ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-19-603-2019Comparison of surface ozone simulation among selected regional models in MICS-Asia III – effects of chemistry and vertical transport for the causes of differenceComparison of surface ozone simulation in MICS-Asia IIIAkimotoHajimeakimoto.hajime@nies.go.jpNagashimaTatsuyaLiJieFuJoshua S.https://orcid.org/0000-0001-5464-9225JiDongshenghttps://orcid.org/0000-0002-7889-4417TanJianihttps://orcid.org/0000-0003-3296-6339WangZifaNational Institute for Environmental Studies, Onogawa, Tsukuba 305-8506, JapanInstitute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, ChinaDepartment of Civil and Environmental Engineering, University of Tennessee, Knoxville, TN 37996, USAHajime Akimoto (akimoto.hajime@nies.go.jp)16January20191916036152August201829August20183December201817December2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/19/603/2019/acp-19-603-2019.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/19/603/2019/acp-19-603-2019.pdf
In order to clarify the causes of variability among the model outputs for
surface ozone in the Model Intercomparison Study Asia Phase III (MICS-Asia III),
three regional models, CMAQ v.5.0.2, CMAQ v.4.7.1, and NAQPMS
(abbreviated as NAQM in this paper), have been selected. Detailed analyses of
monthly averaged diurnal variation have been performed for selected grids
covering the metropolitan areas of Beijing and Tokyo and at a remote oceanic
site, Oki. The chemical reaction mechanism, SAPRC99, used in the CMAQ models
tended to give a higher net chemical ozone production than CBM-Z used in
NAQM, agreeing with previous studies. Inclusion of the heterogeneous
“renoxification” reaction of HNO3 (on soot surface)→NO+NO2
only in NAQM would give a higher NO concentration resulting in a
better agreement with observational data for NO and nighttime O3 mixing
ratios. In addition to chemical processes, the difference in the vertical
transport of O3 was found to affect the simulated results significantly.
Particularly, the increase in downward O3 flux from the upper layer to
the surface after dawn was found to be substantially different among the
models. Larger early morning vertical transport of O3 simulated by CMAQ 5.0.2
is thought to be the reason for higher daytime O3 in July in this
model. All three models overestimated the daytime ozone by ca. 20 ppbv at
the remote site Oki in July, where in situ photochemical activity is minimal.
Introduction
In the Model Intercomparison Study Asia Phase III (MICS-Asia Phase III), one
of the targets was to narrow down the difference in the model simulation
results by using common key input parameters such as precursor emissions,
meteorological fields, and boundary conditions to allow a more focused
discussion on the causes of the difference among model outputs. In most of
the past model intercomparison studies for chemical transport models (CTMs)
for air quality, such key parameters were not common to all the models,
which made the discussion of the causes of the differences among the model
outputs difficult, and the results often demonstrated that the ensemble mean
of simulated mixing ratios agreed reasonably well with observations even
though the disagreement among the models was often significantly large (for
example, Han et al., 2008; Fiore et al., 2009).
In order to improve the state of model intercomparison studies,
participants of the MICS-Asia III studies agreed to use common emission data
(M. Li et al., 2017), meteorological fields (specified Weather Research and
Forecasting Model (WRF)), and boundary conditions by either of two global
CTMs
(GEOS-Chem and CHASER) provided within the project (Wang et al., 2019; J. Li et al., 2019). The following
12 regional models have been submitted to the MICS-Asia III using the
designated common emissions, meteorological fields, and boundary conditions:
six WRF-CMAQ (Community Multiscale Air Quality Modeling System, two v.5.0.2,
one v.5.0.1, and three v.4.7.1), four WRF-Chem (Weather Research and
Forecasting (WRF) model coupled with Chemistry), one WRF-NHM (JMA
Nonhydrostatic Model)/Chem, and one WRF-NAQPMS (Nested Air Quality Prediction
Modeling System, which is abbreviated to NAQM in this paper hereafter for
simplicity). It turned out, however, that even though these 12 models used
the specified common key input components, large variabilities in the
spatial distribution and absolute mixing ratios among the models were found
for ozone (O3) (J. Li et al., 2019).
In the present study, three regional models, two WRF-CMAQ, v.5.02 and
v.4.7.1, and WRF-NAQM were selected among the 12 abovementioned models to
elucidate the causes of differences, and detailed comparisons were made for
selected grids covering the metropolitan areas of Beijing and Tokyo and at
a remote oceanic site at Oki in April and July in 2010. We selected the two
models of CMAQ because CMAQ models have been widely used to assess the air
quality for ozone in Asia (e.g., Yamaji et al., 2008; Kurokawa et al., 2009;
Fu et al., 2012), and the difference in the simulated results between
different versions (v.5.02 and v.4.7.1) is of concern. Furthermore, we
selected WRF-NAQM because this is one of the regional CTMs developed in
Asia, giving substantially lower mixing ratios of surface ozone compared
to most WRF-CMAQ models including the two selected models (J. Li et al., 2019). The metropolitan areas of the two megacities of Beijing
and Tokyo have been selected for the comparison to test whether regional
models can be applied to the mitigation policy of urban ozone pollution.
Oki, an EANET (Acid Deposition Monitoring Network in East Asia) monitoring
station located in the southern part of the Sea of Japan, was selected as a
remote reference site located between the two megacities, as in situ
photochemical production of O3 is known to be minimal there (Jaffe et
al., 1996).
