Evaluating the accuracy of NO x emission fluxes over East Asia by comparison between CMAQ-simulated and OMI-retrieved NO 2 columns with the application of averaging kernels from the KNMI algorithm

K. M. Han, S. Lee, I. S. Chang, and C. H. Song School of Environmental Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, 500-712, Korea Advanced Environmental Monitoring Research Center (ADEMRC), Gwangju Institute of Science and Technology (GIST), Gwangju, 500-712, Korea Air Quality Research Division, National Institute of Environmental Research (NIER), Incheon, 404-708, Korea


Introduction
There has been growing public concern about serious smog events in East Asia due to large amounts of anthropogenic pollutants in the atmosphere.Among the pollutants, nitrogen oxides (NO x = NO + NO 2 ) play a key role in tropospheric chemistry, such as ozone and secondary aerosol formation.Also, in global climate change, atmospheric NO x is believed to make indirect negative contributions to radiative forcing in the atmosphere (Wild et al., 2001).For example, secondary nitrates (NO − 3 ) formed via the condensation of atmospheric HNO 3 , NO 3 , and N 2 O 5 into particles contribute, on average, 30.7 % to aerosol direct radiative forcing (ADRF) in East Asia during the winter season, which cannot be ignored in the estimation of direct radiative forcing in East Asian (Park et al., 2014).HNO 3 formation via the reaction of OH + NO 2 during the daytime and heterogeneous nitrate formation via the condensation of N 2 O 5 onto atmospheric particles during the nighttime are believed to be the main chemical and physico-chemical processes removing NO x from the atmosphere (Han and Song, 2012).
Recently, several studies have reported annual increases in NO x emissions in China (Zhang et al., 2007(Zhang et al., , 2009;;Kurokawa et al., 2013).For example, based on the Greenhouse gas and Air pollution INteractions and Synergies (GAINS) model simulations, China makes the largest contribution to global NO x emissions, and its contribution was estimated to be 25 % for 2010 (Cofala et al., 2012).Also, when several emissions scenarios are applied to future GAINS simulations, the contribution of China is estimated to increase, to ∼ 29 % in the years between 2015 and 2035 (Cofala et al., 2012).However, large uncertainty in bottom-up NO x emissions over East Asia has also been reported (e.g.Streets et al., 2003;Zhang et al., 2007;Klimont et al., 2009;Xing et al., 2011).In the meantime, several studies have also reported rapid increases in atmospheric NO x mixing ratios over China, based on Global Ozone Monitoring Experiment (GOME), Ozone Monitoring Instrument (OMI), and SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) observations (Richter et al., 2005;van der A et al., 2006;Itahashi et al., 2014).These satellite observations have provided Figures useful global/regional information on the mixing ratios and spatial distributions of NO x , and have also been used to investigate the accuracy of the global and regional NO x emissions (e.g.Martin et al., 2006;Uno et al., 2007;Wang et al., 2007;Han et al., 2009).However, these satellite-retrieved column quantities are not "real" or "true" values, having different vertical sensitivities of the satellite measurements at different altitudes in the atmosphere.To consider this vertical sensitivity of the satellite observations, averaging kernels (AKs) have been introduced into comparison studies between chemistry-transport model (CTM)-simulated and satellite-retrieved NO 2 columns (hereafter, denoted as Ω NO 2 ) (e.g.Rodgers, 2000;Eskes and Boersma, 2003).In particular, Eskes and Boersma (2003) reported that the use of AKs is crucial in interpreting the retrieved Ω NO 2 near the surface, because of the low sensitivity of satellite observations of NO 2 near the surface areas.In this context, several studies have used AKs to evaluate the accuracy of surface NO x emissions over several regions (e.g.Herron-Thorpe et al., 2010;Lamsal et al., 2010;Ghude et al., 2013).
Evaluating the accuracy of the NO x emission fluxes in East Asia is also important in more accurately predicting mixing ratios of atmospheric gases and aerosols and estimating DRF by aerosols.The main objective of this study was thus to investigate the accuracy of the bottom-up NO x emissions in East Asia, comparing OMI-retrieved tropospheric NO 2 columns (Ω NO 2 ,OMI ) from KNMI/DOMINO v2.0 daily products with the CTM-calculated NO 2 columns (Ω NO 2 ,CTM ).To conduct this investigation, we also applied the AKs retrieved from the KNMI algorithm to the CTM simulations over East Asia for a direct comparison between the CTM-estimated and satellite-observed Ω NO 2 in this study (refer to Sect.3.1).
Precise evaluations of the accuracy of bottom-up NO x emissions via comparisons between Ω NO 2 ,OMI and Ω NO 2 ,CTM are frequently hampered by several uncertainty factors.Thus, another issue in this study was to explore these uncertainty factors, such as uncertainties in monthly variations of NO x emissions, strength of NO x emissions, reaction probabilities of N 2 O 5 etc.These issues will be discussed further in Sect.

