A top-down assessment using OMI NO2 suggests an underestimate in the NOx emissions inventory in Seoul, South Korea during KORUS-AQ

Energy Systems Division, Argonne National Laboratory, Argonne, IL 60439 USA Consortium for Advanced Science and Engineering, University of Chicago, Chicago, IL 60637, USA Department of Atmospheric and Oceanic Sciences, Institute of the Environment and Sustainability, University of 10 California – Los Angeles, Los Angeles, CA 90095, USA Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, MD 21046, USA NASA Goddard Space Flight Center, Code 614, Greenbelt, MD 20771, USA Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO 63108, USA 15 Konkuk University, 05029 Seoul, South Korea School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA Department of Chemical and Biochemical Engineering, University of Iowa, Iowa City, IA 52242, USA Correspondence to: Daniel L. Goldberg (dgoldberg@anl.gov) 20 Abstract. In this work, we investigate the NOX emissions inventory in Seoul, South Korea using a regional Ozone Monitoring Instrument (OMI) NO2 product derived from the standard NASA product. We first develop a regional OMI NO2 product by re-calculating the air mass factors using a high-resolution (4 × 4 km) WRF-Chem model simulation, which better captures the NO2 profile shapes in urban regions. We then apply a model-derived spatial averaging kernel to further downscale the retrieval and account for the sub-pixel variability. These two modifications 25 yield OMI NO2 values in the regional product that are 1.37 larger in the Seoul metropolitan region and >2 times larger near substantial point sources. These two modifications also yield an OMI NO2 product that is in better agreement with the Pandora NO2 spectrometer measurements acquired during the Korea U.S.-Air Quality (KORUS-AQ) field campaign. NOX emissions are then derived for the Seoul metropolitan area during the KORUS-AQ field campaign using a top-down approach with the standard and regional NASA OMI NO2 products. We first apply the top-down 30

larger effect on the AMF calculation than modifications to the Korean emissions inventory. Although the current work is focused on South Korea using OMI, the methodology developed in this work can be applied to other world regions using TROPOMI and future satellite datasets (e.g., GEMS and TEMPO) to produce high-quality regionspecific top-down NOX emission estimates.

Introduction
Nitrogen oxides (NOX ≡ NO+NO2) are a group of reactive trace gases that are toxic to human health and can be converted in the atmosphere into other noxious chemical species. In the presence of abundant volatile organic compounds and strong sunlight, NOX can participate in a series of chemical reactions to generate a net accumulation of O3, another toxic air pollutant with a longer atmospheric lifetime. NOX also participates in a series of reactions to 5 create HNO3, a key contributor to acid rain, and particulate nitrate (NO3 -), a component of fine particulate matter (PM2.5), an additional health hazard. There are some natural emissions of NOX (e.g., lightning), but the majority of the NOX emissions are from anthropogenic sources (van Vuuren et al., 2011).
There is a rich legacy of NO2 measurements by remote sensing instruments (Burrows et al., 1999). One of these instruments is the Dutch-Finnish Ozone Monitoring Instrument (OMI), which measures the absorption of solar 10 backscatter in the UV-visible spectral range. NO2 can be observed from space because it has strong absorption features within the 400 -465 nm wavelength region (Vandaele et al., 1998). By comparing observed spectra with a reference spectrum, the amount of NO2 in the atmosphere between the instrument in low-earth orbit and the surface can be derived; this technique is called differential optical absorption spectroscopy (DOAS) (Platt, 1994).
With a pixel resolution varying from 13 × 24 km 2 to 26 × 128 km 2 , the OMI sensor was developed for global to regional scale studies rather than for individual urban areas. Even at the highest spatial resolution of 13 × 24 km 2 , the sensor has difficulty observing the fine structure of NO2 plumes at or near the surface (e.g., highways, power plants, 25 factories, etc.) (Chen et al., 2009;Ma et al., 2013;Flynn et al., 2014), which are often less than 10 km in width (Heue et al., 2008). This can lead to a spatial averaging of pollution (Hilboll et al., 2013). A temporary remedy, until higher spatial resolution satellite instruments are operational, is to use a regional air quality simulation to estimate the subpixel variability of OMI pixels. Kim et al. (2016) utilize the spatial variability in a regional air quality model to spatially downscale OMI NO2 measurements using a spatial averaging kernel. The spatial averaging kernel technique 30 has shown to increase the OMI NO2 signal within urban areas, which is in better agreement with observations in these regions (Goldberg et al., 2017).
Furthermore, the air mass factor and surface reflectance used in obtaining the global OMI NO2 retrievals are at a coarse spatial resolution (Lorente et al. 2017;Kleipool et al., 2008). While appropriate for a global operational retrieval, this is known to cause an underestimate in the OMI NO2 signal in urban regions (Russell et al., 2011). The air mass factors in the operational OMI NO2 retrieval are calculated using NO2 profile shapes that are provided at a 1.25° × 1° spatial resolution in the NASA product (Krotkov et al. 2017) and 2° × 3° spatial resolution in the DOMINO product (Boersma et al., 2011). Developers of the NASA product provide scattering weights and additional auxiliary information so that users can develop their own tropospheric vertical column product a posteriori .

