AS3.19

The Sentinel-5P TROPOMI instrument provides unique observations of atmospheric trace gases at a high resolution of about 5 km with near-daily global coverage, resolving individual sources like thermal power plants, industrial complexes, fires, medium-scale towns, roads and shipping routes. These datasets are especially well suited to test high-resolution regional-scale air quality (AQ) models and provide valuable input for regional emission inversion systems. In Europe, the Copernicus Atmosphere Monitoring Service (CAMS) has implemented an operational regional AQ forecasting capability for Europe based on an ensemble of 7 up to 11 European models. In the presentation we show comparisons between TROPOMI observations of nitrogen dioxide (NO2) and the CAMS AQ forecasts and analyses of NO2. We discuss the different ways of making these comparisons, and present the quantitative results for time series for regions and cities between May 2018 to March 2021, for summer and winter months and individual days. We demonstrate the importance of the free tropospheric contribution to the tropospheric column, and include profiles from the CAMS configuration of the ECMWF’s global integrated model above 3 km altitude in the comparison. The models generally capture the fine-scale daily and averaged features observed by TROPOMI in much detail. In summer, the quantitative comparison of the NO2 tropospheric column shows a close agreement, but in winter we find a significant discrepancy in the average column amount over Europe. Recently a new TROPOMI NO2 reprocessing with processor version 2.3.1 has become available, and impact of this new version on the comparisons is discussed. 

As spin-off, we present a new TROPOMI NO2 level-2 data product for Europe, based on the replacement of the original TM5-MP generated global a priori profile (1x1 degree resolution) by the regional CAMS ensemble profile at 0.1x0.1 degree resolution. This a-priori replacement leads to significant changes in the TROPOMI retrieved tropospheric column, with typical increases at the emission hotspots in the order of 20%. 

The European NO2 product is compared with ground-based remote sensing measurements of 6 PANDORA instruments of the Pandonia global network and 8 MAX-DOAS instruments. As compared to the standard S5P tropospheric NO2 column data, the overall bias of the new product is smaller owing to a reduction of the multiplicative bias linked to the profile shape.

How to cite: Eskes, H., Douros, J., van Geffen, J., Boersma, F., Compernolle, S., Pinardi, G., Blechschmidt, A., Peuch, V.-H., Colette, A., and Veefkind, P.: Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS-regional air quality ensemble, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4538, https://doi.org/10.5194/egusphere-egu22-4538, 2022.

Nitrogen oxides (NO_x = NO + NO₂) play a major role in tropospheric chemistry through their impact on ozone and hydroxyl radical (OH) distributions, and therefore on the oxidizing capacity of the atmosphere. Whereas anthropogenic NO_x emissions are dominant globally, natural sources are responsible for ca. 30 % of the total emissions into the atmosphere. These sources include soil emissions (due to microbial nitrification and denitrification) and lightning (due to thermal dissociation of O₂ followed by recombination with N₂). Chemistry-transport models (CTMs) rely on bottom-up (BU) inventories, the uncertainty of which is acknowledged, especially for natural sources. Soil NO_x is mainly emitted as nitric oxide (NO) and current global BU estimates range from 4 to 15 Tg N yr^-1 with nearly 70% occurring in the tropics. Satellite retrievals of tropospheric NO₂ columns are used as top-down constraints in CTMs to derive NO_x emissions from various sources (anthropogenic, biomass burning, soil, lightning) such as illustrated in Martin et al. (2003), Jaeglé et al. (2005), Müller et Stavrakou (2005), Stavrakou et al. (2008) or Vinken et al. (2014). This is realized through the method of source inverse modelling, which consists in the optimization of emissions in a CTM in order to minimize the discrepancy between observed and simulated NO₂ columns.

