During a solar eclipse, sunlight incident on the Earth is reduced due to the (partial) shadow of the Moon. Atmospheric trace gas concentrations which are influenced by the amount of available sunlight, such as nitrogen dioxide (NO₂), may be affected due to the disrupted photolysis processes. Large-scale observations of the increased NO₂ concentrations caused by the solar eclipse would improve our understanding of the sensitivity of NO₂ in the atmosphere to short-term variations in sunlight. Spaceborne
measurements can provide valuable information about the large-scale spatial distribution of NO₂, which is provided daily by the TROPOMI instrument aboard the Sentinel-5 Precursor satellite by measuring and retrieving locally reflected sunlight. However, the TROPOMI NO₂ retrieval is unable to derive reliable concentrations during a solar eclipse, as solar eclipses are not taken into account in its retrieval algorithm. In this research, we have adjusted the NO₂ retrieval of TROPOMI such that it can handle solar eclipses and study the large-scale response of NO₂ during two solar eclipses over Europe in 2021 and 2022. We found a large-scale increase of NO₂ in the adjusted measurements, which linearly correlated with the degree of obscuration. We compared the measured NO₂ increase with the values from the atmospheric chemistry model TM5 including an applied eclipse implementation and we found a close agreement in most areas that are not highly polluted. Our measurements and model predict a NO₂ increase of 60%±12% and 70%±7% for an obscuration fraction of 1, respectively. More advanced chemistry modelling work is needed to explain the measurements in highly populated areas. We conclude that our results demonstrate that the TROPOMI algorithm is capable of correctly measuring NO₂ after an adjustment of the NO₂ retrieval. We have shown that it is possible to adjust an atmospheric trace gas retrieval for the influence of a solar eclipse. Moreover, we are the first to provide evidence for an increase in NO₂ during a solar eclipse using space-based measurement techniques and to quantify this increase on a large scale with the same instrument. Our measurements can be used to test atmospheric chemistry models, possibly improving their sensitivity to solar eclipses but also artificial shadows on the Earth induced by sunlight-intercepting geoengineering approaches.

COVID-19 caused a historic collapse in fossil fuel demand, a general decline in economic activity, and hydrocarbon price volatility. This resulted in an unprecedented scenario to evaluate the contribution of the O&G (Oil and Gas) industry NO₂ (nitrogen dioxide) emissions in the Permian basin (United States), currently the second largest hydrocarbon-bearing area on Earth. TROPOMI (Tropospheric Monitoring Instrument), on board the Sentinel-5P satellite, has captured the impact of the oil and gas industry emissions during the COVID-19 lockdown. A generalized drop (∼30%) of NO₂ emissions derived using the divergence method in comparison with 2019 was observed following the decline in production and drilling (13% and 68% respectively) during the lockdown. NO₂ tropospheric columns were less impacted with a smaller decrease (∼4%) across the basins. This study demonstrates that the impact of the COVID-19 lockdown on NO₂ emissions was not only present in urban areas but also in vast O&G production regions, which shows the potential of TROPOMI to assess future pollution mitigation strategies for this industry.

