefore, the real ambient OH concentration can be
obtained by multiplying the differential OH signal by the scavenging
efficiency and by the instrument sensitivity. More details about the
prototype chemical-modulation reactor used with PKU-LIF and the calculation
method can be seen in Tan et al. (2017).
Trace gas measurements
A large number of trace gases and aerosol properties related to the
atmospheric oxidation chemistry investigation were measured simultaneously.
Instruments were placed in sea containers with their sampling inlets mounted
5 m above ground. The detail of instrumentation is described by
H. Wang et al. (2020). In Table 1, the measured species related to
photochemistry study are listed together with the performance of
instruments.
Measured species and performance of the instruments.
a Signal-to-noise ratio = 1. b Laser-induced fluorescence.
c Chemical conversion to OH via NO reaction before detection. d Process-specific, 5 orders of magnitude lower than maximum at noon. e Photolytic conversion to NO before detection, home-built converter. f Long-path absorption photometry. g VOCs including C2–C11 alkanes, C2–C6 alkenes, and C6–C10 aromatics. h Gas
chromatography equipped with a mass spectrometer and a flame ionization
detector. i The sum of monoterpene.
O3, NO, NO2, SO2, and CO were detected by a series of
commercial analyzers from Thermo Inc. O3 was measured by a UV
photometric analyzer (model 49i). Both NO and NO2 were measured by a
trace-level analyzer (model 42i) using chemiluminescence. Therein,
NO2 measurement was accomplished by a home-built photolytic converter
to avoid interference from other NOy species. HONO measurement was
conducted with a long-path absorption photometer with a time resolution of 1 min. A gas chromatograph coupled with a flame ionization detector and mass
spectrometer (GC-FID-MS) was deployed to measure volatile organic compounds
(VOCs) including non-methane hydrocarbons (C2–C11 alkanes, C2–C6 alkenes,
C6–C10 aromatics, isoprene, sum of monoterpenes) and oxygenated VOCs
including methyl vinyl ketone (MVK)/methacrolein (MACR), methyl ethyl ketone
(MEK), acetaldehyde (ACD), and acetone (ACT) at a time resolution of 1 h. The
sum of monoterpenes was also detected by proton-transfer-reaction mass
spectrometry (PTR-MS). Formaldehyde and glyoxal were measured by
commercial and home-built instruments, namely Hantzsch and CEAS,
respectively. Additionally, meteorological parameters including temperature,
relative humidity, pressure, wind speed, and wind direction were all
measured simultaneously. Photolysis frequencies were calculated by integrated
actinic flux measured by a spectroradiometer.
Model description
An observation-constrained box model based on the RACM2-LIM1 mechanism
(Goliff et al., 2013; Peeters et al., 2014) was used to simulate the OH
and HO2 radical concentrations. Briefly, observations of the photolysis
frequencies j(O1D), j(NO2), j(HONO), j(H2O2), j(HCHO),
and j(NO3); O3; NO; NO2; CO; CH4; SO2; HONO; C2–C12
VOCs; certain oxygenated VOCs such as HCHO, acetaldehyde, glyoxal, and
acetone; and the meteorological parameters were used to constrain the
model with a time resolution of 5 min. Photolysis frequencies of other
species were calculated in the model using the following function of solar
zenith angle (χ) and scaled to the ratio of measured to calculated
j(NO2) to represent the effect from clouds:
J=l×(cosχ)m×e-n×secχ,
where the optimal values of parameters l, m, and n for each photolysis
frequency were adopted (Saunders et al., 2003). The organic compounds
were not treated individually but assigned to different lumped species
according to the reactivities with OH. The classification of the constrained
organic compounds in RACM2 were listed in Table 2 in detail. The sum of
monoterpenes was allocated to α-pinene in the model, and the
uncertainty due to such simplification was discussed in Sect. 4.2.2.
Isomerization of isoprene-derived peroxy radicals was also considered. Other
lumped secondary species were unconstrained due to the technical limits but
generated numerically by the model calculation.
Assignment of measured and constrained VOCs in RACM2 during
this study.
An additional first-order loss term equivalent to a lifetime of 8 h was
given to all species to represent physical losses by means of deposition,
convection, and advection. The observed-to-model ratio of PAN concentration
was 1.09 using this physical loss rate, while the modeled PAN concentration
agreed to measurements from late morning to midnight but slightly lower
than measurements in the early morning (Fig. S2), which might be related to
the effect of boundary layer height variation. To test the influence of diurnal
boundary layer height variation, we performed a sensitivity test by
imposing a loss rate dependent on boundary layer height (BLH, reanalysis data from European Centre
for Medium-Range Weather Forecasts) to all species. In
this scenario, the model continuously underpredicted the concentration in
the early morning, and additionally, the model overestimated the observed
PAN in the midday and afternoon (Fig. S2). This was because the boundary-layer-height-dependent loss rate was largest at night, which made the loss
of PAN greater and further worsened the measurement–model comparison.
Therefore, the treatment of a first-order loss term equal to 8 h for all
species in the model might not reflect the loss due to deposition but gave a
reasonable approximation on the overall physical loss of the model-generated
intermediates. Nevertheless, the modeled OH and HO2 concentrations
were insensitive to the imposed loss rate (Fig. S3). The concentrations
differed less than 0.5 % between two cases for both OH and HO2. In
addition, a sensitivity test without HCHO and glyoxal constrained indicated
that the model would under-predict the HCHO and over-predict the glyoxal
concentrations (Fig. S2), which might be related to the significant primary
emission of HCHO and missing sinks of glyoxal in the current mechanisms.
However, the missing sources and sinks of HCHO and glyoxal are not the scope
of this study. To avoid interruption from incapability of model performance,
both HCHO and glyoxal were constrained to observations in this study.
According to the Monte Carlo simulation tests, the estimated 1σ uncertainty
of the model calculation was 32 % and 40 % for OH and HO2,
respectively, arising mainly from the uncertainties of both observational
constraints and kinetic rate constants, among which the rate constants
between HO2 and NO, dilution time, and NO concentration were of most
significant importance in this study.
ResultsMeteorological and chemical conditions
The meteorological condition encountered during the campaign was
characterized by high temperature (up to 35 ∘C), high relative
humidity (54 % on average), and strong solar radiation. The wind speed was
usually below 2 m s-1 during the daytime. Back trajectory analysis
demonstrated that the air masses were predominately transported from the
south and east during the campaign (Fig. S4). High O3 concentrations
were frequently observed on days when the air masses transported to the
measurement site had passed through the south, especially the large southwest
city clusters. As shown in Fig. 1, the daytime O3 concentrations
exceeded the Chinese national air quality standard level II (hourly averaged
limit 93 ppb) on several days and reached as high as 150 ppb on 5 and 6
June.