Models
Basic features and the whole simulated domain of the regional models, CMAQ
v.4.7 (Foley et al., 2010), v.5.0 (CMAS, 2011), and NAQM (J. Li et al.,
2016), used in this study are given elsewhere in this issue (J. Li et al.,
2019). The employed horizontal resolution was 45 km for all
the models, and the maximum height and number of vertical layers were
45 km
and 40 layers, respectively, in the CMAQ models and 20 km and 20 layers,
respectively, in the NAQM, so that the vertical resolution in the
troposphere was about the same. The lowest layer for which the simulated
data of ozone were extracted in this paper was 50 m from the ground. Model
calculations by CMAQ v.5.0.2 and v.4.7.1 and NAQM were conducted at the
University of Tennessee, USA; National Institute for Environmental Studies,
Japan; and Institute of Atmospheric Physics, China, respectively. All the
models used the common meteorological fields from WRF simulation and common
emissions of MIX (0.25∘×0.25∘) for 2010 (M. Li
et al., 2017) developed in the MICS-Asia III project. The initial and
boundary conditions were supplied by global models, CHASER for CMAQ v.4.7.1
and NAQM and GEOS-Chem for CMAQ v.5.0.2. It was agreed that either CHASER
or GEOS-Chem may be used in MICS-Asia III since they were confirmed to
give reasonably good agreement for the O3 field in the Asian domain.
Other than these three key components (emissions, meteorological field, and
boundary conditions), the three models employed different sub-models and
parameters for the gas-phase and aerosol chemistry module, dry
deposition parameters, boundary layer scheme, etc. As for the gas-phase
chemistry, CMAQ v.4.7.1 and v.5.0.2 incorporated SAPRC99 (Carter, 2000), and
NAQM employed CBM-Z (Zaveri and Peters, 1999). In CMAQ v.4.7.1 (Foley et
al., 2010), major upgrades were made on the aerosol treatment from the
previous version: (a) updates to the heterogeneous N2O5
parameterization, (b) improvement in the treatment of secondary organic
aerosol (SOA), (c) inclusion of dynamic mass transfer for coarse-mode
aerosol, and (d) revisions to the cloud model. The NAQM and CMAQ v.4.7.1
employed ISORROPIA v.1.7 (Nenes et al., 1998), and CMAQ v.5.0.2 incorporated
ISORROPIA v.2.1 (Fountoukis and Nenes, 2007) for inorganic aerosol chemistry
modules. In addition, CMAQ v.4.7.1 and v.5.0.2 included AERO5 and AERO6
(Binkowski and Roselle, 2003), respectively, as an organic aerosol
chemistry module. The sub-modules for dry deposition and wet deposition
employed in the three models were essentially the same. The Asymmetric
Convective Model version 2 (ACM2) for the planetary boundary layer (PBL)
(Pleim, 2007) was employed in both CMAQ v.4.7.1 and v.5.0.2. The Yonsei
University (YSU) boundary layer scheme was used for calculating boundary
layer
height for NAQM (J. Li et al., 2016). As for the advection module, the
models by Yamartino (1993) and Walcek and Aleksic (1998) were used for CMAQ
(v.4.7.1 and v.5.0.2) and NAQM, respectively. For the computation of the
vertical transport for advection, CMAQ v.5.0.2 used the PPM (piecewise
parabolic method) scheme, compared to CMAQ v.4.7.1, which used the
vertical velocity directly from WRF.
Comparison domain and observational data
All the comparisons between the model simulations and the model using
observational data were made for monthly averaged diurnal variations in the
mixing ratios of O3 and NO in April and July. April and July were
chosen here because in situ photochemical build-up of O3 in April is
insignificant but the daytime maximum mixing ratio of O3 is relatively
high, reflecting the well-known spring maximum of O3 for the background
in the Northern Hemisphere including East Asia (Monks, 2000; Pochanart et
al., 2003), while in July a much higher in situ photochemical buildup of
O3 is observed in urban areas in East Asia. Two representative
megacities, Beijing and Tokyo, were selected as urban areas for the
comparison. As a remote reference site, Oki, an EANET site situated between
Beijing and Tokyo, was selected. The Oki site is located on a cliff of an
island where the local emissions of NOx and volatile organic compounds (VOCs) are insignificant so
that in situ production of O3 is also minimal (Jaffe et al., 1996;
Pochanart et al., 2002). Since the NO levels at Oki are too low to
obtain any meaningful data using the conventional chemiluminescence NOx
monitoring instrument, comparison with modeling results was carried out only for O3 at this site in this
study. All the calculations were conducted for
the whole year of 2010 using the meteorological field and emission data for
this year.
Grids for comparison of the model simulation and observation; Beijing
and Tokyo metropolitan areas and Oki EANET site.
The domains of the Beijing, Tokyo, and Oki sites were centered at 39.9∘ N,
116.3∘ E; 36.0∘ N, 139.3∘ E; and
36.3∘ N and 133.1∘ E, respectively. The selected domains
for Beijing and Tokyo consisted of nine (3×3) and three (2+1) grids,
respectively, covering the metropolitan areas of the cities as shown in Fig. 1.
Data of a single grid covering the island were used for the Oki site. The
observational data used for Tokyo were 1 h averaged values in 2010 of the
average of 118 (for O3) and 126 (for NO) non-roadside monitoring
stations within the selected grid (Fig. 1). The data were obtained from
Atmospheric Environment Monitoring Data Files in the Environmental
Information Database stored at the National Institute for Environmental
Studies (NIES), Japan. In Beijing, unfortunately, no routine monitoring data
of 1 h averaged values of O3 in 2010 are open to the public.
