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.

This paper reports on consolidated ground-based validation results of the atmospheric NO₂ data produced operationally since April 2018 by the TROPOspheric Monitoring Instrument (TROPOMI) on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite. Tropospheric, stratospheric, and total NO₂ column data from S5P are compared to correlative measurements collected from, respectively, 19 Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), 26 Network for the Detection of Atmospheric Composition Change (NDACC) Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 Pandonia Global Network (PGN)/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatio-temporal co-location of the satellite and correlative data, e.g. by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5P data show, on average, (i) a negative bias for the tropospheric column data, of typically −23 % to −37 % in clean to slightly polluted conditions but reaching values as high as −51 % over highly polluted areas; (ii) a slight negative median difference for the stratospheric column data, of about −0.2 Pmolec cm⁻², i.e. approx. −2 % in summer to −15 % in winter; and (iii) a bias ranging from zero to −50 % for the total column data, found to depend on the amplitude of the total NO₂ column, with small to slightly positive bias values for columns below 6 Pmolec cm⁻² and negative values above. The dispersion between S5P and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec cm⁻²) but exceed those for the tropospheric column data (0.7 Pmolec cm⁻²). While a part of the biases and dispersion may be due to representativeness differences such as different area averaging and measurement times, it is known that errors in the S5P tropospheric columns exist due to shortcomings in the (horizontally coarse) a priori profile representation in the TM5-MP chemical transport model used in the S5P retrieval and, to a lesser extent, to the treatment of cloud effects and aerosols. Although considerable differences (up to 2 Pmolec cm⁻² and more) are observed at single ground-pixel level, the near-real-time (NRTI) and offline (OFFL) versions of the S5P NO₂ operational data processor provide similar NO₂ column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.

Global observations of tropospheric nitrogen dioxide (NO₂) columns have been shown to be feasible from space, but consistent multi-sensor records do not yet exist, nor are they covered by planned activities at the international level. Harmonised, multi-decadal records of NO₂ columns and their associated uncertainties can provide crucial information on how the emissions and concentrations of nitrogen oxides evolve over time. Here we describe the development of a new, community best-practice NO₂ retrieval algorithm based on a synthesis of existing approaches. Detailed comparisons of these approaches led us to implement an enhanced spectral fitting method for NO₂, a 1° × 1° TM5-MP data assimilation scheme to estimate the stratospheric background and improve air mass factor calculations. Guided by the needs expressed by data users, producers, and WMO GCOS guidelines, we incorporated detailed per-pixel uncertainty information in the data product, along with easily traceable information on the relevant quality aspects of the retrieval. We applied the improved QA4ECV NO₂ algorithm to the most current level-1 data sets to produce a complete 22-year data record that includes GOME (1995–2003), SCIAMACHY (2002–2012), GOME-2(A) (2007 onwards) and OMI (2004 onwards). The QA4ECV NO₂ spectral fitting recommendations and TM5-MP stratospheric column and air mass factor approach are currently also applied to S5P-TROPOMI. The uncertainties in the QA4ECV tropospheric NO₂ columns amount to typically 40 % over polluted scenes. The first validation results of the QA4ECV OMI NO₂ columns and their uncertainties over Tai'an, China, in June 2006 suggest a small bias (−2 %) and better precision than suggested by uncertainty propagation. We conclude that our improved QA4ECV NO₂ long-term data record is providing valuable information to quantitatively constrain emissions, deposition, and trends in nitrogen oxides on a global scale.

Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements of nitrous acid (HONO) and its precursor NO₂ (nitrogen dioxide) as well as aerosols have been performed daily in Beijing city centre (39.98° N, 116.38° E) from July 2008 to April 2009 and at the suburban site of Xianghe (39.75° N, 116.96° E) located ~60 km east of Beijing from March 2010 to December 2012. This extensive dataset allowed for the first time the investigation of the seasonal cycle of HONO as well as its diurnal variation in and in the vicinity of a megacity. Our study was focused on the HONO and NO₂ near-surface concentrations (0–200 m layer) and total vertical column densities (VCDs) and also aerosol optical depths (AODs) and extinction coefficients retrieved by applying the Optimal Estimation Method to the MAX-DOAS observations. Monthly averaged HONO near-surface concentrations at local noon display a strong seasonal cycle with a maximum in late fall/winter (~0.8 and 0.7 ppb at Beijing and Xianghe, respectively) and a minimum in summer (~0.1 ppb at Beijing and 0.03 ppb at Xianghe). The seasonal cycles of HONO and NO₂ appear to be highly correlated, with correlation coefficients in the 0.7–0.9 and 0.5–0.8 ranges at Beijing and Xianghe, respectively. The stronger correlation of HONO with NO₂ and also with aerosols observed in Beijing suggests possibly larger role of NO₂ conversion into HONO in the Beijing city center than at Xianghe. The observed diurnal cycle of HONO near-surface concentration shows a maximum in the early morning (about 1 ppb at both sites) likely resulting from night-time accumulation, followed by a decrease to values of about 0.1–0.4 ppb around local noon. The HONO / NO₂ ratio shows a similar pattern with a maximum in the early morning (values up to 0.08) and a decrease to ~0.01–0.02 around local noon. The seasonal and diurnal cycles of the HONO near-surface concentration are found to be similar in shape and in relative amplitude to the corresponding cycles of the HONO total VCD and are therefore likely driven mainly by the balance between HONO sources and the photolytic sink, whereas dilution effects appear to play only a minor role. The estimation of OH radical production from HONO and O₃ photolysis based on retrieved HONO near-surface concentrations and calculated photolysis rates indicate that in the 0–200 m altitude range, HONO is by far the largest source of OH radicals in winter as well as in the early morning at all seasons, while the contribution of O₃ dominates in summer from mid-morning until mid-afternoon.