Time series of measured photolysis frequencies
(j(O1D), j(NO2)), relative
humidity (RH), ambient temperature (T), and concentrations of
O3, Ox (= O3+ NO2),
NO, NO2, CO, SO2, HONO,
formaldehyde (HCHO), and glyoxal (CHOCHO). The dotted horizontal line
represents the Chinese national air quality standard level II of
O3 (hourly averaged limit 93 ppb). The grey areas
denote nighttime.
Figure 2 shows mean diurnal profiles of the key parameter observations. The
averaged period is selected when HOx measurements were available (23 May–17 June excluding the break). Solar radiation was intense during the
whole campaign, indicated by photolysis frequencies j(O1D) and
j(NO2). NO concentration peaked at 4 ppb during morning rush hour and
then dropped to 0.2 ppb at noon. O3 concentration started to increase
after sunrise and reached the peak of 86 ppb around noon and lasted until
sunset. Subsequently, O3 concentration decreased and partially
converted to NO2 due to the absence of sunlight. The total oxidant
(Ox), the sum of O3 and NO2, also decreased after sunset.
Along with the increased NO2 at night, HONO concentration increased and
reached the maximum of up to 1.3 ppb at sunrise and then declined rapidly
due to the fast photolysis. The averaged HONO concentration was 0.6 ppb on
a daytime basis. Peroxyacyl nitrates (PANs) is an indicator for active
photochemistry, which increased since sunrise, reaching a maximum of 1.6 ppb at
12:00 and then decreasing in late afternoon during this campaign. However,
other oxidation products, including HCHO and glyoxal, similar to CO and
SO2, peaked at 08:00 CNST rather than at noon and in the late afternoon and
decreased afterwards, indicating an anthropogenic emission-related origin of
these species. Since this campaign was conducted during a harvest season,
agriculture biomass burning might be responsible for the elevated HCHO and
glyoxal in the early morning (Guo et al., 2021; J. W. Liu et al., 2020; Wang et
al., 2017; Silva et al., 2018).
Isoprene showed a broad peak of 0.2 ppb from 09:00 to 15:00, which was
several times lower than during the previous summer campaigns (Lu et al.,
2012, 2013; Tan et al., 2017). The sum of monoterpene
concentrations varied from 0.2 to 0.4 ppb, showing a diurnal peak around
noon. Though the speciation is not known, the daytime monoterpene
concentration was comparable to monoterpene-dominated pine forest (Kim et
al., 2013; Hens et al., 2014). The role of monoterpene in HOx chemistry
is discussed in Sect. 4.2.2.
Mean diurnal profiles of measured photolysis frequencies
(j(O1D), relative humidity (RH), ambient temperature
(T), and concentrations of O3,
Ox (= O3+ NO2),
NO, NO2, CO, SO2, HONO,
formaldehyde (HCHO), glyoxal (CHOCHO), biogenic VOCs (monoterpenes,
isoprene), and PAN. Data are averaged over the period with
HOx radical measurement. Colored areas denote the
standard deviation of variability (1σ). The grey areas
denote nighttime.
OH and HO2 radical observation
Figure 3 shows the time series of the observed and calculated OH and
HO2 radical concentrations. Continuous measurement of HOx radicals
was interrupted by rainfall and calibration or instrument maintenance.
Distinct diurnal variation was observed for both OH and HO2 radicals.
The daily maxima of OH and HO2 concentration were in the range of
(8–24)×106 and (4–28)×108 cm-3,
respectively. The mean diurnal profiles showed that averaged OH and HO2
peak concentrations (1 h averaged) were 1.0×107 and
1.1×109 cm-3, respectively (Fig. 4). Additionally, the
chemical modulation tests performed on 7 June, an O3 polluted day,
indicated that the unknown OH interference, if existed, was insignificant and
below the detection limits during this campaign (Fig. S5).
Time series of observed and modeled OH and
HO2 concentration, and the modeled grouped OH
reactivity (kOH). Vertical dashed
lines denote midnight. The grey areas denote nighttime.
The mean diurnal profiles of measured and modeled OH and
HO2 concentrations (a) as well as the
discrepancies between observation and model (b) in different
scenarios (Scenario 1: base case; Scenario 2: without
α-pinene constrained; Scenario 3: with
HO2 heterogeneous uptake process considered by
assuming the uptake coefficient of 0.2; Scenario 4: with
HO2 heterogeneous uptake process considered by
assuming the uptake coefficient of 0.08). Colored areas denote 1σ uncertainties of measured (red) and base case modeled (blue)
radical concentrations, respectively. The grey areas denote nighttime.
For comparison, the daytime measured OH concentration in this campaign
together with the OH concentrations in the Yufa and Wangdu campaigns in the NCP
region and in the Backgarden, Heshan, and Shenzhen campaigns in the PRD region, where
OH radical observations were available in China, are summarized in Table 3
and Fig. 5. Overall, the OH radical concentration in the present study was
relatively higher than during other campaigns except for the Backgarden
campaign in 2006 (Hofzumahaus et al., 2009). A recent winter
observation in Shanghai in the YRD region reported an averaged noontime OH
concentration of 2.7×106 cm-3 (Zhang et al., 2022),
which was comparable to or even higher than that observed in winter in
Beijing (1.7–3.1×106 cm-3) (Tan et
al., 2018c; Ma et al., 2019; Slater et al., 2020). It demonstrated the strong
atmospheric oxidation capacity in this region among the three megapolitan
areas (NCP, PRD, and YRD) in China from the perspective of OH concentration.
Summary of filed measurements and model simulation for
j(O1D),
O3, NOx, OH,
HO2, P(ROx),
F(Ox), and OPE at local noon in urban and suburban
environments.
LocationMonthTypej(O1D)O3NOxOHHO2P(ROx)F(Ox)OPEsReferencesYear/10-5 s-1/ppb/ppb/106 cm-3/108 cm-3/ppb h-1/ppb h-1Pabstthum, Germany,July–AugustRural1.5421.553.52.21.7a2.2b1.3Holland et al. (2003),52.85∘ N, 12.94∘ W,1998Volz-Thomas et al. (2003),50 km NW of BerlinPlatt et al. (2002)Nashville, USA,June–JulySuburban3.0a60a4.4a107.51.19c8.2Martinez (2003),36∘11.4′ N, 86∘42.0′ W,1999Thornton et al. (2002)8 km NE of downtown areaLa Porte, USA,August–SeptemberSuburban3.0706207.54.925d5.1Mao et al. (2010)29∘40′ N, 95∘01′ W,200040 km SE of HoustonNew York (Queens College), USA,June–AugustUrban2.548287.0e1.0e4.834d7.1Mao et al. (2010),40∘44′ 15′′ N, 73∘49′ 18′′ W,2001Ren et al. (2003a, b)in the borough of QueensMexico City, Mexico,April–MayUrban4.51151812f15f8.665d7.6Mao et al. (2010),19∘25′ N,2003Shirley et al. (2006)∼7 km SE of downtown areaEssex (Writtle University College), England,July–AugustRural1.0g46.5g10.8g2g0.7g11.6g7.2g,h0.6Emmerson et al. (2007)51∘44′ 12′′ N, 0∘25′ 28′′ E,200325 miles NE of central LondonTokyo (University of Tokyo),July–AugustUrban2.532126.3e1.4e2.213.9j6.3Kanaya et al. (2007, 2008)Japan, 35∘39′ N, 139∘41′ E,2004(6.8)i(2.0)inear city centerBackgarden, China,JulyRural3.55111.41417k10.718l1.7Lu et al. (2012),23.487∘ N, 113.034∘ E,2006Lou et al. (2010)60 km NW of downtown GuangzhouYufa, China,August–SeptemberRural1.8718.85.57.2k7.015l2.1Lu et al. (2013)39.5145∘ N, 116.3055∘ E,2006∼40 km south of the Beijing downtown areaMexico City, Mexico,MarchUrban4.090494.6e1.9e7.531c4.1Dusanter et al. (2009a, b),19∘ N, 100∘ W,2006Molina et al. (2010)∼7 km SE of downtown areaUniversity of Houston (70 m a.g.l.), USA,August–SeptemberUrban (tower)3.16841512.55.345d8.5Mao et al. (2010)29.7176∘ N, 95.3413∘ W,20065 km SE of downtown Houston
Continued.
