Since EGU18, this session continues to showcase how scientific data and analyses can be transformed into actionable information services and successful climate solutions for a wide range of user-communities. Actionable information results from data with the required spatial and temporal granularity and compositional details able to explicitly target, attribute and track GHG emissions and reductions where climate action is achievable.
This session seeks contributions from researchers, inventory compilers, government decision and policy makers, non-government and private sector service providers that show the use and impact of science-based methods for detecting, quantifying, tracking GHG emissions and the resulting climate mitigation. We especially welcome presentations of work guided by IG3IS good practice research guidelines at urban and national scale and for specific economic sectors. The scope of the session spans measurements of all GHGs and from all tiers of observation.
Hydrofluorocarbons (HFCs) are used as refrigerants, propellants or insulating foams. They don’t deplete the ozone layer like their predecessors, (hydro)chlorofluorocarbons ((H)CFCs). However, HFCs are still potent greenhouse gases and are regulated under the Kyoto Protocol (1997) and, more recently, the Kigali Amendment to the Montreal Protocol. The Kigali amendment targets reductions in HFC production and consumption over the coming decades.1, 2 Observing halogenated substances in the atmosphere provides an independent means to verify compliance with these international treaties. From these observations, regional and global emission estimates can be obtained by combining them with atmospheric modelling or using a reference tracer with known emissions.3, 4 Due to rapid industrialization and high demand for refrigeration and air conditioning, the eastern Asian region contributes significantly to global HFC emissions. Therefore, it is crucial to understand the emission patterns in this region to assess global compliance.
We have conducted a large-scale controlled-release tracer experiment to estimate regional halocarbon emissions of the greater Seoul metropolitan area (South Korea). Ethyl fluoride (HFC-161)5 and hexafluorobutane (HFO-1336mzzE), which are virtually absent in the background atmosphere, were released at one location in the City of Seoul. Release times were selected to align with favorable meteorological conditions that allowed air masses to reach the AGAGE station Gosan (Jeju Island, 490 km south of Seoul). The site is equipped with an instrument for in-situ halocarbon measurements. Intermediately located along the path of air mass transport, sites at the Global Atmosphere Watch (GAW) Observatory Anmyeondo and Mokpo National University (138 km and 320 km from Seoul, respectively) were used for additional flask sampling. The atmospheric transport model FLEXPART6 was used to forecast the tracer plume's trajectory and dispersion, and the release and sampling times were adjusted accordingly.
During two releases in November 2024 and April 2025, both tracers were detected at the flask sampling sites Anmyeondo GAW Observatory and Mokpo National University, as well as at Gosan station. The measurements show a strong correlation of our tracer substances with various HFCs. Preliminary emission estimates for the greater Seoul metropolitan area are derived using the tracer ratio method, and its limitations are discussed. Finally, a comparison to a full regional inversion, based on the continuous observations at Gosan, is conducted.
References
[1] Kyoto Protocol to the United Nations Framework Convention on Climate Change. adopted on December 11th, 1997; Kyoto, 1998, 1-22.
[2] Kigali Amendment to the Montreal Protocol on Substances that Deplete the Ozone Layer. adopted on October 15th, 2016; United Nations, Kigali.
[3] Matt Rigby, Sunyoung Park, Takuya Saito, Luke M. Western, Alison L. Redington, et al., Nature, 2019, 569 (7757), 546-550.
[4] Peter G. Simmonds, Matthew Rigby, Alistair J. Manning, Sunyoung Park, Kieran M. Stanley, et al., Atmospheric Chemistry and Physics 2020, 20 (12), 7271-7290.
[5] Dominique Rust, Martin K. Vollmer, Stephan Henne, Arnoud Frumau, Pim van den Bulk, et al., Nature, 2024, 633, 96-100.
[6] Ignacio Pisso, Espen Sollum, Henrik Grythe, Nina I. Kristiansen, Massimo Cassiani, et al., Geoscientific Model Development, 2019, 12 (12), 4955-4997.
How to cite: Müller, M. J., Vollmer, M. K., Henne, S., Yun, J., Choi, H., Park, S., Emmenegger, L., and Reimann, S.: Investigating Regional Halocarbon Emissions: The Seoul Tracer Release Experiment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2527, https://doi.org/10.5194/egusphere-egu26-2527, 2026.
Methane (CH4) emissions from livestock production remain one of the largest and most uncertain components of national greenhouse gas inventories, largely because direct measurements at operational facilities are limited. This measurement gap constrains the accuracy of agricultural CH4 estimates and the development of effective mitigation strategies. Strengthening the empirical basis for these inventories is therefore essential. Emerging close-range tools, such as uncrewed aerial vehicle (UAV) plume-sampling systems, can enhance monitoring, reporting, and verification (MRV) by providing high-resolution, facility-level observations.
To evaluate this approach, this study conducted a five-day field campaign at a commercial cattle feedlot in southern Alberta, Canada, housing approximately 28,000 cattle. UAV plume sampling was deployed alongside continuous CH4 measurements from an open-path laser (OPL) to estimate CH4 emission rate downwind of the facility. For both techniques, emission rates were derived using inverse dispersion modeling, for a direct comparison of performance and assessing the extent to which UAV-based sampling can complement established ground-based flux measurements.
Uncrewed aerial vehicle-derived CH4 emission rates varied from 149 to 392 g head-1 day-1 (mean ± SE: 280 ± 22), in near-perfect agreement with OPL-derived emissions of 152-438 g head-1 day-1 (280 ± 22). Daily mean emissions differed by only 0.08% during overlapping sampling periods, and statistical distributions were highly consistent across methods. Hour-to-hour variability reflected transient atmospheric dynamics and associated changes in plume dispersion, rather than methodological bias. UAV flights also revealed spatial plume gradients not captured by the fixed OPL geometry, and consistent hourly emission estimates were found when UAV flights collected at least four usable plume samples per hour. Performance declined under very low-wind or highly turbulent conditions, clarifying key operational constraints for future deployments.
Overall, these findings demonstrate that UAV-based plume sampling can provide CH4 emission estimates consistent with established ground-based systems, providing a validated pathway for quantifying emissions from commercial feedlots. The approach aligns with the Integrated Global Greenhouse Gas Information System (IG3IS) good-practice principles and provides empirical information that can improve IPCC Tier 2 emission factors for open-lot beef operations.
How to cite: Dash, S. S., Coates, T. W., and Madramootoo, C. A.: High-resolution measurement-based methane quantification from beef cattle feedlots to improve agricultural GHG inventories, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-671, https://doi.org/10.5194/egusphere-egu26-671, 2026.
Methane (CH₄) is the second most important greenhouse gas after CO₂, and its emissions from the agricultural sector, particularly rice paddies and dairy farms, remain highly uncertain and challenging to quantify. While recent advancements in satellite technology, such as high spatial resolution instruments, have enabled the detection of methane sources from global to facility scales, agricultural emissions still pose challenges. These emissions are typically diffuse and area-like, making them less detectable by targeted satellites like GHGSat and EMIT, which are better suited for isolated point sources such as oil/gas facilities or landfills. Additionally, agricultural emissions exhibit significant spatiotemporal variability driven by climate conditions, water management practices in rice paddies, and differences in farm types.
In the AGATE project of ESA, we apply an improved divergence method to estimate monthly methane emissions using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations at a 0.1° grid resolution. We focus on major agricultural regions, including the Po Valley in Italy, as well as India and Bangladesh, over the period 2019-2022. To better isolate agricultural emissions, we separate area-like sources (e.g., rice paddies) from isolated point sources. The locations of identified big emitters are cross-validated using bottom-up emission inventories and targeted satellite observations (e.g., EMIT, Carbon Mapper) to minimize the influence of non-agricultural sources. Furthermore, to better understand the seasonality of methane emissions, we analyze the correlations between methane emission variations and auxiliary datasets, such as rice paddy maps and ammonia emissions derived from satellites.
How to cite: Liu, M., van der A, R., van Weele, M., Ioannidis, E., Liu, R., Chen, Z., and Ding, J.: Quantifying Agricultural Methane Emissions Using Satellite Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5426, https://doi.org/10.5194/egusphere-egu26-5426, 2026.
As national and sub-national governments, companies, and communities plan methane mitigation action, there is a need for robust emissions tracking systems, especially for major sectors like waste where countries have made commitments to reduce emissions. Landfills are a major source of methane emissions in many jurisdictions spread across the world, so there is a need in the waste sector for monitoring frameworks that are applicable at scale but also provide facility-level insights to guide decision making.
Given the complexity of landfill emissions both in terms of variability and underlying causes, models are a common tool used for planning and tracking landfill methane mitigation, but past studies show potential biases in models and inventories compared to observations. In this work, we bring together both process-level insights as provided in bottom-up models and our top-down observations from the Tanager-1 satellite by (1) improving the accuracy and consistency of satellite-derived annual average emission rates and (2) developing methodologies for reconciling the two unique datasets. The goal of this work is to use satellite methane observations to identify improved bottom-up model parameters, focusing on the modeling frameworks used by national and sub-national jurisdictions.
As a point source imaging satellite, Tanager-1 is well suited for tracking emissions at landfills as it provides facility-scale methane emissions data, but existing algorithms and workflows for creating the emissions data have been primarily validated based on controlled release experiments which mimic environments more similar to the oil and gas sector than landfills. We identify methods that are robust and best suited to landfills by performing sensitivity tests for our quantification methods, testing algorithms and parameters, and identifying causes of bias unique to landfill environments (e.g., albedo, topography). The next step is translating our Tanager-1 observations to annual averages. We present a new methodology for temporally averaging satellite observations that accounts for null detects through scene-specific probability of detection limits. Finally, we compare our annual average satellite-based emission estimates to bottom-up models typically used by jurisdictions for official reporting (e.g., IPCC, LandGEM, US GHGRP), focusing on select countries where there is sufficient spatiotemporal coverage with Tanager-1. We use statistical methods to adjust parameters in the bottom-up models to reconcile the model estimates with observed emissions, allowing region-specific model parameter adjustments to account for potential climatic and meteorological factors. Finally, we discuss the implications of our initial results in terms of improvements to official national reporting and compare to inverse modeling results.
How to cite: Scarpelli, T., Cusworth, D., Kim, J., O'Neill, K., Duren, R., and Howell, K.: Using point source imaging satellite observations to guide landfill methane model improvements at the national and sub-national scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7927, https://doi.org/10.5194/egusphere-egu26-7927, 2026.
Methane (CH4) is a key short-lived climate forcer, yet robust monitoring of its anthropogenic sources remains limited by inconsistent national reporting and incomplete inventories, especially from coal mining. Global anthropogenic CH4 emissions are about 369 million tonnes per year, of which coal mine methane (CMM) contributes roughly 40 million tonnes per year, which is comparable to emissions from the gas sector. In 2023 only 15% of coal production reported annual CMM emissions in national greenhouse gas inventories and this limits the scientific basis for monitoring and verification of progress towards the Global Methane Pledge and the Paris Climate Agreement.
We present Ember’s Coal Mine Methane Data Tracker as a new open, global, evidence based dataset for understanding CMM emissions, reporting quality and methane targets. The Data Tracker compiles and harmonises national greenhouse gas inventory submissions to the United Nations Framework Convention on Climate Change (UNFCCC). It integrates these data with historic coal production statistics from the US Energy Information Administration (EIA), International Energy Agency (IEA) coal production forecasts and independent emission estimates (IEA Methane Tracker, Global Energy Monitor (GEM) Global Coal Mine Tracker).