Experimental methods
The basic approach of this study was to compare the CTM-calculated NO 2 columns with satellite-derived NO 2 columns to investigate the accuracy of bottom-up NO x emissions in East Asia.Thus, the two major tools (CTM simulations and satellite observations) for this study are described briefly in Sect. 2.

Modeling descriptions
First, for the CTM simulations, the US EPA/Models-3 CMAQ (Community Multi-scale Air Quality) v4.7.1 model was used (Byun and Schere, 2006).To drive CMAQ model simulations, two main drivers are needed: (i) meteorological fields and (ii) emission fields.For the former, PSU/NCAR MM5 (Pennsylvania state University/National Center for Atmospheric Research Meso-scale Model 5) v3.7.1 was used with National Centers for Environmental Prediction (NCEP) reanalyzed data sets (Stauffer andSeaman, 1990, 1994).To prepare more accurate meteorological fields, four-dimensional data assimilation (FDDA) using QuickSCAT 10 m wind data sets was also carried out.For the latter, three anthropogenic emission inventories were used: INTEX-B (Intercontinental Chemical Transport Experiment-Phase B), CAPSS (Clean Air Policy Support System), and REAS (Regional Emission Inventory in Asia) emission inventories over China, Korea, and Japan, respectively (Ohara et al., 2007;Hong et al., 2008;Zhang et al., 2009).For biogenic emissions, the MEGAN+MOHYCAN (Model of Emissions of Gases and Aerosols from Nature + MOdel for Hydrocarbon emissions by the CANopy) inventory was obtained from the official website, at http://tropo.aeronomie.be/models/mohycan.htm(Müller et al., 2008).Biogenic emissions are an important factor during the summer, even in this type of NO x study, because the mixing ratios of biogenic species can influence the NO 2 -to-NO ratios via changing the levels of HO x and RO 2 radicals (Horowitz et al., 2007;Han et al., 2009).
The accuracy of the biogenic emissions used in this study was also evaluated over the same domain, East Asia, in our previous study (Han et al., 2013).Introduction

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Full Table 1 summarizes base-case simulation and several sensitivity runs for this study.For the base-case simulation, monthly variations of the anthropogenic NO x emissions from Zhang et al. (2009) were considered for China, while those from Han et al. (2009) were used for South Korea and Japan.As shown in Fig. 1, the monthly variations in NO x emissions over China were highly uncertain.This uncertainty is explored and discussed in Sect.3.2.1.
The modeling period was from 1 January to 31 December 2006.In this study, 2006 was chosen for the CMAQ model simulations, because relatively accurate emission data of INTEX-B over China are available for this year.The horizontal domain covers from 100 • E to 150 • E and from 20 • N to 50 • N with a grid-resolution of 30 km × 30 km.
The vertical domain covers from 1000 hPa to 118 hPa with 14 terrain following σcoordinates.For considering aerosol dynamics and thermodynamics, the aerosol module of AERO4 was selected (Binkowski and Roselle, 2003).
For the consideration of gas-phase chemistry, the SAPRAC-99 (Statewide Air Pollution Research Center-99) mechanism was selected (Carter, 2000).Then, to consider unknown OH radical processes (Lelieveld et al., 2008), the SAPRAC-99 mechanism was modified partly, based on the work of Butler et al. (2008) in the following way (Reaction R1): Here, ISOPO2 and ISOPOOH represent isoprene-derived peroxy radical and peroxide, respectively.Other schemes used in the CMAQ model simulations were the global mass-conserving scheme (YAMO) for horizontal and vertical advection (Yamartino, 1993), the asymmetric convective model (ACM) algorithm for convective cloud mixing, and ACM (ver.2) for vertical diffusion (Pleim, 2007).
For synchronization with the OMI-observed tropospheric Ω NO 2 , CMAQ-estimated tropospheric Ω NO 2 data were collected and then averaged between 13:00 and 14:00 local time (LT), because the OMI sensor scans the atmosphere over East Asia approximately at 13:45 LT.For further detailed analyses, eight highly-populated focus regions were defined in this study, and are presented in Fig. 2.

OMI-retrieved NO 2 columns and AKs
The OMI instrument on board the NASA/EOS-Aura satellite, a nadir-viewing imaging spectrometer, provides information on the properties of aerosols and clouds as well as global levels of atmospheric species such as ozone, NO 2 , SO 2 , OClO, BrO, and HCHO on a daily basis via observing backscattered UV-VIS radiances from 270 to 550 nm (Levelt et al., 2006).Two-dimensional charge-coupled device (CCD) detectors equipped in the OMI instrument observe the atmosphere with a spatial resolution of 13 km × 24 km.CCD1 covers the UV channel of 270-310 nm and 310-365 nm.The visible channel, ranging from 365 to 500 nm, is covered by CCD2 to observe NO 2 .
In this study, daily levels of OMI-retrieved tropospheric NO 2 columns from KNMI/DOMINO v2.0 products were used (Boersma et al., 2007(Boersma et al., , 2011)).The KNMI/DOMINO v2.0 algorithm (hereafter, KNMI algorithm) for retrieving the tropospheric NO 2 columns from the OMI radiance data proceeds in the following sequence.
First, a slant NO 2 column density was determined from spectral fitting, using the differential optical absorption spectroscopy (DOAS) method.Second, the stratospheric NO 2 contribution was removed by subtracting the stratospheric portions of slant NO 2 columns calculated from the global CTM, TM4, from the total slant NO 2 columns.Finally, the tropospheric slant NO 2 columns were converted into vertical NO 2 columns, using the air mass factor (AMF), defined as the ratio of the measured slant column to the vertical column.This AMF is a function of several factors, such as the satellite Introduction