LocationMonthTypej(O1D)O3NOxOHHO2P(ROx)F(Ox)OPEsReferencesYear/10-5 s-1/ppb/ppb/106 cm-3/108 cm-3/ppb h-1/ppb h-1University of Houston (70 m a.g.l.), USA,April–MayUrban (tower)–472.58.8e6.3e318j6Ren et al. (2013),29.7176∘ N, 95.3413∘ W,2009Lee et al. (2013)5 km SE of downtown HoustonParis, France,JulySuburban2.2354.34.21.3m0.75n7.1o9.5Michoud et al. (2012)48.718∘ N, 2.207∘ E,2009∼14 km SW of ParisPasadena, USA,May–JuneSuburban2.145193.52.04.0338.3Griffith et al. (2016)34.1408∘ N, 118.1223∘ W,2010(2.5)p(72)p(9)p(4.0)p(5.0)p(5.3)p(23)p,q(4.3)∼18 km NE of downtown Los AngelesLondon, England,July–AugustUrban–24.213.12.12.04.95.6g1.1Whalley et al. (2018),51∘31′ 16′′ N, 0∘12′ 48′′ W,2012(37.4)r(24.3)r(3.0)r(0.6)rWhalley (2016)in central LondonWangdu, China,June–JulyRural1.8888.28.37.74.814.7b3.1Tan et al. (2017)38.71∘ N, 115.15∘ E,2014∼35 km SW of Baodingand 170 km SW of BeijingHeshan, China,October–NovemberSuburban1.35126.94.82.35.118.1b3.5Tan et al. (2019a)22.728∘ N, 112.929∘ E,2014∼6 km SW of the city of Heshan and50 km SW of Guangzhou and FoshanBeijing, China,May–JuneUrban2.4100259.03.07.07.8t2.4tWhalley et al. (2021),39.97 ∘ N, 116.38∘ E,2017Shi et al. (2019)in central BeijingTaizhou, China,May–JuneSuburban2.1823.610.611.46.811.4j1.7This study32.56∘ N, 119.99∘ E,2018∼200 km NW of Shanghai
a Take from a typical day. b Calculated from measured peroxy
radical with NO reaction. c Calculated from measured HO2 with NO.
d Calculated from measured HO2 and scaled RO2 (measured
HO2 times the ratio of modeled RO2 to HO2) with NO. e Median. f Median and revised. g 11:00–15:00 mean. h Calculated by summing all of the reaction rates for NO-to-NO2
conversions. i For smog-free day and smog day (in parentheses)
separately. j Calculated from measured HO2 and modeled RO2
with NO. k HO2∗ (HO2 and partial RO2). l Calculated from modeled HO2 and RO2 with NO. m Total
peroxy radicals (HO2+ RO2). n 08:00–16:00 mean. o Calculated by measured total peroxy radicals (HO2+ RO2) with NO.
p For weekdays and weekend days (in parentheses) separately. q Calculated from measured HO2∗ with NO. r For westerly
flow and easterly flow (in parentheses) separately. s Calculated by the
ratio between F(Ox) and P(ROx). t Daily mean.
Summary of OH radical concentrations (noontime,
11:00–13:00) measured in five summer field campaigns in China. Yufa (YF) and
Wangdu (WD) campaigns in the North China Plain, Heshan (HS) and Backgarden (BG)
campaigns in the Pearl River Delta, and Taizhou (TZ, this study) campaign in
Yangtze River Delta. The box–whisker plot shows the
90th, 75th,
50th, 25th, and
10th percentile values of noon OH radical
concentrations in each campaign. The diamond shows the mean values of noon
OH radical concentrations.
We also found strong correlation between observed OH radical concentration
and photolysis frequency (j(O1D)) during the EXPLORE-YRD campaign, with
the correlation coefficient R2 and the correlation slope being 0.85 and
4.8×1011 s cm-3, respectively (Fig. 6). Notably, the
slopes were in the range of (4.0–4.8)×1011 s cm-3 for
all the previous filed campaigns in the NCP and PRD regions, for both summer and
winter (Tan et al., 2017, 2018c; Lu et al., 2012; Ma et al.,
2019). It suggested that the atmospheric oxidation capacity to sustain the
radical concentrations was comparable under various chemical conditions in
the three major urban agglomerations. In addition, the intercept of the linear
fit for this campaign was about 7.6×105 cm-3, which
was comparable to the Wangdu campaign in 2014 (7.7×105 cm-3) and lower than the Yufa and Backgarden campaigns in 2006
(1.6×106 and 2.4×106 cm-3,
respectively). It represented the non-photolytically produced OH
concentration.
Correlation between measured OH and
j(O1D). Grey scatter plot represents the 5 min
observation result for the EXPLORE-YRD campaign. A linear fit which takes
both measurement errors into account is applied. The linear fit lines and
correlation slopes, intercept, and coefficients are also shown.
Modeled OH reactivity
OH reactivity (kOH) is the pseudo first-order loss rate coefficient of the
OH radical and indicates the inverse of the chemical lifetime of the OH
radical. It can be defined by the sum of the OH reactant concentrations
multiplied by their reaction rate constants versus OH radical (Fuchs et
al., 2017; Yang et al., 2016, 2019; Lou et al., 2010):
kOH=∑ikOH+Xi[Xi].
In this study, the kOH was calculated from measured NO, NO2, CO,
CH4, SO2, C2–C12 VOCs (including isoprene and monoterpene), HCHO,
acetaldehyde, glyoxal, and acetone and model-generated intermediate species
(mainly referring to the unconstrained oxygenated VOCs). The calculated
kOH ranged between 5 and 40 s-1 (Fig. 3).
