Comparing OMI-based and EPA AQS in situ NO<sub>2</sub> trends: towards understanding surface NO<sub><i>x</i></sub> emission changes

Abstract

Abstract. With the improved spatial resolution of the Ozone Monitoring Instrument (OMI) over earlier instruments and more than 10 years of service, tropospheric NO 2 retrievals from OMI have led to many influential studies on the relationships between socioeconomic activities and NO x emissions. Previous studies have shown that the OMI NO 2 data show different relative trends compared to in situ measurements. However, the sources of the discrepancies need further investigations. This study focuses on how to appropriately compare relative trends derived from OMI and in situ measurements. We retrieve OMI tropospheric NO 2 vertical column densities (VCDs) and obtain the NO 2 seasonal trends over the United States, which are compared with coincident in situ surface NO 2 measurements from the Air Quality System (AQS) network. The Mann-Kendall method with Sen's slope estimator is applied to derive the NO 2 seasonal and annual trends for four regions at coincident sites during 2005-2014. The OMI-based NO 2 seasonal relative decreasing trends are generally biased low compared to the in situ trends by up to 3.7 % yr −1 , except for the underestimation in the US Midwest and Northeast during December, January, and February (DJF). We improve the OMI retrievals for trend analysis by removing the ocean trend, using the Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data in air mass factor (AMF) calculation. We apply a lightning flash filter to exclude lightning-affected data to make proper comparisons. These data processing procedures result in close agreement (within 0.3 % yr −1 ) between in situ and OMI-based NO 2 regional annual relative trends. The remaining discrepancies may result from inherent difference between trends of NO 2 tropospheric VCDs and surface concentrations, different spatial sampling of the measurements, chemical nonlinearity, and tropospheric NO 2 profile changes. We recommend that future studies apply these procedures (ocean trend removal and MODIS albedo update) to ensure the quality of satellite-based NO 2 trend analysis and apply the lightning filter in studying surface NO x emission changes using satellite observations and in comparison with the trends derived from in situ NO 2 measurements. With these data processing procedures, we derive OMI-based NO 2 regional annual relative trends using all available data for the US West (−2.0 % ± 0.3 yr −1 ), Midwest (−1.8 % ± 0.4 yr −1 ), Northeast (−3.1 % ± 0.5 yr −1 ), and South (−0.9 % ± 0.3 yr −1 ). The OMI-based annual mean trend over the contiguous United States is −1.5 % ± 0.2 yr −1 . It is a factor of 2 lower than that of the AQS in situ data (−3.9 % ± 0.4 yr −1 ); the difference is mainly due to the fact that the locations of AQS sites are concentrated in urban and suburban regions.

Evidence of heterogeneous HONO formation from aerosols and the regional photochemical impact of this HONO source

“…Xu et al (2013) reported that this ratio varied in different seasons and locations. Hence, the applied correction ratios by Zhang et al (2018) may not be representative for the different campaigns in this study. By using a consistent NO 2 dataset, the R 2 between the unknown HONO source and the product of SWR×NO 2 ×aerosol surface area was the highest when compared to other correlations in Liu et al (2014).…”

“…PAN). We further applied the observed seasonal ratios of coincident NO 2 measurements of by the chemiluminescence instrument to the more selective photolytic instrument reported by Zhang et al (2018) to correct the NO 2 data and found that the R 2 (0.87) increased after the correction (figure 2(f)). Xu et al (2013) reported that this ratio varied in different seasons and locations.…”

Temporal Analysis of OMI-Observed Tropospheric NO2 Columns over East Asia during 2006–2015

“…For more consistent trend analysis, the observed pixels were considered only for the rows 6-23 because of the known row anomaly issue [35,43]. Besides, the observed pixels contaminated by bright surface albedo (larger than 0.3) and clouds (cloud radiance fraction larger than 50%) were not involved in the analysis [38].…”

“…The uncertainty in the tropospheric NO2 columns from the DOMINO v2.0 algorithm, caused mainly by the AMF calculation was 1.0 × 10 15 molecules cm −2 with a relative error of 25% [38]. For more consistent trend analysis, the observed pixels were considered only for the rows 6-23 because of the known row anomaly issue [35,43]. Besides, the observed pixels contaminated by bright surface albedo (larger than 0.3) and clouds (cloud radiance fraction larger than 50%) were not involved in the analysis [38].…”

Inferring the anthropogenic NO<sub><i>x</i></sub> emission trend over the United States during 2003–2017 from satellite observations: was there a flattening of the emission trend after the Great Recession?

“…2 Model and data description 2.1 REAM REAM has been applied and evaluated in many research applications including ozone simulation and forecast, emission inversion and evaluations, and mechanistic studies of chemical and physical processes (Alkuwari et al, 2013;Cheng et al, 2017Cheng et al, , 2018Choi et al, 2008a, b;Gu et al, 2013Gu et al, , 2014Koo et al, 2012;Liu et al, 2012Liu et al, , 2014Wang et al, 2007;Yang et al, 2011;Zhang et al, 2017Zhang et al, , 2018Zhang and Wang, 2016;Zhao and Wang, 2009;Zhao et al, 2009aZhao et al, , 2010. REAM as used in this work, the model domain of which is shown in Fig.…”