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Full viewing geometry, surface albedo, surface pressure, and vertical distributions of clouds, aerosols, and trace gases.In this study, to reduce retrieval errors, measured scenes with surface albedo values larger than 0.3 were excluded, as suggested by Boersma et al. (2011).Also, observed pixels with cloud radiance fractions (CRF) larger than 50 % were filtered out, which are approximately equivalent to cloud fractions (CF) smaller than 20 % (van der A et al., 2006).Thus, OMI-retrieved tropospheric NO 2 columns under almost "cloud-free" conditions were used in this study.
The AKs were also applied to the CMAQ model simulations.The AKs are analytically expressed in Eq. ( 1) (Rodgers, 2000;Eskes and Boersma, 2003): where G y and K x represent the sensitivities of the retrieval (R) to the measurement (y) and the forward model (F ) to the state (x), respectively.Also, K x is known as a weighting function or Jacobian matrix.Thus, as shown in Eq. ( 1), the AKs represent the sensitivity of the retrieved quantities (here, vertical NO 2 column, x) to the true atmospheric state (x).Using the AKs, the retrieved quantity ( x) can be expressed by Eq. (2): where x a and ε represent a prior estimate and total error in measured signal relative to the forward model, respectively.Information on the AKs and retrieved quantity are included in the daily KNMI products (http://www.temis.nl/airpollution/no2col/no2regioomi_v2.php).Fig. 2).As shown in Fig. 3, the AKs are strongly altitude-dependent in the troposphere.For example, near the surface, the AKs are smaller than unity, indicating that the OMI observations are under-sensitive, by the extent deviated from the vertical line of unity.In contrast, in the upper troposphere, the AKs are larger than unity, indicating that the OMI measurements are over-sensitive, by the extent deviated from the line of unity (an AK of unity means that the OMI instruments can directly measure the true NO 2 column densities).Additionally, the AKs are generally lower in warm seasons than in cold seasons.These lower values in the AKs during the summer are probably related to the lower surface albedos and the lower concentrations of aerosols during the summer (Eskes and Boersma, 2003).
Once the CMAQ model simulations were done, the AKs were applied under the almost cloud-free conditions.The tropospheric Ω NO 2 values were also retrieved from the OMI observations via the KNMI algorithms.Then, using the two NO 2 columns, a comparison study was made between the two Ω NO 2 products.Figure 4 illustrates the main processes of the comparison study.

Results and discussions
The objective of this study was to examine the accuracy of the NO x emission fluxes over East Asia by comparing two tropospheric Ω NO 2 obtained from the CMAQ model simulations and OMI observations (Sect.3.1).Several sensitivity analyses were also conducted to examine the influences of the uncertainty factors that can result in discrepancies between Ω NO 2 ,CMAQ and Ω NO 2 ,OMI (Sect.3.2).Obviously, not all the influential factors can be explored within the framework of this study.Thus, several selected issues that may be important are also discussed further in Sect.3.2.4.Introduction

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Full Figure 5 presents the comparison analysis between the Ω NO 2 ,CMAQ and Ω NO 2 ,OMI for the four seasons over East Asia before and after the applications of the AKs.As shown in Fig. 5, the CMAQ model simulations (the first and second columns) show spatially and seasonally consistent patterns with OMI observations (the third column).For example, the high values of the Ω NO 2 ,OMI over the densely populated and economically developed mega-city regions such as Beijing, Shanghai, Hong Kong, Seoul, and Tokyo (refer to Fig. 2 regarding their locations) are well captured by the CMAQ model simulations.Also, the low values of the Ω NO 2 from the CMAQ model simulation during the summer are well matched with those from the OMI observations.The lower levels of the Ω NO 2 during the summer are possibly caused by active NO x chemical losses via the reaction of NO 2 with OH radicals (Han et al., 2009).Because of this "fast chemical NO x loss rate" during the summer, summer is not the season of primary interest in this study, because there are many uncertainties and unknowns related to these chemical NO x loss rates and processes in the CTMs (Han et al., 2009).These uncertainties and unknown factors will be discussed further in Sect.3.2.4.In the same context, more attention was paid to winter (spring and fall) in this study, because there are fewer uncertainties and unknowns related to the chemical NO x loss rates during these seasons.Additionally, the levels of the Ω NO 2 during the winter are distinctly high.Introduction

