Introduction
Air quality improvement has become a hot topic in China from citizens to
government. Therefore, a series of air pollution control provisions have been
implemented by the Chinese government in the last 15 years to improve the air
quality in China since early 2000. As a result, emissions and concentrations
of primary pollutants (sulfur dioxide, nitrogen oxide, and coarse particles) have been showing a fast decreasing trend nation wide in
recent years (Kan et al., 2012). However, secondary pollution characterized
by high concentrations of ozone and fine particles (PM2.5) has now
become the major concern. In particular, severe pollution events (haze)
frequently happened in winter of the last few years, with very poor
visibility and extremely high concentrations of PM2.5, secondary
aerosol, and its precursors (Sun et al., 2006; Yang et al., 2011; Guo et al.,
2014; Zhao et al., 2013).
The OH radical is the major atmospheric oxidizing agent during the daytime,
converting primary pollutants to secondary ones. OH radicals initiate the
oxidation of pollutants, e.g., CO, non-methane hydrocarbons (NMHCs), producing
peroxy radicals (HO2+RO2), which can regenerate OH by
their reaction with NO. Within this ROx cycling, primary
pollutants are converted to secondary pollutants, e.g., to CO2,
H2SO4, HNO3, and oxygenated organic compounds (OVOCs),
some of which are important precursors for aerosol. In addition, NO is
oxidized to NO2 in the reaction with peroxy radicals leading to net
ozone production and NO regeneration via further photolysis. The efficient
coupling of the ROx and the NOx cycles
facilitates the fast degradation of primary pollutants with the disadvantage
of the formation of secondary pollutants. In wintertime, the radical
chemistry is less active than in summertime because the solar radiation is
weaker due to the higher solar zenith angle. For example, one of the
important OH primary sources, photolysis of ozone, is strongly reduced by the
smaller photolysis rate and the lower water vapor abundances at low
temperatures during wintertime. Global models predict OH concentrations to be
on average only 0.4×106cm-3 in northern China in
January, 1 order of magnitude lower than what is predicted for summertime
for the lower troposphere in northern China (Lelieveld et al., 2016).
Previous field measurement also observed relative low OH concentrations
(1.4-1.7×106cm-3 of diurnal maximum) for urban and
suburban locations in winter (Heard et al., 2004; Ren et al., 2006; Kanaya et
al., 2007). The significant difference between summer and winter OH
concentrations indicates the radical chemistry only plays a minor role in
winter. Especially during particle pollution events, the dimming effect of
aerosol will further attenuate the solar radiation and thus lower the
radical chemistry activity. However, composition analysis showed the
contribution of secondary components to the aerosol increase during pollution
events in the NCP (Guo et al., 2014; Zhao et al., 2013; Huang et al., 2014),
which suggests that oxidation plays an important role in aerosol formation.
However, a rigorous test of the current understanding of radical chemistry by
direct in situ measurements of ambient OH concentration is missing during
wintertime in China. Therefore, the role of radicals in air pollution is
unclear for wintertime, especially during heavy pollution episodes in China.
Only a handful field campaigns have been carried out in northern China to
elucidate key processes of tropospheric radical chemistry (Hofzumahaus et
al., 2009). In the summer of 2006, a comprehensive campaign took place at a suburban
site in Beijing (Yufa), 40 km south of the center of the city (Lu et al.,
2013). High concentrations of OH radicals were observed. In addition, the
measured OH reactivity showed a high concentration of reactive trace gases.
The combination of high OH concentration and high OH reactivity resulted in a
fast formation of secondary pollutants. Box model calculations could not
reproduce the ambient OH concentrations for the low NOx
regime (Lu et al., 2013). In the summer of 2014, another field campaign was
conducted at a rural site in the center of the NCP (Wangdu) to study the
formation of secondary pollutants (Tan et al., 2017; Fuchs et al., 2017b).
Also, for this environment, there is a tendency of underestimating OH for
conditions when NO was less than 300 pptv. Additionally, a missing peroxy
radical source was found for the high NOx regime showing a
severe underestimation of local ozone production (Tan et al., 2017).
To reveal the cause of heavy air pollution episodes and the link between
radical chemistry and air pollution during wintertime, a field campaign
“Beijing winter finE particle STudy – Oxidation, Nucleation and light
Extinctions” (BEST-ONE) was carried out at the suburban site (Huairou) in
the Beijing area from January to March 2016. For the first time,
concentrations of OH, HO2, and RO2 radicals, and of the
reactivity of OH, kOH, were simultaneously measured in China
during wintertime in addition to numerous measurements of other trace gas
concentrations and aerosol properties. In this study, the ambient radical
concentrations are analyzed for different chemical conditions by calculating
the budget of radicals and by comparing measurements to model calculations.