Therefore, unpublished data from two sites (IAP tower campus and Yangfang)
obtained by IAP, and literature values published in Xu et al. (2011) and
Chen et al. (2015), have been referred to in this work. The O3 and
NOx instruments at the IAP site (39.9∘ N, 116.3∘ E), which is an urban site
surrounded by residential infrastructure and a freeway to the east (ca. 200 m),
were on the rooftop of a building (10 m above the ground). Yangfang (39.5∘ N,
116.7∘ E) is a suburban site in the north of Beijing, ca. 40 km away from IAP.
The instruments were 10 m above the ground on the campus of a university
with little influence from local sources and sinks. The O3 and NOx
instruments were an ultraviolet photometric analyzer (model 49i, Thermo
Fisher Scientific (Thermo), USA) and a chemiluminescence analyzer (model 42i
TL, Thermo, USA), respectively. One of the Beijing data that we used is the
monthly averaged daily maximum concentration of O3 in April and July in
2014–2015 averaged over two suburban sites, Daxing (39.7∘ N, 116.4∘ E) and
Shunyi (40.1∘ N, 116.7∘ E), presented by Chen et al. (2015). Another datum is the
averaged diurnal variation at three urban–suburban sites, Fengtai, Shunyi,
and Baolian, in July and August 2007, which are given in the paper by Xu
et al. (2011). All the denoted observational sites in Beijing are located
within the selected nine model grids shown in Fig. 1.
The observational data for Oki are the 1 h averaged EANET data in 2010
provided on request by the Network Center, Asia Center for Air Pollution
Research (ACAP) (http://www.acap.asia, last access: 24 May 2018).
Monthly averaged diurnal variation in Beijing, (a)O3 in April,
(b)O3 in July, (c) NO in April, and (d) NO in July.
Monthly averaged diurnal variation in Tokyo, (a)O3 in April,
(b)O3 in July, (c) NO in April, and (d) NO in July.
Results
Figure 2a–d depict the simulated and observed mixing ratios of the
monthly averaged diurnal variations in the O3 and NO concentrations in
April and July in Beijing, and Fig. 3a–d show similar results in Tokyo.
The comparisons of the values simulated by CMAQ 5.0.2 and 4.7.1
(hereafter, “v.” for version will be omitted for simplicity) and NAQM are
plotted in each figure together with the observational data.
In Beijing, observational data of surface ozone at the routine monitoring
stations managed by the Beijing municipal government were, unfortunately,
not available until 2013 (Chen et al., 2015). The average of two
observational data sets obtained by IAP in 2010 is marked by the dashed
lines with filled circles in Fig. 2a and b for O3 and in Fig. 2c and d
for NO. Other published observational data of diurnal variation
in O3 in Beijing in April are available by Xu et al. (2011) at four
sites, two urban (Fengtai and Baolian), one suburban (Shunyi), and one rural
(Shangdianzi) in summer (21 June–12 September) in 2007. Since the diurnal
variation in the urban and suburban sites is consistent, the average of
these three sites is plotted in Fig. 2b, marked by a dashed line with
triangles. No monthly average diurnal variation in O3 is available for
April in Beijing in the literature. Chen et al. (2015) reported the monthly
averaged daily maximum mixing ratio of O3 to be ca. 60 ppbv at an urban
site (Dongsi) and ca. 75 and 65 ppbv at two suburban sites (Daxing and
Shunyi, respectively) within the selected grids in this study. If we simply
take the average of these three values, the daily maximum mixing ratio is
ca. 65 ppbv (not shown in Fig. 2a). Only the IAP data are plotted for NO
with solid lines in Fig. 2c and d.
As can be seen in Figs. 2a, b and 3a, b, the diurnal pattern
of the simulated surface ozone shows a maximum in the late afternoon around
14:00–16:00 local time in both Beijing (CST, UTC+8) and Tokyo (JST, UTC+9), agreeing well with the
observations. The simulated mixing ratios of O3 by CMAQ 4.7.1 are the
highest, and those simulated by NAQM are the lowest in both Beijing and
Tokyo in both April and July. The diurnal variations in O3 simulated by
CMAQ 4.7.1 are in parallel with the NAQM values for whole days in all cases,
but the predicted mixing ratios by CMAQ 4.7.1 are higher by ca. 20 and 40 ppbv
than those predicted by NAQM in April and July, respectively,
in both Beijing and Tokyo. The O3 mixing ratios predicted by CMAQ 5.0.2
have peculiar seasonal characteristics; i.e., the mixing ratio is
slightly higher but close to that predicted by NAQM within 10 ppbv in both
Beijing and Tokyo in April, whereas in July the daytime O3 maximum
predicted by CMAQ 5.0.2 is very close to that predicted by CMAQ 4.7.1, much
higher than the value by NAQM. In Tokyo, the simulated mixing ratios of CMAQ 5.0.2
and NAQM are closer to the observations in April, and NAQM gives a
closer matching with observations in July, while CMAQ 4.7.1 overestimates
the values in both months as shown in Figs. 2b and 3b. A comparison
with the observations will be discussed later, including the uncertainty of
the observational data in Beijing.