To reconstruct national emissions from 1990 onwards, we calculate country and year specific CH4 emission intensities wherever both reported emissions and coal production exist. Emission intensity is defined as CH4 emissions (in kilotonnes) per million tonnes of coal produced. This approach also enables consistent comparison of reported emissions across countries and over time.
We fill gaps in the intensity time series using values from neighbouring years so that each country has a continuous record. We then multiply these completed intensity series by observed production to estimate unreported emissions. Ember’s gap filled series indicates that global active CMM emissions exceeded 34 million tonnes in 2023, whereas official UNFCCC inventories reported only 4.62 million tonnes, less than 14% of the inferred total. For 2024, the latest compilation of submissions implies 34.5 million tonnes of reported CMM, with underreporting of up to 21.2 million tonnes when compared with independent datasets.
We introduce a quantitative confidence score from 0 to 6 for each country’s reported CMM emissions, combining recency of UNFCCC reporting, consistency with independent estimates from both top down and bottom up approaches, and methodological robustness. Applied to major producers, this score shows that most large coal producing countries fall in the low-to-moderate confidence range, with only a small number, such as Poland (score 5), achieving higher confidence in their reported CMM inventories.
By providing a transparent, harmonised framework for CMM monitoring, we demonstrate that systematic underreporting pervades national inventories. This gap is driven by widespread reliance on low tier IPCC methods, with 86% of reported CMM emissions relying on emission factors rather than direct measurement. Our quantitative confidence score (ranging from 0 to 6) highlights this reliance, showing that low scoring countries correlate directly with significant underestimation. This evidence necessitates the need for transparent, measurement based Monitoring, Reporting and Verification (MRV) frameworks to establish the rigorous CH4 accounting required by global climate commitments.
How to cite: Horner, R., Assan, S., and Liepa, A.: A global coal mine methane tracker to highlight inventory gaps and target mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9574, https://doi.org/10.5194/egusphere-egu26-9574, 2026.
We use atmospheric inverse modelling to provide observation-based estimates of methane emissions at the national scale in Europe. We apply the numerical weather prediction model ICON-ART to obtain an ensemble of methane concentrations by varying the meteorology, the lateral boundary conditions and emission fields. By comparing to ground based observations of the ICOS network, we employ a 4D LETKF to assimilate both the concentrations and emissions concurrently. We create an ensemble of emissions in two ways: We can perturb the underlying emission field with a gaussian random field, or we can separate it into regions and economic sectors and scale these. We compare the two approaches and the resulting emission estimates to national greenhouse gas inventories and synthesis inversion results with a focus on Germany. The first results are presented for 2021 and we identify a considerable mismatch with the reported emissions in central Europe.
How to cite: Becker, N., Keil, N. H., Bruch, V., and Kaiser-Weiss, A.: Concurrent data assimilation of methane concentrations and fluxes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2817, https://doi.org/10.5194/egusphere-egu26-2817, 2026.
Accurate estimation of methane (CH₄) emissions is essential for assessing mitigation progress,
yet substantial uncertainties persist at the national scale. In South Korea, CH₄ emissions are
predominantly anthropogenic, with the waste and agricultural sectors contributing
approximately 82% of total national emissions. This study analyzes national-scale CH₄
emission estimates for South Korea during 2010–2021 using multiple atmospheric inversion
systems participating in the Methane Inversion Inter-Comparison for Asia (MICA) project.
Results from inversions using only in situ observations indicate that prior emissions over South
Korea were likely overestimated. Prior estimates range from 1.5 to 1.7 Tg yr⁻¹ for most years,
whereas posterior emissions are, on average, about 15% lower than the prior estimates. A
notable exception is the LMDZ inversion model, which yields posterior estimates that are 40
67% lower than prior values. This substantial reduction is primarily associated with the waste
sector. Sectoral attribution reveals substantial inter-model differences. LMDZ shows a
decreasing waste-sector emission trend in Exp. 1 but an increasing trend when only satellite
observations are assimilated (Exp. 2), whereas the STILT-based inversion consistently
indicates increasing waste-sector emissions. Given that the waste sector dominates national
CH₄ emissions, these discrepancies strongly influence total emission estimates. The prior
waste-sector emissions, derived from EDGAR v7, exceed those reported in South Korea’s
national greenhouse gas inventory (GIR), contributing to the observed overestimation.
Additionally, the inversion-derived posterior estimates consistently indicate an overestimation
of prior agricultural emissions during the summer months. Model performance evaluation over
the region of interest indicates varying levels of agreement between simulated and observed
CH₄ mole fractions, with correlation coefficients ranging from 0.24 to 0.85 and posterior biases
ranging from −65.6 to 0.34 ppb, highlighting the choice of transport model is important. Overall,
this study highlights the value of multi-model inversion inter-comparisons for constraining
national-scale CH₄ emissions, diagnosing sector-specific uncertainties, and identifying
structural differences among inversion frameworks that can guide future improvements.
How to cite: Takele Kenea, S., Shin, D., Seo, W., Lee, S., Wang, F., Maksyutov, S., Janardanan, R., Lee, S., Belikov, D. A., Patra, P. K., Montenegro, N., Berchet, A., Saunois, M., Martinez, A., Liang, R., Zhang, Y., Ren, G., Lin, H., Hyvärinen, S., and Tsuruta, A. and the Sangwon Joo, Sumin Kim: National-scale methane emissions in South Korea (2010–2021): insights from multiple inversion systems , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16089, https://doi.org/10.5194/egusphere-egu26-16089, 2026.
Uncertainty in atmospheric transport models, especially boundary-layer mixing and turbulence, still limits confidence in top-down GHG emission estimates. In inversion workflows, observation selection is commonly supported by empirically tuned filters based on modelled meteorological variables (e.g., boundary-layer height, wind speed). The selection prioritises periods when transport is expected to be well represented. This motivates continued work to characterise atmospheric mixing and its associated uncertainties using observations.
In the UK GEMMA programme, we investigate whether observation-based atmospheric mixing state can provide complementary information to support uncertainty characterisation in UK CH₄ inversions. We demonstrate the framework at UK sites with radon measurements and at a newly instrumented site in Scotland where only meteorological measurements are available. Where radon is measured, we use it as an independent tracer of near-surface mixing and compare observed radon with radon simulated using the Met Office NAME dispersion model and a radon flux map. This comparison is used to define transport-performance classes (periods of relatively better vs poorer agreement) and associated atmospheric mixing state. At the Scotland site, we derive atmospheric mixing regimes from in situ meteorological measurements alone, using a vertical profile sampled every 10 m to characterise stratification and mixing.
We show how the resulting atmospheric mixing state and transport-performance classes can be used in two operational ways: (i) as additional information to support observation selection alongside existing practice, and (ii) to define regime-dependent uncertainty characterisation within inversion frameworks rather than assuming a single fixed error model. We illustrate the approach using two UK CH₄ inverse methods (InTEM and RHIME) and discuss how observation-based mixing information can improve transparency and reproducibility in hybrid (inventory + atmospheric) emissions estimation for IG3IS-aligned information services.
How to cite: Kikaj, D., Andrews, P., Danjou, A., Manning, A., Rigby, M., Chung, E., Forster, G., Wenger, A., Rennick, C., Safi, E., O’Doherty, S., Stanley, K., Pitt, J., and Gardiner, T.: Can observation-based atmospheric mixing state reduce filtering sensitivity in GHG inversions? Lessons from the UK GEMMA programme, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19832, https://doi.org/10.5194/egusphere-egu26-19832, 2026.
Nitrous oxide (N₂O) is the third most important long-lived greenhouse gas after CO₂ and CH₄, yet large uncertainties remain in its regional emission estimates. In this study, we apply the regional inverse modeling system CIF-CHIMERE to quantify N₂O surface fluxes over the EU27+3 region (European Union, United Kingdom, Norway, and Switzerland) for the period 2005–2023, providing a long-term and high spatiotemporal resolution assessment of N2O fluxes. The inversion is primarily constrained by in situ atmospheric N₂O measurements from the ICOS (Integrated Carbon Observation System) ground-based station network across Europe, and uses the CIF-CHIMERE transport model coupled with a four-dimensional variational (4D-Var) data assimilation framework to estimate posterior N2O fluxes. For 2005–2023, inversions are conducted at a spatial resolution of 0.5° × 0.5°, while for 2018–2023 the resolution is refined to 0.2° × 0.2°. In both configurations, hourly surface fluxes are estimated, enabling analysis of diurnal, seasonal, and interannual variability. The inversions significantly improve the representation of localized emission patterns and short-term flux dynamics. Overall, the results provide a top-down dataset for evaluating bottom-up inventories and for improving the understanding of regional and temporal variability in N₂O emissions across EU27+3.
How to cite: Shi, T., Berchet, A., and Ciais, P.: Quantifying N₂O Flux over the EU27+3 Region Using CIF-CHIMERE Model for 2005–2023, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14957, https://doi.org/10.5194/egusphere-egu26-14957, 2026.
Methane emissions from waste and agriculture account for 46.6 % of Aotearoa New Zealand’s (ANZ) gross greenhouse gas emissions in 2023. Despite the significance of methane emissions, the only way to estimate their magnitude is based on emission factor methods, which include large uncertainties. We present newly developed tools to directly measure methane emissions from wastewater treatment facilities, animal effluent storage systems and herds of dairy cows. We deploy in situ analysers on mobile observation platforms (vehicle and drone) and quantify methane emission fluxes using the tracer gas technique. The accuracy of this method is estimated in multiple ways: i) a controlled release experiment, ii) through comparison to a mass-balance modelling approach, iii) through comparison to co-located chamber measurements for methane emissions from effluent ponds, iv) through comparison to co-located measurements of animal emissions using the “GreenFeed” technique. The comparisons show excellent agreement, providing much needed assurance of analytical performance to our mobile techniques. Our tools support ANZ’s farmers and waste managers to better understand current emissions, as well as to assess the efficacy of investments into emission mitigation. Additional tests explore new isotope techniques with the goal to quantify methane fluxes from different components within a plant, for example methane derived from digestors versus methane derived from biosolids in wastewater treatment systems, or methane from the open face of a landfill versus emissions from an area that is covered.
How to cite: Sperlich, P., Stiegler, C., Geddes, A., Sutton, H., Smith, B., Leitch, M., Gray, S., Brailsford, G., Moss, R., Bukosa, B., Mikaloff-Fletcher, S., Pirooz, A., Turner, R., Turnbull, J., Laubach, J., Rowe, S., McNaughton, L., Spaans, O., Brian, K., and Wymei, E.: Towards accurate quantification of New Zealand’s methane emissions from waste and agriculture, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19514, https://doi.org/10.5194/egusphere-egu26-19514, 2026.
Zurich aims for net-zero direct greenhouse gas emissions by 2040, a target supported by 75 % of voters. Progress is tracked through a detailed CO2 inventory covering energy, transport, industry, and waste. Under the European ICOS Cities project, a monitoring program was launched using two approaches: (i) a network of mid- and low-cost CO2 sensors combined with atmospheric inverse modeling, and (ii) CO2 flux measurements from an eddy-covariance system on a city-center high-rise building, paired with footprint modeling.
Here, we focus on the mid-cost (ZiCOS-M) and low-cost (ZiCOS-L) NDIR (nondispersive infrared) CO2 networks, which were both operational for at least 3 years since 2022.
