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ACPD
8, 17297–17341, 2008
NOxemissions and
NOx-related
chemistry in East
Asia
K. M. Han et al.
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Atmos. Chem. Phys. Discuss., 8, 17297–17341, 2008
www.atmos-chem-phys-discuss.net/8/17297/2008/
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmospheric
Chemistry
and Physics
Discussions
This discussion paper is/has been under review for the journal Atmospheric Chemistry
and Physics (ACP). Please refer to the corresponding final paper in ACP if available.
Investigation of NOxemissions and
NOx-related chemistry in East Asia using
CMAQ-predicted and GOME-derived NO2
columns
K. M. Han1, C. H. Song1, H. J. Ahn1, C. K. Lee1,2, A. Richter3, J. P. Burrows3,
J. Y. Kim4, J. H. Woo5, and J. H. Hong6
1Department of Environmental Science and Engineering, Gwangju Institute of Science and
Technology (GIST), Gwangju, Korea
2Dept. of Physics and Atmospheric Science, Dalhousie Univ., Halifax, Nova Scotia, Canada
3Institute of Environmental Physics, University of Bremen, Otto-Hahn-Allee 1, 28359 Bremen,
Germany
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NOxemissions and
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4Hazardous Substance Research Center, Korea Institute of Science and Technology (KIST),
Seoul, Korea
5Dept. of Advanced Technology Fusion, Konkuk University, Seoul, Korea
6Air Pollution Cap System Division, National Institute of Environmental Research (NIER), In-
cheon, Korea
Received: 21 July 2008 – Accepted: 4 August 2008 – Published: 17 September 2008
Correspondenco to: C. H. Song (chsong@gist.ac.kr)
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Abstract
This study examined the estimation accuracy of NOxemissions over East Asia with
particular focus on North China and South Korea due to their strong source (North
China)-receptor (South Korea) relationship. In order to determine contributions of North
China emissions to South Korean air quality accurately, it is important to examine the5
accuracy of the emission inventories of both regions. In this study, NO2columns from
the US EPA Models-3/CMAQ model simulations carried out using the 2001 ACE-ASIA
(Asia Pacific Regional Aerosol Characterization Experiment) emission inventory over
East Asia were compared with the GOME-derived NO2columns. There were large dis-
crepancies between the CMAQ-predicted and GOME-derived NO2columns in the fall10
and winter seasons. In particular, while the CMAQ-predicted NO2columns produced
larger values than the GOME-derived NO2columns over South Korea (receptor region)
for all four seasons, the CMAQ-predicted NO2columns produced smaller values than
the GOME-derived NO2columns over North China (source region) for all seasons with
the exception of summer. It is believed that there might be some estimation error in15
the NOxemissions as well as large uncertainty in NOxloss rates over North China
and South Korea. Regarding the latter, this study further focused on the biogenic VOC
emissions that were strongly coupled with NOxchemistry in East Asia. It was found
that the rates of NOxloss determined by CMAQ modeling studies might be signifi-
cantly low due to the possible overestimation of biogenic isoprene emissions during20
summer, particularly in China. In addition, due to the possible overestimation of iso-
prene emissions, the CMAQ-modeled NO2/NOxratios might show an incorrectly high
level, compared with the actual NO2/NOxratios. In addition to the retarded NOxchem-
ical loss rates and overestimated NO2/NOxratios, the omission of soil NOxemissions
over North China during summer can lead to an underestimation of NOxemissions25
over North China during summer. Overall, it is estimated that the NOxemissions in
North China are underestimated possibly by ∼50% over an entire year. In order to
confirm the uncertainty in NOxemissions, the NOxemission over South Korea was fur-
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ther investigated using the ACE-ASIA inventory, REAS (Regional Emission inventory
in ASia) and CAPSS (Clean Air Policy Support System) by NIER (National Institute
of Environmental Research) in Korea. The NOxemissions from ACE-ASIA and the
REAS inventories appear to be approximately 2 times larger for mega-cities in Korea
than that from the CAPSS inventory. In contrast, the NOxemissions of ACE-ASIA and5
REAS inventories are only 10% smaller for North China than the recently-estimated
“date-back” ANL (Argonne National Laboratory) inventory. A comparison between the
CMAQ-predicted and GOME-derived NO2columns indicated that both the ACE-ASIA
and REAS inventories have some uncertainty in NOxemissions over North China (A)
and South Korea (C), which can lead to some error in modeling the formation of ozone10
and secondary aerosols in South Korea and North China.
1 Introduction
Nitrogen oxides (NOx≡NO+NO2) emitted from anthropogenic sources, such as fossil
fuel combustion and biomass burning, as well as natural sources, such as lightning and
microbiological processes in soil, play important roles in tropospheric ozone chemistry15
and secondary aerosol formation. Several studies have focused on NOxemissions
from China to determine their influence on air quality and aerosol radiative forcing in
East Asia (e.g. Uno et al., 2007; Wang et al., 2007; Zhang et al., 2007). Recent studies
using satellite measurements reported that NO2columns (or NO2vertical column den-
sity, VCD) have increased significantly in Central East Asia since 2001 (Richter et al.,20
2005; van der A et al., 2006; He et al., 2007). Such increases in NOxemissions over
China were confirmed partly by a bottom-up emission inventory study (Zhang et al.,
2007). In order to test the accuracy of NOxemissions, several studies were carried out
over East Asia comparing the 3-D model-predicted NO2columns with satellite-derived
NO2columns (Kunhikrishnan et al., 2004; Ma et al., 2006; Uno et al., 2007). The25
comparisons revealed large inconsistencies between the NO2columns from the 3-D
CTM (Chemical Transport Model) simulations and the satellite-derived NO2columns.
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NOxemissions and
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Asia
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For example, Uno et al. (2007) reported that the 3-D CTM-derived NO2columns with
the REAS (Regional Emission inventory in ASia) emission inventory were lower by
a factor of 2–4 over polluted Central East China, compared with the GOME (Global
Ozone Monitoring Experiment)-retrieved NO2columns. In addition, Ma et al. (2006)
also reported that the 3-D CTM-derived NO2columns with the ACE-ASIA (Asia Pacific5
Regional Aerosol Characterization Experiment) emission inventory underestimated the
NOxemissions over China during the summer for the year 2000 by more than 50%
compared with the GOME-derived NO2columns. However, there has been no detailed
investigation carried out as to how and why the NO2columns over China were under-
predicted by 3-D CTM simulation using the ACE-ASIA or REAS inventory. In addition,10
there are no reports on the possibly important relationship between the rates of NOx
loss and biogenic isoprene emissions in East Asia, even though it could be an impor-
tant factor for evaluating NOxemissions. Biogenic emissions are important because
they can control the levels of OH radicals, which can affect the NOxchemical loss rates.