In this study, we present top-down monthly soil NOemissions at 0.5° resolution over Africa and South America for 2019 based on spaceborne tropospheric NO₂ columns from the TROPOspheric Monitoring Instrument (TROPOMI). In a first step, we evaluate three global BU inventories against each other and against flux observations over the Tropics. The following BU estimates are considered: (1) YL-MAG, based on the Yienger and Levy parameterization (1995), (2) CAMS, provided by the Copernicus Atmosphere Monitoring Service (Granier et al., 2019; Simpson and Darras, 2021), and (3) HEMCO, calculated using Harvard–NASA Emission Component software (Weng et al., 2020). The last two estimates rely on the parameterization of Hudman et al. (2012). We assess YL-MAG, CAMS and HEMCO inventories against in situ measurements of biogenic soil NO fluxes compiled from literature distinguishing between seasons (dry/wet) and biomes. Based on this evaluation, the best BU inventory is selected and further used as a priori information in the regional MAGRITTE CTM (Müller et al., 2019) run at 0.5°×0.5° resolution for the year 2019. Monthly top-down NO_x fluxes (from the anthropogenic, biomass burning, soil and lightning categories) are inferred from TROPOMI NO₂ columns using an inversion framework based on the adjoint of MAGRITTE. The top-down soil NO fluxes and NO_x abundances are subsequently validated against in situ measurements over the two tropical regions.

How to cite: Opacka, B., Müller, J.-F., and Stavrakou, T.: Evaluation of soil NO emissions in the tropics using field data and TROPOMI NO2 columns., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1287, https://doi.org/10.5194/egusphere-egu22-1287, 2022.

The Arctic is experiencing rapid climate change. The increasing temperature not only reduces the sea-ice extent but will also have doubled the number of lightning flashes by the end of the century. The unlocked Arctic ocean can also lead to increased human activities such as shipping and expanded oil and gas production. In addition, the increase of lightning will cause more wildfires. All of these above will give rise to emissions of nitrogen oxides (NO_x).

In this study, we track and estimate three-year (2019-2021) Arctic NO_x emissions by combing the TROPOspheric Monitoring Instrument (TROPOMI) observations, Visible Infrared Imaging Radiometer Suite (VIIRS) data, and the Vaisala’s Global Lightning Dataset (GLD360). The NO_x emissions are divided into two different categories and estimated separately: 1) NO_x emissions from lighting; 2) surface NO_x emissions from all other sources.

The continuous overlapping orbits of TROPOMI passing over the Arctic provide unique opportunities for tracking the lightning NOx (LNO_x) and calculating both LNO_x lifetime and production efficiency. Previous studies focused on the LNO_x emissions in the tropical and mid-latitude regions and estimated the global LNO_x within the range of 2 to 8 T N yr-1. This study can add the missing LNO_x productions in high latitudes.

Besides, a Cloud-Snow Differentiation (CSD) method is applied to get more high-precision TROPOMI observations over large boreal snow-covered areas by discriminating snow-covered surfaces from clouds. The derived NO_x emissions from power plants, natural gas industries, and soil will play an important role in updating the present-day NO_x inventories which have a limited number of data sets. This study highlights the potential of TROPOMI as well as future satellite missions for monitoring Arctic NO_x emissions.

How to cite: Zhang, X., Yin, Y., van der A, R., Ding, J., Eskes, H., van Geffen, J., Vagasky, C., and Lapierre, J.: Arctic lightning and anthropogenic NOx emissions estimated from TROPOMI observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8640, https://doi.org/10.5194/egusphere-egu22-8640, 2022.

Nitrogen oxides (NOx = NO2 + NO) are primary pollutants that are mainly produced by anthropogenic activities. They play a key role in oxidation processes in the troposphere. They control the photochemical production of Ozone (O3) and affect the concentration of the hydroxyl radical (OH), thus causing air quality degradation. Industrialized countries with high air quality degradation such as China, are implementing mitigation strategies with the aim of improving air quality. The evaluation of these strategies requires having precise and rapidly updated emission inventories. Inverse modeling approaches based on satellite observations are useful tools as they can provide independent inventories to complement the traditional bottom-up inventories. In this study, we propose to evaluate the potential of the new 4D-Var inverse modeling system, CIF (Community Inversion Framework), coupled with the CHIMERE chemistry-transport model to inverse NOx emissions. We focus on the case study of NOx emissions over China for the year 2015 and use OMI satellite NO2 observations as constraints. The HTAP NOx emissions from 2010 are used to prescribe prior emissions and the inversion is performed at 0.5° resolution. The posterior NOx emissions are validated against surface NO2 concentration measurements and compared to the recent MEIC bottom-up inventory from the year 2015.

