On-site sanitation systems (OSS) generate greenhouse gases (GHGs) during the decomposition of fecal matter. The reported measurements of these emissions are confined to a restricted number of research examining septic tanks in high-income nations. We conducted field measurements of onsite containments to generate emissions data for Nepal. This represents the first empirical investigation of greenhouse gas emissions from onsite containments in low- and middle-income countries. Emissions were recorded from a panel of pit latrines (n=18), holding tanks (n=6), septic tanks (n=3), between December 2021 and December 2022. A calibrated static flux chamber was designed was and deployed to collect gases samples at each containment site. Portable gas analyzers were employed to quantify methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O). Results will be provided in detail. Preliminary investigation showed a substantial range in emissions rates notably CH4 across different types of onsite sanitation containments. Statistical test indicated methane emission rates varied considerably within containment types (P value<0.05). N2O was not discovered in any of the sample containments. Our preliminary findings indicate that onsite containment emissions are greater than anticipated and may be a key area for improvement in order to get net zero emissions.
How to cite: Poudel, P., Tuladhar, S., Ghimire, A., Howard, G., Camargo-Valero, M. A. C.-V., Evans, B., Reddy, O., and Sharma, S.: Field based greenhouse gas emission measurement from onsite containments in Nepal., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1108, https://doi.org/10.5194/egusphere-egu25-1108, 2025.
Cities are major contributors to global anthropogenic CO2 emissions and their share relative to national emissions is increasing in many countries. This makes urban areas critical targets for effective CO2 mitigation strategies. To monitor and verify mitigation efforts, measurement-based emission estimates of anthropogenic CO2 emissions can be used. Many cities, however, lack the infrastructure to precisely constrain these emissions.
To support the development of urban sensor networks within the Integrated Greenhouse Gas Monitoring System (ITMS) for Germany, we use Observing System Simulation Experiments (OSSEs). In these experiments, we evaluate synthetic in-situ sensor networks in Berlin and Munich with regards to their potential to constrain anthropogenic CO2 emissions.
Our OSSEs use a Bayesian inversion framework, with atmospheric transport simulated by the Lagrangian Particle Dispersion Model FLEXPART-WRF and meteorology computed by the Weather Research and Forecasting (WRF) model at a 1 km resolution for urban areas over an entire year.
We analyze the effect of number, location and precision of CO2 sensors, as well as of co-located CO concentration measurements. We suggest favorable city-specific sensor network configurations and identify key factors for efficient network designs across the two cities. Our results support the deployment of efficient and effective sensor networks for measurement-based CO2 emission monitoring and verification in Berlin, Munich and similar cities and will be the basis for future planned sensor network installations in Germany.
How to cite: Lüken-Winkels, C., Pilz, L., Cello, S., and Vardag, S. N.: Designing CO2 sensor networks for German cities: Insights from synthetic studies in Berlin and Munich , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10382, https://doi.org/10.5194/egusphere-egu25-10382, 2025.
Road transportation in U.S. urban areas accounts for roughly two-thirds of on-road CO2 emissions. Yet the drivers of those transportation emissions and differences among cities are not well-understood owing to limited availability of detailed data until recently. Here, we use high-resolution Floating Car Data to analyze street-level transportation emissions in 457 U.S. urban areas (hereinafter referred to as cities) in 2022, and decompose the key drivers of differences among them. Our study reveals that cities with greater population densities tend to have lower per capita road transportation emissions due to lower travel demand (R2 = 0.36) without significant increases in traffic congestion that represent only a fraction of the total (2-10%). Furthermore, we find that variations in vehicle fleets (e.g., electrification) are still a secondary driver of city-scale transportation emissions. These findings underscore the importance of tailored interventions to mitigate cities’ transportation emissions and may be used to support more sustainable urban transportation systems.
How to cite: Bonnemaizon, X., Ciais, P., Zhou, C., Ben Arous, S., Megel, N., Berghaüser, G., and J. Davis, S.: Drivers of CO2 emissions from road transport in U.S. urban areas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1263, https://doi.org/10.5194/egusphere-egu25-1263, 2025.