Results
Chemical and meteorological conditions
The observed trace gas concentrations were highly variable depending on the
particular meteorological conditions. In Fig. 1, the Hybrid Single-Particle
Lagrangian Integrated Trajectory (HYSPLIT) back-trajectory
analysis (Stein et al., 2015) shows two typical air mass transportation
pathways. The dominating wind direction in Beijing during wintertime is
northwest from the Siberian tundra or from the Mongolian desert. The
back-trajectory analysis shows that the air parcels arriving in the city of
Beijing either came directly from these remote regions, bringing clean air to
the UCAS site, or first reached the Beijing downtown region and then turned to
the UCAS site, loaded with pollutants from the Beijing city region. As
presented in Fig. 3, the CO concentration, a proxy for anthropogenic
pollution, shows a distinct variation from day to day. The chemical
conditions could be classified into three groups, namely background, clean,
and polluted, respectively, based on observed kOH, CO, and
PM2.5. The measured OH reactivity was used to separate polluted
from clean periods by a threshold value of kOH=15s-1 (daily average). This corresponds also to CO mixing ratios
higher than 1 ppmv and PM2.5 higher than
50 µg m-3 since these parameters
were highly correlated. The difference between the background and clean
episodes is whether there is a diurnal variation in the CO and
kOH, and PM2.5. Only 3 days (22 and 23 January and
23 February) were classified as background conditions, during which strong
northern winds (wind speed up to 10 m s-1) were observed. The strong
wind enhanced the dilution of pollutants coming from the Beijing region and
prevented the buildup of a stable nocturnal surface layer. As shown in
Fig. 4, the mean diurnal profiles of CO, NO2, HONO, and
O3 were flat over most of the day. For the majority of the
campaign, the wind velocity was relatively small, as reflected by the local
wind speed measurements (less than 2 m s-1). Hence, although the air
masses originated over the Mongolian desert or the Siberian tundra, the
observations at the UCAS site were slightly influenced by local emission from
the nearby village and from close-by car traffic. The time periods for these
relatively clean episodes are listed in Table 2.
Time series of measured photolysis frequencies (j(O1D),
j(NO2)), ambient temperature (T), particle mass concentrations with
aerodynamic diameter below 2.5 µm (PM2.5), and
concentrations of absolute water vapor (H2O), CO, O3,
Ox (=O3+NO2), NO, NO2,
SO2, and HONO. The grey areas denote nighttime. The labels in the
first row denote the classification for background (B, green), clean (C,
blue), and polluted (P, red) episodes.
The pollution episodes happened on 15, 16, 20, 21, 27, 28, 29 January, 21
February, and 1 to 4 March. The back-trajectory analysis shows that for those
days the air masses were at least partly influenced by the Beijing city area
which is located south of the UCAS site. In addition, the relative humidity
increased from 28 % for the clean episodes to 44 % for the pollution
episodes for daytime averaged conditions, suggesting that the site was
influenced by humidified air from the south. The concentrations of trace
gases and particle matter increased noticeably during the pollution episodes
(Table 2).
Comparison of observed parameters for different episodes (24 h
average values with 1σ standard deviations of ambient variabilities).
Parameter
Background
Clean
Polluted
Temperature (∘C)
-10.2±6.3
-4.0±4.9
-3.9±4.4
Pressure (hPa)
1025±4
1015±4
1012±6
RH (%)
19.6±8.3
29.5±11.7
46.3±17.2
Wind speed (m s-1)
3.7±1.6
2.4±1.3
1.9±0.8
j(O1D) (10-5 s-1)
0.67±0.24
0.63±0.23
0.56±0.29
j(NO2) (10-3 s-1)
6.52±0.78
6.04±0.63
4.23±1.66
OH (106 cm-3)
2.73±1.19
3.58±2.32
2.36±0.74
HO2 (108 cm-3)
1.04±0.62
0.93±0.72
0.52±0.23
RO2 (108 cm-3)
0.70±0.34
0.76±0.46
0.71±0.41
O3 (ppb)
34.7±4.7
27.9±9.0
11.6±10.1
NO (ppb)
0.30±1.61
2.06±7.93
9.27±12.54
NO2 (ppb)
2.0±4.0
10.3±13.6
32.5±15.1
HONO (ppb)
0.05±0.05
0.23±0.24
0.98±0.90
CO (ppm)
0.22±0.22
0.33±0.19
1.19±0.70
HCHO (ppb)
0.47±0.38
1.29±0.69
2.41±0.97
kOH (s-1)
5.4±3.7
10.1±5.6
26.9±9.5
PM2.5 (µg m-3)
0±20
6±15
55±36
Dates
01.22, 01.23, 02.23
01.06, 01.07, 01.10,
01.15, 01.16, 01.20,
01.11, 01.03, 01.24,
01.21, 01.27, 01.28,
01.25, 01.26, 01.31, 02.01,
01.29, 02.21, 03.01,
02.20, 02.24, 02.25, 02.26,
03.02, 03.03, 03.04
02.27, 02.28, 02.29
j(O1D), (NO2), HO2, and
RO2 concentrations are noontime averaged values () peak values
of 1 h averages.
The dissimilarities between different episodes can be easily illustrated
using the mean diurnal profiles (Fig. 4). Solar radiation, indicated by the
measured photolysis frequencies, e.g., j(O1D), was comparable during the background and
the clean episodes but reduced by about 20%-30% during the
pollution episodes (Table 2). NO concentrations were below 1 ppbv during the
background episodes. The diurnal peak of NO (4 ppbv) appeared at 09:00 CNST
during the clean episodes. During the polluted episodes, NO concentrations
increased significantly to reach a maximum of 15 ppbv around sunrise.
O3 remained nearly constant at 38 ppbv at all times during daytime
of the background episodes, which can be regarded as a continental wintertime
O3 background concentration. During clean episodes, the wind
velocity was reduced, so that local NO emissions could accumulate and titrate
away the ozone, especially during nighttime when vertical mixing was reduced.
NO2 showed an anti-correlation with O3 because of this
interconversion. Besides the titration effect, there were deposition
processes and non-photochemical reactions which additionally diminished the
concentration of ozone (and of NO2) in the shallow surface layer
during night hours. However, the maximum O3 concentrations during
afternoon hours, when vertical mixing inhibited the accumulation of the local
NO emissions, were comparable to those observed during the background
episodes (Fig. 4). These processes led to a slow reduction of the
near-surface ozone concentration starting at sunset, to a minimum just at
sunrise, and to a fast recovery within the next 2–3 h. This increase of
O3 in the morning hours during the clean episodes was at least
partly a result of entrainment of background O3 from the residual
layer.
