Michel Van Roozendael

Pollutants information can be retrieved from visible (VIS) and ultraviolet (UV) diffuse solar spectra exploiting Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments. In May 2021, the Italian research institute CNR-ISAC acquired and deployed a MAX-DOAS system SkySpec-2D. It is located in the “Giorgio Fea” observatory in San Pietro Capofiume (SPC), in the middle of the Po Valley, where it has constantly acquired zenith and off-axis diffuse solar spectra since the 1st October 2021. This work presents the retrieved tropospheric NO₂ and aerosol extinction profiles (and their columns) derived from the MAX-DOAS measurements using the newly developed DEAP retrieval code. The code has been validated both using synthetic differential Slant Column Densities (dSCDs) from the Fiducial Reference Measurements for Ground-Based DOAS Air-Quality Observations (FRM4DOAS) project and real measured data. For this purpose, DEAP results are compared with the ones obtained with three state-of-the-art retrieval codes. In addition, an inter-comparison with satellite products from Sentinel-5P TROPOMI, for the tropospheric NO₂ Vertical Column Densities (VCDs), and MODIS-MAIAC for the tropospheric Aerosol Optical Depth (AOD), is performed. We find a bias of −0.6 × 10¹⁵ molec/cm² with a standard deviation of 1.8 × 10¹⁵ molec/cm² with respect to Sentinel-5P TROPOMI for NO₂ tropospheric VCDs and of 0.04 ± 0.08 for AOD with respect to MODIS-MAIAC data. The retrieved data show that the SPC measurement site is representative of the background pollution conditions of the Po Valley. For this reason, it is a good candidate for satellite validation and scientific studies over the Po Valley.

The UV–Visible Working Group of the Network for the Detection of Atmospheric Composition Changes (NDACC) focuses on the monitoring of air-quality-related stratospheric and tropospheric trace gases in support of trend analysis, satellite validation and model studies. Tropospheric measurements are based on MAX-DOAS-type instruments that progressively emerged in the years 2010 onward. In the interest of improving the overall consistency of the NDACC MAX-DOAS network and facilitating its further extension to the benefit of satellite validation, the ESA initiated, in late 2016, the FRM4DOAS project, which aimed to set up the first centralised data processing system for MAX-DOAS-type instruments. Developed by a consortium of European scientists with proven expertise in measurements, data extraction algorithms and software design specialities, the system has now reached pre-operational status and has demonstrated its ability to deliver a set of quality-controlled atmospheric composition data products with a latency of one day. The processing system has been designed using a highly modular approach, making it easy to integrate new tools or processing updates. It incorporates advanced algorithms selected by community consensus for the retrieval of total ozone, lower tropospheric and stratospheric NO₂ vertical profiles and formaldehyde profiles. The ozone and NO₂ products are currently generated from a total of 22 stations and delivered daily to the NDACC rapid delivery (RD) repository, with an additional mirroring to the ESA Validation Data Centre (EVDC). Although it is still operated in a pre-operational/demonstrational mode, FRM4DOAS was already used for several validation and science studies, and it was also deployed in support of field campaigns for the validation of the TROPOMI and GEMS satellite missions. It recently went through a CEOS-FRM self-assessment process aiming at assessing the level of maturity of the service in terms of instrumentation, operations, data sampling, metrology and verification. Based on this evaluation, it falls under class C, which is a good rating but also implies that further improvements are needed to reach full compliance with FRM standards, i.e., class A.

Nitrogen oxides (NO_x = NO + NO₂) are of great concern due to their impact on human health and the environment. In recent years, machine learning (ML) techniques have been widely used for surface NO₂ estimation with rapid developments in computational power and big data. However, the uncertainties inherent to such retrievals are rarely studied. In this study, a novel ML framework has been developed, enhanced with uncertainty quantification techniques, to estimate surface NO₂ and provide corresponding data‐induced uncertainty. We apply the Boosting Ensemble Conformal Quantile Estimator (BEnCQE) model to infer surface NO₂ concentrations over Western Europe at the daily scale and 1 km spatial resolution from May 2018 to December 2021. High NO₂ mainly appears in urban areas, industrial areas, and roads. The space‐based cross‐validation shows that our model achieves accurate point estimates (r = 0.8, R² = 0.64, root mean square error = 8.08 μg/m³) and reliable prediction intervals (coverage probability, PI‐50%: 51.0%, PI‐90%: 90.5%). Also, the model result agrees with the Copernicus Atmosphere Monitoring Service (CAMS) model. The quantile regression in our model enables us to understand the importance of predictors for different NO₂ level estimations. Additionally, the uncertainty information reveals the extra potential exceedance of the World Health Organization (WHO) 2021 limit in some locations, which is undetectable by only point estimates. Meanwhile, the uncertainty quantification allows assessment of the model's robustness outside existing in‐situ station measurements. It reveals challenges of NO₂ estimation over urban and mountainous areas where NO₂ is highly variable and heterogeneously distributed.