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Several users have re-calculated the air mass factor using a regional air quality model (Russell et al., 2011;Kuhlmann et al, 2015;Lin et al., 2015;Goldberg et al., 2017;Laughner et al., 2019), which can better capture the NO2 profile shapes in urban regions. Other techniques to improve the air mass factor involve correcting for the surface pressure in mountainous terrain  and accounting for small-scale heterogeneities in surface reflectance (Zhou et al., 2010;Vasilkov et al., 2017). These a posteriori products have better agreement with ground-based spectrometers 10 measuring tropospheric vertical column contents (Goldberg et al., 2017). When available, observations from aircraft can constrain the NO2 profile shapes used in the air mass factor calculation (Goldberg et al., 2017).
In this paper, we apply both techniques (the spatial averaging kernel and an air mass factor adjustment) to develop a regional OMI NO2 product for South Korea. We then use the regional product with only the air mass factor adjustment to derive NOX emission estimates for the Seoul metropolitan area using a statistical fit to an exponentially modified Gaussian (EMG) function (Beirle et al., 2011;Valin et al., 2013;de Foy et al., 2014;Lu et al., 2015); the methodology is described in-depth in Section 2.5.

OMI NO2
OMI has been operational on NASA's Earth Observing System (EOS) Aura satellite since October 2004 (Levelt et 20 al., 2006). The satellite follows a sun-synchronous, low-earth (705 km) orbit with an equator overpass time of approximately 13:45 local time. OMI measures total column amounts in a 2600 km swath divided into 60 unequal area "field-of-views", or pixels. At nadir (center of the swath), pixel size is 13 × 24 km 2 , but at the swath edges, pixels can be as large as 26 × 128 km 2 . In a single orbit, OMI measures approximately 1650 swaths and achieves daily global coverage over 14 -15 orbits (99 minutes per orbit). Since June 2007, there has been a partial blockage of the 25 detector's full field of view, which has limited the number of valid measurements by blocking consistent rows of data; The tropospheric AMF has been derived to be a function of the optical atmospheric/surface properties (viewing and solar angles, surface reflectivity, cloud radiance fraction, and cloud height) and a priori profile shape (Palmer et al., 5 2001;Martin et al., 2002) and can be calculated as follows  in Eq. (2): where x is the partial column. The optical atmospheric/surface properties in the NASA retrieval are characterized by the scattering weight and are calculated by a forward radiative transfer model (TOMRAD), which are output as a lookup table. The scattering weights are then adjusted real-time depending on observed viewing angles, surface albedo, 10 cloud radiance fraction, and cloud pressure.
We follow previous studies (e.g., Palmer et al., 2001, Martin et al., 2002, Boersma et al., 2011, Bucsela et al., 2013 and assume that scattering weights and trace gas profile shapes are independent. The a priori trace gas profile shapes (xa) must be provided by a model simulation. In an operational setting, NASA uses a monthly-averaged and yearspecific Global Model Initiative (GMI) global simulation with a spatial resolution of 1.25° lon × 1° lat (~110 km × 15 110 km in the mid-latitudes) to provide the a priori profile shapes. For this study, we derive tropospheric VCDs using a priori NO2 profile shapes from a regional WRF-Chem simulation. A full description of this methodology can be found in Goldberg et al. (2017); it is also described in brief in section 2.1.1. We filter the Level 2 OMI NO2 data to ensure only valid pixels are used. Daily pixels with solar zenith angles ≥ 80°, cloud radiance fractions ≥ 0.5, or surface albedo ≥ 0.3 are removed as well as the five largest pixels at the swath edges (i.e., pixel numbers 1 -5 and 56 -60).