The observed mixing ratios of NO show a peak value at around 07:00 (CST in Beijing and JST in Tokyo), a
decrease during morning, followed by a slow decay in the afternoon, and they
start to build up during nighttime in both April and July, in both
Beijing and Tokyo. The peak values of the mixing ratios in the morning are
ca. 13–14 and 6 ppbv in April and ca. 11 and 5–6 ppbv in July in Beijing and
Tokyo, respectively. The minimum mixing ratios in the evening are ca. 1.7
and 1.4 ppbv in April and 2.3 and 1.3 ppbv in July in Beijing and Tokyo,
respectively. Thus, it can be noted that the NO mixing ratios in Beijing are
nearly double those in Tokyo.
The simulated mixing ratios of NO are generally in the order of NAQM>CMAQ 5.0.2>CMAQ 4.7.1, but they vary considerably
among the models. In April, CMAQ 5.0.2 gives morning peak values of
13–14 ppbv in Beijing and ca. 5 ppbv in Tokyo, which agrees well with the
observations. NAQM overpredicts the NO mixing ratio in April in Beijing but
gives a reasonable agreement with the observations in Tokyo as shown in Figs. 2c
and 3c. In contrast, CMAQ 4.7.1 gives a broad daytime peak of only
ca. 2 ppbv in Beijing and ca. 1 ppbv in Tokyo in April, which is quite
different from other models, and it considerably underpredicts the
observational data. In July, only NAQM gives a morning peak mixing ratio of
ca. 8 ppbv in Beijing and 5.5 ppbv in Tokyo, agreeing fairly well with the
observations including diurnal variation (Figs. 2d and 3d). In contrast,
both CMAQ 5.0.2 and 4.7.1 give morning peaks as low as 1–2 ppbv and a nearly
zero mixing ratio during nighttime, which are significantly lower than the
observational values.
It can be noted that the simulated and observed levels of O3 are highly
anti-correlated with those of NO. For example, the reasonably good
agreements of O3 simulated by CMAQ 5.0.2 and NAQM in April and by NAQM
in July in Tokyo correspond to the reasonably good agreement of NO in each
case. Much higher overestimates of O3 by CMAQ 4.7.1 in April and by
both CMAQ 5.0.2 and 4.7.1 in July correspond to the substantial
underestimates of NO.
Monthly averaged diurnal variation in O3 at Oki (a) in
April and
(b) in July.
Figure 4a and b show the monthly averaged diurnal variation in O3
mixing ratios at Oki in April and July, respectively. As shown in Fig. 4a,
all three models give consistent mixing ratios of O3 at 60–65 ppbv
in April, agreeing well with observations within 10 ppbv. In July, although
the simulated mixing ratios of O3 agree well with each other within
10 ppbv, they are in the range of 50–70 ppbv as compared to the
observational level of 35–45 ppbv. Thus, all three models overestimate
the O3 mixing ratio by nearly 20 ppbv. Although the characteristics
of remote sites showing only a slight daytime buildup of O3 are well
reproduced by the models, the substantial overestimate of the simulated
O3 mixing ratio in July compared to the observational values should be
of concern.
Discussion
The causes of the differences in the simulated results among the three
models mentioned above must be due to either chemical or transport processes
incorporated in the models. Here, possible causes of differences of those
processes are discussed individually.
Comparison of chemical mechanism sub-modules
One of the differences in the three models in this study is the chemical
reaction mechanism sub-module. CMAQ 5.0.2 and 4.7.1 incorporate SAPRC99
while NAQM employs CBM-Z. It has been well known that different
photochemical mechanisms used in regional CTMs
produce different results in the prediction of O3. Jimenez et al. (2003)
compared seven photochemical mechanisms including CBM-IV (Gery et
al., 1989) and SAPRC99 using a box model. Comparisons of CBM-IV, CBM-V
(Sarwar et al., 2008), and SAPRC99 incorporated into regional CTMs have been made by Faraji et al. (2008) and Luecken et al. (2008).
The main differences among these mechanisms have been noted to be
the lumping technique describing organic compounds into surrogate groups
(Jimenez et al., 2003), the differences in the products of the reaction of
aromatics with OH radicals, and the overall branching ratio of radical
generation and termination reactions (Faraji et al., 2008). The results of
these studies gave a consistent picture that SAPRC99 gives higher
concentrations of O3 than CBM-IV in both the box model calculation and regional model simulation over the United States. The O3
concentration obtained by CBM-V is reported to be between the CBM-IV and
SAPRC99 values (Luecken et al., 2008). The reason for the higher
concentration of O3 by SAPRC99 has been deduced to be due to the more
efficient peroxy radical production in the photochemical reaction scheme of
SAPR99 compared to that of the CBM modules.
Comparison of net chemical O3 production in (a) Beijing in
April, (b) Beijing in July, (c) Tokyo in April, and (d) Tokyo in July.
Figure 5a–d show the net chemical production of O3 in Beijing and
Tokyo in April and July calculated in this study. Here, the net chemical
production, N(O3), was calculated by the equation N(O3)=F(O3)-D(O3)={k1[HO2][NO]+k2[RO2][NO]}-{k3[O(1D)][H2O]+k4[OH][O3]+k4[HO2][O3]+k5[O3][olefin]} in NAQM.