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Atmospheric aerosols are solid or liquid particles suspended in the air. The majority of them are produced by natural processes, including sea salt from oceans, mineral dust from (semi-)arid regions, carbon containing particles from wildfires, and sulfates and ash from volcanic activities. Anthropogenic aerosols are produced by industrial activities, power generation, transportation, agriculture, and human-induced biomass burning events. Depending on the meteorological conditions, aerosol particles can stay in the atmosphere for several hours to several months and can be transported over long distances, causing adverse effects on human health, visibility and climate. This thesis focuses on the aerosol optical properties, particularly the light absorption of the aerosol particles that has significant effects on the Earth’s climate system. This thesis starts with a general introduction of atmospheric aerosols, including its sources, categories, physical properties and measurement techniques (Chapter 1). Next, the Ultra-Violet Aerosol Index (UVAI) is introduced, which is calculated from satellite measurements of the radiance at two wavelengths in the UV. UVAI contains information of aerosol absorption, and it has a very long and almost continuous data record starting in 1978. Direct use of UVAI is challenging because it is not a geophysical quantity, but a numerical index. The objective of this thesis is to derive quantitative properties on aerosol absorption from the UVAI (e.g. single scattering albedo, absorption aerosol optical depth) that can be directly used in aerosol radiative transfer assessments. Two types of methods have been developed, i.e. physically-based methods and statistically-based methods. The first compares the observed UVAI to the one simulated by radiative transfer models. The second uses Machine Learning algorithms trained by existing data sets. The physically-based methods have been applied to quantify aerosol absorption of several large scale wildfires (Chapter 2 and 3). An important challenge of these method is that assumptions have to be made on the aerosol micro-physical properties, leading to significant uncertainties in the results, whereas theMachine Learning-based methods can avoid this kind of assumptions. Chapter 3 investigates the feasibility to quantify aerosol absorption from UVAI using a Machine Learning algorithm. Despite the higher computational efficiency and better results, the application of such data-driven methods is still restricted by the limited data on the aerosol vertical distribution. Therefore, in Chapter 4, a database of aerosol height is created from a chemistry transport model. This database is applied in Chapter 5, where a Deep Neural Network method is used to derive the quantitative aerosol absorptive properties from the OMI/Aura UVAI for the period from 2006 to 2019. In comparison to ground-based observations, the results of the Deep Neural Network agree better than satellite retrievals and also better than chemistry transport model simulations. This thesis demonstrates the feasibility of deriving quantitative aerosol absorptive properties from the satellite retrieved UVAI.We use traditional radiative transfer simulations meanwhile investigating the new possibilities of data-driven methods in aerosol remote sensing. Although the retrieval results are encouraging, there remain limitations and challenges which need to be addressed. These are discussed in Chapter 6 with corresponding suggestions and prospects. Despite the challenges, it is expected that a synthetic database of global aerosol absorption can be derived fromUVAI observations provided by multiple satellite products. Such a data set will make great contributions to quantify the effect of absorbing aerosols on the climate system.@en

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 50ĝ€¯% 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.

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U.S. oil and natural gas production volumes have grown by up to 100% in key production areas between January 2017 and August 2019. Here we show that recent trends are visible from space and can be attributed to drilling, production, and gas flaring activities. By using oil and gas activity data as predictors in a multivariate regression to satellite measurements of tropospheric NO2 columns, observed changes in NO2 over time could be attributed to NOx emissions associated with drilling, production and gas flaring for three select regions: the Permian, Bakken, and Eagle Ford basins. We find that drilling had been the dominant NOx source contributing around 80% before the downturn in drilling activity in 2015. Thereafter, NOx contributions from drilling activities and combined production and flaring activities are similar. Comparison of our top-down source attribution with a bottom-up fuel-based oil and gas NOx emission inventory shows agreement within error margins.@en

The Tropospheric Monitoring Instrument (TROPOMI), aboard the Sentinel-5 Precursor (S5P) satellite, launched on 13 October 2017, provides measurements of atmospheric trace gases and of cloud and aerosol properties at an unprecedented spatial resolution of approximately 7 × 3:5 km² (approx. 5:5 × 3:5 km² as of 6 August 2019), achieving near-global coverage in 1 d. The retrieval of nitrogen dioxide (NO₂) concentrations is a three-step procedure: slant column density (SCD) retrieval, separation of the SCD in its stratospheric and tropospheric components, and conversion of these into vertical column densities. This study focusses on the TROPOMI NO₂ SCD retrieval: the retrieval method used, the stability of the SCDs and the SCD uncertainties, and a comparison with the Ozone Monitoring Instrument (OMI) NO₂ SCDs. The statistical uncertainty, based on the spatial variability of the SCDs over a remote Pacific Ocean sector, is 8.63 μmol m^-2 for all pixels (9.45 μmol m^-2 for clear-sky pixels), which is very stable over time and some 30 % less than the long-term average over OMI-QA4ECV data (since the pixel size reduction TROPOMI uncertainties are ~ 8 % larger). The SCD uncertainty reported by the differential optical absorption spectroscopy (DOAS) fit is about 10 % larger than the statistical uncertainty, while for OMI-QA4ECV the DOAS uncertainty is some 20 % larger than its statistical uncertainty. Comparison of the SCDs themselves over the Pacific Ocean, averaged over 1 month, shows that TROPOMI is about 5 % higher than OMI-QA4ECV, which seems to be due mainly to the use of the so-called intensity offset correction in OMI-QA4ECV but not in TROPOMI: turning that correction off means about 5 % higher SCDs. The row-torow variation in the SCDs of TROPOMI, the "stripe amplitude", is 2.15 μmol m, while for OMI-QA4ECV it is a factor of ~ 2 (~ 5) larger in 2005 (2018); still, a so-called stripe correction of this non-physical across-track variation is useful for TROPOMI data. In short, TROPOMI shows a superior performance compared with OMI-QA4ECV and operates as anticipated from instrument specifications. The TROPOMI data used in this study cover 30 April 2018 up to 31 January 2020.