OMI-WRF-Chem NO2
We modify the air mass factor in the OMI NO2 retrieval based on the vertical profiles from a high spatial (4 × 4 km 2 ) resolution WRF-Chem simulation. The vertical profiles are scaled based on a comparison with in situ aircraft if the aircraft observations during the campaign show that mean NO2 concentrations between 0 -500 m are low by 50%, then we scale all of the modeled NO2 in this altitude bin by this same amount. To re-calculate the air mass factor for each OMI pixel, we first compute sub-pixel air mass factors for each WRF-Chem model grid cell, using the same method as outlined in Goldberg et al. (2017). The sub-pixel air mass factor for each WRF-Chem grid cell is a function of the modelled NO2 profile shape and the scattering weight calculated by a radiative transfer model. We then average all sub-pixel air mass factors within an OMI pixel (usually 10-100) to generate a single tropospheric air mass factor for each individual OMI pixel. This new air mass factor is used to convert the total slant column into a total vertical column using Equation 1. Model outputs were sampled at the local time of OMI overpass. For May 2016, we used 5 daily NO2 profiles and terrain pressures (e.g., , Laughner et al., 2016) to re-calculate the AMF. For other months and years, we used May 2016 monthly mean values of NO2 and tropopause pressures for the a priori profiles, which are used in the calculation of the AMF.
Once the tropospheric vertical column of each OMI pixel was re-calculated, the product was oversampled (de Foy et al., 2009;Russell et al., 2010) for April -June over a 3-year period (2015-2017; 9 months total). During this timeframe, there are approximately 9 valid OMI NO2 pixels per month over any given location on the Korean peninsula. In the top-down emissions derivation, we use all nine-months of OMI data for the analysis.

NO2 observations during KORUS-AQ
We use in situ NO2 observations from the KORUS-AQ field campaign to test the regional satellite product. KORUS-AQ was a joint Korea-US field experiment designed to better understand the trace gas and aerosol composition above

Pandora NO2 data
Measurements of total column NO2 from the Pandora instrument (Herman et al., 2009) are used to evaluate the OMI 25 NO2 satellite products. The Pandora instrument is a stationary, ground-based, sun-tracking spectrometer, which measures direct sunlight in the UV-Visible spectral range (280-525 nm) with a sampling period of 90 seconds. The Pandora spectrometer measures total column NO2 using a DOAS technique similar to OMI. A distinct advantage of the Pandora instrument is that it does not require complex assumptions for converting slant columns into vertical columns, compared to zenith sky measurements (e.g., MAX-DOAS). The spatial and temporal variation of NO2 30 columns in Korea as observed by Pandora has been documented elsewhere (Chong et al., 2018;Herman et al., 2018).
In our comparison, valid OMI NO2 pixels are matched spatially and temporally to Pandora total column NO2 observations. To smooth the data and eliminate brief small-scale plumes that would be undetectable by a satellite, we average the Pandora observations over a two hour period (± one hour of the overpass time) before matching to the OMI NO2 data (Goldberg et al., 2017). During May 2016, there were seven Pandora NO2 spectrometers operating during the experiment (five instruments were situated within the Seoul metropolitan area and their locations are shown in Figure 5); this corresponded to fifty instances in which valid Pandora NO2 observations matched valid OMI NO2 column data.

DC-8 aircraft data
We compare the model simulation to in situ NO2 data gathered by the UC-Berkeley Cohen group (Thornton et al., 2000;Day et al, 2002) on the DC-8 aircraft. The instrument quantifies NO2 via laser-induced fluorescence at 585 nm.
This instrument does not have the same positive bias as chemiluminescence NO2 detectors, so there is no need to modify NO2 concentrations by applying an empirical equation (e.g., Lamsal et al., 2008). We also compare the model 10 simulation to chemiluminescence NOy data gathered by the NCAR Weinheimer group (Ridley et al., 2004) We utilize one-minute averaged DC-8 data from all fourteen flights during May -June 2016. A typical flight path included several low-altitude spirals over the Seoul Metropolitan Area and a long-distance transect over the Korean peninsula or the Yellow Sea. One-minute averaged data is already pre-generated in the data archive. Hourly output from the model simulation is spatially and temporally matched to the observations. We then bin the data into different 15 altitude ranges for our comparison.