The CMAQ models give the net chemical production as the difference in the
O3 mixing ratio between the calculation steps of the chemistry module
with a process analysis package. The net chemical production was calculated
in each grid and then the average was taken for all the grids. As revealed
in Fig. 5, the CMAQ models gave higher net ozone productions than the NAQM
models did, which is consistent with the results of earlier studies, showing
that the photochemical reaction scheme of SAPRC99 gives a higher O3
production than do the CBM modules. The reaction scheme of CBM-Z is the
revision of CBM-IV, and the major revision is described as (1) inclusion of
revised inorganic chemistry, (2) explicit treatment of lesser reactive
paraffins, (3) revised parameterization for reactive paraffin, olefin, and
aromatic reactions, (4) inclusion of alkyl and acyl peroxy radical
interactions and their reaction with NO3, (5) inclusion of organic
nitrates and hydroperoxides, and (6) refined isoprene chemistry. Although
any intercomparison including CBM-Z has not been reported, the overall
photochemical reactivity would be assumed to be similar to CBM-V, which
gives a higher O3 value than CBM-IV and a lower value than SAPRC99.
Thus, the maximum values of daytime net O3 production in CMAQ 5.0.2 and
4.7.1 in July are ca. 10 and 7–9 ppbvh-1 compared to ca. 6 and
2 ppbvh-1 in NAQM in Beijing and Tokyo, respectively, showing
substantially larger values for CMAQ than for NAQM.
It can be noted that the net O3 production in NAQM shows a peak in the
early morning after the break of dawn in both Beijing and Tokyo, which could
be a cause of overestimation or earlier rise of O3 in the morning by
the NAQM simulation as seen in Figs. 2a, b and 3a, b, although
the effect is marginal in the case of Beijing in April. The cause of the
early morning peak of net O3 production in NAQM might be due to the
photolysis of higher HONO that is produced by the heterogeneous reaction of
NO2. More quantitative sensitivity analyses should be performed to
confirm these effects.
In April, the net chemical production of O3 is, in general, negative in
all the models for both Beijing and Tokyo, except for that in CMAQ 4.7.1
around midday and that in NAQM in early morning, which show slightly positive
values. A tendency of higher net O3 production is seen particularly for
CMAQ 4.7.1, which may be the main cause of higher O3 by this model
in both Beijing and Tokyo in April (Figs. 2a and 3a). The daytime
net O3 production simulated by CMAQ 5.0.2 is similar to that simulated
by CMAQ 4.7.1 in July but is substantially lower in April. Since the
chemistry mechanism of SAPRC99 is used in both CMAQ versions, the difference
may be related to the vertical transport of some relevant species.
Effects of heterogeneous “renoxification” reaction of
HNO3
Figures 2 and 3 show the common feature of anti-correlation of O3 and
NO concentrations as noted above. This feature is most clearly seen for the
comparison of O3 and NO concentrations in July in both cities,
demonstrating a large overestimation of O3 and a large underestimation
of NO by CMAQ 4.7.1 and 5.0.2, while much lower O3 and much higher NO
are estimated by NAQM. The situation in April also confirms this finding.
It should be noted that the rate constants of the most sensitive gas-phase
reactions affecting the balance of O3 and NO (Finlayson-Pitts and
Pitts, 2000; Akimoto, 2016), such as
NO+O3→NO2+O2,NO+HO2→NO2+OH,NO+RO2→NO2+RO,
have been well established (Burkholder et al., 2015, and earlier evaluations
of the series), and more or less the same reaction rates are employed in both SAPRC99 and CBM-Z. As for the heterogeneous processes affecting NOx,
the reaction
N2O5+H2O (on particle)→2HNO3
is included in common in the heterogeneous inorganic chemistry sub-module
ISORROPIA and employed in the CMAQ and NAQM models.
It has been noted that the simulated gaseous HNO3 concentration and
HNO3/NOx ratio were found to be 2–10 times higher when using
global and regional CTMs than the observational data
during the PEM-West (Singh et al., 1996), TRACE-P (Talbot et al., 2003), and
PEM-Tropics A and SONEX (Brunner et al., 2005) aircraft campaigns over the
Pacific and Atlantic oceans. The same result has also been reported by ground
observations in the remote troposphere at Mauna Loa (Hauglustaine et al.,
1996) and in the polluted boundary layer of the Beijing–Tianjin–Hebei region
(Y. Li et al., 2015).
Another concern regarding recent NOx chemistry has been focused on the
high concentration of HONO in the urban atmosphere, which is thought to be
produced by the heterogeneous reaction of NO2 and H2O on the
aerosol and ground surface (for example, Y. Li et al., 2011; Gonçalves
et al., 2012; Wong et al., 2013). Inclusion of the additional heterogeneous
source of HONO not only affects the photochemical O3 formation due to
the increase in OH radicals but also increases HNO3 due to the increase
in the reaction OH+NO2+M→HNO3+M. Y. Li et
al. (2015) have shown that the inclusion of the heterogeneous formation of
HONO gives more HNO3, which tends to give a larger overestimation of
gaseous HNO3 in the Beijing–Tianjin–Hebei region.
In order to solve the problem of overestimation of HNO3, the
heterogeneous reaction of HNO3 on soot surface to reproduce NO and
NO2 has been proposed to be a renoxification process early by Lary et
al. (1997) in the analysis of the aircraft observation data above. The
heterogeneous reaction of HNO3 on soot surfaces to produce
NO/NO2
has been confirmed experimentally in laboratory studies (Disselkamp et al.,
2000; Muñoz and Rossi, 2002), although the product ratio and reaction
mechanism are not well established yet. The steady-state uptake coefficient
γss of this reaction has been reported to be (4.6±1.6)×10-3 for black soot using geometric surface area (Muñoz
and Rossi, 2002).