The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm has been used to retrieve cloud information from measurements of the O₂ A-band around 760 nm by GOME, SCIAMACHY and GOME-2. The cloud parameters retrieved by FRESCO are the effective cloud fraction and cloud pressure, which are used for cloud correction in the retrieval of trace gases like O₃ and NO₂. To improve the cloud pressure retrieval for partly cloudy scenes, single Rayleigh scattering has been included in an improved version of the algorithm, called FRESCO+. We compared FRESCO+ and FRESCO effective cloud fractions and cloud pressures using simulated spectra and one month of GOME measured spectra. As expected, FRESCO+ gives more reliable cloud pressures over partly cloudy pixels. Simulations and comparisons with ground-based radar/lidar measurements of clouds show that the FRESCO+ cloud pressure is about the optical midlevel of the cloud. Globally averaged, the FRESCO+ cloud pressure is about 50 hPa higher than the FRESCO cloud pressure, while the FRESCO+ effective cloud fraction is about 0.01 larger. The effect of FRESCO+ cloud parameters on O₃ and NO₂ vertical column density (VCD) retrievals is studied using SCIAMACHY data and ground-based DOAS measurements. We find that the FRESCO+ algorithm has a significant effect on tropospheric NO₂ retrievals but a minor effect on total O₃ retrievals. The retrieved SCIAMACHY tropospheric NO₂ VCDs using FRESCO+ cloud parameters (v1.1) are lower than the tropospheric NO₂VCDs which used FRESCO cloud parameters (v1.04), in particular over heavily polluted areas with low clouds. The difference between SCIAMACHY tropospheric NO₂ VCDs v1.1 and ground-based MAXDOAS measurements performed in Cabauw, The Netherlands, during the DANDELIONS campaign is about −2.12×10¹⁴molec cm⁻².

We present the new version (v14) of the BIRA-IASB algorithm for the retrieval of formaldehyde (H₂CO) columns from spaceborne UV–visible sensors. Applied to OMI measurements from Aura and to GOME-2 measurements from MetOp-A and MetOp-B, this algorithm is used to produce global distributions of H₂CO representative of mid-morning and early afternoon conditions. Its main features include (1) a new iterative DOAS scheme involving three fitting intervals to better account for the O₂–O₂ absorption, (2) the use of earthshine radiances averaged in the equatorial Pacific as reference spectra, and (3) a destriping correction and background normalisation resolved in the across-swath position. For the air mass factor calculation, a priori vertical profiles calculated by the IMAGES chemistry transport model at 09:30 and 13:30 LT are used. Although the resulting GOME-2 and OMI H₂CO vertical columns are found to be highly correlated, some systematic differences are observed. Afternoon columns are generally larger than morning ones, especially in mid-latitude regions. In contrast, over tropical rainforests, morning H₂CO columns significantly exceed those observed in the afternoon. These differences are discussed in terms of the H₂CO column variation between mid-morning and early afternoon, using ground-based MAX-DOAS measurements available from seven stations in Europe, China and Africa. Validation results confirm the capacity of the combined satellite measurements to resolve diurnal variations in H₂CO columns. Furthermore, vertical profiles derived from MAX-DOAS measurements in the Beijing area and in Bujumbura are used for a more detailed validation exercise. In both regions, we find an agreement better than 15 % when MAX-DOAS profiles are used as a priori for the satellite retrievals. Finally, regional trends in H₂CO columns are estimated for the 2004–2014 period using SCIAMACHY and GOME-2 data for morning conditions, and OMI for early afternoon conditions. Consistent features are observed, such as an increase of the columns in India and central–eastern China, and a decrease in the eastern US and Europe. We find that the higher horizontal resolution of OMI combined with a better sampling and a more favourable illumination at midday allow for more significant trend estimates, especially over Europe and North America. Importantly, in some parts of the Amazonian forest, we observe with both time series a significant downward trend in H₂CO columns, spatially correlated with areas affected by deforestation.