Displays

Accurate estimation of anthropogenic CO₂ emissions (ACE) is of great importance for climate change mitigation, however, it is quite challenging.  Co-emitted gases, e.g. CO and NO_x, have been reported to be useful for tracking ACE. Here we estimated ACE of China based on “proxy” species (i.e. CO and NO_x) inversions with emission ratios of CO₂ and the “proxy” species and evaluated the estimates using ground CO₂ measurements of three tower stations in population-dense areas based on the Stochastic Time-Inverted Lagrangian Transport (STILT) modeling driven by the Global Data Assimilation System (GDAS) meteorology. An ensemble of ACE of China were estimated from different combinations of anthropogenic CO or NO_x flux estimates and emission ratios, where the CO or NO_x fluxes were estimated from in-situ measured concentration data or satellite column concentration data, and the emission ratios were derived from two emission inventory datasets, i.e. multi-resolution emission inventory for China (MEIC) and Peking University global emission inventories (PKU-FUEL). We found all CO₂ simulations with “proxy” based ACE estimates (either using in-situ or satellite data) in one year clearly fitted better to observations than those with inventory datasets did, especially during winter and early spring. Meanwhile, large mismatches between simulations and observations were found for some periods, which indicated the use of CO or NO_x to track ACE may be not suitable for a whole year. Our preliminary result demonstrates the potential to use atmospheric “proxy” species to track anthropogenic CO₂ emissions in China.

How to cite: He, W., Jiang, F., Feng, S., Nguyen, N. T., Wang, H., and Ju, W.: Evaluating anthropogenic CO2 emissions of China estimated from atmospheric inversions of “proxy” species against ground CO2 measurements, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1905, https://doi.org/10.5194/egusphere-egu2020-1905, 2020.

Currently sampling of the atmosphere for gas emission measurements involves building towers or hiring airplanes - capital-intensive methods. Easy access to unmanned aerial vehicles (UAV) has opened-up new opportunities for remote gas sampling. The project Iso-2-Drone aims to develop and produce a modular UAV-based gas monitoring system for emission measurements to substitute current technologies. A key feature of the UAV-attached gas sampler design was the ready-to-use nature of the system. This meant that the system was designed to mesh with commonly available equipment, using collection vessels which can be easily and immediately measured by common continuous flow - isotope ratio mass spectrometer (CF-IRMS) instrumentation. The target compounds comprise the three major natural greenhouse gases CH₄, CO₂ and N₂O to be measured at natural isotopic abundance and ambient levels.

We use 20 mL headspace vials for CH₄ and CO₂ sampling. Vials can be conditioned on-sight with our sample preparation prototype using repeatedly evacuating and synthetic air refilling cycles to prevent ambient air contamination. On the UAV-attached sampler atmospheric air is sampled passively by pressure compensation of the vacuum. N₂O is sampled actively via adsorption tubes, filled with Molecular Sieve 5Å and conditioned in the lab. Both a prototype device and two UAV-attached samplers have been designed, built and are currently tested.

The measurement setup in the lab comprises of two autosamplers, a purge & trap system (VSP 4000, IMT Innovative Maschinentechnik GmbH) and a headspace sampler (CTC CombiPal, Chromtech GmbH) in order to switch from ppb range necessary for CH₄ and N₂O to a ppm range for CO₂. For CO₂ measurements the CTC injects 600 µl of sampled air to a Restek Micropacked Column (Shin Carbon ST 100/120, 2m x 1mm ID and 1/16” OD) within a Thermo Scientific Trace GC Ultra heated up from 40°C to 110°C, maintained for 5 min, before heating up to 180°C by 12°C per minute. Thereby CO₂ is properly separated from the potentially interfering N₂O. For CH₄ the residual air sample is cryo-focused at -140°C in a HayeSep D filled trap, transferred to the GC and targeted with a Poraplot Q (30m x 0.32mm) held at 35°C. Using the similar GC method and autosampler N₂O is desorbed after switching the autosampler to thermal desorption mode. All three analytes pass an oxidation/reduction reactor (1030°C) before they are introduced into the IRMS (Thermo Scientific DeltaV Advantage) via a universal gas interface (Thermo Scientific Conflo IV). The IRMS continuously scans the intensity of the mass-to-charge ratios of mass 44, 45, 46 for CH₄ and CO₂ and 28, 29 for N₂0 converted to N₂. δ¹³C and δ¹⁵N are referenced against calibrated laboratory reference gases.