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Before the launch of the TROPOspheric Monitoring Instrument (TROPOMI), only two other satellite instruments were able to observe aerosol plume heights globally, the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The TROPOMI aerosol layer height is a potential game changer, since it has daily global coverage, and the aerosol layer height retrieval is available in near real time. The aerosol layer height can be useful for aviation and air quality alerts, as well as for improving air quality forecasting related to wildfires. Here, TROPOMI's aerosol layer height product is evaluated with MISR and CALIOP observations for wildfire plumes in North America for the 2018 fire season (June to August). Further, observing system simulation experiments were performed to interpret the fundamental differences between the different products. The results show that MISR and TROPOMI are, in theory, very close for aerosol profiles with single plumes. For more complex profiles with multiple plumes, however, different plume heights are retrieved; the MISR plume height represents the top layer, and the plume height retrieved with TROPOMI tends to have an average altitude of several plume layers. The comparison between TROPOMI and MISR plume heights shows that, on average, the TROPOMI aerosol layer heights are lower, by approximately 600 m, compared to MISR, which is likely due to the different measurement techniques. From the comparison to CALIOP, our results show that the TROPOMI aerosol layer height is more accurate over dark surfaces, for thicker plumes, and plumes between approximately 1 and 4.5 km. MISR and TROPOMI are further used to evaluate the plume height of Environment and Climate Change Canada's operational forecasting system FireWork with fire plume injection height estimates from the Canadian Forest Fire Emissions Prediction System (CFFEPS). The modelled plume heights are similar compared to the satellite observations but tend to be slightly higher with average differences of 270-580 and 60-320 m compared to TROPOMI and MISR, respectively.

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TROPOspheric Monitoring Instrument (TROPOMI), on-board the Sentinel-5 Precurser satellite, is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared. From these spectra several important air quality and climate-related atmospheric constituents are retrieved, including nitrogen dioxide (NO2) at unprecedented spatial resolution from a satellite platform. We present the first retrievals of TROPOMI NO2 over the Canadian Oil Sands, contrasting them with observations from the Ozone Monitoring Instrument satellite instrument, and demonstrate TROPOMI's ability to resolve individual plumes and highlight its potential for deriving emissions from individual mining facilities. Further, the first TROPOMI NO2 validation is presented, consisting of aircraft and surface in situ NO2 observations, and ground-based remote-sensing measurements between March and May 2018. Our comparisons show that the TROPOMI NO2 vertical column densities are highly correlated with the aircraft and surface in situ NO2 observations, and the ground-based remote-sensing measurements with a low bias (15–30 %); this bias can be reduced by improved air mass factors.@en

The main objective of this thesis is to design a new aerosol layer height retrieval in order to improve the operational NO2 retrieval, both in the troposphere, from space-borne instruments for highly polluted events and under cloud-free conditions. This thesis focuses on the exploitation of the OMI satellite measurements acquired in the visible wavelength range (405-490 nm). In addition, we develop numerical methods and tools (e.g. machine learning) in order to support the operational processing of big data amounts from the forthcoming new-generation satellite instruments for air quality and climate research.@en

This overview paper highlights the successes of the Ozone Monitoring Instrument (OMI) on board the Aura satellite spanning a period of nearly 14 years. Data from OMI has been used in a wide range of applications and research resulting in many new findings. Due to its unprecedented spatial resolution, in combination with daily global coverage, OMI plays a unique role in measuring trace gases important for the ozone layer, air quality, and climate change. With the operational very fast delivery (VFD; direct readout) and near real-time (NRT) availability of the data, OMI also plays an important role in the development of operational services in the atmospheric chemistry domain.

