Introduction
Air pollution in Chinese megacity regions has become an issue of
great concern for citizens and the government. Ambitious
restriction strategies have already been implemented for the reduction
of the primary air pollutants such as sulfur dioxide (SO2),
nitrogen oxides (NOx) and particular matter
(PM10) for more than a decade. Significant emission
reductions of those primary air pollutants were achieved. However,
high concentrations of secondary air pollutants, e.g., ozone
(O3) and small particles (PM2.5), still occur and
the air quality has been steadily deteriorating in some
locations . As denoted in the empirical kinetics modeling approach
, the reduction in primary pollutants may not
directly reduce O3 due to the nonlinearity of atmospheric
photochemistry. Thus, a critical question is to find an optimized
way to control the abundance of secondary air pollutants through the
reduction of primary pollutants.
As shown in a large number of studies, hydroxyl radical
(OH) chemistry controls the atmospheric oxidation globally
. However, also other oxidants can be
of importance on a regional scale like NO3 ,
Criegee intermediates and chlorine radicals . In
China, studies of atmospheric oxidants are still sparse
. In summer 2006, we performed two
field campaigns (PRIDE-PRD2006 and CareBeijing2006)
focusing on hydroxyl and hydroperoxy (HO2) radical
measurements in a rural area in the Pearl River delta (PRD) and in
a suburban area (Yufa) close to Beijing. The major results from these two
campaigns were the following:
There were high concentrations of daytime and
nighttime HOx (= OH + HO2) radicals in the
Chinese developed megacity regions, indicating a strong atmospheric
oxidation capacity.
The high daytime OH concentrations
at high concentrations of volatile organic compounds (VOCs)
and low NOx concentrations could only be explained by
introducing an additional OH regeneration process in the
model that converts peroxy radicals to OH like NO
does. An equivalent of 0.8 ppbv and 0.4 ppbv of
NO was required in PRD and Beijing on average, respectively.
The high daytime OH concentrations at high VOC and high
NOx conditions could be understood by model calculations
. A
retrospective analysis shows that the magnitude
of unexplained OH concentrations observed in these two
studies in China is similar to other OH observations at
high VOC low NOx conditions .
Because isoprene was the most important OH reactant during
many of these campaigns, theoretical and laboratory investigations
were done to investigate its photochemical degradation.
Isomerization and decomposition reactions of organic peroxy
radicals formed from isoprene were found to be competitive with
the reaction of peroxy radicals with NO for conditions of
these campaigns . They led to the direct reformation of radicals and
the production of hydroperoxy aldehydes (HPALDs), which can
photolyze and produce additional radicals. Isoprene chemistry was
less important in our two field campaigns in China 2006 compared
to other campaigns that were conducted in forested areas, so that
new findings in the degradation of isoprene alone could not close
the gap between measured and modeled OH .
As a continued effort to explore the hydroxyl radical chemistry in
Chinese megacity areas, OH, HO2, RO2
radical concentrations and OH reactivity were measured for
1 month at a rural site (Wangdu) in the North China Plain in
summer 2014 as part of a comprehensive field campaign. Several
improvements were achieved in comparison to the previous
campaigns (PRIDE-PRD2006 and CareBeijing2006).
Interference
tests were performed for OH measurements applying a new
prototype chemical-modulation device.
Unlike before,
HO2 was measured without interferences by RO2
species that are formed from alkenes and aromatic VOCs.
Total
RO2 was measured together with OH and HO2
in contrast to the two previous campaigns.
In addition, the
sum of RO2 species that are formed from alkenes and
aromatic VOCs was measured as a separate class of RO2.
Oxygenated
VOCs (e.g., formaldehyde, acetaldehyde, isoprene
oxidation products) were measured, whereas such observations were
missing in the previous two campaigns.
All improvements provide
better constraints for the interpretation of the radical
chemistry. The radical measurements were obtained by a newly
built, compact instrument that combines resources from Peking
University and Forschungszentrum Jülich. In this paper, we
report results of radical measurements and model calculations
compared to results from previous campaigns investigating HOx
chemistry in China.
Methodology
Measurement site
The campaign took place between 7 June and 8 July 2014. The
measurement site is close to the town of Wangdu (population 260 000
inhabitants), which is without major industry. The
Taihang Mountains are located 50 km northwest of Wangdu
and the Bohai Sea 200 km east. The next large city,
Baoding, is 35 km northeast of Wangdu. Beijing and
Shijiangzhuang, two of the largest cities in the North China
Plain, are located 170 km northeast and 90 km
southwest of the site, respectively. Time is given in this paper as
CST (Chinese national Standard Time = UTC + 8 h).
Sunrise was at 04:30 CST and sunset at 20:00 CST.
Measured quantities used for data analysis and model calculations.
Parameters
Measurement technique
Time resolution
Detection limit a
1σ Accuracy
OH
LIFb
32 s
0.32 ×106 cm-3
±11 %
HO2
LIFb, c
32 s
0.10 ×108 cm-3
±16 %
RO2
LIFb, c
32 s
0.05 ×108 cm-3
±18 %
kOH
LP-LIFd
180 s
0.3 s-1
±10 % ±0.7 s-1
Photolysis frequency
spectroradiometer
20 s
e
±10 %
O3f
UV photometry
60 s
0.5 ppbv
±5 %
NOg
chemiluminescence
180 s
60 pptv
±20 %
NO2g
chemiluminescenceh
60s
300 pptv
±20 %
HONOi
LOPAPj, CEASk
30s
7 pptv
±20 %
CO, CH4, CO2, H2O
cavity ring down
60 s
l
m
SO2
pulsed UV fluorescence
60 s
0.1 ppbv
±5 %
HCHO
Hantzsch fluorimetry
60 s
25 pptv
±5 %
Volatile organic compoundsn
GC-FID/MSo
1 h
20 to 300 pptv
±15 to 20 %
Volatile organic compoundsp
PTR-MS
20 s
0.2 ppbv
±15 %
a Signal to noise ratio = 1.
b Laser-induced fluorescence.
c Chemical conversion via NO reaction before detection.
d Laser photolysis – laser-induced fluorescence.
e Process specific, 5 orders of magnitude lower than maximum at noon.
f O3 was measured by two photometers (Environment S.A. (41M) and
Thermo (49i)); data were taken from the Thermo (49i) instrument, which agreed
well with the data from the Environment S.A. instrument (see text).
g NO and NO2 were measured by three chemiluminescence
instruments (Eco Physics CLD TR780 and two Thermo 42i-TL models); data were
taken from the Thermo (42i-TL) instruments which agreed well with each other;
the data accuracy represents the unexplained difference between the data from the
Thermo and Eco Physics instruments (see text).
h Photolytical conversion to NO before detection, home built converter.
i HONO was measured by two different, home-built (FZJ, PKU) LOPAP
instruments and one CEAS instrument (NOAA); data were taken from the FZJ-LOPAP
instrument; the data accuracy represents the unexplained differences between
the data of the three instruments (see text).
j Long-path absorption photometry.
k Cavity-enhanced absorption spectrometer.
l Species specific, for CO: 1 ppbv; CH4:1 ppbv; CO2: 25 ppbv; H2O: 0.1 % (absolute water vapor content).m Species specific, for CO: 1 ppbv; CH4:±1 ppbv; CO2: ±25 ppbv; H2O: ±5 %.
n VOCs including C2-C11 alkanes, C2-C6 alkenes, C6-C10 aromatics.
o Gas chromatography equipped with mass spectrometer and a flame ionization detector.
p OVOCs including acetaldehyde, methyl vinyl ketone and methacrolein.
Instruments were set up in a botanical garden, which was surrounded
by farmland. Wheat and willows were the dominant plant species, a few
of which were growing within 10 m of the instruments.
There was no car or truck traffic in the botanical garden; the
closest road was 2 km away. Most of the instruments were
placed in seven sea containers. Two of them were stacked on top of
three others and two more containers were placed approximately
5 m away.
Instrumentation
A large number of trace gases and aerosol properties were measured
during this campaign. Most of the instrument inlets were placed
7 m above the ground at the height of the upper
containers. Table summarizes the details of
the trace gas measurements. OH, HO2 and
RO2 radicals were measured by laser-induced fluorescence
described in detail below. The OH reactivity
(kOH), which is the inverse chemical lifetime of
OH, was directly measured by a laser pump and probe
technique .
Most of the inorganic trace gases (O3, CO,
CO2, NO and NO2) were simultaneously
monitored by more than one instrument. Measurements of
O3, CO and CO2 measurements agreed well
within the instrumental accuracies.
O3 measurements were performed by two commercial
instruments using ultraviolet (UV) absorption (Environment S.A.
model 41M and Thermo Electron model 49i). Also SO2,
CO, CO2 concentrations were measured by commercial
instruments (Thermo Electron models 43i-TLE, 48i-TLE and 410i). In
addition, a cavity ring-down instrument (Picarro model G2401)
provided measurements of CO, CO2, CH4 and
H2O concentrations.
Chemiluminescence technique was used to detect NO and also
NO2 after conversion to NO. Two commercial
instruments were deployed by Peking University (PKU) (Thermo
Electron model 42i NO-NO2-NOx analyzer),
one of which (PKU-PL) was equipped with a home-built photolytic
converter, for the detection of NO2, and the other with a
catalytic converter (PKU-Mo). The NO2 data from PKU-Mo
were not used here, since catalytic converters can cause
interferences from other nitrogen–oxygen compounds (e.g., HNO3,
PAN). Another instrument was operated by
Forschungszentrum Jülich (FZJ) (Eco Physics model TR 780, with a
photolytical converter for NO2).
Instruments were located in the upper two containers to have inlet
lengths as short as possible in order to minimize the correction
for shifts in the NO-to-NO2 ratio by the reaction
of NO with O3 in the inlet lines. The effect of changes
of the NO-to-NO2 ratio by peroxy radicals is negligible
due to their small concentrations and their high loss rate in
the inlet line. The distance
between inlets was less than 5 m. Measurements of the two
PKU instruments and the FZJ instrument differed overall by ±20 %, which cannot be explained by their calibration errors. The
reason for this discrepancy is not clear. Calibrations of the FZJ
instrument were less reproducible (10 %) than in previous
deployments, while
calibration measurements of the PKU instrument varied only by 1 to
2 %. Fortunately, the calibrations did not show a trend over
time, indicating that there was no accumulation of contaminations
in the inlet lines. Because of the more stable calibrations of the
PKU instruments, the NO and NO2 data used as model
input (Sect. 2.4) were taken from the PKU-Mo and PKU-PL instruments,
respectively. However, the difference between measurements of
different instruments is considered as
additional uncertainty in the NO2 and NO
measurements.
Six instruments detected HONO using different techniques.
Home-built instruments from FZJ and from PKU
utilized long-path absorption photometry (LOPAP).
In addition, three instruments applied cavity-enhanced absorption
spectroscopy (CEAS) for the detection of HONO. They were
operated by the US National Oceanic and Atmospheric Administration (NOAA)
, by the Anhui Institute of Optics and Fine
Mechanics (AIOFM) and by the University of Shanghai for Science
and Technology (USST). A gas and aerosol collector (GAC), which is
based on the wet denuder/ion chromatography technique, could also
detect HONO . The measurements between
multiple instruments agreed within 30 %.