The city of Zurich, Switzerland, aims to achieve net-zero greenhouse gas emissions by the year 2040. To support the city in monitoring its path towards this ambitious goal, an emission monitoring program has been established with two complementary approaches. The first involves a network of CO2 mid- and low-cost sensors in combination with atmospheric transport inverse modelling. The second, presented here, combines CO2 flux measurements from an Eddy-covariance system installed on a 17 m mast on top of a 95 m tall building in the city center with flux footprint modeling and a high-resolution emission inventory.
Here we present a detailed comparison between hourly simulated and observed CO2 fluxes for a period of two years (August 2022 – August 2024) to evaluate the inventory and its partitioning into source sectors. The simulated fluxes were obtained by multiplying the footprints with the sectorially resolved emissions from the inventory, all available on a 10 m x 10 m grid. The sectorial emissions were scaled by temporal factors describing diurnal, day-of-week and seasonal variability. Traffic emissions, for example, were scaled using actual traffic counts from 182 counters and heating emissions were scaled with a heating-degree-day factor based on outdoor temperatures. In addition to anthropogenic emissions, biospheric CO2 fluxes from trees, lawns and cropland were simulated at 10 m x 10 m resolution with the Vegetation Photosynthesis and Respiration Model (VPRM), driven by local temperature and radiation measurements and Sentinel-2 satellite observations.
The simulated hourly fluxes, which change in time due to the varying footprints and temporal scaling factors, were found to be strongly correlated with the observed fluxes but were, on average, higher, suggesting that the inventory overestimates the actual emissions from the city. The comparison also allowed us to improve the temporal scaling factors of certain sectors, for example, to better represent the reduced emissions during holidays or the heating demand during the transition periods between winter and summer. Accurately representing the temporal variability is important, as it allows disentangling source sectors that follow different temporal profiles. The results demonstrate the capability of tracking the CO2 emissions of a central part of Zurich with a single, well-placed flux tower with an accuracy that is suitable for evaluating the expected emission reductions in the coming decades.
How to cite: Brunner, D., Bernet, L., Constantin, L., Molinier, B., Kljun, N., Hilland, R., Christen, A., Super, I., Li, J., Chen, J., Stagakis, S., and Emmenegger, L.: Measurement and modelling of Eddy-covariance fluxes of CO2 in the city of Zurich, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10046, https://doi.org/10.5194/egusphere-egu25-10046, 2025.
We present the CHETNA (City-wise High-resolution Carbon Emissions Tracking and Nationwide Analysis) project, an innovative framework designed to generate near real-time high-resolution carbon emissions data for 100 Indian cities across five major sectors: power, traffic, residential, industrial, and aviation. Utilizing advanced technologies including artificial intelligence, large-scale open data scraping, satellite imagery, sophisticated energy models, and field surveys, CHETNA will address critical gaps in emissions tracking and modeling at the city level. CHETNA’s methodologies focus on regions with limited official datasets and inadequate high-resolution data, providing essential insights to support urban planning, climate mitigation, and sustainable urbanization efforts both in India and globally.
India, the world’s third-largest emitter of greenhouse gases (GHGs), plays a pivotal role in global climate mitigation efforts. Its rapidly urbanizing population, expanding economy, and coal-dominated energy structure present both challenges and opportunities for sustainable development. To meet its Paris Agreement commitments, India has pledged to reduce its GHG emissions intensity—emissions per unit of GDP—by 33%–35% by 2030, relative to 2005 levels. However, critical data gaps persist, particularly at the city level, hindering effective city-specific climate action and data-driven decision-making in India’s urban decarbonization.