We are currently tuning the methods and testing the prototypes and will present the lasted results and open questions at the conference.

How to cite: Leitner, S., Feichtinger, W., Mayer, S., Mayer, F., Krompetz, D., Hood-Nowotny, R., and Watzinger, A.: UAV-based gas monitoring systems for the underpinning of urban, agricultural and industrial emission roadmaps – a methodological approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2684, https://doi.org/10.5194/egusphere-egu2020-2684, 2020.

Satellite observations of NO₂ provide valuable information on the location and strength of NO_x emissions, but spatial resolution is limited by horizontal transport and smearing of temporal averages due to changing wind fields. The divergence (spatial derivative) of the mean horizontal flux, however, is highly sensitive for point sources like power plant exhaust stacks.

In a previous study, point source emissions have been identified and quantified exemplarily for Riyadh, South Africa, and Germany with a detection limit of about 0.11 kg/s down to 0.03 kg/s for ideal conditions, based on TROPOMI NO₂ columns and ECMWF wind fields (Beirle et al., Science Advances, 2019).

Here we extend this study and derive a global catalog of NO_x emissions from point sources. The specific challenges for e.g. high latitudes (longer NO_x lifetime) or coastlines (potentially persistent diurnal wind patterns) are investigated.

How to cite: Beirle, S., Borger, C., Dörner, S., and Wagner, T.: Global catalog of NOx point sources derived from the divergence of the NO2 flux, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3649, https://doi.org/10.5194/egusphere-egu2020-3649, 2020.

Direct estimates of air pollution emissions using a combination of satellite observations and meteorological reanalyses are becoming increasingly advanced and widely used. In this presentation, a new such methodology, able to resolve NO_x emissions from multiple, adjacent emissions sites of varying sizes, but still applicable over larger scales, is presented. This method was applied to TROPOMI NO₂ observations over the entirety of the Canadian oil sands, deriving emissions from small-to-large surface and in-situ mining operations. Initial comparisons with best available bottom-up emissions estimates show good consistency, and that TROPOMI is able to discern NO_x emissions at the 1 kt[NO₂]/yr level.

How to cite: McLinden, C., Fioletov, V., Griffin, D., and Dammers, E.: High-resolution mapping of NOx emissions from the Canadian Oil Sands from TROPOMI, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3959, https://doi.org/10.5194/egusphere-egu2020-3959, 2020.

The time lag between the occurrence of emissions and the compilation of an inventory is inevitable. When an emissions inventory is used to simulate air quality, uncertainties in the emissions are propagated into uncertainties in the modelled pollutant concentrations. Such uncertainties can be particularly high in regions undergoing rapid emission changes. Beijing, for instance, has implemented a series of pollution control measures over the past several years and various studies have confirmed significant decreases in the emissions of pollutants such as CO and NO_X. Hence, it is crucial to quantify and constrain the uncertainties in existing emission estimates for this region.

We sample the uncertainties in an emissions inventory for Beijing using a high-resolution advanced Gaussian dispersion model with perturbed emissions ensembles (PEEs), and constrain these uncertainties using a comprehensive set of in situ observations, including vertically resolved measurements made from a tower in central Beijing using low-cost sensors. We first construct a PEE by varying key emission parameters including source sectors, vertical and diurnal profiles within their uncertainty ranges estimated through expert elicitation. By removing the baseline contribution to the concentrations, we are able to evaluate the performance of the PEE in simulating the local signal. Based on knowledge gained from the initial PEE, we design a second PEE with optimised uncertainty ranges with which we constrain the uncertainties in the base emission estimates.

Our study shows the applicability of perturbed emissions ensembles and high-resolution, three-dimensional observations in systematically sampling and constraining emission uncertainties. This method has wide implications for air quality modelling, particularly in regions with rapid emission changes or for studies in which emissions inventories are out-dated.