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Full These high values would be better for a comparison study between the Ω NO 2 ,CMAQ and Ω NO 2 ,OMI , as shown in Fig. 5.When panels a and c in Fig. 5 are compared, it can be seen that the Ω NO 2 ,CMAQ is in general greatly larger than the Ω NO 2 ,OMI over the entire domain.The large differences between the two NO 2 columns can be confirmed again in panel a of Fig. 6.Previously, Han et al. (2009Han et al. ( , 2011) also compared the CMAQ-calculated NO 2 columns with satellite-retrieved NO 2 columns, without using the AKs, to investigate the accuracy of bottom-up NO x emissions over East Asia.Based on this comparison, Han et al. (2011) concluded that the bottom-up NO x emissions used in the CTM simulation over East Asia could be overestimated.However, such a comparison without applying the AKs is like comparing apples and oranges, and is not reasonable.Such studies have been conducted over East Asia, with misleading conclusions (e.g.Ma et al., 2006;He et al., 2007;Uno et al., 2007;Shi et al., 2008;Han et al., 2009Han et al., , 2011)).In this context, we now wish to correct our previous conclusions (Han et al., 2011) here, applying the AKs to the CMAQ model simulations, using the linear relationship presented in Eq. ( 2).
After the application of the AKs to the CMAQ model simulations, the comparison becomes independent of a prior profile shape used in the retrieval (Eskes and Boersma, 2003).In this study, when the panels b and c in Fig. 5 are compared, it can be seen that the CMAQ-calculated NO 2 columns considering the AKs are much more comparable to the OMI-retrieved NO 2 columns, possibly indicating that the bottom-up NO x emission used in the CMAQ model simulations would not be very greatly overestimated, unlike the previous conclusion in Han et al. (2011).Figure 6 more directly shows the effects of the application of the AKs.When the AKs are applied, the differences are greatly diminished, and are even negative, particularly over the CEC regions.The Ω NO 2 ,CMAQ,AK becomes smaller than the Ω NO 2 ,OMI over the CEC, SC, SK, JP1, and JP2 regions.Also, possible overestimations of the bottom-up NO x emissions were found in the CEC2 and SB regions, particularly during the winter.In Table 2, we summarize the average tropospheric NO 2 columns and normalized mean errors (NMEs) with and without considering the AKs for the eight focus regions.After the applications of the AKs, it can Introduction

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Full be seen that the NMEs are greatly reduced during all seasons, except for the summer, over the entire domain (from ∼ 103 % to ∼ 46 %, from ∼ 112 % to ∼ 45 %, and from ∼ 135 % to ∼ 40 %, during spring, fall, and winter, respectively).Collectively, we found that the application of the AKs is an essential component for a direct comparison between model-and satellite-derived NO 2 columns.It was found that after the applications of the AKs, the differences between the two NO 2 columns were greatly reduced.To further investigate the eight regions of interest, scatter plots and statistical analyses were carried out in Sect.3.1.2.

Scatter plots and statistical analyses
Figure 7 presents the seasonal scatter plot analysis between the Ω NO 2 ,CMAQ,AK and Ω NO 2 ,OMI for the seven focus regions defined in Fig. 2. The statistical analysis related to the scatter plots was also conducted in terms of the Pearson correlation coefficient (R) and linear regression slope (S).Although the correlation coefficients were sometimes lower than 0.7 in Fig. 7, the two NO 2 columns correlated well, with R values between 0.71 and 0.94 (also, refer to the "R" values colored in Fig. 8).Slopes lower than 1.0 (see dashed lines in Fig. 7) were also found in the "blue" regions in Fig. 6  were also analyzed to assess the degrees of correlations and agreement, respectively.
These 10 performance metrics are defined in Table A1 (see Appendix A).As shown in Fig. 8, the IOAs (as a measure of the degree of model prediction errors, Willmott, 1981) showed high values, between 0.78 and 0.93, over the entire domain.However, the Introduction

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Full IOAs sometimes showed relatively low values during the summer over several regions where large relative differences were found (e.g.SB, JP1, and JP2 regions during the summer), because the IOA decreased with the large difference between Ω NO 2 ,CMAQ,AK and Ω NO 2 ,OMI .
As shown in Fig. 8, large MEs were found over the CEC region (2.05 × 10 15 to 4.55 × 10 15 molecules cm −2 ) and MBs mostly ranges between 2 × 10 15 and 2 × 10 15 molecules cm −2 in East Asia, except in CEC.In the seasonal perspective, all statistical parameters of the relative differences (i.e.MNGE, MNB, NME, NMB, MFE, and MFB) showed large values for the summer in all the regions, because the OMI-retrieved quantity in the denominator of the equations (see Table A1) for the summer were relatively small vs. the values of the absolute differences in the numerator.Also, as discussed in the previous section, there are large uncertainties and many unknown factors and processes in the tropospheric chemistry of the CMAQ model during the summer.In the regional perspective, the relative differences showed large values in the SC, SB, JP1, and JP2 regions, where tropospheric Ω NO 2 were relatively low (i.e. the same reason leading to larger relative errors and biases in the summer).Collectively, the statistical analyses indicated that the bottom-up NO x emissions used in this study are possibly underestimated in East Asia.However, it should be noted that possible overestimations in NO x emissions were also found over the CEC2 and SB regions.