HONO measurements from the FZJ-LOPAP instrument are used
as model constraint, because it showed the best detection limit and
temporal coverage during the campaign. Results of model calculations
only change less than 10 % if either measurements by the PKU-LOPAP
or NOAA-CEAS are instead used as constraint. The other CEAS
HONO instruments measured only during a few
days. The GAC HONO measurement is known to be affected by
interferences from ambient NO2 and was therefore not used here.
A total of 59 organic species were measured by a gas chromatograph (GC)
equipped with a mass spectrometer and a flame ionization detector
(FID) . This instrument provided concentrations of
C2 to C11 alkanes, C2 to C6
alkenes, and C6 to C10 aromatics. In addition,
measurements of VOCs were performed
by a proton transfer reaction – mass spectroscopy system (PTR-MS,
Ionicon). These measurements included isoprene, acetaldehyde, the
sum of methyl vinyl ketone (MVK) and methacrolein
(MACR), benzene, toluene, styrene, C8 aromatics,
C9 aromatics and acetonitrile.
Daytime measurements of the two instruments agreed well for those
species which were detected by both instruments. During nighttime,
however, PTR-MS measurements gave much larger values compared to
measurements by GC for some periods and some species. The reason
for that is not clear, but could have been caused by interferences
by other species that occur at the same mass in the PTR-MS.
Because of this uncertainty, mainly measurements by GC are taken as
constraints for model calculation here. Measurements of acetaldehyde,
MVK and MACR were only done by PTR-MS.
Formaldehyde (HCHO) was measured by a
commercial instrument utilizing the Hantzsch method (Aerolaser
GmbH model AL4021).
A 20 m high tower with meteorological instrumentation
was set up 15 m south of the containers, where
temperature, pressure, relative humidity, wind speed
and wind direction were measured at two different heights (10 and
20 m). The height of the planetary boundary layer (PBL)
could be estimated by a ceilometer (the minimum detectable PBL
height was 200 m). Photolysis frequencies were calculated
from the spectral actinic photon flux density measured by a
spectroradiometer , whose inlet dome was placed on
top of the highest container.
More trace gases were detected, but will not be discussed in
detail here; peroxyacyl nitrates (PAN) and peroxypropionyl
nitrate (PPN) were measured by gas chromatography with an
electron-capture detector . H2O2 was
collected by a scrubbing coil collector and detected by
high-performance liquid chromatography (HPLC) coupled with
post-column derivatization and fluorescence detection
. Chemical ionization mass spectroscopy (CIMS) was
utilized to measure nitryl chloride (ClNO2) and
N2O5, but measurements were only conducted after 21 June .
A cavity-enhanced absorption spectrometry instrument was deployed
to detect glyoxal, HONO and NO2 .
Aerosol properties were characterized in detail during the
campaign, but will be discussed elsewhere. Measurements included
particle number density and size distribution and also chemical
composition.
Laser-induced fluorescence instrumentation for the detection of radicals
Instrument description
OH, HO2 and RO2 concentrations were
measured by laser-induced fluorescence (LIF) technique. LIF is a
direct method to detect OH radicals . In addition,
HO2 and RO2 radicals can be detected by
fluorescence after chemical conversion to OH .
Schematic drawing of the LIF instrument for the detection
of OH, HO2 and RO2. The laser module and
the measurement module were installed inside and on top of a sea
container, respectively. The laser light of 308 nm is split into three parts
(BS: beam splitter; L: lens) and guided by optical fibers to the
measurement cells, the kOH instrument and the reference
cell. Ambient air is sampled into low-pressure fluorescence cells
that are separated by windows (W). Reactive gases (NO,
CO) are added into the HOx- and ROx- cells
and the ROx converter. Baffle arms (BAs) and fluorescence
cells are continuously purged with N2. The position and
the power of the laser beam are monitored by a photodiode (PD) and
a position-sensitive diode (PSD).
The Peking University laser-induced fluorescence instrument,
PKU-LIF, was deployed in this campaign for the first time. It
consists of two LIF measurement cells to detect both OH and
HO2. It was built by Forschungzentrum Jülich and is
therefore similar to instruments from this organization
that have been described earlier
.
Additionally, a third measurement cell was provided by
Forschungzentrum Jülich for the detection of the sum of
RO2 radicals .
The instrument consists of a laser and a measurement module
(Fig. ). The laser radiation for the OH
excitation at 308 nm is generated by a pulsed,
frequency-doubled, tunable dye-laser system that is pumped by a
commercial Nd:YAG laser (Spectra-Physics model Navigator) at
532 nm (repetition rate: 8.5 kHz; pulse duration at full width half maximum (FWHM): 25 ns). The laser light is guided to the measurement
cells, to the kOH instrument and to an OH
reference cell by optical fibers. The laser power is divided with
a ratio of 0.6 : 0.32 : 0.08, resulting in a laser power inside the
measurement cells of typically 20 mW. The signal of the
reference cell, in which a large concentration of OH is
produced by pyrolysis of water vapor on a hot filament, is used as
a wavelength reference and allows for the automatic correction of
possible drifts of the laser wavelength.
All components of the measurement module are housed in a
weather-proof, air-conditioned box placed on top of the upper
container. For the OH and HO2 detection cells,
ambient air is sampled at a flow rate of 1 slpm (standard
liters per minute, at standard conditions of 25 ∘C
and 1 atm) through conically shaped nozzles (Beam Dynamics, orifice
diameter 0.4 mm) into low-pressure cells (p=4 hPa). RO2 is measured by a differentially
pumped system consisting of a chemical conversion reactor (p=25 hPa), followed by a fluorescence detection cell
(p=4 hPa). Ambient air of 7 slpm is sampled through
a nozzle (orifice diameter 1.0 mm) into the reactor, half
of which is sampled through a second orifice into the fluorescence
cell. Nitrogen sheath flows of 1 slpm are surrounding the
gas expansions of sampled air in all fluorescence cells. Reactive
gases for the conversion of peroxy radicals can be injected via
ring-shape nozzles in the fluorescence cells and via an injection
needle in the RO2 conversion reactor.
The laser light crosses the three fluorescence cells in a single
pass. Microchannel plate (MCP) photomultiplier detectors (Photek, PMT 325)
are used to detect fluorescence photons collected by lens systems.
The detection system is mounted perpendicular to the gas beam and laser light axis.
The MCPs are gated to switch off the gain
for the duration of the
laser pulses. The OH fluorescence is recorded by a gated
photon-counting system (Becker & Hickl, PMS 300) in a 500 ns
time window starting approximately 100 ns after the
laser pulse when laser stray light has dropped to an acceptable level.
The total photon count rate is composed of the OH
fluorescence, solar stray light that enters the cell through the
orifice and laser stray light. The solar stray light is detected
separately during a second counting window (duration of
25 µs starting 25 µs after the laser pulse),
when the OH fluorescence signal has diminished. The long
integration time ensures accurate subtraction of the solar
background signal, after it has been scaled to the shorter
OH fluorescence counting window. The remaining other
background signals are separated from the OH fluorescence
by wavelength modulation of the laser. Background and fluorescence
signals are measured together, when the laser wavelength is tuned
on the OH absorption line, and only background signals are
detected, when the laser wavelength is tuned off the absorption line.
During one measurement cycle, the laser wavelength is tuned to four
different online and two offline positions to make sure that the
maximum of the OH absorption line is captured as well as the
background signal. A full wavelength cycle gives a time resolution
of 32 s.
Interferences in the OH measurement
It is known that O3 photolysis by 308 nm radiation
with subsequent reaction of O1D with water vapor can
produce artificial OH inside the measurement cell. This
interference was characterized in laboratory experiments and
parameterized using the laser power and the O3 and water vapor
concentrations. A correction is applied that is
small compared to ambient OH concentrations during daytime:
50 ppbv of O3 could cause
an equivalent of 3 ×105 cm-3 OH for
typical laser power (20 mW) and water concentration (1 %) in this campaign.
Potential interferences from ozonolysis reactions and NO3
have been investigated for OH and HO2 detection
cells that are similar to the detection cells of the PKU-LIF
instrument . No significant interference was
found from the ozonolysis of simple alkenes (e.g., ethene,
propene), isoprene and monoterpenes at ozonolysis reaction rates
of several ppbvh-1, i.e., at reaction rates that are
considerably higher than found in the atmosphere. Therefore, it is
not expected that measurements in this campaign are affected by
ozonolysis products. Interferences from NO3 were reported
. The underlying mechanism is still unknown. The
magnitude of the interference is 1.1 × 105 cm-3
OH in the presence of 10 pptv NO3. No
significant interference is expected at NO3 concentrations
that are predicted by model calculations for the present campaign
at nighttime (average 10 pptv).
Wavelength modulation used in this work to distinguish between
OH fluorescence and background signals is not capable of
discriminating ambient OH signals from signals caused by
artificially produced OH in the detection cells. Because
interferences from unknown internal processes have been reported
for two other LIF instruments , we
have tested a chemical modulation scheme that was proposed and
used by these authors. For this purpose, ambient OH is
removed by an OH scavenger (propane) that is added to the
sampled ambient air just before entering the fluorescence cell, so
that any remaining OH signal could be attributed to
internally produced OH. The propane concentration has to be
chosen such that most of the ambient OH is removed while it
is small enough to prevent OH losses inside the
fluorescence cell. When the scavenger is replaced by nitrogen, the
sum of ambient OH and possible interference OH is
measured. By switching between propane and nitrogen addition,
ambient OH signals can be discriminated from artifacts.
In the campaign, we applied a prototype device for chemical
modulation that was temporarily attached to the OH
detection cell during selected periods (Table ).
The device consisted of a Teflon
tube with an inner diameter of 1.0 cm and a length of
10 cm. About 20 slpm of ambient air were drawn
through the tube by a blower. Air of 1 slpm was sampled
into the OH detection cell. At the entrance of the Teflon
tube, either propane mixed with nitrogen or pure
nitrogen was injected into the air flow by a small tube (stainless
steel, outer diameter 1/16′′). Due to technical problems with the
control electronics, the device failed to operate in the first
half of the campaign. In the second part of the campaign, it
showed instabilities causing an increased uncertainty in the
determination of the OH scavenging efficiency.
The two signals with and without propane have contributions from ambient
OH (SOH), from the known ozone interference
(SO3) and any potential additional interference
signal (Sint):
SN2=SOH+SO3+SintSprop=(1-ϵ)SOH+SO3+Sint.
ϵ is the efficiency with which ambient
OH is removed when propane is added.
Unexplained OH signal and chemical conditions during the OH interference tests.
The mean value and the 1σ standard deviation of the unexplained OH signal are
calculated from the differences between SN2 and SOH + SO3
shown in Fig. for each test. The differences are expressed as
equivalent ambient OH concentrations (see text).
No.
Date
Time (CST)
OH
kOH (s-1)
NO (ppbv)
ISO
O3
T (∘C)
Unexplained signal
(106cm-3)
(ppbv)
(ppbv)
(106 cm-3)
1
06.29
13:00–15:00
7.0
15.2
0.16 ± 0.11
2.8
126
34
0.65 (±0.34)
2
06.30
09:50–11:00
10.4
15.4
1.39 ± 0.51
2.1
81
31
0.97 (±0.14)
3
06.30
14:40–16:10
8.5
8.8
0.14 ± 0.05
2.0
110
34
1.15 (±0.21)
4
07.02
10:50–11:30
4.6
10.0
1.19 ± 0.27
n/aa
52
26
0.74 (±0.24)
5
07.05
16:30–17:40
3.3
9.2
0.08 ± 0.02
1.6
94
32
0.99 (±0.04)
6
07.05
18:00–21:00
1.5
16.7
0.02 ± 0.03
1.4
77
31
0.53 (±0.30)
a No VOC was measured during the chemical modulation experiment.
