We present a status overview of the TEMPO mission including its operation, validation and status of baseline data products and upcoming algorithm improvements, implementation of Near-Real-Time (NRT) and other data products.
TEMPO is NASA’s first Earth Venture Instrument (EVI) selected in 2012, and the North America component of the geostationary air quality constellation along with GEMS (launched in Feb. 2020) over Asia and Sentinel-4 (to launch in 2025) over Europe. It is the first spaceborne instrument providing revolutionary hourly daytime air pollution over North America from Mexico City to the Canadian oil sands, and from the Atlantic to the Pacific, at neighborhood scale (~10 km2 at boresight). It uses UV/visible spectroscopy (293-493 nm, 538-741 nm) to measure key elements of tropospheric air pollution chemistry including O3, NO2, HCHO and aerosols, and capture the inherent high variability in the diurnal cycle of emissions and chemistry. TEMPO was successfully launched on board IS-40E into the geostationary orbit at 91°W in April 2023. It conducted its first light Earth observations in early August 2023, kicking off a new era of air quality monitoring from space over North America. It started its nominal operation in October 2023 for a 20-month of baseline Phase E. The baseline mission has been recently extended to September 2026, with further extension via NASA senior reviews. At night, TEMPO can observe city lights, gas flaring, maritime lights from fishing and offshore oil platforms, clouds and snow in the moonlight, lightning, aurorae, and nightglow without interfering with its primary daytime air quality/chemistry mission. Baseline V3 data products were released to the public in May 2024 from NASA’s Atmospheric Science Data Center (ASDC). These data products were upgraded from beta to provisional level in December 2024 after the validation team approval. TEMPO near-real-time (NRT) and other science quality data products were funded by NASA Satellite Needs Working Group (SNWG) to assist in air quality forecasting and modeling efforts and develop better pollution control strategies.
How to cite: Liu, X., Chance, K., Chong, H., Davis, J., Fitzmaurice, J., Gonzalez Abad, G., Houck, J., Hou, W., Nowlan, C., Park, J., Suleiman, R., and Wang, H. and the TEMPO team: A New Era of Air Quality Monitoring from Space over North America with TEMPO: Early Years in Orbit, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14388, https://doi.org/10.5194/egusphere-egu25-14388, 2025.
The Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign took place in February and March 2024 as part of a global initiative to collect detailed atmospheric data across various locations in Asia. This campaign utilized aircraft, satellites, and ground-based instruments to improve the understanding of winter air quality in the region. Since 2020, the Geostationary Environment Monitoring Spectrometer (GEMS) has been providing hourly observations of air quality in Asia for the first time.
During the ASIA-AQ campaign, GEMS data offered comprehensive observations of aerosols, ozone, NO₂, SO₂, HCHO, CHOCHO, and other pollutants over a wide area. These observations were compared with independent measurements from aircraft and ground-based instruments, including high spectral resolution lidar (HSRL), GEO-CAPE Airborne Simulator(GCAS), PANDORA, AERONET etc. This study highlights the intercomparison and evaluation of the GEMS dataset for various scenarios, such as urban pollution, biomass burning, emissions from power plants, and volcanic eruptions observed during the campaign.
How to cite: Kim, J., Park, R., Jeong, U., Lee, H., Kim, J.-H., Park, S., Ahn, M.-H., Chang, L., Lee, W.-J., Hong, H., Jung, Y. J., Bak, J., Kim, M., Kang, W., Chae, Y., Seo, Y., Crawford, J., Janz, S., Judd, L., and Hair, J. W.: Intercomparison of Geostationary Environment Monitoring Spectrometer (GEMS) observations during the ASIA-AQ Field Campaign, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15056, https://doi.org/10.5194/egusphere-egu25-15056, 2025.
Onboard of MetOp satellite series, Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform spectrometer based on the Michelson interferometer. IASI acquires interferograms from which high-resolution atmospheric emission spectra are provided, enabling the derivation of temperature and humidity profiles (among other parameters) with exceptional spectral resolution. In this study, we will use the IASI archive to evaluate a retrieval approach in the interferogram domain, which we anticipate will be well-suited for near-real-time (NRT) analysis of extensive spectral datasets expected from next-generation tropospheric sounders like MTG-IRS. The Partially Scanned Interferograms (PSI) method, applied to the retrieval of trace gases from IASI, has only rarely been studied. However, existing studies suggest its potential for specific gases, including CO, CO₂, CH₄, and N₂O, which could enable highly accurate trace gas column density retrievals at the resolution of a single IASI footprint.
We will present the interferogram retrieval approach of CO from IASI simulations. These results are based on the set of simulations of IASI interferograms for which the identified regions (optical path differences), sensitive to the carbon monoxide species, are noised and then used for retrievals. Furthermore, the study which aims to compare the performance of the interferogram retrieval approach compared to the conventional (i.e. from the spectral domain) will also be presented. The expected advantage compared to the usual methods is an efficient use of the information contained in all IASI channels that are available in the absorption bands of a specific gas species. Finally, using interferogram points sensitive to parameters of interest, we will also present a proof of concept of a neural network algorithm for classification of the interferograms predicting the surface temperature and the abundance of H2O and CO.
The simulation of IASI spectra was conducted using the LATMOS Atmospheric Retrieval Algorithm (LARA), a robust and validated radiative transfer model based on Least Squares estimation [Segonne et al., 2021]. The climatological library TIGR [Chédin et al., 1985; Chevallier et al., 1998] was used to generate IASI interferograms with LARA. TIGR comprises 2311 atmospheric scenarios, each characterized by temperature, water vapor, and ozone concentration values across a specified pressure grid from the surface to the top of the atmosphere. The study focuses on carbon monoxide, a key trace gas for understanding air quality and climate forcing. Carbon monoxide exhibits a characteristic “comb” absorption pattern within the 2050–2350 cm⁻¹ wavenumber range [Serio et al., 2012]. Simulations were performed for surface temperatures ranging from -15 to +15 K, in 5 K increments from the base temperature, to assess the impact of thermal contrast [Baudin et al., 2016]. Additionally, the study explores the potential of correlating interferogram characteristics with surface temperature and H₂O content, aiming to enhance the accuracy of CO column retrievals.
How to cite: Eco, N., Payan, S., and Croizé, L.: Towards MTG-IRS retrieval of CO using IASI from the interferogram domain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16544, https://doi.org/10.5194/egusphere-egu25-16544, 2025.
The Sentinel-5P/TROPOMI Aerosol Layer Height (ALH) is based on an optimal estimation (OE) approach, fitting cloud-free measurements to synthetic reflectances in the strongest oxygen absorption (O2-A) band, provided by a neural network trained with high resolution simulated reflectances. The ALH has been continuously improved since its release in 2019, focusing especially on (bright) land surfaces, over which the ALH product showed underestimated aerosol layer heights (biased towards the surface). In this presentation the latest updates of the ALH product will be discussed, including the introduction of the Directional Lambertian-Equivalent Reflectance (DLER) climatology to improve the surface albedo characterisation over land. Secondly, by adding the surface albedo in the feature vector of the OE inversion, using the DLER as prior information, retrievals improved considerably over land, especially in the case of bright surfaces. New retrievals over land now largely match the retrievals over ocean, which have shown a good comparison with validation data since its release, most notably with CALIOP weighted extinction heights. The albedo is fitted for both land and ocean surfaces, but the implementation is different over land and ocean because of the large range of land surface albedos. Over ocean, the retrievals are optimised by tuning the a priori error settings, while over land the a priori surface albedo values are relaxed so the fitting procedure can incorporate the albedo effects in the retrieval. About 1.5 times more converged results were obtained with the current implementation, with low land-ocean contrasts in the aerosol layer height retrievals. The average difference with CALIOP weighted extinction height decreased for selected cases from about −1.9 km to −0.9 km over land and from around −0.8 km to +0.1 km over ocean.
We will show the latest results of the TROPOMI ALH products, by comparisons with CALIOP and GEMS Aerosol layer height retrievals, and show some preliminary results with EarthCARE data. Implementing of this algorithm for Sentinel-3/OLCI O2-A band measurements and the algorithm developments for the upcoming Sentinel-4 and Sentinel-5 mission will also be highlighted.
How to cite: de Graaf, M., Sneep, M., ter Linden, M., Tilstra, L. G., and Veefkind, J. P.: Surface albedo fitting in the optimal estimation routine of the TROPOMI oxygen A-band aerosol layer height product., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9106, https://doi.org/10.5194/egusphere-egu25-9106, 2025.
Nitrogen dioxide (NO2) plays a key role in the formation of urban smog and its adverse impact on human health. However, the routinely deployed measurements of NO2 are essentially limited to:
- tropospheric NO2 columns with global daily coverage, measured by the TROPOMI satellite instrument with a horizontal resolution of up to 3.5 x 5.5 km2
- near-surface NO2 concentrations, measured by in situ instruments at typically 0-8 m above ground with sparse spatial coverage
- NO2 profiles from MAX-DOAS measurements, with extremely sparse coverage and significant retrieval uncertainties
Regional chemistry and transport models (CTMs) facilitate the prediction of air pollutants with dense spatial coverage at urban-scale resolutions (e.g. 10 x 10 km2 or better), thereby yielding a valuable extension to the available observational data. Furthermore, significant progress has recently been made in developing neural network surrogate models with the ability to partially replace the computationally expensive CTM simulations. The NitroNet model, for example, can predict tropospheric NO2 profiles based on TROPOMI satellite observations and other ancillary variables, such as emission data and meteorological information.
A particular challenge is the validation of such models at altitudes within the boundary layer due to the lack of suitable observations, but data from measurement campaigns can partially fill these gaps. The CINDI-3 measurement campaign took place in May 2024 and comprised diverse spectroscopic measurements of NO2 concentrations in the lowest few hundred meters above ground. Moreover, CINDI-3 is the first CINDI campaign since the launch of the TROPOMI instrument, whose measurements are the main input to the NitroNet model.
We present an intercomparison of lower-tropospheric NO2 profiles from CINDI-3 long-path DOAS measurements, the CAMS CTM, and the NitroNet neural network. We provide a comprehensive overview of the near-surface NO2 profile shapes, as well as the level of agreement between measurements and simulation results obtained with different modelling approaches (here: a classic CTM simulation and a neural network based surrogate model).