On the other hand, an accurate estimation of NOxemissions in China is important15
because NOxemissions from North China tend to persistently affect the air quality
of South Korea (e.g. Arndt et al., 1998). From 2003, the Korean government began to
implement an ambitious pollution abatement policy aimed at improving the air quality of
Seoul Metropolitan area, called the “Total Air Pollution Load Management System”, by
reducing the levels of secondary pollutants, such as O3, PANs (Peroxy Acetyl Nitrates),20
and nitrate (Korean Ministry of Environment, 2006). This policy included a specific
plan to reduce the total NOxemissions from the Seoul Metropolitan area by 53%, from
309 387 Ton yr−1(2001) to 145 412 Ton yr−1(2014). The reference and target years
for the Total Air Pollution Load Management System are 2001 and 2014, respectively.
However, the critical and largest uncertainty in implementing this policy is to evaluate25
and quantify accurately the influences of the emissions outside the policy domain on
the air quality of the Seoul Metropolitan area. Due to the strong and persistent source-
receptor relationship between North China and South Korea, it is important to use
accurate emission inventories for both the source (“North China”) and the receptor
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NOxemissions and
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regions (“Seoul Metropolitan area” or “South Korea”) for the reference and target years,
2001 and 2014.
This study examined the accuracy of NOxemissions from North China and South
Korea using the CMAQ-simulated and GOME-derived NO2columns. In addition to
the uncertainty in NOxemission itself, this paper also discussed the possibly impor-5
tant uncertainty factors that could cause inconsistencies between the CMAQ-derived
and GOME-derived NO2columns. Particularly, the HOx-NOx-isoprene photochemistry
in East Asia was examined in detail on account of its strong relationship with the in-
consistency between the CMAQ-simulated and GOME-derived NO2columns in East
Asia.10
2 Experimental methods
In this study, a three-dimensional Eulerian CTM simulation over East Asia was carried
out in conjunction with the Meteorological fields generated from the PSU/NCAR MM5
(Pennsylvania state University/National Center for Atmospheric Research Meso-scale
Model 5) model in order to compare the CTM-predicted NO2columns with the satellite15
(GOME)-derived NO2columns.
2.1 US EPA models-3/CMAQ modeling
In this study, a 3-D Eulerian CTM, US EPA Models-3/CMAQ (Community Multi-scale Air
Quality) model was used in conjunction with the MET fields generated from PSU/NCAR
MM5 modeling over an approximately 3 week period for four seasons: Late Fall (920
November 2001–27 November 2001), Spring (25 March 2002–13 April 2002), Late
Summer (24 August 2002–13 September 2002), and Winter (11 February 2003–28
February 2003) (Byun and Ching, 1999; Byun and Schere, 2006). The details of the
modeling conditions were reported by Song et al. (2008). For the MET fields, the
2.5◦
×2.5◦resolved re-analyzed National Centers for Environmental Prediction (NCEP)25
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data with automated data processing (ADP) of the global surface and upper air obser-
vations were employed using four-dimensional data assimilation (FDDA) techniques
(Stauffer and Seaman, 1990, 1994). The MET fields were generated at “1 h intervals”
during the four episode periods. The CMAQ modeling system then used the meteoro-
logical fields generated from the PSU/NCAR MM5 and emission fields. The schemes5
selected in CMAQ modeling are as follows: the piece-wise parabolic method (PPM)
for advection (Collela and Woodward, 1984); 4th generation carbon bond mechanism
(CBM 4) for gas phase chemistry (Gery et al., 1989); the Carnegie-Mellon University
(CMU) aqueous chemistry mechanism for cloud chemistry (Pandis and Seinfeld, 1989;
Fahey and Pandis, 2003); the AERO3 module for particulate dynamics and aerosol10
thermodynamics (Binkowski and Roselle, 2003); and the Wesley scheme for the dry
deposition of both gaseous and particulate species (Wesley, 1989). The 4th genera-
tion carbon bond mechanism for gas-phase chemistry included explicit VOC species,
such as ALD2 (higher aldehyde, C>2), ETH (ethane), FORM (formaldehyde), ISOP
(isoprene), OLE (olefin), PAR (paraffin), TOL (toluene), and XYL (xylene). As indi-15
cated above, far more detailed atmospheric chemistry and physical processes, aerosol
dynamics, and thermodynamic gas-aerosol processes were considered in these calcu-
lations than in other global and regional chemistry-transport modeling studies in order
to better consider the atmospheric fate of NOx. For example, in this study the analysis
was not restricted to “clear sky conditions”. In other words, it fully considered cloud20
chemistry, wet scavenging, and the effects of clouds on the photolysis reaction rates.
The horizontal domain of CMAQ modeling reported in Fig. 1 covered the region
from approximately 100◦E to 150◦E and 20◦N to 50◦N, which included Korea, Japan,
China, and parts of Mongolia and Russia with a 108 km×108 km grid resolution. For
vertical resolution, 24 layers were used with σ-coordinates using the model-top at25
180 hPa. For comparison, NO2vertical column loading was integrated from the sur-
face to 250 hPa (approximately corresponding to ∼10 km a.s.l. in these calculations).
The CMAQ-modeled NO2columns were averaged between 10:00 LST and 12:00 LST
because the GOME measurements were taken approximately at 10:30LST over East
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Asia. The total number of the grid points in the CMAQ model calculation was 36 432.
Figure 1 also shows the four main study regions used for the comparison studies: i)
North China (Region A, 30◦N–42◦N; 110◦E–125◦E); ii) South China (Region B, 22◦N–
30◦N; 108◦E–122◦E); iii) South Korea (Region C, 33.5◦N–40◦N; 125◦E to 130◦E); and
iv) Japan (Region D, 31◦N–40◦N; 130◦E–142◦E). Here, the remote continental areas5
in China were excluded from our analysis, partly because they are remote areas, and
the NO2columns showed a similar order of magnitude of the absolute errors to the
GOME measurements (∼1015 molecules cm−2).
