Introduction
The hydroxyl radical (OH) is the most dominant oxidant in the troposphere,
initiating daytime photochemistry, removing the majority of reactive gases,
and leading to the formation of secondary products (e.g., ozone (O3),
PANs (peroxyacyl nitrates), and aerosol) that can affect air quality,
climate, and human health (Stone et al., 2012). OH is formed primarily
through the photolysis of O3, nitrous acid (HONO), hydrogen peroxide
(H2O2), the reactions of O3 with alkenes, and the
hydroperoxyl radical (HO2) to OH conversion process (HO2+NO)
(Platt et al., 1980; Crutzen and Zimmermann, 1991; Atkinson and Aschmann,
1993; Fried et al., 1997; Paulson et al., 1997). Recent field experiments
have found that the contribution of the photolysis of HONO to daytime OH
production can reach up to 56, 42, and 33 % in urban, rural and
forest areas, respectively (Ren et al., 2003; Kleffmann et al., 2005; Acker
et al., 2006), more than that of the photolysis of O3. However, most
current air quality models fail to predict observed HONO concentrations,
underestimating daytime HONO in particular (Czader et al., 2012;
Gonçalves et al., 2012; Li et al., 2011), due to the incomplete
knowledge of HONO sources.
Summary of observed HONO mixing ratios at noon (black font) and the
calculated unknown daytime HONO source (blue font) from field studies.
It is generally accepted that the photolysis of HONO (Reaction R2) in the
early morning could be a major source of OH. After sunrise, HONO mixing
ratios are usually in low concentrations due to the strong photolysis of
HONO. However, many field experiments have found daytime HONO mixing ratios
that are unexpectedly higher than the theoretical steady value
(∼ 10 ppt), in both urban and rural areas: e.g., 0.15–1.50 ppb in Asia (Su et al., 2008; Wu et al., 2013; Spataro et al., 2013),
0.01–0.43 ppb in Europe (Kleffmann et al., 2005; Acker and Möller, 2007;
Sörgel et al., 2011; Michoud et al., 2014), 0.02–0.81 ppb in North
America (Zhou et al., 2002a, b; Ren et al., 2010; Villena et al., 2011; N.
Zhang et al., 2012; Wong et al., 2012; VandenBoer et al., 2013), 2.00 ppb
(maximum) in South America (Elshorbany et al., 2009), and 0.015–0.02 ppb in
Antarctica (Kerbrat et al., 2012) (Fig. 1). These high HONO mixing ratios,
particularly in the daytime, cannot be explained well by gas-phase
production (Reaction R1), HONO emissions, and nighttime hydrolysis
conversion of NO2 on aerosols, suggesting that an unknown daytime HONO
source (Punknown) could exist.
OH+NO→HONOHONO+hν→OH+NOHONO+OH→NO2+H2O
The Punknown was calculated by Su et al. (2008) at Xinken (Guangzhou,
China), with a maximum of 4.90 ppb h-1. Spataro et al. (2013) proposed
a Punknown value of 2.58 ppb h-1 in Beijing. In fact,
Punknown values, ranging from 0.06 to 4.90 ppb h-1, have
been obtained from many field studies across the globe, as shown in Fig. 1,
suggesting Punknown could contribute greatly to the daytime production
of OH and HO2.
The most important formation pathway for nocturnal HONO could be the
hydrolysis reaction of nitrogen dioxide (NO2) on humid surfaces
(Reaction R4) (Kleffmann et al., 1999; Alicke et al., 2002;
Finlayson-Pitts et al., 2003):
2NO2+H2O→HONO+HNO3.
Ammann et al. (1998) found HONO formation via the heterogeneous reduction of
NO2 on the surface of soot (Reaction R5), and Reaction (R5) can be
enhanced by irradiation (Monge et al., 2010):
NO2+redads→HONO+oxads.
George et al. (2005) and Stemmler et al. (2006, 2007) showed the
heterogeneous reduction of NO2 on organic surfaces (Reaction R6)
(e.g., humic acid) to produce HONO:
NO2+HCred→HONO+HCox.
Li et al. (2008) proposed a homogeneous reaction of photolytically excited
NO2 with H2O (Reaction R7), but this reaction has been proven to
be unimportant in the real atmosphere (Carr et al., 2009; Wong et al., 2011;
Amedro et al., 2011). Zhang and Tao (2010) suggested the homogeneous
nucleation of NO2, H2O, and ammonia (NH3) for the production
of HONO (Reaction R8), but Reaction (R8) has not yet been tested in
laboratory studies, nor observed in field experiments:
NO2+hυ(λ>420nm)→NO2∗NO2*+H2O→HONO+OHNO2*+M→NO2+M2NO2+H2O(g)+NH3→HONO+NH4NO3(s).
Zhou et al. (2002b, 2003, 2011) demonstrated that the photolysis of adsorbed
nitric acid (HNO3) and nitrate (NO3-) at ultraviolet
wavelengths (∼ 300 nm) (Reaction R9) can produce HONO:
HNO3/NO3-+hυ→HONO/NO2-+O.
Additionally, HONO could be emitted from soils (Su et al., 2011; Oswald et
al., 2013), and may be important in farmland and forest areas.
Based on these mechanisms outlined above, some modeling studies have been
carried out to simulate HONO concentrations (e.g., An et al., 2011; Czader et
al., 2012; Gonçalves et al., 2012). Sarwar et al. (2008) incorporated
Reactions (R4), (R9), and HONO emissions into the Community Multiscale Air
Quality (CMAQ) model, but still underestimated HONO mixing ratios during
daytime. Li et al. (2010) considered both aerosol and ground surface
reactions, and HONO emissions, in the WRF-Chem model (Weather Research and
Forecasting model coupled with Chemistry), and found that HONO simulations
were significantly improved. However, Li et al. (2010) used a relatively
high emissions ratio of 2.3 % for HONO / NO2 to compute the direct
emissions of HONO, which could have overestimated the HONO concentrations in
the air (An et al., 2013). Czader et al. (2012) added Reactions (R6), (R7),
and HONO emissions into the CMAQ model. The HONO simulations matched well
with observations at night, but were significantly lower than observations
at noon. Wong et al. (2013) reported good agreement between simulated and
observed daytime HONO when HONO emissions, photolytically enhanced daytime
formation mechanisms on both aerosols and the ground, and Reaction (R7),
were included. However, according to our recent studies (Tang et al., 2014),
this result depended heavily on the selection of uptake coefficients of
NO2 heterogeneous chemistry. Overall, the topic of HONO sources remains
under discussion today, and so it is a challenge for modelers to decide
which mechanism(s) to be coupled into an air quality model.