The typical mean diurnal variation in kOH showed a peak in the early
morning and then dropped by nearly 50 % to a minimum in the afternoon
(Fig. 7a). The averaged kOH for periods with OH radical measurement was
10.8 s-1 on a daytime basis (08:00–16:00), and a total of 36 % of the
modeled kOH could be attributed to the inorganic compounds (Fig. 7b).
CO was the single largest contributor to kOH, with a campaign average
contribution of 19 %. NO and NO2 together contributed 15 % of the
modeled kOH. Alkanes, alkenes, and aromatics contributed an additional
15 % of the modeled kOH. The reactivity from isoprene made a small
contribution (5 %) to the modeled kOH compared to other campaigns
conducted in suburban China, where isoprene typically contributed about
20 % of the total kOH (Lou et al., 2010; Fuchs et al., 2017). The
contribution that monoterpene made was 4 %, which was a substantial
fraction considering that the daytime monoterpene level was usually low in
suburban and urban areas.
(a) The mean diurnal profiles of speciated OH reactivity.
The grey areas denote nighttime. (b) Breakdown of modeled OH reactivity for
daytime conditions (08:00–16:00).
The OVOCs made up a large portion, accounting for approximately 40 % of
the modeled kOH. The model-generated OVOCs made a comparable contribution
to the measured ones (22 % vs. 18 %), and the model-generated
contribution to OH reactivity was insensitive to the imposed physical loss
rate (Fig. S3). This characteristic was similar to what was observed in
London and Wangdu (Whalley et al., 2016; Fuchs et al., 2017), where major
OVOCs including HCHO, acetaldehyde, and acetone were directly measured, and
the measured OVOCs together with the model-generated OVOCs accounted for a
large portion of the total reactivity (44 % and 25 %, respectively). It
was noteworthy that, in both campaigns, kOH was directly measured and
the kOH budget was largely closed. In some previous studies in urban
and suburban areas, however, missing kOH ranging from less than 30 %
to over 50 % of the total reactivity was often observed (Kovacs et al.,
2003; Lou et al., 2010; Shirley et al., 2006; Yang et al., 2016). The common
feature of these observations was that the measurement of OVOCs was
completely missing. In fact, model simulations had proven that the
model-generated OVOCs from the photooxidation of measured VOCs could
quantitatively explain the missing kOH in most of these campaigns during
daytime, and the majority of the model-generated OVOCs were HCHO,
acetaldehyde, glyoxal, and the isoprene oxidation products. Therefore, in
recent studies, with the improved coverage of the measurement of major OVOC
species, together with the model-generated secondary species, the calculated
kOH was largely in agreement with the measured kOH in urban and
suburban areas during the daytime. However, a significant difference could
still be observed in areas affected by dramatic anthropogenic influences,
for instance in central Beijing (Whalley et al., 2021). About 30 % of the
measured kOH remained unaccounted for, even if the measured and
model-generated OVOCs were taken into account, which only contributed
6.5 % of the total reactivity, implying that the missing reactivity could
be attributed to the undetected or unrecognized species under complex
environments.
DiscussionSources and sinks of ROx radicals
The sum of OH, HO2, and RO2 radicals is known as the ROx radical.
The interconversion within the ROx radical family is relatively
efficient via radical propagation reactions, in which the number of consumed
and number of produced radicals are equal and do not change the total ROx
concentrations. In this section, we concentrate on the radical initiation
processes that produce radicals from non-radical molecules and chain
termination processes that destroy radicals. The radical primary production
consists of photolysis reactions and alkene ozonolysis. Radical termination
processes include reactions with nitrogen oxides and recombination of peroxy
radicals.
Figure 8 presents the mean diurnal profiles of ROx radical production
and destruction rates based on the model calculation. The P(ROx) and
L(ROx) show distinct diurnal variation with a maximum of 6.8 ppb h-1
at noontime. In other campaigns (Table 3), diurnal maximum P(ROx)
varies from 1.1 ppb h-1 at a suburban site in Nashville to about 11.6 ppb h-1 at a rural site near London during a heat wave (Martinez,
2003; Emmerson et al., 2007). The P(ROx) in the EXPLORE-YRD campaign is
comparable to that found in Mexico 2003, Mexico 2006, and Yufa 2006 (Mao
et al., 2010; Dusanter et al., 2009b; Lu et al., 2013) .
Hourly mean diurnal profiles of primary sources and sinks
of ROx radicals from model calculations. The grey
areas denote nighttime.
The daytime averaged radical chemistry production rate was 5.7 ppb h-1,
of which 83 % was attributed to the photolytic process. HONO photolysis was
the dominant primary source for the entire day and contributed up to 42 %
of P(ROx) on a daytime basis. Two recent winter campaigns in the same
region also found HONO photolysis dominated the radical primary source,
contributing 38 % to 53 % of the total radical sources, despite the
overall radical production rates being several times lower than that in
summertime (Lou et al., 2022; Zhang et al., 2022). In fact, the photolysis
of HONO is one of the most important radical primary sources in worldwide
urban and suburban areas for both summer (Ren et al., 2003b; Dusanter et
al., 2009b; Michoud et al., 2012; Whalley et al., 2018; Tan et al., 2017) and
winter (Ren et al., 2006; Kanaya et al., 2007; Kim et al., 2014; Tan et
al., 2018c; Ma et al., 2019). In addition, carbonyl compound (including HCHO)
photolysis was also an important contributor to radical primary sources
under urban and suburban conditions (Kanaya et al., 2007; Griffith et al.,
2016; Emmerson et al., 2007). In this study, carbonyl compound photolysis
accounted for on average 24 % of P(ROx), in which 14 % was from
HCHO solely. The dominant primary radical source in remote regions, ozone
photolysis (generating O1D and subsequently reacting with H2O to
produce OH), also played a significant role in this study, contributing
17 % to P(ROx). Furthermore, the non-photolytic radical source alkene
ozonolysis peaked at around 10:00 in the morning, and the most important
O3 reactant was monoterpene (35 % on a daytime basis). It was worth
noting that P(ROx) reduced significantly after sunset while there was a
small peak of 1.5 ppb h-1 at dusk. The nighttime radical
chemistry was mainly initiated by NO3 oxidation (82 %) with
monoterpene in the first half of the night, but the NO3 chemistry was
suppressed from midnight to sunrise by the increasing NO concentration
because of the efficient titration effect (H. Wang et al., 2020).