To ensure a robust and scalable system for sectoral high-resolution CO₂ emission tracking, CHETNA employs an integrated workflow that combines GHG emission inventories and high-resolution sectoral activity modeling. For sectors such as power, large industrial, and aviation, where reliable national or regional emission inventories are available from open data sources, we developed sophisticated downscaling models to generate gridded emission maps based on those open-source datasets. For sectors lacking comprehensive emission inventories, such as traffic and residential, we adopted a bottom-up approach. Activity models were developed for each sector using machine learning, field-collected data (e.g., traffic sensor and field survey data), and satellite imagery. These activity models were then coupled with advanced emission models. For instance, a fleet-speed-emission model was developed for the traffic sector, while a building-climate-energy model was implemented for the residential sector. In addition to CO₂ emissions, CHETNA provides air pollutant co-emissions by integrating detailed activity data with pollutant-specific emission factors. This approach allows for the assessment of air quality benefits resulting from GHG mitigation efforts, highlighting the co-benefits of reduced air pollutants.
The dataset generated with the CHETNA project enables policymakers to develop city- and sector-specific strategies, contributing to India's sustainable urban development. Its sectoral high-resolution data would provide insights for guiding urban planning, air pollutant reduction, optimizing transportation systems, enhancing energy efficiency, and implementing effective industrial regulations. Representing a significant advancement in urban GHG emissions monitoring, CHETNA also offers a scalable and replicable framework for other counties or cities facing similar challenges.
This presentation provides an overview of the CHETNA project, outlining its scope, general concept, workflow design, and simplified methodologies for each sector. At EGU25, we will also present detailed sectoral methodologies and results, including traffic, residential, power, and small industrial sectors.
How to cite: Zhou, C., Ciais, P., Jana, A., Sarkar, A., Mittakola, R.-T., Tibrewal, K., De Sarkar, K., Sharma, A., Parmar, V., Benkhelifa, F., Zhu, B., Goldmann, C., and Phuleria, H.: CHETNA-Overview: City-wise High-resolution Carbon Emissions Tracking and Nationwide Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13577, https://doi.org/10.5194/egusphere-egu25-13577, 2025.
Cities with their large, dense populations are concentrated sources of CO2 emissions to the atmosphere. Although more than 60% of global fossil fuel CO2 emissions are from cities, yet we lack high-quality city-level emissions inventories and/or independent verification datasets across the majority of global cities. Several cities have also adopted ambitious goals of reaching net-zero emissions by 2030 or 2050. In fact, most recently at COP28, several cities, including those in non-Annex I countries, signed up to be part of the Coalition for High Ambition Multilevel Partnerships for Climate Action (CHAMP ; UNFCCC COP28), thereby obligating themselves to report emissions on a timely basis. So, how can we assist city-scale and local policy and decision-making entities to utilize information from space-based observations to monitor and track GHG emissions? In this presentation, I will show the application of OCO-2 and OCO-3 data across a suite of global cities worldwide. I will show that well-defined and robust mathematical frameworks can exploit the information content in dense, fine-scale, space-based CO2 data to deliver not only whole-city or total emission estimates but also attribute them to individual sectors, such as large point sources, on-road emissions, etc. I will also show some examples from recent studies that illustrate the value of exploiting co-located emissions of other species (such as CO, NO2, CH4) to obtain novel insights into sectoral emission characteristics. Examples from OCO-3, TROPOMI and EMIT data will be shown to demonstrate the value of assimilating information from disparate tracers for reliable source attributions. Even though there are methodological challenges in setting up a multi-species framework, the problem is not insurmountable. Development and refinement of such multi-species frameworks need to start now in order to unlock the true potential of space-based datasets. This is also crucial to optimally utilizing the information from future space-based CO2 emission monitoring sensors, such as Carbon Mapper, ESA’s CO2M, JAXA’s GOSAT-GW and other planned missions. The presentation will conclude with a discussion of implications of space-based datasets for tracking city- and country-level progress towards meeting proposed CO2 emission reduction goals and its value and benefit for advancing bottom-up emission inventories.
How to cite: Chatterjee, A., Ahn, D., Roten, D., Kiel, M., Nelson, R., Kurosu, T., Wu, D., Lin, J., and Gurney, K.: Monitoring urban CO2 emissions from space: current status and future potential, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4839, https://doi.org/10.5194/egusphere-egu25-4839, 2025.