To investigate the importance of the mechanisms described above, correlation
tests between the Punknown and NO2, HNO3, irradiation or the
photolysis frequency of NO2 [J(NO2)], were conducted in field
experiments (Acker and Möller, 2007; Sörgel et al., 2011; Villena et al.,
2011; Wong et al., 2012). Many of these studies demonstrated that there is a
clear dependency of the Punknown on irradiation /J(NO2) during daytime, particularly at noon. Rohrer et al. (2005) proposed that
the photolytic HONO source at the surface of the chamber strongly depended
on light intensity. Acker and Möller (2007) summarized field experiments in
several European countries and showed a strong correlation
(R2=0.81) between the Punknown and J(NO2). Wong et al. (2012)
also indicated that the Punknown showed a clear symmetrical
diurnal variation with a maximum around noontime, closely correlated with
actinic flux (NO2 photolysis frequency) and solar irradiance; the
correlation coefficient was over 0.70.
Besides irradiation /J(NO2), good correlations between the Punknown and NO2 mixing ratios have been found from both field and laboratory
studies, supporting the viewpoint that NO2 is the primary precursor of
HONO. Through estimating the Punknown, Acker and Möller (2007) speculated
that the daytime HONO levels might be explained by a fast electron transfer
onto adsorbed NO2. Sörgel et al. (2011) indicated that the
conversion of NO2 most likely accounted for light-induced HONO
formation, about an order of magnitude stronger than HONO formation during
nighttime. High correlations between the Punknown and NO2 mixing
ratios have also been found (e.g., R2=0.77 in Qin et al., 2006,
R2=0.80 in Villena et al., 2011, and R2=0.62 in Elshorbany
et al., 2009), indicating that the photosensitized conversion of NO2
is more likely to be the daytime HONO source. This is the reason why the
recent CalNex 2010 (California Research at the Nexus of Air Quality and
Climate Change) study found a very strong positive correlation (R2= 0.985) between HONO flux and the product of NO2 concentration and solar
radiation at the Bakersfield site (Ren et al., 2011).
Based on the studies introduced above, the Punknown calculated from
field experiments may be a practical method to help quantify the daytime
HONO source. In this study, field experiment data from 13 different field
campaigns across the globe were used to express the Punknown as a
function of NO2 mixing ratios and J(NO2) (see Sect. 2.2). We then
added the Punknown into the WRF-Chem model to assess the impacts of the
Punknown on the concentrations and production and loss rates of HONO,
OH, HO2, and organic peroxy radicals (RO2).
Data and methods
Observed data
Anthropogenic emissions were based on the year 2006/2007. Limited
measurements of HONO, OH, and HO2 in the coastal regions of China were
made in the summers of 2006/2007, so these limited measurements were used
for model evaluation. Observed air temperature (TA), relative humidity (RH),
wind speed (WS), and direction (WD) near the ground were obtained from the
National Climatic Data Center, China Meteorological Administration (H. Zhang
et al., 2012). Surface mixing ratios of O3 and NO2 in Beijing were
obtained from the Beijing Atmospheric Environmental Monitoring Action,
carried out by the Chinese Academy of Sciences (Li et al., 2011; Wang et
al., 2014), except those in Guangzhou, which were sourced from Qin et al. (2009). HONO observations were conducted using two annular denuders at the
campus of Peking University (39∘59′ N, 116∘18′ E) in Beijing on 17–20 August 2007 (Spataro et al., 2013) and a
long path absorption photometer at the Backgarden (BG) supersite
(23∘30′ N, 113∘10′ E), about 60 km
northwest of Guangzhou on 3–31 July 2006 (X. Li et al., 2012). The
measurement systems are described in detail in Spataro et al. (2013) and X.
Li et al. (2012). OH and HO2 were measured by laser-induced fluorescence
at the BG supersite on 3–30 July 2006 (Lu et al., 2012).
Parameterization of HONO sources
Besides HONO gas-phase production from Reaction (R1), three additional HONO
sources [HONO emissions, Reaction (R4) (nighttime), and the Punknown]
were coupled into the WRF-Chem model in this work.
HONO emissions were calculated using [0.023×fDV+0.008×(1-fDV)]×fTS, where fDV denotes the nitrogen oxides (NOx) emissions
ratio of diesel vehicles to total vehicles, and fTS is the NOx
emissions ratio of the traffic source to all anthropogenic sources (Li et
al., 2011; An et al., 2013; Tang et al., 2014). Reaction (R4) was inserted
into the Carbon-Bond Mechanism Z (CBM-Z) during nighttime only. The
heterogeneous reaction rate was parameterized by k=aDg+4νγ-1As (Jacob, 2000), where a is the
radius of aerosols, ν is the mean molecular speed of NO2, Dg is a gas-phase molecular diffusion coefficient taken as 10-5 m2 s-1
(Dentener and Crutzen, 1993), and As is the aerosol
surface area per unit volume of air, calculated from aerosol mass
concentrations and number density in each bin set by the Model for
Simulating Aerosol Interactions and Chemistry (MOSAIC). Hygroscopic growth
of aerosols was considered (Li et al., 2011).
Correlation of the unknown daytime HONO source (Punknown) (ppb h-1) with (a) [NO2] (ppb) and (b) [NO2] × J(NO2) (ppb s-1),
based on the field experiment data shown in Fig. 1.