2.2 Emissions
Emission is an important input parameter in a modeling study. Poor agreement be-10
tween 3-D modeling studies and satellite measurements is expected if the emission
inventories incorrectly reflect the seasonal and spatial emission flux from the various
sources. In order to consider anthropogenic emissions, 1◦
×1◦resolved emission data
for 9 major species, including SO2, NOx, CO, NMVOCs (Non-Methane Volatile Or-
ganic Compounds), CH4, NH3, CO2, BC, and OC, were obtained from the official ACE-15
ASIA and TRACE-P (Transport and Chemical Evaluation over Pacific) emission web
site at the University of Iowa (http://www.cgrer.uiowa.edu/EMISSION DATA/index.htm).
Streets et al. (2003) provided detailed information on the emission inventory (hereafter,
labeled the ACE-ASIA inventory) used in this study. The ACE-ASIA emission inventory
included NOxemissions from fossil fuels and biofuel combustion as well as biomass20
(vegetation) burning in East Asia. However, the inventory did not consider the NOx
emissions from lightning and microbial activity in soil. In general, emission from light-
ning is believed to make a small contribution to the total NOxbudget (Martin et al.,
2003). On the other hand, Wang et al. (2007) reported that soil NOxemissions might
be important, accounting for up to ∼43% of the combustion source during summer in25
East Asia. In addition, the original ACE-ASIA emission inventory was built up for the
year 2000. Therefore, the NOxemissions for East Asia were modified slightly by multi-
plying a factor of 1.05 in order to account for an annual increase in NOxemissions from
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China for the year 2001 (Zhang et al., 2007).
Anthropogenic NMVOC emissions were assumed to be constant without any sea-
sonal variation. In this study, chemical speciation (chemical species splitting) of the
total NMVOC emissions in East Asia was performed using the SPECIATE database
built up by the US EPA. The major biogenic 1◦
×1◦resolved emissions data of iso-5
prene and monoterpene were obtained from the Global Emissions Inventory Activity
(GEIA, http://www.geiacenter.org/), which was created as an activity of the Interna-
tional Geosphere-Biosphere Program (IGBP).
2.3 NO2retrieval algorithm from ESA/ERS-2 GOME platform
GOME was launched on the ERS-2 satellite by European Space Agency (ESA) in April10
1995. It is a nadir-scanning double-monochromator, and obtains approximately 30 000
radiance spectra each day covering the ultraviolet and visible wavelengths from 240 to
790 nm at a moderate spectral resolution of 0.17 to 0.33 nm. Because GOME is a nadir
viewing instrument, both tropospheric and stratospheric absorptions contribute to the
measured signals. The ground scene of GOME typically has a footprint of 320×40 km2.15
Total ground coverage is obtained within 3 days at the equator with a 960 km wide track
swath (4.5 s forward scan and 1.5 s backward scan).
The NO2analysis for GOME is based on a Differential Optical Absorption Spec-
troscopy (DOAS) retrieval method (Richter and Burrows, 2002; Richter et al., 2005).
The wavelength range of 425–450 nm was used for the NO2DOAS fit because the20
differential absorption is large and interference by other species is small. In addition to
the NO2cross-section (Burrows et al., 1998), the cross-sections of O3(Burrows et al.,
1999), O4(Greenblatt et al., 1990), H2O (Rothman et al., 1992), a synthetic Ring spec-
trum (Vountas et al., 1998), and an undersampling correction (Chance, 1998) were
included in the fit. In order to calculate the tropospheric NO2slant column, the strato-25
spheric contribution of NO2to the measured slant column was removed by subtracting
the slant column taken on the same day at the same latitude in the 180◦–230◦longi-
tude region from the total slant column using the reference sector method (Richter and
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Burrows, 2002). Cloud screening was applied to remove measurements with a cloud
fraction >0.3, as determined from the GOME measurements using the FRESCO (Fast
Retrieval Scheme for Clouds from the Oxygen A-band) algorithm (Koelemeijer et al.,
2001). The tropospheric slant column was then converted to a vertical tropospheric
column using the appropriate air mass factor (AMF). The AMF is defined as the ratio of5
the observed slant column to the vertical column and was calculated with using the ra-
diative transfer model (SCIATRAN) (Rozanov et al., 1997). The monthly averaged AMF
on a 2.5◦
×2.5◦grid was determined using the NO2vertical profiles (shape facor) from
a global chemical transport model, MOZART-2 (Model for Ozone and Related Tracers).
The error budget of satellite measurements of tropospheric NO2columns from10
GOME has been discussed in detail (e.g. Richter and Burrows, 2002; Martin et al.,
2003; Boersma et al., 2004; Richter et al., 2005). The main contributions to the
error are random fitting uncertainties, uncertainties related to the subtraction of the
stratospheric contributions, uncertainties from residual clouds, and AMF. The total un-
certainties in the retrieval of tropospheric NO2columns over continental source re-15
gions is largely determined by the AMF calculation due to surface reflectivity, clouds,
aerosols, and the trace gas profile. An overall assessment of errors leads to 5×1014–
1×1015 molecules cm−2for monthly averages over polluted areas (Richter and Bur-
rows, 2002; Richter et al., 2005).
3 Results and discussions20
In order to properly determine the contributions from North China emissions to air qual-
ity of South Korea, it is important to evaluate the accuracy of the NOxemission invento-
ries over both regions and understand NOx-related gas-phase chemistry. Initially, 3-day
backward trajectory analysis was conducted (Sect. 3.1) to confirm the strong source-
receptor relationship between North China (A) and South Korea (C). Subsequently,25
the CMAQ-predicted NO2columns are then spatially and seasonally compared with
the GOME-derived NO2columns (Sect. 3.2). The ACE-ASIA NOxemissions in North
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China (A, source region) and South Korea (C, receptor region) are then compared with
other recently-released inventories such as, REAS, “date-back” ANL (Argonne National
Laboratory) inventory, and CAPSS (Sect. 3.3).