The consumption of natural gas in China, predominantly in cities, has nearly tripled over the past decade. However, there is an absence of measurement-based assessment of methane emissions from natural gas consumption in Chinese cities. Moreover, it is challenging to separate the contribution of natural gas relative to other major urban methane sources (e.g., wastewater, landfills) using only methane observations. Here, we use in-situ and total-column ethane observations across the Yangtze River Delta, one of China’s most important metropolitan areas, between 2012 and 2021, to quantify methane emissions from the natural gas sector. Ethane is co-emitted with methane in natural gas and has no significant biogenic sources, and therefore serves as a tracer to separate the contribution of natural gas from other methane sources. To interpret ethane observations, we apply atmospheric chemical transport simulations with the GEOS-Chem model to account for transport, mixing, and chemical decay. Our analysis reveals that surface ethane concentrations have increased by 0.25–0.3 ppb a-1 at city-cluster sites, in contrast to a stable global background concentration and a slightly negative trend in regional total-column measurements. The simulation indicates that a substantial natural gas leakage rate (2.5–4.1%) is required to replicate the observed trend. This leakage rate implies that natural gas consumption emits 0.55–0.9 Tg methane emissions annually in the Yangtze River Delta, accounting for about 5.1–8.4% of the regional total emissions. Our findings indicate that natural gas usage is a substantial contributor to methane emissions and their growth in East China.
How to cite: Zhang, Y., Zhao, Y., Wang, X., Wang, R., Qiu, B., Zhao, S., Zhang, Y., Xu, Z., Pei, X., Wang, Z., Sun, Y., Huang, C., and Zhou, Y.: Quantify natural gas methane emissions from a city cluster in East China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5425, https://doi.org/10.5194/egusphere-egu25-5425, 2025.
Detecting, reporting, and mitigating fugitive methane leaks has been identified as one way of lowering national methane emissions in the United States. To that effect, the United States Environmental Protection Agency has launched a new super emitter program that relies on technologies that can detect and report methane leaks for mitigation. NOAA is exploring the option of utilizing its fleet of geostationary and polar-orbiting satellite sensors to operationalize the short wave infrared Multi Band Multi Pass methane detection algorithm developed by Harvard University. Prior to transitioning the technology to NOAA operations, a careful evaluation of retrievals from the two sensors, Advanced Baseline Imager on GOES-R series and Visible Infrared Imaging Radiometer Suite on JPSS series is needed. NOAA satellites can detect only large methane plumes (tons per hour) and benchmarking the capability is critical to work with stakeholders such as the EPA. To do that, NOAA is partnering with facility operators that conduct timed large methane releases during pipeline blowdown events to validate satellite methane detections and quantification of emissions. NOAA, in partnership with the Pipeline Research Council International, conducted its first pipeline blowdown experiment on October 8, 2024, deploying methane-monitoring technologies across ground, air, and space to track a controlled methane release. Three NOAA geostationary satellites viewing the plume from different geometries detected the plume along with various ground and airborne instruments - all systems reported methane flux estimates that are closer to the values reported by the pipeline operator. Results of this controlled release experiment will be presented along with plans to conduct additional experiments, jointly with NASA, to validate methane plumes from civilian satellite data as well as those detected by commercial plume mappers such as GHGSat, CarbonMapper, and MethaneSat.
How to cite: Kondragunta, S., Varon, D., and He, T.: Assessing Methane Detection Capabilities of Operational Satellite Sensors using Controlled Release Experiments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3293, https://doi.org/10.5194/egusphere-egu25-3293, 2025.