Previous studies (Sörgel et al., 2010; Villena et al., 2011; Wong et
al., 2012) have shown Punknown∝[NO2]⋅J(NO2). To
quantify the relationship between the Punknown and NO2 mixing
ratios and irradiation, daytime Punknown, NO2 mixing ratios and
J(NO2), based on all the available data sets from 13 different field
campaigns across the globe (Table S1 in the Supplement), were plotted in Fig. 2. As expected,
good correlation (R2=0.75) between the Punknown and NO2
mixing ratios was obtained (Fig. 2a). Furthermore, the correlation (R2) between
the Punknown and [NO2]⋅J(NO2) was increased to 0.80, with a
linear regression slope of 19.60 (Fig. 2b). For the coastal regions of China
(mainly including Liaoning, Beijing, Tianjin, Hebei, Shandong, Jiangsu,
Anhui, Shanghai, Zhejiang, Jiangxi, Fujian, and Guangdong), the correlation
between the Punknown and [NO2]⋅J(NO2) was 0.48,
with
a linear regression slope of 17.37 (Fig. S2b in the Supplement), which is within the maximum
Punknown uncertainty range of 25 % (Table S1). The Punknown
could be expressed as a function of NO2 mixing ratios and J(NO2),
i.e., Punknown≈19.60[NO2]⋅J(NO2). This formula is very similar
to Punknown≈α⋅J(NO2)⋅[NO2]⋅[H2O]⋅(S/Vg+S/Va) proposed by Su et al. (2008), and Punknown≈3.3×10-8[NO2]⋅Qs suggested by Wong et al. (2012) as an additional
daytime source of HONO through analysis of observed data, where S/Va
is the aerosol surface area-to-volume ratio, S/Vg is the ground
surface area-to-volume ratio, α is a fitting parameter, and Qs
is solar visible irradiance. Recently, Li et al. (2014) suggested that high
HONO mixing ratios in the residual layer in the studied Po Valley in Italy
were mainly from a gas-phase source (SHONO) that consumed NOx (Li
et al., 2015), and SHONO was proportional to the photolysis frequency
of HONO [J(HONO)], basically consistent with our result that the
Punknown was proportional to NO2 mixing ratios and the photolysis
frequency of NO2 [J(NO2)].
Model setup
Model domains used in this study. Domain 2 covers the
Beijing–Tianjin–Hebei (BTH), Yangtze River delta (YRD), and Pearl River
delta (PRD) regions.
The WRF-Chem model version 3.2.1 (Grell et al., 2005; Fast et al., 2006), with the CBM-Z (Zaveri and Peters,
1999) and the MOSAIC (Zaveri et al., 2008,), was used in this study. The detailed physical
and chemical schemes for the simulations can be found in Tang et al. (2014).
Two domains with a horizontal resolution of 27 km were employed in this
study: domain 1 covered East Asia, whereas domain 2 covered the coastal
regions of China, including the Beijing–Tianjin–Hebei region (BTH), the
Yangtze River delta (YRD), and the Pearl River delta (PRD) (Fig. 3), which
are the three most rapidly developing economic growth regions of China. Rapid economic development and urbanization has led to a serious
deterioration in air quality in these three regions. Beijing, Shanghai, and
Guangzhou are three representative cities of the three regions, so this
study focuses on the three regions, including the three representative
cities. There were 28 vertical model layers from the ground to 50 hPa, and
the first model layer was ∼ 28 m above the ground. Meteorological
initial and boundary conditions were obtained from the NCEP (National
Centers for Environmental Prediction) 1∘ × 1∘
reanalysis data set. Chemical initial and boundary conditions were
constrained with the output of MOZART-4 (Model for Ozone and Related
chemical Tracers, version 4) (Emmons et al., 2010), every 6 h. Monthly anthropogenic emissions in 2006/2007 and
biogenic emissions were the same as those used by Li et al. (2011) and An et
al. (2013).
Six simulations (cases R, Rwop, and Rp, performed for the entire
months of August 2007 and July 2006), with a spin-up period of 7 days,
were conducted to assess the Punknown effects on the concentrations and
budgets of HONO, OH, HO2, and RO2. Case R only considered Reaction (R1) as a reference; Case Rwop included case R with HONO emissions, and
Reaction (R4) only at night; case Rp contained case Rwop with the
Punknown [≈19.60[NO2]⋅J(NO2)]. The Punknown and
Reaction (R4) were added to the CBM-Z, and diagnostic variables (i.e.,
production and loss rates of HONO, OH, HO2, RO2, O3, and
other species) were inserted into the CBM-Z to quantify the Punknown
impacts on the budgets of HONO, OH, HO2, and RO2 (Wang et al.,
2014).
Results and discussion
Comparison of simulations and observations
The statistical metrics of mean bias (MB), mean error (ME), root-mean-square
error (RMSE), normalized mean bias (NMB), normalized mean error (NME), index
of agreement (IOA), and correlation coefficient (CC), were used. The MB, ME,
and RMSE are given in the same units as the measurements (absolute metrics).
The MB quantifies the tendency of the model to over- or underestimate values,
while the ME and RMSE measure the magnitude of the difference between modeled
and observed values, regardless of whether the modeled values are higher or
lower than observations. One disadvantage of absolute metrics is that they
make intercomparisons of model performance in clean and polluted environments
or across different pollutants difficult to interpret. Consequently, a range
of relative metrics are often used. These metrics are presented either in
fractional or percentage units. The NMB and NME all normalize by observed
values. The IOA and CC provide a sense of the strength of the relationship
between model estimates and observations that have been paired in time and
space. Perfect agreement for any metric alone may not be indicative of good
model performance, so multiple metrics must be considered when evaluating
model performance. Simulations of TA, RH, WS and WD were compared with
observations, as shown in Wang et al. (2014). The MB, ME, RMSE, NMB, NME,
IOA, and CC were comparable with those of Wang et al. (2010) and L. Li et
al. (2012) using MM5 (the fifth-generation Pennsylvania State
University/National Center for Atmospheric Research Mesoscale Model), and H.
Zhang et al. (2012) using the WRF model. For O3 in Beijing of the BTH
region and Guangzhou of the PRD region, the NMB, NME, and IOA were -22.80,
58.70, and 0.79 %, respectively (Table 1 for case R), comparable to the
values of 30.2 % for NMB, 55.8 % for NME, and 0.91 for IOA, reported in L.
Li et al. (2012) using the CMAQ model. When HONO emissions, Reaction (R4), and
the Punknown were included, the NMB, NME, and IOA increased to
-2.20, 66.10 %, and 0.80, respectively (Table 1 for case Rp).
The NO2 fluctuations were generally captured (Fig. 4) but the simulated
amplitude of NO2 was underestimated in some cases (Fig. 4). This
underestimation could be related to the uncertainty of NOx emissions.
For NO2 in case R, the NMB, NME, and IOA were -13.50, 42.10 %, and
0.57, respectively (Table 1), similar to the results of Wang et
al. (2010) using the CMAQ model (NMB of -33.0 %, NME of 50.0 %, and IOA
of 0.61). Compared with case R, NO2 simulations (Table 1 for case
Rp) were further underestimated for case Rp due to
the underestimation of NOx emissions in Guangzhou.