Sensitivity analyses
Although it is believed that the application of the AKs is the most important factor in this study, there are several issues (uncertainty factors) we have still to examine.These are discussed further in this section.study still has several uncertainties.Among them, first, the monthly variations of NO x emissions over China were investigated, using different monthly variations as shown in Fig. 1.In this sensitivity run, we applied a more drastic monthly variation of the NO x emissions in China (thick-black line in Fig. 1) (Han et al., 2009).The main reason we did this is that, as shown in Figs.5-7, the Ω NO 2 ,CMAQ,AK was underestimated, compared with the Ω NO 2 ,OMI , over several regions (such as CEC and main mega-city areas like Hong Kong and Shanghai) in China, particularly during the cold months.

Monthly variation in NO
The results are presented in Fig. 9.The spatial distributions of the Ω NO 2 ,CMAQ,AK and Ω NO 2 ,OMI are shown in Fig. 9 for the four seasons.As indicated in Supplement Table S1 in the supplementary materials, the application of the AKs again greatly reduced the errors and biases between the two tropospheric NO 2 columns in this sensitivity test.As expected, the Ω NO 2 ,CMAQ,AK in Fig. 9a generally increased for the spring and winter, whereas it decreased for the summer and fall, compared with the values in Fig. 5b.These increases in the Ω NO 2 ,CMAQ,AK for the winter produced better agreement with the Ω NO 2 ,OMI , particularly over the CEC region (see the average NO 2 columns and NMEs in Tables 2 and Supplement Table S1).However, as shown in Tables 2 and Supplement Table S1, the situations became worse, except for the CEC region, showing significant increases in NMEs, compared with the NMEs in cases using of the monthly variation of the INTEX-B inventory.Even larger (more serious) differences between the two NO 2 columns in Fig. 9c

Another NO x emission inventory (REAS): Case 3
There is another NO x emission inventory available in China: the REAS emission inventory.Thus, in this section, the REAS emission inventory, a frequently used bottomup inventory established by the National Institute of Environmental Studies (NIES) in Japan, was tested over China for January (a cold month).Because the REAS inventory does not include monthly variation, the same monthly variation of the INTEX-B inventory was also applied to this sensitivity study.The strength of the NO x emissions between the INTEX-B and REAS inventories differed greatly over China.For example, the total annual NO x emissions from the INTEX-B inventory were 5.41, 4.85, 3.49, and 1.24 Tg yr −1 over the CEC, CEC2, SC, and SB regions, respectively, whereas those from the REAS inventory were 4.21, 3.40, 3.04, and 0.88 Tg yr −1 , respectively, over the same regions.
The results are presented in Fig. 10 and Supplement Table S2.The application of the AKs to the CMAQ model simulations were also taken into account in this comparison (see Table S2 in the Supplement).As expected, the Ω NO 2 ,CMAQ,AK decreased significantly over China, when the REAS NO x emissions are used (refer to Table S2 in the Supplement).Although the absolute differences between the Ω NO 2 ,CMAQ,AK and Ω NO 2 ,OMI became smaller over the CEC2 and SB regions, much large underestimates were found over the CEC region, compared with the case of the INTEX-B inventory as shown in Fig. 10.Recently, Kurokawa et al. (2013) reported that the NO x emissions from the REAS inventory were possibly underestimated, by a factor of ∼ 0.9, over China for the year of 2006.
Collectively, our results indicate that (i) the NO x emission fluxes from the REAS inventory were also underestimated over China (particularly, over the CEC region), columns.For better agreement between the Ω NO 2 ,CMAQ,AK and Ω NO 2 ,OMI over China, a combination of the two emission inventories may be a good practical attempt in the CMAQ model simulations over East Asia, based on this result.That is, the INTEX-B NO x emissions data tended to produce better results over the CEC region, whereas the REAS NO x emissions data tended to generate better results over the CEC2 and SB regions.However, this issue needs a more sophisticated approach and should be investigated further.