Precise quantification of greenhouse gas (GHG) emissions is important for better urban sustainability. Transportation is one of the primary contributing sources of greenhouse gas emissions. To quantify better on-road GHG emissions, it is essential to decode fleet distribution. However, globally, many cities do not have the infrastructure to calculate a fleet distribution. Therefore, there will always be an uncertain error in the on-road GHG emissions estimation. However, very high-resolution satellite data can be helpful to overcome this gap due to its global temporal coverage. Hence, this study proposes a deep learning method, Faster Region-based Convolutional Neural Network (Faster R-CNN), and You Look Only Once (YOLO) based vehicle detection to identify the vehicles and vehicle categories from the very high-resolution satellite data and estimate the fleet distribution. The results show that our model can identify, Passenger Cars, Buses, Trucks, and Large Passenger Cars with the precision of 93.30%, 79.50%, 78.90%, and 81.15%, respectively. We applied this model to temporally available satellite images of Phoenix and calculated the fleet distribution and calculated the FFCO2 based on that fleet distribution and compared it with FFCO2 estimated using CURB dataset fleet distribution. Results show that CURB data-based FFOC2 is over-predicting by 22%, while using fleet distribution estimated by this method, FFCO2 over-predicting by 17% w.r.t VULCAN. These findings demonstrate the effectiveness of satellite-based fleet distribution estimation for improving FFCO₂ quantification in cities lacking robust data infrastructure. This approach provides a scalable and data-driven pathway to more accurate urban emissions modeling, enabling better-informed urban planning and sustainability efforts.
How to cite: Aslam, B., Hocking, T., Dass, P., Kato, A., and Gurney, K.: Bridging the Fleet Distribution Data Gap with Satellite Imagery and Deep Learning for GHG Estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8355, https://doi.org/10.5194/egusphere-egu25-8355, 2025.
The accuracy of greenhouse gas (GHG) emission estimates is significantly limited by uncertainties in atmospheric transport models (ATMs). These uncertainties largely arise from difficulties in accurately representing sub-grid turbulence and mixing processes. Furthermore, the use of modelled meteorological data to filter observations before inversion frameworks results in the exclusion of 40–75% of continuous GHG measurements, thereby reducing the reliability of emission estimates.
To overcome these challenges, we propose the use of radon measurements - a naturally occurring radioactive noble gas with well-characterised sources and sinks. Radon will be used as a metric to define atmospheric mixing classes, providing a novel approach to validate ATM performance and address its inherent uncertainties. These mixing classes, which reflect varying atmospheric stability conditions, offer a valuable benchmark for evaluating model parameterisations and meteorological inputs.
Our study utilises radon measurements from the Weybourne Atmospheric Observatory (UK) and Cabauw Tower (Netherlands) to assess the reliability of meteorological inputs and parameterisation in ATMs. Preliminary results demonstrate that radon-derived mixing classes can reduce biases in data filtering while improving the representation of atmospheric transport dynamics. This innovative method helps to bridge gaps in current inversion frameworks, enabling more accurate GHG emission estimates and supporting the development of evidence-based climate policies.
How to cite: Kikaj, D., Lils, C., Chambers, S. D., Forster, G., and Frumau, A.: Beyond Bias: Radon-Based Technique for Reducing Uncertainty in Greenhouse Gas Verification Frameworks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17412, https://doi.org/10.5194/egusphere-egu25-17412, 2025.
Systematic – regular and routine – observations are vital for understanding and monitoring the Earth climate system. Systematic observation networks, traditionally built and operated by the public sector, provide relevant data that inform climate models and respective pathways, forecasts, products and services. Critical in that regard, in particular, are high precision, accurate, and comprehensive greenhouse gas measurements. Initiatives for enhancing such networks and observations for scaling up climate data collection and monitoring are important, as is doing this in a sustainable manner by leveraging opportunities and advancing cooperation though public-private partnerships. This presentation highlights recent NOAA initiatives in that regard, including recent partnerships with United Airlines and with Maersk for data collection from commercial aircraft and commercial shipping vessels.
How to cite: Grubišić, V. and Sweeney, C.: Public-Private Partnerships in Climate System Observations , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14965, https://doi.org/10.5194/egusphere-egu25-14965, 2025.
EYE-CLIMA is a Horizon Europe project that aims to improve estimates of emissions of climate forcers (CO2, CH4, N2O, SF6, HFCs, and black carbon aerosols) by using atmospheric observations. Atmospheric observations can be used, with the help of an atmospheric transport model, in a statistical optimization framework to estimate surface-to-atmosphere fluxes – a method known as atmospheric inversion. These fluxes can be used to estimate national and sub-national emissions (and removals) and can help support national monitoring and reporting and ultimately the Global Stocktake process.