ZiCOS-M consists of 26 monitoring sites, 21 in the city and 5 outside the urban area. Daily calibrations using two reference gas cylinders, and corrections of the sensors’ spectroscopic response to water vapour were performed. The hourly mean root mean squared error (RMSE) was 0.98 ppm (0.46 - 1.5 ppm) and the mean bias ranged between 0.72 and 0.66 ppm compared to parallel measurements with a high-precision reference gas analyser for a period of 2 weeks or more. CO2 concentrations in the city were highly variable with site means ranging from 434 to 460 ppm, and Zurich’s mean urban CO2 increment was 15.4 ppm above the regional background.
ZiCOS-L consists of 56 sites with paired sensors. The sensors require in-field training for model calibration before deployment and further post-processing steps to account for drift and outliers. After data processing, the hourly RMSE was 13.6±1.4 ppm, and the mean bias 0.75±1.67 ppm when validated against parallel reference measurements from ZiCOS-M. CO2 concentrations were highly variable with site means in Zurich ranging from 438 to 465 ppm, reflecting mainly the influence of sources in the nearby surroundings. Vegetation (mainly grassland) amplified the morning concentration on average in summer by up to 20 ppm due to ecosystem respiration, while heavy traffic increased the morning rush hour concentration by 15 ppm. Despite its lower measurement accuracy, the ZiCOS-L network enables the study of concentration dynamics at a spatial and temporal scale that is not accessible by any other means.
The ZiCOS-M data was extensively used to derive top-down CO2 emissions. Similar modelling activities are currently ongoing with the ZiCOS-L data, and both are compared to emissions derived from the eddy covariance system and to the city's emission inventory.
Grange SK, … Emmenegger L, The ZiCOS-M CO2 sensor network: measurement performance and CO2 variability across Zurich. https://doi.org/10.5194/acp-25-2781-2025.
Creman L, … Bernet L, The Zurich Low-cost CO2 sensor network (ZiCOS-L): data processing, performance assessment and analysis of spatial and temporal CO2 dynamics. https://doi.org/10.5194/egusphere-2025-3425
Brunner D, … Emmenegger L, Building-resolving simulations of anthropogenic and biospheric CO2 in the city of Zurich with GRAMM/GRAL. https://doi.org/10.5194/acp-25-14279-2025.
Hilland R, … Christen A, Sectoral attribution of greenhouse gas and pollutant emissions using multi-species eddy covariance on a tall tower in Zurich, Switzerland. https://doi.org/10.5194/acp-25-14279-2025.
Ponomarev N, … Brunner D, Estimation of CO2 fluxes in the cities of Zurich and Paris using the ICON-ART CTDAS inverse modelling framework. https://doi.org/10.5194/egusphere-2025-3668.
How to cite: Emmenegger, L., Creman, L., Fischer, A., Grange, S. K., Hüglin, C., Rubli, P., and Brunner, D.: Design, operation, and insights from Zurich’s mid- and low-cost ICOS Cities CO2 sensor network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2748, https://doi.org/10.5194/egusphere-egu26-2748, 2026.
Quantifying urban CO2 emissions from space can be approached using different methodologies, including direct plume-based analyses, but combining satellite observations with atmospheric transport models requires the ability to realistically reproduce fine-scale spatial gradients over cities. Using the Grand Paris area as a testbed, we investigate the sensitivity of simulated near-surface CO2 concentrations to urban physics parameterization and horizontal resolution within the WRF-Chem modeling framework coupled to a high-resolution fossil fuel emission inventory. At mesoscale resolution (900 m), a hierarchy of urban representations ranging from simulations without urban physics to multi-layer urban canopy models is evaluated, showing that the Building Energy Model (BEM) provides the most physically consistent simulation of surface energy fluxes, boundary-layer development, and near-surface CO2 variability. Building on this configuration, we compare mesoscale simulations with Large-Eddy Simulation (LES) runs at 300 m and 100 m resolution. Model results are evaluated against dense urban CO2 observations from the high-precision Picarro network, a complementary mid-cost sensor network from ICOS-Cities, and surface sensible and latent heat flux observations from the ICOS ETC Level-2 fluxes data product. An extensive urban observation network including wind lidars and ceilometers from Urbisphere project provides an exceptional constraint for the evaluation of boundary-layer structure and vertical mixing at fine scales. The LES simulations substantially enhance the representation of spatial heterogeneity and localized CO2 enhancements associated with major emission sources, which are smoothed or underestimated at mesoscale resolution. However, increased resolution also amplifies sensitivity to local wind fields and emission inventory uncertainties. These results highlight that both urban physics and model resolution critically shape the ability of transport models to reproduce observed urban CO2 gradients.
How to cite: Rafalimanana, A., Lauvaux, T., Abdallah, C., Chariot, M., Ramonet, M., Doc, J., Laurent, O., Lopez, M., Raznjevic, A., Krol, M., Järvi, L., David, L., Sanchez, O., Christen, A., Looschelders, D., Bignotti, L., Loubet, B., Grimmond, S., and Morrison, W.: Monitoring urban atmospheric CO2 plumes from space: sensitivity to urban physics and scale effects over Paris, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5198, https://doi.org/10.5194/egusphere-egu26-5198, 2026.
Accurate quantification of the sources and sinks of long-lived air pollutants is fundamental for effective emissions management, particularly in urban areas where emissions are generally more intense. Stakeholders commonly use so-called bottom-up methods to estimate emissions for urban areas. This type of emission accounting is typically carried out for annual totals, often with a latency of one or more years. Alternative methods that provide estimates with higher temporal resolution and lower latency could be helpful for stakeholders seeking targeted strategies to reduce emissions. A top-down urban emissions estimation system for the Washington, DC, and Baltimore, MD, metropolitan area, called the Urban Atmospheric Monitoring and Modeling System (Urban-AMMS), is being developed to provide accurate, up-to-date urban emissions data. Urban-AMMS has several components, including tower-based, aircraft, and mobile van measurements platforms, whose data are assimilated by the CarbonTracker-Lagrange analytical inverse model; an ensemble of HYSPLIT backward dispersion simulations driven by in-house high-resolution WRF simulations (spatial resolution of 1 km) enhanced with urban meteorological observations; biospheric models; and bottom-up inventories used for a prior estimate of emissions in the domain. The inversion system is tailored to account for the underlying variability in urban fluxes of an inert tracer (CO2) by solving for hourly fluxes and incorporating explicit spatiotemporal covariance of prior errors, as well as high-resolution source-receptor sensitivities estimated by WRF-HYSPLIT. Here, we present an overview of Urban-AMMS, including initial results and sensitivity analyses to investigate the effects of prior spatial aggregation, background handling, and the temporal covariance of prior errors. Numerical experiments show improvements in estimates of urban surface fluxes at both the city and grid cell scales. Still, the reliability of inverse fluxes depends on prior uncertainty, as observed in previous studies. These findings provide critical insights for the inverse estimation of long-lived air pollutants in complex urban environments.
How to cite: Cahuich-Lopez, M., Loughner, C., Ngan, F., Karion, A., Hu, L., Lopez-Coto, I., Mueller, K., Marrs, J., Miller, J., McDonald, B., Harkins, C., Lyu, C., Li, M., Gurney, K., Zinn, S., Ren, X., Cohen, M., Diamond, H., Stein, A., and Whetstone, J.: Urban Atmospheric Monitoring and Modeling System (Urban-AMMS): A Top-Down Approach to Investigate Sources and Variability of an Inert Tracer in the Washington, DC, and Baltimore, MD, Metropolitan Area, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14303, https://doi.org/10.5194/egusphere-egu26-14303, 2026.
Accurate estimation of greenhouse gas (GHG) emissions at the infrastructure scale remains essential to climate science and policy applications. Powerplant and vehicle emissions often form the majority of fossil fuel CO2 (FFCO2) emissions in much of the world at multiple scales. Climate Trace, co-founded by former U.S. Vice President Al Gore, is a new AI-based effort to estimate pointwise and roadway-scale GHG emissions, among other sectors. However, limited independent peer-reviewed assessment has been made of this dataset. Here, we update a previous analysis of Climate Trace powerplant FFCO2 emissions in the U.S. and present a new analysis of Climate Trace urban on-road CO2 emissions in U.S. urban areas. This is done through comparison to an atmospherically calibrated, multi-constraint estimates of powerplant and on-road CO2 emissions from the Vulcan Project (version 4.0).
Across 260 urban areas in 2021, we find a mean relative difference (MRD) of 69.9% in urban inroad FFCO2 emissions. Furthermore, differing versions of the Climate Trace on-road emissions releases shift from over to under-estimation in almost equal magnitudes. These large differences are driven by biases in Climate Trace’s machine learning model, fuel economy values, and fleet distribution values. An update to the powerplant FFCO2 emissions analysis (from a 2024 paper) show both improved and degraded convergence of emissions. We continue to recommend that sub-national policy guidance or climate science applications using the GHG emissions estimates in these sectors made by Climate Trace should be done so with caution.
How to cite: Gurney, K., Aslam, B., Dass, P., Gawuc, L., Hocking, T., Barber, J., and Kato, A.: Assessing the accuracy of the Climate Trace global vehicular and power plant CO2 emissions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22515, https://doi.org/10.5194/egusphere-egu26-22515, 2026.
Long-term coal spontaneous combustion (CSC) represents a severe and persistent threat, resulting in substantial waste of energy resources, significant environmental degradation, and serious risks to human health and safety. To better understand the emission characteristics of CSC, we conducted ground-based measurements of XCO₂, XCH₄, XCO and aerosol optical depth (AOD) using a Fourier-transform infrared spectrometer (EM27/SUN) within the COCCON network, in the Wugonggou coal-fire region near Fukang, Xinjiang.
Our results indicate that TROPOMI satellite data systematically underestimated XCO, with a mean bias of 4.53 ± 5.53 ppb (4.54%). For distinct enhancement events observed by COCCON, ΔXCO₂ and ΔXCO exhibit a strong correlation (R² = 0.6082), with a slope of 9.782 ppb/ppm (9.782 × 10⁻³ ppm/ppm). This value is lower than the CAMS inventory ratio of 13.52 × 10⁻³. This discrepancy arises primarily from their distinct spatial representativeness. The COCCON instrument, located within the coal fire region, captures intense local combustion emission. In contrast, the CAMS product represents a daily average over a much larger model grid cell, which dilutes strong local point sources like coal fires within a broader regional background. Additionally, correlation analysis shows that ΔXCO is more closely linked to AOD (R² = 0.2283) than either ΔXCO₂ or ΔXCH₄, underscoring the distinct behavior of CO in coal-fire plumes.
How to cite: Tu, Q., Fang, J., Hase, F., Butz, A., Barreto, Á., García, O., and Qin, K.: ΔXCO/ΔXCO2 characteristics over coal-fire areas in Xinjiang, China using a portable EM27/SUN FTIR spectrometer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15734, https://doi.org/10.5194/egusphere-egu26-15734, 2026.
Observation based quantification of surface CO2 fluxes relies on the consistent integration of atmospheric observations with numerical transport models. We present the development and demonstration of an ensemble-based data assimilation system that couples atmospheric CO2 observations to the ICON-ART modeling framework using a Local Ensemble Transform Kalman Filter (LETKF).
Starting with a flux estimate provided by CarbonTracker Europe High-Resolution we start with a dynamic model with hourly resolution with a focus on fluxes in Europe for 2021. We then create an ensemble of perturbed prior fluxes within assumed uncertainties using prescribed spatial and temporal correlation structures. We simulate the transport of these ensemble members in ICON-ART in limited area mode, while varying the meteorological conditions to represent meteorological uncertainties. Subsequently, we use the LETKF to update the state vector of concentrations and CO2 fluxes daily, resulting in an posterior estimate of surface CO2 fluxes over Europe.