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A global picture of atmospheric aerosol vertical distribution with a high temporal resolution is of key importance not only for climate, cloud formation, and air quality research studies but also for correcting scattered radiation induced by aerosols in absorbing trace gas retrievals from passive satellite sensors. Aerosol layer height (ALH) was retrieved from the OMI 477 nm O2 − O2 band and its spatial pattern evaluated over selected cloud-free scenes. Such retrievals benefit from a synergy with MODIS data to provide complementary information on aerosols and cloudy pixels. We used a neural network approach previously trained and developed. Comparison with CALIOP aerosol level 2 products over urban and industrial pollution in eastern China shows consistent spatial patterns with an uncertainty in the range of 462–648 m. In addition, we show the possibility to determine the height of thick aerosol layers released by intensive biomass burning events in South America and Russia from OMI visible measurements. A Saharan dust outbreak over sea is finally discussed. Complementary detailed analyses show that the assumed aerosol properties in the forward modelling are the key factors affecting the accuracy of the results, together with potential cloud residuals in the observation pixels. Furthermore, we demonstrate that the physical meaning of the retrieved ALH scalar corresponds to the weighted average of the vertical aerosol extinction profile. These encouraging findings strongly suggest the potential of the OMI ALH product, and in more general the use of the 477 nm O2 − O2 band from present and future similar satellite sensors, for climate studies as well as for future aerosol correction in air quality trace gas retrievals.@en

Sunlight scattering and absorption by aerosols perturb the atmospheric radiation. This brings both scientific and technical challenges to the research community. Aerosols are an important player in the climate system (Boucher et al., IPCC report, Chapter 5: Clouds and aerosols, 2015), and also affect satellite measurements of reflected sunlight. Because long-time series of OMI trace gas observations are so crucial for analysing the air pollution trends between urban and regional scales, the uncertainties due to aerosols must be reduced. While, overall, the horizontal distribution of aerosol optical thickness (AOT) can be relatively well described, uncertainties in aerosol layer height (ALH) significantly contribute to the overall uncertainty. In the absence of clouds, ALH remains the most important error source on the observations of trace gases in the troposphere (e.g. NO2, HCHO, SO2) from UV-Vis air quality space-borne sensors over areas dominated by high AOT (i.e. > 0.5), and absorbing particles (Krotkov et al., 2008; Boersma et al., 2011; Barkley et al., 2012; Hewson et al., 2015; Castellanos et al., 2015; Chimot et al., 2016). Because atmospheric models still have large uncertainties on the vertical distribution of aerosols the use of passive satellite measurements with global coverage, such as OMI, becomes advantageous. We have developed a unique Multilayer Perceptron Neural Network (NN) algorithm (i.e. machine learning) to retrieve ALH from the OMI 477 nm O2-O2 visible absorption band and over cloud-free scenes (Chimot et al., 2017a). This band has high sensitivity to strong aerosol loading and has fewer challenges than other bands (e.g. O2-A). This retrieval approach strongly benefits from a synergy with MODIS as prior AOT information is needed. We will present the evaluation of the OMI ALH performances over cloud-free scenes. A 3-year time series (2005- 2007) over the urban and large industrialized area of north-east China shows accuracy in the range of 260-800 m for scenes with AOT (550 nm) > 0.5 (Chimot et al., 2017a). In addition, comparisons with CALIOP aerosol observations demonstrate a high correlation of the spatial distributions (Chimot et al., 2017b). Furthermore, analyses of biomass burning episodes over South America and Russia suggest the strong potential of UV-visible passive satellite sensors to probe thick smoke layer height (Chimot et al., 2017b). Improvement of these retrievals can be obtained by reducing uncertainties on the assumed aerosol type and surface albedo. Preliminary yearly global maps of OMI aerosol layer height will be shown. Finally, we will discuss how this first development may support the correction of aerosol radiation effect on the retrieved OMI tropospheric NO2 product, a key element of fossil-fuel emissions. This work illustrates the possibility, in the future, to derive a long-time series aerosol layer height with global coverage and to analyse the aerosol radiative effects, over cloud-free scenes, from current and nextgeneration UV-visible satellites: e.g. OMI, TROPOMI on-board Sentinel-5-Precurosr, Sentinel-4, Sentinel-5. Moreover, the proposed machine learning technique is a promising asset for facing the challenge of big satellite data. @en