3.1 Backward trajectory analysis
In this study, a 3-day backward trajectory analysis for each month of 2001 was carried5
out using the NOAA HYSPLIT model (Draxler, 1999) to confirm the persistent source
(North China, A)-receptor (South Korea, C) relationship, as well as to determine how
frequently the air masses travel from North China (A) to South Korea (C). Approx-
imately 20 days per month were selected. The trajectory ends at a point (37.5◦N,
127.0◦E) at an altitude of 1 km under which Seoul is located. As shown in Fig. 2, the10
air masses traveled from North China (A) to South Korea (C) during almost the entire
year. However, in July, the air masses appear to be affected by the emissions from
South China (B). In August and September, the air masses arrive in Seoul from the
North, East, South (August) and the Northeast (September). Although air masses do
not always travel from North China (A) to Seoul during July, August, and September,15
it is clear that the South Korean air quality is most strongly and persistently affected
by the emissions from North China (A) throughout almost the entire year. Therefore,
in the framework of the source-receptor relationship, this study focused particularly on
the emissions from two regions, North China (A) and South Korea (C).
3.2 CMAQ-predicted and GOME-derived NO2columns and NOx-related chemistry in20
East Asia
3.2.1 CMAQ-predicted vs. GOME-derived NO2columns
Figure 3 shows the spatial distributions of the CMAQ-predicted NO2columns and the
GOME-derived NO2columns for four episodes over East Asia. Figure 4 shows a close-
up of the area of South Korea for better visualization. There were large discrepancies25
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Asia
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between the two quantities in the late fall and winter seasons (i.e. cold seasons), as well
as strong seasonal variations, particularly in the GOME-derived NO2columns. Inter-
estingly, the CMAQ-predicted NO2columns were larger than the GOME-derived NO2
columns for all seasons over South Korea (C) (Fig. 4). On the other hand, the CMAQ-
predicted NO2columns over North China (A) were smaller than the GOME-derived5
NO2columns for all seasons except for summer (Fig. 3). Note that the differences
between the CMAQ-predicted and the GOME-derived NO2columns in the third col-
umn in Figs. 3 and 4 have negative values (“blue colors”) over North China (A) and
positive values (“red-orange colors”) over South Korea (C). Scatter plots between the
CMAQ-predicted and GOME-derived NO2columns were also made for North China10
(A), South China (B), South Korea (C), and Japan (D) for further confirmation. Fig-
ure 5 shows that there are no clear seasonal trends in South China (B) and Japan
(D), whereas the CMAQ-predicted NO2columns over South Korea (C) are obviously
larger than the GOME-derived NO2columns for all seasons. In addition, the CMAQ-
predicted NO2columns over North China (A) were clearly smaller than the GOME-15
derived NO2columns for all seasons except for summer. These results are further con-
firmed through statistical analyses (see Sect. 3.2.2). These results suggest that there
are some errors in the estimations of NOxemissions over North China (A) and South
Korea (C) in the ACE-ASIA NOxemission inventory. Of course, this inference should
be valid only when the following assumption is held: In CMAQ modeling, emission is20
the largest uncertainty, and the other atmospheric chemical and physical processes
are reasonably accurate. In this study, we chose the GOME-derived NO2columns as
reference values, although the GOME measurements also have uncertainties, mainly
from the assumptions made in the radiative transfer calculations (Richter et al., 2005).
In this study, we focused on the highly polluted East Asian regions where the monthly25
averages of NO2columns reach ∼2×1016 molecules cm−2(see Figs. 3, 4, and 5; also
refer to Uno et al., 2007). The NO2columns are larger than the magnitudes of overall
errors of the GOME NO2column measurements, 5×1014–1×1015 molecules cm−2. An
attempt was also made to determine why the difference between the CMAQ-predicted
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Asia
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and GOME-derived NO2columns became minimal only during summer in Fig. 3. This
issue is discussed in detail in Sect. 3.2.3.
3.2.2 Statistical analysis
Table 1 shows the seasonal and regional statistical analyses between the CMAQ-
predicted and GOME-derived NO2columns. The following four statistical parameters5
were introduced for statistical analyses: i) Root Mean Square Error (RMSE, absolute
error); ii) Mean Normalized Gross Error (MNGE, relative error); iii) Mean Bias (MB,
absolute bias); and iv) Mean Normalized Bias (MNB, relative bias). The four statistical
parameters are defined in Eqs. (1) to (4):
RMSE =v
u
u
t1
N
N
X
1NO2,CMAQ−NO2,GOME2(1)10
MNGE =1
N
N
X
1
NO2,CMAQ−NO2,GOME
NO2,GOME !×100 (2)
MB =1
N
N
X
1NO2,CMAQ−NO2,GOME(3)
MNB =1
N
N
X
1NO2,CMAQ−NO2,GOME
NO2,GOME ×100 (4)
In Table 1, RMSE analysis showed that the magnitudes of the “absolute” differences
were much larger over North China (A) and South Korea (C) than over South China (B)15
and Japan (D) (These are denoted as “bold fonts” in Table 1). Large uncertainties are
expected over North (A) and South Korea (C). The MNGEs range from 39.6% to 59.0%
over North China (A) and from 54.5% to 121.2% over South Korea (C). Relative bias
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analysis (MNB) showed that the CMAQ-predicted NO2columns tends to have positive
biases ranging from 41.1% to 117.6% compared with the GOME-derived NO2columns
over South Korea (C). In contrast, over North China (A), the CMAQ-predicted NO2
columns tend to have low biases ranging from −8.6% to −56.9% with the exception
of summer. These statistical analyses are also in line with the results reported in5
Sect. 3.2.1.
3.2.3 Summer anomaly due to possible overestimation of isoprene emissions in East
Asia
The first and second columns in Fig. 3 show seasonal variations of the NO2columns
over East Asia. The CMAQ-derived NO2columns show weak seasonal variations in10
North China (Region A), whereas the GOME-derived NO2columns show strong sea-
sonal variations. The differences between the two NO2columns reduce to almost zero
during summer, and are also small over North China (A) during spring (Fig. 3). Table 1
shows that the MNB has a positive value over North China (A) only during summer.
There are two factors that may be involved in these phenomena: i) seasonal varia-15
tions in NOxemissions; and ii) seasonal variations in the NOxchemical loss rates.
First, the distribution of NO2columns can be influenced by the seasonal variations in
NOxemissions. However, Streets et al. (2003) reported almost no seasonal variations
in anthropogenic NOxand SO2emissions, which is in contrast to black carbon (BC)
emissions. The monthly fraction of the NOxemissions is almost constant, as shown in20
Fig. 6, even though the fractions of NOxemissions increase slightly in December and
January. Therefore, the seasonal changes in NOxemissions are not a likely cause of
the seasonal variations in the NO2columns (Note that uniform NOxemission fluxes
were also assumed in the CMAQ model based on the almost constant NOxemissions
shown in Fig. 6). Secondly, in order to explain this anomalous (or unexpected) phe-25
nomenon in NO2concentration, this study examined the seasonal variations in NOx
chemical loss rates in East Asia. The chemical mechanisms for NOxloss are HNO3
and nitrate formation (i.e. N(V) formation) in the atmosphere. Both nitric acid and par-
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ticulate nitrate form from NOxby Reactions (R1) through to (R5) as follows:
NO2+OH +M→HNO3(M: third body) (R1)
NO3+HCHO →HNO3+CHO (R2)
NO3+CH3CHO +O2→HNO3+CH3CO3(R3)
NO3
Hetro.