One of the main goals of EYE-CLIMA is to develop atmospheric inversions into a useful tool for improving national greenhouse gas inventories (NGHGIs). This entails establishing good practice guidelines for atmospheric inversions (with a particular focus on the national scale) including a full assessment of the uncertainties, as well as developing the methodology to prepare sectorial emission estimates from atmospheric inversions and make these comparable to what is reported in national greenhouse gas inventories (NGHGIs). EYE-CLIMA collaborates with NGHGI agencies on pilot projects comparing and reconciling inventory and atmospheric inversion-based emission estimates, as well as on establishing a good practice for atmospheric observation-based verification of NGHGIs.
This presentation will present an overview of the EYE-CLIMA methodology and the pilot projects with NGHGI agencies. In particular, the pilot projects cover: i) land use, land use change and forestry (LULUCF) emissions and removals of CO2 in France, ii) N2O emissions from agriculture in Germany, and iii) CH4 emissions from agriculture and waste in France and Germany.
How to cite: Winiwarter, W., Thompson, R., Stohl, A., Peylin, P., Ciais, P., Boesch, H., Aalto, T., Berchet, A., Kanakidou, M., Peters, G., Shchepashchenko, D., Chang, J.-P., Fuß, R., Pisso, I., Engelen, R., Arneth, A., Buchmann, N., Reimann, S., Platt, S., and Krishnankutty, N.: EYE-CLIMA: A Horizon Europe project using atmospheric inversions to improve national estimates of greenhouse gas emissions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10769, https://doi.org/10.5194/egusphere-egu25-10769, 2025.
Within the Integrated Greenhouse Gas Monitoring System for Germany (ITMS), the main aim is to provide regular, accurate, observation-based emission estimates that will enhance the transparency in GHG emission reporting needed to build the necessary trust on the path to net zero emissions. Reliable inverse atmospheric transport modelling using atmospheric GHG observations is one of the main ingredients for this. However, multiple studies have shown rather large differences in GHG flux estimates from regional inverse modelling, related to differences in implementation of atmospheric transport processes such as vertical mixing and convective transport. Within the ITMS-M (modelling) project, a number of approaches are taken towards either improving atmospheric transport and mixing, or to reduce the impact of related uncertainties in atmospheric transport. These approaches include the utilization of vertical profiles from ICOS tall towers (using stable layer tracer enhancements during night time, expressed as partial columns as input to the inversion), profile information from IAGOS and mixing height information from ceilometer networks (diagnosing/correcting for uncertainties in daytime vertical mixing), but also multi-tracer inversions using correlated model-data-mismatch errors (utilizing independent knowledge on e.g. Radon surface fluxes in a Rn-CH4 inversion). We will give an overview of these approaches and current status of their developments within ITMS.
How to cite: Gerbig, C., Akinyede, R., Custódio, Ð., Gałkowski, M., Ho, D., Maier, F., Munassar, S., Xu, Y., and Koch, T.: Steps towards improved inverse modelling of GHG fluxes: recent work within ITMS, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11561, https://doi.org/10.5194/egusphere-egu25-11561, 2025.
Success of the Paris Agreement relies on rapid reductions in fossil fuel CO2 (ffCO2) emissions, which can be independently verified using atmospheric data. However, estimating changes in ffCO2 from atmospheric CO2 is challenging due to large and variable contributions from natural fluxes and background concentrations. Nitrogen oxides (NOx = NO + NO2), which are a major contributor to surface air pollution that have adverse effects on human health, are co-emitted with CO2 during incomplete fossil fuel combustion. Because atmospheric NOx has a relatively short lifetime (hours to days), low background concentrations, and limited natural sources, it is possible to link elevated NO2 satellite columns to their parent emissions.
We present results from an Ensemble Kalman Filter (EnKF) based model inversion using the GEOS-Chem atmospheric chemistry and transport model, along with NO2 TROPOMI observations, to estimate NOx emissions across mainland Europe. Leveraging sector-specific CO2:NOx emission ratios, we then convert the NOx posterior dataset to ffCO2. Additionally, we present preliminary findings for an alternative methodology that relies less on prior knowledge of emission ratios. This approach uses a combined CO2:NOx inversion, integrating TROPOMI NO2 and OCO-2 CO2 measurements to directly constrain ffCO2.