During the EXPLORE-YRD campaign, the ROx termination processes were
mainly dominated by the OH+NO2 reaction before 08:00 and by peroxy
radical self-reaction in the afternoon (Fig. 8). On a daytime basis, nitrate
formation and peroxy radical recombination both accounted for half of
L(ROx). The peroxy radical recombination including HO2+ RO2, HO2+ HO2, and RO2+ RO2 reactions contributed 33 %,
15 %, and 1 % to L(ROx), respectively. Because the HO2 and
RO2 concentrations were usually similar, the different contributions
between three kinds of peroxy radical recombinations were caused by different
reaction rate constants. In RACM2, the HO2+ RO2 reaction rate
varied from 5.1×10-12 cm3 molec.-1 s-1
(methyl peroxy radical at 298 K) to 1.6×10-11 cm3 molec.-1 s-1 (isoprene-derived RO2 at 298 K). In
comparison, the effective HO2+ HO2 reaction rate constant was
3.5×10-12 cm3 molec.-1 s-1 assuming an
ambient H2O mixing ratio of 2 %. The self-combination of methyl
peroxy radical rate constant was 3.5×10-13 cm3 molec.-1 s-1, 1 order of magnitude smaller than the
other radical recombination reactions. The reversible reaction between the
peroxyacyl radical and PANs became a net radical sink in the morning because
relatively high NO2 and low temperature shifted the thermodynamic
equilibrium to form PANs. The net formation of PANs followed by physical
losses contributed on average 12 % of L(ROx). In addition, part of the
RO2 species reacts with NO to form organic nitrate rather than recycle
to HO2 radical, resulting in 6 % of the radical losses during the
daytime. As for the nighttime, since the radicals formed from NO3
oxidation were dominantly OLND (peroxy radicals of NO3-alkene adduct
reacting via deposition) and OLNN (peroxy radicals of NO3-alkene adduct
reacting to form carbonitrates and HO2) in RACM2, the nighttime radical
losses were dominated by the formation of organic nitrates from OLND and
OLNN reaction with themselves and other peroxy radicals. The radical
termination processes in winter were quite different from that in summer.
During wintertime, the peroxy radical recombination was almost negligible,
and the radical termination was almost all contributed by the reactions with
NOx (Zhang et al., 2022; Tan et al., 2018d; Ma et al., 2019; Slater et al.,
2020).
OH and HO2 measurement–model comparison
OH and HO2 radical concentrations were simulated by a box model, which
showed generally good agreement with observations (Fig. 3). A significant
discrepancy between observed and modeled HO2 concentrations occurred
on 12 and 13 June. On these two days, maximum HO2 increased to
2.6×109 cm-3, twice the campaign averaged maximum,
while modeled HO2 concentration remained nearly the same as the
campaign averaged maximum. We investigated the discrepancy between observed
and modeled HO2 against different chemical compositions but could not
identify the cause of elevated HO2 concentration on these two days. In
the following analysis, the observation–model comparison mainly focused on
the mean diurnal average to extract the overall feature of the campaign.
OH underestimation in low-NO regime
As shown in Fig. 4, the modeled OH concentration reproduced the observed OH well in the early morning but underestimated the observation since 10:00, with the largest discrepancy occurring at noon. The HO2 measurement–model
comparison showed similar diurnal variation, but the largest discrepancy
shifted to 1 h later together with the diurnal maximum. On a daytime basis,
the modeled OH and HO2 radical concentrations were on average 30 %
and 28 % smaller than measurements, respectively. The discrepancies can be
explained by their respective combined 1σ uncertainties of measurement and
model calculation (10 % and 13 % for measurement and 32 % and 40 %
for model calculation). In fact, the HO2 discrepancy in the mean
diurnal profile was mainly caused by two outlier days, which disappeared in
the median diurnal profile (Fig. S6). However, the discrepancy of OH was
also observed in the median diurnal profile, indicating a persistent OH
underestimation during the afternoon.
The OH underestimation discrepancy showed dependence on the NO
concentration. Figure 9 illustrates the dependence of observed and modeled
HOx radicals on NO concentration. To remove the influence of photolysis
on the OH radical, OH concentration was normalized to j(O1D) prior to NO
dependence analysis. The observed median OHnorm was almost constant
over the whole NO regime, while the modeled value tended to decrease
towards lower NO (<0.3 ppb). The modeled OHnorm was 42 %
smaller than the observed one at a NO mixing ratio below 0.1 ppb (Fig. 9),
which was beyond the measurement–model combined uncertainty. This
discrepancy was mainly caused by the data obtained in the afternoon. The
observed and modeled HO2 agreed throughout the NO regime (Fig. 9) and
was consistent with the median diurnal profiles.
Dependence of measured and modeled OH,
HO2, and P(Ox) on NO
concentrations for daytime conditions
(j(O1D) >0.5×10-5 s-1). Box–whisker plot
shows the median, the 75th and 25th percentiles, and the 90th and 10th percentiles
of the measured results for each NO interval bin. Only median values are
shown for modeled results. Numbers in the upper panel represent the data points
incorporated in each NO interval. Results from the base case, with an additional
recycling process by a species X (equivalent to
100 ppt NO) and with an additional HO2 heterogeneous
uptake process (assuming γ to be 0.08), are all plotted.
Such OH underestimation in the low-NO regime (typically with NO concentration
less than 1 ppb) was frequently found in environments with intense biogenic
emissions, especially isoprene (Tan et al., 2001; Ren et al.,
2008; Lelieveld et al., 2008; Whalley et al., 2011; Stone et al., 2011a; Lu et
al., 2012, 2013; Hofzumahaus et al., 2009). We included up-to-date
chemical mechanisms related to H-shift processes to consider the impact of an
additional OH source, such as the H-shift mechanism of isoprene-derived
peroxy radicals (Peeters et al., 2014). However, during this
campaign, isoprene concentration was only 0.2 ppb, contributing 5 % of the
modeled OH reactivity. The H-shift mechanism of isoprene-derived peroxy
radicals only increased 1.2 % of the modeled OH concentration and thus
played a minor role in OH chemistry. Therefore, other processes should account
for the OH underestimation in low-NO conditions.
To resolve the OH underestimation, a genetic mechanism X was proposed for the
Backgarden 2006 campaign, in which X served as NO that converted RO2 to
HO2 and then HO2 to OH (Hofzumahaus et al., 2009).
Sensitivity tests demonstrated the requested amount of X was equivalent to
100 ppt NO for the EXPLORE-YRD campaign (Fig. 9). Comparatively, the X
concentration is the same as in the Wangdu campaign (Tan et al., 2017)
but smaller than that identified in Backgarden (0.8 ppb, Hofzumahaus
et al., 2009), Yufa (0.4 ppb, Lu et al.,
2013), and Heshan (0.4 ppb, Tan et al.,
2019a), where the biogenic isoprene and OH reactivities were 3 to 5
times and twice as high as during this campaign, respectively (Table 3).
It should be pointed out that the preceding quantified X of 100 ppt
equivalent NO was supposed to be the lowest limit in this study, if missing
reactivity existed. Therefore, we performed a series of sensitivity tests,
by adding a genetic reaction converting OH to RO2 that was equivalent to
30 % of the total OH reactivity to account for the possible
missing reactivity in this study. The adopted degree of missing reactivity
was comparable to that observed in central Beijing (Whalley et al.,
2021), which represented a significant portion of potential missing
reactivity. Furthermore, the formed RO2 species was varied to investigate
the influence of different RO2 types on the modeled radical
concentrations including MO2 (methyl peroxy radical), ETEP (peroxy
radical formed from ethene), and ACO3 (acetyl peroxy radical). In these
cases, the modeled OH decreased by 1.1–1.7×106 cm-3 compared to the base case, and the requested amount of
X increased to be equivalent to 200–300 ppt of NO depending on
the specific RO2 types (Fig. S7).