This work provides the foundation for an ICON-ART-based CO2 flux assimilation system and establishes a technical basis for future extensions toward longer assimilation periods, refined error modeling, and the assimilation of anthropogenic emission signals.
How to cite: Böttcher, J., Becker, N., Kaiser-Weiss, A., and Harms, M.: Development of an Ensemble-Based Data-Assimilation System for CO2 Fluxes Using ICON-ART, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3437, https://doi.org/10.5194/egusphere-egu26-3437, 2026.
The Land Use, Land-Use Change, and Forestry (LULUCF) and agriculture sectors are increasingly central to global climate policy. They play a crucial role in climate mitigation strategies, as land acts as a carbon sink that needs to be enhanced and as a source of greenhouse gas (GHG) emissions that must be reduced. In the European context, the LULUCF Regulation (EU 2018/841), revised in 2023, aims for 310 Mt CO2eq net removals by 2030 and requires spatially explicit land-use representations to monitor land dynamics and assess policy impacts.
Within the Horizon project AVENGERS (Attributing and Verifying European and National Greenhouse Gas and Aerosol Emissions and Reconciliation with Statistical Bottom-up Estimates), a methodology was developed to generate an IPCC-compliant land-use map by integrating multiple Copernicus Land Monitoring Service (CLMS) products. In national GHG inventories, the operational use of spatial explicit data is often limited due to restricted temporal coverage, inconsistencies with national statistics, and challenges in interpreting mixed classes and land-use/land cover definitions. This methodology provides a transparent approach to reconcile inventory data with high-resolution spatial datasets.
The approach combines the CLC Plus Backbone geometry with CORINE Land Cover (CLC) and ancillary CLMS datasets, including the High-Resolution Layer Crop Types and Priority Areas monitoring products (e.g., Coastal Zones, Riparian Zones, and Protected areas). Multiple layers were integrated using overlay techniques and priority rules, resulting in an harmonized map at 10-m spatial resolution. CLC attributes were aggregated to IPCC land use categories, allowing direct comparison between mapped areas and inventory surfaces.
Preliminary validation involved cross-checks with national land-use activity data to ensure reliability of mapped areas across LULUCF categories. The resulting maps enable the spatialization of inventory-based LULUCF and agriculture emissions, producing gridded emission datasets based on improved spatially explicit land-use information. These datasets are suitable for use as input (priors) in atmospheric inversion modelling, a top-down emissions estimation method supporting policy evaluation.
The methodology is designed to be replicable across all European countries covered by CLMS data and to be updated approximately every 2–3 years, in line with the regular update cycle of CLMS products. The methodological framework is modular and flexible, based on a spatial data storage and management scheme developed by ISPRA, which allows the integration of additional datasets and adaptation to different territorial contexts. The approach was applied and tested in three national case studies for the year 2018—Italy, Sweden, and the Netherlands—with specific adaptations introduced to account for distinct territorial characteristics. This first implementation represents a promising step and provides a solid foundation for further refinements and future developments, supporting the production of high-resolution land-use maps helpful for national inventory agencies and inversion modelling experts.
How to cite: Cecili, G., De Fioravante, P., Pellis, G., Vitullo, M., and Fiore, A.: The use of CLMS products for improving the spatialization of greenhouse gases emissions from LULUCF and agriculture sectors , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12319, https://doi.org/10.5194/egusphere-egu26-12319, 2026.
Bamboo, a perennial grass species, exhibits rapid growth rates surpassing many native trees, offering substantial potential for atmospheric carbon capture and subsequent sequestration into durable products. Despite this promise, the carbon sequestration capacity of bamboo forests and its variability under different land management practices and environmental conditions remain underexplored. This study examines carbon sequestration in a representative bamboo forest in Anji, eastern China, employing a novel observation-based approach utilizing multiple atmospheric tracers (CO₂, CO, and ¹⁴C-CO₂) measurements to attribute fluxes accurately. The study also includes regular biomass inventory to be able to compare CO2 fluxes between two approaches. Departing from conventional inventory-based estimates of carbon emissions and uptakes, observations-based method yields detailed insights into individual carbon-cycle processes within bamboo ecosystems and identifies the most effective tracers for quantifying regional CO₂ fluxes. Leveraging high-resolution atmospheric CO₂ observations, coupled with advanced modeling systems and analytical tools—including machine learning techniques to reconstruct and correct prior Net Ecosystem Exchange (NEE) fluxes for the bamboo forest—we derive carbon fluxes while accounting for variations in management strategies and environmental factors. These findings enhance our understanding of bamboo's role in global carbon mitigation, informing sustainable forestry practices and climate policy. This work highlights the transformative potential of tracer-based methodologies for precise, scalable carbon flux assessments in managed ecosystems.
The study is supported by the Quadrature Climate Foundation (Grant No. 01-21-000133).
How to cite: Fang, S., Tarasova, O., Li, Y., Turnbull, J., Lin, Y., Brailsford, G., and Mikaloff-Fletcher, S.: Unveiling Carbon Sequestration Dynamics in Bamboo Forests, China: An Observation-Based Approach Using Atmospheric Tracers, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2643, https://doi.org/10.5194/egusphere-egu26-2643, 2026.
In the context of the Paris Agreement on climate change and of a global effort to reduce greenhouse gas emissions, the monitoring of anthropogenic carbon dioxide (CO2) emissions is needed to assist policy makers but represents a major challenge. While current inventories provide rather robust annual emission totals at country scale, they lag behind real time by many months and they lack spatial and sub-annual details. Here we map the daily surface fossil fuel CO2 emissions at a 1/16 degree resolution over Europe, with the year 2021 as an example, based on spaceborne atmospheric composition observations.
As the high-resolution satellite monitoring of atmospheric CO2 remains challenging, especially at a local spatial scale and a daily time scale, we take advantage of the co-emission of CO2 and nitrogen oxides (NOX) during fossil fuel combustion: we exploit images of nitrogen dioxide (NO2) concentrations retrieved from the measurements of the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5P satellite.
From the TROPOMI NO2 concentrations, we retrieve daily maps of NOX emissions based on the divergence of the mass fluxes within the NO2 images. We combine the changes of these maps from one year to the next with low latency national CO2 emissions from Carbon Monitor (https://carbonmonitor.org/), and with a baseline of monthly spatially-distributed CO2 emissions for a previous year (here 2020) from GridFED (https://mattwjones.co.uk/co2-emissions-gridded/) from which we removed aviation and shipping emissions beforehand.
The resulting maps of emission increments from 2020 to 2021 capture changes in highly emitting areas: major urban or industrial areas, and main transport corridors. The emissions for the year 2021 show good consistency with existing inventories. The dataset also produces realistic seasonal variability at a local scale and captures daily variability, although temporally smoothed due to a 5-day rolling average of Carbon Monitor data.
This method is both temporally and spatially scalable and can therefore be extended to the entire world and to additional years, which provides encouraging prospects for the continuation of this work.
How to cite: Héraud, A., Chevallier, F., Broquet, G., Ciais, P., Martinez, A., and Rey-Pommier, A.: Daily and 1/16 degree maps of CO2 fossil fuel emissions based on satellite retrievals of pollutant atmospheric data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20826, https://doi.org/10.5194/egusphere-egu26-20826, 2026.
In this research, we propose a simple and effective method for gas analysis of semiconductor and display industries. To achieve this, residual gas analyzer (RGA) was adopted and two high-global warming potential (GWP) gases such as CF4 and NF3 commonly used in industrial application were focused. The experiment was conducted in four key steps: identifying gas species using optical emission spectroscopy (OES), calibrating RGA with a quadrupole mass spectrometer (QMS), constructing a five-point calibration graph to correlate RGA and Fourier-transform infrared spectroscopy (FT-IR) data, and estimating the concentration of unknown samples using the calibration graph. The results under plasma-on conditions demonstrated correlation and accuracy, confirming the reliability of our approach. In other words, the method effectively captured the relationship between RGA intensity and gas concentration, providing valuable insights into concentration trends. Thus, our approach serves as a useful tool for estimating gas concentrations and understanding the correlation between RGA intensity and gas composition.
Reference
[1] B. G. Jeong, S. H. Park, D. H. Goh, and B. J. Lee, Metrology 5 (2025) 60
How to cite: Jeong, B. G.: Real-Time Monitoring and Quantification of Fluorinated Greenhouse Gases in Semiconductor/Display Manufacturing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4566, https://doi.org/10.5194/egusphere-egu26-4566, 2026.
Achieving effective greenhouse gases (GHGs) mitigation policy requires accurate quantification of contribution from each emission source based on in-situ measurements. In this study, we investigated the spatial distribution of CO2 and CH4 emitted from different emission sources by conducting mobile measurements using a GLA331-GGA analyzer (ABB–LGR Inc.) mounted on a vehicle. We conducted seven mobile measurements in spring (N = 3), summer (N = 2), and fall (N = 2) over Seoul Metropolitan Area (SMA) in 2025. By comparing the correlation between two GHGs from various emission sources, we selected representative sites including livestock facilities (cattle and swine barns), industrial complexes, urban, wastewater treatment plants, LNG power plants, rural areas. Background GHGs concentrations were defined as the daily 5th percentile for each measurement day, and correlations between enhancements (ΔCO2 and ΔCH4) were assessed. Along with real time measurements, stable carbon isotopes samplings were also conducted to provide complementary constraints on concentration variability and the contributions of end-member of each emission source. For stable isotope measurements, two ambient air samples were collected per site using canisters (Entech, Simi Valley, CA, USA) and analyzed with Picarro G2131-i for δ13C–CO2 (δ13C) and Picarro G2132-i for δ13C–CH4 (δ13CH4). Strong co-variability between the two GHGs was observed at several emission sources and seasons, including springtime cattle barns (R = 0.75), LNG power plants (R = 0.83), industrial complexes (R = 0.74), and swine barns (R = 0.64); summertime cattle barns (R = 0.66) and LNG power plants (R = 0.67); and fall industrial complexes (R = 0.70) and cattle barns (R = 0.97). These correlations suggested that CO2 and CH4 were likely emitted concurrently from shared sources or similar emission activities in SMA region. The observed δ13C values ranged from −8.2‰ to −12.5‰, while δ13CH4 ranged from −47.2‰ to −48.6‰. Seasonal mean δ13C values were −11.2‰ in spring, −9.2‰ in summer, and −10.1‰ in fall, consistent with a summertime influence from enhanced biospheric respiration, with the most depleted values occurring in spring. In contrast, δ13CH4 exhibited relatively small seasonal variability, as indicated by the coefficient of variation (sd/mean; 0.004 in spring, 0.013 in summer, and 0.012 in fall), but still provided useful constraints on source attribution. In addition, a Bayesian isotope mixing model (the ‘simmr’ package in R) was applied to quantify relative source contributions indicating that coal combustion contributed most strongly to δ13C, whereas wastewater treatment and natural gas were the dominant contributors to δ13CH4.
How to cite: Choi, H., Jun, J., Lee, S., Kim, S., and Choi, Y.: Characteristics of CO2 and CH4 from different emission sources using mobile measurements and stable carbon isotope analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3366, https://doi.org/10.5194/egusphere-egu26-3366, 2026.