How to cite: Savas, D., Dufour, G., Coman, A., Siour, G., Fortems-Cheiney, A., Pison, I., Berchet, A., and Bessagnet, B.: Evaluation of the new 4D-variational inverse modeling system, CIF-CHIMERE: Inversion of NOx emissions over China using OMI NO2 observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5282, https://doi.org/10.5194/egusphere-egu22-5282, 2022.

The existing lignite reserves in Turkey are used for energy generation in coal-fired power plants (CPPs) and cause high amount of SO₂ pollution due to low calorific value and high sulfur content. Therefore, large-capacity CPPs in Turkey are the most important sources of SO₂ pollution. Shutdown and semi-shutdown periods were observed in the recent years for these CPPs with old technologies and insufficient treatment technologies. The daily electricity productions were also investigated and high variability, even in operating periods, were observed. Six major CPPs utilizing lignite were temporarily shut down on January 1, 2020 on the grounds that they did not have the necessary treatment system and exceeded the limit values, and reopened in June 2020 after requirements were partially or fully met. The impact of the temporary shutdowns for the aforementioned CPPs on SO₂ emissions, and SO₂ pollution using TROPOMI SO₂ retrievals was investigated here. 2-year period (2019-2020) covering the shutdown and reopening periods was selected and the impacts on SO₂ retrievals were examined. Although there are limited number of ground measurement stations in the region for the study period (2019-2020), ground-level SO₂ concentrations were also used for comparison with retrievals and investigation of the impact of shutdown periods.   

To estimate monthly SO₂ emissions from point sources using retrievals, a previously developed method by Fioletov et al. (2015) fitting SO₂ retrievals to a three-dimensional (3-D) parameterization were used for processing SO₂ columns and wind speed data. For emission estimations, TROPOMI Level-2 (L2) SO₂ product was processed and low quality data were removed according to quality criteria. In order to reduce noise in the data, SO₂ retrievals were averaged for 1×1 km² gridded domains around CPPs using an oversampling method and monthly averages were estimated. Different oversampling distances were applied to obtain the clearest signal, and 10 km radius was selected indicating reduced noise but sufficient spatial variability. The 3-D parameterization method was applied to SO₂ retrievals near the CPPs that were shut down. Since the uncertainty in the emission inventories can be quite high, these emission estimates using TROPOMI SO₂ retrievals give us a chance to assess the available annual estimates such as EMEP. This method aims to obtain more realistic and accurate emissions by estimating monthly SO₂ emissions for CPPs with shutdowns.

How to cite: Deger, S. S. and Kaynak, B.: Investigation of Monthly Emission Variations using TROPOMI SO2 Retrievals for Lignite-Fired Power Plants with Temporary Shutdowns, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12258, https://doi.org/10.5194/egusphere-egu22-12258, 2022.

The Multi-Angle Imager for Aerosols (MAIA) project is a NASA project focused on improving our understanding of the associations between speciated particulate matter (PM) air pollution and human health. The MAIA satellite instrument is currently being built at the NASA Jet Propulsion Laboratory (JPL), and NASA and JPL will identify a host satellite, with launch currently expected circa 2024 for a three-year baseline mission. The instrument will collect data on aerosol optical properties over a set of globally distributed target areas, which will be used in a geostatistical regression model, in combination with surface monitor data and chemical transport modeling, to derive surface-level PM concentrations. Epidemiologists on the MAIA Science Team will conduct studies in the eleven planned Primary Target Areas (PTAs) using the MAIA products to associate PM with various health outcomes. Observations are also planned in Secondary Target Areas (STAs) to provide data of interest to the community.

MAIA has a strong presence in Europe with two PTAs (Barcelona and Rome) and one STA, Belgrade, planned in the region. In the PTAs, the MAIA project will be providing data products including per-observation aerosol property data and per-observation and daily surface-level PM data (including total PM10 and PM2.5, and sulfate, nitrate, organic carbon, elemental carbon, and dust PM2.5). The project’s capacity to process the full suite of data products in any given STA is dependent on the associated observational objectives and availability of resources. MAIA data will be available free of charge from the NASA Atmospheric Science Data Center. This presentation will cover details of the MAIA target areas in Europe, prospective health studies, potential synergies with Sentinel-4 data, and how potential users can receive resources including MAIA simulated data.