As long as ambient OH does not change while switching
between the two measurement modes, the difference between the two
signals can be used to calculate the signal from ambient
OH:
SOH=ϵ-1(SN2-Sprop).
Together with the known ozone interference, the signal that is
expected to be observed in the absence of an additional
interference can be calculated and compared to the total signal
that is measured with no OH scavenger added
(SN2).
The knowledge of ϵ is
essential for an accurate quantification of potential
interferences. The removal efficiency was tested and optimized
in the field using the OH calibration device as a
radical source. The value of ϵ was found to depend on
the flow rates of the added gases (propane and nitrogen). Propane
was added as a 5 % mixture in nitrogen with a flow rate between 0.02
and 0.2 L min-1 (liters per minute) which was further diluted
in a carrier flow of pure nitrogen (0.04 to 0.5 L min-1).
The dependence of ϵ on the flow rates showed that
mixing of the injected propane into the high flow of ambient air
was inhomogeneous, similar to results reported in .
Because of technical difficulties with the flow regulation,
the removal efficiency was redetermined before each ambient
titration test. The values obtained for ϵ ranged between
80 and 97 %, with an accuracy of 10 % (1σ) at fixed nominal
propane and nitrogen flows.
Kinetic calculations show that the added propane removes less than
0.3 % of internally produced OH. The calculation assumes that
the added propane is homogeneously mixed in the sampled air, yielding
an expected OH lifetime which is larger than 0.1 s and
therefore much longer than the residence time (3 ms) in the
low-pressure detection cell. Therefore, the propane concentrations
used in the chemical-modulation tests
are not expected to influence possible OH interference signals.
Another systematic error could arise from the depletion of ambient
OH by wall loss in the attached Teflon tube. Calibrations
of OH sensitivities with and without the chemical-modulation
device only differed by 5 %, which indicates that wall loss was not
important.
Measurement of HO2 and possible interference
The detection of HO2 is achieved by chemical conversion to
OH in its reaction with NO . Three
types of interferences are known for the current instrument
design.
A small OH signal is observed when NO is injected
into the fluorescence cell in the absence of ambient radicals.
This background signal was regularly determined during each
calibration and was stable over the entire campaign. The
equivalent HO2 concentration of this signal is 3 ×107 cm-3 for the NO mixing ratios applied in
this campaign (see below). In addition, ambient
NO3 radicals can cause interferences in
HO2 detection similar to OH (see above). The
estimated interference is 1 ×107 cm-3 at
10 pptv of NO3 , which is
comparable to the detection limit.
Specific RO2 radicals have the potential to be converted
to OH on the same timescale as HO2. Therefore,
they can contribute to ambient HO2 measurements
. In the following, the class of interfering
peroxy radicals is called RO2#. It includes, for example,
RO2 species derived from alkenes, isoprene and aromatic compounds.
In previous papers (e.g., ), the quantity [HO2*]
was defined as the sum of the true HO2 concentration and
the interference from RO2 species i, which is detected with
different relative sensitivities, αRO2i:
[HO2*]=[HO2]+∑αRO2i[RO2]i.
RO2 radicals from alkenes, for example, have αRO2i
values of about 0.8, when NO is sufficiently high to achieve almost
complete HO2 to OH conversion in the detection cell
. A significant reduction of the relative
interference from RO2 can be achieved by using a smaller amount
of added NO. Although less NO will cause a smaller HO2
conversion efficiency, possible interferences from RO2 will be even
more strongly reduced because RO2 conversion to OH requires
one more reaction step with NO. For this reason, the NO
concentration used for the conversion of HO2 during this campaign
was chosen to be significantly smaller (≤ 20 ppmv) than
in previous field campaigns (500 ppmv) . At this low concentration,
it is expected that interferences from RO2 become almost negligible .
In order to test the importance of the remaining RO2# interference
in the HO2 measurements, the added NO was periodically switched
between two different concentration values every few minutes. Any RO2#
interference is then expected to produce a systematic difference between HO2
measurements with smaller and higher NO concentrations. At the beginning of
the campaign, NO mixing ratios were changed between 5 ppmv and
20 ppmv, yielding HO2 conversion efficiencies of 11 and 35 %,
respectively. On average, HO2* was 15 % higher when the larger NO
value was applied, indicating the influence of RO2#. After 14 June,
the mixing ratios were switched between values of 2.5 ppmv and 5 ppmv,
giving HO2 conversion efficiencies of 6 and 11 %, respectively.
In this case, HO2* was on average 3 % higher when the larger NO
value was applied. The ratios of HO2* measurements obtained for a pair
of alternating NO concentrations showed no temporal trend or diurnal variation
in each part of the campaign.
The HO2* ratios were used to derive correction factors for the determination
of interference-free HO2 concentrations. For small NO concentrations
as used in this work, we assume that the interference from RO2# is directly
proportional to the applied NO concentration. Based on this assumption, we derived
HO2* / HO2 ratios of 1.02, 1.05 and 1.2 for the addition of 2.5, 5 and
20 ppmv NO, respectively. These ratios were then used as correction factors
to generate a consistent data set of interference-free HO2 concentrations from
the HO2* measurements. After all, the correction was small enough that deviations
from this assumption would not significantly affect our results.
Measurement of RO2 and possible interference
In the RO2 detection system, the chemical conversion of
RO2 and of HO2 to OH is accomplished by a
two step process as described in . In the first
chamber (conversion reactor), the addition of 0.7 ppmv
NO and 0.11 % CO at a pressure of
25 hPa leads to the conversion of OH and
RO2 to HO2. The amount of NO in the reactor
is optimized for complete conversion of CH3O2 to
HO2. Similar conversion efficiencies apply to the majority
of other atmospheric RO2 species, including those
resulting from OH reactions with simple alkanes,
monoalkenes and isoprene . If these are the
dominant RO2 species, then all sampled ROX (= OH + HO2 + RO2) radicals are present as
HO2 at the exit of the conversion reactor. In the second
chamber (fluorescence cell at a pressure of 4 hPa),
HO2 is converted to OH by increasing the NO
mixing ratio to 0.5 %. In contrast to the pure HO2
detection described above, there is no need to keep the
HO2 conversion efficiency small to avoid simultaneous
RO2 conversion. Therefore, the NO concentration is
much higher compared to the NO concentration in the
HO2 detection system. This measurement mode gives the
total RO2 concentration when the contributions of
OH and HO2 measured in the other two cells are
subtracted.
The ROx system can be operated in a second mode. CO
is still added to the converter causing conversion of OH
to HO2, but NO is switched off, so
that RO2 radicals are not converted to HO2. In the
fluorescence cell, however, RO2# species are converted
to OH on the same timescale as HO2 at the high
NO concentration. As a result, this operational mode
measures HO2* (Eq. ). The relative detection
sensitivities, αRO2i, of the ROx system in the
HO2* measurement mode were determined in laboratory experiments
for RO2 radicals derived from small alkenes (e.g., ethene, propene).
The values were found to be the same as those reported by for
an HO2 detection system with high HO2-to-OH conversion
efficiency. Accordingly, other αRO2i
values were adopted from and for these experimental
conditions.
The concentration measurements of HO2 (from the HO2 cell)
and of HO2* (from the ROx system) allow to estimate the
total concentration of RO2# : [RO2#] = ([HO2*]-[HO2]) / αRO2#.
Here, αRO2# denotes an average, relative detection sensitivity
for RO2# species which contribute to HO2*. A value of
αRO2# = 0.8 ± 0.2 is applied here, representing the range of
specific αRO2i values for the most relevant RO2 species
from alkenes, isoprene and aromatics. Any error in this average value adds to the
uncertainty of the calculated RO2# concentration.
Like for the HO2 detection system, the presence of
NO alone causes background signals of 5.0×107 cm-3 and 3.5×107 cm-3 in
the operational modes with and without NO addition in the
conversion reactor. In addition, NO3 causes an
interference signal, which is equivalent to 1×107 cm-3 RO2 per 10 pptv NO3
. Measurements were corrected for the NO
background signal, but no correction was applied for potential
interferences from NO3, because no NO3 measurement
was available. Model calculated NO3 concentrations suggest
that there was no significant interference from NO3
for conditions of this campaign.
A bias in the measurement of RO2 may be caused in polluted
air by peroxy radicals, which are produced in the low-pressure
converter of the RO2 instrument by thermal decomposition
of peroxy nitric acid (HO2NO2), methyl peroxy nitrate
(CH3O2NO2) and PAN . In the atmosphere,
HO2NO2 and CH3O2NO2 are in a fast thermal
equilibrium with HO2 and CH3O2, respectively,
together with NO2. The possible interference scales with
NO2, which was highest during the Wangdu campaign in the
morning (median value of 15 ppbv). For this condition,
according to model calculations by , HO2NO2
and CH3O2NO2 are expected to produce interferences of
+2.6 and +9 % for the detected HO2 and CH3O2
radicals, respectively. Since HO2 and CH3O2
contributed about 50 % (measured) and 10 % (modeled) to the
total ROx in the morning, the estimated interference
for measured RO2 is only +2 %. The interference
from PAN decomposition in the instrument was calculated by
to be 0.1 pptv per ppbv of PAN. Since the modeled
PAN concentrations for the Wangdu campaign are less than 1 ppbv,
no significant interference is expected from this compound.
Another bias could be due to the perturbation of the reactor
chemistry from high ambient NO concentrations .
For the measurements in the ROx and HO2* mode,
the corresponding interferences are estimated to be less than
+1 and +3 %, respectively, at 15 ppbv NO.
Calibration and detection limits
The calibration of the LIF instrument is achieved by a radical
source that provides equal concentrations of OH and
HO2 radicals by water vapor photolysis at 185 nm,
described in detail in . The radical
concentrations delivered by the source can be calculated from the
measured water vapor concentration, the gas flow and the intensity
of the 185 nm radiation with a 1σ accuracy of 10 %.
Addition of CO or CH4 to the calibration gas
quantitatively converts the OH into HO2 or CH3O2,
respectively. These modes are used for the calibration of the
HOx and ROx channels, respectively .
During the campaign, calibrations were done approximately every
third day. No trends with time for any of the sensitivities were observed.
Thus, averaged sensitivities over the entire campaign
were applied to calculate radical concentrations. The variability
of the measured sensitivities is considered as an additional
calibration uncertainty. The reproducibilities (1σ standard
deviation) of the sensitivities were 5 % for the OH cell
and 5 or 10 % for the HOx cell at high or low NO,
respectively. The reproducibilities of the sensitivities of the
ROx system were 7 % for the detection mode without
NO in the conversion reactor and 12 % for the mode with
NO.
The detection limit depends on the sensitivity, the laser power,
the value of the background signal and the integration time
. For nighttime conditions in the absence of
sunlight, the detection limits were
0.32×106 cm-3,
0.10×108 cm-3 and
0.11×108 cm-3 for OH, HO2 and
RO2, respectively (for a signal-to-noise ratio of 1,
a measurement time of 30 s and a laser power of 20 mW
during this campaign). During daytime, the detection limits
for OH and HO2 are significantly higher, because
higher background signals from solar radiation are present. The
typical solar background was about 40 cnts s-1 which is a
factor of 20 higher than the typical background signals obtained
at night. Therefore, the detection limit was reduced by a factor of 5.