−→ NO−
3(R4)5
N2O5+H2OHetro.
−→ 2H++2NO−
3(R5)
Therefore, the NOxchemical loss rate (LNOx) can be constructed by Eq. (5):
LNOx
≡k1[NO2] [OH]+k2NO3[HCHO]+k3NO3[ALD2]
+k4,h NO3+2k5,h[N2O5] (5)
where, the first, second and third terms in the right hand side of Eq. (5) represent the10
NOxchemical loss rate due to HNO3formation via Reactions (R1), (R2), and (R3),
respectively. The fourth and fifth terms represent heterogeneous nitrate formation by
Reactions (R4) and (R5), respectively. In Eq. (5), the heterogeneous mass transfer
coefficients (s−1) of k4,h and k5,h for NO3and N2O5radicals were calculated using the
Schwartz formula (ki=γiSivi/4) (Schwartz, 1986). In the Schwartz formula, γi,Si, and15
virepresent the reaction probability, aerosol surface density (µm2cm−3), and molecular
mean velocity (cm s−1) for species i, respectively. Among the five pathways for remov-
ing NOx,LNOx is dominated by Reaction (R1), i.e. the first term in Eq. (5). The third
column in Fig. 7 shows the model-predicted, spatial distributions of the NOxchemical
loss rates at the surface. As expected, the rates of NOxloss are much faster during20
spring and summer than during fall and winter. In addition, the NOxchemical loss
rates are rapid, particularly around metro-city areas, such as Beijing, Shanghai, Taipei,
Seoul, Busan, and Tokyo, where OH concentrations are relatively high. However, Fig. 7
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shows an unexpected phenomenon. The LNOxin spring is, on average, larger than that
in summer, particularly over Regions A, B, and C (the three regions are indicated by
the three white boxes in Fig. 7). It is possible that this is caused by biogenic VOC
emissions in summer (the anthropogenic NMVOC emissions were kept constant in the
CMAQ modeling of the four episodes). The first column in Fig. 7 shows the biogenic5
isoprene concentrations at the surface for the four episodes. The isoprene concen-
trations were highest during summer. Biogenic isoprene emissions influence the for-
mation of ozone, and affect rates of NOxremoval by controlling the levels of hydroxyl
radicals. Hydroxyl radicals (OH) are produced by O1D+H2O Reaction (R6) and HONO
photo-dissociation (R7). Hydroxyl radicals (OH) are converted to perhydroxyl radicals10
(HO2) or organic peroxyl radicals (RO2) through Reactions (R8), (R9), and (R10) (in
the Reaction R10, RH indicates non-methane hydrocarbons). Formaldehydes (HCHO)
are also an important source of hydroxyl radicals in that perhydroxyl radicals (HO2) are
produced by HCHO photo-dissociation (R11) and a HCHO+OH Reaction (R12).
O1D +H2O→2OH (R6)15
HONO +hv →NO +OH (R7)
OH +CO +O2→CO2+HO2(R8)
OH +CH4+O2→CH3O2+H2O (R9)
OH +RH +O2→RO2+H2O (R10)
HCHO +hv +2O2→2HO2+CO (R11)20
HCHO +OH +O2→HO2+CO +H2O (R12)
Perhydroxyl radicals (HO2) or organic peroxyl radicals (RO2) convert NO to NO2, and
are converted back to hydroxyl radicals (OH). Figure 8 gives an illustration of these
relationships. Most importantly, the isoprene emissions can create a shift in the HOx
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cycle. Excessive amounts of isoprene can deplete OH radicals, producing HO2or RO2
through Reaction (R13). Subsequently, under a NOx-limited and isoprene-abundant
environment, HO2and RO2radicals are removed from the atmosphere producing two
products, hydrogen peroxides (H2O2) and organic hydroperoxides (ROOH), through
Reactions (R15) and (R16), respectively.5
OH +Isoprene(C5H8)→RO2(multi-steps) (R13)
OH +Monoterpene(C10H16)→RO2(multi-steps) (R14)
HO2+HO2→H2O2(R15)
RO2+HO2→ROOH (R16)
Therefore, in summer, the modeled OH concentrations at the surface in China are quite10
low, due to the active conversion of OH to HO2and RO2by the presence of abundant
isoprene (see the second column in Fig. 7). Therefore, if too large biogenic isoprene
emissions are used in CMAQ modeling, the modeled (or virtual) NOxlevels could be
higher than the actual NOxlevels. Indeed, the CMAQ-predicted NOxchemical loss
rates were slow during summer in the CMAQ modeling, as shown in Fig. 7. In con-15
trast, the GOME-derived NO2columns during summer appear to be lower as a result
of more active actual chemical destruction rates than virtual destruction rates. Such
slow NOxchemical loss rates resulted in higher NO2levels in the CMAQ modeling. In
addition to isoprene, monoterpenes also convert OH to RO2via Reaction (R14). How-
ever, the CMAQ “v4.3” model only considered the gas-phase isoprene chemistry. The20
“MONO-TERP” species in the version of CMAQ 4.3 model did not take into account
the gas-phase chemistry of monoterpenes, but considered secondary organic aerosol
(SOAs) formation for monoterpenes (CMAS, 2003, 2006). The formation of SOAs from
monoterpenes in East Asia was reported by Song et al. (2008). If gas-phase monoter-
pene chemistry is introduced in CMAQ modeling, the NOxchemical loss rates would25
become even slower.
