Reconciling the differences between OMI-based and EPA AQS in situ NO2 trends

With the improved spatial resolution than earlier instruments and more than ten years of service, tropospheric NO2 retrievals from the Ozone Monitoring Instrument (OMI) have led to many influential studies on the relationships between socioeconomic activities and NOx emissions. This study focuses on how to improve OMI NO2 retrievals for more reliable trend analysis. We retrieve OMI tropospheric NO2 vertical column densities (VCDs) and obtain the NO2 seasonal trends over 15 the United States, which are compared with coincident in situ surface NO2 measurements from the Air Quality System (AQS) network. The Mann-Kendall method is applied to derive the NO2 seasonal and annual trends for four regions at coincident sites during 2005-2014. The OMI-based NO2 seasonal relative trends are generally biased high compared to the in situ trends by up to 3.7% yr, except for the underestimation in the Midwest and Northeast during Dec-Jan-Feb (DJF). We improve the OMI retrievals for trend analysis by removing the ocean trend, using the MODerate-resolution Imaging Spectroradiometer 20 (MODIS) albedo data in air mass factor (AMF) calculation, and applying a lightning flash filter to exclude lightning affected OMI NO2 retrievals. These improvements result in close agreement (within 0.3% yr) between in situ and OMI-based NO2 regional annual relative trends. Thus, we recommend future studies to apply these procedures to ensure the quality of satellitebased NO2 trend analysis, especially in regions without reliable long-term in situ NO2 measurements. We derive optimized OMI-based NO2 regional annual relative trends using all available data for the West (-2.0%±0.3 yr), the Midwest (-1.8%±0.4 25 yr), the Northeast (-3.1%±0.5 yr), and the South (-0.9%±0.3 yr). The OMI-based annual mean trend over the contiguous United States is -1.5%±0.2 yr. It is a factor of 2 lower than that of the AQS in situ data (-3.9%±0.4 yr); the difference is mainly due to the fact that the locations of AQS sites are concentrated in urban and suburban regions.


Introduction
Nitrogen dioxide (NO2) is an air pollutant. At high concentrations, it aggravates respiratory diseases and can lead to acid rain formation (e.g., Lamsal et al., 2015). It is also a key player to produce another pollutant, ozone (O3), through photochemical reactions in the presence of Volatile Organic Compounds (VOCs) under sunlight. Tropospheric NO2 is emitted both anthropogenically and naturally (e.g., Gu et al., 2016). Anthropogenic fossil fuel combustions and biomass burnings emit 5 mostly nitrogen monoxide (NO) under high temperature, which is later oxidized by O3 into NO2. Major natural NO2 sources include lightning and soils.
Surface NO2 concentrations are regulated by the U.S. Environmental Protection Agency (EPA) through the National Ambient Air Quality Standards (NAAQS). NO2 is measured routinely at the EPA Air Quality System (AQS) sites (Demerjian, 2000). Although the AQS network continually provides valuable hourly NO2 measurements, AQS sites are mostly located in 10 urban and suburban regions, leaving large regions of rural areas unmonitored. Satellite data provide a better spatial coverage than the in situ measurements.
To understand how various factors and the retrieval procedure affect the resulting OMI derived trends and their differences from those derived from the surface AQS measurements, we utilize a regional 3-D chemistry transport model (CTM), a 30 radiative transfer model (RTM), and the Mann-Kendall method (Mann, 1945;Kendall, 1948) to calculate OMI-based NO2 seasonal relative trends during Dec-Jan-Feb (DJF), Mar-Apr-May (MAM), Jun-Jul-Aug (JJA), and Sept-Oct-Nov (SON) (Section 2). To reconcile with the AQS based regional NO2 trends, we find that three procedures are essential to ensure the quality of trend analysis using OMI tropospheric NO2 VCDs, including the ocean trend removal, the MODerate-resolution Imaging Spectroradiometer (MODIS) albedo update in calculating the air mass factors (AMFs), and the lightning filter (Section 3.1). With these procedures implemented, the differences between OMI-based and AQS in situ annual relative trends are within 0.3% yr -1 of coincident measurements for all the four regions. Finally, we estimate the OMI-based annual relative trends across the nation in Section 3.2. Conclusions are given in Section 4. 5