Model performance statistics for O3 and NO2 in Beijing in
August 2007 and Guangzhou in July 2006.
Species
Case
MB
ME
RMSE
NMB
NME
IOA
(ppb)
(ppb)
(ppb)
(%)
(%)
O3
Rp
-0.65
19.40
25.44
-2.20
66.10
0.80
R
-6.69
17.21
25.24
-22.80
58.70
0.79
NO2
Rp
-9.50
17.31
21.40
-29.10
53.00
0.51
R
-4.40
13.75
17.61
-13.50
42.10
0.57
MB: mean bias; ME: mean error; RMSE: root-mean-square error; NMB: normalized
mean bias; NME: normalized mean error; IOA: index of agreement.
Comparison of simulated and observed hourly mean mixing ratios of
NO2 and O3 in (a) Beijing on 14–28 August 2007 and (b) Guangzhou
on 11–23 July 2006.
HONO simulations only with the gas-phase production (case R) were always
substantially underestimated compared with observations (Fig. 5), similar to
the results of Sarwar et al. (2008), Li et al. (2011), and An et al. (2013).
When HONO emissions and Reaction (R4) were included, HONO simulations were
significantly improved, especially at night (Fig. 5 and Table 2 for case
Rwop). For Beijing, the nighttime RMSE and NME were reduced by
0.90 × 106 molecules cm-3 and 44.70 %, whereas the NMB
and IOA were increased by 50.00 % and 0.29, respectively (Table 2). For
Guangzhou, the nighttime RMSE and NME were reduced by
0.44 × 106 molecules cm-3 and 32.90 %, and the NMB and
IOA were enhanced by 58.80 % and 0.18, respectively. When the
Punknown was included, daytime HONO simulations were considerably
improved (Fig. 5 and Table 2 for case Rp). Compared with case
Rwop, the daytime NME in Beijing was reduced by 19.60 %, and the
NMB and IOA in Beijing were increased to -24.30 from -62.00 % and 0.73
from 0.64, respectively (Table 2); the daytime NME in Guangzhou was reduced
by 8.10 %, and the NMB in Guangzhou was increased to -61.20 from
-76.50 % (Table 2).
Model performance statistics for daytime (06:00–18:00 LST) and
nighttime (19:00–05:00 LST) HONO in Beijing in August 2007 and Guangzhou in
July 2006.
Species
Case
MB
ME
RMSE
NMB
NME
IOA
CC
(106 molec cm-3)
(106 molec cm-3)
(106 molec cm-3)
(%)
(%)
HONOdaytime (Beijing)
Rp
-0.54
0.98
1.41
-24.30
44.50
0.73
0.57
Rwop
-1.37
1.41
1.83
-62.00
64.10
0.64
0.63
R
-2.07
2.07
2.58
-93.80
93.80
0.46
0.31
HONOnighttime (Beijing)
Rp
-0.73
0.84
1.09
-42.20
49.10
0.77
0.74
Rwop
-0.82
0.91
1.16
-47.90
53.20
0.75
0.75
R
-1.68
1.68
2.06
-97.90
97.90
0.46
0.76
HONOdaytime (Guangzhou)
Rp
-0.38
0.43
0.58
-61.20
69.60
0.58
0.56
Rwop
-0.48
0.49
0.65
-76.50
77.70
0.55
0.56
R
-0.60
0.60
0.80
-95.60
96.20
0.43
-0.30
HONOnighttime (Guangzhou)
Rp
-0.42
0.75
1.05
-32.90
58.50
0.66
0.43
Rwop
-0.49
0.83
1.15
-38.40
64.30
0.63
0.38
R
-1.25
1.25
1.59
-97.20
97.20
0.45
-0.01
CC: correlation coefficient.
Model performance statistics for OH and HO2 in Guangzhou in
July 2006.
Species
Case
MB
ME
RMSE
NMB
NME
IOA
CC
(106 molec cm-3)
(106 molec cm-3)
(106 molec cm-3)
(%)
(%)
OH
Rp
-1.35
4.37
6.22
-17.60
57.00
0.84
0.75
Rwop
-3.00
4.58
6.25
-112.20
126.50
0.81
0.72
R
-3.36
4.85
6.55
-123.00
136.60
0.79
0.70
HO2
Rp
-3.80
3.81
5.59
-78.50
78.60
0.61
0.66
Rwop
-4.19
4.20
6.14
-86.60
86.70
0.54
0.59
R
-4.22
4.23
6.16
-87.20
87.30
0.54
0.57
Comparison of simulated and observed hourly mean HONO mixing ratios
at the Peking University site in (a) Beijing on 17–20 August 2007 (Spataro
et al., 2013) and (b) the Backgarden site in Guangzhou on 11–25 July 2006
(X. Li et al., 2012).
Comparison of simulated and observed hourly mean mixing ratios of OH
and HO2 at the Backgarden site in Guangzhou in July 2006 (Lu et al.,
2012).
Simulated diurnal variations of OH and HO2 showed consistent patterns
with the observed data (Fig. 6). When HONO emissions and Reaction (R4) were
considered (case Rwop), OH and HO2 enhancements were ≤ ∼ 6 % in most cases compared with case R (Fig. 6 and Table 3),
but the Punknown led to 10–150 % improvements in OH
simulations on 5–12 July 2006 (Fig. 6). The 20–90 % overestimation of
OH mixing ratios on 20–25 July 2006 (Fig. 6) needs further investigation.
Compared with case R, the NME was reduced by 79.60 % (i.e., 136.60 -
57.00 %), whereas the NMB was increased by 105.40 % (123.00 -
17.60 %), and the IOA was improved to 0.84 from 0.79 (Table 3). When the
Punknown was considered, HO2 simulations were substantially
improved (Fig. 6), the IOA was improved to 0.61 from 0.54, and the CC was
improved to 0.66 from 0.57 (Table 3). However, HO2 simulations were
still substantially underestimated (Fig. 6). One of the major reasons for the
HO2 underestimation could be related to the considerable underestimation
of anthropogenic volatile organic compounds (VOCs) (Wang et al., 2014).
Punknown simulations and its impacts on
production and loss rates of HONO
Simulated unknown daytime HONO source (ppb h-1) in the (a) BTH,
(b) YRD, and (c) PRD regions in August 2007 (BJ, Beijing; TJ, Tianjin; SJZ,
Shijiazhuang; SH, Shanghai; NJ, Nanjing; HZ, Hangzhou; GZ, Guangzhou; ZH,
Zhuhai; SZ, Shenzhen).