How to cite: Yuan, L., Carruthers, D., Hood, C., Jones, R. L., Popoola, O. A. M., Stocker, J., and Archibald, A. T.: Reducing uncertainty in emission estimates using perturbed emissions ensembles and novel observations: A focus on Beijing, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4781, https://doi.org/10.5194/egusphere-egu2020-4781, 2020.

The Tropospheric Monitoring Instrument (TROPOMI) on ESA Copernicus Sentinel-5P satellite (S5-P) monitors the total column concentration of carbon monoxide (CO) as one of its primary targets.  In this study, we present an approach to analyze the large amount of TROPOMI CO data and to estimate urban emissions on sub-city scales for metropolises like Mexico City. The results demonstrate the advance in using TROPOMI observations for monitoring regional air quality. To this end, we analyze about two years of TROPOMI CO measurements with 551 overpasses over Mexico City using tracer simulations of the regional Weather Research and Forecasting (WRF) model. Ten separate CO tracers for emissions of the districts Tula, Pachuca, Tulancingo, Ciudad de Mexico, Toluca, CDMX, Cuernavaca, Cuautla, Tlaxcala, and Puebla are used to conclude on the emissions of different urban districts. A regularized source inversion minimizes the difference with respect to a prior emission estimate. Here, the degree of freedom of the inferred sources is a powerful tool to filter on measurement information and forward model errors e.g. due to erroneous wind fields. We compare the estimated emissions with those of the national inventory ``Inventario Nacional de Emisiones de Contaminantes Criterio'' (INEM) multiplied by 0.48 to make it applicable for the years 2017 to 2019. Overall, TROPOMI confirms the total INEM CO emissions form the area but indicates clear differences in relative distribution of the emissions between the districts. For example, TROPOMI yields 0.11 Tg/yr and 0.10 Tg/yr CO emissions for the urban districts Tula and Pachuca in the North of Mexico City, which exceeds the INEM emissions of <0.008 Tg/yr. Also, for the central part of Mexico City (CDMX) the TROPOMI estimate with 0.14 Tg/yr differs significantly from the inventory with 0.25 Tg/yr. Moreover, we found that the retrieved emissions for CDMX and Ciudad de Mexico follow a clear weakly cycle with a minimum during the weekend in agreement with ground-based in situ measurements of the ``Secretaria del Medio Ambiente'' (SEDEMA) and column measurements of a Fourier Transform Spectrometer in Mexico City operated by the National Autonomous University of Mexico (UNAM). To improve further the TROPOMI CO data exploitation, our study clearly indicates the need for improvements of regional models like WRF, in particular with respect to the prediction of the local wind fields.

How to cite: Borsdorff, T., Garcia Reynoso, A., Stremme, W., aan de Brugh, J., Grutter, M., and Landgraf, J.: Monitoring CO emissions from urban districts in Mexico City using about 2 years of TROPOMI CO observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5594, https://doi.org/10.5194/egusphere-egu2020-5594, 2020.

A six-year long high-resolution Chinese air quality reanalysis datasets (CAQRA) covering the period 2013-2018 has been developed in this study by assimilating over 1000 surface air quality monitoring sites from China National Environmental Monitoring Centre (CNEMC) based on the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). This reanalysis provides the surface fields of six conventional air pollutants in China, namely PM_2.5, PM₁₀, SO₂, NO₂, CO and O₃, at high spatial (15km×15km) and temporal (1 hour) resolutions. This paper aims to document this dataset by providing the detailed descriptions of the assimilation system and presenting the first validation results for the reanalysis fields of air pollutants in China. A twenty-fold cross validation (CV) method was used to assess the quality of CAQRA. The CV results show that the CAQRA has excellent performances in reproducing the magnitude and variability of the air pollutants in China with the biases (normalized mean bias) of the reanalysis data about -2.6 (-4.9%) μg/m³ for PM_2.5, -6.8 (-7.6%) μg/m³ for PM₁₀, -2.0 (-8.5%) μg/m³ for SO₂, -2.3 (-6.9%) μg/m³ for NO₂, -0.06 (-6.1%) mg/m³ for CO and -2.3 (-4.0%) μg/m³ for O₃. The interannual changes of the air quality in China were also well represented by the CAQRA in terms of the six air pollutants. Comparisons with previous datasets of daily PM_2.5, SO₂ and NO₂ concentrations indicate that the CAQRA is more accurate with smaller RMSE values. We also compared our reanalysis dataset to the CAMSRA (The Copernicus Atmosphere Monitoring Service reanalysis) produced by ECMWF (European Centre for Medium-Range Weather Forecasts), which suggests that the CAQRA has higher accuracy in representing the surface air pollutants in China due to the assimilation of surface observations. This reanalysis dataset can provide us comprehensive pictures of the air quality in China from 2013 to 2018 with a complete spatial and temporal coverage, which can be used in the assessment of health impacts of air pollution, validation of model simulations and providing training data for the statistical or AI (Artificial Intelligence) based forecast.