EPA AQS surface NO2 measurements
The in situ surface NO2 measurements from the U.S. EPA AQS network are used in this research. Sites with a continuous measurement gap of more than 50 days are removed and the observations of 140 remaining cites are used (Fig. 1). The AQS chemiluminescent analyzers are equipped with molybdenum converters to measure ambient NO2 concentrations. These 10 analyzers are known to have high biases, since the converters are not NO2 specific and they measure some fractions of peroxyacetyl nitrate, nitric acid and organic nitrates (Demerjian, 2000;Lamsal et al., 2008). In addition to chemiluminescent analyzers, several NO2 specific photolytic instruments were deployed since 2013. By utilizing the data from both chemiluminescent and photolytic measurements at coincident sites during the overpassing time of OMI, we calculate the observed NO2 concentration ratio between both measurements in Fig. S1 in the Supplement. The ratio peaks at 2.3 in June and 15 decreases to 1.3 in November, indicating that the chemiluminescent analyzers overestimate by 27%-132% than photolytic instruments. This finding is in agreement with Lamsal et al. (2008). We correct the chemiluminescent NO2 data by the observed ratio assuming that the inter-annual change is small and the high bias of the chemiluminescent measurements is identical at all sites. This correction may contribute to the differences between in situ and OMI based absolute NO2 trends but do not significantly affect the relative trends (since the correction is canceled out in computing relative trends). In this study, we only 20 examine the relative trends and therefore the analysis results are not affected by the uncertainties in the in situ NO2 measurement corrections.

REAM model
We use a 3-D Regional chEmical trAnsport Model (REAM) in the simulation of NO2 profiles. REAM has widely been used in atmospheric NO2 studies, including vertical transport (Choi et al., 2005;Zhao et al., 2009a;Zhang et al., 2016a), emission 25 inversions (Zhao et al., 2009b;Yang et al., 2011;Gu et al., 2013Gu et al., , 2014Gu et al., , 2016, and regional and seasonal variations (Choi et al., 2008a(Choi et al., , 2008b. The model has a horizontal resolution of 36 km with 30 vertical layers in the troposphere, 5 vertical layers in the stratosphere, and a model top of 10 hpa. In this study, the domain of REAM is about 400 km larger on each side than the contiguous United States (CONUS). Meteorology inputs driving transport process are simulated by the Weather Research and Forecasting model (WRF) assimilations constrained by National Centers for Environmental Prediction Climate Forecast 30 System Reanalysis (NCEP CFSR, Saha et al., 2010) 6-hourly products. The KF-eta scheme is used for sub-grid convective transport in WRF (Kain and Fritsch,1993). We run the WRF model with the same resolution as in REAM but with a domain 10 grids larger on each side than that of REAM. REAM updates most of the meteorology inputs every 30 minutes while those related to convective transport and lightning parameterization are updated every 5 minutes. The chemistry mechanism expands that of a global CTM GEOS-Chem (V9-02) with aromatics chemistry (Bey et al., 2001;Liu et al., 2010Liu et al., , 2012aLiu et al., , 2012bZhang et al., 2017). For consistency, the GEOS-Chem (V9-02) simulation with 2°× 2.5° resolution is used to generate initial and 5 boundary conditions for chemical tracers.  (Choi et al., 2005). 10 NOx production per flash is set to 250 moles NO per flash, and the emissions are distributed vertically following the C-shaped profiles by Pickering et al. (1998). For recent model evaluations of REAM with observations, we refer readers to Zhang et al.