On the other hand, the OH measurement–model discrepancy could be attributed
to measurement artifacts (Mao et al., 2012; Novelli et al., 2014, 2017; Rickly and Stevens, 2018; Fittschen et al., 2019). Previous
studies proposed that stabilized Criegee intermediates (SCIs) produced from
reaction of ozone with alkenes and trioxides (ROOOH) produced from reaction
of larger RO2 with OH might cause artificial OH signals using LIF
techniques (Novelli et al., 2017; Fittschen et al., 2019). However,
chemical modulation tests on an ozone-polluted day when both O3 and
ROOOH (modeled) concentrations were high (7 June) indicated insignificant
interference for OH measurement in this study (Fig. S8). Furthermore, little
relevance of ROOOH and the degree of disagreement between measurement and
model was found in this study (Fig. S9), and thus, there is no hint for
significant OH measurement interference during the EXPLORE-YRD campaign.
However, one should note that the precision is not good enough to rule out
the possibility.
Monoterpene influence
The observed monoterpenes varied from 0.2 to 0.4 ppb, showing a broad peak
around noon (Fig. 2). The high monoterpene concentration and daytime peak
indicate a strong daytime source given its short lifetime due to oxidation
(24 min for α-pinene or 8.2 min for Limonene,
OH =1.0×107 cm-3, O3= 80 ppb). The diurnal
variation was different from forest environments where maxima usually
appeared at night (Kim et al., 2013; Wolfe et al., 2014; Hens et al.,
2014). The relatively low nighttime monoterpenes could be related to the strong
NO3 chemistry in this study (H. Wang et al., 2020).
In the base model run, observed monoterpene concentrations were all
allocated to α-pinene, accounting for 0.5 s-1 of kOH (Fig. 7). Detailed mechanisms referring to α-pinene oxidation in RACM2 are
listed in Table S1 in the Supplement. A sensitivity test without monoterpenes constrained
showed the kOH would decrease by 1.0 s-1. Apart from the decrease
in monoterpene itself, half of the decrease in kOH was attributed to the
degradation products of α-pinene oxidation. Consequently, the
daytime OH and HO2 concentrations would increase by 7 % (5×105 cm-3) and 4 % (3×107 cm-3), respectively
(Fig. 4).
We also performed a sensitivity test to attribute the sum of monoterpenes to
limonene, another monoterpene species in RACM2. In this case, the OH
concentration would decrease by 11 %, while the HO2 concentration
would slightly increase by 1 % relative to the base case. The reduced
modeled OH concentration resulted from the 3 times faster reaction
rate constant of limonene with OH (1.6×10-10 cm-3 s-1 at 298 K) than that of α-pinene (5.3×10-11 cm-3 s-1 at 298 K). It indicated that the different assumptions
of monoterpene speciation had a minor impact on modeled OH and HO2
concentrations in this study.
In recent studies, Whalley et al. (2021) highlighted that large RO2
species, such as those derived from α-pinene and ozone reaction, form RO
species upon reaction with NO, and these RO species can isomerize to form
another RO2 species rather than forming HO2 directly and thus
might have an impact on the modeled OH and HO2 concentration. We also
performed a sensitivity test to substitute the reactions of α-pinene
with ozone in RACM2 with those considering RO isomerization in MCM3.3.1. The
modeled OH and HO2 concentrations decreased by 2.0×104 cm-3 and 2.5×107 cm-3, respectively, compared to the
base model (Fig. S3), indicating that α-pinene-derived RO isomerization had
little impact on the modeled OH and HO2 concentrations in this
study.
Other studies conducted in forested environments with a strong influence of
monoterpenes from pine tree emissions found discrepancies of up to a factor of 3 in HO2 measurement–model comparison (Kim et al., 2013; Wolfe et
al., 2014; Hens et al., 2014). In the present study, however, HO2
concentration was well reproduced by the chemical model within combined
uncertainty during daytime with high monoterpene concentrations.
Nevertheless, we cannot draw a solid conclusion that the monoterpene
oxidation chemistry in an environment with both strong anthropogenic and
biogenic influences can be captured by the applied chemical mechanisms with
respect to HOx concentration, since missing HO2 sources and sinks
might exist simultaneously but cancel out each other. Given that there were
no OH reactivity or RO2 observations in this study, we cannot rule out
these possibilities.
HO2 heterogeneous uptake
A recent model study proposed that HO2 heterogeneous uptake processes
play an important role in HOx radical chemistry and thus suppress ozone
formation in China (Li et al., 2019). The RACM2-LIM1 mechanisms used in
our study only consist of gas phase reactions without heterogeneous chemistry.
Therefore, in this section, we performed a sensitivity test with HO2
radical uptake considered to investigate the potential impact on the
modeled radical concentrations by adding a radical termination process
(Reaction R6).
HO2+aerosol→products
The heterogeneous loss rate of the HO2 radical is limited by the free
molecular collision because the aerosol surface is mainly contributed to by
submicron particles. The HO2 radical uptake process can be simplified as a
pseudo first-order reaction, and the first-order kinetics constant can be
calculated by Eq. (3):
3kHO2=VHO2×Sa×γ4,4VHO2=8RTπ×0.033.VHO2 represents the mean molecular velocity of HO2 determined by
Eq. (4). Sa is the humid aerosol surface area calculated by the SMPS
measured particle number and size distribution in each size bin corrected by
the hygroscopic growth factor. γ is the effective HO2 uptake
coefficient on aerosol giving the probability of HO2 loss by impacting
the aerosol surface.
The effective uptake coefficients vary from 10-5 to 0.82 from multiple
laboratory studies (Thornton et al., 2008; Taketani et al., 2009; Taketani
and Kanaya, 2010; George et al., 2013; Lakey et al., 2015; Zou et al., 2019). A
relatively high value of 0.2 was found in aerosol samples collected in the North
China Plain, which was attributed to the abundant dissolved copper ions in
aqueous aerosol (Taketani et al., 2012). A study
based on radical experimental budget analysis determined the effective
HO2 uptake coefficient to be 0.08±0.13 in the North China Plain
(Tan et al., 2020). In our sensitivity tests, both
coefficients were applied and simulated separately.
As shown in Fig. 4, the incorporation of the HO2 heterogeneous uptake
process worsened the model–measurement agreement with both OH and HO2
radicals for both cases. The modeled OH and HO2 radicals were reduced
by 10 % and 20 %, respectively, for the coefficient of 0.2 and by 5 %
and 10 % for the coefficient of 0.08. For the case the coefficient of
0.08, the increased radical loss rate from the HO2 uptake process was 0.4 ppb h-1 on a daytime basis, which was smaller than that during the Wangdu
campaign (0.6±1.3 ppb h-1). The discrepancy between the two studies
was caused by the lower aerosol surface areas during the EXPLORE-YRD
campaign (750 compared to 1600 µm2 cm-3). The measured and
modeled HO2 concentrations agreed within 33 % on a daytime basis,
which was less than the 40 % uncertainty of the HO2 simulation. However,
this discrepancy enlarged to 51 % as the coefficient increased to 0.2,
exceeding the uncertainty of HO2 simulation. The agreements between
measurement and model calculation of OH and HO2 indicated that the base
model without heterogenous reaction captured the key processes for OH and
HO2 radical chemistry in this study.
