We present an improved tropospheric nitrogen dioxide column retrieval algorithm (DOMINO v2.0) for OMI based on better air mass factors (AMFs) and a correction for across-track stripes resulting from calibration errors in the OMI backscattered reflectances. Since October 2004, NO₂ retrievals from the Ozone Monitoring Instrument (OMI), a UV/Vis nadir spectrometer onboard NASA's EOS-Aura satellite, have been used with success in several scientific studies focusing on air quality monitoring, detection of trends, and NO_x emission estimates. Dedicated evaluations of previous DOMINO tropospheric NO₂ retrievals indicated their good quality, but also suggested that the tropospheric columns were susceptible to high biases (by 0–40%), probably because of errors in the air mass factor calculations. Here we update the DOMINO air mass factor approach. We calculate a new look-up table (LUT) for altitude-dependent AMFs based on more realistic atmospheric profile parameters, and include more surface albedo and surface pressure reference points than before. We improve the sampling of the TM4 model, resulting in a priori NO₂ profiles that are better mixed throughout the boundary layer. We evaluate the NO₂ profiles simulated with the improved TM4 sampling as used in the AMF calculations and show that they are highly consistent with in situ NO₂ measurements from aircraft during the INTEX-A and INTEX-B campaigns in 2004 and 2006. Our air mass factor calculations are further updated by the implementation of a high-resolution terrain height and a high-resolution surface albedo climatology based on OMI measurements. Together with a correction for across-track stripes, the overall impact of the improved terrain height and albedo descriptions is modest (<5%) on average over large polluted areas, but still causes significant changes locally. The main changes in the DOMINO v2.0 algorithm follow from the new LUT and the improved TM4 sampling that results in more NO₂ simulated aloft, where sensitivity to NO₂ is higher, and amount to reductions in tropospheric NO₂ columns of up to 20% in winter, and 10% in summer over extended polluted areas. We investigate the impact of aerosols on the NO₂ retrieval, and based on a comparison of concurrent retrievals of clouds from OMI and aerosols from MODIS Aqua, we find empirical evidence that OMI cloud retrievals are sensitive to the presence of scattering aerosols. It follows that an implicit correction for the effects of aerosols occurs through the aerosol-induced cloud parameters in DOMINO, and we show that such an empirical correction amounts to a 20 %AMF reduction in summer and ±10% changes in winter over the eastern United States.

The Ozone Monitoring Instrument (OMI) onboard NASA's Aura satellite has been providing global observations of the ozone layer and key atmospheric pollutant gases, such as nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), since October 2004. The data products from the same instrument provide consistent spatial and temporal coverage and permit the study of anthropogenic and natural emissions on local-to-global scales. In this paper, we examine changes in SO₂ and NO₂ over some of the world's most polluted industrialized regions during the first decade of OMI observations. In terms of regional pollution changes, we see both upward and downward trends, sometimes in opposite directions for NO₂ and SO₂, for different study areas. The trends are, for the most part, associated with economic and/or technological changes in energy use, as well as regional regulatory policies. Over the eastern US, both NO₂ and SO₂ levels decreased dramatically from 2005 to 2015, by more than 40 and 80 %, respectively, as a result of both technological improvements and stricter regulations of emissions. OMI confirmed large reductions in SO₂ over eastern Europe's largest coal-fired power plants after installation of flue gas desulfurization devices. The North China Plain has the world's most severe SO₂ pollution, but a decreasing trend has been observed since 2011, with about a 50 % reduction in 2012–2015, due to an economic slowdown and government efforts to restrain emissions from the power and industrial sectors. In contrast, India's SO₂ and NO₂ levels from coal power plants and smelters are growing at a fast pace, increasing by more than 100 and 50 %, respectively, from 2005 to 2015. Several SO₂ hot spots observed over the Persian Gulf are probably related to oil and gas operations and indicate a possible underestimation of emissions from these sources in bottom-up emission inventories. Overall, OMI observations have proved valuable in documenting rapid changes in air quality over different parts of the world during last decade. The baseline established during the first 11 years of OMI is indispensable for the interpretation of air quality measurements from current and future satellite atmospheric composition missions.