WRF-Chem model simulation
For the high-resolution OMI NO2 product, we use a regional simulation of the Weather Research & Forecasting (Skamarock et al., 2008) coupled to Chemistry (WRF-Chem) (Grell et al., 2005)  WRF-Chem was configured with 4 bin MOSAIC aerosols (Zaveri et al., 2008), a reduced hydrocarbon trace gas chemical mechanism (Pfister et al., 2014) including simplified secondary organic aerosol formation (Hodzic and Jimenez, 2011), and with capabilities to assimilate satellite aerosol optical depth both from low-earth orbiting and geostationary satellites (Saide et al., 2013(Saide et al., , 2014.

Emission Inventory
The

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Emissions were first processed to the monthly time-scale at a spatial resolution of 3 km in South Korea and 0.1° for the rest of Asia using SMOKE-Asia (Woo et al., 2012). Information from GIS, such as population, road network, and land cover, were applied to generate gridded emissions from the region-based (17 metropolitan and provincial boundaries of South Korea) emissions. The GIS-based population and regional boundary data compiled by the

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An exponentially modified Gaussian (EMG) function is fit to a collection of NO2 plumes observed from OMI in order to determine the NO2 burden and lifetime from the Seoul metropolitan area. The original methodology, proposed by Beirle et al. (2011), involves the fitting of OMI NO2 line densities to an EMG function. OMI NO2 line densities are the integral of OMI NO2 retrieval perpendicular to the path of the plume; the units are mass per distance. We define integration length scale as the across plume width. The across plume width is dependent on the NO2 plume size and can vary between 10 km (for small point sources) to 240 km (for large urban areas). Visual inspection of the rotated oversampled OMI NO2 plumes is the best way to determine the spatial extent of the emission sources ).
The EMG model is expressed as Equation (3): where α is the total number of NO2 molecules observed near the hotspot, excluding the effect of background NO2, β; xo is the e-folding distance downwind, representing the length scale of the NO2 decay; µ is the location of the apparent source relative to the city center; σ is the standard deviation of the Gaussian function, representing the Gaussian smoothing length scale; Φ is the cumulative distribution function. Using the 'curvefit' function in IDL, we determine 10 the five unknown parameters: α, xo, σ, µ, β based on the independent (distance; x) and dependent (OMI NO2 line density) variables. Using The NO2 plume concentration is a function of the emission source strength, wind speed, and wind direction. Originally, the method separated all NO2 plumes by wind direction, and fit an EMG function to NO2 in eight wind directions (Beirle et al., 2011;Ialongo et al., 2014;Liu et al., 2016). Newer methodologies rotate the plumes so that all plumes 25 are in the same direction (Valin et al., 2013;de Foy et al., 2014;Lu et al., 2015). This process increases the signal-tonoise ratio and generates a more robust fit. In this work, we filter OMI NO2 data and rotate the NO2 plumes and as described in Lu et al. (2015), so that all plumes are decaying in the same direction. We rotate the retrieval based on the re-analyzed 0-500 m wind speed direction from the ERA-Interim. In doing so, we develop a re-gridded satellite product in an x-y coordinate system, in which the urban plume is aligned along the x-axis. Following de Foy et al. emission estimates. We fit an EMG function to the line density as function of the horizontal distance. This yields a single value at each point along the x-direction.

Results
In this section, we describe the regional high-resolution satellite product and our validation efforts. All OMI NO2 results presented here are vertical column densities. First, we show a continental snapshot of OMI NO2 (OMI-Standard) over East Asia using the standard NASA product. Then, we show a regional NASA OMI NO2 satellite product (OMI-Regional) using AMFs generated from the WRF-Chem a priori NO2 profiles. We compare the OMI-Regional product with NO2 VCDs from the original WRF-Chem simulation. We evaluate the OMI-Regional product by comparing to KORUS-AQ observations. Finally, we use the OMI-Standard and OMI-Regional products to estimate NOX emissions from the Seoul metropolitan area.