During polar spring, periods of elevated tropospheric bromine drive near complete removal of surface ozone. These events impact the tropospheric oxidative capacity and are an area of active research with multiple approaches for representing the underlying processes in global models. We present a method for parameterizing emissions of molecular bromine (Br₂) over the Arctic using satellite retrievals of bromine monoxide (BrO) from the Ozone Monitoring Instrument (OMI). OMI retrieves column BrO with daily near global coverage, and we use the GEOS‐Chem chemical mechanism, run online within the Goddard Earth Observing System Earth System Model to identify hotspots of BrO likely associated with polar processes. To account for uncertainties in modeling background BrO, hotspots are only identified where the difference between OMI and modeled columns exceeds a statistical threshold. The resulting hotspot columns are a lower‐limit for the portion of OMI BrO attributable to bromine explosion events. While these hotspots are correlated with BrO measured in the lower troposphere over the Arctic Ocean, a case study of missing detections of near‐surface BrO is identified. Daily flux of Br₂ is estimated from hotspot columns of BrO using internal model parameters. When the emissions are applied, BrO hotspots are modeled with a 5% low bias. The sensitivity of the resulting ozone simulations to the treatment of background uncertainties in the BrO column is demonstrated. While periods of isolated, large (>50%) decreases in surface ozone are modeled, this technique does not simulate the low ozone observed at coastal stations and consistently underestimates ozone loss during March.

Ozone depletion over the polar regions is monitored each year by satellite and ground-based instruments. In this study, the vortex-averaged ozone loss over the last three decades is evaluated for both polar regions using the passive ozone tracer of the chemical transport model TOMCAT/SLIMCAT and total ozone observations from Système d'Analyse par Observation Zénithale (SAOZ) ground-based instruments and Multi-Sensor Reanalysis (MSR2). The passive tracer method allows us to determine the evolution of the daily rate of column ozone destruction, and the magnitude of the cumulative loss at the end of the winter. Three metrics are used to estimate the linear trend since 2000 and to assess the current situation of ozone recovery over both polar regions: 1) The maximum ozone loss at the end of the winter; 2) the onset day of ozone loss at a specific threshold and 3) the ozone loss residuals computed from the differences between annual ozone loss and ozone loss values regressed with respect to sunlit volume of polar stratospheric clouds (VPSC). This latter metric is based on linear and parabolic regressions for ozone loss in the Northern and Southern Hemispheres, respectively. In the Antarctic, metrics 1, and 3, yield trends of -2.3 and -1.8 % dec^-1 for the 2000–2021 period, significant at 1 and 2 standard error (σ), respectively. For metric 2, various thresholds were considered, all of them showing a time delay for when they are reached. The trends are significant at the 2σ level and vary from 3.5 to 4.2 day dec^-1 between the various thresholds. In the Arctic, metric 1 exhibits large interannual variability and no significant trend is detected; this result is highly influenced by the record ozone losses in 2011 and 2020. Metric 2 is not applied in the Northern Hemisphere due to the difficulty of finding a threshold value in a consistent number of winters. Metric 3 shows a negative trend in Arctic ozone loss residuals of -1.7 ±1 % dec^-1, significant at 1σ level. This is therefore the first quantitative detection of ozone recovery in the Arctic springtime lower stratosphere.

Ozone depletion over the polar regions is monitored each year by satellite and ground-based instruments. In this study, the vortex-averaged ozone loss over the last three decades is evaluated for both polar regions using the passive ozone tracer of the chemical transport model TOMCAT/SLIMCAT and total ozone observations from Système d'Analyse par Observation Zénithale (SAOZ) ground-based instruments and Multi-Sensor Reanalysis (MSR2). The passive tracer method allows us to determine the evolution of the daily rate of column ozone destruction, and the magnitude of the cumulative loss at the end of the winter. Three metrics are used to estimate the linear trend since 2000 and to assess the current situation of ozone recovery over both polar regions: 1) The maximum ozone loss at the end of the winter; 2) the onset day of ozone loss at a specific threshold and 3) the ozone loss residuals computed from the differences between annual ozone loss and ozone loss values regressed with respect to sunlit volume of polar stratospheric clouds (VPSC). This latter metric is based on linear and parabolic regressions for ozone loss in the Northern and Southern Hemispheres, respectively. In the Antarctic, metrics 1, and 3, yield trends of -2.3 and -1.8 % dec^-1 for the 2000–2021 period, significant at 1 and 2 standard error (σ), respectively. For metric 2, various thresholds were considered, all of them showing a time delay for when they are reached. The trends are significant at the 2σ level and vary from 3.5 to 4.2 day dec^-1 between the various thresholds. In the Arctic, metric 1 exhibits large interannual variability and no significant trend is detected; this result is highly influenced by the record ozone losses in 2011 and 2020. Metric 2 is not applied in the Northern Hemisphere due to the difficulty of finding a threshold value in a consistent number of winters. Metric 3 shows a negative trend in Arctic ozone loss residuals of -1.7 ±1 % dec^-1, significant at 1σ level. This is therefore the first quantitative detection of ozone recovery in the Arctic springtime lower stratosphere.