How to cite: Kuhn, L., Beirle, S., Ziegler, S., Pozzer, A., and Wagner, T.: Intercomparison of lower-tropospheric NO2 profiles from the CINDI-3 measurement campaign, the CAMS regional model, and the NitroNet neural network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2882, https://doi.org/10.5194/egusphere-egu25-2882, 2025.
Satellite observation plays an important role in air quality monitoring. Nitrogen dioxide (NO2) is an important atmospheric trace gas that significantly impacts air quality, public health, and ecosystems. While satellite NO2 observations have been widely used in the application of machine learning (ML) to estimate surface NO2 distributions, this surface NO2 modeling deserves further investigation, including examining satellite’s contributions to the estimation of NO2 at different concentration levels. In addition, as satellite data evolve towards higher spatiotemporal resolution, the demand for high-resolution a-priori NO2 profiles is increasing. Generating such profiles using traditional numerical models is computationally expensive, but ML offers an efficient solution to address this challenge. This presentation illustrates two developments based on ML technology: one focusing on the application of satellite observations to estimate surface NO2 distributions, and the other on the development of high-resolution a-priori NO2 profiles for satellite retrievals. Western Europe is used as the study area.
The first part addresses estimating high-resolution surface NO2 concentrations (1 km, daily) using the Boosting Ensemble Conformal Quantile Estimator (BEnCQE). This model integrates diverse datasets, including TROPOMI NO2 tropospheric vertical column densities (TVCDs), and demonstrates reliable performance validated against European Environmental Agency (EEA) surface observations (r = 0.80, R² = 0.64, RMSE = 8.08 µg/m³). Quantile regression in BEnCQE provides uncertainty estimates and feature importance analysis across different NO2 levels. Results show that satellite observations significantly contribute to background NO2 predictions but have less influence on high-concentration estimates, likely due to the relatively coarse spatial resolution of current satellite data. These findings highlight the need for higher-resolution satellite missions, such as CO2M (2 km resolution), to better capture localized pollution.
The second part focuses on generating high spatial resolution a-priori NO2 profiles for satellite retrievals. We developed Deep Atmospheric Chemistry NO2 (DACNO₂), a convolutional neural network framework, to produce 3D NO2 distributions (8 levels from the surface to 5,000 m, 2 km spatial resolution, daily). Using a multi-constraint training approach that combines coarse-resolution CAMS-EU synthetic NO2 data (10 km) and fine-scale EEA surface observations (2 km), DACNO2 captures detailed spatial gradients near emission hotspots while maintaining broad physical consistency. The evaluation shows good performance aligned with EEA observations (r = 0.81, R² = 0.64, RMSE = 5.10 µg/m³) and CAMS-EU synthetic NO2 (r = 0.94, R² = 0.89, RMSE = 1.11 µg/m³). The implementation of DACNO2 is efficient, taking only minutes to compute one day's result using GPU acceleration.
Overall, this presentation introduces ML-based works on application and development aspects for satellite NO2 observations to advance the coupling of ML technology and satellite observations of pollution.
How to cite: Sun, W., Tack, F., Clarisse, L., and Van Roozendael, M.: Enhancing Satellite-Based NO2 Monitoring with Machine Learning: From Near Surface Concentration Estimation to A-Priori Profile Development, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9700, https://doi.org/10.5194/egusphere-egu25-9700, 2025.
Ammonia (NH3) is an atmospheric pollutant mainly emitted by the agricultural sector, which has an effect on public health since it is a precursor of fine particles (PM2.5). The diurnal variability of NH3 in the atmosphere and its transformation into particles are poorly constrained and strongly depend on meteorological parameters, in particular temperature. Polar orbit satellite observations, such as the Infrared Atmospheric Sounder Instrument (IASI), are keys to assess spatio-temporal variabilities of NH3 with observations twice a day. Geostationary instruments offer a comprehensive assessment of NH3 diurnal variability and its dependence on atmospheric temperature.
This study analyses the contribution of geostationary instruments to observe ammonia variabilities and their link to atmospheric temperature. We use IASI observations, as well as the Geostationary Interferometric Infrared Sounder (GIIRS) on board China’s FengYun-4B satellite launched in June 2021 that measure atmospheric ammonia over East Asia and parts of South Asia and Southeast Asia every 2 hours at 12 km at nadir. We also evaluate the InfraRed Sounder (IRS) instrument that will be launched onboard the Meteosat Third Generation (MTG) satellite into geostationary orbit over Europe and Africa in late 2025. IRS will offer the ability to assess NH3 diurnal variabilities with frequent measurements (every 30-45 minutes) and better spatially resolved observations than IASI (4 km x 4 km at the Equator and Greenwich meridian).
In this work, GIIRS NH3 total columns are validated with IASI observations between July 2022 and June 2024. Then, the link between NH3 variabilities and atmospheric temperature are analyzed over Asia using GIIRS observations. Finally, a NH3 sensitivity analysis considering measurement noises is made for the future IRS-MTG mission and is discussed with respect to IASI.
How to cite: Guendouz, N., Viatte, C., Zeng, Z.-C., Boynard, A., Safieddine, S., Standfuss, C., Turquety, S., Van Damme, M., Clarisse, L., Coheur, P., Armante, R., Prunet, P., and Clerbaux, C.: Contribution of Geostationary Satellites to the Observation of Atmospheric NH3, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17035, https://doi.org/10.5194/egusphere-egu25-17035, 2025.
In East Africa, scarcity of air quality data in rapidly expanding cities has hindered planners’ ability to mitigate air pollution effectively. We explored how low-cost air quality sensors, reference monitors, and satellite-derived products from Sentinel-5p and MODIS can be integrated to generate ground-truthed, high-resolution (1 km × 1 km) daily maps of PM2.5 concentrations over the megacities of Kampala, Nairobi, and Dar es Salaam. These maps and accompanying codes, which in principle can be applied to other data-scarce cities, would be made available during the session. The findings reveal that average PM2.5 concentrations sometimes exceed recommended air quality thresholds, with significant seasonal and spatial variability. Challenging existing preconceptions, the study found that PM2.5 levels could be higher in suburban zones than in city centres, due to seasonal vegetation shifts and combustion-related activities. This work further demonstrates the feasibility of combining low-cost sensors with satellite data to improve air quality monitoring especially in data-scarce regions.
How to cite: Chua, S., Oguge, O., Sserunjogi, R., Okure, D., Manyele, A., Lehtipalo, K., Zaidan, M. A., and Petäjä, T.: Integration of satellites and low-cost sensors for high-resolution air quality mapping in East African megacities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6271, https://doi.org/10.5194/egusphere-egu25-6271, 2025.
Effective measures to improve air quality and optimize health benefits require teamwork to scale from satellite observations with broad coverage and surface-based measurements with research-grade instruments and local arrays of low-cost sensors. Results must be evaluated with models and communicated to regulatory agencies for corrective action. The bulk of the most vulnerable communities lack monitors and satellites can help fill in the gaps. Historically these efforts have been carried out by scientists but historically the worst air quality has been in disadvantaged communities with inadequate monitoring and justifiably distrustful of government agencies. Communities must be encouraged to express their concerns and empowered to seek mitigation action. A neighborhood in Baltimore has long complained of coal dust but suffers from myriad air pollution problems – a legacy of residential communities surrounded by industry and heavy traffic. A community in suburban Washington has formed an effective environmental justice action team to measure, display, and report AQ problems. Success includes improved enforcement of regulations on pollution sources and a $147M grant to replace diesel with electric vehicles around the Port of Baltimore. Unfortunately, these are just two of dozens of afflicted communities in the area. We report on successes and challenges and how these efforts might be generalized and scaled up.
How to cite: Dickerson, R., Aburn, T., Aubourg, M., Dressen, J., Canty, T., He, H., Li, Z., Okanga, T., Ren, X., Singh, A., Stratton, P., and Wei, J.: Air Quality on the Neighborhood Scale: All Means Necessary, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20332, https://doi.org/10.5194/egusphere-egu25-20332, 2025.
Sulfur dioxide (SO2) from volcanic eruptions can have a significant impact on atmospheric chemistry and climate, and pose a threat to aviation. Satellite observations provide a unique opportunity to track the spatial distribution, vertical structure and temporal evolution of volcanic SO2 plumes. In this study, we use observations from the FengYun-3 (FY-3) series of meteorological satellites, which have formed a Hyperspectral Infrared Atmospheric Sounder (HIRAS) constellation in dawn-dusk, mid-morning and afternoon sun-synchronous orbits. The constellation provides six global observations in one day in the thermal infrared with equatorial overpass times of 5:30 am/pm (FY-3E), 10:00 am/pm (FY-3F) and 2:00 am/pm (FY-3D). SO2 layer height and total column are obtained from HIRAS spectra observations for two volcanic eruptions in 2024 in the tropics and high latitudes, respectively. The retrieval results from the three sounders are generally consistent. Intercomparisons with existing data from IASI satellites, model simulations and Microwave Limb Sounder are performed to assess the robustness of the retrieved data products. Our results show that the global FengYun-3 HIRAS constellation captures well the spatial and vertical evolution of lofted volcanic SO2 plumes after eruptions. This study represents an important application of a global constellation of FengYun hyperspectral infrared sounders for monitoring global variations in atmospheric composition.
How to cite: Zeng, Z.-C., Clarisse, L., Franco, B., Qi, C., Lee, L., and Hu, X.: Observing volcanic SO2 from a constellation of FengYun hyperspectral infrared sounders, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1814, https://doi.org/10.5194/egusphere-egu25-1814, 2025.