A shade ring was installed during the campaign to shield the cell
from direct solar radiation. The
detection limit of the ROx system is not different during
daytime and nighttime, because no significant solar radiation can
enter the fluorescence cell through the conversion reactor.
Assignment of measured VOCs to species in the RACM 2 .
RACM
Measured hydrocarbons
CH4
methane
ETH
ethane
HC3
propane, i-butane, n-butane, 2,2-dimethylbutane
HC5
i-pentane, n-pentane, cyclopentane, n-hexane, 2,3-dimethylbutane, 2-methylpentane,
3-methylpentane, n-heptane, 2,4-dimethylpentane, 2,3-dimethylpentane,
methylcyclopentane, 2-methylhexane, MTBE
HC8
cyclohexane, 3-methylhexane,2,2,4-trimethylpentane, 2,3,4-trimethylpentane,
n-heptane, methylcyclohexane, 2-methylheptane, 3-methylheptane,
n-octane, n-nonane, n-decane
ETE
ethene
DIEN
1,3-butadiene
OLI
trans-2-butene, cis-butene, trans-2-pentene, cis-2-pentene
OLT
propene,1-butene, i-butene, 1-pentene, 1-hexene, styrene
ACE
ethyne
ISO
isoprene
BEN
benzene
TOL
toluene, ethylbenzene, i-propylbenzene, n-propylbenzene
XYM
m-ethyltoluene, 1,3,5-trimethylbenzene, 1,2,4-trimethylbenzene,
1,2,3-trimethylbenzene, m-diethylbenzene
XYO
o-xylene, o-ethyltoluene
XYP
m-p-xylene, p-ethyltoluene, p-diethylbenzene
HCHO
formaldehyde
ACD
acetaldehyde
MVK/MACR
methyl vinyl ketone and methacrolein
Isoprene oxidation mechanism replacing the isoprene chemistry in RACM 2.
Reaction
Reaction rate constant (cm3s-1)
Reference
ISOP → MACR + HCHO + OH
0.31 × 1.8 × 1011× exp(-9752/T)
a
ISOP → MVK +HCHO + OH
0.62 × 1.04 × 1011×exp(-9746/T)
a
ISOP → HPALD1 + HO2 + HPCARPO2
0.5 × 0.62 × (9.5 × 107exp(-7009/T) + 1.79 × 10-7exp(3722.5/T) ×ktr f)
a
ISOP → HPALD2 + HO2 + HPCARPO2
0.5 × 0.31 × (3.8 × 1013exp(-10 745/T) + 5.82 × 10-2exp(476.3/T) ×ktr f)
a
HPALD1 + HV → OH + HO2 + 0.5 × HKET
+0.5 × MGLY + 0.5 × ALD + HCHO
100 × jMACR
b
HPALD2 + HV → OH + HO2 + 0.5 × HKET
+0.5 × GLY + 0.5 × ALD + HCHO
100 × jMACR
b
HPALD1 + OH → OH
4.6 × 10-11
b
HPALD2 + OH → OH
4.6 × 10-11
b
HPCARPO2 → CO + OH +OP2
0.1
a
HPCARPO2 +NO → NO2 + MGLY + OH + OP2
2.9 × 10-12exp(-300/T)
a
HPCARPO2 + HO2 → OP2
7.5 × 10-13exp(-700/T)
a
ISHP + OH → IEPOX + OH
1.9 × 10-11exp(-390/T)
c
ISHP + OH → 0.7 × ISOP + 0.3 × MACR + 0.3 × OH
0.38 × 10-11exp(-200/T)
c
IEPOX + OH → IEPOXO2
5.78 × 10-11exp(-400/T)
c
IEPOXO2 + NO → IEPOXO+NO2
2.54 × 10-12exp(-360/T)
IEPOXO2 + HO2 → IEPOXO + OH + O2
0.074 × 10-11exp(-700/T)
c
IEPOXO → 0.125 × OH + 0.825 × HO2 + 0.251 × CO
+0.725 × HKET + 0.275 × GLY +0.275 × ALD
+0.074 × ORA1 + 0.275 × MGLY +0.375 × HCHO
1 × 106
c
MCP → HKET + OH + CO
2.9 × 107exp(-5297/T)
d
MACP + NO → 0.65 × MO2 + 0.65 × CO
+0.35 × ACO3 + NO2 + HCHO
2.54 × 10-12exp(-360/T)
d
MCP + NO → N2 + HO2 + HKET + CO
2.54 × 10-12exp(-360/T)
d
MVKP + HO2 → OP2
0.34 × 2.91 × 10-13exp(-1300/T)
e
MVKP + HO2 → ACO3 + OH + ALD
0.48 × 2.91 × 10-13exp(-1300/T)
e
MVKP + HO2 → HO2 + OH + ORA2
0.18 × 2.91 × 10-13exp(-1300/T)
e
a .
b .
c .
d .
e .
f ktr = NO × 2.43 × 10-12
exp(-360/T) + HO2 × 2.05 × 10-13
exp(-1300/T) + ACO3 × 8.4 × 10-14
exp(-221/T) + MO2 × 3.4 × 10-14
exp(-221/T).
Model calculations
A box model is used to simulate the concentrations of OH,
HO2, RO2 and RO2# and the total
OH reactivity. The model is based on the compact Regional
Atmospheric Chemical Mechanism version 2 (RACM) described in
. This mechanisms includes 17 stable inorganic
species, 4 inorganic intermediates, 55 stable organic compounds
and 43 intermediate organic compounds. Compounds that are not
explicitly treated in the RACM are lumped into species with
similar functional groups. The assignment of organic compounds
that were measured during this campaign to species in the RACM is
listed in Table .
Some modifications were applied to the RACM. The isoprene
mechanism was replaced by the more detailed mechanism listed in
Table . It is based on the Leuven isoprene
mechanism (LIM) proposed by . Here, we use the
updated LIM for bulk RO2 reactions described in
. In addition, the chemistry of the first-generation products of the isoprene oxidation, MVK and
MACR and isoprene hydroperoxides (ISHP), are
revised. MACR has been shown to regenerate OH via
RO2 isomerization and decomposition . OH is also formed by the reaction of
RO2 from MVK with HO2 with a significant
yield . The products of the reaction of isoprene
hydroperoxides formed in the reaction of isoprene RO2 with
HO2 have been revised by , showing that
epoxides can be formed in an OH neutral reaction.
The modified RACM 2 in this work has been compared to the modified
RACM-MIM-GK which was used previously for model studies of the HOx
chemistry in China . In the present study, modeled
HOx concentrations differ no more than 5 % between the old
and new modified RACM mechanisms. It is also noteworthy that HOx
results of the modified RACM-MIM-GK agreed well with predictions of the more
explicit Master Chemical Mechanism v3.2 .
Model calculations are constrained to measured trace gases,
including inorganic species (H2O, NO, NO2,
O3, HONO, CO) and organic species (methane
and nonmethane organic compounds listed in Table ).
Because only the sum of MVK and MACR were measured, a ratio of 0.6 : 0.4
was used to divide the sum measurement to
individual species. In addition, physical parameters like
photolysis frequencies, temperature and pressure are constrained
to measured values.
For model calculations, the measured time series are synchronized
to 5 min time intervals. This is done either
by averaging or by linear interpolation, if the time resolution of
the measurement is shorter or longer than 5 min, respectively.
Measurements of the two instruments for ozone and CO are
combined in order to fill data gaps.
Slightly more than 60 % of the measured OH reactivity
can be explained by the measured concentrations of CO,
NOx and hydrocarbons during daytime.
More than 90 % of the OH reactivity
can be explained if also measured oxygenated VOC species are
included .
Consequently, there were no large amounts of other
relevant OH reactants in the atmosphere which would
otherwise have contributed significantly to the measured
reactivity. For this reason, long-lived product species which were
not measured are constrained to zero in the model, in order to
avoid unrealistic build-up of additional reactivity. This
constraint is consistent with the assumption that most of the
measured pollutants were emitted nearby and were not
photochemically aged. Only aldehydes (ALD) are not set to
zero, because they lead to the formation of reservoir species for
organic peroxy radicals (peroxy acyl nitrates, PAN and
PPN), which are kept as free parameters. In addition,
HPALD that is formed in the new isoprene chemistry is not
constrained to zero. In order to avoid unrealistic accumulation of
oxygenated VOC species (mostly aldehydes), an artificial,
constant loss is added, which limits their lifetime to 24 h.
For comparison with experimental data, the modeled concentrations
of individual RO2 species are summed up in two categories
which simulate the measured total RO2 and RO2#
concentrations (cf. Sect. 2.3.4). Modeled RO2 contains
those species that can be detected by the measurement system.
The largest class of RO2 that is not included
in the calculated RO2 are NO3-alkene adducts (RACM
name OLND), because their reaction with NO does not
produce HO2. The largest concentration of OLND is
predicted in the early evening (approximately 1×108 cm-3). In contrast, the majority of modeled
RO2 during daytime consists of species which are detected.
In the model, the observable RO2 species contribute with
equal weight to the total RO2, whereas laboratory
calibrations of the RO2 instrument have shown slightly
different (less than ±20 %) detection sensitivities for the
measured RO2 species . Modeled
RO2# represents a subclass of RO2 species
which are produced in RACM 2 from alkenes, aromatics and
long-chain (> C4) alkanes.
The relatively large uncertainty of the model calculations is a
combination of uncertainties in the measurements used as model
constraints and reaction rate constants (for details, see
). Differences in the measurements of NO
(20 %) and HONO (30 %) from different instruments
change modeled OH concentrations by only 7 and 10 %,
if measurements from one or the other instrument is taken as
constraint. The uncertainties of measurements and
modeling need to be taken into account in the comparison. The uncertainty
of radical measurement is mainly determined by the 1σ measurement accuracies
(OH: ±11 %, HO2: ±16 %, RO2: ±18 %).
A series of tests based on Monte Carlo simulations show that the uncertainty
of the model calculations is approximately 40 %.
Results and discussion
OH chemical modulation tests
Chemical modulation tests as described in Sect. 2.3.2 were
conducted on 29 June (afternoon), 30 June (morning and afternoon),
2 July (afternoon) and 5 July (afternoon and evening). The time
periods of the tests and the atmospheric chemical conditions are
given in Table . All test results are shown
in Fig. , where the measured OH signal
SN2 (without OH scavenger) is compared
to the sum of the expected signals from ambient OH
(SOH) and the known O3 interference
(SO3). Statistical error bars shown in Fig.
are derived from 1σ measurement precisions of SN2
and Sprop. In addition, the sum of SOH
and SO3 has a systematic error (not shown in
Fig. ), which is dominated by the uncertainty
(±10 %, 1σ) of the removal efficiency
(ϵ) needed to calculate SOH
(see Eq. ).
Results of chemical OH modulation tests performed
during the campaign. In each test, the total measured OH
signal without OH scavenger (SN2)
is compared to the sum of the known contributions from ambient OH
(SOH) and the
interference from O3 (SO3). The error bars
denote the 1σ statistical error.
A fluorescence signal of 30 cnts s-1 (counts per second) corresponds to an OH
concentration of 1.0 ×107 cm-3.
The signals SN2 (Fig. ) are
on average higher than the corresponding sum of
SOH and SO3. The differences
vary within the range between 0.53 ×106 and
1.2 ×106 cm-3 (Table )
and could be the result of an
unknown OH interference or of the systematic
experimental error in the determination of
SOH + SO3. The differences
are subject not only to statistical errors,
which are shown as the error bars in Fig. ,
but also to the uncertainty arising from the
calculation of SOH (Eq. ).