Satellite-based observations of tropospheric trace gas concentrations are used extensively to test models, infer emissions and their trends. These observations are also needed to develop emission-based scenarios for radiative forcing by tropospheric ozone and secondary aerosols, both from anthropogenic and natural sources. Although the detection of trace gases from space is feasible and operational data streams are in place for most relevant sensors, limited effort has been devoted to the generation and quality assessment of consistent multi-decadal climate data records. The ESA Climate Change Initiative (CCI) program was established for the systematic generation of Essential Climate Variables (ECVs). It builds on long-term global satellite observational datasets. Regarding atmospheric composition, the ECVs covered have recently been extended with trace gases that lead to the formation of ozone and aerosols.Here we present first results from the ‘Precursors for aerosols and ozone ECVs’ project, funded by ESA, and the first steps in developing long-term climate data records on the precursor gases nitrogen dioxide (NO2), formaldehyde (HCHO), carbon monoxide (CO), sulfur dioxide (SO2), ammonia (NH3), and glyoxal (CHOCHO). The project’s main goal is to build consistent and harmonized long-term data records from satellite instruments including GOME, SCIAMACHY, GOME-2, OMI, TROPOMI, IASI and MOPITT. A second goal is to demonstrate the fitness-for-purpose of the data records for various users including the European Copernicus Atmosphere Monitoring Service, currently operated by ECMWF. In this presentation, we report on what users need from such data records, and on round robin activities to test and evaluate the different retrieval approaches used in the scientific community. One key motivation will be to reconcile retrieval approaches so far independently developed in the EU QA4ECV and EUMETSAT AC-SAF projects, with the aim to harmonize products from all sensors.

Sentinel-5 precursor (S-5p), launched on 13 October 2017, is the first mission of the Copernicus Programme dedicated to the monitoring of air quality, climate, ozone and UV radiation. The S-5p characteristics, such as the fine spatial resolution, introduce many new opportunities and challenges, requiring to carefully assess the quality and validity of the generated data products by comparison with independent measurements and analyses. While routine validation is performed within the ESA Mission Performance Center (MPC) based on a limited number of Fiducial Reference Measurements (FRM), additional validation activities including airborne and ground-based campaigns are conducted in research mode as part of the S-5p Validation Team (S5PVT). A series of decentralized campaign activities take place since 2021, which have been identified to address key priorities for S5-p validation as well as key S5-p products (see s5pcampaigns.aeronomie.be for an overview of all campaigns performed in the context of the ESA QA4EO and SVANTE projects). Here, we will focus on recurrent observations with the airborne hyperspectral imager SWING, developed by BIRA-IASB. A SWING instrument is regularly installed in the Cessna T207A from FUB, while a second SWING instrument is permanently installed in a BN-2 from INCAS, alongside other instruments such as in-situ samplers.  Recurrent validation activities are performed over the cities of Berlin, Germany and Bucharest, Romania to map the horizontal distribution of tropospheric NO2 and its urban/industrial sources, in close coincidence with the overpass of the TROPOMI sensor. During each flight, approximately 5 to 15 TROPOMI pixels can be fully covered by airborne measurements. The recurrent nature of this type of campaigns, meaning that a flight can take place whenever weather conditions allow, results in a large statistical set of reference data covering various meteorological and geo-physical conditions (including autumn and winter conditions), as well as different satellite overpass configurations. In this context, a general airborne data format, following the Climate and Forecast (CF) conventions, has been developed at BIRA, as well as harmonized tools for the airborne L2 data processing and the TROPOMI validation in order to obtain consistent results over the different reference data sets. The developed validation strategies and developed tools are suitable for other airborne imagers than SWING and will be suitable as well for validation of future atmospheric missions such as Sentinel-5 and Sentinel-4. First results of the assessment of the TROPOMI tropospheric NO2 L2 product based on observations from recurrent airborne SWING campaigns will be discussed.

Space‐borne observations are used to characterize the fate of formaldehyde and glyoxal in wildfire plumes. Their distribution measured by the Tropospheric Monitoring Instrument reveals striking differences between the two compounds near intense fires. In typical situations, the glyoxal‐to‐formaldehyde ratio is highest near the fire (∼0.1) and decreases downwind of the source area due to the larger contribution of pyrogenic emissions to the glyoxal abundance and to the longer lifetime of formaldehyde. However, a pronounced glyoxal depletion is detected above high‐level clouds, not seen for formaldehyde, likely due to processing in pyrocumulonimbus clouds generated by the fires. This depletion suggests glyoxal retention upon droplet freezing and/or its outgassing in hydrated form in the upper troposphere. The absence of a sizable loss of formaldehyde during convection indicates that its hydration in liquid droplets and subsequent outgassing as methanediol represent at most a minor sink of formaldehyde.