This research study analyzes atmospheric ammonia (NH₃) surface concentrations in France, Belgium (Flanders region), and the Netherlands, highlighting their geographic, temporal, and diel variability from 2015 to 2023 using ground-based measurements (31 sites) and combined satellite data (IASI and CrIS). NH3 is the major alkaline gas in the atmosphere, affects air quality and aerosol formation, and degrades ecosystems, making its monitoring essential. The highest annual average NH₃ levels were observed in the Netherlands (7.4 ± 4.1 µg/m³), followed by Belgium (4.5±3.4 µg/m³) and France (3.7±2.1 µg/m³) as per in-situ observations. Rural areas characterized by agricultural practices showed higher levels than other land-use types, peaking in spring and summer due to fertilizer application and manure volatilization. Rural sites reached 8.5 ± 4.0 µg/m³ and 5.4 ± 3.9 µg/m³, in the Netherlands and Belgium (Flanders region) respectively. Urban areas recorded noticeable NH₃ concentrations either across Belgium (Flanders region) (3.5±2.0 µg/m³) and France (4.4 ± 2.0 µg/m³) which may be attributable to vehicular traffic, wastewater management, industrial operations, and the geographical dispersion of agricultural emissions. Seasonal variations observed notable NH₃ peaks in spring and summer, due to agricultural intensification and increased temperatures, while winter had the lowest concentrations due to decreased emissions. Diel patterns showed midday peaks in rural areas due to increased volatilization, while urban areas showed morning peaks related to traffic emissions. Satellite-derived NH₃ data from combined IASI and CrIS sensing showed moderate to strong correlations with ground-based measurements (R = 0.32–0.8), while satellites tended to underestimate local concentrations. Unlike surface measurements, satellite data revealed NH₃ concentrations across land-use types were little different, with means and standard deviations as follows: Crops (2.35 ± 2.08 µg/m³), High-Density Urban (2.39 ± 2.20 µg/m³), Low-Density Urban (2.35 ± 2.14 µg/m³), and Rural (2.26 ± 2.00 µg/m³). Comparable trends were noted in entire Belgium (+0.023 µg/m³ per year) and the Netherlands (+0.043 µg/m³ per year), where NH₃ concentrations were higher in 2020 and decreased in the following years possibly due to improved air dispersion and increased precipitation. The results highlight the key role of agriculture, which is the dominant source of NH₃ emissions, but urban regions also contribute significantly through vehicular and industrial activities. Effective mitigation techniques are crucial, including optimal fertilizer application, sophisticated manure management, and stringent urban emission regulations. These plans are in line with regional and national regulations, such as France’s PREPA plan, which aims to reduce NH₃ emissions by 13% by 2030 (Chatain et al., 2022). The integration of satellite and ground-based data offers a thorough understanding of NH₃ dynamics, facilitating the formulation of specific regulatory frameworks to reduce emissions, protect ecosystems, and improve air quality in these areas.
References
Chatain, M., Chretien, E., Crunaire, S., & Jantzem, E. (2022). Road Traffic and Its Influence on Urban Ammonia Concentrations (France). Atmosphere, 13(7), Article 7. https://doi.org/10.3390/atmos13071032
How to cite: Katoch, V., Font, A., Bourin, A., Perdrix, E., Shephard, M., Clarisse, L., Staelens, J., Berkhout, H., Damme, M. V., and Riffault, V.: Geographic and Temporal Variability of atmospheric surface Ammonia (NH3) in France, Belgium, and the Netherlands (2015 – 2023) across different land-use types: Insights from Ground-Based and combined Satellite Observations., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10824, https://doi.org/10.5194/egusphere-egu25-10824, 2025.
Short-term air quality measures have been commonly conducted for major events held in China, while their effectiveness on emission reduction was insufficiently analyzed due to deficient capability of tracking the fast-changing emissions of cities. Here we combined a machine learning algorithm, multiple satellite measurements, and an air quality model, and deduced 7-day moving averages of NOX emissions for host and neighboring cities of 11 events held from 2010 to 2023 in Yangtze River Delta (YRD). We find the benefits of short-term controls on emissions for these events have been weakened over time, due to gradually tightened long-term controls and to a more cautious strategy of air quality improvement for recent events. The main sector of emission abatement for events shifted from power to industry and transportation, reflecting the diverse progresses of regular controls for different sectors. As a legacy, short-term controls supported better design of long-term air quality policies.
How to cite: Wang, H., Zhao, Y., He, Q., Hao, K., Qin, K., Zheng, B., and Lin, J.: Declining opportunity and enhanced targeting of short-term emission controls for major events in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1754, https://doi.org/10.5194/egusphere-egu25-1754, 2025.
Emissions from power plants contribute significantly to the overall greenhouse gas and air pollutant levels. Satellite observations of compounds such as NO2 and CO can help improve CO2 emission estimates, as proposed in the CORSO (CO2 Monitoring and Verification Support Research on Supplementary Observations) project. One of the key objectives of CORSO is to improve global and local capabilities in using observations of co-emitted species (NO2 and CO) to better estimate anthropogenic CO2 emissions. In this presentation, we will discuss a methodology developed to detect hotspots associated with power plants using NO2 data from the TROPOMI instrument on the Copernicus Sentinel-5P satellite, as well as observations from the Geostationary Environment Monitoring Spectrometer (GEMS) over Eastern and Southeast Asia. The final goal is to improve the accurate detection of hotspot locations to identify missing sources in emission inventories and refine their geolocation. It is important to note that the detectability of the anthropogenic signal from co-emitted species is generally much higher than that of CO2. Various statistical methods have been tested to identify high-probability hotspots in the NO2 tropospheric column densityfrom TROPOMI and GEMS. This includes an exploratory spatial data analysis for cluster detection (Getis-Ord Gi*), which evaluates each spatial variable’s neighborhood to determinewhether its values are significantly higher or lower than those in the surrounding area. The results indicate agreement between the hotspots identified through the Gi* method and the locations of power plants from the literature. These identified hotspot coordinates can be used to enhance the quanification of emissions and address mislocation in power plant emissions withinemission inventories.
How to cite: Doumbia, T., Granier, C., Merly, H., Kuhlmann, G., Collado, O., and Guevara, M.: Detection of hotspot areas using Sentinel-5P and GEMS imagery for evaluating bottom-up emission inventorie, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8135, https://doi.org/10.5194/egusphere-egu25-8135, 2025.
Since carbon monoxide (CO) in the atmosphere adversely affects air quality and climate, knowledge about its sources is crucial. However, current global bottom-up emission estimates retain significant uncertainties. In this study, we attempt to reduce these uncertainties by optimizing emission estimates for the second half of the year 2018 on a global scale with a focus on the northern hemisphere through the top-down approach of inverse modeling. Specifically, we introduce data from the TROPOspheric Monitoring Instrument (TROPOMI) into the TM5-4DVAR model. For this purpose, we developed novel methods to handle the unprecedented amount of data provided by TROPOMI. We compare the results from three inversion experiments that optimize CO emissions based on different observational data. In one experiment we only assimilate TROPOMI data, in a second experiment we only assimilate NOAA surface flask measurements, and in a third experiment we assimilate both datasets. We show that the inversion that assimilates only satellite observations reproduces flask measurements south of 55° N almost as well as the inversions that assimilated these measurements. These results show that the assimilation of TROPOMI data alone may provide reliable CO source estimates globally.
How to cite: Vrekoussis, M., Nüß, J. R., Daskalakis, N., Piwowarczyk, F. G., Gkouvousis, A., Schneising, O., Buchwitz, M., Kanakidou, M., and Krol, M. C.: Global carbon monoxide emissions constrained by TROPOMI, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10148, https://doi.org/10.5194/egusphere-egu25-10148, 2025.
This study focuses on TROPOMI based observations and retrieval uncertainties of tropospheric CO and HCHO in Central Asia, an area experiencing rapid development. The data is applied within the Model Free Inversion Estimation Framework (MFIEF), to calculate daily, grid-by-grid CO emissions and uncertainty range from 2019 to 2023.
The framework herein has been expanded by integrating the data with explicit observational uncertainties, and applying a new and unbiased analytical system to perform comprehensive uncertainty analysis. Results show that the observational uncertainties have a significant impact on the calculated emissions, with approximately 55% of the data deemed unreliable. After filtration, the remaining data reveals more distinct high-value areas, while excluding the small number of extremely high values which were as likely to be due to observational noise as the large number of very low emissions pixels. Remaining pixels generally conform to know industrial, power, coal, steel, mining, and urban areas, enhancing the reliability of emission estimates. A few interesting exceptions, as discussed herein include large underground coal fires.
The CO emissions exhibit distinct temporal and spatial patterns. In urban and industrial areas of China from 2019 to 2022, emissions show a downward trend, followed by a slight increase in 2023, while in underground coal fire areas and in non-Chinese areas of Central Asia there are different trends observed. Emissions are highest during the months with the least UV radiation and coldest temperatures, such as December and January. Spatially, high emissions are concentrated in urban and industrial areas, while natural areas have relatively lower emissions, with the notable exception of underground burning coal fields which are found to be roughly as significant as large steel, power, and industrial sites.
Comparisons with EDGAR indicate our results have both different spatial distribution and temporal variation. Our results show a greater likelihood of decreasing over time and more variability (daily to weekly scale). This provides a scientific basis for understanding CO emissions in Central Asia while also contributing to the improvement of emission inventories, air quality models, as a basis dataset for CO and CH4 retrievals, and even for attribution studies to be performed.
How to cite: Feng, Y., Cohen, J. B., Li, X., and Qin, K.: CO emissions inversion using satellite CO observations and uncertainty in tandem identify and Attribute New and Unknown Sources over Central Asia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9171, https://doi.org/10.5194/egusphere-egu25-9171, 2025.
Retrievals of column average methane mixing ratio (XCH4) from satellites can provide a wealth of information on the fluxes of CH4. The TROPOMI instrument onboard the satellite Sentinel-5P is particularly interesting for flux estimation using atmospheric inversions as it provides global coverage with relatively high-resolution ground pixels of 5.5 × 7.5 km2 (at nadir). Furthermore, there are several different retrieval products available for TROPOMI XCH4, making it possible to compare these and quantify the effect that differences between them have on CH4 fluxes from atmospheric inversions.
In this study, we have compared XCH4 from three retrieval products: i) the official ESA product provided by SRON, ii) the Weighting Function Modified Differential (WFMD) product from the University of Bremen, and iii) the blended TROPOMI-GOSAT product (S5P-BLND) from Harvard University, in atmospheric inversions over Europe for the year 2020. In addition, we compare fluxes from each of these with those derived with an inversion using ground-based observations from the ICOS atmospheric network. For flux estimation, we use the Bayesian inversion framework, FLEXINVERT, and the Lagrangian atmospheric transport model, FLEXPART, driven by ECMWF ERA5 meteorological reanalysis data.