How to cite: Kong, L., Tang, X., Zhu, J., Wang, Z., Wu, H., and Li, J.: Developing High-resolution Air Quality Reanalysis Dataset over China for Years 2013-2018 Based on Ensemble Kalman Filter and Surface Observations from CNEMC, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6848, https://doi.org/10.5194/egusphere-egu2020-6848, 2020.

Even though the actual levels of anthropogenic pollution around South Eastern Europe do not reach the ones experienced in numerous Central and Western locations such as the Po Valley, the Benelux regions, the English Channel, etc., both nitrogen and sulphur oxides remain a cause for concern for air quality issues in the area. S5P/TROPOMI offers a high enough spatial resolution of 3.5x7km² (x5.5km² since August 2019) coupled with a high signal-to-noise to allow the monitoring of air quality levels, as well as the calculation of emissions, around the overpass time of the satellite. In that respect, LOTOS-EUROS Chemical Transport Model (CTM) simulations for year 2018 will be used in conjunction to the S5P/TROPOMI NO₂ v01.03.02 and SO₂ v01.01.07 columns to update the current emission inventory used in CAMS, provided recently by TNO for year 2015.

The process is validated at every step; the CTM surface concentrations are being compared to the European Environmental Agency E1a & E2a in situ air quality station data while the satellite vertical columns are compared to MAX-DOAS ground-based measurements. The diurnal variability of the NO₂ depicted by the in situ and the CTM is examined, as a source of understanding the effect of the apriori emission fields, the OH radical chemistry, the planetary boundary layer definition, etc., within the model structure. The seasonal variability of the SO₂ columns observed by the satellite and ground-based instruments reveals the amount of insufficiently filtered power plants and smelting activities in the area, including transboundary transport around the Balkan Peninsula.

Area sources, such as cities and industrial regions, as well as shipping plumes around the Aegean Sea, the Bosporus Strait and the Eastern Mediterranean, will be characterized vis-à-vis their updated emissions and discussed.

Acknowledgements:

We acknowledge support of this work by the project “PANhellenic infrastructure for Atmospheric Composition and climatE change” (MIS 5021516) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). This work was co-funded by ESA within the Contract No. 4000117151/16/l-LG “Preparation and Operations of the Mission Performance Centre (MPC) for the Copernicus Sentinel-5 Precursor Satellite”.

How to cite: Koukouli, M.-E., Skoulidou, I., Segers, A., Manders-Groot, A., Kuenen, J., van Geffen, J., Eskes, H., Hedelt, P., Loyola, D., Stavrakou, T., Tzoumaka, V., Kelessis, A., Karagkiozidis, D., and Balis, D.: Quantifying South Eastern Europe NOx and SO2 emissions using S5P/TROPOMI; from the urban to the regional scale., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8043, https://doi.org/10.5194/egusphere-egu2020-8043, 2020.

Since 2000 there have been two significant changes impacting loadings of aerosols and trace gasses on the troposphere. First, there has been a rapid expansion of urbanization and access to energy sources, coupled with significant deforestation, all leading to a rapid increase in emissions and a change in its distribution in space and time. Secondly, we now have access to multiple daily to weekly measurements of aerosols and related trace gasses on a global scale. Combining the data from these different remotely sensed platforms in space and on the ground, coupled with an understanding of the basic physical and chemical differences of different sources and substances should allow us to understand and begin to quantify how the emissions have changed over time. However, we have serious issues when it comes to analyzing changes which are rapid in either space or time, with traditional Kalman filters and 3D/4D variance techniques tending to smooth out such changes.