How to cite: Nastan, A.: The NASA Multi-Angle Imager for Aerosols (MAIA): Providing Actionable Air Quality Data in Europe and Around the Globe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10475, https://doi.org/10.5194/egusphere-egu22-10475, 2022.

The Permian basin is the largest oil and gas production region in the U.S.A. Because of the non-conventional exploration of the basin, the region is covered with hundreds of small production facilities. Observations from the surface and from aircrafts have shown that there are continuous emissions of amongst others CH₄ and NO_x. Although they come from the same facilities, the sources of CH₄ and NO_x differ: NO_x is predominantly produced by engines and generators used for drilling and to run the facilities, whereas CH₄ is produced from planned and accidental releases from the production, storage and transportation of oil and gas. The TROPOMI satellite instrument allows continuous monitoring of CH₄ and NO₂ and has confirmed the large contribution of the oil and gas industry to the CH₄ and NO₂ concentrations in the Permian basin.

Using the divergence method, we have derived emissions for both CH₄ and NO₂ from the TROPOMI data, with a high spatial resolution of better than 5x5 km² for NO₂ and better than 10x10 km² for CH₄. The results show that the Permian CH₄ emissions in the basin are not dominated by a few large emission events, but are also impacted by many smaller releases across the basin. The basin can therefore be seen as a large area source that varies in space and time. Mitigation of these releases is challenging, rather than solving a few large leaks in a few facilities, the equipment and management of many small sites have to be improved to reduce emissions.

In this contribution we present results of the emission retrievals. Using CAMS model data, we show the potential of the divergence method and its sensitivity. Analyses will be presented of the TROPOMI derived emissions, showing the spatial variability of CH₄ and NO_x emissions over the region and their relation to the underlying oil and gas production and drilling activities.

How to cite: Veefkind, P., Serrano Calvo, R., Dix, B., Liu, M., van der A, R., de Gouw, J., and Levelt, P.: Satellite Derived CH4 and NOx Emissions from the Oil and Gas Industry in the Permian Basin in the U.S.A., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9372, https://doi.org/10.5194/egusphere-egu22-9372, 2022.

Satellite observations of greenhouse gases are used to constrain some global forecast models and are included in reanalysis models. The Copernicus Atmosphere Monitoring Service (CAMS) produces twice daily atmospheric composition forecasts using a data assimilation approach, with satellite observations forming part of the initial conditions in this forecast model. Global reanalysis of atmospheric composition, through data assimilation, is also produced by CAMS, where currently the fourth generation of the ECMWF global reanalysis (EAC4) is used.

The TROPOspheric Monitoring Instrument (TROPOMI) makes daily, high-resolution observations of atmospheric methane (CH₄) in the short-wave infrared band from space. The high-resolution of TROPOMI observations allows for urban and regional-scale CH₄ emissions evaluation but cloud coverage and data quality can limit the number of days with useful data. TROPOMI CH₄ observations are not included in the data assimilation of the CAMS global atmospheric composition forecast model nor in CAMS EAC4 and can therefore be used to independently evaluate atmospheric CH₄forecasts and EAC4 model estimates.

There are eight days in 2019-2020 with TROPOMI CH₄ observations that have sufficient coverage and pixel-density across the UK for comparison with CAMS daily CH₄ forecasts and EAC4 reanalysis values. We find average negative biases of ~55 ppb in CAMS forecasts and ~50 ppb in CAMS reanalysis compared to TROPOMI observations for these eight days across the UK. Differences could be due to i) the anthropogenic emissions used in the models; ii) biases in the stratosphere part of the CAMS models; iii) the TROPOMI retrieval algorithm, where biases could arise from the surface albedo and aerosol optical thickness values for certain pixels. To better understand and attribute the biases in CAMS we plan to explore parts of the CAMS model that relate to the stratospheric bias. Correct for the biases in CAMS yields average differences of around ±30 ppb across the UK suggesting additional discrepancies resulting from random error.