Mean diurnal profiles of measured photolysis frequencies
(j(O1D) and concentrations of NO, CO, O3,
Ox (=O3+NO2), NO2, HONO, and
SO2 for background (green), clean (blue), and polluted (red)
episodes. The grey areas denote nighttime.
During the pollution episodes, the ozone concentrations showed a distinct
diurnal variation due to a strong titration by NO. The concentration of
NO2 was high at night but decreased after sunrise, which could be
due to the poor dilution conditions at night and fast photolysis after
sunrise. Ox is the sum of NO2 and O3,
which is considered as a more conservative metric than O3 because
it is not affected by the interruption of fresh NO emission, especially in
urban environments (Kley et al., 1994; Kleinman et al., 2002).
Ox increased from 30 ppbv at 07:00 CNST to 50 ppbv at 16:00 CNST
during the pollution episodes. After sunset, Ox started to
decrease due to physical losses, like deposition and transportation of
O3 and NO2, or chemical conversion to N2O5.
The fast increase in Ox concentrations during daytime was a
specific feature for the pollution episodes, which indicated the strong
photochemistry happening during the haze events.
SO2 is a tracer for regional air pollution originating from coal
combustion, e.g., power plants and residential heating. For background and
clean episodes, SO2 exhibited low concentrations around 1 ppbv. It
increased from 5 ppbv at sunrise to 10 ppbv at 16:00 CNST during the pollution
episodes. The large increase of SO2 concentrations during pollution
episodes which showed a distinct diel increase could be related to the
pollutants' accumulation process.
Typically, HONO accumulated during the night (most probably from
heterogeneous reactions of NO2 on humid surfaces) and started to
decrease after sunrise due to fast photolysis. This was observed during clean
or polluted episodes when the measured HONO showed a distinct diurnal minimum
during the afternoon and started to increase between sunset and sunrise up to
values around 1 ppbv during the night. In contrast, the averaged HONO
concentrations were about 0.05 ppbv without obvious diurnal variation for
background conditions (Table 2).
Comparisons between measured and calculated OH, HO2, RO2
radical concentrations, and OH reactivity
Figure 5 shows the measured and modeled OH, HO2, and RO2
radical concentrations and OH reactivity. The largest OH concentration
appeared around noontime with large day-to-day variability. The daily maximum
varied in the range 1×106 to 1×107cm-3. The
highest OH concentrations were observed during the second half of the
campaign (27 and 28 February) when the temperature was above the freezing
point and the photolysis frequencies for the short wavelength region, e.g.,
j(O1D), were higher than in January (Fig. 3).
Time series of observed (red) and modeled (blue) OH, HO2,
the sum of RO2, and of kOH. The OH measurements were
achieved by wavelength modulation and chemical modulation (CM is an
abbreviation for chemical modulation; see text in Sect. 2.2.1). The grey
areas denote nighttime. The labels in the first row denote the classification
for background (B, green), clean (C, blue), and polluted (P, red) episodes.
In general, the model could reproduce the observed OH concentrations within
30 %. It is important to notice that the good agreement between measured
and modeled OH concentrations was mainly due to the availability of observed
HONO concentrations. A sensitivity test showed that the calculated OH radical
concentrations were reduced by 43 % if the model was not constrained to
the HONO measurements. This underlines that the currently known gas-phase
formation of HONO from the reaction of OH with NO is not sufficient to
sustain the high HONO concentrations observed during this winter campaign.
The result agrees with numerous field and laboratory studies which have
reported additional daytime sources of HONO (e.g., Kleffmann et al., 2005),
for example, from the heterogeneous conversion of nitrogen species (nitrates,
nitric acid, NOx) at surfaces and soil emissions (see
overview in Meusel et al., 2018, and references therein). The present study
demonstrates that at least some of the additional HONO sources are also
effective at cold conditions and play an important role for the wintertime
oxidation of pollutants in urban atmospheres. It also confirms the need for
in situ HONO measurements during field studies focusing on the investigation
of atmospheric radical chemistry (Alicke et al., 2003; Su et al., 2011; Lu et
al., 2012; Kim et al., 2014).
The overall correlation between observed OH concentrations and photolysis
frequencies is shown in Fig. 6. Both observed and modeled OH concentrations
show a good correlation with j(O1D) and with j(NO2) with
the coefficient of determination R2 being larger than 0.7, which can be
expected in summer when the radical chemistry is initiated mainly by
photolysis processes including ozone photolysis. However, j(O1D)
increased by a factor of 2 from the start to the end of the campaign while
j(NO2) remained nearly constant (Fig. 3). The correlation between
calculated OH and j(NO2) is more compact and linear than that
between OH and j(O1D), reflected in a slightly higher correlation
coefficient (Fig. 6). In the model, the OH concentrations tend to depend
strongly on j(NO2). This can be explained by HONO photolysis being
the dominant primary source of OH radicals (see Sect. 4.1) and the well-known
linear correlation between j(NO2) and j(HONO) (Kraus and
Hofzumahaus, 1998). The OH observations do not show such a difference;
j(O1D) and j(NO2) both exhibit the same good correlation
coefficient.
The correlation between observed (a, b) and modeled
(c, d) OH concentrations and photolysis frequencies of j(O1D)
(a, c) and j(NO2) (b, d). The pink dots denote
the results obtained on 27 and 28 February, which are not included in the
correlation analysis. The units of the slope and the intercept are
1011 cm-3 s-1 and 106 cm-3, respectively.