Among all, the uncertainty in the removal efficiency (ϵ)
has the largest impact on the derived differences. The differences
between SN2 and SOH + SO3
and their uncertainty are listed in Table . No correlation
of differences with time of day or with the chemical conditions is observed.
The differences
fall quantitatively into the 2σ range of the
accuracy of SOH + SO3 and are
therefore at the limit of detection of the experimental
setup used in the campaign. Because the test results are
not sufficiently accurate to draw firm conclusions about
an unknown interference, the OH data in this work
were not corrected for a potential interference. Instead,
the differences found in Fig. are treated
as an additional uncertainty of the OH measurements
presented in this paper.
In the case of an interference, it would be a small fraction
of the measured OH during daytime. The measured
nighttime OH, however, would be much more affected.
Because the existence of an unknown OH interference
cannot be strictly ruled out, the interpretation of the
radical chemistry will therefore concentrate on daytime conditions.
More precise and accurate chemical modulation tests with an improved
experimental setup are needed in future field campaigns.
Meteorological and chemical conditions
Meteorological conditions were characterized by high temperatures
of up to 37 ∘C and high humidity. The wind velocity
was usually below 2 m s-1. Back trajectory analysis using
the NOAA HYSPLIT (Hybrid Single Particle Lagrangian Integrated
Trajectory Model) model showed that air masses
were often transported from south or east where large city
clusters are located. Solar radiation was strong during this
campaign with few exceptions of hazy or cloudy days (15–19, 25
June and 1 to 4 July; Fig. ).
Afternoon CO mixing ratios increased during several periods,
indicating accumulation of anthropogenic emissions on a regional
scale. They are separated by sudden drops during rain events on 19
June and 4 July and on 27 and 28 June when clean air was
transported from the north.
During the first half of the campaign, burning of agricultural
waste after harvesting in surrounding fields was observed. This
was confirmed by high acetonitrile
mixing ratios (> 1 ppbv) from 12 to 19 June.
Biomass burning was accompanied by a reduced
visibility and an increase in aerosol mass concentrations
(PM2.5) with maximum values of 150 µg cm-3
on 16 June (campaign average value: 70 µg cm-3).
Time series of O3 and NO2 often showed trends
similar to CO, but were also strongly influenced by
photochemistry. Maximum daily ozone mixing ratios ranged between
100 and 140 ppbv depending on the strength of radiation.
Because solar radiation was attenuated between 14 and 19 June
during the first pollution episode, O3 peaked already on
14 June. O3 was sometimes completely titrated by
nitric oxide at night.
Time series (5 min data) of measurements during this
campaign for j(O1D), j(NO2), CO, NO,
NO2, HONO, O3 and isoprene (ISO) used as
constraints for model calculations. Vertical dashed lines denote
midnight. Grey areas indicate nighttime. Several species were measured by two instruments
provided by PKU and FZJ. Measurements of both instruments for
O3 and CO agreed well, so that data sets were
combined to close data gaps. Only the combined data set is shown
here, but different colors indicate the origin of data.
NO2 and NO mixing ratios measured by the PKU
instruments were generally 20 % smaller than those measured by
the FZJ instrument. The horizontal lines denote the limit of
detection for two NO instruments (10 pptv for FZJ;
60 pptv for PKU). Both time series are shown, but
measurements from the PKU instruments were used as model
constraints.
Time series of measured and modeled OH,
HO2, RO2#, total RO2 concentrations and
kOH. Vertical dashed lines denote midnight. See text
for details on the definition of RO2# and total
RO2. Grey vertical lines denote 1σ standard
deviation for measured radicals' concentration with respect to
5 min variability. Grey areas indicate nighttime.
Isoprene mixing ratios exhibited a typical diurnal profile with
maximum values between a few hundred pptv and nearly
4 ppbv in the afternoon. These values indicate that
chemical conditions were also influenced by presumably local biogenic
emissions.
Time series of measurements and model calculations
The time series of measured and modeled OH, HO2,
RO2 and kOH are shown in Fig. .
Distinct diurnal profiles are observed for all radical species.
The daily maxima of OH, HO2 and RO2
appeared around noontime and concentrations ranged between
(5–15) ×106 cm-3,
(3–14) ×108 cm-3 and
(3–15) ×108 cm-3, respectively. On 18, 19 and 25 June and from
1 to 3 July, radical concentrations were low due to attenuated
solar radiation. On 28 June, OH increased to exceptionally
high concentrations of up to 3×107 cm-3 for a
short period of time, which was accompanied by an increase of the
HONO mixing ratio to 2 ppbv, leading to enhanced
OH production from HONO photolysis. During this
time, farmland next to the measurement site was treated with water
and artificial nitrogen-containing fertilizer, which may have
caused large local HONO emissions.
In general, the model reproduces the measured time series of
OH, HO2 and RO2 well. Differences between
modeled and measured radical concentrations are generally smaller
than the combined 1σ uncertainties of radical
measurements (10 %) and model calculations (40 %).
A closer look at the modeled and measured radical concentrations
reveals some systematic trends. Modeled OH concentrations
tend to be smaller than measurements during afternoon hours and
modeled RO2 concentrations tend to be lower in the early
morning and higher in the evening than corresponding RO2
measurements. In contrast, differences between modeled and
measured HO2 concentrations are small at all times.
Because of the similarity of the model–measurement agreement for
different days, further analysis of daytime radical concentrations
will be done on the basis of median diurnal profiles (Sect. 3.4).
The OH observed at night are mostly above the limit of
detection (3 ×105 cm-3) with concentrations
around 5 ×105 cm-3, whereas the model predicts
concentrations below the limit of detection. In a few nights, the
measured OH is even higher (e.g., 1–3 ×106 cm-3 on 13 June). The reason why the
measured OH values are significantly higher than the model
prediction is not clear. It could be caused by missing chemistry
in the model or vertical gradients in the nocturnal boundary layer,
as discussed in . Furthermore, we cannot exclude an
unknown interference of the same magnitude. The known interference
from NO3 is probably not sufficient as an explanation
; the expected interference would be
1 ×105 cm-3 for this campaign, which is 5 times less
than the averaged nighttime OH measurement.
Thus, if interferences played a role, they would probably have a
different origin.
Comparison of hourly median diurnal profiles of
OH, HO2, RO2, RO2# concentrations
and kOH and the ozone production rate P(O3)
(thick lines give median values, colored areas give the 25 and
75 % percentiles). S0 denotes results from the base
model run. S1 shows results when the VOC
concentrations in the model are increased to match the observed
OH reactivity. S2 shows results
when an additional primary RO2 source (2 ppbvh-1)
is added in the model for the time between 06:00 and 12:00 CST. Grey
areas indicate nighttime.
The time series of measured OH reactivity shows a change on
20 June (Fig. ). During the first 2 weeks, diurnal
profiles of kOH are more structured and show higher
values with maximum values of up to 40 s-1 compared to
values after 20 June, when kOH is only around
10 s-1 in the afternoon and exhibits a less distinct
diurnal profile. The first period coincides with the accumulation
of pollutants like CO, nitrogen oxides and particles
(Fig. ). In addition, harvesting and biomass burning
activities caused local emissions of OH reactants, which
may explain the short-term increases in OH reactivity
during this period, especially during nighttime, when fresh
emissions are released into the shallow nocturnal boundary layer
and highest OH reactivity is observed. After 20 June,
biomass activities close to the measurement place were less often
observed and heavy rainfall cleaned the air.
In the first period of the campaign, the model often underpredicts
the measured OH reactivity, especially at night. This is
likely caused by unmeasured atmospheric compounds from local
emission sources like biomass burning. In the second period, the
modeled and measured reactivities agree well during day and night for
most of the time.
Median values of measured species for morning and
afternoon hours. Time is indicated in CST.
06:00–10:00
12:00–16:00
j(O1D) (10-5 s-1)
0.63
1.3
j(NO2) (10-3 s-1)
3.5
4.9
OH (106 cm-3)
3.8
6.9
HO2 (108 cm-3)
1.9
7.4
RO2 (108 cm-3)
3.2
8.8
kOH (s-1)
20
11
NO (ppbv)
2.5
0.25
NO2 (ppbv)
12
3.3
HONO (ppbv)
0.78
0.51
O3 (ppbv)
39
93
CO (ppmv)
0.70
0.54
CH4 (ppmv)
2.2
2.0
ISO (ppbv)
0.59
0.84
ETH (ppbv)
4.1
2.7
HC3 (ppbv)
4.0
2.0
HC5 (ppbv)
2.5
1.0
HC8 (ppbv)
0.57
0.22
ETE (ppbv)
3.3
0.93
OLI (ppbv)
0.25
0.20
OLT (ppbv)
0.83
0.21
BEN (ppbv)
1.3
0.71
TOL (ppbv)
1.6
0.69
HCHO (ppbv)
8.4
7.5
ACD (ppbv)
2.6
1.9
MACR (ppbv)
0.36
0.28
MVK (ppbv)
0.54
0.43
Hourly median diurnal profiles of measured
j(O1D), O3, NO, NO2, HONO,
CO, isoprene (ISO) and HCHO
(thick lines give median values, colored areas give the
25 and 75 % percentiles). Grey areas indicate nighttime.
Correlation between j(O1D) and measured (upper panel)
and modeled OH (lower panel). A linear fit is applied which takes errors in
both measurements into account.
Median diurnal profiles
Differences between measurements and model calculations are
further analyzed using median diurnal profiles with a time resolution
of 1 h (Fig. ). Data are only included when
measurements of all key species used as model constraints and
radical measurements are available at the same time. Therefore,
4 days are excluded from the analysis from the entire data set.
On 13 June, data gaps are larger than 6 h for nearly
all instruments. No measurements of VOCs are available on 14 June,
no measurements of photolysis frequencies on 22 June and no
radical measurements on 4 July.
As described in Sect. 3.3, chemical conditions were slightly different
before and after 20 June. We found similar results of model–measurement
comparisons for radicals from the two periods for daytime conditions.
Therefore, the following interpretation and discussion will focus on
campaign-averaged diurnal profiles. Chemical conditions of data
included in the median profile are summarized in Table
and median diurnal profiles of important photochemical parameters are shown
in Fig. .
Hourly median diurnal profiles of measured and modeled
RO2 concentrations. Measurements can distinguish between
total RO2 concentrations and the subclass of
RO2#. Modeled RO2 species are shown as colored
areas. MO2 are methyl peroxy radicals. ETHP are ethyl peroxy
radicals. HC3P are alkyl peroxy radical (carbon number is equal to 3 or 4).
ACO3 + RCO3
are acetyl peroxy radicals. In the evening, “other” RO2 radicals
are mainly RO2 species produced by the reaction of VOCs with
NO3. ISOP are isoprene peroxy radicals. The “other” RO2#
include peroxy radicals from long alkanes, alkenes, aromatics and
isoprene oxidation products (MVK and MACR). Grey areas indicate nighttime.
The median diurnal profiles of the measured and modeled OH
concentrations agree within their errors of 10 % (1σ)
and 40 %, respectively, from sunrise to mid-afternoon. When
the median NO mixing ratio (cf. Fig. )
drops gradually from 0.3 ppbv to 0.1 ppbv
in the afternoon, a systematic difference evolves, with
measured OH concentrations being approximately
1 ×106 cm-3 higher than the model
calculations. The discrepancy is of similar magnitude
to the averaged unexplained OH determined in
the chemical modulation experiments (Table ).