How to cite: Saboya, E. and Graven, H.: Methane observations from TROPOMI suggest CAMS products underestimate methane across the UK, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12826, https://doi.org/10.5194/egusphere-egu22-12826, 2022.

The 3 IASI instruments on-board the Metop satellites have been sounding the atmospheric composition since 2006. Up to ~30 atmospheric gases can be measured from IASI spectra, allowing the improvement of weather forecasting, and the analysis and monitoring of atmospheric chemistry and climate variables.

The early detection of extreme events such as fires, pollution episodes, volcanic eruptions, industrial accidents, is key to take appropriate decisions regarding safety to protect inhabitants and the environment in the target areas. With IASI providing global observations twice a day in near real time, a new way for the systematic and continuous detection of exceptional atmospheric events to support operational decisions is possible.

In this work, we explore and improve a method for the detection and characterization of extreme events using the recorded spectra, which relies on the principal component analysis (PCA) method. We assess this PCA-based system by analysing IASI raw and reconstructed spectra along with their differences (residuals) for various past and documented extreme events. The benefits and limitations of this approach are discussed with comparison with available CO and SO₂ IASI products. A methodological innovation, based on the refined analysis of extreme residuals (outliers) for the detection of fires, volcanic eruptions and pollution event is proposed, and could be used for the automatic and systematic detection of unexpected events.

How to cite: Vu Van, A., Boynard, A., Prunet, P., Jolivet, D., Lezeaux, O., Henry, P., Camy-Peyret, C., and Clerbaux, C.: Detection of extreme events from IASI observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8473, https://doi.org/10.5194/egusphere-egu22-8473, 2022.

Agriculture contributes to air pollution and is affected by atmospheric composition, meteorology and climate change. One of the important gases emitted from agricultural activities is ammonia (NH₃), which makes up a large portion of the anthropogenic reactive nitrogen in the environment. Agricultural ammonia emissions contribute to the formation of inorganic fine particulate matter (PM_2.5), causing multiple negative effects on human health and the overall air quality. Many studies proved the capability of the Infrared Atmospheric Sounding Interferometer (IASI) instrument aboard the Metop satellites in measuring ammonia from space. The series of 3 instruments provides a continuous view of the global atmosphere since 2006, allowing us to study NH₃ and many other pollutants relevant to air quality.

In this presentation, we explore the interaction between atmospheric ammonia on the one hand, and land and meteorological conditions on the other hand. To start, IASI NH₃ total columns and ERA5 land surface temperatures are used to estimate the NH₃ emission potential from the soil in agricultural fields. In addition to temperature, the emission potential is affected by the soil physical properties, fertilizer application practices and the concentrations of NH₃ near the surface. The results are used to validate the emission potential of NH₃ as derived from chemistry transport model (CTM) simulations.

Then, we look at the spatio-temporal variability of ammonia, focusing on different source regions around the globe. The NH₃ land-atmosphere exchanges depend on land cover and land use management, and on meteorology. We study this relationship in two test regions and periods: an agricultural region in Syria that was subject to land use change during the conflict, and over agricultural regions around safe megacities.

In Syria we show that the detected changes in NH₃ concentrations is driven by land use/cover rather than meteorology.

Over megacities, in particular, Paris, Toronto and Mexico, the result is quite different. We show that the NH₃ variability is mainly driven by meteorology, and interestingly, we can detect the fertilizers application period by looking at the NH₃ – temperature relationship.

How to cite: Abeed, R., Viatte, C., Clerbaux, C., Clarisse, L., Van Damme, M., Coheur, P.-F., and Safieddine, S.: Land use change and meteorology effect on atmospheric ammonia as seen by IASI, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7650, https://doi.org/10.5194/egusphere-egu22-7650, 2022.

Atmospheric formaldehyde (HCHO) is a secondary product in the destruction of non-methane volatile organic compounds (NMVOCs), through both natural and anthropogenic processes. With a relatively short lifetime of a few hours, the HCHO concentrations are usually localised close to their source. Measuring HCHO from space is therefore highly relevant in obtaining information on NMVOC emissions and their role in air quality and climate. HCHO retrievals from space have so far been limited to polar orbiting sensors with a fixed local overpass time.