OMI-based NO2 VCDs
We retrieve the tropospheric NO2 VCDs using the tropospheric slant column densities (SCDs) from the Royal Dutch 15 Meteorological Institute (KNMI) Dutch OMI NO2 product (DOMINO v2, Boersma et al., 2011). OMI onboard the Aura satellite was launched in July 2004 and is still active. OMI overpasses the equator at about 13:30 Local Time (LT) and obtains global coverage with a 2600 km viewing swath spanning 60 rows. It has a ground level spatial resolution up to 13 km x 24 km (at nadir). The spatial extent of the OMI pixels will not affect our analysis as we focus on regional trend analysis. SCDs are retrieved by matching a modeled spectrum to an observed top-of-atmosphere reflectance with the Differential Optical 20 Absorption Spectroscopy (DOAS) technique within a fitting window of 405-465nm. The stratospheric portion of SCDs are estimated and subsequently removed with a global CTM TM4 with stratospheric ozone assimilation (Dirksen et al., 2011).
Deriving tropospheric VCDs from the remaining tropospheric SCDs requires the calculation of AMFs. Being an optically thin gas, tropospheric AMF for NO2 can be calculated from AMF for each vertical layer ( ) weighted by NO2 partial VCDs at the corresponding layer ( ) (Boersma et al., 2004), as shown in equation (1). 25 As the vertical distribution of NO2 is usually unknown, we typically substitute by an a priori profile ( , ) from a CTM. is the sensitivity of NO2 SCD to VCD at a given altitude (Eskes and Boersma, 2003), and is computed using the Double Adding KNMI (DAK) RTM (Lorente et al., 2017). As a result, the retrieved tropospheric NO2 VCD computation depends on the a priori NO2 vertical profile, the surface reflectance, the surface pressure, the temperature profile, and the viewing geometry (Boersma et al., 2011). Previous studies have addressed the sources of uncertainties in NO2 retrievals, including surface reflectance resolutions (Russell et al., 2011;Lin et al., 2014), lightning NOx (Choi et al., 2005a;Martin et al., 2007;Bucsela et al., 2010), a priori CTM uncertainties (Russell et al., 2011;Heckel et al., 2011;Lin et al., 2012;Laughner et al., 2016), surface pressure and reflectance anisotropy in rugged terrain (Zhou et al., 2009), cloud and aerosol radiance (Lin et al., 2014(Lin et al., , 2015, and boundary layer dynamics (Zhang et al., 2016a). We find that the NO2 VCD trend analysis is particularly sensitive to the first two factors and we will discuss these in the following sections. 5 AMFs are derived using the pre-computed altitude-dependent AMF lookup table, which is generated by the DAK RTM.
We use the NO2 profiles from REAM, temperature and pressure from CSFR, viewing geometry and cloud information from DOMINO v2 product. We use the REAM results of 2010 to avoid the uncertainty introduced by yearly variation of NO2 profiles. The yearly variations of meteorology and anthropogenic emission changes have little impact in polluted areas on 10 trend analysis results using OMI data . We use the surface reflectance from DOMINO v2 product as default (Kleipool et al., 2008), and update it using a surface reflectance product with a higher temporal resolution (Section 2.3.2). The derived tropospheric NO2 VCD relative trends with default surface reflectance are referred as "Standard".

Ocean trend removal
For trend and other analyses of OMI tropospheric VCDs, the data of anomalous pixels must be removed. The row anomaly 15 initially occurred in June 2007 and subsequently in later years affected rows 26-40 (Schenkeveld et al., 2017). Additional anomalies can be found in some years in rows 41-55. For trend analysis from 2005-2014, we exclude rows 26-55, consistent with our understanding of the row anomaly (Schenkeveld et al., 2017), and following the flagging in the DOMINO v2 data product. In addition, the data of coarse spatial resolution from rows 1-5 and rows 56-60 are also excluded, as suggested by Lamsal et al. (2015). Furthermore, we exclude OMI data with cloud fraction > 0.3 to minimize retrieval uncertainties due to 20 clouds and aerosols (Boersma et al., 2011;Lin et al., 2014). 2b) with minimal marine traffic. This trend reflects the increase in the magnitude of the stripes (step-wise SCD variability from one row to another) in time, which originates from the use of a constant (2005-averaged) solar irradiance reference spectrum in the DOAS spectral fits throughout the mission and the weak increase of noise in the OMI radiance measurements (Boersma, 25 personal communication, 2017;Zara et al., 2018). Fig. 2a shows that there is a positive annual trend of 1.750.45x10 13 molecules cm -2 yr -1 . The ocean trend is insensitive to the region selection in the remote North Pacific (varies within 10%). We only analyze OMI tropospheric column trends over the CONUS for grid cells with 2005-2014 averaged VCDs > 1x10 15 molecules cm -2 , which tends to minimize the effect of the background noise. However, removing this background ocean (absolute) trend has a non-negligible effect in reducing the OMI relative trend (Fig. 1). We refer to such derived (relative) 30 trend data as "Ocean". An alternative method is to subtract monthly SCDs of the remote ocean region from the OMI tropospheric SCD data. Although the end results are essentially the same as the trend removal method, noises are added to the SCD data (Fig. 2a), making it more difficult to understand the effects of the MODIS albedo update and the lightning filter (next sections). We therefore choose to use the (absolute) trend removal method here.