High Punknown values were found in the coastal regions of China (Fig. 7), especially in the BTH, YRD, and PRD regions due to elevated emissions of
NOx (Zhang et al., 2009). The largest daytime average Punknown
value reached 2.5 ppb h-1 in Tianjin of the BTH region (Fig. 7a),
whereas it was 2.0 ppb h-1 in Shanghai of the YRD region (Fig. 7b). The
largest daytime average Punknown value reached 1.2 ppb h-1 in
Guangzhou and Shenzhen of the PRD (Fig. 7c), lower than the values of 2.5 ppb h-1 and 2.0 ppb h-1. One major reason is the underestimation
of daytime NO2 mixing ratios in the PRD (Fig. 4b).
Production [P(HONO)] and loss [L(HONO)] rates of HONO for cases R
(dashed lines) and Rp (solid lines) in (a, b) Beijing, (c, d) Shanghai,
and (e, f) Guangzhou in August 2007.
For case R, daytime HONO production was primarily from the reaction of OH
and nitric oxide (NO) (Reaction R1), with a maximum production rate of
0.69 ppb h-1 in Beijing, 1.20 ppb h-1 in Shanghai, and 0.72 ppb h-1 in Guangzhou near noon due to high OH mixing ratios (Fig. 8a, c,
e). The loss rate of HONO was 0.62 ppb h-1 in Beijing, 1.09 ppb h-1 in Shanghai, and 0.65 ppb h-1 in Guangzhou via Reaction (R2),
much higher than the 0.01–0.02 ppb h-1 in Beijing, Shanghai, and
Guangzhou via Reaction (R3) (Fig. 8b, d, f), indicating that Reaction (R2)
accounted for approximately 99 % of the total loss rate of HONO.
When the additional HONO sources (HONO emissions, Reaction (R4), and the
Punknown) were coupled into the WRF-Chem model, nighttime HONO was
formed mainly via Reaction (R4) (0.30–1.42 ppb h-1 in Beijing,
0.20–0.45 ppb h-1 in Shanghai, and 0.25–0.84 ppb h-1 in
Guangzhou) (Fig. 8a, c, e). HONO emissions contributed 0.04–0.62 ppb h-1 to HONO production (Fig. 8a, c, e). Simulated Punknown values
ranged from 0.42 to 2.98 ppb h-1 in Beijing, from 0.18 to 2.58 ppb h-1 in
Shanghai, and from 0.06 to 1.66 ppb h-1 in Guangzhou (Fig. 8a, c, e). The simulated Punknown values in Beijing (Fig. 8a) were in
good agreement with the results of Spataro et al. (2013), with an average
unknown daytime HONO production rate of 2.58 ppb h-1 in the studied
summer period. However, the simulated Punknown values in Guangzhou
(Fig. 8e) were lower than the 2.36–4.90 ppb h-1 reported by Su et al.
(2008), due mainly to the underestimation of the daytime NO2 mixing
ratios in the PRD region. The additional HONO sources produce more HONO,
which subsequently photolyzes to yield more OH. Therefore, the formation of
HONO through Reaction (R1) was greatly enhanced, with a maximum of 4.70 [1.44] (due to the Punknown) ppb h-1 in Beijing, 4.25
[3.13] ppb h-1 in Shanghai, and 1.58 [0.40] ppb h-1 in
Guangzhou in the morning (Fig. 8a, c, e), much higher than the
0.69 ppb h-1 in Beijing, 1.20 ppb h-1 in Shanghai, and
0.72 ppb h-1 in Guangzhou, respectively, for case R (Fig. 8a, c, e).
Meanwhile, the loss rate of HONO via Reaction (R2) was significantly
enhanced, with a maximum enhancement of 5.20 (i.e., 5.82 - 0.62) [1.97] (due to
the Punknown) ppb h-1 in Beijing, 4.31 (i.e., 5.40 - 1.09)
[1.44] ppb h-1 in Shanghai, and 1.96 (i.e., 2.61 - 0.65)
[1.18] ppb h-1 in Guangzhou (Fig. 8b, d, f). The HONO loss rate via
dry deposition ranged from 0.28 to 0.45 ppb h-1 (not shown), roughly
equivalent to the contribution of HONO emissions, suggesting that dry
deposition of HONO cannot be neglected in high NOx emission areas. The
maximum Punknown uncertainty range of 25 % (Table S1), a 25 %
increase (decrease) in the slope factor (19.60) led to a 9.19–18.62 %
increase (a 8.40–14.32 % decrease) in the maximum production and loss rate
of HONO (Fig. S3 in the Supplement).
Punknown impacts on concentrations of OH, HO2, and RO2
Daytime (06:00–18:00 LST) percentage enhancements of (a) OH, (b)
HO2, and (c) RO2 due to the unknown daytime HONO source (case
Rp- case Rwop) in the coastal regions of China in August 2007.
Incorporation of the Punknown into the WRF-Chem model led to
substantial enhancements in the daytime average mixing ratios of OH in the
coastal regions of China, e.g., 60–190 % in the BTH region, 60–210 % in
the YRD region, and 60–200 % in the PRD region (Fig. 9a). The maximum
enhancement of HO2 reached 250 % in the BTH region, 200 % in the YRD
region, and 140 % in the PRD region (Fig. 9b). Similarly, a daytime average
increase of 100–180, 60–150, and 40–80 % in RO2 (i.e., CH3O2
(methylperoxy radical) + ETHP (ethyl peroxy radical) + C2O3
(peroxyacyl radical) + others) were found in the BTH, YRD, and PRD regions,
respectively (Fig. 9c).
Vertically, the Punknown enhanced the monthly meridional-mean
daytime (06:00–18:00 LST) mixing ratios of OH, HO2, and RO2 by
5–38, 5–47, and 5–48 %, respectively, within 1000 m above the ground in
the coastal regions of China (Fig. 10). Strong vertical mixing in the daytime
in summer led to a roughly uniform vertical enhancement of OH, HO2, and
RO2 within 1000 m at the same latitude (Fig. 10). Different
Punknown values in different latitudes produced distinct
differences in the enhancements of OH, HO2, and RO2, with a maximum
located near 35∘ N (Fig. 10).