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As mentioned in Sect. 2.2, the GEIA emission inventory was used to consider the bio-
genic emissions in CMAQ modeling. In this inventory, an isoprene flux of 20.0 Tg yr−1
over East Asia was estimated. However, Steiner et al. (2002) estimated an isoprene
flux of only 13.6 Tg yr−1over East Asia, using the land-cover conditions derived from
the AVHRR satellite. Furthermore, Fu et al. (2007) recently estimated an even lower5
isoprene flux of 10.8 Tg yr−1over East Asia from an inversion analysis of the GOME-
retrieved HCHO columns. Overall, the isoprene flux used in this study might be ap-
proximately 1.5 to 2 times larger than those reported by Steiner et al. (2002) and Fu
et al. (2007). As indicated by previous discussions, the rates of NOxloss during sum-
mer in the CMAQ model simulations were lower than expected, which was attributed10
mainly to the lower OH concentrations. This might be due to the possible use of over-
estimated isoprene emissions in the modeling study. The rates of NOxloss would be
faster if the recent isoprene emissions in East Asia estimated by Fu et al. (2007) were
used. Therefore, this study again confirmed that the biogenic emissions of the GEIA
inventory could be overestimated in East Asia. Figure 9 shows that the NOxchemical15
loss rates were changed at 2 km. The hydroxyl radical (OH) concentrations increase
with decreasing isoprene concentrations, with a corresponding gradual increase in NOx
chemical loss rates.
In addition, the use of overestimated biogenic isoprene emissions in the CMAQ mod-
eling can affect the NO2/NOxratios. It is generally believed that there is some confi-20
dence in the ability of CTMs to simulate the actual NO2/NOxratios (Martin et al., 2003).
The current analysis was also based on the assumption that the actual NO2/NOxra-
tios could be successfully simulated by CMAQ modeling (Note that the GOME platform
only measures the NO2columns, not the NOxcolumns). Therefore, the correct NO2
fractions are critical in this analysis (Leue et al., 2001). The NO2/NO ratios at a pseudo-25
steady state can be estimated by Eq. (6):
[NO2]
[NO]
=kO3+k0[HO2]+k00 CH3O2+k000 [RO2]
J1
(6)
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where J1is the NO2photolysis reaction constant (s−1); and k,k0,k00 , and k000
(cm3molecules−1s−1) are the atmospheric reactions constants for the NO-to-NO2con-
version reactions through NO+O3, NO+HO2, NO+CH3O2, and NO+RO2, respectively.
The concentrations of HO2and RO2might also be overestimated if the biogenic iso-
prene levels are over-predicted using the overvalued isoprene emissions in the CMAQ5
modeling (see Reaction R13; also refer to Fig. 8). This can lead to high NO2/NO ratios
in Eq. (6), which may result in incorrectly high NO2/NOxratios in the CMAQ model-
ing. Figure 10 shows the NO2/NOxratios over East Asia for the four episodes. The
ratios were higher in summer than in the other seasons. Note the NO2/NOxratios in
Regions A and C. In addition, the NO2/NOxratios in Region C were quite high during10
summer, >0.88, which is probably due to the high isoprene flux. By this reasoning, the
CMAQ-modeled NO2columns shown in Figs. 3 and 4 might overestimate the actual
NO2columns, which can lead to a further over-prediction in the CMAQ-simulated NO2
column shown in Fig. 3.
However, in the ACE-ASIA NOxemission inventory, the NOxemissions from micro-15
biological activity in soil were not considered, as mentioned previously. According to
a recent satellite observation-constrained top-down NOxinventory study reported by
Wang et al. (2007), soil NOxemissions can sometimes account for up to 43% of com-
bustion sources during the summer in China, depending on the application of fertilizers
as well as seasonally variable temperatures and precipitation. Both the retarded NOx
20
chemical loss rates and highly biased NO2/NOxratios can lead to an over-prediction
in the CMAQ-simulated NO2columns, whereas a consideration of soil NOxemissions
over North China during the summer might offset the effects from the overestimated
biogenic isoprene emission fluxes. Overall, the natural emission of biogenic isoprene
and soil-derived NOxduring the warm seasons in East Asia is uncertain and requires25
further investigation.
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3.3 Emission inventories in East Asia
3.3.1 NOxemission inventories for South Korea
As discussed previously, it was found that the CMAQ-predicted NO2columns over
South Korea (C) may be overestimated by 41.14% to 117.63% compared with the
GOME-derived NO2columns. This finding can be confirmed with the emission fluxes5
recently released from the NIER for South Korea: CAPSS inventory. The CAPSS
emission inventory for South Korea has been built up since 1999 as a part of the
“Total Air Pollution Load Management System”. The CAPSS was established follow-
ing the SNAP 97 (Selected Nomenclature for Air Pollution), which was used as the
CORINAIR (CORe INventory of AIR emission) emission inventory system of the EEA10
(European Environment Agency). The CAPSS is an 1 km×1 km-resolved, very de-
tailed emission inventory that employs a hybrid approach (a combination of bottom-up
and top-down approaches), including intensive surveys on large-scale point sources
(such as power plants, smelting facilities, and chemical & petrochemical plants), mo-
bile sources with different automobile categories and classes, area sources with re-15
gional fuel-type consumption statistics, and non-road mobile sources such as vessels,
aviation, and construction equipment. In detail, the CAPSS has the following major 11
classification codes: i) electric generating utility (EGU) combustion, ii) non-electric gen-
erating utility (NEGU) combustion, iii) industrial combustion, iv) industrial processes,
v) storage and transport, vi) solvent utilization, vii) on-road mobile, viii) non-road mo-20
bile, ix) waste treatment, x) biogenic, and xi) agriculture (Heo et al., 2002; also, refer to
http://airemiss.nier.go.kr).
The NOxemission fluxes of the CAPSS inventory was compared with those of two
other inventories available for South Korea for 2001: ACE-ASIA and REAS. The NOx
emission fluxes of the REAS inventory were obtained from the official REAS emission25
web site (http://www.jamstec.go.jp/frcgc/research/p3/emission.htm). Figure 11 shows
the annual distribution of the NOxemissions of the CAPSS inventory for the year 2001,
showing high emission fluxes in metro-city areas such as Seoul, Incheon and Busan
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(refer to Fig. 1, regarding the locations of these cities). Table 2 shows the NOxemis-
sion fluxes of the CAPSS, ACE-ASIA and REAS emission inventories. The comparison
shows that the NOxemission fluxes of the ACE-ASIA and REAS inventories were ap-
proximately double that of the CAPSS inventory over Seoul and Incheon, and were
approximately 3 times larger over the Busan and Ulsan areas. This is in line with the5
conclusions drawn from a previous comparison study between the CMAQ-predicted
and the GOME-derived NO2columns, i.e. the NOxemission fluxes of the ACE-ASIA
inventory over South Korea were overestimated by 41.1%–117.6%. In addition, sulfur
dioxide (another important primary pollutant) emission of the CAPSS inventory has a
similar inconsistency to those of the ACE-ASIA and REAS inventories. As shown in10
Fig. 12, the SO2emission fluxes of the ACE-ASIA and REAS inventories were also
larger than those of the CAPSS inventory, particularly around metro-city areas. Table 3
shows that the SO2emission fluxes of the ACE-ASIA and REAS were even ∼10 times
larger over the Seoul and Incheon areas than those of the CAPSS inventory, and ∼2
times larger over the Busan and Ulsan areas. Therefore, in order to correctly consider15
the emissions from South Korea (C), the NOxemissions from the ACE-ASIA and REAS
inventory should be replaced by the NOxemissions of the CAPSS inventory.