As discussed in Sect. 4.2.1, a series of sensitivity tests had been
performed to test the effect of missing reactivity on the modeled radical
concentrations (Fig. S7). It turned out that when OH converted to MO2,
the modeled HO2 would increase by 6.2×107 cm-3
compared to the base case, which makes more room for the HO2
heterogeneous loss. However, considering the potential effect of missing
reactivity on HO2, the measured and modeled HO2 discrepancy
(41 %) would still be beyond the uncertainty of HO2 simulation for a
coefficient of 0.2. On the contrary, for cases where OH converted to ETEP and
ACO3, the modeled HO2 decreased by 1.3×107 and 1.5×107 cm-3, respectively, compared to the
base cases, possibly due to the faster radical termination rates through
RO2+ HO2 in both these cases compared to that of MO2.
Nevertheless, the model sensitivity tests suggested that the HO2 uptake
coefficient was less than 0.2, if the HO2 heterogeneous loss played a
role during this campaign.
Local ozone production rate
Peroxy radical chemistry is intimately tied to the atmospheric ozone
production. All peroxy radicals which could react with NO to form NO2
lead to ozone formation (F(Ox)), as expressed in Eq. (5). In this
study, the ozone formation contributed by RO2 was derived from model
calculation due to the absence of RO2 measurement. The reaction rate
constant between HO2 and NO is approximately 8.5×10-12 cm3 molec.-1 s-1 at 298 K, while the rate constant for the
reaction of RO2 with NO varies significantly (ranging 5-fold)
depending on the specific speciation in RACM2. In addition, the NO2 yield
from RO2 and NO reaction also differs for different RO2 groups in
RACM2. Part of the RO2 radicals react with NO, forming organic nitrates
rather than producing NO2 and recycling the peroxy radicals. The
nitrate yield increases with higher carbon numbers and branch structure.
Therefore, the NO2 production from RO2+ NO reaction is
manipulated by the effective reaction rate considering both reaction rate
constant and NO2 yield for different RO2 species i (Eq. 5).
F(Ox)=kHO2+NOHO2NO+∑ikRO2i+NO[RO2]i[NO]
On the other hand, formed O3 could be involved and consumed in the
radical chain reactions by initiating the radicals from photolysis and
reaction with alkenes and propagating the radicals from reaction with OH and
HO2. Furthermore, part of the NO2 would react with OH to generate
nitric acid rather than photolysis (L(Ox)). Additionally, NO2
could also react with O3 to form the NO3 radical, which could further
combine with another NO2 to form N2O5 or oxidize VOCs to form
organic nitrates, leading to 2 to 3 times faster Ox loss than NO3
radical formation. Considering the fact that the NO3 radical could be
easily photolyzed to regenerate NO2 and O3 or be titrated by NO to
regenerate NO2, the contribution from the net NO3 radical formation
pathway was taken into account by taking the largest Ox loss per
NO3 net formation of 3 in Eq. (6).
L(Ox)=J(O1D)O3×φ+kO3+AlkenesAlkenesO3+kO3+OHOHO3+kO3+HO2HO2O3+kOH+NO2OHNO2+3×(kNO2+O3NO2O3-kNO+NO3NONO3-jNO3[NO3])
Thus, the net ozone production rate (P(Ox)) could be deduced from the
difference between Ox formation and Ox loss rates as expressed in
Eq. (7).
P(Ox)=F(Ox)-L(Ox)
Figure 10a shows the mean diurnal profiles of the calculated F(Ox) and
L(Ox) in this study. Fast ozone formation rate of up to 20 ppb h-1
was observed at 09:00, while the maximum ozone loss rate of 4 ppb h-1
shifted 2 h later at noon, when the ozone formation rate reduced to
11.4 ppb h-1. This rate was comparable to other campaigns conducted in
rural areas, while the ozone production rates increased significantly in
urban areas, where the noontime ozone formation rates varied from 13.9 ppb h-1 in Tokyo to 65 ppb h-1 in Mexico (Table 3).
(a) Mean diurnal profiles of the speciation ozone
formation rate (F(Ox)) from different peroxy radical
species (upper panel) and the speciation ozone destruction rate
(L(Ox), lower panel) calculated based on the measured
OH and HO2 and modeled RO2 radicals. (b) Daily (08:00–16:00) integrated net ozone production calculated
from the observed and modeled radical concentration, respectively. The
discrepancies between two model scenarios run (Scenario 1: without
α-pinene constrained; Scenario 2: with
HO2 heterogeneous uptake considered by assuming γ to be
0.08) from the base case are also shown.
Fast ozone formation is the consequence of both strong primary source and
efficient radical propagation. The latter can be evaluated by the ratio
between F(Ox) and P(ROx) known as ozone production efficiency
(OPE). As discussed in Sect. 4.1, the radical primary source was relatively
high during the EXPLORE-YRD campaign, and thus, the OPE was only 1.7, which
was smaller than or comparable to other rural campaigns (Table 3). Urban
campaigns in the US, Mexico, and Tokyo showed significantly higher OPE
varying from 6 to 10 (Table 3) probably benefitting from the moderate NOx
level. In comparison, OPE was smaller in four megacities in China (Beijing:
3.4, Shanghai: 3.1, Guangzhou: 2.2, Chongqing: 3.6) than in the US cities
ranging from 3 to 7 because of the suppression of high NOx in Chinese
cities (Tan et al., 2019b). However, during the
EXPLORE-YRD campaign, the low OPE indicates that the radical propagation
chain length was relatively short due to low-NO conditions.
As shown in Fig. 10b, the integrated net ozone production was 68.3 ppb d-1 over the entire daytime (08:00–16:00). The daily integrated
P(Ox) calculated based on the modeled peroxy radicals was 6.9 ppb
lower than that derived from observation (Fig. 10b). The discrepancy for
observations and model P(Ox) mainly appears at NO concentrations
larger than 1 ppb (Fig. 9). This behavior has been observed in a number of
previous urban radical measurement campaigns (Kanaya et al., 2008, 2012; Martinez, 2003; Ren et al., 2003a, 2013; Elshorbany et
al., 2012; Brune et al., 2016; Whalley et al., 2018; Tan et al., 2017), which
was caused by the model underprediction of the observed HO2
concentrations under high NO concentration (typically NO greater than 1
ppb). Although some of the previous HO2 measurement might suffer from
unrecognized interference from RO2 species, this kind of interference
has been minimized by lowering down the added NO concentration in recent
studies (Griffith et al., 2016; Brune et al., 2016). However, the
underestimation of ozone production from HO2 radical persists,
indicating that the photochemical production mechanism of ozone under a
polluted urban environment is still not well understood.