MODIS albedo update
The albedo data used to calculate the in "Standard" and "Ocean" versions of trend analysis are from the DOMINO v2 products, which are the climatology of averaged OMI measurements during 2005-2009 with a spatial resolution of 0.5°×0.5° 5 (Kleipool et al., 2008) and is valid for 440 nm. We recalculate the using the MODIS 16-day MCD43B3 albedo product with 1km spatial resolution, which combines data from both MODIS onboard Aqua and Terra satellites (Schaaf et al., 2002;Tang and Zhang, 2007). Aqua and Terra have an equatorial overpassing time of 13:30 LT and 10:30 LT, respectively. The band 3 (459nm-479nm) is used to match the NO2 fitting window (405nm-465nm). The albedo is spatially integrated to the geometry of OMI pixels and is temporally interpolated to match OMI overpassing dates. In order to maintain the consistency 10 of the DOMINO retrieval algorithm (Boersma et al., 2011), we only use the MODIS data to improve the temporal variations of albedo data used in the retrieval. We scale the MODIS albedo data such that the mean albedo during 2005-2009 is the same as the OMI climatology at 0.5°×0.5°. We recalculate OMI tropospheric VCDs using the MODIS albedo data as described. We recalculate the relative OMI trend and remove the ocean (absolute) trend (Section 2.3.1). We refer to this version of OMI relative trend data as "MODIS". 15

Lightning event filter
Over North America, lightning is a major source of NOx in the free troposphere and its simulations in CTMs are uncertain (e.g., Zhao et al., 2009a;Luo et al., 2017). The large temporospatial variations of lightning NOx make it difficult to compute satellite based NO2 trends by changing the vertical distributions of NO2 affecting the AMF calculation (e.g., Choi et al., 2008b;Lamsal et al., 2010) and the SCD values. Given the difficulty to simulate lightning NOx accurately across different years, we 20 use a lightning filter to remove potential effects of lightning NOx on the basis of the flash rate observations of cloud-to-ground (CG) lightning flash data detected by the National Lightning Detection Network TM (NLDN) (Cummins and Murphy, 2009;Rudlosky and Fuelberg, 2010). NLDN only reports the ground point of a CG lightning flash, while the CG lightning flash can extend horizontally to tens of kilometers. A CG lightning flash can affect the NO2 retrievals not only in the model grid cell where the CG lightning is located but also the nearby model grid cells. The atmospheric lifetime of NOx in the free troposphere 25 can be up to 1 week. Therefore, we exclude the OMI NO2 data within a radius of 90 km of the NLDN-reported CG lightning location (about two model grid cells around the grid cell where the CG lightning is located) for a period of 72 hours after the lightning occurrence. Since lightning usually occur along the track of a thunderstorm, the 90 km radius is more a constraint on lightning NOx effects across the track. The extended period of 72 hours is to ensure that we exclude data affected by lightning NOx. Figure 4 shows the distribution of the number of days of 2005-2014 with lightning detection. The Southwest monsoon 30 and the South regions have more lightning days than the other areas. While there are fewer lightning flashes in the Northeast than the South (Fig. 3), large amounts of lightning NOx can be produced by high flash ratios of severe thunderstorms and they can be transported northward from the South to the Northeast (Choi et al., 2005). We therefore further filter OMI NO2 data in the Northeast on the basis of CG lightning flash rates in the South. If the average CG flash rate in the South exceeds the 95 th percentile value of the NLDN observations, which is 0.035 flash km -2 day -1 (Fig. S2 in the Supplement), we exclude in the analysis the Northeast OMI data in the following 72 hours. Excluding the OMI data based on CG lightning data implicitly removes the data affected by cloud-to-cloud lightning collocated with CG lightning. The lightning filter removes about 2%, 5 27%, 20%, and 19% of OMI data, which are coincident with AQS data, for the West, the Midwest, the Northeast and the South, respectively. We refer to this version of OMI relative trend data as "Lightning filter".