Daytime (06:00–18:00 LST) meridional-mean percentage enhancements
of (a) OH, (b) HO2, and (c) RO2 due to the unknown daytime HONO
source (case Rp- case Rwop) in the coastal regions of China in
August 2007.
Punknown impacts on the budgets of OH,
HO2, and RO2
OH radicals are produced mainly through the reaction of HO2+ NO, the
photolysis of O3 and HONO, and the reactions between O3 and alkenes
(Fig. 11). For case R, the predominant contribution to P(OH) (production rate
of OH) was the reaction of HO2+ NO (Fig. S1a, c, e), and the photolysis
of O3 was the second most important source of OH (Fig. S1a, c, e). When
the three additional HONO sources were added, the most important source was
the reaction of HO2+ NO, with a diurnal maximum conversion rate
reaching 9.38 [7.23] (due to the Punknown) ppb h-1 in Beijing,
2.63 [1.15] ppb h-1 in Shanghai, and 4.88 [1.43] ppb h-1 in
Guangzhou near noon (Fig. 11a, c, e). The photolysis of HONO became the
second most important source of OH in Beijing and Guangzhou before
10:00 LST, and in Shanghai before 12:00 LST; the diurnal peaks were 3.72
[3.06] ppb h-1 in Beijing at 09:00 LST, 0.89 [0.62] ppb h-1 in
Shanghai at 11:00 LST, and 0.97 [0.78] ppb h-1 in Guangzhou at
09:00 LST (Fig. 11a, c, e), which were comparable to or lower than the
3.10 ppb h-1 reported by Elshorbany et al. (2009). Kanaya et
al. (2009), who also conducted similar studies at Mount Tai (located in a
rural area) of China, using an observationally constrained box model,
suggested that the reaction of HO2+ NO was the predominant OH source,
with a daytime average of 3.72 ppb h-1, more than the
1.38 ppb h-1 of the photolysis of O3. Using an observationally
constrained box model, Hens et al. (2014) reported similar results in a
boreal forest, in which the dominant contributor to OH was the reaction of
HO2+ NO, ranging from 0.23 to 1.02 ppb h-1 during daytime. The
production rates of OH in our study were higher than in Kanaya et al. (2009)
and Hens et al. (2014) due to higher NOx emissions in urban areas than
in rural areas.
Averaged production [P(OH)] and loss [L(OH)] rates of OH for case
Rp in (a, b) Beijing, (c, d) Shanghai, and (e, f) Guangzhou in August
2007. (HONO+hv)net means the net OH production rate from HONO
photolysis (subtracting OH + NO results in HONO).
Recently, Li et al. (2014) proposed an assumed HONO source through the
reaction between NO2 and the hydroperoxyl-water complex
(HO2⚫ H2O), and suggested that
the impact of HONO on hydrogen oxide radicals (HOx) budget could be
overestimated because this source mechanism consumed HOx radicals.
However, Ye et al. (2015) argued that the HONO yield for the reaction above
is too small (with an upper-limit yield of 0.03) to explain the observation
of HONO in the study of Li et al. (2014), and Li et al. (2015) agreed that
the reaction of HO2⚫ H2O + NO2 is not a significant
HONO source, suggesting that HONO remains an important net OH precursor, as
demonstrated by many field studies (e.g., Kleffmann et al., 2005; Acker et
al., 2006) and our simulations.
The dominant loss rate of OH was the reaction of OH + NO2 for both
cases R and Rp (Figs. 11b, d, f and S1b, d, f). The diurnal
maximum loss rates were 1.98 ppb h-1 in Beijing, 1.12 ppb h-1
in Shanghai, and 1.70 ppb h-1 in Guangzhou for case R (Fig. S1b, d,
f), whereas these values were 5.61 [4.38] (due to the
Punknown) ppb h-1 in Beijing, 2.00 [1.00] ppb h-1 in
Shanghai, and 2.65 [1.02] ppb h-1 in Guangzhou for case Rp
(Fig. 11b, d, f). The reactions of OH + VOCs to form HO2 and RO2
were the second most important loss path of OH, with a diurnal maximum of
0.75–1.73 ppb h-1 for case R (Fig. S1b, d, f) and 1.57 [0.82] (due to
the Punknown) to 5.37 [4.05] ppb h-1 for case
Rp in Beijing, Shanghai, and Guangzhou (Fig. 11b, d, f). The third
most important OH loss path was the reaction of OH + CO to form HO2;
the diurnal maximum rates were 0.46–1.47 ppb h-1 for case R (Fig. S1b, d, f) and
0.93 [0.49] (due to the Punknown) to 3.58
[2.86] ppb h-1 for case Rp in Beijing, Shanghai and
Guangzhou (Fig. 11b, d, f).
The averaged radical conversion rates in the daytime (06:00–18:00 LST) are
illustrated in Fig. 12. OH radicals are produced mainly via the photolysis of
O3, HONO, and hydrogen peroxide (H2O2), and the reactions
between O3 and alkenes, after which OH radicals enter the ROx
(i.e., OH + HO2+ RO2) cycle (Fig. 12 and Tables 4, S2, and S3).
Daytime (06:00–18:00 LST) average budgets of OH, HO2 and
RO2 radicals (reaction rates, ppb h-1) for cases (a) R and (b)
Rp in Beijing/Shanghai/Guangzhou in August 2007.
Daytime (06:00–18:00 LST) average OH budgets in
Beijing/Shanghai/Guangzhou in August 2007. (Major OH production and loss rates and contributions are shown in bold.)