The semiconductor and display industries are significant sources of fluorinated greenhouse gas (F-GHG) emissions in the electronics, making accurate emission estimation essential for addressing climate change. The Republic of Korea, a leading country in the semiconductor and display industries, requires precise evaluation of the environmental impact of these industries due to its global competitiveness. Currently, The Republic of Korea relies on default emission factors provided by the 2006 IPCC guidelines for estimating F-GHG emissions. However, this approach does not account for the latest mitigation technologies implemented in Republic of Korea, resulting in a conservative overestimation of actual F-GHG emissions. To address this issue, this study conducted direct measurements of F-GHG emissions from semiconductor manufacturing processes in facilities equipped with advanced mitigation technologies. By employing state-of-the-art measurement methods, the study evaluated the use rate of gas (Ui) and generation rate of by-product gas (Bbyproduct, Bi) and compared the results with the default emission factors provided by IPCC G/L (2006 and 2019). Moreover, based on derived country-specific emission factors (Tier 3b), GHG emissions were estimated and compared with tier-based methodologies using 2006 and 2019 IPCC G/L default factors (Tier 2a, 2b, 2c and 3a). The finding highlights the need for developing country-specific emission factors and contribute to the establishment of precise, data-driven policies for reducing GHG emissions in Republic of Korea’s electronics industry. Furthermore, this research serves as valuable reference for other countries aiming to refine their emission estimates with country-specific data and technological advancements, ultimately contributing to global efforts towards carbon neutrality.
How to cite: Inkwon, J. and Bong-Jae, L.: Comparative Analysis of F-GHGs Emission Estimates between IPCC Default Factors and Measurement-based Korea-specific Emission Factors in Semiconductor Manufacturing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4570, https://doi.org/10.5194/egusphere-egu26-4570, 2026.
In efforts to mitigate the effects of global climate change several prominent policies and guidelines which emphasize the importance of sustainable growth have been introduced in recent years. Examples include the 2019 European Green Deal, and the subsequent Clean Industrial Deal in 2025. A key aspect of these goals is the reduction of air pollutant emissions, particularly from fossil fuel combustion, without sacrificing economic growth. The Green Deal commits to an EU wide emission reduction of at least 55% by 2030, as compared to 1990 levels. Remote sensing offers many advantages for tracking progress towards reduction of pollutant emissions. In particular, the global coverage allows for analysis of regions which do not have sufficient ground-based measurement networks. This study presents a method of using spectral analysis with tropospheric NO2 column density and the gross domestic product (GDP) to track and compare progress of the German federal states towards decoupling emissions from economic growth. Most studies evaluating economic decoupling focus on CO2, or CO2 equivalences. There is a current lack of studies which investigate other key combustion products. This study focuses on NO2 as a proxy for emissions related to economic activity. NO2 originates primarily from anthropogenic combustion sources, andhas a short tropospheric lifetime, making it suitable to represent localized fossil fuel emissions. Measurements of NO2 used in this study are obtained from the Ozone Monitoring Instrument (OMI) launched aboard the NASA Aura satellite in 2004. The application of spectral analysis techniques, such as the wavelet analysis, gives additional insight into temporal variability of NO2, to better observe the path of decoupling for each region. Decoupling between GDP and NO2 variability is observed for all regions of Germany in the period between the two most recent global economic recessions (the 2008 financial crisis, and the Covid-19 pandemic). Similar decreasing trends are observed for both the yearly average tropospheric column density and the calculated yearly variability. The variability obtained from the wavelet analysis shows greater sensitivity to changes in NO2 emissions than the absolute tropospheric column density. Further regional differences such as the main economic sectors and types of emission regulations in place are discussed to contextualize the differences present in decoupling processes between the federal states. Overall, NO2 variability is found to be a sensitive and effective indicator for tracking and comparing decoupling progress across different administrative regions.
How to cite: Remy, E., Engert, R., Werner, L., and Bittner, M.: Investigating Germany’s progress in decoupling air pollution emissions from economic activity using satellite-based measurements of NO₂., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5912, https://doi.org/10.5194/egusphere-egu26-5912, 2026.
Reliable and timely information on greenhouse gas (GHG) emissions is essential for evaluating mitigation policies and supporting data assimilation and verification modelling frameworks. In this contribution, we present the sPanisH EmissioN mOnitoring systeM for grEeNhouse gAses (PHENOMENA), a low-latency GHG modelling framework developed within the RESPIRE-CLIMATE Spanish national project, which received formal endorsement from the WMO-IG3IS initiative.
PHENOMENA provides harmonised daily and high spatial resolution (up to 1 km × 1 km) CO2 and CH4 emissions for the main combustion-related sectors, including electricity generation, manufacturing industry (cement and iron and steel), residential and commercial combustion, road transport, shipping and aviation. The system estimates CO2 and CH4 emissions by combining low latency activity data and fuel- and process-dependent emission factors through bottom-up and downscaling approaches. The data collected and pre-processed includes hourly near-real-time traffic counts from the national road network, hourly electricity production data reported by individual power plants, daily Copernicus ERA5-Land surface temperature, monthly industrial production statistics and AIS (Automatic Identification System) data, among others.
PHENOMENA produces multiple GHG emission products, including high resolution maps of daily emissions per sector, as well as daily summaries of emissions aggregated at different regional levels and for the main Spanish metropolitan regions. The emissions computed with PHENOMENA allows representing the intra-weekly and seasonal variability of GHG emissions as well as changes in their spatial patterns, which can be linked to specific policy, socioeconomic, and weather impacts.
The results produced with PHENOMENA are compared to official GHG emission inventories as well as to other state-of-the-art low latency GHG emission datasets, such as the ones produced by the CAMS Carbon Monitor initiative. Overall, these developments demonstrate the capability of PHENOMENA to deliver consistent, multisector and near-real-time GHG emission estimates, supporting national monitoring, policy evaluation and future verification and data-assimilation efforts.
How to cite: Legarreta, O., Castesana, P., Lombardich, I., Tena, C., Piñero-Megías, C., Viñas, A., Gehlen, J., Rizza, L., Pérez García-Pando, C., and Guevara Vilardel, M.: Low latency and high resolution GHG emission estimates to support monitoring and modelling activities in Spain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7798, https://doi.org/10.5194/egusphere-egu26-7798, 2026.
ESG (Environmental, Social, Governance) reporting is essential for industry as it helps secure investment for companies’ development. While Scope 1 are direct emissions and Scope 2 are indirect emissions, most of the industrial players report Scope 2 emissions from the use of energy (electricity and gas): these are carbon emissions that are emitted in the power station that uses fossil fuels (oil, coal, gas, biomass, etc.), see [1].
Conventional way to report company’s carbon emissions of Scope 2 is to obtain electrical meter readings and multiply them by the average carbon intensity of the electric grid that supplies electricity. In the UK, such carbon factors were previously published (annually) by the Department for Environment, Food, and Rural Affairs (Defra), then more recently by the Department for Energy Security and Net Zero (DESNZ). These average annual factors are approximate, and actual fuel mix of the electrical grid varies within a few minutes, depending on the operating power generators.
In some cases, the annual carbon intensity may underestimate the actual intensity of the grid. This usually happens in Europe in winter, when a large number of gas-fuelled generators are active to provide sufficient heating, and at the same time wind conditions are placid, providing little of renewable energy. In other cases, when there is lots of wind-generated energy and less gas-generated energy (for example, on a windy summer day), the average carbon factor may overestimate actual carbon intensity of the grid.
In several case studies, we demonstrate that such discrepancies may reach 10-15% of the total carbon emissions, as they are presented in quarterly or annual ESG reports. The results suggest that the current way of reporting carbon emissions should be revised, so that actual state of the dynamical energy grid would be taken into account for improvement of ESG reporting. Subsequently, this will impact their ESG standing and potential investment, which is crucial for European business as well as for the correct accounting of the impact of European carbon emissions [2].
References
[1] Livina et al, International Journal of Metrology and Quality Engineering, in revision.
[2] Livina et al, in preparation.
How to cite: Stokes, L., Przydrozna, A., and Livina, V.: Validating environmental reporting of carbon emissions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8459, https://doi.org/10.5194/egusphere-egu26-8459, 2026.
Reducing methane (CH4) emissions through environmentally friendly agriculture, such as Alternate Wetting and Drying (AWD), is a critical strategy for climate change mitigation in rice production. To effectively implement and evaluate these mitigation measures, it is essential to monitor agricultural practices and environmental variables at a high spatial resolution. This study develops a standardized data-processing protocol, which leverages Google Earth Engine (GEE) to generate high-resolution remote sensing features necessary for quantifying CH4 emissions.
The protocol integrates multi-sensor satellite data to capture the spatio-temporal dynamics of sustainable rice farming. Central to this protocol is the use of Sentinel-1 Synthetic Aperture Radar (SAR) data to classify water management regimes, specifically distinguishing between continuous flooding (CF) and AWD at the pixel level. Additionally, Sentinel-2 optical imagery is processed to extract key vegetation indices (e.g., NDVI, GRVI) to monitor crop growth. To address environmental factors, coarse-resolution soil moisture data from SMAP is downscaled to resolution by incorporating Sentinel-2 and Digital Elevation Model (DEM) data.
By synthesizing these multi-sensor inputs, the protocol provides the necessary foundation for mapping methane emission hotspots and assessing the impact of environmentally friendly management practices. This high-resolution approach supports the design of region-specific mitigation strategies and the advancement of climate-smart agriculture.
As for future research plans, we will apply the constructed model with the field-measured validation data to the extensive rice paddies in southern Ibaraki Prefecture in Japan to estimate methane emissions on a pixel-by-pixel basis and create hotspot maps. This enables the upscaling of a single-point observation model to a broader area while reflecting regional characteristics. This methodology is expected to serve as a powerful tool for examining highly effective methane reduction measures (such as utilization under the J-Credit system) based on each region's agricultural practices and environmental conditions.
How to cite: Shoyama, K., Hirai, C., and Den, H.: Monitoring Environmentally Friendly Agriculture for Methane Emission Reduction: A High-Resolution Multi-Sensor Remote Sensing Protocol on Google Earth Engine, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8591, https://doi.org/10.5194/egusphere-egu26-8591, 2026.
Inverse modelling of CO2 and CH4 using atmospheric in-situ data relies on simulations of atmospheric transport that arederived from models used in numerical weather prediction. The relevant time scales for inversions range from hours to decades, which is far beyond the time scales of a few weeks for which NWP models are designed. The strong diurnal and seasonal variations in surface to atmosphere fluxes of CO2 covary with atmospheric mixing in the boundary layer, as both are solar radiation driven. This way slight seasonal or diurnal biases in the representation of mixing can be amplified. In addition, different atmospheric models show differences in vertical mixing through turbulent mixing and through moist convection, and thus in the representation of vertical gradients in tracers, which results strong differences in flux estimates from inverse modelling. These facts have been known since several decades by now, but progress in addressing these issues has been slow. Within the atmospheric network of ICOS (Integrated Carbon Observation System) additional meteorological observations are available that provide information on atmospheric mixing heights. Also, IAGOS (In-service Aircraft for a Global Observing System) provides information on vertical gradients which can be related to mixing through turbulence and convection.
ITMS, the Integrated Greenhouse gas Monitoring system for Germany, is implemented in multiple development phases: a first phase with the development of a demonstrator system, followed by the second phase, the development of a first-generation system, and a third and last phase, the transfer to operations. With each phase lasting about four years, the project provides a medium-term framework that allows also addressing some of the longer lasting problems such as transport uncertainty. Within ITMS the CarboScope Regional inversion system (CSR) is used as a reference system for CO2 and CH4 inversions, but also as a testbed for model developments. The presentation will provide an overview of recent results obtained within ITMS. This includes evaluating vertical mixing by using additional meteorological profile data or mixing height information, using additional tracers in inversions such as Radon, and confronting vertical profiles from airborne observations with model equivalents.