Reaction probability of N 2 O 5 : Case 4
We explored the issue of reaction probability of N 2 O 5 (γ N 2 O 5 ) onto aerosols, because a relatively large discrepancy between the Ω NO 2 ,CMAQ,AK and Ω NO 2 ,OMI was found, particularly during the winter season.As mentioned previously, we paid more attention to the cold seasons, such as winter, fall, and spring, because the cold months are better for conducting this type of study due to the uncertain tropospheric chemistry and faster NO x loss rates during the summer.
In particular, during the winter season, the condensation of N 2 O 5 into atmospheric particles is an important NO x loss process (Dentener and Crutzen, 1993;Brown et al., 2004Brown et al., , 2006).Thus, it can affect the CMAQ-simulated NO 2 columns (Ω NO 2 ,CMAQ,AK ).Although it is an important physico-chemical NO x loss process during the winter, the magnitude of γ  4).
As shown in Fig. 11 and Table 4, Schemes II, III, and IV tended to produce better Ω NO 2 ,CMAQ,AK data over East Asia than Schemes I and V, compared with Ω NO 2 ,OMI .More recently, Brown et al. (2009) and Bertram et al. (2009) also discussed that the γ N 2 O 5 values being used currently in regional/global CTMs were generally larger than those from their observed γ N 2 O 5 .In addition to the issue of γ N 2 O 5 , it should be noted that the aerosol surface density (A) is another uncertain factor that can influence the Ω NO 2 ,CMAQ,AK , because the rate constant (k N 2 O 5 ) of the physico-chemical reaction also depends on the aerosol surface density (refer to the Schwartz formula, k ).Although all of these issues are arguable, our results show that the γ N 2 O 5 parameterizations can certainly influence the levels of Ω NO 2 in East Asia, particularly during the winter season.Introduction

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More uncertainties and outlooks
As mentioned previously, in this type of analysis all types of temporal variation are potentially important and should therefore be taken into account.A sensitivity analysis on the monthly variation in the NO x emissions in China was performed in Sect.3.2.1,showing that the monthly variations in NO x emissions were an important factor.In contrast, there is only limited information on other temporal variation, such as daily and weekly variation in NO x emissions in East Asia.Unfortunately, no emission inventory in East Asia can provide us with this level of information.Although not shown here, we did also test daily variation in NO x emissions, based on hourly NO 2 concentrations measured in the megacities.However, it has been found that there is no significant impact of this daily variation on the Ω NO 2 ,CMAQ,AK at 13:00 LT (the OMI local pass time).However, this issue should be investigated further.Regarding the issue of the temporal variation, it can be suggested that the future Korean Geostationary Environmental Monitoring Spectrometer (GEMS) sensor, which is planned to be launched in 2018, will/may be able to help to obtain such information on daily and weekly variation in the NO x emissions over East Asia (Kim, 2012).
There is also some level of uncertainty in the NO 2 -to-NO ratios, as discussed previously by Richter et al. (2005) and Han et al. (2009).This factor may be important, because every satellite remote-sensor monitors only NO 2 columns, not NO x columns.The NO 2 -to-NO ratios are affected seriously by anthropogenic and biogenic VOC (AVOC and BVOC) emissions and their mixing ratios.For example, if we assume a photostationary state, the NO 2 -to-NO ratios can be influenced by the mixing ratios of ozone and HO 2 , CH 3 O 2 , and RO 2 radicals, as shown in the following formula: where J 1 is the NO 2 photolysis rate constant (s −1 ) and k 1 , k 2 , k 3 , and k 4 are the reaction constants (cm 3 molecules −1 s −1 ) for NO + O 3 , NO + HO 2 , NO + CH 3 O 2 , and Introduction

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In addition to the issues mentioned above, in the CTM simulations there are additional uncertainties in geogenic NO x emissions (e.g., soil NO x emissions) and pyrogenic NO x emissions (e.g., biomass burning NO x emissions) (Bertram et al., 2005;Jaeglé et al., 2005;Hudman et al., 2010;Lin, 2012).However, for example, geogenic NO x emissions are usually more active during the summer.During the summer, the NO x loss rates are so fast that considerations of additional geogenic NO x emissions would hardly change the CTM-calculated NO 2 columns (Boersma et al., 2009;Han et al., 2009).The same is true for the issues of OH recycling and isoprene-derived alkyl nitrate formation mentioned above.There are uncertainties and unknown chemistry related to isoprene, but, again, due to the fast NO x loss rates during the summer, it has been found that these factors do not greatly affect the Ω NO 2 ,CMAQ,AK during the summer in our test runs (data not shown).This is why we said that the summer was not a season of major interest in this study.Introduction

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Full On the other hand, in the view of satellite observations, there are errors and uncertainties in the retrievals of the NO 2 vertical columns and the AKs.There are also several NO 2 vertical column products from different sensors (e.g.GOME, OMI, SCIA-MACHY, and GOME-2) and from different algorithms (e.g.KNMI, Bremen, BIRA, and NASA).For example, the different NO 2 products sometimes show considerable differences (Herron-Thorpe et al., 2010).Also, Boersma et al. (2011) reported that the tropospheric NO 2 columns retrieved from the DOMINO v2.0 algorithm showed 20 % and 10 % lower values in the winter and summer, respectively, over polluted areas, such as the CEC region in China, compared with values from DOMINO v1.02.Overall, different combinations of these sensors and algorithms can produce different NO 2 column products.Thus, in this type of comparison analysis, all the uncertainty factors mentioned should be taken into account cautiously.