Calculation of new OMI tropospheric column NO2
In Figure 2, we plot the OMI-Standard and OMI-Regional products over South Korea. The left panels are identical 20 and show the OMI-Standard product for Apr -Jun 2015 -2017. The top center panel shows a regional product in which only the air mass factor correction is applied (AMF). The bottom center panel shows a regional product in which the air mass factor correction and spatial averaging kernel are applied (AMF+SK). The regional product yields larger OMI NO2 values throughout the majority of the Korean peninsula. Areas near major cities (e.g. Seoul), power plants, steel mills, and cement kilns have OMI NO2 values that are >1.25 times larger in the regional AMF product 25 and >2 times larger in the regional AMF+SK product. There are two reasons for the larger OMI NO2 signals: the air mass factors in polluted regions are now smaller (Russell et al., 2011;Goldberg et al., 2017) and the spatial weighting kernel allocates a large portion of the OMI NO2 signal into a smaller region (Kim et al., 2016).

OMI-Regional vs. WRF-Chem
We now compare the OMI-Regional product to tropospheric vertical columns from the WRF-Chem model simulation 30 directly. In Figure 3, we compare the regional satellite product (AMF+SK) to the WRF-Chem simulation over the Korean peninsula. In most areas, the modeled tropospheric column NO2 is of smaller magnitude than inferred by the satellite. In the area within 40 km of the Seoul city center, modeled tropospheric vertical columns are 44% smaller than observed tropospheric vertical column in the regional AMF+SK product. We posit four reasons as to why the model simulation calculates columns that are consistently smaller. First, our WRF-Chem simulation uses a reduced hydrocarbon gas-phase chemical mechanism. This fast-calculating mechanism implemented in WRF-Chem for regional climate assessments (Pfister et al., 2014) and used during KORUS-AQ for forecasting does not quickly 5 recycle alkyl nitrates back to NO2; this will cause NO2 to be too low. While an underestimate of the chemical conversion to NO2 in WRF-Chem is a contributor to the underestimate, it likely does not account for the entire discrepancy; Canty et al., (2015) suggests that by shortening the lifetime of alkyl nitrates in the chemical mechanism, NO2 will increase by roughly 3% in urban areas and 18% in rural areas. Second, an underestimate in VOC emissions would have an impact on peroxyacyl and alkyl nitrate formation, and should enhance the effective NOX lifetime

Comparing WRF-Chem to Aircraft Measurements
When comparing the model simulation to in situ observations from the UC-Berkeley NO2 instrument aboard the aircraft, we find that NO2 concentrations are substantially larger than the model when spatially and temporally collocated in the immediate Seoul metropolitan area ( Figure 5). The comparison isolates the NO2 within the lowermost boundary layer as the primary contributor to the tropospheric column underestimate. When comparing aircraft NO2

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to modeled NO2 in other areas of the Korean peninsula, the underestimate is smaller.
When comparing the model simulation of NOy to observations of the same quantity observed from the aircraft, we find a similarly large underestimate. NOy observed on the aircraft is roughly a factor of two larger at all altitudes below 2 km. This suggests that errors in NO2 recycling (NO2 ↔ NOy) are not the main cause of the NO2 discrepancies seen in the satellite and aircraft comparison (also see Figure 9). Instead, there must be errors in the NOy production 30 (i.e., NOX emission rates are too low) or removal rates (i.e., NOy deposition rates are too slow).

Comparison of OMI NO2 to Pandora NO2
To quantify the skill of the regional OMI NO2 product, we compare the new total NO2 vertical columns from the satellite product to the same quantities observed by the Pandora instruments. In Figure 6, monthly averaged observations during May 2016 from the Pandora instrument are overlaid onto the monthly average of the three OMI NO2 satellite products. The two regional OMI NO2 products capture the magnitude and spatial variability of monthly averaged NO2 within the metropolitan region better.
We then compare daily Pandora observations to each daily OMI NO2 value spatially and temporally collocated with the Pandora instrument ( Figure 6). The Pandora observation is a 2-hour mean centered on the mid-afternoon OMI 5 overpass. The slope of the linear best-fit of the standard product is 0.58, indicating that there is a consistent low bias in the satellite product when the Pandora instrument observes large values. A similar result was also found by Herman et al. (2018). The best-fit slope of the OMI-Regional product with only the air mass factor adjustment (AMF) is 0.76, and the OMI-Regional product with the air mass factor adjustment and spatial kernel (AMF+SK) is 1.07, indicating that the regional products capture the polluted-to-clean spatial gradients best. The correlation of daily observations to the satellite retrievals does not improve between retrievals (OMI-Standard: r 2 = 0.57, OMI-Regional (AMF): r 2 = 0.57, and OMI-Regional (AMF+SK): r 2 = 0.58). The lack of improvement in the correlation suggests that the forecasted WRF-Chem simulation is unable to capture the daily variability of NO2 plumes better than a longer-term average.