Only NAQM among the three models studied here incorporates the following
heterogeneous nonstoichiometric reactions on the surface of soot (J. Li et
al., 2015, 2018).
HNO3+soot→→NO+NO2NO2+soot→→0.5HONO+0.5HNO3γHNO3=3.0×10-3 for Reaction (5) and
γHONO=1.0×10-4 for Reaction (6). The
renoxification by Reaction (5) could have contributed to the increase in
NO in Figs. 2c and d and 3c and d, resulting in a better
agreement with the observation. The increase in NO could decrease O3 by
the titration reaction (Reaction 1), which may also give a better
agreement for O3 with the observation, particularly during nighttime.
However, no quantitative sensitivity analysis has been made in the present
study, and it is highly recommended that verification of the importance of
such a heterogeneous renoxification reaction in model simulation be made
against accurate measurements of gaseous HNO3 together with other
NOy in the polluted urban atmosphere.
Effects of vertical transport
Other than the difference in chemical reaction mechanisms, the difference in
transport module could give rise to differences in the output of O3
concentrations. In order to analyze the effects of transport, process
analysis of horizontal and vertical transport of O3 has been conducted.
Since it has been found that there is not much difference in horizontal
transport and surface deposition, and the chemical mechanisms of CMAQ 5.0.2
and CMAQ 4.7.1 are the same, the difference in model performance must be
ascribed to the difference in vertical transport processes.
Comparison of vertical transport of O3 in (a) Beijing in April,
(b) Beijing in July, (c) Tokyo in April, and (d) Tokyo in July.
Figure 6a and b show the comparison of vertical O3 transport
among the three models in Beijing in April and July, respectively, and Fig. 6c
and d show similar plots for Tokyo. The daytime downward vertical
flux of O3 for both CMAQ models in Beijing is nearly the same (22–25 ppbvh-1)
in July and much larger than the values (ca. 6 ppbvh-1) in April.
In contrast, the values of NAQM are ca. 10 ppbvh-1 in both April and July, which is larger than the values of CMAQ
in April, but smaller than those of CMAQ by a factor of 2 in July. The
diurnal variation in vertical O3 flux in Tokyo is quite different from
that in Beijing in July; downward O3 flux is positive only in the
morning till noon and nearly zero or negative in the afternoon. Such
characteristics are common for all three models. The maximum downward
fluxes of O3 in the morning in Tokyo in CMAQ 5.0.2 (ca. 17 ppbvh-1)
and CMAQ 4.7.1 (ca. 13 ppbvh-1) are much higher than those
in NAQM (<5ppbvh-1). Thus, it is concluded that at least a
part of much higher O3 concentrations estimated by CMAQ 5.0.2 and 4.7.1
compared to NAQM shown in Figs. 2b and 3b in Beijing and Tokyo in
July can be ascribed to the higher downward O3 flux estimated by the
CMAQ models compared to NAQM.
A peculiar feature of vertical O3 flux in CMAQ 5.0.2 shown in Fig. 6
is the strong positive morning peak at around 07:00 and 06:00 CST in Beijing in
April and July, respectively, and also at 06:00–07:00 JST in Tokyo in April. Here, it
should be noted that the vertical transport was computed in the PPM scheme
in CMAQ 5.0.2 instead of the direct extraction from WRF in CMAQ 4.7.1 as
described in the method section. The PPM method has been known to introduce
more downward flux of O3 from higher layers to the surface layer.
Another point to be noted is the delayed rise of vertical downward O3
flux by nearly 2 h in NAQM in both April and July in Beijing and Tokyo.
Although this feature is not scrutinized in this study, it should be noted
here that the vertical transport treatment significantly affects the
simulated results of O3 in regional CTMs.
Monthly averaged diurnal variation in (a)O3 concentration in
April, (b) hourly O3 concentration change in April, (c)O3
concentration in July, and (d) hourly O3 concentration change in July in
Beijing.
Monthly averaged diurnal variation in (a) NO concentration in April,
(b) hourly NO concentration change in April, (c) NO concentration in
July,
and (d) hourly NO concentration change in July in Beijing.
Comparison of the transport process in CMAQ v.5.0.2 and v.4.7.1
As seen in Figs. 2 and 3, CMAQ 5.0.2 gives relatively low O3 and
relatively high NO mixing ratios, closer to the values in NAQM in April, but
relatively high O3 and low NO closer to those in CMAQ 4.7.1 in July
in both Beijing and Tokyo. Since the chemical mechanisms of CMAQ 5.0.2
and CMAQ 4.7.1 are the same, the difference in the model performance must be
ascribed to the difference in transport processes. Figure 7a and b show
the comparison of O3 mixing ratios and the change in hourly O3
mixing ratios between CMAQ 5.0.2 and the observations in Beijing in April,
and similar plots for July are shown in Fig. 7c and d. As for the
observational values, data provided by IAP are used for the plots. The large
rise in the change of O3 concentrations at 07:00–08:00 shown in Fig. 7b
and d clearly corresponds to the early morning peak of downward transport
flux of O3 at 06:00–07:00 CST in Fig. 6a and b. Such a sharp rise at
07:00 a.m. is not seen in CMAQ 4.7.1, although a small peak is discernable in April.