How to cite: Gerbig, C., Galkowski, M., Koch, F.-T., Danyeli, L., Maier, F., Munassar, S., Xu, Y., and Rödenbeck, C.: Transport model error in inverse modelling: Developments within the ITMS project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9177, https://doi.org/10.5194/egusphere-egu26-9177, 2026.
An urban greenhouse gas (GHG) monitoring network has been established in the Barcelona Metropolitan Area to support the evaluation of GHG mitigation strategies. The network currently consists of five measurement sites equipped with high-precision Picarro analysers providing continuous observations of carbon dioxide (CO2) and methane (CH4). These measurements, in combination with atmospheric modelling will be used to investigate spatial and temporal variability in urban GHG concentrations.
The five sites (Fabra, ICM, ICTA, IDAEA, and UPC-Agropolis) were strategically selected to represent a range of urban and peri-urban environments, including a natural forest, an urban coastal site, a traffic-influenced highway location on the outskirts of the city, an urban park embedded within a densely built area, and a peri-urban agricultural region. This configuration enables the assessment of how different landuse types and emission sources influence observed GHG mole fractions across the metropolitan area.
Hourly averaged CO2 mole fractions show pronounced differences between sites. Lower values are observed at the forested Fabra site, while the ICTA site, located near a major highway, exhibits the highest mole fractions and the largest variability. These spatial contrasts are consistent with results from previous multi-site measurement campaigns in Barcelona, which indicated that densely urbanized, impermeable landscapes are associated with enhanced CO2 concentrations compared to greener areas, particularly during morning hours dominated by traffic emissions.
Maintaining a continuous urban monitoring network is essential for capturing both spatial and temporal variability in GHG concentrations and for improving our understanding of urban atmospheric processes. Such observations are also critical for constraining and validating atmospheric models and for quantifying changes in emissions over time. Here, we present recent observations from the Barcelona Metropolitan Area GHG network and illustrate their application to the study of greenhouse gas variability in complex urban environments.
How to cite: Monteiro, V., Villalba Mendez, G., Luo, Q., and Curcoll Masanes, R.: Urban greenhouse gas monitoring across the Barcelona Metropolitan Area, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10112, https://doi.org/10.5194/egusphere-egu26-10112, 2026.
Sulfur hexafluoride (SF6) is a highly potent greenhouse gas (GHG). Despite its high global warming potential (GWP), it continues to be produced and used in Germany. The reported emission estimates can be used to calculate expected concentrations at measurements sites. Within the PARIS (Process Attribution of Regional Emissions) project we used the operational numerical weather prediction model ICON (ICOsahedral Nonhydrostatic) and its extension module for aerosol and trace gases (ART) as an Eulerian forward model to calculate the expected mixing concentrations response of Germany's largest point source of SF6. We compared the modelled concentration peaks that occur when the modelled plume crosses the measurement site of the Taunus observatory (TOB) with the respective observed signals (requiring background subtraction). The 4-year period of 2020-2023 was covered, and the uncertainty of the meteorological transport was estimated using a 20-member ensemble in our limited area model for Europe, which was run with a horizontal grid resolution of 6.5 km and 74 vertical levels.The model predicts well when peaks are measured but weWe found that most observed peaks at TOB are considerably higher than in the model, suggesting that prior emissions estimates were too low.
This indicates that the independent, observation-based emission estimate of our ICON-ART based system is in the range of double-digit tons, which is considerably higher than the self-reported SF6 emission estimate for this point source, also if the model uncertainties are taken into account.
How to cite: Harms, M., Meixner, K., Schuck, T., Wagenhäuser, T., Alber, S., Stanley, K., Engel, A., Bruch, V., Rösch, T., Steil, M., and Kaiser-Weiss, A.: Forward modelling of SF6 with ICON-ART, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10768, https://doi.org/10.5194/egusphere-egu26-10768, 2026.
Estuaries are nitrous oxide (N2O) emission hotspots and play an important role in the global N2O budget. However, the large spatiotemporal variability of emission in complex estuary environments is challenging for large-scale monitoring and budget quantification. This study retrieved water environmental variables associated with N2O cycling based on satellite imagery and developed a machine learning model for N2O concentration estimations. The model was adopted in China’s Pearl River Estuary to assess spatiotemporal N2O dynamics as well as annual total diffusive emissions between 2003 and 2022. Results showed significant variability in spatiotemporal N2O concentrations and emissions. The annual total diffusive emission ranged from 0.76 to 1.09 Gg (0.95 Gg average) over the past two decades. Additionally, results showed significant seasonal variability with the highest contribution during spring (31 ± 3%) and lowest contribution during autumn (21 ± 1%). Meanwhile, emissions peaked at river outlets and decreased in an outward direction. Spatial hotspots contributed 43% of the total emission while covering 20% of the total area. Finally, SHapley Additive exPlanations (SHAP) was adopted, which showed that temperature and salinity, followed by dissolved inorganic nitrogen, were key input features influencing estuarine N2O estimations. This study demonstrates the potential of remote sensing for the estimation of estuarine emission estimations.
How to cite: Fan, W. and Xu, Z.: Satellite-Based Estimation of Nitrous Oxide Concentration and Emission in a Large Estuary, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11447, https://doi.org/10.5194/egusphere-egu26-11447, 2026.
Measurement-based emission estimates derived from atmospheric observations provide an independent and important approach for identifying emission sources, quantifying emissions and verifying reported inventories. This is particularly relevant for halogenated gases, which due to their role as ozone depleting substances and potent greenhouse gases are regulated under various international and national frameworks. Here, we present two studies highlighting the urgency and the challenges of the measurement-based emission estimates of sulfur hexafluoride (SF6) and fluoroform (HFC-23) with a particular focus on the influence of point sources.
SF6 and HFC-23 are two of the most potent greenhouse gases with a GWP100 of approximately 24,000 and 14,700, respectively. Previous studies consistently showed a dominant emission source in southern Germany contributing to a large share of European SF6 emissions. Meixner et al., 2025 analysed emission estimates based on 22 European measurement sites revealing an underestimated SF6 emission point source in southern Germany in contrast to the national inventory reports.
Recent studies highlighted major challenges in quantifying HFC-23 emissions (Adam et al., 2024; Rust et al., 2024). We investigate the effects of intermittency in emissions and explore different possibilities based on a priori assumptions about specific emission sources. Forward calculations from these potential emission sources are used to derive expected time series at observational sites. These are compared to observations from different European stations situated in the regions influenced by the potential point sources. We present different approaches based on European atmospheric measurements combined with multiple model approaches, including ICON-ART, FLEXPART and NAME.
Adam, B., Western, L.M., Mühle, J., Choi, H., Krummel, P.B., O’Doherty, S., Young, D., Stanley, K.M., Fraser, P.J., Harth, C.M., Salameh, P.K., Weiss, R.F., Prinn, R.G., Kim, J., Park, H., Park, S., Rigby, M., 2024. Emissions of HFC-23 do not reflect commitments made under the Kigali Amendment. Commun. Earth Environ. 5, 783. https://doi.org/10.1038/s43247-024-01946-y
Meixner, K., Wagenhäuser, T., Schuck, T.J., Alber, S., Manning, A.J., Redington, A.L., Stanley, K.M., O’Doherty, S., Young, D., Pitt, J., Wenger, A., Frumau, A., Stavert, A.R., Rennick, C., Vollmer, M.K., Maione, M., Arduini, J., Lunder, C.R., Couret, C., Jordan, A., Gutiérrez, X.G., Kubistin, D., Müller-Williams, J., Lindauer, M., Vojta, M., Stohl, A., Engel, A., 2025. Characterization of German SF6 Emissions. ACS EST Air 2, 2889–2899. https://doi.org/10.1021/acsestair.5c00234
Rust, D., Vollmer, M.K., Henne, S., Frumau, A., van den Bulk, P., Hensen, A., Stanley, K.M., Zenobi, R., Emmenegger, L., Reimann, S., 2024. Effective realization of abatement measures can reduce HFC-23 emissions. Nature 633, 96–100. https://doi.org/10.1038/s41586-024-07833-y
How to cite: Meixner, K., Rust, D., Schuck, T. J., Wagenhäuser, T., Gad, F., Couret, C., Jordan, A., Vojta, M., Stohl, A., and Engel, A. and the PARIS project: Using atmospheric observations to identify point sources of halogenated trace gases, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11719, https://doi.org/10.5194/egusphere-egu26-11719, 2026.
Strengthening the link between scientific research and official greenhouse gas (GHG) reporting is an important step under the Paris Agreement’s Enhanced Transparency Framework. The PARIS Project, funded by Horizon Europe, is working with eight European countries to develop practical tools for this purpose.
A central innovation of PARIS is the development of draft annexes to National Inventory Documents (NIDs). These annexes provide a structured and transparent interface between official bottom-up inventories and top-down atmospheric estimates. They do not alter formal reporting rules; instead, they document how independent scientific assessments compare with inventory estimates, identify consistencies and discrepancies, and highlight where further investigation or methodological development is warranted. In this way, the annexes enable inventory compilers, policymakers, and scientists to interpret atmospheric results within the legal and institutional framework of national reporting.
The annexes are underpinned by major advances in PARIS observation and modelling capacity. Expanded and harmonised networks for CH₄, N₂O, F-gases, and aerosols, together with multi-model inverse systems and common data standards publicly available on the ICOS Carbon Portal, provide robust, traceable estimates of regional emissions and their sectoral drivers. These scientific outputs are synthesised in the annexes in a form that is directly usable by inventory agencies.
Through close engagement with national inventory teams in the UK, Switzerland, Germany, Ireland and other focus countries, PARIS has co-developed annex templates and begun populating them with results from multiple inversion systems. This process reduces barriers between the research and inventory communities and supports routine, transparent comparison of bottom-up and top-down estimates.
The poster will present the main outcomes of the PARIS project, demonstrating how the outcomes advance and embed atmospheric science in national GHG reporting to strengthen confidence in emission estimates, improve process attribution of regional emissions, and ultimately support more effective climate policy under the Paris Agreement.
How to cite: Walter, S., Manning, A., Röckmann, T., and Ganesan, A. and the PARIS Team: Bridging Science and National GHG Inventories: Insights from the PARIS Project – Process Attribution of Regional Emissions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11963, https://doi.org/10.5194/egusphere-egu26-11963, 2026.
Accurate estimates of greenhouse gas emissions are critical for determining the effectiveness of mitigation strategies under the Paris Agreement. These estimates are commonly derived by atmospheric inversion frameworks, which combine atmospheric transport models with in situ observations to obtain greenhouse gas fluxes. However, regional inversions are often challenged by local-scale signals in atmospheric measurements, that are insufficiently represented by the models. If not properly accounted for, these can introduce biases in inverse flux estimates undermining the reliability of emission estimates.
To address this limitation, observational data has typically been filtered for local influences before being used in inversion simulations, based on assumptions such as stable boundary conditions or wind speed. To make full use of the available dataset, we implemented an observation-dependent model-data uncertainty in the inversion optimisation process, allowing local signals to be explicitly considered. This approach has been applied to CH4 inversions over Europe using the mesoscale Jena CarboScope-Regional (CSR) system at 0.25° × 0.25° resolution.
To determine the time varying model-data uncertainty based on the local influence signal, a leave-one-out cross validation was performed for ground based in situ data of 47 atmospheric stations, excluding one station per inversion simulation. By determining the difference between modelled and observed concentrations, a model-data mismatch was estimated across station categories defined by surrounding land type. These estimates were then combined with local signal features, resulting from low wind speeds, atmospheric stability, and concentration spikes using a multivariate regression. The derived model-data mismatch function was applied to adjust the data weighting in the inversion enabling the inclusion of the observational dataset without discarding any measurements.