Estimating NOX emissions from Seoul
To estimate NOX emissions from the Seoul metropolitan area using a top-down satellite-based approach, we follow the exponentially modified Gaussian (EMG) fitting methodology outlined in Section 2.5. When fit using the EMG method, the photochemical lifetime and OMI NO2 burden can be derived. Using this information, a NOX emission rate can be inferred.

Validating the EMG method using WRF-Chem
The WRF-Chem simulation can serve as a test bed to assess the accuracy of the EMG method, since the bottom-up 20 emissions used for the simulation are known. For this study, we find that for Seoul, an across plume width of 160 km encompasses the entire NO2 downwind plume. Using the NO2 lifetime, NO2 burden, and a 160 km across plume width, we calculate the top-down NOX emissions rate in the WRF-Chem simulation from the Seoul metropolitan area during the early afternoon (Figure 7). We find the effective NO2 photochemical lifetime to be 3.1 ± 1.3 hours and the emissions rate to be 227 ± 94 kton/yr NO2 equivalent. Uncertainties of the top-down NOX emissions are the square 25 root of the sum of the squares of: the NOX / NO2 ratio (10%), the OMI NO2 vertical columns (25%), the across plume width (10%), and the wind fields (30%) . Only the latter three terms are used to calculate the uncertainty of the NO2 lifetime .
The NOX bottom-up emissions inventory calculated using a 40 km radius from the Seoul city center is 198 kton/yr NO2 equivalent. We use a 40 km radius in lieu of a larger radius because an assumption in EMG method is that the 30 emissions must be clustered around a single point (in this case, the city center). Therefore, the calculated emissions rate from the EMG fit is only measuring the magnitude of the perturbing emission source, and not of smaller sources that are further from the city center. Previous studies (de Foy et al., 2014;de Foy et al., 2015) suggest that the background level calculated by the EMG fit accounts for emissions outside the plume that are more regional and diffuse in nature. The agreement between the top-down (227 kton/yr) and bottom-up (198 kton/yr) approaches demonstrates the accuracy and effectiveness of the EMG method in estimating the emissions rate.

Deriving emissions using OMI NO2
We now calculate the top-down NOX emissions rate from the satellite data from the Seoul metropolitan area during the early afternoon (Figure 8). Here we use the OMI standard product and the OMI NO2 retrieval without the spatial averaging kernel; only the new air mass factor is applied to this retrieval. We do not use the retrieval with the spatial averaging kernel when calculating top-down NOX emissions because the spatial averaging is strongly dependent on the wind fields in the WRF-Chem simulation, which are forecasted. Errors in the winds can greatly affect the estimate using this top-down approach (Valin et al., 2013;de Foy et al., 2014).