Despite the general agreement, the model systematically underestimates OH
concentrations for 2 days (27 and 28 February) by a factor of 2 (Fig. 5).
The OH reactivity was relatively small on these days (<10 s-1). A
model sensitivity run with an additional primary OH source of
0.25 ppbv h-1 shows that the modeled OH concentrations would increase
by 50 %, which would be enough to close the gap between measured and
modeled OH for these 2 days. However, the possibility of an instrument
failure of the PKU-LIF during these 2 days cannot be excluded. An
indication may be that the data points on these 2 days do not follow the
generally very good OH–j(O1D) correlation (Fig. 6). Therefore, we
excluded these 2 days from the analysis.
The daytime averaged peroxy radical concentrations are smaller by a factor of
5 compared to summertime at the rural site of Wangdu in the North China Plain
(Tan et al., 2017). The strong reduction in radical concentrations is due to
the attenuated solar radiation and thus smaller primary radical sources. In
addition, NOx concentrations during the BEST-ONE campaign
were larger, resulting in a faster peroxy radical loss and thus shorter
lifetimes.
For the background episodes, the calculated RO2 data are not shown
in Fig. 7 because the observed NO concentrations were close to the limit of
detection of the NO instrument (60 pptv), and thus the model was highly
sensitive to the fluctuations of the NO measurements around zero (Fig. 5).
The observed RO2 radical concentrations do not show a significant
difference between background, clean, and pollution episodes, while a large
difference can be observed for the calculated RO2 concentrations
(Fig. 7). In fact, the observed radical concentrations are rather comparable
in all episodes, while the model predicts a suppression of radical
concentrations in the polluted episodes. This is most obviously seen for
HO2 and RO2. The comparisons between observed and modeled
OH, HO2, and RO2 concentrations for clean and polluted
episodes are shown in Fig. S3. Both the calculated OH and HO2
radical concentrations were lower than observed during polluted days.
Overall, during clean days, the model can reproduce the observed OH,
HO2, and RO2 radical concentrations with an
observed-to-modeled ratio of 1.31, 1.03, and 0.98 for daytime conditions,
respectively (Fig. S3). The nighttime RO2 radicals are often
overestimated by the model calculations by up to a factor of 10, which could
be the result of a NO measurement below the detection limit. The observed NO
concentrations were often below the limit of detection of the
NOx instrument (60 pptv), which did not allow precise
measurements due to the fluctuation of the background signal. In addition, it
also led to large variability in the modeled NO3 and resulted in
overprediction in the nighttime RO2 (Tan et al., 2017). On the
other hand, a small bias in the NO measurement could lead to an
unrealistically long lifetime of RO2 radicals in the model. A model sensitivity run
showed that RO2 concentrations could be significantly reduced if
the constrained NO concentrations in the model were increased by 20 pptv,
which is within the precision of the instrument. A loss of NO inside the
inlet line of the instrument would already be enough to explain this effect
that was within the limit of detection.
Mean diurnal profiles of observed (a) and modeled
(b) OH, HO2, RO2, and kOH for three
different chemical and meteorological conditions. The categories for
background, clean, and polluted episodes are the same as in Table 2, and similar
to those applied to Figs. 9 and 12. The grey areas denote nighttime.
The model underestimates the observed OH concentrations by a factor of 1.8
during polluted days, and the peroxy radical concentrations are significantly
underestimated by up to a factor of 5 (Fig. 7). A detailed analysis of these
days is given in Sect. 4.2. A chemical model based on the Master Chemical Mechanism (MCM) 3.3.1 also predicts
similar results as RACM2, suggesting that such underestimation is not a
result of the “family approach” for organic molecules used in RACM2.
The model was able to reproduce the directly observed OH reactivity within
10 % during all episodes (Fig. 7). This calculation includes the
reactivity from observed VOCs (about 73–83 % of the observed
kOH) and the estimated contributions from OVOCs calculated by the
model (17–27 %). The speciation of the total OH reactivity showed that
the major OH reactants were NOx and CO. On average, CO and
NOx contributed 23 % and 37 % to the total OH
reactivity, respectively. A total of 18 % of the observed reactivity can be
attributed to measured VOC species. In comparison, the model-generated species
contributed 22 % to the total reactivity. For the polluted episodes, the
average OH reactivity increased from 10 to 26 s-1 with a significant
increase in the relative contributions from the inorganic compounds (from
52 % to 63 %).
Discussion
Sources and sinks of ROx radicals
As shown in Fig. 8, the radical chain reactions were mainly initiated by
photolysis processes. HONO photolysis was the most important radical primary
source. It contributed up to 46 % (averaged rate 0.26 ppbv h-1) of
the total primary production rate of radicals for daytime conditions. During
the BEST-ONE campaign, the production rates of radicals varied slightly
between different episodes (Fig. 9). The production rates were highest during
the polluted episodes compared to the background and clean episodes. The
major difference was due to the contribution from HONO photolysis, which
accounted for the major source in all cases contributing 25 %
(0.10 ppbv h-1), 40 % (0.21 ppbv h-1), and 55 %
(0.37 ppbv h-1) of diurnally averaged rates of 0.10, 0.21, and
0.37 ppb h-1 for background, clean, and polluted episodes,
respectively. Alkene ozonolysis was the major radical source of nighttime and
the second largest primary radical source during the day (28 %,
0.16 ppbv h-1). The relative importance of the ozonolysis of alkenes
was slightly larger during the background (39 %) and clean (33 %)
episodes. The ozone photolysis was almost negligible for our wintertime
BEST-ONE campaign. At high solar zenith angles, short wavelength radiation
(<320nm) is suppressed due to the strong attenuation by the
longer pathway through the ozone layer in the stratosphere. In addition, the
water vapor mixing ratio at the low winter temperatures decreases to below
0.3 %, 1 order of magnitude lower than during summertime. Formaldehyde
photolysis contributed on average 17 % to the total ROx
primary production. The photolysis of carbonyl compounds calculated by the
model also made a noticeable portion of the total primary production of
radicals, about 7 % (Fig. 8).