We find that the fluxes derived using WFMD and S5P-BLND products agree better with those derived using ICOS data. The difference in performance of the official ESA product versus the WFMD and S5P-BLND products appears to be related to the albedo bias correction used in the ESA product, which may result in an overcorrection giving too high values of XCH4 in regions of low albedo and too low values over regions of high albedo. Moreover, this study emphasizes the need to compare different retrieval products and, if available, ground-based observations, in atmospheric inversions to identify any biases and address these.
How to cite: Thompson, R., Schneider, P., and Stebel, K.: Using different TROPOMI XCH4 retrieval products in atmospheric inversions of CH4: a comparison and reconciliation over Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9567, https://doi.org/10.5194/egusphere-egu25-9567, 2025.
Widespread and intense dry season burning in subtropical southern Africa (2-20°S), peaking in June to September, results from ignition by humans for agricultural practices and is propagated by a continuous dry savanna landscape. These fires produce large quantities of the reactive nitrogen compounds nitrogen oxides (NOx≡NO+NO2) and ammonia (NH3), influencing tropospheric ozone and aerosol budgets and so affecting climate. Here we use observations of NO2 from the TROPOspheric Monitoring Instrument (TROPOMI) and NH3 from the Infrared Atmospheric Sounding Interferometer (IASI) to evaluate NO2 and NH3 simulated by the GEOS-Chem model driven with 3 distinct biomass burning inventories. These inventories differ in their use of satellite data products to constrain the timing, extent, severity and longevity of fires. The Global Fire Emissions Database version 4 with small fires inventory (GFEDv4s) uses burned area, Fire INventory from NCAR version 2.5 (FINNv2.5) uses fire counts detected as thermal anomalies, and the Global Fire Assimilation System version 1.2 (GFASv1.2) uses fire radiative power. All use similar landcover-specific, temporally static emission factors. The emissions from these inventories for the 2019 burning season are the same for NOx from GFEDv4s and FINNv2.5 (4.3 Tg as NO), peaking in July for GFEDv4s and August for FINNv2.5, and much less for GFASv1.2 (1.5 Tg), peaking in August. For NH3, emissions from GFEDv4s (0.68 Tg) and GFASv1.2 (0.52 Tg) are about half that from FINNv2.5 (1.3 Tg) and peak emission months are the same as NOx for GFEDv4s and FINNv2.5, but a month earlier (July) for GFASv1.2. Averaging kernels from the satellite products are used to mitigate influence of the vertical sensitivity of the instrument and a priori assumption of the vertical profile of NH3 when comparing to GEOS-Chem. We apply TROPOMI NO2 averaging kernels to GEOS-Chem for comparison to TROPOMI and re-retrieve IASI NH3 columns using GEOS-Chem as a priori. In our comparison to TROPOMI, GEOS-Chem monthly mean NO2 driven with GFASv1.2 and GFEDv4s is more spatially consistent (R ≥ 0.9) than FINNv2.5 (R = 0.4-0.6) and is typically just 12-27% less than TROPOMI using GFASv1.2, compared to up to 80% more for GFEDv4s and 55% more for FINNv2.5. The much greater NOx emissions from GFEDv4s and FINNv2.5 contribute to tropospheric ozone chemical production that totals 38-49 Tg in June-September compared to 29 Tg for GFASv1.2, though differences in volatile organic compound emissions will also influence these production rates. Assessment against IASI NH3 is underway.
How to cite: Marais, E., Clarisse, L., Van Damme, M., Wiedinmyer, C., and Murphy, K.: Burning season emissions of reactive nitrogen from fires in subtropical southern Africa determined with TROPOMI and IASI, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11419, https://doi.org/10.5194/egusphere-egu25-11419, 2025.
Methane (CH₄) has a relatively short, compared to other greenhouse gases, atmospheric lifetime (~9 years) and a highly effective radiative forcing, contributing ~31% (1.19 W m-2) of the additional radiative forcing from anthropogenic emissions during the industrial era. Therefore, reducing CH₄ emissions is a necessary target for limiting near-term climate change. Despite progress in understanding processes and improving estimates, uncertainties in CH₄ sources and sinks create discrepancies between bottom-up and top-down estimates. Recent satellites carrying high-resolution, accurate instruments have provided better information on the concentrations and distributions of atmospheric trace gases. High-confidence observations and in-situ measurement networks are essential to reduce discrepancies and increase confidence in the results of data-driven methods.
This study evaluates the feasibility and efficiency of performing inversions integrating satellite-based remote observations, weighted according to their temporal and spatial distribution with in-situ measurements. The inversions assimilate retrieved data from the high-resolution TROPOMI WFMD methane product and background stations from the NOAA network. Compared to the operational ESA product, the TROPOMI WFMD product provides enhanced global daily coverage, especially at higher latitudes, and more realistic uncertainty estimates, offering better insight into the distribution of methane concentrations. Results compare different inversions of global methane emissions for 2019 at 1° × 1° resolution, with and without an adaptive per-pixel weighting factor, performed utilising the TMVar (TM5-MP/4DVar) system. The adaptive inflation factor is applied to the satellite term of the cost function, balancing its contribution relative to the smaller data volume of station measurements. Data from the ground-based TCCON network validate the simulations. This network operates as a standard reference for atmospheric chemistry-transport models and satellite retrievals due to its precision and low uncertainty in total column values.
Preliminary results show no significant differences when including satellite data with the in-situ ones without applying a weighting factor, mainly due to the overwhelming volume of satellite data compared to station measurements. However, introducing a constant weighting factor for satellite observations improves inversion accuracy. Adding an adaptive weighting factor, adjusted based on the observations' temporal and spatial distribution, further enhances the results. This approach outperforms unweighted and constant-weighting methods by addressing the underuse of station data and neglect of satellite information in regions with lower coverage due to lower point density. Therefore, incorporating appropriately weighted sources of information on the total atmospheric state helps to optimise the inversion results and ultimately reduce the error in the constrained surface fluxes.
How to cite: Parraguez Cerda, S., Nüß, J. R., Daskalakis, N., Segers, A., Schneising, O., Buchwitz, M., Vrekoussis, M., and Kanakidou, M.: From ground to orbit: Improving global methane emission inversions with adaptive weighting of TROPOMI and NOAA data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11070, https://doi.org/10.5194/egusphere-egu25-11070, 2025.
Satellite instruments like OMI (2004) and TROPOMI (2018) have transformed global monitoring of tropospheric nitrogen dioxide (NO₂). Here we present a new Level 3 (L3) dataset produced within ESA’s CCI ECV project, created by averaging OMI and TROPOMI NO₂ columns spatially and temporally. This compact, user-friendly dataset is suitable for trend analysis, emission estimation, and climate modeling validation. It includes OMI (2004-2021) and TROPOMI (2018-2021) NO₂ data, uncertainty estimates, and additional variables like the averaging kernel, which informs vertical sensitivity to NO₂. The data set can be found at : https://www.temis.nl/airpollution/no2col/cci-no2-omi.php & https://www.temis.nl/airpollution/no2col/cci-no2-tropomi.php
The L3 dataset consists of OMI and TROPOMI NO2 measurements spatially and temporally averaged in a consistent manner and includes full L3 uncertainty estimates. The uncertainty propagation includes measurement related uncertainties (from L2) as well as a spatial and a temporal representativity component. The data include additional spatiotemporally averaged variables, such as the averaging kernel, which provides relevant information on the vertical sensitivity to NO2. The L3 data have been produced in different resolutions ranging from 0.2 to 2 degrees and have been used for GEOS-Chem model evaluation. We show that the relative L3 uncertainties fall within the 15-20% range in polluted regions, lower than uncertainties in separate level 2 orbit retrievals, and brings tropospheric NO2 columns to within the GCOS ‘goal’ and ‘breakthrough’ requirements. Validation of the L3 against independent MAX-DOAS and PANDORA NO2 columns shows consistency up to 20%.
Our aim is to make the L3 dataset as consistent as possible, by minimizing sensor-related differences to obtain a clearer view of NO2 changes over time. While OMI and TROPOMI have similar retrieval algorithms, there is a 15-minute difference in overpass time, the algorithms use different cloud algorithms, and OMI suffers from the ‘row anomaly’ phenomenon that causes a decrease in spatial coverage and hence sampling differences with TROPOMI. We show that by co-sampling (in space and time) OMI and TROPOMI NO2 columns, the consistency between the datasets improves. For example, for Beijing the relative absolute difference of OMI and TROPOMI for winter months in 2019 and 2020 is 14% and 22% drops to 8% and 11% when co-sampling. Furthermore, the row anomaly phenomenon causes reduced coverage in OMI from 2007 onward. We show how long-term trends in tropospheric NO2 columns are affected by the row anomaly and present a recipe to avoid such spurious trends.
How to cite: Anglou, I., Boersma, F., Glissenaar, I., Rijsdijk, P., Verhoelst, T., Compernolle, S., Narivelo, H., and Eskes, H.: Presenting a Concise OMI and TROPOMI NO2 Afternoon Data Record. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19924, https://doi.org/10.5194/egusphere-egu25-19924, 2025.
Formaldehyde (HCHO) and nitrogen dioxide (NO2) are important precursors of tropospheric ozone through photochemical reactions. HCHO also acts as a marker for the oxidation of volatile organic compounds (VOCs). In the Metropolitan Area of São Paulo (MASP), these precursors are directly emitted by over 7 million vehicles, which burn a complex blend of ethanol and gasoline [1, 2, 3]. The present work analyzed vertical columns of HCHO and NO2 over MASP, using data retrieved from the TROPOMI sensor onboard the Sentinel-5P satellite. The analysis covered a five-year period from 2019 to 2023. A study region spanning ~6,500 km2 within the MASP was divided into 50 areas, each measuring 0.1° x 0.1° in latitude and longitude. This approach enabled the classification of the areas into distinct land-use types: 18 representing urban regions, 15 corresponding to green regions, and 17 covering transitional zones [4].