Our results describe a more accurate and direct approach for estimating fossil fuel CO2 emissions, which we anticipate will offer valuable insights for verifying national emission reductions and informing global climate mitigation strategies.
How to cite: Schooling, C., Palmer, P., and Feng, L.: Using NO2 satellite observations to constrain ffCO2, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10345, https://doi.org/10.5194/egusphere-egu25-10345, 2025.
India, the world’s second-largest brick producer, operates over 100,000 kilns. These kilns emit 170 kt of PM2.5 (15% of the national total) and 120 Mt of CO2 (6% of the national total) annually, along with substantial SOx and NOx emissions. Transitioning from traditional Fixed Chimney Bull’s Trench Kilns (FCBTKs) to cleaner technologies, such as Zigzag Kilns (ZZKs), has the potential to reduce coal consumption by 20% and particulate matter emissions by 70%. However, comprehensive datasets for kiln locations across India remain scarce. This study contributes to the CHETNA project (City-wise High-resolution carbon Emissions Tracking and Nationwide Analysis), which leverages artificial intelligence and advanced datasets to deliver high-resolution, near real-time daily CO2 and air pollutant emissions data for over 100 Indian cities.
To address this gap, we leveraged Sentinel-2 imagery, with a spatial resolution of 10–20 m, to develop a cost-effective and scalable approach. Most existing studies focus on specific geographic areas, such as northern India, and rely on expensive, high-resolution satellite imagery that is often not readily available, limiting their broader applicability. In contrast, our study represents the first nationwide mapping of brick kilns in India, using openly accessible satellite data and advanced machine learning models.
Using a curated dataset of 9,600 geo-tagged labels covering 18,000 km², we developed a method combining Sentinel-2 imagery with convolutional neural networks (CNN) to detect brick kilns and classify their operational technologies (e.g., FCBTK, Zigzag). Labels were annotated using Google Earth layers on QGIS and validated based on distinct visual features, such as oval or rectangular ochre-colored shapes. The model leverages RGB bands to detect active kilns, while the addition of NIR, SWIR, and NDVI metrics enhances its ability to identify abandoned kilns, often concealed by vegetation, and reduces false positives.
The model achieved a precision of 0.90, a recall of 0.89, and an accuracy of 0.91 on the test set. Detected kiln centroids were highly accurate, with precise GPS coordinates matching their actual locations. Nationwide, the model identified 44,000 brick kilns in India for 2022. We benchmarked multiple models to optimize false positive reduction and improve technology classification. Building on these results, we applied the model to neighboring countries in the Indo-Gangetic Plain (IGP), spanning Pakistan, Bangladesh, and parts of Nepal, which also contribute significantly to the brick kiln industry, identifying approximately 20,000 kilns in 2022.
Beyond location mapping, we are generating annual gridded emission maps for CO2 and pollutants such as PM2.5, black carbon, and NOx. These maps provide time-series insights into emission trends, reduce uncertainties in carbon and pollutant emissions, quantify reductions achieved through cleaner technologies, and identify regional hotspots. By focusing on underregulated, high-emission sectors like brick kilns, this study offers critical insights for targeted mitigation strategies and sustainable urban planning. It equips policymakers with tools to evaluate regulations and demonstrates the feasibility of using Sentinel-2 imagery for cost-efficient, large-scale monitoring.
How to cite: Goldmann, C., Arora, S., Zhou, C., Ciais, P., Gieseke, F., Tibrewal, K., and Phuleria, H.: CHETNA-Brick Sector: Estimating GHG and Pollutant Emissions from Brick Kilns in India Using Sentinel-2 Imagery and Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11020, https://doi.org/10.5194/egusphere-egu25-11020, 2025.