Hourly averaged primary sources and sinks of ROx
radicals derived from model calculations for all episodes. The grey areas
denote nighttime.
The importance of HONO photolysis is reported in previous studies for other
locations, both urban and suburban (Alicke et al., 2003; Dusanter et al.,
2009; Mao et al., 2010; Ren et al., 2013). For winter campaigns, HONO
photolysis was found to be the dominant (>50%) daytime
ROx radical source in New York City (NYC) (Ren et al., 2006),
Tokyo (Kanaya et al., 2007), and Boulder, Colorado (Kim et al., 2014).
During the PUMA campaign in Birmingham, HONO photolysis contributed only
36 % to the total OH radical primary sources (Emmerson et al., 2005b).
Meanwhile, the reported value of 36 % is a lower limit, since HONO was not
measured. Instead, it was calculated from the equilibrium between HONO and
OH+NO. As OH and NO can recombine to nitrous acid, the net
effect of HONO photolysis to radical production might be partly compensated.
Kanaya et al. (2007) found that the OH+NO (+M) reaction was
balanced by the HONO photolysis during the morning hours. In our study, the
gas-phase HONO formation only compensated 10 % of the observed HONO
photolysis. Therefore, the net effect of HONO photolysis remains the dominant
radical source in wintertime during the BEST-ONE campaign. In Fig. 9, the
radical production for the Wangdu summer campaign is shown in comparison to
the current campaign. The total daytime radical production rate was reduced
in winter by a factor of 6 compared to summertime. The contribution of HONO
photolysis also changed, contributing 39 % to the total radical
production for the Wangdu summer campaign.
The importance of alkene ozonolysis to the primary ROx
radical production was also found in the IMPACT winter campaign in Tokyo,
where it contributed 49 % of the ROx production on a
24 h basis (Kanaya et al., 2007). In other campaigns, alkene ozonolysis was
comparable to HONO photolysis even during daytime, which contributed 42 %
in NYC (Ren et al., 2006) and 63 % in Birmingham (Emmerson et al.,
2005b), respectively. During the summertime in Wangdu, the contribution from
alkene ozonolysis was only 15 % (Fig. 9). However, the absolute rate was
0.47 ppbv h-1, larger than what was observed at Huairou/Beijing in
wintertime (0.16 ppbv h-1).
Comparison of ROx primary sources and sinks from
model calculations during different episodes for daytime averaged conditions.
The budget analysis for the summer Wangdu campaign is plotted for comparison
(Tan et al., 2017).
Different from our study, the photolysis of ozone was found to be relatively
important in a rural site of Colorado, contributing 15 % to the OH
primary production for noontime conditions. The absolute radical production
rate is comparable in these two campaigns (Table 3); the higher contribution
of ozone photolysis is due to the fact that the Colorado campaign took place
at a slightly later time (February and March) of the year (Kim et al., 2014).
Noontime averaged concentrations of OH, NO2, and OH
reactivity and total radical production rate for campaigns investigating
photochemistry including OH radical measurements during wintertime.
OH
P(ROx)
kOH
NO2
Chain
Reference
(106 cm-3)
(ppbv h-1)
(s-1)
(ppbv)
length
Birmingham, UK (2000, January–February)
1.7
2.8
30a
9.3
2.1
Heard et al. (2004);
Emmerson et al. (2005a, b)
NYC, US (2004, January–February)
1.4
1.4
27
15
3.3
Ren et al. (2006)
Tokyo, Japan(2004, January–February)
1.5
1.4
23a
12
3.1
Kanaya et al. (2007, 2008)
Boulder, US (2011, February–March)
2.7
>0.7b
5c
5
<2.0
Kim et al. (2014)
Beijing, China (2016, January–March)
2.8
0.9
12
6
4.7
This study
a kOH is calculated from the model.
b Only the OH production rate is available and therefore should be
regarded as the lower limit. c The sum of reactivity from VOC,
OVOC, and NO2.
The photolysis of carbonyl compounds can be an important radical source for
urban and suburban areas (Emmerson et al., 2005b). HCHO photolysis
contributed up to 6 % of the total HOx
(=OH+HO2) production in NYC (Ren et al., 2006) and
10 % in Tokyo (Kanaya et al., 2007) for wintertime conditions, comparable
to their contributions in summertime, for example, 8 % for NYC (Ren et
al., 2006) and 18 % for Tokyo (Kanaya et al., 2007).
The termination of ROx radicals is achieved either by the
reaction with NOx or by the peroxy radical self-reactions.
The NOx termination reactions can be further subdivided into
OH+NO, OH+NO2, RO2+NO,
and RO2+NO2. As shown in Fig. 8, the reaction between
OH and NO2 dominated the total radical termination process during
the BEST-ONE campaign, which contributed 49 % of the total
ROx loss for daytime average. The equilibrium between peroxy
radicals and PAN-type compounds was a sink for ROx radicals
due to the low ambient temperature during the winter campaign. The net
PAN-type compounds species formation had a noticeable impact on the radical
budget (25 % daytime averaged). Since the observed and modeled PAN
concentrations agree within 24 %, it gave confidence to the net PAN
formation rate. In London, the net PAN formation was also found to be a major
radical sink, contributing 35 % to the total radical destruction rate
(Whalley et al., 2018). The ROx self-reactions were nearly
negligible (<3%) as the peroxy radical concentrations were small (<1×108cm-3). For comparison, this is 1 order of
magnitude smaller than in the summertime Wangdu campaign, where the radical
termination process was mainly dominated by the hydroperoxide formation path
(Fig. 9).