The three areas with the lowest and highest HCHO columns were two transition regions and one green region (minimum = 10.6 x 1015 molecules/cm2, extreme southwest), and three densely urbanized areas (maximum = 13.9 x 1015 molecules/cm2, central region), respectively. The three areas with the lowest and highest of NO2 columns were green regions (minimum = 2.5 x 1015 molecules/cm2, extreme southwest) and urban areas (maximum = 9.4 x 1015 molecules/cm2, central region), respectively. Both atmospheric constituents displayed higher column densities during the southern hemisphere winter (dry season).
The formaldehyde to nitrogen dioxide ratio (FNR) was calculated for each of the 50 areas, highlighting the São Paulo city center — particularly Marginal Tietê and Marginal Pinheiros — as a hotspot for ozone formation [5]. Despite its proximity to the urban center, the important Atlantic Forest (Reserve of Morro Grande) showed significantly lower concentrations of HCHO and NO2 compared to those recorded in the central areas. TROPOMI HCHO and NO2 data are proving essential for a better characterization of atmospheric chemical processes in the MASP, contributing to describe the formation of secondary pollutants, supporting the objectives of the BIOMASP+ project.
Keywords: Formaldehyde, Nitrogen Dioxide, Ozone, São Paulo megacity, TROPOMI
Acknowledgments: This work is funded and supported by BIOMASP+ Project (FAPESP 2020/07141-2) and Postgraduate Program in Meteorology (IAG-USP).
References
[1] Nogueira, T. et al., Atmosphere, 8(8), 144, 2017, https://doi.org/10.3390/atmos8080144
[2] Chiquetto, J. B. et al., Science of the Total Environment, 945, 2024, https://doi.org/10.1016/j.scitotenv.2024.173968
[3] CETESB, 2024, https://cetesb.sp.gov.br/ar/wp-content/uploads/sites/28/2024/08/Relatorio-de-Qualidade-do-Ar-no-Estado-de-Sao-Paulo-2023.pdf
[4] Pellegatti-Franco, D. M. et al., Urban Climate, 27, 293–313, 2019, https://doi.org/10.1016/j.uclim.2018.12.007
[5] Acdan, J. J. M. et al., Atmospheric Chemistry and Physics, 23(14), 7867–7885, 2023, https://doi.org/10.5194/acp-23-7867-2023
How to cite: Freitas, A., Zacharias, D., Borbon, A., and Fornaro, A.: Five years of satellite-based HCHO and NO2 monitoring over the Metropolitan Area of São Paulo, Brazil: insights from the BIOMASP+ project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13358, https://doi.org/10.5194/egusphere-egu25-13358, 2025.
Lightning is a crucial driver of nitrogen oxides (NOx) in the free troposphere where tropospheric ozone formation is limited by the availability of NOx. NOx production per lightning flash in models is typically represented with a single temporally and spatially static value, likely affecting the accuracy in simulating past, present, and future lightning NOx and consequent changes in tropospheric ozone (O3) and other oxidants. We determine spatially (0.5° × 0.625°) varying hourly NOx production rates (mol N fl⁻¹) using literature reported relationships between NOx yields and lightning flash radiant energies from Lightning Imaging Sensors (LIS) aboard the Tropical Rainfall Measuring Mission (TRMM) and the International Space Station (ISS). The diurnal and spatial variability of the radiant energies from the LIS instruments are assessed to be overall consistent with optical energies from the Geostationary Lightning Mapper (GLM) instrument, which monitors the Americas every 5 minutes. The diurnal variability between the two datasets differs by < 15%. Our lighting NOx production rates are added to GEOS-Chem, yielding global emissions of 6.5 Tg N yr-1 for 2015-2019, closely aligning with 5.8 Tg N yr-1 in the original parameterised representation, but with large differences in the spatial distribution of lightning NOx. Our updated parameterisation causes increases of > 50 pptv in NO2 across the troposphere, particularly in the tropics coincident with deep convection. The resultant changes in tropospheric composition improve agreement with satellite-derived vertical profiles of NO2 obtained via cloud-slicing TROPOMI by decreasing the model underestimate in free tropospheric NO2. Assessment against cloud-sliced O3 is underway.
How to cite: Horner, R., Marais, E., and Murray, L.: Dynamic lightning NOx production rates obtained with space-based low-Earth orbiting and geostationary lightning imagers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9209, https://doi.org/10.5194/egusphere-egu25-9209, 2025.
Accurate, timely, and high-resolution NOx emissions are essential for formulating pollution control strategies and improving the accuracy of air quality modelling at fine scales. Since late 2018, the Tropospheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor (S5P) satellite has provided daily monitoring of NO2 column concentrations with global coverage and a small footprint of 5.5 km × 3.5 km, offering great potential for tracking daily high-resolution NOx emissions. In this study, we develop a data assimilation and emission inversion framework that couples an Ensemble Kalman Filter with the Community Multiscale Air Quality model (CMAQ), to estimate daily NOx emissions at 3-km scales in Beijing and surrounding areas in 2020. By assimilating the TROPOMI NO2 tropospheric vertical column densities (TVCDs) and taking the bottom-up inventory as prior emissions, we produce a posterior NOx emission dataset with a reasonable spatial distribution and daily variations at the 3-km scale. The proxy-based bottom-up emission mapping method at fine scales overestimates NOx emissions in densely populated urban areas, whereas our posterior emissions improve this mapping by reducing the overestimation of urban emissions and increasing emissions in rural areas. The posterior NOx emissions show considerable seasonal variations and provide more timely insight into NOx emission fluctuations, such as those caused by the COVID-19 lockdown measures. Evaluations using the TROPOMI NO2 column retrievals and ground-based observations demonstrate that the posterior emissions substantially improved the accuracy of 3-km CMAQ simulations of the NO2 TVCDs, as well as the daily surface NO2 and O3 concentrations in 2020. However, during the summer, despite notable improvements in surface NO2 and O3 simulations, positive biases in the posterior model simulations persist, indicating weaker constraints on surface emissions from satellite NO2 column retrievals in summer. The posterior daily emissions on the 3-km scale estimated by our inversion system not only provide insights into the fine-scale emission dynamic patterns but also improve air quality modelling on the kilometer scale.
How to cite: Kong, Y., Zheng, B., and Liu, Y.: Tracking daily NOx emissions from an urban agglomeration based on TROPOMI NO2 and a local ensemble transform Kalman filter, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14326, https://doi.org/10.5194/egusphere-egu25-14326, 2025.
The Flux Divergence Approach (FDA) is a technique for deriving NOx emissions from satellite data. Here we will focus on NOx emissions derived from NO2 measured by the Sentinel-5P TROPOMI instrument. The FDA simplifies the complex three-dimensional transport and chemical processes in the atmosphere into a two-dimensional continuity equation for the column-integrated concentration. Emissions are thus calculated by combining spatial distribution patterns from satellite imagery with horizontal wind components that transport the column. Despite its widespread application, the accuracy of this method remains underexplored, primarily because of the limited availability of direct stack emission measurements. Additionally, comparisons with traditional bottom-up inventories provide only a general indication of its performance. In this study, we performed an end-to-end evaluation to assess the capability of the FDA to accurately reproduce known NOx emissions. A high-resolution model (LOTOS-EUROS) was used to generate synthetic, idealized satellite observations for the Netherlands. The FDA method was then applied to these observations, and the resulting emissions were compared with the input emissions used in the model. The results showed that the FDA reproduces the magnitude and spatial distribution of NOx emissions in the Netherlands with high accuracy (absolute bias <9 %). But such a good accuracy is only obtained if high-resolution model information is used as input in the FDA to account for critical factors, including the spatial variability of NO2 lifetime along pollution plumes (linked to OH concentration), the NOx:NO2 ratio, and the NO2 profile shape used for correcting satellite retrievals. These factors exhibit strong spatial and temporal variability on the kilometer scale. Interestingly, the FDA shows limited sensitivity to the specific wind field used, provided it accurately represents the flow within the planetary boundary layer (PBL). Moreover, restricting the analysis to observations within the PBL improves the accuracy of the estimated emissions. In its original form, the FDA generated detailed emission location maps, but it frequently led to notable biases in quantitative emission estimates. To improve accuracy, we therefore propose extending the FDA for NOx emissions from TROPOMI by incorporating additional information from a high-resolution CTM (2 km or better), which provides the necessary spatially and temporally varying inputs, including the replacement of the a-priori profile in the retrieval. Our results indicate that this enhanced FDA approach yields more reliable emission estimates. Although integrating a single high-resolution CTM run increases computational costs, it remains significantly faster than alternative methods, such as ensemble data assimilation or 4D-Var emission inversion systems, which require numerous model runs.
How to cite: Cifuentes, F., Eskes, H., Dammers, E., Bryan, C., and Boersma, F.: Enhancing the flux divergence approach for accurate NOx emission estimation: An evaluation using high-resolution synthetic satellite data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12938, https://doi.org/10.5194/egusphere-egu25-12938, 2025.
Atmospheric nitrogen oxides play a crucial role as a precursor of nitrate and ozone formations. East Asia is still one of the significant contributors to the global NOx budget. Therefore, we evaluated the accuracy of bottom-up and top-down NOx emissions by comparing tropospheric NO2 columns simulated from the CMAQ with those from the TROPOMI over East Asia for four months. The CMAQ simulation using the UNIMIX inventory illustrates an overestimation of NO2 columns by approximately 28-53% in Central East China (CEC) compared to the TROPOMI observation. In contrast, the South Korean (SK) region shows an underestimation of 8-28% for the same period, except for an overestimation of 18% in April. To improve the accuracy of NOx emissions in East Asia, we estimated top-down NOx emissions using an algorithm based on the Finite Difference Mass Balance (FDMB) method with TROPOMI observation data. The top-down approach indicated a decrease of 4-32% in emissions for the CEC region and an increase of 9-24% for the SK region compared to bottom-up estimates. Utilizing the top-down NOx emission in the CMAQ simulation demonstrated enhanced spatial and temporal consistency with the TROPOMI-observed NO2 columns. Additionally, we evaluated the accuracy of the top-down NOx emission from a comparison between the simulation and independent in-situ observations of the AirKorea network.
How to cite: Han, K. M.: Top-down NOx estimation from TROPOMI observation over East Asia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3426, https://doi.org/10.5194/egusphere-egu25-3426, 2025.