Megacities are immense sources of air pollutants, with large impacts on air quality and climate. However, emission inventories in many of them still are highly uncertain, particularly in developing countries. Satellite observations allow top-down estimates of emissions to be made for nitrogen oxides (NO_x = NO + NO₂), but require poorly quantified a priori information on the NO_x lifetime. We present a method for the simultaneous determination of megacity NO_x emissions and lifetimes from satellite measurements by analyzing the downwind patterns of NO₂ separately for different wind conditions. Daytime lifetimes are ~4 hours at low and mid-latitudes, but ~8 hours in wintertime for Moscow. The derived NO_x emissions are generally in good agreement with existing emission inventories, but are higher by a factor of 3 for the Saudi Arabian capital Riyadh.

We present a new algorithm for the near-real time retrieval – within 3 h of the actual satellite measurement – of tropospheric NO₂ columns from the Ozone Monitoring Instrument (OMI). The retrieval is based on the combined retrieval-assimilation-modelling approach developed at KNMI for off-line tropospheric NO₂ from the GOME and SCIAMACHY satellite instruments. We have adapted the off-line system such that the required a priori information – profile shapes and stratospheric background NO₂ – is now immediately available upon arrival (within 80 min of observation) of the OMI NO₂ slant columns and cloud data at KNMI. Slant columns for NO₂ are retrieved using differential optical absorption spectroscopy (DOAS) in the 405–465 nm range. Cloud fraction and cloud pressure are provided by a new cloud retrieval algorithm that uses the absorption of the O₂-O₂ collision complex near 477 nm. On-line availability of stratospheric slant columns and NO₂ profiles is achieved by running the TM4 chemistry transport model (CTM) forward in time based on forecast ECMWF meteo and assimilated NO₂ information from all previously observed orbits. OMI NO₂ slant columns, after correction for spurious across-track variability, show a random error for individual pixels of approximately 0.7×10¹⁵ molec cm⁻². Cloud parameters from OMI's O₂-O₂ algorithm have similar frequency distributions as retrieved from SCIAMACHY's Fast Retrieval Scheme for Cloud Observables (FRESCO) for August 2006. On average, OMI cloud fractions are higher by 0.011, and OMI cloud pressures exceed FRESCO cloud pressures by 60 hPa. A sequence of OMI observations over Europe in October 2005 shows OMI's capability to track changeable NO_x air pollution from day to day in cloud-free situations.

Nitrogen dioxide (NO₂) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, which combines a high signal-to-noise ratio with daily global coverage and high spatial resolution. TROPOMI provides a valuable source of information to monitor emissions from local sources such as power plants, industry, cities, traffic and ships, and variability of these sources in time. Validation exercises of NO₂ v1.2–v1.3 data, however, have revealed that TROPOMI's tropospheric vertical column densities (VCDs) are too low by up to 50 % over highly polluted areas. These findings are mainly attributed to biases in the cloud pressure retrieval, the surface albedo climatology and the low resolution of the a priori profiles derived from global simulations of the TM5-MP chemistry model. This study describes improvements in the TROPOMI NO₂ retrieval leading to version v2.2, operational since 1 July 2021. Compared to v1.x, the main changes are the following. (1) The NO₂-v2.2 data are based on version-2 level-1b (ir)radiance spectra with improved calibration, which results in a small and fairly homogeneous increase in the NO₂ slant columns of 3 % to 4 %, most of which ends up as a small increase in the stratospheric columns. (2) The cloud pressures are derived with a new version of the FRESCO cloud retrieval already introduced in NO₂-v1.4, which led to a lowering of the cloud pressure, resulting in larger tropospheric NO₂ columns over polluted scenes with a small but non-zero cloud coverage. (3) For cloud-free scenes a surface albedo correction is introduced based on the observed reflectance, which also leads to a general increase in the tropospheric NO₂ columns over polluted scenes of order 15 %. (4) An outlier removal was implemented in the spectral fit, which increases the number of good-quality retrievals over the South Atlantic Anomaly region and over bright clouds where saturation may occur. (5) Snow/ice information is now obtained from ECMWF weather data, increasing the number of valid retrievals at high latitudes. On average the NO₂-v2.2 data have tropospheric VCDs that are between 10 % and 40 % larger than the v1.x data, depending on the level of pollution and season; the largest impact is found at mid and high latitudes in wintertime. This has brought these tropospheric NO₂ closer to Ozone Monitoring Instrument (OMI) observations. Ground-based validation shows on average an improvement of the negative bias of the stratospheric (from −6 % to −3 %), tropospheric (from −32 % to −23 %) and total (from −12 % to −5 %) columns. For individual measurement stations, however, the picture is more complex, in particular for the tropospheric and total columns.