Chlorophyll (Chl) and Total Suspended Matter (TSM) are both important water quality parameters since they influence the amount of oxygen & amount of light penetrating the water. Oxygen and light are vital in marine ecosystems. The Dutch governmental organisation, Rijkswaterstaat (RWS), has been monitoring the water quality of the Dutch part of the North Sea for the last 35 years. A research vessel takes off to sample parameters such as chlorophyll and TSM every few weeks at fixed locations. Recently, Sentinel-3 satellites started to provide satellite-based information on air or water quality. It is expected that products from the Ocean Land Colour Instrument (OLCI) sensor, on board of Sentinel-3, can greatly improve both the geographical and temporal coverage of these parameters. For the Dutch coastal waters this is challenging, because they predominantly consist of complex coastal waters. This study focusses on the validation of water quality parameters (Chl and TSM) available from Sentinel-3A OLCI observations. To verify the processing line of the OLCI data products it was desired to evaluate the following variables as well: 1) the aerosol optical thickness used in the atmospheric aerosol correction process, which is an important step for deriving the water-leaving reflectance, and 2) the water-leaving reflectance itself used as the main signal for deriving Chl and TSM. OLCI water quality data products were compared to Rijkswaterstaat in-situ measurements for the months May until September of 2017. Furthermore, OLCI’s Chl and TSM were compared with climatologies of MERIS data. The OLCI water-leaving reflectance and aerosol optical thickness data products were compared with observations from the Belgian AERONET-OC station Thornton. To evaluate the spatial distribution of OLCI's aerosol optical thickness comparisons with nearly coincident MODIS-AQUA observations were made. To evaluate the spatial variability of OLCI's data products boxplots were created of Chl, TSM and the aerosol optical thickness. The water quality products of OLCI consist of a Chl product determined by the OC4Me algorithm and a Chl & TSM product derived from a neural network. OLCI Chl obtained from the OC4Me algorithm showed an overestimation of a factor 2 compared to the in-situ measurements. The Chl results of the neural network compared well with the in-situ measurements showing a correlation coefficient of 0.77. OLCI TSM showed an unrealistic underestimation of a factor 4 compared to in-situ measurements. Boxplots showed that the largest spatial variability is found at stations <50 km from the coast for the three water quality products. This unrealistic underestimation of scattering TSM would imply an underestimation of the water-leaving reflectance in all the bands. Comparing OLCI's water-leaving reflectance with AERONET's showed underestimations in the blue and green bands only. OLCI’s water-leaving reflectance of the red and near-infra-red (NIR) bands correlated well with the AERONET-OC measurements. The aerosol optical thickness data product showed unrealistic overestimations of OLCI compared to AERONET-OC, but had a correlation coefficient of 0.58 when comparing it to MODIS aerosol optical thickness product. The spatial variability of OLCI's aerosol optical thickness is very high with differences of more than 40% per kilometre. In general, all products seem to have unrealistic values around clouds and in coastal areas, especially the aerosol optical thickness product. The pixels in those regions are different from other pixels. These results imply that further research into the software implementation of the radiative transfer models, lookup tables, vicarious calibrations and Neural Networks is needed to understand how retrievals of Chl and TSM concentrations are influenced. Such a fundamental understanding is ultimately also of interest for end users and all parties providing products and services for marine applications.

TEMPO was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (~2.1 km N/S×4.4 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O₃), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), formaldehyde (H₂CO), glyoxal (C₂H₂O₂), bromine monoxide (BrO), IO (iodine monoxide), water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O₃ chemistry cycle. Multi-spectral observations provide sensitivity to O₃ in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