More than two years of hostilities, which began on the 24th of February 2022 with the invasion by Russian armed forces into Ukraine, have altered the emission patterns by destructing industrial facilities and the emerging new local pollution sources from military actions and missile strikes. In this respect, we discuss the changes in air quality across Ukraine during the first two years of the conflict (2022–2023), with a specific focus on fires as a key contributor to pollution. To facilitate spatial and temporal analysis, we distinguish eight regions within the territory of Ukraine, which makes it possible to map the extent and magnitude of fire-related emissions and track their evolution in response to the changes in the front-line location. We utilized data from the European Forest Fire Information System (EFFIS), specifically the MODIS/SENTINEL-2 Burnt Areas product, to identify the extent of burnt areas, analyze fire activity, categorize affected land cover types, and assess the resulting influence on atmospheric pollution levels. To link fire episodes with pollution, we used Sentinel-5 Precursor (S5P) TROPOMI Level 2 data for tropospheric NO2, CO, and the Absorbing Aerosol Index (AAI). Additionally, we incorporated ground-based observations from the Ukrainian National Air Quality Network (NAQN), a system of about 100 monitoring stations in 39 cities across Ukraine that measure atmospheric chemicals, which has not been done before.
It was found that in Ukraine in 2022, the war was a major driver of fire activity, with 66% of the total burned area located within a 30-km zone along the front line. In 2023, this proportion increased to nearly 80% within the same 30-km zone. The retrieved satellite emissions showed that peak fire activity was accompanied by increases in NO2, CO, and aerosol concentrations, which exceeded the historical daily maxima for 2018-2021. Ground-based observations showed mixed tendencies, with concentrations decreasing in 16 cities and significantly rising in others (9 cities). The proximity of monitoring stations to industrial sources complicates the ability to isolate air quality changes that were not directly caused by industry or industrial destruction. Only Sloviansk (Donetsk region, located 20-30 km from the front line) showed reliable increases in NO2, CO, and dust concentrations, which could be directly attributed to military activities. To demonstrate the contribution of increased fire activity to atmospheric pollution, we compared biomass burning emissions, based on fire radiative power, with anthropogenic emission inventories from periods before and during the hostilities. During severe fire seasons (2020, 2022, and 2023), both the Copernicus Atmosphere Monitoring Service (CAMS) and Global Fire Assimilation System (GFAS) inventories reported comparable NOx emission levels, while CO emissions increased by up to 11-fold. In contrast, during a milder fire season (2021), NOx emissions were 2.5 times lower than industrial emissions, whereas CO levels were similar to or slightly exceeded those from industrial sources. This evidence suggests that in Ukraine, pollution from fires, also caused by war, can be as significant as emissions from industrial sources, depending on the intensity of the fire season.
How to cite: Malytska, L., Galytska, E., Ladstätter-Weißenmayer, A., Savenets, M., and Moskalenko, S.: Atmospheric pollution in Ukraine (2022-2023): role of fires in CO, NO2, and aerosol emissions during two years of military conflict , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4718, https://doi.org/10.5194/egusphere-egu25-4718, 2025.
Monitoring the spatiotemporal distribution of formaldehyde (HCHO) is crucial for reducing volatile organic compounds (VOCs) emissions, and the long-term evolution of socio-natural sources contributions to tropospheric HCHO over China is still unclear. We propose an oversampling algorithm for quantitatively tracking the evolution of uncertainty, which lowers the uncertainty of the original Level 2 OMI HCHO data (50% -105%) to 0-50%, and then we examine the evolution of contributions from various emissions sources applying multi-scale correlation. We found that the high formaldehyde emissions caused by human activities in eastern China are crossing the Hu Huanyong Line, which was formerly used to demarcate the population distribution. National-scale analysis indicate that HCHO emissions are significantly correlated with per capita Gross Domestic Product (per capita GDP) (r = 0.948) and Normalized Difference Vegetation Index (r = 0.864), while no substantial correlation with land surface temperature (LST) (r = 0.233). Diagnosis at pixel scale reveals that anthropogenic emissions continue to weaken the contributions of HCHO emissions caused by the increase in vegetation proportion. Our research identifies the evolutionary process and characteristics of the spatiotemporal distribution and socio-nature sources contributions of tropospheric formaldehyde of China from 2005 to 2022.
How to cite: Xia, H.: Revealing the characteristics of long-term Chinese tropospheric formaldehyde under the influence of socio-natural sources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3172, https://doi.org/10.5194/egusphere-egu25-3172, 2025.
The divergence, i.e. the spatial derivative of the horizontal flux, allows to identify point sources due to the strong local gradients, and to quantify emissions from satellite measurements of atmospheric pollutants or greenhouse gases such as NO2 or CH4.
A central assumption made in this approach is that steady state is fulfilled. I.e., spatio-temporal changes of emissions, chemical conditions, or wind fields are not accounted for. Thus it has to be expected that the emission estimate is affected and probably biased in case of deviations from steady state.
Here we investigate quantitatively how far deviations from steady state affect the results of the divergence method. In particular, we quantify the spatial and temporal variability of wind fields and relate them to NOx emission estimates for selected power plants based on individual TROPOMI orbits as well as on WRF-Chem simulations.
The goal is to provide a measure for "steadiness" that could be used to identify and mask out unfavourable conditions. With this filter, it is expected that the remaining emission estimates have lower uncertainties. Other methods for emission estimates that are based on steady state assumption as well, like the calculation of cross-sectional fluxes, will probably also benefit from this.
How to cite: Beirle, S., Kuhn, L., and Wagner, T.: Impact of wind fluctuations on the performance of the divergence method: How steady is the state?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3688, https://doi.org/10.5194/egusphere-egu25-3688, 2025.
Atmospheric ammonia (NH3) is a reactive nitrogen compound that affects air quality and threatens public health. Real-time monitoring of atmospheric NH3 variations using satellite measurements will provide a reliable scientific basis for evaluating emission management strategies for anthropogenic sources. As the world's first geostationary hyperspectral infrared sounder, the Geostationary Interferometric Infrared Sounder (GIIRS) on board the FengYun-4 series of satellites provides an important advance to monitor NH3 total columns with day/night measurements at a 2-hour temporal resolution and a spatial resolution of 12 km at nadir. Using GIIRS NH3 retrievals, this study focused on the analysis of industrial and agricultural NH3 point sources over East Asia and in particular the diurnal cycle of these point sources. The identified point sources are first compared with estimates from the Emissions Database for Global Atmospheric Research (EDGAR) and similar hyperspectral infrared satellites, such as IASI and CrIS. The diurnal variations of NH3 point sources are further compared with simulations from chemical transport model such as GEOS-Chem. The findings demonstrate the unique capability of FY-4B/GIIRS in identifying NH3 point sources and capturing their temporal changes over East Asia, offering critical insights beyond the capabilities of current low-Earth orbit (LEO) instruments.
How to cite: Sheng, M., Zeng, Z.-C., and Hua, J.: Monitoring ammonia point sources over East Asia from the Geostationary Interferometric Infrared Sounder (GIIRS) on board China's FengYun-4 satellite, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10058, https://doi.org/10.5194/egusphere-egu25-10058, 2025.
Ammonia (NH₃) is primarily emitted from agricultural sources, including livestock farming and fertilizer application. Animal farms are significant contributors to ammonia emissions, particularly under low rainfall, as rainfall typically leads to nitrogen leaching and ammonia removal from the soil. In addition to agricultural activities, combustion-related NH₃ emissions, primarily from fossil fuel burning and biomass combustion, also contribute to atmospheric ammonia; however, these sources remain poorly understood. Ammonia emissions mainly arise from the volatilization of NH₃ from NH₄⁺-containing substrates, such as fertilized soils, animal waste, and nitrogen-polluted water, as well as from combustion-related processes, including coal combustion, vehicle exhaust, and biomass burning.
Ammonia significantly impacts air quality as a precursor to fine particulate matter (PM2.5), which has considerable health implications. A study by Vieno et al. (2016) (https://acp.copernicus.org/articles/23/15253/2023/) demonstrated that reducing NH₃ emissions in the United Kingdom could lower PM2.5 levels. Despite this recognized impact, NH₃ monitoring networks are inconsistently implemented across Europe, with only a few countries, such as the Netherlands, the UK, and Belgium, maintaining dedicated NH₃ monitoring systems. Projections indicate that NH₃ emissions are likely to increase due to rising global temperatures and the growing demand for animal products, emphasizing the need for accurate, traceable, and routine NH₃ monitoring to better understand the complexities of ammonia in the atmosphere.
This study aims to identify NH₃ hot-spot regions in Europe based on satellite data from METOP IASI for 2019 to 2022 and compare these findings while accounting for surface variability and reported emission sources. Furthermore, we explore NH₃ CAMS profile analysis and NH₃ observations from the EBAS database of atmospheric measurements.
How to cite: Przeździecki, K., Bhandari, D., Nagmarova, A., Kamiński, J., Starzomska, A., and Strużewska, J.: Temporal variability of NH3 in European hot spots based on satellite and in-situ observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18728, https://doi.org/10.5194/egusphere-egu25-18728, 2025.
Sulfur dioxide (SO2) is an air pollutant that is toxic to humans and, as a precursor to sulfuric acid, has far-reaching consequences for the environment and climate. Anthropogenic emissions are responsible for two-thirds of total SO2 emissions into the atmosphere. Stricter regulations and technical developments, such as the installation of desulfurization systems in coal-fired power plants, are reducing emissions in parts of the world such as Europe. However, inventories show a stagnation in emissions in the Middle East region in recent years. In the global SO2 catalogue provided by NASA, satellite instruments such as TROPOMI (onboard Sentinel-5P) assign many point sources in this region to the production of oil and gas. Two of these hotspots are detected in the southern Sultanate of Oman.
As part of the METHANE-To-Go-Oman campaign in 2023, for the first time the helicopter-borne probe HELiPOD (weight 325kg, length 5m) was used to determine SO2 emissions from selected oil and gas facilities in Oman. The HELiPOD was equipped with a UV fluorescence instrument (Envea AF22e) to measure SO2 in-situ with high precision (1ppb), as well as with a precise wind measurement system. The entire SO2 plume from selected facilities was sampled at different altitudes (50 m to 2000 m) at variable distances (1 km to 4 km) downwind from the source. Flight pattern were designed on a day-to-day basis based on actual wind speed and wind direction measured with a ground-based Doppler wind lidar (Streamline XR, Halo Photonics).