Summary and conclusions
The accuracy of bottom-up NO x emission fluxes from the INTEX-B, CAPSS, and REAS emission inventories were investigated through comparisons between the Ω NO 2 ,CMAQ,AK and Ω NO 2 ,OMI in East Asia.For the comparison study, the CMAQ model simulations were carried out over 12 months in 2006 over East Asia.Also, for the direct comparison between the Ω NO 2 ,CMAQ and Ω NO 2 ,OMI , we applied the AKs to the CMAQ model simulations.
It was found that after the applications of the AKs, the CMAQ-estimated NO 2 columns were much more comparable to the OMI-retrieved NO 2 columns over East Asia and that the normalized mean errors (NMEs), for example, decreased, from ∼ 103 % to ∼ 46 %, from ∼ 112 % to ∼ 45 %, and from ∼ 135 % to ∼ 40 % in East Asia during the spring, fall, and winter, respectively.Overall, the bottom-up NO x emissions were, on annual average, ∼ 28 % (with seasonal variation of ∼ 7 to ∼ 60 %) underestimated in East Asia, although some overestimates were also found partly over the CEC2, SB, and SK regions during the winter.Introduction

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Full To assess the seasonal and spatial discrepancies, several sensitivity studies, shown in Table 1, were performed considering several uncertainty factors such as (i) monthly variation of NO x emission, (ii) strength of NO x emission, and (iii) reaction probabilities of N 2 O 5 .In Table 5, we summarize the relative changes in the NO 2 columns from the sensitivity simulations with respect to those from the standard simulation (Case 1).
From the sensitivity simulations, we found that: monthly variations in NO x emissions have a strong impact on tropospheric NO 2 columns.and Evans and Jacob (2006).Thus, it appeared that the γ N 2 O 5 parameterization would not be a negligible factor, particularly during the winter.
Although we have listed several uncertainty factors in this study, we believe that the application of the AKs was the single most dominant factor that can affect the Ω NO 2 ,CMAQ,AK .Additionally, the strength and monthly variation in NO x emissions can also significantly influence the levels of tropospheric Ω NO 2 ,CMAQ,AK in East Asia.Moreover, we showed that the γ N 2 O 5 parameterization could be another important factor Introduction

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Full The estimation of "top-down" NO x emissions has also been carried out in East Asia (Stavrakou et al., 2008;Lin et al., 2010;Mijling et al., 2013) using satellite-derived NO 2 columns.However, in such top-down estimations, there are other uncertain (limiting) factors, such as lifetimes of NO x (i.e., τ NOx ).The uncertainty is also linked with the factors we discussed in Sect.3.2.4.Improvements in the NO x emissions data or the evaluation of the accuracy of the bottom-up NO x emission fluxes in East Asia can improve air quality modeling and chemical weather forecast over East Asia.Thus, much efforts should be focused on this issue in future, particularly over East Asia.In the same context, many efforts on inverse modeling to improve the accuracy of NO x emissions data over East Asia such as adjoint modeling with measured data and top-down estimations of the NO x emissions with satellite observations could also contribute to improving the performance of air quality modeling and the accuracy of chemical weather forecasting over East Asia (Park et al., 2013).
The Supplement related to this article is available online at doi:10.5194/acpd-14-17585-2014-supplement.Introduction

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Appendix A
For statistical analyses between the CMAQ-calculated and OMI-retrieved NO 2 columns, several statistical parameters below are introduced in Table A1.Introduction