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The Ozone Monitoring Instrument (OMI) has provided daily global measurements of tropospheric NO2 for more than a decade. Numerous studies have drawn attention to the complexities related to measurements of tropospheric NO2 in the presence of aerosols. Fine particles affect the OMI spectral measurements and the length of the average light path followed by the photons. However, they are not explicitly taken into account in the current operational OMI tropospheric NO2 retrieval chain (DOMINO – Derivation of OMI tropospheric NO2) product. Instead, the operational OMI O2 − O2 cloud retrieval algorithm is applied both to cloudy and to cloud-free scenes (i.e. clear sky) dominated by the presence of aerosols. This paper describes in detail the complex interplay between the spectral effects of aerosols in the satellite observation and the associated response of the OMI O2 − O2 cloud retrieval algorithm. Then, it evaluates the impact on the accuracy of the tropospheric NO2 retrievals through the computed Air Mass Factor (AMF) with a focus on cloud-free scenes. For that purpose, collocated OMI NO2 and MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua aerosol products are analysed over the strongly industrialized East China area. In addition, aerosol effects on the tropospheric NO2 AMF and the retrieval of OMI cloud parameters are simulated. Both the observation-based and the simulation-based approach demonstrate that the retrieved cloud fraction increases with increasing Aerosol Optical Thickness (AOT), but the magnitude of this increase depends on the aerosol properties and surface albedo. This increase is induced by the additional scattering effects of aerosols which enhance the scene brightness. The decreasing effective cloud pressure with increasing AOT primarily represents the shielding effects of the O2 − O2 column located below the aerosol layers. The study cases show that the aerosol correction based on the implemented OMI cloud model results in biases between −20 and −40 % for the DOMINO tropospheric NO2 product in cases of high aerosol pollution (AOT  ≥ 0.6) at elevated altitude. These biases result from a combination of the cloud model error, used in the presence of aerosols, and the limitations of the current OMI cloud Look-Up-Table (LUT). A new LUT with a higher sampling must be designed to remove the complex behaviour between these biases and AOT. In contrast, when aerosols are relatively close to the surface or mixed with NO2, aerosol correction based on the cloud model results in an overestimation of the DOMINO tropospheric NO2 column, between 10 and 20 %. These numbers are in line with comparison studies between ground-based and OMI tropospheric NO2 measurements in the presence of high aerosol pollution and particles located at higher altitudes. This highlights the need to implement an improved aerosol correction in the computation of tropospheric NO2 AMFs.@en

Numerous studies have drawn attention to the complexities related to the retrievals of tropospheric NO2 columns derived from satellite UltraViolet-Visible (UV-Vis) measurements in the presence of aerosols. Correction for aerosol effects will remain a challenge for the next generation of air quality satellite instruments such as TROPOMI on Sentinel-5 Precursor, Sentinel-4 and Sentinel-5. The Ozone Monitoring Instrument (OMI) instrument has provided daily global measurements of tropospheric NO2 for more than a decade. However, aerosols are not explicitly taken into account in the current operational OMI tropospheric NO2 retrieval chain (DOMINO v2 [Boersma et al., 2011]). Our study analyses 2 approaches for an operational aerosol correction, based on the use of the O2-O2 477 nm band. The 1st approach is the cloud-model based aerosol correction, also named “implicit aerosol correction”, and already used in the operational chain. The OMI O2-O2 cloud retrieval algorithm, based on the Differential Optical Absorption Spectroscopy (DOAS) approach, is applied both to cloudy and to cloud-free scenes with aerosols present. Perturbation of the OMI cloud retrievals over scenes dominated by aerosols has been observed in recent studies led by [Castellanos et al., 2015; Lin et al., 2015; Lin et al., 2014]. We investigated the causes of these perturbations by: (1) confronting the OMI tropospheric NO2, clouds and MODIS AQUA aerosol products; (2) characterizing the key drivers of the aerosol net effects, compared to a signal from clouds, in the UV-Vis spectra. This study has focused on large industrialised areas like East-China, over cloud-free scenes. One of the key findings is the limitation due to the coarse sampling of the employed cloud Look-Up Table (LUT) to convert the results of the applied DOAS fit into effective cloud fraction and pressure. This leads to an underestimation of tropospheric NO2 amount in cases of particles located at elevated altitude. A higher sampling of the variation of O2-O2 SCD and continuum reflectance as a function of effective cloud parameters in case of low effective cloud fraction values is requested for applying an aerosol correction. The updates of the OMI O2-O2 cloud algorithm, based on the scheduled new OMI cloud LUT, will be presented in terms of impacts of the effective cloud retrievals and reduced biases of tropospheric NO2 columns over cloud-free scenes dominated by aerosols in China. A 2nd approach is investigated, assuming a more explicit aerosol correction. Previous analyses pointed out that the O2-O2 spectra contain information about aerosols: the continuum reflectance is primarily constrained by the Aerosol Optical thickness (AOT) while the O2-O2 Slant Column Density (SCD) mostly results from the combination of AOT and aerosols altitude. We have developed a first prototype algorithm allowing to retrieve information about AOT and aerosol altitude from the O2-O2 DOAS fit. We will discuss preliminary sensitivities and the potential accuracy of the associated explicit aerosol correction, without the use of effective cloud parameters. @en