The Geostationary Environment Monitoring Spectrometer (GEMS), launched on-board the GEO-KOMPSAT-2B satellite in February 2020 is the first geostationary sensor dedicated to air quality and atmospheric composition measurements. GEMS (observing South-East Asia hourly) will be complemented by TEMPO in 2022 (United States) and Sentinel-4 in 2023 (Europe and Northern Africa). Those instruments will provide an unprecedented hourly revisit time in their respective spatial domains. However, geostationary sensors make fundamentally different demands on the HCHO algorithm as compared to polar sensors.

In this work, we present DOAS tropospheric column retrieval results for HCHO from GEMS. In order to fit the SCD, a precise wavelength calibration is applied and potential changes in the instrumental line shape are accounted for. Polarisation spectral structures and scene heterogeneity effects are included, and a background correction and destriping procedure dedicated to geostationary observations is also developed. Air mass factors are calculated using auxiliary data consistent with the TROPOMI operational product. We compare our first results with those from TROPOMI in the early afternoon and with the GEMS HCHO operational product. Finally, we examine the diurnal variations observed with GEMS over different emission sources. MAX-DOAS measurements are used to validate and interpret the observed hourly variations.

How to cite: De Smedt, I., Van Gent, J., Vlietinck, J., Yu, H., Lerot, C., Theys, N., Pinardi, G., Hendrick, F., Romahn, F., Park, R., Lee, G., Kwon, H. A., Loyola, D., and Van Roozendael, M.: Formaldehyde column retrievals from the GEMS mission and evaluation against TROPOMI data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1263, https://doi.org/10.5194/egusphere-egu22-1263, 2022.

Wildfires release in the atmosphere large amounts of aerosols and ozone precursors and have a significant impact on air quality and the climate. Global warming leads to more frequent and intense wildfires such as those that occurred in Australia in January 2020, in Siberia in 2021 or in California in the last few years. The spaceborne TROPOspheric Monitoring Instrument (TROPOMI) launched in October 2017 onboard the Sentinel-5 Precursor platform provides invaluable information on a series of key trace gases (NO_2, HONO, CO, HCHO, CHOCHO)and on aerosols. Its daily global coverage and high spatial resolution are ideal to monitor wildfire emissions and to characterize their spatial extent and temporal evolution.

In this work, we present an analysis of the trace gas distributions observed by TROPOMI for various selected intense wildfire events, with a specific focus on glyoxal (CHOCHO). Primarily emitted gases such as NO₂ show intense signals near the fire sources with limited spatial extent. On contrary, other species like formaldehyde, carbon monoxide and glyoxal are mostly produced via secondary processes, which contribute to extend significantly their spatial spread. Those TROPOMI spatial patterns are consistent with the current knowledge of the different production mechanisms. However, we report here the identification of a very strong reduction of glyoxal slant columns in presence of very high clouds or aerosols, while other gases do not show such behavior. Uptake on aerosols or cloud droplets is a known destruction mechanism for glyoxal and is the most likely cause for this signal reduction. We hypothesize that, owing to its high solubility in water, glyoxal is transferred into the liquid phase within the convective cells of (pyro)cumulus clouds and that (contrary to formaldehyde) it is not degassed upon freezing and therefore remains in the condensed phase in the upper troposphere. We investigate the conditions in which this process is observed by correlating the glyoxal level with e.g. the fire intensity, the presence of high clouds and their altitude (pyrocumulonimbus), atmospheric conditions (temperature and humidity), the nature of the burning eco-system, etc. 

How to cite: Lerot, C., Theys, N., De Smedt, I., Van Roozendael, M., Stavrakou, T., and Müller, J.-F.: Investigation of the glyoxal tropospheric column variability observed from space during wildfire events, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7210, https://doi.org/10.5194/egusphere-egu22-7210, 2022.