The relative importance of various loss pathways was compared between the
different episodes (Fig. 9). The reaction between OH and NO2
contributed 40 % and 61 % to the total ROx loss
during clean and polluted episodes. The net loss of PAN-type compounds became
of relative importance during background episodes, contributing 50 % to
the total radical termination because of the lower temperatures (Table 2).
Model–measurement comparison of peroxy radicals concentrations
Dependence of observed and model-calculated OH, HO2, and
RO2 concentrations on NO concentrations for daytime conditions
(j(O1D)>1.0×10-6s-1). Boxes give
the 75 % and 25 % percentiles, the center lines the median, and
vertical lines the 90 % and 10 % percentiles for each NO interval.
Only median values are shown for model results. Numbers in the upper panel
give the number of data points included in the analysis of each NO interval.
As shown in Fig. 10, the observed RO2 concentrations were
relatively constant over the whole NO regime. However, the model predicted a
strong decreasing trend with higher NO concentrations. This is further
illustrated by the observed-to-modeled ratio, which increased from a value of
1 at 1 ppbv of NO to 9 at 6 ppbv of NO (Fig. 11). As shown in Fig. 11, the
underestimation of both HO2 and RO2 radical
concentrations became larger with higher NOx. In contrast,
the observed-to-modeled ratio of OH concentrations was almost constant with a
value of 1.5 over the full NOx regime, which is within the
combined uncertainties of measurement and model calculations. An
underestimation of HO2 radical concentrations by the model at high
NOx values was also observed in previous studies (Shirley et
al., 2006; Ren et al., 2006, 2013; Emmerson et al., 2007; Kanaya et al.,
2007; Griffith et al., 2016). HO2 measurements in previous field
campaigns could have suffered from interferences from specific RO2
species, so that the reported observed-to-modeled ratios could have been even
larger in these campaigns. It was suggested that one reason for this
discrepancy might be due to the poor mixing and segregation between NO and
peroxy radicals (Dusanter et al., 2009). Alternatively, it can also imply
missing peroxy radical sources in the current chemical mechanisms relevant in
particular for the high NOx regime. In addition, the
underestimation of the measured RO2 radicals by the model could
explain partly the discrepancy observed for HO2 radicals due to
insufficient recycling from the OH oxidation chain.
In the present study, the radical cycling between OH and the peroxy radicals
is relatively well constrained due to the availability of measured
kOH and NO. Since the model reproduces kOH within
10 %, unmeasured VOCs cannot be responsible for a possible
underestimation of the peroxy radical production resulting from the reaction
of VOCs with OH. As the NO concentration in the model was constrained to
measurements, also the main loss processes of peroxy radicals in the high
NOx regime, the reactions with NO, are well determined. In
addition, a possible segregation effect by the fast variability of radical
precursors in combination with non-synchronous observations was minimized by
the experimental setup. The inlets of the LIF instrument and of other
instruments were very close to each other (within 2 m). The sampling height
was about 20 m above ground, 50 m away from the next street. Therefore,
segregation is expected to play only a minor role. This is supported by the
observed O3, NO, and NO2 concentrations, which were close
(within 10 %) to a steady state. All these arguments indicate that the
significant peroxy radical underestimation is probably caused by missing
primary sources in the model.
Dependence of observed-to-modeled ratio of OH, HO2, and
RO2 on NO concentrations for daytime conditions
(j(O1D)>1.0×10-6s-1). The vertical lines denote the
combined uncertainty from radical measurements and model calculations via
error propagation. Numbers in the upper panel give the number of data points
included in the analysis of each NO interval.
Based on the discussion above, we can assume that the peroxy radicals were in
the steady state so that their production and destruction rates were balanced
at all times. The production of peroxy radicals
P(HO2+RO2) can be calculated as follows:
P(HO2+RO2)=kVOC×[OH]+P(HO2)primary+P(RO2)primary.
Here, kVOC denotes the part of OH reactivity which is caused by
CO, CH4, VOC, and OVOC reactions. This value is equal to the
difference between observed total OH reactivity and NOx
reactivity. The known HO2 and RO2 primary sources
contribute less than 5 % compared to the radical recycling rate at high
NOx conditions.
The loss rate of HO2 and RO2 can be expressed as
L(HO2+RO2)=kHO2+NO×[HO2]×[NO]+L(HO2)termination+L(RO2)termination.
For high NOx conditions, the termination rates for
HO2+HO2, HO2+RO2, and
RO2+RO2 are less than 1 % compared to the reaction
rates between HO2 and NO. We also tested the equilibrium between
HO2 and HNO4 as well as between RO2 and
PAN-type compounds. They had only a minor impact on the ROx
budget calculation (<4%). The reaction of RO2 and NO is not
in Eq. (4), because this reaction converts to HO2 and thus is not
an effective loss of peroxy radicals.
Because the production and destruction rates of peroxy radicals must be
balanced, the missing peroxy radical source P′(ROx) can be
determined by the difference between known radical loss rate and production
rate. It is worth noting that all primary sources and termination processes
of HO2 and RO2 are negligible compared to the radical
propagation.