Reaction
Case R
Case Rwop
Case Rp
Rate
Contribution
Rate
Contribution
Rate
Contribution
(ppb h-1)
(%)
(ppb h-1)
(%)
(ppb h-1)
(%)
OH production
HO2+NO
2.778/0.732/1.748
81.73/67.09/71.54
3.242/0.760/1.871
83.74/68.00/72.02
7.101/1.402/2.553
73.34/61.95/67.55
(HONO+hv)net∗
–/–/–
–/–/–
–/–/0.017
–/–/0.66
1.855/0.497/0.489
19.16/21.98/12.93
O1D+H2O
0.465/0.307/0.617
13.68/28.17/25.27
0.479/0.306/0.630
12.36/27.38/24.24
0.568/0.312/0.651
5.86/13.80/17.23
O3+OLET/OLEI
0.101/0.024/0.027
2.98/2.16/1.11
0.095/0.023/0.027
2.45/2.08/1.03
0.080/0.021/0.025
0.83/0.91/0. 65
(H2O2+hv)net∗
0.035/0.023/0.029
1.02/2.07/1.17
0.035/0.023/0.030
0.91/2.03/1.16
0.037/0.022/0.032
0.38/0.97/0.19
HO2+O3
0.009/0.001/0.014
0.28/0.07/0.59
0.010/0.001/0.015
0.26/0.06/0.58
0.026/0.001/0.019
0.27/0.05/0.51
(HNO3+hv)net∗
0.005/0.001/0.002
0.15/0.06/0.10
0.005/0.001/0.002
0.13/0.06/0.09
0.007/0.001/0.003
0.07/0.04/0.07
ROOH+hv
0.003/0.004/0.005
0.09/0.36/0.19
0.003/0.004/0.005
0.09/0.38/0.19
0.007/0.007/0.007
0.07/0.29/0.19
O3+ETH
0.002/< 0.001/< 0.001
0.05/0.02/0.01
0.002/< 0.001/< 0.001
0.04/0.02/0.01
0.001/< 0.001/< 0.001
0.02/0.01/0.01
HO2+NO3
< 0.001/< 0.001/< 0.001
< 0.01/< 0.01/0.01
< 0.001/< 0.001/< 0.001
< 0.01/< 0.01/< 0.01
< 0.001/< 0.001/< 0.001
< 0.01/< 0.01/< 0.01
O3+ISOP
< 0.001/< 0.001/< 0.001
0.01/< 0.01/< 0.01
< 0.001/< 0.001/< 0.001
0.01/< 0.01/< 0.01
< 0.001/< 0.001/< 0.001
< 0.01/< 0.01/< 0.01
Total
3.399/1.091/2.443
100/100/100
3.873/1.118/2.598
100/100/100
9.683/2.263/3.779
100/100/100
OH loss
OH+NO2
1.116/0.474/0.770
39.31/46.63/38.33
1.225/0.501/0.844
38.11/45.86/38.86
3.146/1.045/1.424
38.08/44.29/40.76
OH+CO
0.785/0.203/0.576
27.65/19.97/28.67
0.932/0.227/0.637
29.00/20.78/29.33
2.573/0.506/1.001
31.14/21.45/28.65
OH+OLET/OLEI
0.192/0.054/0.059
6.76/5.31/2.94
0.264/0.065/0.077
8.21/5.95/3.55
0.537/0.206/0.095
6.50/8.73/2.72
OH+HCHO
0.150/0.050/0.146
5.28/4.92/7.27
0.166/0.053/0.156
5.16/4.85/7.18
0.544/0.096/0.242
6.59/4.07/6.93
OH+CH4
0.103/0.057/0.135
3.63/5.61/6.72
0.109/0.059/0.142
3.39/5.40/6.54
0.260/0.115/0.223
3.15/4.87/6.38
OH+ALD2/MGLY/ANOE
0.092/0.018/0.045
3.24/1.77/2.24
0.109/0.020/0.049
3.39/1.83/2.26
0.323/0.047/0.081
3.91/1.99/2.32
OH+SO2
0.054/0.030/0.035
1.90/2.95/1.74
0.064/0.034/0.041
1.99/3.11/1.89
0.172/0.116/0.072
2.08/4.92/2.06
OH+XYL
0.052/0.022/0.023
1.83/2.16/1.14
0.066/0.026/0.029
2.05/2.38/1.34
0.141/0.078/0.045
1.71/3.31/1.29
OH+H2
0.038/0.021/0.050
1.34/2.07/2.49
0.040/0.022/0.052
1.24/2.01/2.39
0.095/0.027/0.075
1.15/1.14/2.15
OH+TOL
0.027/0.007/0.011
0.95/0.69/0.55
0.034/0.008/0.014
1.06/0.73/0.64
0.086/0.025/0.024
1.04/1.06/0.69
OH+HONO
0.003/0.003/0.005
0.11/0.30/0.25
0.006/0.004/0.007
0.19/0.37/0.32
0.069/0.023/0.032
0.84/0.97/0.92
OH+HNOx
0.005/0.001/0.005
0.18/0.10/0.25
0.005/0.001/0.005
0.16/0.09/0.23
0.015/0.002/0.008
0.18/0.08/0.23
OH+O3
0.028/0.006/0.035
0.99/0.59/1.70
0.029/0.006/0.036
0.90/0.55/1.66
0.072/0.005/0.046
0.87/0.21/1.32
OH+H2O2
0.015/0.008/0.027
0.53/0.79/1.34
0.016/0.008/0.029
0.50/0.73/1.34
0.040/0.010/0.043
0.48/0.42/1.23
OH+ETH/OPEN
0.007/0.002/0.004
0.25/0.20/0.20
0.008/0.002/0.005
0.25/0.18/0.23
0.036/0.009/0.011
0.44/0.38/0.31
OH+CH3OOH/ROOH
0.010/0.011/0.014
0.35/1.08/0.70
0.011/0.012/0.014
0.34/1.10/0.64
0.022/0.020/0.022
0.27/0.85/0.63
OH+ISOP
0.019/0.004/0.002
0.67/0.39/0.10
0.020/0.004/0.003
0.62/0.37/0.14
0.017/0.007/0.003
0.21/0.30/0.09
OH+PAR
0.005/0.002/0.004
0.18/0.20/0.20
0.007/0.003/0.005
0.22/0.27/0.23
0.015/0.005/0.007
0.18/0.21/0.20
OH+ONIT/ISOPRD
0.028/0.005/0.016
0.99/0.49/0.80
0.030/0.005/0.018
0.93/0.46/0.83
0.077/0.013/0.025
0.93/0.55/0.72
OH+C2H6
0.002/0.001/0.002
0.07/0.10/0.10
0.003/0.001/0.002
0.09/0.09/0.09
0.008/0.002/0.004
0.10/0.08/0.11
OH+CH3OH/AN OL/CRES
0.002/0.001/0.002
0.07/0.10/0.10
0.002/0.001/0.002
0.06/0.09/0.09
0.007/0.002/0.003
0.08/0.08/0.09
OH+HO2
0.001/< 0.001/0.004
0.04/0.05/0.20
0.002/< 0.001/0.005
0.06/0.05/0.23
0.006/< 0.001/0.008
0.07/0.02/0.23
OH+NO
0.105/0.036/0.039
3.70/3.54/1.94
0.066/0.030/–
2.05/2.75/–
–/–/–
–/–/–
Total
2.839/1.017/2.009
100/100/100
3.214/1.093/2.172
100/100/100
8.261/2.360/3.495
100/100/100
OLET: terminal olefin carbons (C = C); OLEI: internal olefin carbons
(C = C); ROOH: higher organic peroxide; ETH: ethene; ISOP: isoprene; ALD2:
acetaldehyde; MGLY: methylglyoxal; ANOE: acetone; XYL: xylene; TOL: toluene;
HNOx: HNO3+ HNO4; OPEN: aromatic fragments; PAR: paraffin
carbon – C –; ONIT: organic nitrate; ISOPRD: lumped
intermediate species; ANOL: ethanol; and CRES: cresol and higher molar weight
phenols.