In this presentation, we demonstrate the potential of this novel approach to improve the robustness of regional CH4 inversions and to reduce the bias from local-scale signals.
How to cite: Zwerschke, E., Koch, F.-T., Gerbig, C., Mueller-Williams, J., Lindauer, M., Keppler, F., and Kubistin, D.: Impact of Local-Scale Effects in Methane (CH₄) Inversions on Model-Observation Discrepancies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12745, https://doi.org/10.5194/egusphere-egu26-12745, 2026.
Rice cultivation is the largest source of methane (CH4) emissions in Vietnam’s agricultural sector, making accurate quantification of these emissions critical for national GHG inventories and the design of mitigation policies. Currently, for UNFCCC GHG reporting, Vietnam primarily employs IPCC Tier 2 approaches using national emission factors combined with Tier 1 scaling factors. With the implementation of large-scale mitigation projects and Vietnam’s ambition to achieve Net Zero by 2050, Methane Global Pledge commitment by 2030, and joining international carbon markets, there is an urgent need to transition towards higher-tier methodologies. However, also process-based model (Tier 3) outputs are associated with uncertainty, which needs to be benchmarked first with established Tier 1 and 2 emission estimates.
In this study, CH4 emission data from 13 Vietnamese field experiments are split into two groups—one with comprehensive management information (sufficient data) and one with sparse information (limited data)—to test IPCC Tier methods under different activity data conditions. Furthermore, for Tier 3, an inter-comparison is conducted between two biogeochemical models, DNDC and LandscapeDNDC. The evaluation focuses on the performance in estimating rice yields, seasonal CH4 emissions, and daily flux dynamics, while also analyzing the impact of different model parameterization and simulation setups.
Our evaluation shows that Tier 1 significantly underestimates CH4 emissions, whereas Tier 2 provides a substantial improvement and remains robust across varying soil and management conditions. In contrast, Tier 3 outperforms Tier 2 only when comprehensive management data is available, reflecting its distinctive capacity to represent daily emission dynamics and management-driven peaks. Consequently, while Tier 2 remains a practical choice for national inventories, Tier 3 is essential for high-resolution mitigation assessments, particularly for large-scale emission reduction evaluations where detailed management data are comprehensively collected and systematically organized. The process-based model comparison reveals that while DNDC and LandscapeDNDC show similar performance under continuous flooding, they diverge significantly under Alternate Wetting and Drying (AWD) regimes. These discrepancies are primarily attributed to the models' different concepts of representing water table fluctuations.
Building on these results, the Tier 3 approach of LandscapeDNDC was integrated into the web‑based LUI‑RICE platform (https://ldndc.online/rice/). This makes GHG quantification for Vietnamese rice cultivation directly accessible to local stakeholders and policymakers, translating the scientific findings of this study into a practical decision-support application.
How to cite: Nguyen, C., Kraus, D., Nguyen, T., Wassmann, R., Butterbach-Bahl, K., Vo, T. B. T., Mai, V. T., Bui, T. P. L., and Kiese, R.: CH4 emissions in Vietnamese Rice Agriculture: Benchmarking process-based model approaches (Tier 3) against Tier 1/2 Estimates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15692, https://doi.org/10.5194/egusphere-egu26-15692, 2026.
The international community has continuously monitored carbon emissions by publishing National Inventory Reports (NIRs) under the Paris Agreement adopted in 2015 to address the climate crisis. However, current emission estimation methods predominantly rely on bottom-up approaches based on statistical information, which are subject to limitations, including the potential omission of emission sources and the long time required for emission compilation. To overcome these limitations, top-down approaches that estimate emissions using meteorological models and observed atmospheric greenhouse gas concentrations have recently gained increasing attention. This approach has been adopted as a scientific methodology of the Integrated Global Greenhouse Gas Information System (IG3IS), developed under the auspices of the World Meteorological Organization (WMO), and is regarded as a complementary alternative to conventional emission inventories. In this study, carbon dioxide (CO₂) emissions over South Korea were estimated using a top-down approach based on the Stochastic Time-Inverted Lagrangian Transport Model (STILT) and observations from WMO/Global Atmosphere Watch (GAW) stations, and their accuracy was evaluated. The STILT-based inversion results indicate that anthropogenic CO₂ emissions in South Korea for 2019 amount to 589.7 Mt yr⁻¹, which is 83.6 Mt yr⁻¹ lower than the estimate reported in the existing NIR. The downward correction is primarily concentrated in Seoul and the surrounding metropolitan region. Furthermore, to account for the spatial characteristics of CO₂ emission distributions, high-resolution and realistic emission estimates were derived for regions with dense point-source emissions using the Weather Research and Forecasting (WRF) model. The application of top-down approaches for greenhouse gas emission estimation in East Asian countries, together with continuous technological advancement, is expected to provide a scientific foundation for improving the reliability of emission estimates and supporting future climate crisis response strategies.
How to cite: Shin, H. Y., Shin, D., Kenea, S. T., Lee, S., Kim, S., and Lee, Y. G.: Improving the Accuracy of CO₂ Emission Estimates over South Korea Using a Top-down Inversion Framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16769, https://doi.org/10.5194/egusphere-egu26-16769, 2026.
East Asia represents a major source region of greenhouse gas emissions associated with rapid industrialization and increasing energy demand. Among these emissions, halogenated synthetic greenhouse gases such as HFCs and PFCs, which have been widely used as substitutes following international regulations for ozone layer protection, are characterized by high global warming potentials (GWPs).
In South Korea, halogenated greenhouse gases have been monitored at the Gosan station on Jeju Island using the MEDUSA system of the AGAGE network. However, the expansion of observational coverage and the establishment of measurement capabilities remain essential to better characterize regional emission signals. In this study, a cryogenic preconcentration and analysis capability for halogenated greenhouse gases (NIMS-preconcentrator) was developed and and evaluate its capability for monitoring halogenated greenhouse gases.
The analytical setup includes a cryogenic thermal desorption (TD) unit and a pre-concentration trap capable of reaching temperatures down to −170 °C, integrated with an automated valve control module and gas chromatography–mass spectrometry (GC–MS). Measurements were conducted using an offline canister-based sampling approach. Analysis of ambient air samples collected at Anmyeondo (GAW station) resolved about ten halogenated greenhouse gas species, including HFC-134a, HFC-125, and legacy chlorofluorocarbons such as CFC-11 and CFC-12. Concentrations were evaluated using calibration standards, and ongoing performance assessment is conducted using laboratory working standards employed at the Gosan AGAGE station.
This study aims to establish a new measurement capability for halogenated greenhouse gases and to assess its consistency with international observation. Continued operation of this system will support the accumulation of long-term observational datasets and facilitate regional-scale analysis and inter-comparison of high-GWP halogenated greenhouse gases in Northeast Asia.
How to cite: Kim, J.-A., Kang, S., Kwon, D., Park, S., Lee, S., and Kim, S.: Development and Application of a Cryogenic Preconcentration System for Halogenated Greenhouse Gas Measurements in Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17209, https://doi.org/10.5194/egusphere-egu26-17209, 2026.
Residential space heating remains a major source of greenhouse gas emissions in the building sector. In Germany, space heating accounts for the largest share of residential energy consumption, and accurate quantification of associated emissions is essential to meet national climate mitigation targets.
Most research on residential heating emissions focuses on the regional or national levels, while estimates at finer spatial scales remain limited. Data availability further constrains the transferability and usability of current models. Consequently, approaches that deliver spatially and temporally detailed emission estimates and interactive tools to support analysis and decision-making by stakeholders are urgently needed.
We introduce the Climate Action Navigator (CAN), a dashboard for the analysis and visualization of climate mitigation and adaptation spatial data, based entirely on open science principles. One of the tools available in the CAN estimates carbon dioxide emissions from residential heating at fine spatial at temporal scales. The tool applies a bottom-up accounting methodology at 100 m spatial resolution based on publicly available census and building characteristics data in Germany, including building age and dominant energy carriers. The resulting emission estimates are consistent with official city- and national-level inventories, confirming methodological reliability. Germany-wide analyses reveal strong spatial heterogeneity in energy consumption and emissions that correlate with urban morphological characteristics.
Temporal dynamics are captured through an hourly simulation using the Demand Ninja model based on local weather data. The resulting temporal emission patterns can support inverse emission modelling applications as well as aid energy management by, for example, revealing peak heating demand times and locations.
Results are delivered via the CAN interface as intuitive, interactive maps and charts that allow users to compare across neighborhoods, explore temporal emission dynamics, and assess potential mitigation actions. By integrating open-source data with high-resolution modeling and visualization, the Climate Action Navigator bridges the gap between scientific emission quantification and practical decision making. The approach supports transparent attribution and tracking of residential space-heating emissions, thereby advancing evidence-based climate mitigation planning.
How to cite: v. Elverfeldt, K., Kong, G., Ulrich, V., Martin, M., Schott, M., and Block, S.: High-resolution direct GHG emission estimation and simulation from residential space heating using open data , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17591, https://doi.org/10.5194/egusphere-egu26-17591, 2026.
Understanding the temporal dynamics and controls on greenhouse gas exchange between terrestrial ecosystems and the atmosphere is critical for advancing process-level understanding and informing national greenhouse gas budgets and inventories. A large portion of soils in the Netherlands are either drained or restored peatlands, where the high carbon/organic matter content is accompanied by large risk of carbon loss to the atmosphere through enhanced soil respiration (drained sites) and/or enhanced methane emissions (rewetted sites). For this reason, increasing attention is being paid to understanding and quantifying the greenhouse gas budgets of both drained and restored peatland sites across the Netherlands.
To both inform national GHG inventories and improve our understanding of site scale process, we present a multi-site analysis of a network of more than thirty eddy-covariance sites in the Netherlands. We discuss the daily, seasonal, and annual variability of carbon dioxide (CO₂) and methane (CH₄) fluxes measured at these sites. These sites include intensively managed grasslands, arable fields, semi-natural pastures, forested peatlands, wetlands and marshes. These sites encompass a wide range of vegetation types, soil characteristics, and water-management practices, with continuous or semi-continuous high-frequency flux datasets extending across multiple years within the last decade.
We quantify daily, seasonal, and annual CO₂ and CH₄ fluxes and discuss key biophysical drivers, including soil composition and moisture, vegetation dynamics, groundwater levels, and the impacts of climate anomalies such as temperature and precipitation extremes across varying timescales. We discuss differences between sites and potential impacts of soil characteristics, vegetation, land management, and recent climate anomalies.
Our analysis indicates substantial variability in both CO₂ and CH₄ fluxes across sites and seasons. These results highlight the invaluable contributions of both high-resolution flux observations and rigorous data processing methods when disentangling ecosystem controls on gas exchange. These flux observations provide much needed empirical constraints for model evaluation and can facilitate improved representation of peatland and wetland systems in greenhouse gas inventories and process-based models.
How to cite: Andueza Kovacevic, I., Bataille, L., Cabezas, I., Engel, F., Franssen, W., van Huissteden, C., Hutjes, R., Ingle, R., Jans, W., Lippmann, T. J., Zerrudo, J., Zhao, H., Nouta, R., and Kruijt, B.: Carbon dioxide and methane emissions from a network of thirty eddy-covariance sites in the Netherlands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19274, https://doi.org/10.5194/egusphere-egu26-19274, 2026.