P′(ROx)=kHO2+NO×[HO2]×[NO]-P(HO2)primary-P(RO2)primary-kVOC×[OH]+L(HO2)termination+L(RO2)termination
As shown in Fig. 12, P′(ROx) is essentially zero in
background air. In the clean and polluted cases, however, a significant
missing radical source is found during daytime. On polluted days,
P′(ROx) is very large and shows a broad maximum with a
peak value of about 5 ppbv h-1 at 10:00 CNST. The production rate is
2.5 ppbv h-1 on average, nearly a factor of 5 larger than the known
primary source of ROx during the polluted days
(0.62 ppbv h-1). The error of the P′(ROx)
determination is estimated to be ±1.7 ppbv by considering the
uncertainty from measurement and kinetic reaction rates. On the clean days
(Fig. 12), the missing radical source is significant only during morning
hours, peaking at 3 ppbv h-1 at 10:00 CNST.
A missing primary radical source was already proposed to explain a morning
RO2 underestimation by the model during the summer campaign in
Wangdu (Tan et al., 2017). The proposed source originated from photolysis of
ClNO2 generating chlorine radicals, which can oxidize VOC to
RO2. However, the observed ClNO2 (0.5 ppbv, maximum of
diurnal average; Tham et al., 2016) and Cl2 (Liu et al., 2017)
concentrations could only explain 10 % and 30 % of the missing
primary ROx source for the summer Wangdu campaign (Tan et
al., 2017). During the winter campaign in Beijing, the missing primary
ROx source was 2 to 3 times larger than what was found
during the Wangdu summer campaign. The underestimation of RO2
concentrations in the model occurred mainly during the pollution episodes,
when the measurement site was influenced by air masses transported from the
Beijing central area or by local emissions. Since ClNO2 and
molecular chlorine were not measured in this campaign, their possible role in
the production of ROx is difficult to quantify. High
N2O5 concentrations were observed in this campaign with values
up to 10 ppbv during the pollution episodes (Wang et al., 2017a) and also
aerosol chlorine was abundant (up to 7 µg m-3) to facilitate
the production of ClNO2. Therefore, the ClNO2 has the
potential to explain at least part of the missing RO2 source.
However, the production rate of ClNO2 depends on the
N2O5 aerosol uptake coefficient and the ClNO2 yield,
both of which can be highly variable (Tham et al., 2018). Therefore,
measurements of chlorine-chemistry-related species would be essential to
evaluate its effect on the OH–HO2–RO2 radical system, but
they are not available here. Implementing a generic primary RO2
radical source in the model allows increasing the modeled HO2 and
RO2 concentrations. However, matching the modeled RO2
concentrations to the observations leads to an overprediction of OH by more
than a factor of 5. To maintain a relatively good agreement between observed
and modeled OH, an additional OH sink would be needed, but this is difficult
to reconcile with the good agreement between the measured and modeled
kOH.
Mean diurnal profiles of calculated missing ROx
source (see text) and local ozone production determined from measured and
modeled radical concentrations. The grey areas denote nighttime.
From the time series (Fig. 3) and the mean diurnal profiles (Fig. 4) of
Ox, it is possible to deduce that excess Ox
higher than the typical Ox value of 40 ppbv during
afternoon only appeared during the pollution days. This indicates that the
Ox was produced by local photochemical reactions and/or was
transported from the upwind areas. The change of the Ox
concentration is caused by both chemical production/destruction and physical
processes (advection, vertical mixing, deposition, and so on). Therefore, it
is important to note that the large local production rate is not necessarily
observed in the measured local Ox concentration. In this
study, we compared the chemical production rate and the Ox
concentration change to illustrate whether chemical production can support
the Ox concentration increase. The maximum instantaneous
ozone production rate (not including any Ox potential loss
process) can be approximated by the oxidation rate of NO by HO2 and
RO2:
P(O3)=kHO2+NO[HO2][NO]+∑kRO2,i+NO[RO2]i[NO].
The base model underpredicts the observed HO2 and RO2
concentrations significantly, leading to the strong underestimation of this
local ozone production rate (Fig. 12). As discussed above, an additional
primary radical source is required to close the ROx budget,
which would be the dominant contributor to the fast ozone production rate
during pollution episodes. Similarly, ozone production rates that were
directly measured in cities in the US were significantly higher than model
calculations for high NOx regimes (Baier et al., 2017; Brune
et al., 2016; Cazorla et al., 2012).
High OH concentration in Beijing during wintertime
The observed OH concentrations presented in this study are nearly 1 order
of magnitude larger than what global models predict for northern China in winter
(Lelieveld et al., 2016; Huang et al., 2014). The higher-than-expected OH
concentrations indicate that the oxidation capacity of the atmosphere was
high in Beijing and in the North China Plain in wintertime. As shown in
Table 3, the averaged OH concentrations in Beijing for January were also
higher than at other midlatitude field studies in which OH concentrations
were measured in winter. In this study, the observed OH radical
concentrations at noontime ranged from 2.4×106cm-3 in
severely polluted air (kOH∼27s-1) to 3.6×106cm-3 in relatively clean air (kOH∼5s-1). The reported OH maximum concentrations for urban and
suburban locations in the northern hemispheric winter were 1.4×106cm-3 in NYC (Ren et al., 2006), 1.7×106cm-3 in Birmingham (Heard et al., 2004), and 1.5×106cm-3 in Tokyo (Kanaya et al., 2007), respectively
(Table 3). Comparably high OH concentrations of up to 2.7×106cm-3 were observed at a rural site in Colorado in late
February (Kim et al., 2014). In our study, the OH maximum reached 5×106cm-3 from 20 to 28 February with similar j(O1D)
values (maximum 1×10-5s-1 on average) to those observed
at the Colorado site. On the other hand, the OH reactivity was on average less
than 5 s-1, which resulted in much slower OH turnover rates at that
rural site in the US with an estimated maximum of 1.6 ppbv h-1 (Kim et
al., 2014). The combination of high OH concentration and moderate OH
reactivity at Huairou/Beijing resulted in a fast oxidation rate by OH up to
3.6 ppbv h-1 for the campaign average conditions.