In this study, top-down derived SO2 mass flux estimates based on HELiPOD data are presented for six selected point sources from the oil and gas industry in northern and southern Oman. Subsequently, these top-down estimates are compared to bottom-up emission inventories available for the region. In addition, TROPOMI data from the years 2018 to 2023 are analyzed to investigate the temporal development of SO2 point sources in the whole Middle East area. The satellite data show a very good temporal coverage and we were able to identify a new emission source in the northern part of Oman in 2023. In the present global SO2 catalogue provided by NASA this source is not yet included. The signal strength of this northern source is similar to the southern hotspots in the years before. The HELiPOD mass flux estimates also confirm the significant decrease in emissions between 2021 and 2023 from one of the two hotspots in the southern Oman based on TROPOMI data. In general, our study indicates low SO2 emissions from the oil and gas industry in Oman compared to other countries in the Middle East.
How to cite: Maier, N., Förster, E., Huntrieser, H., Witschas, B., Pätzold, F., Bretschneider, L., Lampert, A., Lunt, M., and Roiger, A.: Quantifying Sulfur Dioxide Emissions from Industrial Activities by a Helicopter-borne System and TROPOMI in the Southern Arabian Peninsula, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5690, https://doi.org/10.5194/egusphere-egu25-5690, 2025.
SO2, CO, HCHO, NO2 and O3 satellite data measured by the TROPOspheric Monitoring Instrument (TROPOMI) instrument on board the Copernicus Sentinel-5 Precursor satellite launched in October 2017 provides a comprehensive atmospheric composition dataset at high horizontal resolution (5.5 km -7 km). Here, we used total column SO2, CO, HCHO and tropospheric O3 L2 products for testing Optimal Interpolation (OI) algorithm implemented in the atmospheric modelling system MINNI. The three-dimensional hourly concentrations fields produced with the chemical transport model FARM at European scale were adjusted with satellite retrievals of pollutants simultaneous and, separately, for O3. The three-dimensional optimal interpolation (OI) scheme developed for satellite data assimilation consider the spatial and temporal error structures of the background field through the background error covariance matrix (B). This matrix was estimated in the same way for all five pollutants and, for simplicity, was assumed diagonal considering thus that the retrievals at different points do not influence each other. Besides, model data were corrected only where the observations were available.
This simple OI scheme is computationally feasible but not effective in the same way for all pollutants. There are also differences in assimilating a single species or all together for those formed in the atmosphere such as O3. The differences in the three-dimensional modelled concentrations without and with assimilation for all pollutants and single species are discussed as well as their performances in comparison with ground observations for evaluating the impact of assimilation.
To understand the limitations of the implemented OI algorithm, several experiments were run to investigate the effects of using different definitions of B in different states of atmospheric composition. The comparisons and quantitative evaluations were performed both horizontally and vertically, analysing 2D fields and point time series.
The preliminary results show that the assimilation can improve modelled NO2 and capture SO2 volcano eruptions which are not present in anthropogenic emission inventories. However, the assimilation of the short-lived species like NO2, HCHO and O3 poses many problems, in particular due to their interdependencies, therefore more research is needed.
How to cite: Bolignano, A., Adani, M., Briganti, G., Russo, F., and Mircea, M.: Assimilation of SO2, CO, HCHO and O3 satellite data with Optimal Interpolation implemented in Atmospheric Modelling System MINNI, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16722, https://doi.org/10.5194/egusphere-egu25-16722, 2025.
The Environmental Trace Gases Monitoring Instruments (EMI) are the second generation of spectrometers on-aboard the Gaofen-5 (GF-5) and Daqi-1 (DQ-1) satellites in China, which can be used to analyze trace gases such as O3 , NO2 and BrO. EMI enables continuous daily global trace gas observations at a spatial resolution of 7×13 km2. In this study, the differential optical absorption spectroscopy (DOAS) algorithm was used to invert EMI spectral data to obtain the total column concentrations of O3 , NO2 and BrO of the three satellite loads. Due to the different overpass times of different loads, the daily variation trend of trace gases is obtained through comparative analysis, and the overall quality and reliability of EMI series data sets are verified by cross-comparison. The daily EMI total ozone column (TOC) was compared with the vertical column density (VCD) collected by the TROPOspheric Monitoring Instrument (TROPOMI). The results showed that the spatial distribution of daily EMI series TOC and TROPOMI TOC from September to December 2023 had a good correlation (R≥0.95), and the relative differences were less than 5% when verified with data from ground-based stations. In addition, the EMI series TOC data fusion results were highly correlated with TROPOMI TOCs (R = 0.99), weighted fusion assigns weighting factors to each instrument by inverting RMS of different loads. The consistency of NO2 between EMI loads was high (R ≥ 0.90), and the correlation between the total column concentration of NO2 and TROPOMI was also high (R ≥ 0.80). Retrieval results of BrO products from March to May 2024 in Arctic region also revealed significant increases in BrO concentrations caused by bromine explosion events in the spring. The results of these products highlight the potential of EMI instruments for long-term continuous monitoring of global trace gas distribution and changes, helping to build high-quality atmospheric component concentration data sets.
How to cite: kaili, W., yuhan, L., fuqi, S., and haijin, Z.: Retrieval and Comparison of Multi−Satellite Atmospheric composition concentration Data from the EMI Series Instruments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9438, https://doi.org/10.5194/egusphere-egu25-9438, 2025.
Ground-based Fourier Transform Infrared (FTIR) instruments from the Network for the Detection of Atmospheric Composition Change (NDACC) provide long-term and continuous measurements of many atmospheric trace gases at more that 20 stations. This network is already used in the S5P validation of CO and CH4 (Sha et al., 2021) as well as HCHO (Vigouroux et al., 2020), and NO2 (validation reports https://mpc-vdaf.tropomi.eu/).
Ozone is one of the major FTIR NDACC target gas and its retrieval strategy is harmonized within the network (Vigouroux et al. 2015). Nonetheless, while ozone data from FTIR measurements are contributing to many ozone trend studies (e.g. Vigouroux et al., 2015, Godin-Beekman, et al., 2022, Van Malderen et al. ,2025), they have been poorly used for satellite validation. The reasons are partly historical (Brewen/Dobson are traditionally used for total column validation) and partly scientific (FTIR has low vertical resolution so the ozone sondes and lidars are preferred for profile validation of Limb satellites).
However, the ground-based FTIR ozone products are well suited for the validation of Nadir sounding satellites such as S5P: FTIR retrievals provide both ozone total column with a high precision better than 2% and ozone profiles with low vertical resolution (approximately 4 degrees of freedom for signal - DOFS), which is similar to the S5P ozone profile products (about 5 DOFS). The strength of FTIR data compared to ozone sondes measurements is that they have sensitivity up to about 45 km, allowing the validation of S5P profiles above the 30 km limit reached by the sondes. These higher altitudes can be reached by Lidar data, but the stations equipped with such instruments are sparse and therefore provide lower representativeness of the validation. In addition, FTIR has also a good sensitivity in the troposphere (1 DOFS) which allows for the validation of the specific S5P tropospheric column product as well, although only 3 FTIR stations are located within the 20°S – 20°N band for which the S5P ozone tropospheric product is provided.
We will show validation results for the three different S5P products: total columns, tropospheric columns, and profiles. The profile validation will be made by comparing ozone from both instruments in 4 vertical layers following the FTIR averaging kernels and DOFS. The effect of the different a priori information and vertical sensitivities of both S5P and FTIR will be investigated. Our results (accuracy and precision of the S5P ozone products) will be put in perspective with the past S5P ozone validation studies (Garane et al., 2019; Hubert et al., 2021, Keppens et al., 2024).
This validation exercise is the first step towards the future use of FTIR for the validation of geostationary satellites such as S4/S5 (ESA project CHEOPS S4/5) or TEMPO. One of the major advantage of this reference network is to enable the evaluation of the ozone diurnal cycle, as FTIR acquisitions are made throughout the day in clear sky conditions.
How to cite: Vigouroux, C., Langerock, B., and De Mazière, M. and the FTIR observation Team: Validation of all S5P ozone products (total columns, tropospheric columns and profiles) with a single reference network. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20500, https://doi.org/10.5194/egusphere-egu25-20500, 2025.
East Asia is a region with particularly high emissions of ozone precursors due to the effects of industrialization and urbanization, making continuous monitoring of air pollution and environmental changes crucial. To address this need, the GEMS sensor onboard the GK-2B satellite, launched in 2020, serves as an important tool for real-time observation of the East Asian region from a geostationary orbit. While GEMS offers the advantage of continuous monitoring of air quality in East Asia, its relatively short observation period limits its use for long-term analysis. To overcome this limitation, this study integrates GEMS, OMI and TROPOMI satellite data to construct a 20-year record (2005–2024) and analyze the seasonal and spatial characteristics of ozone variability. Level 2 data from each satellite were processed using a consistent algorithm to generate Level 3 data, and the discrepancies during overlapping observation periods between the satellites were corrected to reduce inconsistencies. The corrected data were validated through cross-verification between the satellites. The validation results showed a slope ranging from 0.95 to 1, an R-squared value above 0.9, and an RMSE of less than 5 DU. To further assess the accuracy of the long-term data generated in this study, validation with ground-based observations is also necessary.
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NRF-RS-2023-00219830) and a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-2024-01-02-042).
How to cite: Jeon, H. J., Kim, J. H., Yu, J.-A., Kim, S.-M., and Park, S. S.: Estimation of Long-Term Ozone Variability in East Asia (2005–2024): Integration of OMI, TROPOMI, and GEMS Satellite Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5821, https://doi.org/10.5194/egusphere-egu25-5821, 2025.