We also investigated the impact of different model scenarios on P(Ox)
by comparing integrated P(Ox) in different cases to that obtained in the
base model (Fig. 10b). A sensitivity test without α-pinene constrained
predicted 6.3 ppb less daily integrated net ozone production than the base case.
Meanwhile, the contribution of α-pinene-derived peroxy radicals
(APIP) to F(Ox) only accounted for 2.3 ppb O3 formation (Fig. 10a). The difference can be attributed to the degradation products of
α-pinene, which also contribute to ozone production. For example,
aldehyde (ALD) is an important daughter product from α-pinene
oxidation, which reacts with OH and forms acyl peroxy radicals. Acyl peroxy
radicals have two advantages in ozone formation. On the one hand, acyl peroxy
radicals have the fastest rate constants with NO among all the peroxy
radicals (2–5 times faster than others). On the other hand,
acyl peroxy radicals react with NO to produce NO2 and methyl or ethyl
peroxy radicals, which can further oxidize the NO to NO2 and generate
HO2. Given that the modeled HO2 concentration increased by 4 %
in the sensitivity test, the reduction in P(Ox) was mainly attributed to
significant reduction in modeled RO2 concentration. In fact, the
modeled RO2 concentration would reduce by 23 % if α-pinene
were not constrained to observation, which indicated α-pinene was an
important RO2 precursor. It proved that monoterpene contributes
significantly to the photochemical production of O3 in this study.
Moreover, we also investigated the impact of the α-pinene-derived RO
species, which can isomerize to form another RO2 rather than forming
HO2 directly on the calculated ozone production rate. It turned out
that including an α-pinene-derived RO isomerization mechanism in the
model run would reduce the daily net O3 production by 1 ppb.
Additionally, the HO2 heterogeneous uptake process in the model run would
reduce the daily net O3 production by 4.8 ppb by assuming the effective
coefficient of 0.08. The reduction in P(Ox) was only slightly smaller
than the relative change in modeled HO2 concentration (10 %) because
62 % of the F(Ox) was contributed by the reaction of HO2 with NO
(Fig. 10a).
Conclusions
A comprehensive field campaign to elucidate the atmospheric oxidation
capacity in the Yangtze River Delta in China was carried out in summer 2018,
providing the first OH and HO2 radical observations in this region.
Daily maximum concentrations of OH and HO2 radicals were in the range
from 8 to 24×106 and 4 to 28×108 cm-3, with mean values of 1.0×107 and
1.1×109 cm-3, respectively. The OH radical had the
second highest concentration among the observations in China, indicating the
strong oxidation capacity in the YRD region from the perspective of OH radical
concentration. The modeled kOH varied from 5 to 40 s-1
over the whole campaign, 40 % of which could be explained by OVOCs, in
which measured and modeled OVOCs made up comparable contributions.
The radical primary source was dominated by HONO photolysis during this
campaign, contributing 42 % of P(ROx). The secondary contributor was
the photolysis of carbonyl compounds (including HCHO), accounting for 24 %
of the total radical primary source. Radical termination was dominated by
the reactions with NOx in the morning and peroxy radical self-reactions
in the afternoon. Specifically, OH + NO2 reaction and peroxy radical
self-reaction from HO2+ RO2 were the most important pathways,
contributing 25 % and 33 % of the total radical loss rates,
respectively.
The comparison between observation and box model simulation showed generally
good agreement for both OH and HO2 radicals on average. However, the OH
radical showed a tendency of underestimation for the low-NO regime
(NO < 0.1 ppb), and the discrepancy (42 %) was beyond the
combined measurement–model uncertainty. The up-to-date H-shift mechanism of
isoprene-derived peroxy radicals could not explain the discrepancy due to
the low isoprene concentration (0.2 ppb) during this campaign. A genetic OH
recycling process equivalent to 100 ppt NO was capable to fill the gaps,
which was also found in previous campaigns in Backgarden, Yufa, Heshan, and
Wangdu in China. In addition, the good simulation for the HO2 radical was
different from other monoterpene-rich forest environments, where HO2
underestimations were found.
Additional sensitivity tests were performed to investigate the impact of
monoterpenes and HO2 heterogeneous uptake on radical chemistry in this
study. Model simulation without monoterpene input or allocating monoterpene
to a different isomer (α-pinene and limonene in this study) showed
that HOx radical concentrations were not sensitive to the monoterpene
in this study. In fact, the modeled RO2 radical concentration would be
reduced by 23 % without monoterpenes constrained. The reduced RO2
radical offset the enhancement of HOx radicals. The combined influence
caused the net daily integrated ozone production to decrease by 6.3 ppb
compared to the base model of 61.4 ppb, which demonstrated the importance of
monoterpene chemistry on the photochemical ozone production in this study.
The role of HO2 heterogeneous uptake was tested by adding a pseudo
first-order reaction loss of HO2 and taking the effective uptake
coefficients of 0.2 and 0.08, respectively. The sensitivity test suggested
the applied chemical mechanism without HO2 heterogeneous uptake could
capture the key processes for HOx radicals, and the effective uptake
coefficient should be less than 0.2, if the HO2 heterogeneous loss
played a role in this study; otherwise, the HO2 measurement–model
discrepancy would be beyond the combined uncertainty. The daily integrated
net ozone production would reduce by 4.8 ppb, if the effective uptake
coefficient was assumed to be 0.08.
Additionally, the noontime ozone production rate was 11.4 ppb h-1,
which was much slower than other campaigns in urban and suburban areas
varying from 13.9 to 65 ppb h-1. Thus, the ozone production efficiency
calculated from the ratio of P(Ox) and P(ROx) was only 1.7 in this
study, which was comparable to the values in rural campaigns but was 3 to 7
times lower than the values in other urban and suburban campaigns,
indicating the slow radical propagation rate and short chain length in this
study.
Data availability
The data used in this study are
available from the corresponding author upon request (k.lu@pku.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-7005-2022-supplement.
Author contributions
YZ and KL organized the field
campaign. KL and YZ designed the experiments. XM and ZT analyzed the data.
XM wrote the manuscript with input from ZT. All authors contributed to
measurements, discussing results, and commenting on the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank the science teams of the EXPLORE-YRD campaign for their support. We appreciate the help of the anonymous reviewers to improve this article.
Financial support
This research has been supported by the Beijing Municipal Natural Science Foundation for Distinguished Young Scholars (grant no. JQ19031), the National Research Program for Key Issue in Air Pollution Control (grant nos. 2019YFC0214801, 2017YFC0209402, 2017YFC0210004, 2018YFC0213801), and the National Natural Science Foundation of China (grant nos. 21976006, 91544225, 91844301).
Review statement
This paper was edited by Yugo Kanaya and reviewed by two anonymous referees.
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