The primary radical production rate at Huairou/Beijing was in the lower range
among all other winter HOx campaigns (Table 3). However, the
high observed OH concentrations and moderate OH reactivities led to a fast OH
turnover rate, which had to be sustained by a large radical chain length. The
radical chain length is used to describe the efficiency of radical
propagation. It can be calculated as follows:
ChL=[OH]⋅kVOC/P(ROx).
The radical chain length was up to five at Huairou/Beijing but was observed to
be in the range from two to three in other winter campaigns (Table 3). This
indicates radical propagation is very efficient during the BEST-ONE campaign.
However, if radical primary production is missing in the calculation, the
calculated radical chain length would be too large. Therefore, for this
study, the chain length should be regarded as an upper limit. For example, in
the NACHTT campaign, only the OH production rate was taken into account by
Kim et al. (2014), which leads to an overestimation of the radical chain
length.
Secondary products are believed to be major contributors to aerosol particles
in haze events (Guo et al., 2014; Cheng et al., 2016; Wang et al., 2016,
2017b). An important question is what is the contribution of gas-phase
reactions to haze formation. Previous studies attempted to quantify the
contributions of gas-phase and aerosol-phase reactions to oxidation processes
during wintertime by chemical models (Huang et al., 2014; Guo et al., 2014;
Nan et al., 2017). In this study, the in situ observations of the OH radical
provide an experimental constraint to quantify the sulfate and nitrate
production by OH oxidation. As discussed above, the high OH concentrations
indicate a fast gas-phase oxidation during wintertime in Beijing. As a
consequence, the nitrate acid production rate P(HNO3) during
wintertime at Huairou/Beijing was 0.28 ppbv h-1 for daytime averaged,
only 3 times slower compared to the campaign in Wangdu during summertime
(0.81 ppbv h-1). In comparison, the SO2 oxidation rate was
0.02 ppbv h-1. One should keep in mind that the formation of gas-phase
sulfate and nitrate via OH oxidation does not necessarily lead to particle
formation, due to the dependence on the gas–aerosol partitioning. The
relatively fast oxidation rate presented here only shows the particle growth
potential from gas-phase reactions. Nevertheless, the in situ measurements
presented in this study provide experimental evidence for a strong gas-phase
production potential of aerosol precursors, which underlines the importance
of taking OH chemistry into account for the understanding of the wintertime
haze formation.
Summary and conclusions
The BEST-ONE campaign was performed at Huairou at the northwest edge of
Beijing in 2016 to elucidate pollution formation mechanisms in the North
China Plain providing the first wintertime observations of OH, HO2,
and RO2 radicals in this region. Relatively high radical
concentrations were observed compared to other winter campaigns in suburban
and urban environments at similar latitudes. OH radical concentrations at
noontime ranged from 2.4×106cm-3 in severely polluted
air (kOH∼27s-1) to 3.6×106cm-3 in relatively clean air (kOH∼5s-1), 2-fold higher than those observed in Birmingham (Heard
et al., 2004), Tokyo (Kanaya et al., 2007), and NYC (Ren et al., 2006) during
wintertime. OH reactivity was measured simultaneously during this campaign
and showed a large variability in values between 5 and 95 s-1. The
experimentally determined OH cumulative turnover rates were on average
20.7 ppbv per day, indicating a fast gas-phase oxidation capacity in this
region even in wintertime. During a strong haze event in early March at the
end of the campaign, the OH turnover rate increased to more than
20 ppbv h-1, which is comparable to summertime conditions.
In addition to the radical observations, numerous parameters were measured
during the BEST-ONE campaign, revealing the key processes of radical chemistry
for wintertime pollution episodes. The photolysis of O3, the
globally most important source of OH radicals, was strongly reduced due to
the low UV-B levels and the small concentration of water vapor during
wintertime. The initiation of ROx radical chain reactions
was instead dominated by the photolysis of HONO, which contributed about
46 % of the observed total primary radical production rate. Most of the
observed HONO cannot be explained by the gas-phase production from the
OH+NO reaction. Similar to summertime, other HONO sources must be
dominating the HONO production, thereby playing a critical role in the
photochemical oxidation of pollutants in urban air in wintertime. The alkene
ozonolysis reactions were the second important radical source, contributing
28 % to the total primary source. The radical chain termination processes
were dominated by the reaction between OH and NO2 (49 %).
The comparison of radical concentrations between observations and box model
simulations based on long-lived trace gas measurements showed only 30 %
difference during clean air episodes. However, RO2 radical
concentrations were underestimated by a factor of 5 by the model in the high
NOx regime. This severe underestimation of RO2
suggests that an important radical source is missing in the current chemical
mechanism during the pollution episodes. As a consequence, the production of
secondary pollutants like ozone driven by this missing radical source during
wintertime haze events in Beijing could be strongly underestimated. Although
the chlorine chemistry has the potential to partly explain the missing
radical source, its effect on radical concentrations could not be quantified
due to the lack of ClNO2 measurements. In the future, the
measurements of chlorine-related species (e.g., ClNO2,
Cl2) would be helpful to gain more insights regarding what the contributions
of ClNO2 are to the radical sources and to the formation of
secondary pollution.