* The reactions of HONO+hv, H2O2+hv, and HNO3+hv
are reversible. “net” in the subscript denotes the subtraction of the corresponding
reverse reactions.
For case R, the reaction of HO2+ NO was the major source of OH
(2.78 ppb h-1 (81.73 % of the total daytime average production rate
of OH) in Beijing, 0.73 ppb h-1 (67.09 %) in Shanghai, and
1.75 ppb h-1 (71.54 %) in Guangzhou) (Fig. 12a and Table 4). The
second largest source of OH was the photolysis of O3 (Table 4). OH
radicals were removed mainly through the reaction of OH + NO2
(1.12 ppb h-1 (39.31 % of the total daytime average loss rate of OH)
in Beijing, 0.47 ppb h-1 (46.63 %) in Shanghai, and
0.77 ppb h-1 (38.33 %) in Guangzhou) (Table 4), whereas those were
converted to HO2 mainly via the reaction of OH + CO (Table 4). For
HO2, the predominant production pathways were the reactions of OH + CO
and CH3O2 + NO and the photolysis of formaldehyde (HCHO)
(Table S2). HO2 radicals were consumed primarily via the reaction of HO2 +
NO (2.78 ppb h-1 (99.34 %) in Beijing, 0.73 ppb h-1
(99.61 %) in Shanghai, and 1.75 ppb h-1 (98.29 %) in Guangzhou)
(Table S2). RO2 radicals were formed mainly from the reactions of OH +
OLET (terminal olefin carbons) / OLEI (internal olefin carbons), OH + ETH
(ethene), OH + methane (CH4), and OH + AONE (acetone). RO2
radicals were consumed primarily via the reaction of CH3O2 + NO
(0.54 ppb h-1 (94.56 %) in Beijing, 0.16 ppb h-1 (95.28 %)
in Shanghai, and 0.33 ppb h-1 (96.07 %) in Guangzhou) (Table S3).
When the three additional HONO sources were inserted into the WRF-Chem model
(case Rp), the daytime average OH production rate was enhanced by
4.32 (i.e., 7.10 - 2.78) [3.86] (due to the Punknown) ppb h-1
in Beijing, 0.67 (i.e., 1.40 - 0.73) [0.64] ppb h-1 in Shanghai, and
0.80 (i.e., 2.55 - 1.75) [0.68] ppb h-1 in Guangzhou via the reaction
of HO2 + NO, and by 1.86 [1.86] ppb h-1 in Beijing, 0.50
[0.50] ppb h-1 in Shanghai, and 0.49 [0.47] ppb h-1 in
Guangzhou via the photolysis of HONO, respectively (Table 4). The
enhancements of the daytime average OH production rate due to the photolysis
of HONO were comparable to or lower than the 2.20 ppb h-1 obtained by
Liu et al. (2012). The daytime average OH loss rate was increased by 2.03
[1.92] (due to the Punknown) ppb h-1 in Beijing, 0.58
[0.55] ppb h-1 in Shanghai, and 0.65 [0.58] ppb h-1 in
Guangzhou via the reaction of OH + NO2, and by 1.78
[1.64] ppb h-1 in Beijing, 0.31 [0.28] ppb h-1 in Shanghai, and
0.42 [0.36] ppb h-1 in Guangzhou via the reaction of OH + CO,
respectively (Table 4). Similarly, the daytime average HO2 production
rate was increased by 0.31 [0.28] (due to the Punknown) to 1.78
[1.64] ppb h-1 in Beijing, Shanghai and Guangzhou via the reaction of
OH + CO, and by 0.63 [0.59] ppb h-1 in Beijing, 0.10
[0.09] ppb h-1 in Shanghai, and 0.19 [0.17] ppb h-1 in
Guangzhou via the reaction of CH3O2+ NO; whereas, the daytime
average HO2 loss rate was enhanced by 0.67 [0.61] (due to the
Punknown) to 4.32 [4.27] ppb h-1 in Beijing, Shanghai and
Guangzhou via the reaction of HO2+ NO (Table S2).
Overall, the net daytime production rate of ROx was increased to 3.48
(i.e., 2.56 + 0.71 + 0.21) [2.06] (due to the Punknown) from 1.20
(i.e., 0.60 + 0.43 + 0.17) ppb h-1 in Beijing, 1.09 (i.e., 0.86 +
0.19 + 0.04) [0.45] from 0.54 (i.e., 0.36 + 0.14 + 0.04) ppb h-1
in Shanghai, and 1.52 (i.e., 1.21 + 0.26 + 0.05) [0.58] from 0.92 (i.e., 0.68
+ 0.20 + 0.04) ppb h-1 in Guangzhou (Fig. 12) due to the three
additional HONO sources, indicating that the ROx source was mainly from
OH production, especially via the photolysis of HONO (Tables 4, S2 and S3).
This result is different from the conclusion of Liu et al. (2012) that the
photolysis of HONO and oxygenated VOCs is the largest ROx source. One of
the primary reasons for this is the underestimation of anthropogenic VOCs
(Wang et al., 2014). For Beijing, the net production rate of ROx was
3.48 ppb h-1, lower than the 6.60 ppb h-1 from the field
studies of Liu et al. (2012). Our results reconfirmed the view of Ma et
al. (2012) that the North China Plain acts as an oxidation pool. The
additional HONO sources produced an increase of 2.03 [1.96] (due to the
Punknown) ppb h-1 in Beijing, 0.56 [0.54] ppb h-1 in
Shanghai, and 0.66 [0.59] ppb h-1 in Guangzhou in the net loss rate
of ROx (Fig. 12).