Thus, the overall agreement
for OH would improve if the unaccounted signal was fully considered
as an OH measurement interference. However, the underestimation of OH
would persist for low NO conditions if a potential unaccounted signal was subtracted.
When NO concentrations are less than 100 pptv, the
observed-to-modeled OH ratio would be reduced from 1.9 to 1.5, indicating
that an OH source would still be missing for low NO conditions.
Although newly proposed isoprene mechanisms have the potential to
enhance the OH regeneration for low NOx conditions,
they only have a small effect on modeled OH concentration
at the conditions of this study with NO concentrations higher than
0.1 ppbv and isoprene concentrations lower than 2 ppbv.
In general, HO2 concentrations are
reproduced by the model during daytime within
the combined uncertainties of measurements and model calculations.
Nevertheless, the model has a tendency to overpredict
HO2 in the afternoon. If we constrain the model
to the observed HO2 concentrations, the
observed-to-modeled OH ratio increases from 1.6 to 1.8 for
daytime-averaged conditions (04:30–20:00 CST).
RO2 and RO2# are significantly underestimated
during the morning hours (06:00–10:00 CST) with an observed-to-modeled
ratio of 3 to 5, which is larger than the combined uncertainty (a factor of 2).
Reasons for discrepancies between measured
and modeled RO2 are further analyzed in Sect. 3.6.
Measured kOH is high at night, peaks in the morning
(22 s-1) and decreases to about 11 s-1 in
the afternoon. Modeled kOH shows a relative flat
diurnal profile (average over the day is 14 s-1).
Whereas good agreement with measurements is achieved during
daytime, measured reactivity is higher during nighttime especially
during the first part of the campaign. This is likely caused by
unmeasured emitted OH reactants. A sensitivity model run,
in which product species are not constrained to zero as in this
model run, does not give significantly different OH
reactivity in the night. A more detailed analysis of the OH
reactivity in this campaign is presented in our companion paper by
.
Correlation of OH with j(O1D)
Strong correlation has been found between j(O1D) and
OH radical concentrations for many field campaigns in
different environments from marine to continental locations
. A strong linear correlation is also observed for
data from this campaign (Fig. ). A linear fit between
measured OH concentrations and measured photolysis
frequencies yields a slope of
4.5 ×1011 scm-3. This value is similar to
values that were derived in previous field campaigns in China in
2006 in the Pearl River delta and Yufa .
The intercept of the linear fit for the campaign in Wangdu is
1.0 ×106 cm-3, which is smaller than
intercepts obtained for the data set from the campaigns in the
Pearl River delta (2.4 ×106 cm-3,
) and Yufa (1.6 ×106 cm-3,
). The intercept gives an estimate of the importance
of radical sources when the production of O1D from ozone
is small. This includes non-photolytic sources (e.g., ozonolysis of
VOCs) and photolytic processes in the early morning before
j(O1D) starts to rise (Fig. ).
Modeled OH also shows a strong dependence on
j(O1D) with a slightly smaller intercept compared to the
fit result using the measurements.
Model–measurement comparison of RO2
Figure shows median diurnal profiles of measured
RO2 and RO2# together with modeled
concentrations of speciated RO2 radicals. The
observed profiles of RO2 and RO2# have
similar shapes with a maximum around 14:00 CST. In the morning
hours, RO2 is dominated by RO2#,
whereas RO2# plays only a minor role in the late
afternoon and at night. The model reproduces the general
behavior of RO2 and RO2# well, with very
good agreement in the afternoon. However, in the morning, the
model underestimates RO2 systematically by a significant
amount of (1–2) ×108 cm-3. This is mainly
caused by an underestimation of RO2#. After sunset,
in the first half of the night, the model overestimates RO2.
This discrepancy is apparently related to organic peroxy radicals,
which do not belong to RO2#.
In the group of modeled RO2# species, isoprene peroxy
radicals (ISOP) make the largest contribution during daytime. Other
modeled RO2# include peroxy radicals from alkenes,
aromatics, long-chain (> C4) hydrocarbons, and MVK and MACR.
Among the RO2 radicals which do not belong to the
RO2# group, peroxy radicals of short-chain (< C5) alkanes
are dominating: methyl peroxy radicals (MO2), ethyl peroxy
radicals (ETHP) and peroxy radicals from HC3P
(e.g., propane). Acetyl peroxy radicals (ACO3 + RCO3)
are also a substantial fraction of RO2.
The strong underprediction of the observed RO2 by more
than a factor of 4 in the morning cannot be explained by the
measurement errors and interferences discussed in Sect. 2.3.4 and 2.3.5. In order to explore potential reasons for
this underprediction, several sensitivity tests were performed.
First, the impact of a faster OH on RO2
conversion by an increased amount of VOC was tested
(model sensitivity run S1). Second, an additional primary source
of RO2 was introduced into the chemical
mechanism (S2). Third, the possibility of a slower
removal rate of RO2 was tested (S3).
The first possibility (S1) is supported by the observation
that the modeled OH reactivity in the base run (S0)
is smaller than the measured OH reactivity in the
morning until about 09:00 CST. If this missing reactivity is
caused by unmeasured VOCs, the true RO2 production
from reactions of VOCs with OH would be larger than
the modeled one. To fill this gap, the total concentration
of the measured VOCs is increased to match the measured kOH
in the time window from 06:00 to 09:00 CST. The relative partitioning
of the VOCs is not changed. The model run (S1) with the upscaled
VOC reactivity resolves part of the RO2 discrepancy
until 09:00 CST (Fig. ). The observed-to-modeled
RO2 ratio is improved from 2.8 to 1.7 without affecting
the good model–measurement agreement for OH and HO2.
Further sensitivity tests show that the modeled RO2 is
not sensitive to the speciation of the additional VOC reactivity,
since the required change of kOH is relatively small
(< 20 %). Because no missing OH reactivity is found
after 09:00 CST, the gap between measured and observed RO2
cannot be explained by unmeasured VOCs later.
In sensitivity test S2, an additional primary source of
RO2 (OLTP) from terminal alkenes is introduced into
the model. A source strength of 2 ppbvh-1
from 06:00 to 12:00 CST would be required to achieve a
good model–measurement agreement (within 20 %)
for both RO2 and RO2#.
The modeled OH and HO2 concentrations also increase
and are slightly overpredicted by about 10 and 20 %,
respectively. This can still be considered as agreement within
the error of measurements and model calculations.
After 12:00 CST, the difference between modeled and measured
RO2 becomes smaller than 15 %, within the range of the accuracy of RO2 measurements.
A candidate for an additional primary RO2 source would be
reactions of VOCs with chlorine atoms, which are produced by photolysis
of nitryl chloride (ClNO2) . ClNO2
is formed from the heterogeneous reactions of Cl- ions with nitrogen
pentoxide (N2O5) and accumulates during nighttime. After sunrise,
ClNO2 is expected to be completely photolyzed within a few hours. The resulting
Cl atoms can abstract H atoms from saturated hydrocarbons or can
add to alkenes. The alkyl radicals produce RO2 which, in the case
of alkene-derived peroxy radicals, carry a chlorine atom. ClNO2 was
measured by a CIMS instrument at the Wangdu field site from 20 June to 8 July
. The concentrations increased at night and reached
on average high values of 0.5 ppbv at 08:00 CST, followed by a decay
to zero until 11:00 CST. In their study, investigated the role
of ClNO2 photolysis on the photochemical formation of RO2
and ozone during the Wangdu campaign. They used the Master Chemical Mechanism (MCM) v3.3 with an additional
chlorine chemistry module by . We repeated the study by
adding the same chlorine chemistry to our modified RACM 2 mechanism and
found the same additional formation rates of RO2 and
O3 as reported by . In our model run, a ClNO2
source is assumed that leads to a linear increase of ClNO2 concentrations
during nighttime to a maximum value of 0.5 ppbv at 08:00 CST for every day.
After 08:00 CST, the modeled source is turned off. ClNO2 starts to photolyze
after 06:00 CST with a photolysis frequency that was calculated from the measured
actinic flux. A maximum Cl production rate of 0.2 ppbvh-1
is obtained at 08:00 CST, yielding an additional RO2 production with a
similar rate. Compared to the additional RO2 production rate required
for model run S2, this is an order of magnitude too small. The mechanism is
also not capable of sustaining the additional RO2 production during the
whole morning, because ClNO2 is photolytically depleted within 2–3 hours.
Even if the modeled source strength is increased to match the highest ClNO2
mixing ratio of 2 ppbv observed on 21 June , the additional
primary RO2 production of 0.5 ppbvh-1 is still not sufficient.
Thus, although ClNO2 photolysis was a relevant radical source, it alone
cannot explain the missing source of RO2 radicals in the morning.
A further model test (S3) was performed, in which the rate of RO2 removal was
artificially reduced by decreasing the reaction rate constants between RO2
and NO. Such a reduction would be justified if the rate constant for
RO2 + NO would be systematically too large in the model. Another
reason could be a systematic measurement error of the NO concentration,
or a segregation effect between RO2 and NO due to inhomogeneous
mixing in the case of local NO emissions. In order to account for the discrepancy
between modeled and measured RO2 in the morning, the loss rate would have
to be changed by a factor of 4, which seems unrealistically high for each of the
above-mentioned possibilities. Also, there is no plausible reason why a systematically wrong
rate constant or NO measurement error would appear only during morning hours.
The overprediction of RO2 by the model in the evening
could be related to the differences in the chemistry of
RO2 during day and night. Because VOC oxidation
by NO3 is a major contribution to RO2 production at night,
the inability of the model to predict RO2 at night could be due to
the difficulties in reproducing NO3 in a box model. One complication is that
the NO concentrations are close to the limit of detection of the instrument
(60 pptv), which leads to a large variation in NO3 concentrations
in the model because of the fast reaction between NO3 and NO.
Assuming no RO2 production from NO3
chemistry would bring measured and modeled RO2 into
agreement.
NO dependence of the radical concentrations
NOx plays a crucial role in ROx chemistry due to
radical propagation via peroxy radical reactions with NO
and radical loss by the reaction of OH with NO2
. Because of these two counteracting processes,
maximum OH concentrations are expected at NOx
mixing ratios around 1 ppbv when other conditions
controlling OH are constant.
NO dependence of OH, HO2 and
RO2 concentrations and instantaneous ozone production rate
(P(O3)net) for daytime conditions (j(O1D)
>0.5×10-5 s-1). OH concentrations are
normalized to the average of j(O1D)
(1.5×10-5 s-1). Boxes give the 75 and 25 %
percentiles, the center lines the median and vertical lines the 90
and 10 % percentiles for NO intervals of
Δln(NO)/ppbv = 0.57. Numbers in the upper panel
give the number of data points included in the analysis of each
NO interval. Only median values are shown for model
results. Results from the base model and with additional radical
recycling by a species X (equivalent to 100 pptv
NO) are plotted.
Figure shows the dependence of the measured and modeled
radical concentrations on the NO mixing ratio. In order to remove the
influence of the OH production strength by photolysis seen in Fig. ,
OH concentrations are normalized to j(O1D)
measurements. In addition, only daytime values at NO
concentrations above the detection limit of the NO
instrument are included in this analysis
(j(O1D) >0.5×105 s-1,
NO >60 pptv). Measured OH concentrations
appear to be nearly independent of the NO concentration
after normalization to j(O1D). Median values of
measurements are almost constant for NO mixing ratios of up
to 5 ppbv. This behavior is only expected for NO
mixing ratios between 0.3 and 3 ppbv as indicated by the
base model calculations. Median modeled OH concentrations
are nearly half of the median measured values at NO mixing ratios below
100 pptv. This discrepancy is also seen in the median
diurnal profile of measured and modeled OH
(Fig. ), but it is less pronounced because NO
mixing ratios only dropped below 0.3 ppbv for certain
times and not for every day.