Results and discussion
We group the analysis results into different regions: (a) West, (b) Midwest, (c) Northeast, and (d) South (Fig. 1), following the regional divisions by the United States Census Bureau. To make a fair comparison between the in situ and OMI-based trends, 10 we only use spatially and temporally coincident in situ and OMI NO2 observations in Section 3.1. We apply the Mann-Kendall method with the Sen's slope estimator to calculate the relative trend of NO2 for each season, i.e. DJF, MAM, JJA, and SON, during 2005-2014. We compute the uncertainties of the trends with the 95 th percentile confidence intervals using the Mann-Kendall method. Note that when we compare in situ and OMI-based trends, the lightning filter also removes in situ NO2 data, which are coincident with the OMI NO2 data affected by lightning. This leads to slightly different in situ NO2 trends between 15 Fig. 4 and Fig. 6 (Section 3.2.3). We first compute the trends using the "Standard" OMI VCD data. The ocean trend removal, MODIS albedo update, and lightning filter are then added in sequence to compute three different OMI-based NO2 trends (in addition to "Standard") to compare to the AQS in situ results. A subtlety in the comparison is that the coincident data change when the lightning filter is applied. As a result, the AQS in situ results in this set of comparison differ from those in the other three sets. 20 Fig. 4 shows that both AQS in situ and "Standard" OMI-based seasonal relative trends are negative for all seasons across the regions. OMI-based trends generally underestimate the decreasing trends by up to 3.7% yr -1 (the absolute difference between relative trends) except for the large overestimation in the Midwest and the Northeast regions during DJF. The overestimates in these two regions are 3.0% yr -1 and 1.1% yr -1 , respectively. On average, the differences between OMI-based and in situ 25 seasonal relative trends are 1.6% yr -1 , -0.3% yr -1 , 1.0% yr -1 , and 1.4% yr -1 for the West, the Midwest, the Northeast, and the South regions, respectively. Note that the relative trends are calculated using coincident measurements for the comparisons.

In situ and "Standard" OMI-based trends
The NO2 relative trends from both datasets are expected to be close on a regional basis where surface emissions of NOx dominate the observed surface concentrations and tropospheric VCDs of NO2. The focus of this work is to reconcile the difference between AQS in situ and OMI-based trends, which will be discussed in the following sections. 30

Improvement due to ocean trend correction
After removing the ocean trend as discussed in Section 2.3.1, the OMI-based NO2 decreasing trends are more pronounced as shown in Fig. 4 ("Ocean", blue line) by 0.1-0.9% yr -1 . The regional relative trends have different sensitivities to the ocean trend removal due to different tropospheric VCDs levels. In general, the discrepancies between OMI-based and in situ trends are reduced except for the Midwest and the Northeast regions during DJF, which are already biased low. The averaged 5 differences between OMI-based and in situ seasonal relative trends for the West, the Midwest, the Northeast, and the South regions are 1.2% yr -1 , -1.1% yr -1 , 0.4% yr -1 , and 1.0% yr -1 . Only in the Midwest region, removing the ocean trend enlarges the difference due to the large winter bias.

Improvement due to MODIS albedo update
The adoption of the up-to-date MODIS albedo (Section 2.3.2) greatly reduces the difference of relative trends in the Midwest 10 during DJF from -3.6% yr -1 ("Ocean") to 1.3% yr -1 ("MODIS"), the improvement of DJF trend difference is more moderate from -1.7% to 0.5% (Fig. 4). There are no significant changes of the comparisons in other regions or other seasons. Fig. 5 shows the albedo seasonal relative trends for the 4 regions coincident with AQS in situ NO2 data. The OMI DOMINO v2 incorporates a climatology albedo dataset (Kleipool et al., 2008) with snow/ice albedo adjustment using the NASA Near-realtime Ice and Snow Extent (NISE) dataset (Boersma et al., 2011). The climatology albedo data exhibits no trends. Thus, the 15 trends of albedo mainly originate from the yearly variation of NISE detected snow/ice, followed by OMI sampling variation.
The noticeable seasonal trend of the OMI DOMINO v2 albedo dataset is the 3.9% yr -1 increase in DJF of the Midwest and a smaller DJF increase (1.0%) of the Northeast. In contrast, the MODIS albedo dataset exhibits a smaller positive DJF trend (0.8% yr -1 ), 3.1% yr -1 less than the trend from DOMINO v2, in the Midwest, and a small negative DJF trend (-0.8%) in the Northeast. The comparison to the AQS data shows that the MODIS albedo update leads to better agreement between satellite 20 and in suit trends in winter in these regions (Fig. 4).