This implies that such a feature is due to the characteristics of the
vertical transport module of CMAQ 5.0.2. Similar plots for NO are shown in
Fig. 8a–d. In April, the NO mixing ratio by CMAQ 5.0.2 rises in early
morning, which corresponds well with the observation. The cause of such an
early morning rise of NO mixing ratio and change in hourly mixing ratio is
assumed to be the increase in traffic in the morning. In July, however,
although the observation of NO mixing ratio and hourly change shows a
similar morning peak in April, the CMAQ 5.0.2 simulation does not give any
such morning peak, which would correspond to a very low NO mixing ratio
simulated by CMAQ 5.0.2 together with CMAQ 4.7.1 as seen in Fig. 2d.
Although the phenomena could be caused by rapid oxidation of NO to NO2
in summer, the reason is unknown at this stage.
It should be noted that after the large rise at 07:00–08:00 CST, the hourly change of
O3 mixing ratio simulated by CMAQ 5.0.2 agrees well with the observed
O3 change in the late morning and afternoon as shown in Fig. 7b and
d. This implies that the large morning surge gives a much earlier rise of
O3 compared to the observation. It can be noted, however, that the
morning surge at 07:00 CST in July is ca. 15 ppbv, which is not much higher than ca. 10 ppbv in April. Thus, although the morning surge is larger in July
than in April, this would not be the main cause of the much higher predicted
O3 concentration in the morning in July compared to April. A large
difference in the simulated concentration of nighttime O3 can be seen
between April and July in CMAQ 5.0.2 and also between CMAQ 5.0.2 and CMAQ 4.7.1
in April. The nighttime O3 is as low as 10–20 ppbv in Beijing
and 20–30 ppbv in Tokyo in both CMAQ 5.0.2 and NAQM in April, agreeing with
observation. However, the nighttime O3 simulated by CMAQ 4.7.1 is as
high as 30 and 45 ppbv in April in Beijing and Tokyo, respectively. In July,
the nighttime O3 is 20–30 ppbv in Beijing and ca. 20 ppbv in Tokyo in
NAQM, which is close to the observation, while both CMAQ models give 40–50 ppbv
in both Beijing and Tokyo, which is substantially higher than the
observation. The high nighttime O3 simulated by the CMAQ models would
contribute at least partly to the high daytime O3 in July. Although the
coarse resolution of 45 km grid tends to give a higher nighttime O3 due
to less effective NO titration, it would not be enough to explain such a
high nighttime O3 in CMAQ 4.7.1 for both April and July and CMAQ
5.0.2 for July since the NAQM simulation with the same grid size reproduces
the nighttime O3 as low as 20 ppb, agreeing better with the
observation. It would be important to quantify the effect of the
heterogeneous production of nighttime NO from HNO3 to evaluate its
impact on nighttime O3.
Comparison of simulations with the observational data of
O3 in Beijing and Tokyo
Both CMAQ 5.0.2 and NAQM give reasonably good agreement of O3 mixing
ratios with the observational data in April in Tokyo. It can be noted that
both CMAQ 5.0.2 and NAQM give higher mixing ratios by 10–15 ppb after dawn.
For CMAQ 5.0.2, as mentioned above, the overestimate could be caused by the
peak of downward O3 flux in the early morning. NAQM gives a similar
overestimate of the O3 mixing ratio by ca. 10 ppbv in the early
morning, but this phenomenon could be caused by the peak of the net chemical
ozone production (Fig. 5) rather than the vertical transport. Although the
cause of the early morning peak of the net O3 production has not been
elucidated in this study, it may be related to the photolysis of HONO
accumulated during nighttime since the heterogeneous production of HONO
(Eq. 6) is included in NAQM.
In July, NAQM is the sole model giving a good agreement with the observation
in Tokyo. It can be noted, however, that the calculated concentration is
higher than the observation by ca. 10 ppbv in early morning similar to
April. Such a higher rise of the O3 mixing ratio in the early morning
is discernible in July in Tokyo in CMAQ 5.0.2. The same phenomenon can also
be seen in July in Tokyo, and the cause is assumed to be the early morning
peaks of downward flux of O3 and net O3 production in CMAQ 5.0.2
and NAQM, respectively. It should be noted that the enhanced mixing ratios
of O3 in early morning are persistent at least till noon, giving higher
values of simulated mixing ratios.
Substantially higher simulated O3 mixing ratios in CMAQ 4.7.1 than the
observation in both April and July, and in CMAQ 5.0.2 in July in Tokyo
(Fig. 3a and b) may at least partially be caused by the higher
nighttime mixing ratios of O3, which would contribute to the baseline
mixing ratio for the whole day. It would be expected that if the nighttime
O3 could be reduced to the observational level, a better agreement of
O3 with observation would be expected for the whole day.
As for the observational data in Beijing, the daily maximum of O3
mixing ratio in July is ca. 90 ppbv in Xu et al. (2011) and ca. 60 ppbv by
the IAP data, while the nighttime minimums are both 10–20 ppbv consistently.
Since the maximum O3 mixing ratio in summer is expected to be higher in
Beijing than in Tokyo (ca. 60 ppbv) due to higher NO (see Figs. 2d and
3d) and NO2 levels by a factor of ∼2 (not shown), the higher
observational data in Beijing than in Tokyo in Fig. 2c could be more
representative for the average of the calculated grids in Beijing. Although
there is still a large uncertainty in the monthly averaged observational
data of O3 in Beijing in 2010, a tendency of overestimation by CMAQ 5.0.2
and 4.7.1 and underestimation by NAQM in Beijing in July can be
suggested.