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Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | the CMAQ modeling, initial conditions (ICs) were prepared from 1 week-long spin-up model simulations, and boundary conditions (BCs) were obtained from global CTM simulations, MOZART (Model for OZone And Related chemical Tracers) (Emmons et al., 2010).The MOZART model simulation data for the BCs were obtained Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Figure 3
Figure 3 presents the vertical distributions of the seasonally-averaged AKs retrieved from the KNMI algorithms over Central East China (CEC) and other regions (defined in 17593
Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | such as the CEC, SC, SB, JP1, and JP2 regions.These low slopes indicate the possible "underestimation" of the bottom-up NO x emissions used in the CMAQ model simulations, as discussed in Sect.3.1.1.Further statistical analyses were conducted.For absolute differences, Mean Error (ME) and Mean Bias (MB) were utilized.For relative differences, Mean Normalized Gross Error (MNGE), Mean Normalized Bias (MNB), Normalized Mean Error (NME), Normalized Mean Bias (NMB), Mean Fractional Error (MFE), and Mean Fractional Bias (MFB) were used.The Pearson correlation coefficient (R) and index of agreement (IOA) Discussion Paper | Discussion Paper | Discussion Paper | x emissions: Case 2 Although both the Ω NO 2 ,CMAQ,AK and Ω NO 2 ,OMI became much more comparable with each other after the applications of the AKs as shown in Figs. 5 and 6, this comparison Discussion Paper | Discussion Paper | Discussion Paper | were found over other regions of China (CEC2, SC, and SB) than those shown in Fig. 6b in terms of errors and biases.Collectively, it appears that the monthly variations of the OMI observations are better captured by the CMAQ model simulations using the monthly variations of the INTEX-B inventory than those from Han et al. (2009).Further detailed analyses over the eight focus regions were carried out, and the scatter plots and statistical analyses are presented in Supplement Figs.S1 and S2 of the supplementary materials.Although it can be concluded that the monthly variation of the INTEX-B NO x inventory appears to be more realistic, the monthly variations in the NO x emissions in China are important, though still uncertain.Accurate information on this important issue should be examined in future comparison studies.Discussion Paper | Discussion Paper | Discussion Paper | (ii) both NO x emission inventories (INTEX-B and REAS) show underestimation over the CEC region and the Hong Kong area, and (iii) accurate spatial distributions of NO x emissions and the strength of NO x emissions are important factors to reduce the degree of disagreement between the CTM-estimated and satellite-retrieved NO 2 Discussion Paper | Discussion Paper | Discussion Paper | N 2 O 5 has been a controversial issue.In this study, five Ω NO 2 ,CMAQ,AK from the CMAQ model simulations with five different γ N 2 O 5 parameterizations were compared with the Ω NO 2 ,OMI over East Asia.These five parameterizations are from the works of: (i)Dentener and Crutzen (1993), (ii)Riemer et al. (2003), (iii) a combination ofRiemer et al. (2003) andEvans and Jacob (2005), (iv)Davis et al. (2008), and (v)Brown et al. (2006).The mathematical expressions for these parameterizations are summarized briefly in Table3.In theDentener and Crutzen's parameterization (1993), they used a fixed value of γ N 2 O 5 of 0.1 in their global CTM simulation (Scheme I in Table3).InRiemer et al.'s parameterization (2003), γ N 2 O 5 is a main function of the acidity of the particles (Scheme II).In the combined parameterization of Evan andJacob (2006)  and Discussion Paper | Discussion Paper | Discussion Paper | Riemer et al. (2003), γ N 2 O 5 is a function of relative humidity (RH), temperature, and the acidity of the particles (Scheme III, standard scheme).In Davis et al. (2008)'s parameterization, γ N 2 O 5 is a function of all the factors, such as RH, temperature, the acidity of the particles, and the mixing state (Scheme IV).Finally, for Brown et al. (2006)'s parameterization, we used a fixed minimum value of γ N 2 O 5 of 10 −3 in the CMAQ model simulation (Scheme V).The comparison results are presented in Fig. 11.As shown in Fig. 11 and Table 4, the Ω NO 2 ,CMAQ,AK with the Brown et al. (2006) parameterization were 19 % larger than those with the standard Scheme (III) over East Asia.This indicates that Brown et al.'s parameterization resulted in the smallest NO x loss rates (or nitrate formation rates) via this physico-chemical reaction pathway.In contrast, the application of the Dentener and Crutzen's parameterization to the CMAQ model simulation produced the smallest Ω NO 2 ,CMAQ,AK in East Asia, indicating the fastest NO x loss rates, due to the large γ N 2 O 5 .These results suggest that Brown et al.'s γ N 2 O 5 (0.001) may be smaller than the real value, while Dentener and Crutzen's γ N 2 O 5 (0.1) is probably larger.Other than Brown et al.'s and Dentener and Crutzen's parameterizations, it was found that there was almost no significant or practical difference in the Ω NO 2 ,CMAQ,AK among the other three Schemes, II, III, and IV (also, refer to Table Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | in the winter.Because other possible uncertainty factors still exist, as discussed in Sect.3.2.4,further analyses are necessary in future studies.
Han et al. (2009)ges ranged from −31.15 % to 66.03 % over China, when the monthly factors fromHan et al. (2009)were used.However,Han  et al.'s monthly variations (2009)resulted in even larger discrepancies between Ω NO 2 ,CMAQ,AK and Ω NO 2 ,OMI over several regions in China.-AsshowninTable 5, when REAS inventory data over China were used in the CMAQ model simulations, the Ω NO 2 ,CMAQ,AK were lower by −31.50 % to −58.33 % over China than those from the case with the INTEX-B inventory.Based on this, the NO x emissions from the REAS NO x emissions appeared to be more underestimated over China than the INTEX-B NO x emissions.-In the sensitivity test of γ N 2 O 5 , almost no difference in the Ω NO 2 ,CMAQ,AK was found among the conventional γ N 2 O 5 parameterizations.However, the Ω NO 2 ,CMAQ,AK from Brown et al. (2006)'s parameterization were 18.25 % larger over East Asia than the Ω NO 2 ,CMAQ,AK from the combined parameterization of Riemer et al. (2003)

Table 2 .
Average tropospheric NO 2 columns, standard deviations, and the normalized mean error (NME) with and without the application of AKs for four seasons.

Table 4 .
Average tropospheric NO 2 columns, standard deviations and the ratios of the Ω NO 2 ,CMAQ,AK to the Ω NO 2 /OMI , when different γ N 2 O 5 parameterizations were applied to the CMAQ model simulations for January.