Effective climate mitigation requires obtaining greenhouse gas (GHG) information and accounting that is scientifically robust and actionable for decision-making. The CARBONICA project has developed and implemented a robust climate-positive action plan for carbon farming implementation across the widening countries of Greece, Cyprus and North Macedonia, generating climate information services that operate at regional, national, and international scales. An extended management practices inventory has been developed and implemented in pilot sites across 15 crops between the 3 countries, fully aligned with the IPCC, the Natural Climate Solutions World Atlas, the GHG Protocol, and climate related EU laws and initiatives. GHG accounting is supported by a robust MRV system combining soil sampling, field inputs following IPCC Scope guidance, and management practices, covering direct, indirect, and upstream emissions across the farm system, with all procedures are fully compliant with ISO 14064-2. Farm-level data are also collected using the validated Field Diagnostic Toolbox, which includes soil CO₂ flux monitoring using spectroscopy to support accurate assessment of emissions and carbon removals.
This enables explicit attribution of emissions and carbon removals to farms, regions, and in general, the agrifood sector, supporting monitoring, reporting and validating of mitigation measures for positive climate action. LCA modelling on a pilot site (1ha peach orchard) has shown significant results in emissions reductions and carbon removals. The model was used once on the baseline (business-as-usual scenario) in 2024, and once after the management practices no- till and residues incorporation were implemented in the orchard, for the year 2025. The total greenhouse gas emissions from the pilot peach orchard decreased from 2,660 kg CO₂e in 2024 to 1,280 kg CO₂e in 2025, with emissions per ton of produced fruit dropping from 147.63 kg CO₂e to 71.04 kg CO₂e. Beyond the reduction of the emission sources, the demonstrated change in the soil carbon stock was also significant. While the 2024 cultivation season showed a net-zero change compared to the baseline scenario, the implementation of no-till and crop residue incorporation during the 2025 season created an active carbon sink, resulting in a net removal of 597.76 kg of CO₂e from the atmosphere into the soil. Thus, the project successfully demonstrated a twofold climate benefit: a major reduction in operation emissions and a significant sequestration of atmospheric carbon into the soil.
The results presented above are part of a third-party validated carbon farming project, facilitated through CARBONICA. This work also contributes to IG3IS-aligned applications demonstrating the operational use of multi-source GHG observations for real-world solutions in carbon farming.
How to cite: Kitsou, D., Chantzi, P., Gkoutzikostas, D., Rousonikolos, V., Galanis, G., Papastergiou, A., and Zalidis, G.: From GHG Observations to Actionable Climate Information Services, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20089, https://doi.org/10.5194/egusphere-egu26-20089, 2026.
Quantitative evidence is increasingly required to assess the mitigation potential of cities in achieving global carbon neutrality. However, although urban green spaces contribute simultaneously through biophysical carbon sequestration and reductions in energy demand driven by urban heat island mitigation, few studies have systematically compared and evaluated these two effects within a unified framework at the global scale.This study quantifies the total contribution of urban green spaces to carbon neutrality across global cities and decomposes this contribution into carbon sequestration and cooling driven energy savings, assessing their relative importance and spatial patterns.The urban heat island effect is estimated using remote sensing derived land surface temperature differences between urban and non urban areas, while carbon sequestration by urban green spaces is simultaneously quantified based on satellite based observations.These two contributions are then integrated and compared. Furthermore, this study examines how the relative importance of the two effects varies across major climate zones and how heterogeneity manifests in distinct spatial patterns. Finally, this study investigates how vegetation related indicators, socio economic variables, and urban structural characteristics influence the two effects across climate zones with AI based approaches and identify contextual conditions under which the mitigation benefits of urban green spaces are amplified or attenuated even under similar urban green space availability.This study provides a global assessment of the contribution of urban green spaces to carbon neutrality and offers empirical evidence to support the design of climate and context specific nature based mitigation strategies in cities.
How to cite: Kim, S. and Choi, Y.: The dual role of urban green spaces in carbon neutrality: carbon sequestration and cooling driven energy savings at the global scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20959, https://doi.org/10.5194/egusphere-egu26-20959, 2026.
Sulfur hexafluoride (SF₆) is an extremely potent (GWP100 = 24,300) and long-lived greenhouse gas whose atmospheric concentrations continue to rise due to anthropogenic emissions. Europe represents a particularly relevant test case for investigating SF₆ emissions, as successive EU F-gas regulations over the past two decades have aimed to substantially reduce emissions. A key question is whether these regulatory measures are reflected in observed emission trends and whether reported national inventories are consistent with observation-based estimates.
In this study, we quantify European SF₆ emissions for the period 2005–2021 using a large ensemble of atmospheric inversions with a strong focus on uncertainty characterization. Uncertainties are assessed using an extensive set of sensitivity tests in which key inversion parameters are systematically varied, while final uncertainties are quantified via a Monte Carlo ensemble that randomly samples combinations of these parameters. This allows us to identify the main sources of uncertainty and to evaluate the robustness of inferred emission trends.
Our analysis focuses on countries with relatively dense observational coverage - the United Kingdom, Germany, France, and Italy - while also examining aggregated emissions for the EU-27. The inversion results reveal declining SF₆ emissions in all studied regions except Italy, broadly consistent with the timing of EU F-gas regulations (842/2006, 517/2014). In several countries, inferred emissions exceed reported national inventories, although the agreement generally improves in more recent years. At the EU-27 scale, emissions exhibit a pronounced decline between 2017 and 2018, coinciding with a marked reduction in emissions from southwestern Germany, suggesting regional actions were taken as the 2014 regulation took effect.
Our sensitivity tests highlight the crucial role of dense and sustained atmospheric monitoring networks for robust inversion-based emission estimates. In particular, expansions of the UK observing system in 2012 and 2014 lead to significant reductions in emission uncertainties, demonstrating the importance of comprehensive observational networks in refining emission estimates.
How to cite: Vojta, M., Plach, A., Thompson, R. L., Purohit, P., Stanley, K., O'Doherty, S., Young, D., Pitt, J., Arduini, J., Lan, X., and Stohl, A.: Quantifying European SF6 emissions (2005-2021) using a large ensemble of atmospheric inversions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12776, https://doi.org/10.5194/egusphere-egu26-12776, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 5
EGU26-21511 | ECS | Posters virtual | VPS3
Assessing Spatial and temporal heterogeneity of Soil Carbon emissions across anthropized land use Gradient in the Sudanian savannaTue, 05 May, 15:03–15:06 (CEST) vPoster spot 5
Field-based observations of Carbon dioxide (CO₂) exchange between soils and atmosphere are critical to accurately account for terrestrial carbon cycling in data-scarce West African savanna ecosystems. This study quantified soil CO₂ fluxes over two consecutive years (2023–2024) using a static chamber approach across four contrasting land-use systems namely forest, grassland, cropland, and rice fields. Measurements were conducted on weekly basis using replicated chambers to assess both spatial heterogeneity and interannual variability. Soil CO₂ fluxes were analysed in relation to key environmental drivers, including water-filled pore space (WFPS) and soil temperature, using mixed-effects statistical models to account for repeated chamber measurements. Across all land uses, CO₂ emissions increased markedly in 2024 compared to 2023. Median seasonal CO₂ fluxes ranged from 0.59 to 1.46 t C ha⁻¹ season⁻¹ in forest systems, 1.91 to 5.07 t C ha⁻¹ season⁻¹ in grasslands, 1.75 to 5.09 t C ha⁻¹ season⁻¹ in croplands, and 1.84 to 2.61 t C ha⁻¹ season⁻¹ in rice fields. Grasslands and croplands consistently exhibited the highest CO₂ emissions, with maximum values reaching 7.18 and 5.38 t C ha⁻¹ season⁻¹, respectively, highlighting the strong influence of land management and disturbance intensity. Forest soils showed comparatively lower CO₂ fluxes, reflecting reduced soil disturbance and more stable microclimatic conditions. Statistical analyses revealed that soil temperature was a dominant driver of CO₂ emissions across all ecosystems, while soil moisture exerted a secondary but significant control, particularly in managed systems. Higher WFPS and elevated soil temperatures during the wet season were associated with enhanced CO₂ release, indicating intensified microbial respiration and root activity. Interannual contrasts suggest that wetter and warmer conditions in 2024 amplified soil respiration across all land uses. Overall, our results demonstrate pronounced spatial and temporal variability in soil CO₂ fluxes in the Sudanian savanna and underscore the sensitivity of carbon emissions to land-use change and hydro-climatic variability. These findings provide critical baseline data for improving regional carbon budgets and for informing mitigation strategies in data-scarce tropical savanna regions.
How to cite: Oussou, F. E. and the WASCAL CONCERT Team: Assessing Spatial and temporal heterogeneity of Soil Carbon emissions across anthropized land use Gradient in the Sudanian savanna, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21511, https://doi.org/10.5194/egusphere-egu26-21511, 2026.
EGU26-21709 | ECS | Posters virtual | VPS3
Greenhouse gas emissions and socio-environmental costs of anthropogenic fires in Tucumán (Argentina): A remote sensing and environmental economics approachTue, 05 May, 15:06–15:09 (CEST) vPoster spot 5
Anthropogenic fires linked to the burning of agricultural biomass residues represent a recurring environmental disturbance in the province of Tucumán, northwest Argentina. These events are predominantly associated with land clearing and post-harvest burning in sugarcane fields concentrated in the region’s lowland plains, where agro-industrial activity is most intense. Such practices contribute significantly to greenhouse gas (GHG) emissions, vegetation degradation, and a range of socio-economic impacts. This study integrates satellite-derived fire indices with environmental economic tools to quantify the spatial and temporal effects of fire over the past five years (2021–2025) and assess their implications for climate change mitigation and policy.
Multispectral data from Sentinel‑2 and atmospheric composition products from Sentinel‑5P were processed via Google Earth Engine to calculate vegetation and fire severity indices including NDVI, dNBR, and BAI. Additionally, tropospheric CO and CO₂ concentrations were used to evaluate atmospheric impacts. The spatial distribution of fire activity—primarily in the eastern and southern lowlands—was cross-referenced with the Global Fire Emissions Database (GFED) and IPCC Tier 1 emission factors to estimate fire-related GHG emissions. Preliminary analyses indicate an average of 45,000 ha affected annually, mainly in sugarcane-dominated landscapes, resulting in estimated emissions of approximately 382,500 t CO₂-equivalent per year.
To evaluate broader socio-environmental impacts, economic losses were estimated across multiple dimensions: reduced land productivity, costs of ecosystem restoration, loss of ecosystem services (e.g. carbon sequestration, water retention), and public health expenses related to degraded air quality. Additional indirect impacts include traffic accidents due to smoke-induced low visibility and recurring property damage reported in local media. These preliminary estimates suggest combined annual damages of approximately USD 46.5 million, underscoring the considerable burden imposed by current fire management practices.
It is important to note that this work presents ongoing research, and all results are preliminary. The estimates provided will be further refined through continued integration of field data, emission modeling, and economic valuation methods.
This integrative approach demonstrates the value of combining Earth observation technologies with environmental economics to support climate-oriented decision-making. By quantifying the environmental and economic impacts of anthropogenic fires, this study provides critical evidence for the development of cross-sectoral policies aimed at regulating biomass burning, improving land management practices, and strengthening resilience to climate risks. The case of Tucumán underscores the urgent need for sustainable alternatives to current residue management practices and for aligning agricultural production with mitigation goals.
How to cite: Reynoso Posse, F., Zbrun Luoni, J. P., and Aguilera Sammaritano, M.: Greenhouse gas emissions and socio-environmental costs of anthropogenic fires in Tucumán (Argentina): A remote sensing and environmental economics approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21709, https://doi.org/10.5194/egusphere-egu26-21709, 2026.