Methane (CH4) is a critical atmospheric trace gas and a potent greenhouse gas contributing to global warming, yet its relationship with climate variables remains underexplored, particularly in eastern Saudi Arabia, which hosts over 70% of the country’s oil fields. This study presents the first comprehensive evaluation of the spatiotemporal variability of CH4 and its climatic drivers in eastern Saudi Arabia, using novel TROPOMI (Tropospheric Monitoring Instrument)/Sentinel-5P data and innovative approaches applied over the period from 2019 to 2024. Google Earth Engine (GEE)-based analysis shows significant annual and seasonal changes, with CH4 concentrations increasing from 1892 ppb to 1927 ppb. Seasonal patterns show maximum concentrations in summer and autumn and minimum concentrations in winter and spring. ArcGIS-based spatial trend analysis indicates an area-averaged increase of 5.61 ppb per year across the majority of the examined regions. These spatiotemporal variabilities are driven by anthropogenic factors (e.g., oil and gas activities, urbanization, and agriculture) and natural climatic factors (e.g., wetlands, soil activity, and changing climate variables). Emission sources are validated using the Global Fuel Exploitation Inventory (GFEIv2) dataset. Geographically Weighted Regression (GWR) modelling is adopted to understand the spatial-scale connections between CH4 and climate variables. The results, with a model fit R² of 0.85, reveal that CH4 is negatively correlated with temperature, solar radiation, and precipitation, but positively correlated with humidity and wind speed. For instance, in 2023, the mean correlation coefficient between CH4 and temperature was -3.89, indicating that CH4 concentrations decreased by 3.89 ppb across most of the studied areas per year for each unit increase in temperature. This decrease may be attributed to accelerated oxidative processes at higher temperatures, as the eastern region of Saudi Arabia is known for its consistently high temperatures. To assess the degree of importance of these connections, the Random Forest model is employed, outperforming statistical models with an R2 of 0.75 and an RMSE of 2.79 ppb. The findings highlight that temperature and incoming solar radiation have the highest importance, followed by humidity, wind speed, and precipitation, in driving CH4 variability. These results provide valuable insights to guide future research efforts across the Middle East and support policymakers in developing effective strategies for monitoring and managing atmospheric methane.
How to cite: Rahman, M. M., Shults, R., Rajendran, S., Arshad, A., and Keshk, H.: Novel Insights into Atmospheric Methane Variability and Climatic Drivers in Eastern Saudi Arabia Using TROPOMI/Sentinel 5P Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1282, https://doi.org/10.5194/egusphere-egu25-1282, 2025.
Air pollution over the oceans has received less attention compared to densely populated urban areas of continents. Over the past four decades, satellite-derived Aerosol Optical Depth (AOD) data reveal significant spatial and temporal variations across global oceans. The global mean AOD is approximately 0.112, with higher levels in the Central Atlantic (~0.206), North Indian Ocean (~0.201), and Western North Pacific (~0.197). Latitudinal analysis shows that the highest AOD values are concentrated in the Northern Hemisphere, particularly between latitudes 0° and 70° N. except for the Gulf of California and Hudson Bay, AOD values in the other fourteen surveyed inland seas surpass the mean levels found at similar latitudes in oceanic regions. Over the last four decades, AOD trends have revealed a significant decrease across about 89.5% of global oceanic grids, while an increase in AOD is observed in low-latitude oceanic areas (30° S-30° N). The turning-points of the AOD in each inland sea confirm the success of regional emission control policies initiated on the adjacent continents. Our findings suggest that regional emission control policies have been successful in many areas, but further adjustments, such as relocating heavy industries away from coastal areas, are needed to reduce pollution in regions like the Bohai Sea.
How to cite: Li, L., Bi, X., and Feng, Y.: Four-decade Trends and Latitudinal Variations of Satellite-derived Aerosol in Global Oceanic Regions , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4902, https://doi.org/10.5194/egusphere-egu25-4902, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 5
EGU25-4493 | Posters virtual | VPS3
Comparison of nitrogen dioxide tropospheric columns retrieved by TEMPO and PandoraWed, 30 Apr, 14:00–15:45 (CEST) | vP5.25
Monitoring emissions of nitrogen dioxide is crucial for understanding the atmospheric composition and its impacts on air quality and climate. This study aims to evaluate the accuracy of retrievals of nitrogen dioxide tropospheric column by the Tropospheric Emissions: Monitoring of Pollution (TEMPO) by comparing them against retrievals of the ground-based Pandora instruments.
The TEMPO is a visible and ultraviolet spectrometer flying aboard of a commercial telecommunications satellite, Intelsat 40e, in geostationary orbit over 91˚ W longitude, thus maintaining a continuous view of North America. High resolution measurements of radiance reflected by the Earth's back to the instrument's detectors enable retrievals of columns of nitrogen dioxide involved in the chemical dynamics of Earth’s atmosphere. TEMPO V03 Level 1, 2, and 3 data were recently made available from the Atmospheric Science Data Center (ASDC) via NASA EarthData Search.
Direct-Sun Pandora spectrometer is used to retrieve columnar amounts of trace gases in the atmosphere by the means of differential optical absorption spectroscopy at numerous locations around the globe.
ASDC has developed a set of Jupyter notebooks dedicated to TEMPO vs. Pandora comparisons of the columns of individual trace gases including one dealing with NO2 tropospheric column. The notebooks allow a user to select a specific Pandora station and a timeframe of interest. The code downloads all relevant TEMPO L2 granules as well as the Pandora dataset. The latter is sub-set to the selected timeframe. Time series of the gas column retrievals along with their uncertainties are then derived with accounting for the quality flags from both datasets. Since Pandora measurements are significantly more frequent, a procedure computing weighted averages of them at the times of TEMPO retrievals was incorporated to the notebooks allowing direct comparison of gaseous columns from two sensors against each other.
The results derived by the ASDC tool show only qualitative agreement between the TEMPO and Pandora retrievals of nitrogen dioxide tropospheric column. it was also found that the discrepancies between the two are site dependent which may point to a potential problem with Pandora quality flags. Two attempts were made to improve comparison. Since TEMPO algorithm allows for negative NO2 tropospheric columns, such retrievals were removed from consideration. There are also multiple TEMPO retrievals accompanied by uncertainty greater that the retrieved column. Removal of such retrievals constitutes another approach to improve comparison.
The findings of this study will contribute to the understanding of the reliability and applicability of space-based trace gases monitoring for air quality applications. The results will enhance our understanding of atmospheric processes related to tropospheric NO2.
How to cite: Radkevich, A., Mahmoud, H., and Kaufman, D.: Comparison of nitrogen dioxide tropospheric columns retrieved by TEMPO and Pandora, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4493, https://doi.org/10.5194/egusphere-egu25-4493, 2025.
EGU25-1923 | Posters virtual | VPS3
Spatiotemporal reconstruction of gas pollutants with high resolution and coverage using hyperspectral remote sensing and artificial intelligenceWed, 30 Apr, 14:00–15:45 (CEST) | vP5.26
Satellite remote sensing has the advantage of wide spatial coverage and high data consistency, which is an important technology for global atmospheric environment monitoring. However, due to the influence of cloud cover, satellite remote sensing faces the problem of data missing; moreover, the direct object of hyperspectral satellite remote sensing is the total amount of pollution gases in the atmosphere, which is different from the near-ground concentration that directly affects human health. To solve these problems, this research developed a remote sensing technology combining satellite spectral analysis and artificial intelligence. We use artificial intelligence to increase the spatial coverage of satellite and ground-based remote sensing, and make future short term predictions and their applications. Preliminary results show that the reconstruction of satellite remote sensing data supported by artificial intelligence is of great significance for environmental pollution monitoring and control.
How to cite: Liu, C., Hu, Q., Li, Q., and Zhang, C.: Spatiotemporal reconstruction of gas pollutants with high resolution and coverage using hyperspectral remote sensing and artificial intelligence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1923, https://doi.org/10.5194/egusphere-egu25-1923, 2025.
EGU25-2443 | Posters virtual | VPS3
Two Typical Case Studies of Volcanic Eruption Trace Gases Based on EMI ObservationsWed, 30 Apr, 14:00–15:45 (CEST) | vP5.27
Volcano eruption is one of the most destructive natural disasters, and its direct release of toxic gases and volcanic ash can lead to atmospheric pollution, posing significant threats to human health and ecological balance. To investigate the environmental impact of volcanic emissions, we retrieved the vertical column densities (VCDs) of sulfur dioxide (SO2) and bromine monoxide (BrO) using the Chinese highest-resolution atmospheric trace gas remote sensing satellite payloads: the Environmental Trace Gas Monitoring Instrument (EMI) series on-board the GaoFen (GF5-02) and DaQi (DQ-1) satellites.
Here, we present our study on two significant volcanic emission events. On January 15, 2022, a violent eruption occurred near the South Pacific Island nation of Tonga, which is a typical submarine volcano. During this eruption, the volcanic plume ascended directly into the stratosphere (above 20 km), releasing a substantial amount of SO2 and spreading rapidly westward (~30 m/s). In contrast, the majority of the BrO dispersed southeastward slowly (~10 m/s) within the altitude range of 8–15 km on January 16. The differences in eruption height and timing resulted in the transport of SO2 and BrO in distinct directions in the Southern Hemisphere.
Another case is the Sundhnukagigar volcano on Iceland's Reykjanes Peninsula, which is a typical fissure volcano. A significant eruption began at 21:00 on August 22nd, following an earthquake swarm; this was the largest eruption in the region since December 2023. Satellite data indicated that the volcanic eruption released high concentrations of SO2, with the maximum SO2 VCD exceeding 15 Dobson Units (DU). By the morning of the 26th, part of the air mass had been transported northward to the Arctic Svalbard region. Simultaneously, ground observations from Ny-Ålesund revealed that an unprecedented Arctic haze event occurred, with the SO2 VCD reaching approximately 40 times the usual level. It is also important to note that, in the context of global warming, the ongoing activity of Iceland's volcanoes will further exacerbate the melting of local glaciers and permafrost. This, in turn, disrupts the gravitational balance of the overlying crust, leading to an intensification of volcanic activity. Therefore, it is essential to employ multi-instrument, multi-scale, and high-resolution observations to monitor volcanic activity and assess its impact on both regional and global climate and the environment.
How to cite: Luo, Y., Li, Q., Wu, K., Qian, Y., Zhou, H., and Si, F.: Two Typical Case Studies of Volcanic Eruption Trace Gases Based on EMI Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2443, https://doi.org/10.5194/egusphere-egu25-2443, 2025.