In April in Beijing, Chen et al. (2015) reported the daily maximum mixing
ratio of O3 at ca. 65 ppbv in 2014–2015, which is substantially higher
than the IAP data of ca. 40 ppbv in 2010. An increase in surface ozone has
been reported in Beijing at the rural sites of Shangdianzi during the period
of 2004–2015 with regard to the maximum daily average 8 h mixing ratios
(MDA8) (Ma et al., 2016). Although the long-term increasing trend indicates
an average rate of 1.13±0.01ppbyr-1, no monthly data were
reported, and the year-by-year variability is substantial. If we assume that
the monthly averaged MDA8 values in April 2010 are lower by 10 ppbv than
those in 2015, the uncertainty of the daily maximum observational value in
April in Beijing would be in the range of 40–55 ppbv. Thus, within the
uncertainty range, a tendency of overestimation by CMAQ 4.7.1 and an
underestimation by NAQM could be suggested.
As for the discussion of reproducibility of the model simulation, a
comparison of 3-year averaged values in more recent years after 2013,
when routine monitoring data at considerably more sites within the targeted
grids are available, would be highly desirable, particularly in Beijing.
Overestimation of O3 at Oki, a remote oceanic
site
At the remote site of Oki, an overestimation by ca. 20 ppb for daytime
O3 has been seen in July in Fig. 4b by all three models with a
spatial resolution of 45 km. Such an overestimation of summertime O3 at
Oki by the CMAQ models has been reported by Lin et al. (2009) with MM5-CMAQ
v.4.6 (27km×27km), while a much better agreement with the
observation was previously reported by Yamaji et al. (2006) (80km×80km) using RAMS-CMAQ v.4.4 and J. Li et al. (2007) using
NAQM (81km×81km). The seasonal variation in O3 at remote
sites around Japan has shown a springtime monthly maximum of ca. 60 ppb and
a summertime monthly minimum of 35–40 ppb (Pochanart et al., 1999, 2002),
which is consistent with the observational data shown in Fig. 4. The
summertime minimum at Oki and other remote islands in this region are well
established to be due to prevailing clean marine air (Pochanart et al.,
2002; Yamaji et al., 2006).
Since the overestimation does not depend on the spatial resolution of the
model, as noted above, and the daytime buildup of O3 due to local
photochemical activity is <10ppbv in the observation and 5–15 ppbv
in the simulation as shown in Fig. 4b, the overestimation of O3
concentration in July by all three models cannot be ascribed to the
direct influence of nearby terrestrial emissions of precursors in mainland
Japan. The overestimation could be due to either a more frequent influence
of terrestrial air masses by WRF compared to the real meteorology or higher
O3 concentration in the oceanic air around this area affected by the
influence of non-episodic terrestrial emissions including long-range
transport. The reproduction of observed concentrations by models at Oki
would be important for the analysis of air quality in Japan since air
masses passing through Oki provide a flowing-in background mixing ratio to
mainland Japan.
Summary
In order to identify the causes of the substantial variability among the
simulated modeling results for surface ozone in MICS-Asia III even though
using the same emissions, meteorological field, and boundary conditions,
three regional models, namely CMAQ 5.0.2 and 4.7.1 and NAQM, were selected and a
detailed comparison was made in the selected grids covering the metropolitan
areas in Beijing and Tokyo and at the remote oceanic site of Oki. The
analyses were made for the monthly averaged diurnal change of surface ozone
in April and July 2010.
The simulated O3 concentration was the highest in CMAQ 4.7.1, followed
by that in CMAQ 5.0.2 and NAQM in both Beijing and Tokyo in April, while
both CMAQ models gave much higher O3 values than NAQM did in July. At
Oki, the simulations for O3 by all three models agree well with
each other and with the observation in April. In July, however, all the
models overestimated daytime O3 by ca. 20 ppb compared to the
observation.
Three causes for the difference among model outputs have been identified and
discussed.
The chemistry mechanism sub-module SAPRC99 used in the CMAQ was found to
give higher net ozone production values than CBM-Z in NAQM, agreeing with
previous studies.
Higher NO concentrations have been predicted by NAQM than by CMAQ, possibly
due to the inclusion of a heterogeneous renoxification reaction of
HNO3 (on soot surface)→NO+NO2, which gave a better
agreement with observational concentration, particularly for nighttime NO and
O3.
A vertical downward O3 flux was found to substantially affect the
diurnal pattern and mixing ratios of O3.
All the concentration data of O3 and NO used in Figs. 2–4 and 7–8
were from the submitted data for MICS-Asia III. The availability of the data may be
specified in the overview paper for O3 by Li et al. (2019). The process analysis data for
CMAQ v.5.0.2 and v. 4.7.1 and NAQM used in Figs. 5–6 were provided by each co-author, Joshua S. Fu., Tatsuya Nagashima,
and Jie Li, respectively.
HA analysed the data and wrote the first draft of the paper. TN, JL, and JSF provided
the process analysis data for their own models and conducted discussions for the paper. DJ provided
the observational data of IAP, and JT performed a process analysis calculation using their model. ZW
contributed to the availability of modeling data of MICS-Asia III and to overall discussion of the paper.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Regional assessment of air pollution and climate
change over East and Southeast Asia: results from MICS-Asia Phase III”. It is not associated with a conference.
Acknowledgements
This research was supported by the Environment Research and Technology
Development Fund (S12-1) of the Ministry of the Environment, Japan, and by
the Natural Science Foundation of China (41620104008).
Edited by: Gregory R. Carmichael
Reviewed by: two anonymous referees
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