3.3.2 NOxemission inventories for China
There are several NOxemission inventories in China available for 2001 including
i) ACE-ASIA/TRACE-P inventory (Streets et al., 2003), ii) REAS (Ohara et al., 2007),20
iii) EDGAR (Emission Database for Global Atmospheric Research) (Olivier et al., 1999,
2002), and iv) GEIA. Here, the ACE-ASIA and REAS inventories were used for a com-
parison study of the NOxemission fluxes from China.
As discussed previously, when the ACE-ASIA inventory was used, the CMAQ-
predicted NO2columns over North China (A) were underestimated by 8.6%–56.9%,25
compared with the GOME-derived NO2columns. In order to confirm this, the three
emission inventories for China were inter-compared: ACE-ASIA, REAS, and “date-
back” ANL inventory. Here, the “date-back” ANL inventory was estimated based on
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an emission inventory developed recently by Zhang et al. (2007) for 2006. The ANL
inventory for 2006 is an “upgraded” and “updated” version of the ACE-ASIA emission
inventory. The former (“upgraded”) indicates that the ANL inventory was improved and
methodologically evolved. The latter (“updated”) means that the ANL inventory reflects
the rapidly-growing NOxemissions from China (Zhang et al., 2007). The ANL inventory5
for 2006 also accounted for new emission factors, technology renewal, and bottom-up
approaches for various emission sources. Since the ANL inventory is only available for
“2006”, an attempt was made to “date-back” the ANL inventory to “2001” by retaining
the “upgraded” components of the ANL inventory but dating-back the “updated” parts
of the ANL inventory to 2001. For this work, the NOxemission shapes of the ANL10
inventory were retained but the NOxemissions over China were reduced using China’s
statistical data as well as the increase in energy and fossil fuel consumption (Zhang et
al., 2007).
Figure 13 shows the annual distribution of the NOxemission fluxes of ACE-ASIA,
“date-back” ANL, and REAS inventories in the upper panels. The differences in the15
NOxemission fluxes are shown in the bottom panels of Fig. 13. While the differences
between the REAS and ACE-ASIA inventories (Fig. 13e) were relatively small, the dif-
ferences between “date-back” ANL and ACE-ASIA inventories (Fig. 13d) and between
the “date-back” ANL and REAS inventories (Fig. 13f) were relatively large. The NOx
emission fluxes of the ACE-ASIA inventory are smaller than those of the “date-back”20
ANL inventory over North China (A). This was confirmed by analyzing the NOxemission
fluxes of the ACE-ASIA, “date-back” ANL and REAS emission inventory in Table 4. The
comparison shows that the NOxemission fluxes of the “date-back” ANL inventory were
∼10% larger than those of the ACE-ASIA and REAS inventory over North China (A),
and were approximately 30% larger than that of the ACE-AISA inventory over South25
China (B). The total amount of NOxemission fluxes over North China (A) were largest
in the “date-back” ANL inventory and smallest in the REAS inventory. Overall, the NOx
emission fluxes of the ACE-ASIA and REAS inventories were probably underestimated
over North China (A), as was reported by Ma et al. (2006) and Uno et al. (2007). It is
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believed that although the NOxemissions of the “date-back” ANL inventory may be the
closest to the real situations for 2001, the NOxemission fluxes of the “date-back” ANL
inventory were still low based upon comparisons of the CMAQ-predicted and GOME-
derived NO2columns, in which there was 8.6%–56.9% underestimation in NOxemis-
sions over North China. Again, the correct emission inventory is a critical input for5
examining the source-receptor relationships. The importance of using the correct NOx
emission fluxes from North China for 2001 (“reference year” of the new Korean envi-
ronmental policy of the “Total Air Pollution Load Management System”) to examine the
impact of Chinese emissions (source) on South Korean (receptor) air quality cannot be
overemphasized.10
4 Summary and conclusions
This study reports on comprehensive comparisons between the CMAQ-predicted and
GOME-derived NO2columns in order to determine the accuracy of the NOxemis-
sion inventory over North China (A) and South Korea (C). Since both regions have a
strong source-receptor relationship, an accurate knowledge of the emissions over both15
the regions is vital for understanding the contributions of North China emissions to
South Korean air quality. When the ACE-ASIA emission inventory for 2001 was used,
the CMAQ-predicted NO2columns were low by 8.6%–56.9% over North China (A)
and by 41.1%–117.6% over South Korea (C) compared with the GOME-derived NO2
columns. This was further confirmed partly by comparing several emission inventories.20
The ACE-ASIA and REAS emission inventories showed large uncertainties over North
China (A) and South Korea (C). The NOxemission fluxes of the ACE-ASIA inventory
over South Korea and North China were overestimated by ∼50% and underestimated
by ∼10%, respectively, compared with the CAPSS and “date-back” ANL inventories.
Based on these analyses, the “date-back” ANL and CAPSS inventories appear to pro-25
vide a better estimation of the real situation over North China (A) and South Korea
(C), respectively, even though the NOxemissions of the “date-back” ANL inventory is
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still low. In this study, the HOx-NOx-isoprene photo-chemistry in East Asia was also
examined because they are strongly coupled with the NOxchemical loss rates in North
China (A) and South Korea (C). In particular, the biogenic emissions of isoprene and
monoterpenes are the key parameter to control OH radicals, whose concentrations
sequentially control the NOxconcentrations through nitric acid and particulate nitrate5
formation. Recent studies reported on lower biogenic NMVOC emissions in East Asia,
which would imply that with the current emission inventory, the results of CMAQ mod-
eling may significantly retard the rates of NOxloss in East Asia, hence increase the
NOxlevels, particularly during summer. Therefore in future, biogenic emissions should
also be corrected to model the NOxconcentrations more precisely during summer in10
East Asia.