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For the standard product, the effective NO2 photochemical lifetime is 4.2 ± 1.7 hours, while in the regional product, the effective lifetime is 3.4 ± 1.4 hours. In the standard product, we derive the NOX emissions rate to be 353 ± 146 kton/yr NO2 equivalent, while in the regional product it is 484 ± 201 kton/yr NO2 equivalent. Emission estimates using satellite products with coarse resolution air mass factors will yield top-down emission estimates that are lower than reality. In this case, the regional satellite product yields NOX emission rates that are 37% higher; we would 15 expect similar results from other metropolitan regions. The top-down approach for the model simulation yielded a NOX emission rate of 227 kton/yr, while the top-down approach using the satellite data yielded a 484 kton/yr NOX emission rate: a 53% underestimate in the emissions inventory.
It should be noted that the NO2 photochemical lifetime derived here is a fundamentally different quantity than the NO2 lifetime observed by in situ measurements (de Foy et al., 2014;Lu et al., 2015) or derived by model simulations 20 (Lamsal et al., 2010). This is because the lifetime calculation is extremely sensitive to the accuracy of the wind direction (de Foy et al., 2014) and spatial pattern of the emissions. Inaccuracies in the wind fields introduce noise that shorten the tail of the fit. As a result, NO2 photochemical lifetimes derived here are considered "effective" photochemical lifetimes and are generally shorter than the tropospheric column NO2 lifetimes derived by model simulations (Lamsal et al., 2010). NOx sources at the outer portions of urban areas will lead to an artificially longer 25 NO2 lifetime. This partially compensates for the bias introduced by the wind direction. The heterogeneous topography and oscillating thermally driven wind flows (such as the Yellow Sea breeze) in the Seoul metropolitan area are effects that may bias the effective photochemical lifetime calculation. We partially account for this bias by only selecting days with strong winds (>3 m/s); on days with faster winds speeds, the sea & mountain breeze effects are secondary to the synoptic flow.

Model simulation with increased NOX emissions
To test whether an increase in the NOX emission rate is appropriate for the Seoul metropolitan area, we conduct a simulation with NOX emissions in the Seoul metropolitan areawithin a 40 km radius of the city centerincreased by a factor of 2.13, and analyze the results for May 2016. The 2.13 increase is representative of the change suggested by the top-down method (OMI-Regional: 484 kton/yr vs. WRF-Chem original: 227 kton/yr). This simulation was performed slightly differently than the original simulation in that it was a continuous month-long simulation and the outer domain was nudged to the reanalysis.
When comparing the new model simulation to in situ observations from the UC-Berkeley NO2 and NCAR NOy 5 instruments aboard the DC-8 aircraft, we find that NO2 concentrations are a bit high, but NOy concentrations are in good agreement with WRF-Chem in the boundary layer when spatially and temporally collocated in the immediate Seoul metropolitan area (Figure 9). The NO2-NOy partitioning is captured well by both model simulations, and there is no significant change in the NO2-NOy ratio when using increased NOX emissions.
When comparing the new WRF-Chem simulation to the OMI-Regional product for May 2016 (Figure 10), we now 10 find no significant biases in the Seoul metropolitan area. In the area within 40 km of the Seoul city center, NO2 columns are now only 11% smaller in the new model simulation. The better agreement in NO2 and NOy from a combination of aircraft and satellite data suggests that an increase in NOX emissions by a factor of 2.13 is appropriate.
Finally, we re-process the air mass factors for May 2016 using the newest WRF-Chem simulation. In Figure 11, we show the OMI-Standard product, the OMI-Regional product with no scaling of the a priori profiles from the original 15 WRF-Chem simulation, the OMI-Regional product with scaling of the original a priori profiles, and the OMI-Regional product with a priori profiles from the new WRF-Chem simulation. While using the new a priori profiles increases the OMI NO2 retrieval further by 8%, this change is much smaller than the 37% increase associated with switching models and model resolution (i.e., Standard vs. Regional product).

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In this work, we use a high-resolution (4 × 4 km 2 ) WRF-Chem model simulation to re-calculate satellite NO2 air mass factors over South Korea. We also apply a spatial averaging kernel to better account for the sub-pixel variability that cannot be observed by OMI. The regional OMI NO2 retrieval yields increased tropospheric columns in city centers and near large industrial areas. In the area within 40 km of the Seoul city center, OMI NO2 values are 1.37 larger in the regional product. to the WRF-Chem model simulation, we find similar underestimates of NO2 in the Seoul metropolitan area. The effective photochemical lifetime derived in the Seoul plume is 4.2 ± 1.7 hours using the standard OMI NO2 product and 3.4 ± 1.4 hours using the regional product. The regional product yields shorter NO2 lifetimes, although it is not a statistically significant difference. Finally, we show that a WRF-Chem simulation with an increase in the NOX emissions by a factor of 2.13 yields a better comparison with aircraft observations of NO2 and NOy, and is in better agreement with the OMI-Regional NO2 product developed herein.