The approach uses the rate in the change of the difference of the variance  of the loadings of NO₂ (from OMI) which is short-lived, CO (from MOPITT) which is long-lived, and AOD (from MISR) which is short-lived in the presence of rain, and intermediate-lived under dry conditions. This combination is used to generate new a priori, which in turn have a significantly different spatial, temporal resolution than currently existing emission datasets. The magnitudes are then scaled by using a simple forward-inverse modeling framework based on an approximation of an EnKF approach, using measurements not used in the a priori fitting: AOD from AERONET and MODIS, surface measurements of trace gasses from various national and international projects, and other sources.

Our results of this new approach demonstrate that these rapidly varying sources in space and time can contribute from an additional 10% to up to 500% of emissions over these various rapidly changing regions, as compared with existing present-day inventories. The results seem to be robust for changes occurring over time scales from a week to two months, and spatial scales of 25km x 25km and larger. The technique is able to capture significant single events, inter-annual and intra-annual variation. In specific, we observe clear decreases in sources from urban North America and urban Western Europe, both increases and decreases over East Asia, and significant increase in biomass burning sources from North America, and both biomass burning and urban sources from Southeast Asia, Africa, and regions of South America.

Finally, weaknesses in the model assumptions associated with vertical transport, mis-characterized removal and in-situ processing, remotely sensed measurement biases (i.e. cloud cover), and the mathematics of sampling of the differences of the variance are discussed. In some cases, uncertainties in emissions can be expanded to cover these observations, and in other cases are highlighted for future work.

How to cite: Cohen, J.: Using Multiple Satellites with a New Forward Variance Maximization and Coupled Inverse Filter Method to Quantify the Emissions of Biomass Burning and Urban Sources which have Changed Dramatically Over the Past 2 Decades, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12769, https://doi.org/10.5194/egusphere-egu2020-12769, 2020.

Ammonia (NH₃) emission inventories are an essential input in chemical transport models and are helpful for policy-makers to refine mitigation strategies. However, current estimates of Chinese NH₃ emissions still have large uncertainties. In this study, an improved inversion estimation of NH₃ emissions in China has been made using an ensemble Kalman filter and the Nested Air Quality Prediction Modeling System. By first assimilating the surface NH₃ observations from the Ammonia Monitoring Network in China at a high resolution of 15 km, our inversion results have provided new insights into the spatial and temporal patterns of Chinese NH₃ emissions. More enhanced NH₃ emission hotspots, likely associated with industrial or agricultural sources, were captured in northwest China, where the a posteriori NH₃ emissions were more than twice the a priori emissions. Monthly variations of NH₃ emissions were optimized in different regions of China and exhibited a more distinct seasonality, with the emissions in summer being twice those in winter. The inversion results were well-validated by several independent datasets that traced gaseous NH₃ and related atmospheric processes. These findings highlighted that the improved inversion estimation can be used to advance our understanding of NH₃ emissions in China and their environmental impacts.

How to cite: Tang, X., Kong, L., Zhu, J., Wang, Z., Pan, Y., Wu, H., Wu, L., Wu, Q., He, Y., Tian, S., Xie, Y., Liu, Z., Sui, W., Han, L., and Carmichael, G.: Improved Inversion of Monthly Ammonia Emissions in China Based on the Chinese Ammonia Monitoring Network and Ensemble Kalman Filter, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13077, https://doi.org/10.5194/egusphere-egu2020-13077, 2020.

During May and June 2018, the intensive campaign CoMet (Carbon dioxide and Methane mission) made atmospheric measurements of greenhouse gases over Europe, with the upper Silesian coal basin (USCB) in southern Poland as a specific focus area. CoMet aimed at characterising the distribution of CH₄ and CO₂ over significant regional sources with the use of a fleet of research aircraft, as well as to validate remote sensing measurements from state-of-the-art instrumentation installed on-board against a set of independent in-situ observations.