The correct emission inventory is critical for examining source-receptor relationships.
Using the corrected emission inventories for North China (A) and South Korea (C),
a one year-long Models-3/CMAQ modeling over East Asia is currently underway to
examine and quantify more accurately the influences of Chinese (source) emissions15
on the South Korean (receptor) air quality.
Acknowledgements. This study was funded mainly by the Korea Ministry of Environment as
an Eco-technopia 21 project under grant 121-071-055, and was also supported by the Korea
Science and Engineering Foundation (KOSEF) grant (MEST) (No. R17-2008-042-01001-0).
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Table 1. Statistical analysis for the comparisons between the CMAQ-predicted and GOME-
derived NO2columns over East Asia.
RMSEaMNGEbMBaMNBb
CMAQ vs. GOME
A
Spring 8.17 52.27 −1.27 −8.59
Summer 1.40 39.57 0.05 5.43
Fall 35.67 58.42 −5.03 −56.90
Winter 28.61 59.04 −3.48 −22.21
B
Spring 1.61 77.92 0.43 61.34
Summer 2.47 42.00 0.06 22.02
Fall 3.21 31.03 −0.64 −10.36
Winterc– – – –
C
Spring 24.06 90.88 3.26 79.78
Summer 18.35 121.21 2.95 117.63
Fall 38.75 54.47 3.32 41.14
Winter 35.92 70.49 4.30 65.57
D
Spring 4.45 111.92 0.84 94.78
Summer 3.90 81.00 0.87 55.51
Fall 6.29 46.62 −0.58 −7.83
Winter 4.46 74.76 0.29 32.79
A: North China; B: South China: C: South Korea: D: Japan
aUnit, ×1015 molecules cm−2
bUnit, %
cDue to missing values
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Table 2. Comparison of NOxemissions among the CAPSS, REAS, and ACE-ASIA inventories
over South Korea for 2001.
Region ACE-ASIAaREASaCAPSSaACE-ASIA/CAPSS REAS/CAPSS
Seoul 232 059 328 256 135 771 1.71 2.42
Incheon 399 787 378 100 176 379 2.27 2.14
Busan and Ulsan 361 520 496 088 129 188 2.80 3.84
Daegu 115 438 106 483 103 422 1.12 1.03
Other region 249 236 332 832 375 032 0.66 0.89
Total 1 358 040 1641 758 919 792 1.48 1.78
aTon yr−1
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Table 3. Comparison of SO2emissions among the CAPSS, REAS, and ACE-ASIA inventories
over South Korea for 2001.
Region ACE-ASIAaREASaCAPSSaACE-ASIA/CAPSS REAS/CAPSS
Seoul 169 054 300 418 22 846 7.40 13.15
Incheon 309 937 268 019 34 093 9.90 7.86
Busan and Ulsan 174 177 194 050 92 894 1.88 2.09
Daegu 68 811 55 048 23 902 2.88 2.30
Other region 134 453 227 625 147 937 0.91 1.54
Total 856 433 1 045 159 321 673 2.66 3.25
aTon yr−1
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Table 4. Comparisons of NOxemission among ACE-ASIA, “date-back” ANL, and REAS inven-
tories over China for 2001.
Region ACE-ASIAa“date-back” REASa“Date-back” REAS/
ANLaANL/ACE-ASIA ACE-ASIA
North China (A) 6 063 087 6 586 730 5 995 179 1.09 0.99
South China (B) 2 145 335 2 726 533 2 609 364 1.27 1.22
Other region 2 790 983 3 521 353 2 889 506 1.26 1.04
Total 10 999 405 12 834 616 11 494 050 1.17 1.04
aTon yr−1
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Fig. 1. Modeling domain of the study. Four regions were defined: i) A: North China, ii) B: South
China, iii) C: South Korea, and iv) D: Japan.
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Fig. 2. Three-day backward trajectory analysis for air masses arriving in Seoul, Korea in
2001. The trajectories are obtained at 1 km a.s.l., and are shown at 1 h intervals: (a) Jan-
uary, (b) February, (c) March, (d) April, (e) May, (f) June, (g) July, (h) August, (i) September,
(j) October, (k) November, and (l) December.
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Fig. 3. Seasonal variations in the CMAQ-derived NO2columns (unit: ×1015 molecules cm−2)
in the first column and GOME-derived NO2columns in the second column (unit:
×1015 molecules cm−2). The differences between the CMAQ-derived and GOME-derived NO2
columns are shown in the third column.
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Fig. 5. Scatter plots between the CMAQ-derived and GOEM-derived NO2columns for
(a) spring, (b) summer, (c) fall, (d) winter, and (e) all seasons. North China (red circles),
South China (open circles), South Korea (blue triangles), and Japan (green triangles).
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Fig. 6. Seasonal variations in the anthropogenic NOx(red thick line), SO2(blue dot line), and
BC (black dash line) emissions in China (Streets et al., 2003).
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Fig. 7. Seasonal variations in the CMAQ-derived isoprene concentration (unit: ×ppb)
at the surface level (the first column), CMAQ-derived hydroxyl radical (OH) concentra-
tion (unit: ×10−2ppt) at the surface level (the second column), and NOxloss rates (unit:
×106molecules cm−3s−1) at the surface level (the third column).
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Fig. 8. An illustration of the simplified HOx/RO2-NOx-biogenic VOC photochemistry.
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Fig. 9. Similar to Fig. 7, except for the isoprene concentrations (unit: ×102ppt), OH concentra-
tions (unit: ×10−2ppt), and NOxloss rates (unit: ×105molecules cm−3s−1) at the 2 km.
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Fig. 10. NO2/NOxratios modeled by CMAQ model: (a) Fall, (b) Spring, (c) Summer, and
(d) Winter.
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Fig. 11. Annual NOxemission fluxes over South Korea: (a) ACE-ASIA, (b) REAS, and
(c) CAPSS.
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Fig. 13. Annual NOxemissions inventory over China: (a) ACE-ASIA, (b) “date-back” ANL, and
(c) REAS. The differences between the annual NOxemission inventories are also shown in:
(d) “date-back” ANL – ACE-ASIA, (e) REAS – ACE-ASIA, and (f) “date-back” ANL – REAS.
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