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It should be noted that the Seoul metropolitan area has complex geographical features, which adds further uncertainty to this analysis. The area has large topographical changes over short distances, including many hills (> 500 m) within the metropolitan area. Furthermore, the city is in close proximity to the Yellow Sea, which causes the area to be affected by sea breeze fronts, especially in the springtime, which is our period of focus. The localized mountain and sea breezes may not be fully captured by our 4 × 4 km 2 WRF-Chem simulation used to derive the OMI-Regional product or the ERA-interim dataset used to calculate top-down NOX emissions. The effects of these features on local air quality have been documented elsewhere in the literature (Kim and Ghim, 2002;Lee et al., 2008;Ryu et al., 2013).
Nevertheless, the 4 × 4 km 2 simulation will capture topography and mesoscale phenomena better than a coarse global model and further supports the benefits of WRF-Chem over a global model to derive NO2 vertical column contents.
We hypothesize that the temporal allocation of NOX emissions in the bottom-up emission inventory is a large 15 remaining uncertainty. The satellite-derived emission rates are instantaneous rates at the time of the OMI overpass (~13:45 local time). This is a different quantity than a bottom-up NOX emission inventory, which is often a daily averaged or monthly averaged emission rate. For this study, we only attempt to derive a mid-afternoon NOX emission rate. Subsequently, we make sure to compare this to the mid-afternoon NOX emission rate from WRF-Chem. While bottom-up studies provide estimates of the diurnal variability of NOX emissions, these are very difficult to confirm 20 from top-down approaches. Due to a consistent mid-afternoon overpass time, OMI or TROPOMI cannot address this issue. Due to boundary layer dynamics, this is also very difficult to constrain from ground-based and aircraft measurements. In the future, observations from a geostationary satellite instruments such as the Geostationary Environment Monitoring Spectrometer (GEMS) and Tropospheric Emissions: Monitoring Pollution (TEMPO), will be integral in constraining the ratio of the mid-afternoon emissions rate to the 24-hour averaged emission rate.

Acknowledgments
This publication was developed using funding from the NASA KORUS-AQ science team and the NASA Atmospheric   5 Figure 2. (a) OMI-Standard NO2 product averaged over a 9-month period, Apr -Jun 2015 -2017, (b) the OMI-Regional NO2 product with only the air mass factor adjustment averaged over the same timeframe, and (c) the ratio between the two products. (d) Same as the top left plot, (e) the OMI-Regional NO2 product with the air mass factor adjustment and spatial kernel averaged over the same timeframe, and (f) the ratio between the two products.   from the OMI-Regional product with only the air mass factor adjustment (AMF) during the same timeframe, (c) same quantities from the OMI-Regional product with the air mass factor adjustment and spatial kernel (AMF+SK) during the same timeframe, and (d) a comparison between total column contents from the three OMI NO2 products and 5 Pandora NO2 during May 2016. An average of Pandora 2-hour means co-located to valid daily OMI overpasses are overlaid in the spatial plots.

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NO2 plume rotated based on wind direction for Seoul, Korea from WRF-Chem (4 × 4 km 2 ) for May 2016, and (c) NO2 line densities integrating over the 240 km across plume width (-120 km to 120 km along the y-axis) and the corresponding EMG fit. NOx emission estimates are shown in units of kton/yr NO2 equivalent and represent the midafternoon emissions rate.

Figure 8.
Top panels represent the oversampled (4 × 4 km 2 ) OMI NO2 plume from Seoul rotated based on wind direction over a 9-month period, Apr -Jun 2015 -2017, centered on May 2016. Bottom panels represent the OMI NO2 line densities integrating over the 240 km across plume width (-120 km to 120 km along the y-axis of the top panels) and the corresponding EMG fit. Left panels are from the OMI-Standard NO2 product and right panels are from 5 the OMI-Regional NO2 product. NOx emission estimates are shown in units of kton/yr NO2 equivalent and represent the mid-afternoon emissions rate.  Figure 11. (a) The OMI-Standard product during the month of May 2016, (b) the OMI-Regional NO2 product with the WRF-Chem air mass factor adjustment and spatial kernel during the same period, (c) same as (b) but using WRF-Chem NO2 profiles scaled based on the aircraft comparison, and (d) same as (b) but using the WRF-Chem simulation with NOx in the Seoul metropolitan area emissions increased by a factor of 2.13.