Improvement due to lightning filter
As discussed in Section 2.3.3, lightning NOx affects the retrievals of satellite tropospheric NO2 VCDs. Fig. 6 shows that the lightning filter significantly reduces the difference between the OMI-based relative trend and that of the AQS data by 0.5-1.4% yr -1 in the Northeast and 0.9-1.3% yr -1 in the South. As a result, the seasonal trend differences are within 0.9% yr -1 in 25 these two regions except during SON. The lightning filter has little effect on the West and the Midwest. While lightning NOx can be significant during the monsoon season in some regions of the West (Fig. 3), the average tropospheric NO2 VCDs are usually < 1x10 15 molecules cm -2 and lightning affected regions are therefore excluded in trend analysis.
The effect of lightning filter (Fig. 6) cannot be shown in Fig. 4 because the coincident OMI and AQS data points are fewer after applying the lightning filter. We examine the improvements of ocean trend removal, MODIS albedo update, and lightning previously discussed improvements such as OMI albedo update for the Midwest and the Northeast during DJF are shown. By subtracting the AQS trends, we can now find clear improvements of the lightning filter for the South and the Northeast. There remains seasonal variation of OMI-based trend biases relative to in situ data but the discrepancies of the annual trends after the three discussed procedures are relatively small at 0.3% yr -1 , -0.3% yr -1 , -0.1% yr -1 , and 0.0% yr -1 , in the West, the Midwest, the Northeast, and the South regions (Fig. 1), respectively. The remaining seasonal difference of the trends reflects in part the 5 nonlinear photochemistry (Gu et al., 2013) and the effects of NOx emission changes on NO2 retrievals .

OMI-based NO2 trends
Table 1 summarizes the regional annual trends of coincident AQS in situ and OMI data. The "Standard" OMI data (following the DOMINO v2 algorithm) tend to show less NO2 reduction than AQS data. After applying the three corrections discussed in the previous section to the OMI data, the agreement with the AQS trends is within the uncertainties of the trends. While 10 lightning NOx is part of OMI NO2 observations, we treat the influence of lightning on the OMI tropospheric VCD trend as a bias for comparison purposes in this study since AQS data are not as strongly affected by lightning.
Without the lightning filter, AQS decreasing trends are stronger than the decreasing trends of OMI data (Fig. 7). The lightning trend in the NLDN data is unclear due in part to the changing instrument sensitivity (Koshak et al., 2015). If lightning NOx is not accounted for in OMI retrieval, tropospheric NO2 VCDs are overestimated. On the other hand, lightning 15 accompanies low pressure systems which mix the atmosphere vertically and tend to reduce surface NO2 concentrations when anthropogenic emissions are high such as urban and suburban regions. Therefore, lightning has opposite effects on surface and satellite trends. The low-pressure dilution effect on surface NO2 concentrations depends on anthropogenic emissions (since the end point of dilution is the background NO2 value). Therefore, the weaker decreasing surface trends likely reflects a reduction of low-pressure dilution effect. Similarly, as anthropogenic emissions decrease, the positive bias of tropospheric 20 VCDs due to lightning NOx becomes larger, likely resulting in weaker decreasing trends. We consider the lightning effects on surface NO2 trends to be mostly meteorological driven not by lightning NOx directly (e.g., Ott et al., 2010;Lu et al., 2017) and hence the filtered OMI NO2 data are likely closer to emission related concentration changes.
The AQS in situ NO2 annual relative trends (coincident with OMI data with lightning filter) are most significant in the Northeast (-5.2±0.6% yr -1 ) and the West (-4.2±0.5% yr -1 ), followed by the South (-3.0±0.5% yr -1 ) and the Midwest (-2.8±0.6% 25 yr -1 ) regions. The nationwide annual trend is -4.1±0.4% yr -1 , which is consistent with the previous studies Lu et al., 2015;Tong et al., 2015;de Foy et al., 2016b;Duncan et al., 2016;Krotkov et al., 2016). The significant NO2 reductions result from updated technologies and strict regulations (Krotkov, et al., 2016). The corrected OMI-based NO2 trends (coincident with AQS data) show similar reduction rates in the West (-3.8±0.4% yr -1 ), the Midwest (-3.1±0.5% yr -1 ), the Northeast (-5.3±0.7% yr -1 ) and the South (-3.0±0.5% yr -1 ) regions. The nationwide annual trend is -3.9±0.3% yr -1 . 30 One advantage of satellite observations over a surface monitoring network is spatial coverage. The OMI data ("Lightning filter") coincident with the AQS data show a national annual trend of -3.9±0.3% yr -1 similar to the AQS in situ trend of -4.1±0.4% yr -1 . Using all data available (Fig. 8, Table 1), the OMI data ("Lightning filter") show a much lower trend of -1.5±0.2%yr -1 , about half of the AQS trend (-3.9±0.4% yr -1 ). Fig. 9 shows that the AQS sites, which are mostly urban and suburban sites, tend to be located in regions with high tropospheric NO2 VCDs. The OMI decreasing trend with corrected data is a function of tropospheric NO2 VCDs, increasing from 0% yr -1 to -6% yr -1 (Fig. 9). The national annual trend is close to the value of clean regions which contribute much more than polluted regions. The larger decrease near the anthropogenic source regions reflect in part the nonlinear photochemistry (Gu et al., 2013) and in part to a stronger influence of NOx sources such 5 as soils in rural regions.