In order to link atmospheric mixing ratios to source emission rates, high-resolution simulations with WRF-GHG v 3.9.1.1. (10 km x10 km Europe + nested 2 km x 2 km domain over the USCB), driven by short-term meteorological forecasts from the ECMWF IFS model and forecasts from CAMS (Copernicus Atmospheric Monitoring Service) for initial and lateral tracer boundary conditions were performed. Biogenic fluxes of CO2 were calculated online using the VPRM model driven by MODIS indices. Anthropogenic emissions over Europe were taken from the database of TNO, Department of Climate, Air and Sustainability (7 km x 7 km), augmented with an internal emissions database developed within CoMet that uses coal mine ventilation shaft emission measurements in combination with recent updates of the E-PRTR (European Pollutant Release and Transfer Register).

Tagged tracers were used to simulate a robust set of over 100 distinct anthropogenic sources of CH₄ and CO₂ from the study area, and these forward simulations were then used as the transport operator in an analytical Bayesian inversion system. Here we discuss the results of an analysis performed with the use of selected in-situ data measured over the course of the three-week campaign, including results and sensitivity tests.

How to cite: Gałkowski, M., Marshall, J., Koch, F.-T., Chen, J., Fiehn, A., Roiger, A., Eckl, M., Kostinek, J., Swolkień, J., and Gerbig, C.: Estimating emissions of methane and carbon dioxide sources using analytical Bayesian inversion system based on WRF-GHG tagged tracer simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16082, https://doi.org/10.5194/egusphere-egu2020-16082, 2020.

Since the last decade, India has encountered severe problems in air quality and became the most air polluted country in the world.  With satellite observations, we can monitor the changes of NO2 column concentrations in India. However, the information on emissions is very limited. In this study, we use the KNMI DECSO (Daily emission Estimates constrained by Satellite Observation) algorithm to estimate NOx emissions from OMI observations from 2007 to 2018. The results show that NOx emissions have increased by about 40% in the last 12 years. We compare NOx emissions from DECSO and the HTAP bottom-up NOx emissions with the location and capacity of power plants in India. The comparison between DECSO and HTAP shows that the emissions estimated from satellite are more accurate on spatial and temporal scale. We also run the CHIMERE v2013 model with emissions from DECSO and HTAP respectively and compare the model simulations with NO2 in-situ measurements of the Indian national network. The comparison shows that model simulation with DECSO has lower bias and better correlation with in-situ observations than that with HTAP.  

How to cite: Ding, J., van der A, R., and Mijling, B.: Rapid growth of NOx emissions in India observed from Space, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17942, https://doi.org/10.5194/egusphere-egu2020-17942, 2020.

A clear understanding of carbon monoxide (CO) emissions is important at various scales. On the local scale CO is toxic to living organisms, and on the global scale CO plays in role in the budget of  the hydroxyl radical (OH). OH, in turn, is important for the oxidizing capacity of the atmosphere. Additionally, CO is a precursor of the greenhouse gases ozone and carbon dioxide, hence CO influences also climate on a global scale.

Approximately one quarter of the global atmospheric CO load emanates from wildfires. However, these emissions are sometimes underrepresented in the emission datasets. Among the reasons for this discrepancy are clouds and smoke plumes hampering observations of land cover and active fires and uncertainties in emission factors. These issues are less relevant for top-down approaches like inverse modeling, which allow tracing back an atmospheric signal to its source even if it is only observed days after emission.

In this study, we attempt to improve the emission estimates of an existing inventory by applying an inverse modeling approach to the CO emissions of the California wildfires in 2018, that devastated more than 7500 square kilometers of forested and residential area. More specifically, we used the Fire Emission Inventory from NCAR (FINN) together with the CO observations from the TROPOMI instrument onboard the Sentinel 5 Precursor (S5P) satellite and the TM5-4dvar inverse model. The high resolution of the TROPOMI observations enables better spatial constraints compared to previous instruments. Preliminary results suggest significant positive emission increments compared to FINN.

How to cite: Nüß, J. R., Daskalakis, N., Schneising, O., Buchwitz, M., Krol, M. C., and Vrekoussis, M.: Optimizing CO emissions from the 2018 Californian fires using S5P – an inverse modelling study, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20387, https://doi.org/10.5194/egusphere-egu2020-20387, 2020.