OH behavior similar to that shown in Fig.
has been reported for PRD and Yufa and also
for other field campaigns selected for conditions with high
OH reactivity (> 10 s-1) . In
contrast, campaigns in relatively clean air have shown a
decreasing trend of OH at low NO concentrations as
expected from the reduced radical recycling efficiency
.
Measurements and model calculations show similar decreasing trends
for both HO2 and RO2, with increasing NO
concentrations. This is expected because the lifetimes of these
radical species are mainly limited by their reactions with
NO. As also seen in the median diurnal profiles
(Fig. ), modeled and measured HO2
concentrations agree within 20 % over the entire range of
NO concentrations, whereas the measured RO2
decreases less than the modeled RO2 as NO
increases. At 3 ppbv NO, the modeled RO2
concentration is less than 1×108 cm-3,
whereas the median measured RO2 is
3.5×108 cm-3. As a consequence, the measured
peroxy radicals yield higher calculated net ozone production rates
than predicted by the model (see Sect. 3.8).
Two sensitivity model runs were done. In the first sensitivity
run, the model did not include the updated isoprene mechanism,
which is part of the base model run. The overall impact of the new
isoprene chemistry is rather small, the maximum increase in the
median OH and HO2 concentrations due to the
additional OH recycling is less than 1×106 cm-3 and 1×108 cm-3,
respectively, at NO mixing ratios lower than
0.1 ppbv. This is lower than the variability of
measurements.
In the second sensitivity run, radical recycling was enhanced by
introducing an artificial species X that behaves like
NO, but does not produce ozone (Fig. ). This has
been successfully applied to describe unexplained high OH
concentration in other campaigns including our
previous observations in China . Similar to the observations in the previous campaigns, a
constant mixing ratio of X would bring modeled and
measured OH into agreement for the entire range of
NO concentrations (Fig. ). Here, the
concentration of X needs to be equivalent to
100 pptv NO. Modeled HO2 and RO2
concentrations do not change much if this mechanism is applied.
OH concentrations in this campaign are better predicted by
the base model compared to our previous field campaigns that were
conducted in China. In all three campaigns, median diurnal
profiles of measured and modeled OH agree in the morning,
but measured median OH starts to be increasingly higher
than modeled OH after noon. In this campaign, the
difference is a factor of 1.4 at 16:00 CST and a factor of 2 at
sunset (20:00 CST). Differences are within the 2σ uncertainty
of measurements for most of the time. In contrast, the difference
was a factor of 2.6 to 4.5 in previous campaigns for higher
OH reactivity conditions. Consequently, also the amount of
additional recycling that is required to bring modeled and
measured OH into agreement is less in this campaign
(100 pptv NO equivalent) compared to Yufa
(400 pptv) and PRD (800 pptv) in 2006. The major
differences between this campaign and the others are as follows: (1) OH
concentrations in this campaign are smaller; (2) NO
mixing ratios (100 pptv) were lower in previous
campaigns, reducing the OH recycling efficiency from the
reaction of peroxy radicals with NO; (3) measured OH
reactivity is around 12 s-1 in this campaign, but was
at least 50 % larger in the other campaigns.
Ozone production rate
Peroxy radical measurements allow the calculation of net ozone
production . The photolysis of NO2
produces O3 and NO. Because O3 can also be
consumed in the back reaction of NO to NO2, net
ozone production is only achieved if the reformation of
NO2 does not involve O3. This is the case if
peroxy radicals (HO2 and RO2) react with
NO. Therefore, net ozone production can be calculated from
the reaction rate of peroxy radicals with NO using measured
and modeled peroxy radical concentrations (Fig. ).
Production (P(O3)net) is reduced by the loss of NO2
via its reaction with OH and further losses of ozone
(L(O3)) by photolysis and
reactions with OH, HO2 and alkenes:
P(O3)net=kHO2+NO[HO2][NO]+∑kRO2+NOi[RO2i][NO]-kOH+NO2[OH][NO2]-L(O3)L(O3)=θj(O1D)+kOH+O3[OH]+kHO2+O3[HO2][O3]+∑(kalkene+O3i[alkenei])[O3].
θ is the fraction of O1D from ozone photolysis that
reacts with water vapor.
The calculation of the net ozone production from the measured
concentration of total RO2 is
complicated by differences in the reaction rate constants of
NO with different RO2 species. An effective rate
constant is determined from the rate constants of the different
RO2 species in RACM 2 weighted by their relative abundance
calculated by the model for each instant of
time. The effective rate constant increases in the morning
and reaches a maximum 8.5 ×10-12 cm3s-1 in
the afternoon and decreases to a value of
6.5 ×10-12 cm3s-1 after dusk. For
comparison, the rate constant for the reaction of CH3O2
with NO is 7.5 ×10-12 cm3s-1. A
systematic underestimation of the calculated ozone production rate
may arise from RO2 species, which react with NO and
form NO2, but do not produce HO2. Such RO2
species would possibly contribute to the ozone formation, but are
not detected in our instrument. As explained in Sect. 2.4, this
behavior is found in the Wangdu campaign for peroxy radicals which
are formed by reactions of alkenes with NO3. However,
because NO3 is easily photolyzed, these particular peroxy
radicals do not play a role during daytime and do not contribute to
photochemical ozone production.
Net ozone production has a distinct diurnal profile that peaks in
the morning (Fig. ). The peak value of
19 ppbvh-1 (median) derived from measurements is
higher than that calculated in the model (14 ppbvh-1)
and shifted to earlier times (Fig. ). The variability
of this peak value is much larger than seen in the model with
values up to several tens of ppbvh-1.
If the diurnal ozone production rates are integrated for
daytime (04:30–20:00 CST), the model yields about 20 ppbv
O3 less than the experimental value of 110 ppbv
derived from the radical measurements. The difference between
observed and modeled ozone production is mainly caused by the
underestimation of the modeled RO2 concentration in the
morning. As discussed in Sect. 3.6, two generic mechanisms may partly explain the discrepancy.
One possibility are unmeasured VOCs, which would explain the model
underestimation of the OH reactivity in the morning and would
increase RO2 by their reactions with OH. Model run S1
with adjusted VOCs shows a slightly improved agreement of the modeled
and measured ozone production rates (Fig. ) but enhances
the daily integrated ozone production only by 4 ppbv. The other
possibility is an additional primary RO2 source of 2 ppbvh-1,
which is considered in model run S2. It would enhance the daily integrated
ozone production by 30 ppbv, which is on the order of magnitude
of the P(O3) underestimation.
As also mentioned in Sect. 3.6, one possibility for an additional primary
RO2 source is the reaction of VOCs with chlorine atoms from
ClNO2 photolysis, which is not considered in RACM 2. With a maximum
ClNO2 concentration of 0.5 ppbv in the morning, an additional daily
integrated ozone production of about 2 ppbv is calculated. It should
be noted that RO2 radical species, which are produced by additional
reactions of chlorine atoms with alkenes, may behave kinetically different
than RO2 radicals from OH reactions. In the chlorine chemistry
module that we adopted from , Cl-substituted RO2 radicals
have the same rate constants like OH-substituted RO2 radicals, because
kinetic data are missing for Cl-substituted compounds. It is, however, unlikely
that this simplification has a strong influence on the calculated net ozone production.
During the first period of the campaign (8 to 14 June), daily
maximum ozone mixing ratios increased from 50 to 150 ppbv
(Fig. ). However, the connection between the
photochemical ozone production rate and ozone concentrations
measured over several days at a distinct location is complicated.
Additional ozone loss processes, for
example, deposition and indirect loss via reactive nitrogen
chemistry during the night (NO3 and N2O5) that
are not included in Eqs. () and (), need to be taken into account.
Furthermore, the effect of high ozone production in the morning on
midday ozone mixing ratios is reduced due to the dilution by the
increase of the boundary layer height. Also, regional transportation of
ozone can be of importance, if the spatial distribution of ozone
production and/or loss processes is inhomogeneous. The cumulative
ozone production observed during the first period of the campaign
is approximately 700 ppbv. This high total ozone
production indicates that most of the locally produced ozone was
removed by transport or deposition.
Other HOx field studies have also found that models
underpredicted the observed ozone production rate in urban
atmospheres . In these studies,
the observed production rates were determined from measured
HO2 concentrations only, without the contribution of
RO2 for which measurements were not available. In general,
the ozone production from HO2 was underpredicted by chemical
models at NO mixing ratios greater than 1 ppbv, reaching
a factor of about 10 between 10 ppbv and 100 ppbv NO.
In campaigns before 2011, unrecognized interferences from RO2#
species may have contributed to the deviation between measurement and model
results. The interference, however, is expected to account for less than a
factor of 2, because HO2 and RO2 concentrations are approximately
equal and RO2# is only a fraction of
the total RO2 (e.g., Fig. ). This expectation has been
confirmed in recent studies, where the interference was taken into account
and the significant underprediction of the ozone production from HO2
still persists . During the CalNex-LA
2010 campaign in Pasadena (California), part of the discrepancy could be explained
by unmeasured VOCs, which were recognized as missing OH reactivity
. Another major reason for the HO2 underprediction
could be an incomplete understanding of the HO2 chemistry at high NOx
concentrations . Similar arguments as for
the underprediction of HO2 apply to RO2. have
pointed out that modeled RO2 and the associated ozone production could be
severely underestimated (60 %) in the London atmosphere due to the presence
of larger VOCs (mainly monoterpenes). In the Wangdu campaign, missing reactivity from
unmeasured VOCs is much smaller. As shown above, unmeasured VOCs caused an underprediction
of the daily ozone production of less than 5 %.
Total photochemical ozone production
rates were directly measured in a sunlit environmental chamber during the SHARP campaign
in Houston (Texas) 2009 . The comparison with ozone production
rates determined from measured HO2 and from modeled HO2 and RO2
suggests that the model underestimated both HO2 and RO2 at high NOx
in the morning. The underprediction of the daily ozone production was a factor of 1.4.
At Wangdu, we find an underprediction of the daily ozone
production by a factor of 1.2, which is mainly caused by an underprediction of RO2.
In conclusion, all field studies indicate that the photochemical formation of ozone in a
polluted urban atmosphere is not well understood either due to incomplete chemical characterization
of the air composition, or incomplete understanding of the peroxy radical chemistry at high NOx.
Hourly median diurnal profiles of modeled rates of
primary ROx production and termination reactions.
Grey areas indicate nighttime.
Budget analysis based on model results
The budget analysis for OH, HO2 and RO2
radicals is based on the results of model calculations. There are
two classes of radical reactions. On the one hand, ROx
radicals are produced or destroyed by reactions in which
ROx radical species are not reactants and products at the
same time. On the other hand, ROx species are converted
into each other by radical recycling reactions. In polluted air
during daytime, the conversion reactions are fast, so that the
ROX species are in an equilibrium. Under these conditions,
the impact of primary production and destruction is similar on all
radical species.
The partitioning of ROX, however, depends on the relative
rates of the conversion reactions.
Primary radical production and destruction
Median diurnal profiles of primary radical production and
destruction rates of ROx radicals are shown in
Fig. . Highest turnover rates occur after
noon, reaching maximum values around 5 ppbvh-1.