Atmospheric hydrogen cyanide (HCN) is one of the most abundant cyanides in the global atmosphere. Understanding its physical and chemical nature is important considering its influence on the nitrogen cycle. The key processes driving tropospheric HCN variability are biomass burning, as the main source, and ocean uptake, as the main tropospheric sink. In the upper troposphere and stratosphere, the main HCN loss mechanisms are oxidation by hydroxyl radicals (OH) and by reaction with O(¹D). The resulting HCN lifetime varies from 2–5 months in the troposphere to several years in the stratosphere.

Given its relatively long atmospheric lifetime, HCN is a good tracer of many biomass burning events. Peats contain a high concentration of partially decayed organic matter and once burned they can emit large quantities of carbon dioxide, particulate matter, and other trace gases, including HCN, which affect regional air quality. The widespread peatlands in Indonesia are seasonally drained and cleared of natural vegetation to prepare soil for agricultural activities, making them a prime fuel source and enhancing the potential for burning to occur. During 2015, one of the most intense and prolonged fire seasons in recent decades was observed in Indonesia due to the drought conditions by the abnormally strong 2015/2016 El Niño event.

In this work, we use the TOMCAT three-dimensional (3-D) chemical transport model (CTM) to investigate the atmospheric response to the Indonesia 2015 peat fire season with a focus on HCN. The HCN concentrations over the Indonesian region have been modelled at a 2.8° × 2.8° spatial resolution from the surface to ~60 km. The modelled HCN distribution has been compared with the HCN observations over the Indonesia region measured by the Infrared Atmospheric Sounding Interferometer (IASI) instruments on-board the MetOp satellites. Retrievals of HCN columns from IASI measured radiances were made on an 8-layer equidistant altitude grid from 0 to 21 km using the optimal estimation method University of Leicester IASI Retrieval Scheme (ULIRS).

Using IASI measurements, we are able to investigate the HCN plume propagation over the entire region and how the El Niño influenced the enhancement of the HCN concentration during the 2015 wildfire season, in particular, a large peak of HCN concentration was observed across the end of October and the beginning of November. We find that TOMCAT is able to simulate and reproduce the magnitude of the unprecedented HCN emissions observed by IASI instrument over Indonesia and the Indian Ocean. Emission factors for Indonesian peat have been derived from IASI satellite data and incorporated into the TOMCAT model. The results provided are comparable to the emission factors of peat derived from lab measurements of burning peat collected in other regions of the world. The implications of our results for understanding the HCN biomass burning emissions and its variability are then discussed.

How to cite: Bruno, A. G., Harrison, J. J., Moore, D. P., Chipperfield, M. P., and Pope, R. J.: Hydrogen cyanide emissions of Indonesia 2015 peat fire season: satellite observations and modelling study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10005, https://doi.org/10.5194/egusphere-egu22-10005, 2022.

In the future, spectral image monitoring instruments from ESA missions will
provide spatial observations of pollutants (CO2, CH4, ...). Like TROPOMI,
these instruments have a large swath allowing the monitoring of city plumes.
Currently, the resulting plume images are assimilated (i) either with quick in-
verse methods relying on simplifying hypotheses for the transport and fate of
the pollutants, or (ii) with rigorous inverse methods using observation operators
and local metrics based on pixel-wise comparisons. Local metrics are, however,
prone to heavily penalyse small offsets between images which is known as the
double penalty issue. This makes local metrics not always well suited for plume
comparison within an emission inversion framework.
Therefore, we propose to use non-local metrics inspired by optimal transport
and the Wasserstein distance. Such metrics have the advantage to treat plumes
as coherent objects and hence avoid the double penalty issue. Furthermore,
these new metrics developed here are split into several terms that can be related
to errors in source location, wind field, emission rate, and whose respective
contributions to the global metric can be evaluated.
A sensitivity study towards these error sources is made for each metric. To
this end, a large catalogue of realistic tracer plumes is built. The ultimate goal
here is to discuss which metrics is the best suited for updating anthropogenic
emission inventories.

How to cite: Vanderbecken, P., Farchi, A., Bocquet, M., Dumont le Brazidec, J., Roustan, Y., Broquet, G., and Potier, E.: Comparison of non-local metrics towards the assimilation of pollutant plumes without thedouble penalty, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1754, https://doi.org/10.5194/egusphere-egu22-1754, 2022.