Conclusions
Using data from the DOMINO v2 algorithm, we find that the computed OMI-based seasonal NO2 (relative) trends underestimate the decreasing trends of the EPA AQS data by up to 3.7% yr -1 . We attribute most of the discrepancies to OMI retrievals since the standard retrieval algorithm was not specifically designed for trend analysis. While lightning NOx is part 10 of OMI NO2 observations, we treat the influence of lightning on the OMI tropospheric VCD trend as a bias for comparison purposes in this study since AQS data are not as strongly affected by lightning. Furthermore, lightning NOx effects need to be removed when using satellite observations to understand the effects of changing anthropogenic emissions.
In this study, we show that removing the background ocean trend (likely a result of the increasing stripes), adopting MODIS albedo data (with better temporospatial resolutions), and excluding lightning influences can bring OMI tropospheric NO2 VCD 15 trends in close agreement (within 0.3% yr -1 ) with those of the AQS data. The largest effects of MODIS albedo update are in winter in Midwest and Northeast and those of lightning filter are in the South and the Northeast. After applying these corrections, the derived OMI-based annual regional NO2 trends change by a factor of > 2 for the South, the Midwest, and the West and seasonal changes can be even larger. We derive optimized OMI-based NO2 regional annual relative trends using all available data for the West (-2.0%±0.3 yr -1 ), the Midwest (-1.8%±0.4 yr -1 ), the Northeast (-3.1%±0.5 yr -1 ), and the South (-20 0.9%±0.3 yr -1 ).
The national annual trend of the corrected OMI data is -1.5±0.2%yr -1 , about half of the AQS trend (-3.9±0.4% yr -1 ). It reflects that the AQS sites are mostly located in the urban and suburban regions, where OMI data show much larger decreasing trends (up to -6% yr -1 ) than rural regions (down to 0% yr -1 ). The reasons for the dependence of OMI derived trends on tropospheric NO2 VCDs and the seasonal/regional trend differences are still not completely understood. Further studies are 25 necessary to improve our understanding of these trends. The observation-based lightning filter implemented in this study is preliminary. Incorporating chemical transport modeling may improve this filter. Moreover, the results presented here represent an alternative and indirect way to assess the importance of lightning NOx for National Climate Assessment (NCA) analyses described in Koshak et al. (2015), and Koshak (2017). Inversion studies (e.g., Zhao and Wang, 2009;Gu et al., 2013Gu et al., , 2014Gu et al., , 2016 will be needed to quantify the emission and AMF changes corresponding to the OMI tropospheric NO2 VCD trends. 30 Vaisala Inc. for providing the NLDN data used in this study. K. Folkert Boersma acknowledges funding from the EU FP7 5 project QA4ECV (grant no. 607405).

Data access
The datasets used in this research have been obtained online as follows: •          The spatial coverage is shown in Figure 1.