HONO, O3 and HCHO photolysis account for
approximately two-thirds of the daytime radical production.
HONO, O3 and HCHO concentrations as well as their
photolysis frequencies are well constrained by measurements.
HONO photolysis alone is the most important single primary
source with maximum values of nearly 2 ppbvh-1 at 13:00 CST,
with 38 % of the total radical production.
O3 photolysis contributes 15 % to the total radical
production rate. Formaldehyde photolysis is a major source for
HO2, accounting for 18 % of total daytime primary
production.
Other production processes of OH includes alkene
ozonolysis, which also produces HO2 and RO2. The
remaining part of the daytime production can be attributed to the
photolysis of carbonyl compounds.
Recent findings in the understanding of the oxidation of isoprene
found that photolabile hydroperoxy aldehydes (HPALD) can be
formed in environments where radical recycling via NO is
not efficient . HPALD photolysis can be a
significant radical source in this case. In this campaign, this
reaction is almost negligible (Fig. ), because
modeled HPALD concentrations are only around 100 pptv.
In the morning (until 10:00 CST), the major loss of ROx is the
reaction of OH with NO2. At later times, radical
destruction is dominated by the loss via peroxy radical
self reactions: HO2 + HO2, HO2 + RO2
and RO2 + RO2. HO2 and RO2
concentration values and their diurnal profiles are similar.
Because the reactions of HO2 with RO2 have the
largest reaction rate constant of the three types of peroxy
radical self reactions, these reactions make the largest
contribution. The effect of radical destruction by RO2
self reactions could be underestimated in the model, because only
reactions of RO2 with methyl peroxy radicals and acetyl
peroxy radicals are included in the RACM mechanism.
The reaction of OH with NO is the only known gas-phase
production of HONO which can compensate the
OH production by HONO photolysis. During this
campaign, however, HONO formation in the gas phase is
always much smaller compared to HONO photolysis making
HONO a net source of OH. This also means that the
high HONO concentrations during the day cannot be explained
by production from the reaction of OH with NO. The
importance of HONO photolysis to HOx chemistry
has been reported from urban to forest environments
.
The observation of an unusually high HONO concentration of
2 ppbv at noon on 28 June (Fig. ), when the
nearby agricultural field was treated with artificial nitrogen
fertilizer, suggests that HONO emissions from surrounding
farmland may have played an important role at the measurement site
in Wangdu. An imbalance of the two gas-phase reactions of
HONO has also been found in many other field campaigns – for
example, in previous field campaigns in China in 2006
. Heterogeneous formation of HONO is thought
to explain part of the missing daytime source and references
therein and photolysis of particulate nitrate is proposed
to be of potential importance for tropospheric HONO
production (Ye et al., 2016).
Further radical-terminating OH losses include reactions
with unsaturated dicarbonyls (DCB1, DCB2, DCB3) and acetyl
nitrate species (PAN, MPAN, etc.) in RACM 2.
Compared to our previous campaign in Yufa in 2006, the primary
radical production in this campaign is significantly less in the
morning mainly because of smaller OH production from
HONO photolysis. In the afternoon, however, radical
production was mainly due to ozone and formaldehyde photolysis in
Yufa. The relative contributions of radical destruction processes
are similar in this campaign compared to Yufa, but radical loss
due to reactions with nitrogen oxides is less important in the
morning and slightly enhanced in the afternoon in this campaign.
Radical propagation reactions
Figure shows the distribution of turnover
rates of radical recycling reactions. These conversion reactions
establish the partitioning of total ROx species into
OH, HO2 and RO2.
The conversion of OH to HO2 (43 % of the total
OH destruction rate) is dominated by the reaction of
OH with CO and HCHO contributing 25 and
13 % to the total OH destruction during daytime. Isoprene
and its oxidation products (MVK and MACR) are the
dominant organic OH reactants in the afternoon. In
contrast, alkenes and aldehydes reactions with OH dominate
the conversion from OH to RO2 in the morning.
The radical recycling from RO2 to HO2, and also
from HO2 to OH, is mainly driven by NO
reactions. NO reactions with methyl peroxy radicals
(MO2) and isoprene-derived radicals (ISOP) each account
for 26 % of the total conversion rate of RO2 to
HO2 during daytime. Alkane-derived (ALKAP) and alkene-derived (ALKEP) peroxy radicals contribute another 20
and 13 %, respectively. Their relative importance is largest in
the morning.
Hourly median diurnal profiles of turnover rates (model
results) of radical propagation reactions between OH,
HO2 and RO2 radicals.
ALKAP: alkane-derived peroxy radicals;
ALKEP: alkene-derived peroxy radicals;
AROMP: aromatic-derived peroxy radicals.
Grey areas indicate nighttime.
Acyl peroxy radicals (ACO3 and RCO3) do not
directly convert to HO2, but form other RO2
species (MO2 and ETHP in RACM). A second reaction
step with NO is required to form HO2. Therefore,
they are not included in the budget in Fig. .
However, this conversion reaction contributes to ozone production as
discussed above. The daytime average turnover rate of this type of
conversion reaction is 0.9 ppbvh-1.
Direct conversion of RO2 radicals to HO2 and
OH by isomerization reactions with subsequent decomposition
has been found to be competitive with radical recycling via
reactions with NO in the isoprene oxidation mechanism
. The effective isomerization rate
of isoprene-derived RO2 is 0.01 s-1 for
conditions of this campaign in the afternoon hours (temperature:
303 K). This loss rate is small compared to the loss of
isoprene-derived RO2 via the reaction with NO. The
average NO mixing ratio is 0.19 ppbv (for the
subset of days shown in Fig. ), giving a loss
rate of 0.04 s-1. Therefore, only 20 % of RO2
from ISOP undergoes isomerization, so that
radical recycling from ISOP to HO2 via
isomerization is small. This also explains why HPALD
photolysis as a primary ROx source is not important in this
campaign. In contrast to RO2 from isoprene, one
RO2 species from MACR (MACP) nearly
exclusively isomerizes for afternoon conditions of the campaign.
However, the overall impact of this radical recycling reaction is
also small, because the median production rate of MACP is
only 0.14 ppbvh-1 in the afternoon.
The maximum turnover rate of recycling reactions is slightly
shifted to earlier times compared to the maximum turnover rate of
primary radical production. This is mainly due to the dominance of
conversion reactions of RO2 and HO2 with
NO. This can be best seen in the median diurnal profile of
the HO2 conversion to OH, which peaks earlier than
the OH conversion to HO2 and RO2
(Fig. , lower panel). Because the total
OH production and destruction rates are equal in the model
calculation, this imbalance is compensated by the larger primary
OH production (Fig. ).
Compared to the turnover rates in Yufa 2006, radical conversion is
less strong in the morning in this campaign, mainly due to smaller
peak NO concentrations leading to a reduced reformation of
OH from HO2. This is accompanied by lower
HO2 production in the reaction of OH with
formaldehyde. In the afternoon, the strength of radical conversion
reaction is similar in both campaigns.
Summary and conclusions
A comprehensive set of measurements was achieved to characterize
the photochemistry at the rural site Wangdu in the North China
Plain in 2014. Air pollution was likely transported from
surrounding industrial areas and farmland in the North China Plain
and few days were influenced by clean air coming from the north.
A new LIF instrument was used to measure concentrations of
OH, HO2, RO2 and a special group of
organic peroxy radicals (RO2#) which are produced
from alkenes and aromatics. Furthermore, total OH reactivity
was measured by a laser pump-and-probe instrument. In order to test if
OH measurements included artifacts from OH
production inside the measurement cell, chemical modulation tests
were performed. These tests identified unexplained OH
signals equivalent to (0.5–1) ×106 cm-3 with a
systematic experimental 1σ uncertainty of 0.5 ×106 cm-3. Given this uncertainty, the unexplained
OH signal may have been caused by an experimental bias of
the chemical modulation setup, but also an unknown OH
interference cannot be excluded. In the case of an interference, its
contribution to the maximum OH concentration would have
been only 10 %; thus, it would have a minor impact on
the interpretation of daytime OH measurements. However, it
cannot be excluded that nighttime OH measurements were
significantly affected by interferences. An improved setup of
this system will be used in future field campaigns.
Daily maximum concentrations of OH, HO2 and
RO2 ranged from 5 ×106 to 15 ×106
cm-3, 3 ×108 to 14 ×108 cm-3
and 3 ×108 to 15 ×108 cm-3, respectively.
Model calculations using a modified RACM 2 mechanism reproduce
the measured radical concentrations generally well in this campaign.
The modified RACM 2 contains an extension based on recent findings
in the isoprene chemistry , which
leads to a small increase of the modeled OH for the conditions
of this campaign.
The model–measurement comparison for OH shows a tendency towards
not as good agreement at low NO concentrations. At concentrations above
0.3 ppbv NO, OH is well
described by the model, but is increasingly underpredicted at lower NO
in the afternoon by up to a factor of 2. The unexplained OH signals
from the chemical modulation test cannot explain this trend. Introduction of an
additional radical recycling process which has the same effect as 100 pptv
NO can close the gap between modeled and measured OH, but the nature
of the process remains unknown. This behavior is qualitatively in agreement with
previous results from two field campaigns in China, in the Pearl River delta and
in the North China Plain, where the required equivalent NO is 800 and 400 pptv .
An opposite trend is found for RO2 radicals. At higher NO
concentrations in the morning, the model shows an underprediction of the
measured RO2, which reaches a factor of 10 at about 4 ppbv
NO. The underprediction is mainly related to RO2# species,
whose concentrations were half of the total RO2 concentrations. The reaction
of OH with unknown VOCs, estimated from missing OH reactivity,
can explain part of the RO2 discrepancy until 09:00 CST, but not later
in the morning. Good agreement between measured and modeled RO2
and RO2# can be achieved by assuming an additional primary
source of 2 ppbvh-1 of RO2 (from alkenes) until noon.
Reactions of VOCs with chlorine atoms from the photolysis of ClNO2
were a likely source of additional RO2 after sunrise, but the measured
ClNO2 concentrations (< 2 ppbv) reported by
can explain only (10–20) % of the required additional RO2 source
early in the morning. Another source which sustains additional RO2
production until noon is therefore needed.
As a consequence of the model underprediction of RO2, the total
net ozone production from HO2 and RO2 radicals is also
underestimated by the model. The median measured concentrations of
HO2 and RO2 yield a daily integrated ozone production
of 110 ppbv, which is 20 ppbv more than predicted by
the modified RACM 2. About 10 % of the discrepancy can be explained
by ClNO2 chemistry during the Wangdu campaign. The
underprediction of the photochemical ozone production at high NOx
in the morning is in general agreement with other studies in urban
environments, underlining the need for better understanding of the
peroxy radical chemistry in polluted air.
Radicals are primarily produced by photolysis reactions and
radical loss is dominated by reactions with nitrogen oxides in the
morning and peroxy radical self reactions in the afternoon. This
is similar to our previous campaign 2006 in Yufa that is also
located in the North China Plain . OH
production from HONO photolysis in the afternoon was the largest
primary radical source in this campaign. Because NO
concentrations are lower than in 2006 in the morning, radical
conversion rates are smaller. Higher OH concentrations and
OH reactivity measured in 2006 and smaller OH
recycling from the reaction of HO2 with NO in the
afternoon led to the need of a larger enhancement of the radical
recycling efficiency for the campaign in 2006 compared to results
from this campaign.