Quantifying carbon emissions in the Indian region is fraught with uncertainties, largely due to the limited availability of atmospheric trace gas observations and robust techniques. Atmospheric inverse modeling approaches, though highly potential, are often constrained by sparse observational datasets over India. To address these challenges, this study investigates a novel data-driven methodology that leverages satellite-based NO2 and CO2 concentrations for plume detection and associated emission quantification. Specifically, we utilize highly accurate and precise NO2 measurements from the TROPOMI instrument onboard Sentinel-5P to identify and trace emission hotspots, such as thermal power plants and densely populated urban centers, which significantly contribute to regional emissions. Using this NO2-driven plume detection as a proxy, we explore the potential of atmospheric dry-air column CO2 concentrations to quantify hotspot emissions. The present study utilises the modeled dry-air column CO2 concentrations, which observations can later replace. The focus is given to illustrate a methodology that can combine both NO2 and CO2 concentrations derived from satellite instruments to infer the spatial distribution of CO2 emission over a region that is rapidly evolving and industrialized, like India. The above task is particularly in preparation for upcoming satellite missions like CO2M, which will offer co-located NO2 and CO2 observations that can be utilized for cost-effective solutions for carbon monitoring. Hence, the study outcome will not only improve our understanding of regional emissions but also establish a framework for leveraging future satellite missions to assist in establishing carbon emission reduction policies.
How to cite: Sukumaran, J., Pillai, D., Dhiman, A., and Thilakan, V.: A Hybrid Approach to Carbon Monitoring in India by combining Satellite-based NO2 and CO2 mixing ratios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16892, https://doi.org/10.5194/egusphere-egu25-16892, 2025.
Nitrous Oxide (N₂O) is a long-lived and highly potent greenhouse gas, recognized as the third most significant contributor to radiative forcing, with a substantial proportion of its emissions originating from a large area source, agricultural soils, due to the application of mineral fertilizer and livestock manure. As part of the Horizon Europe project EYE-CLIMA, we performed atmospheric inversions to improve the estimates of N2O fluxes across Europe at two spatial resolution scales. The first inversion, spanning the period from 2005 to 2023, was performed at a resolution of 0.5° × 0.5°. The second inversion, covering the period from 2018 to 2023, was carried out at a higher resolution of 0.2° × 0.2°. The method integrates the Community Inversion Framework (CIF) with the Lagrangian particle dispersion model, FLEXPART v11 (CIF-FLEXPART), to estimate N2O emissions using ground-based measurements of atmospheric N2O concentrations. Comprehensive prior N2O flux estimates were generated by incorporating monthly data from key source categories, including agriculture, other anthropogenic activities such as combustion, industry or waste treatment, biomass burning, natural soils, and ocean fluxes. For consistency, observed atmospheric concentrations of N2O were sourced from a newly harmonized dataset for Europe, compiled collaboratively by EYE-CLIMA and the Horizon Europe projects AVENGERS and PARIS.
Following the inversion, the modelled concentrations showed improved agreement with observations, capturing the seasonal cycle and increasing trend from 2005 onward. Statistical analyses revealed high correlations between modelled and observed concentrations at most stations. The N2O emissions from the inversion differ from the prior estimates in intensity and spatial distribution with increased emissions in regions of specifically high agricultural activity and reductions in other areas. Monthly flux variations exhibited a consistent seasonal cycle, with peak emissions occurring in early summer (May–June) and lower emissions during winter months. Across all years, total posterior emissions were lower than the prior estimates. While the phase of the seasonal cycle remained consistent from year to year, interannual variability in the amplitude was observed.
How to cite: Krishnankutty, N., Thompson, R., Berchet, A., Winiwarter, W., Henne, S., and Karstens, U.: Inverse modelling of N2O fluxes over Europe: An EYE-CLIMA initiative, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12856, https://doi.org/10.5194/egusphere-egu25-12856, 2025.
HFC-23 (trifluoromethane) is a potent greenhouse gas, believed to be emitted to the atmosphere primarily as a by-product during the production of the refrigerant and feedstock HCFC-22 (chlorodifluoromethane). Due to the high global warming potential of HFC-23 (GWP100 ~ 14,700), the Kigali Amendment to the Montreal Protocol requires countries to limit their emissions of HFC-23 as much as possible and report these emissions to the United Nations Environment Programme. Global reported emissions have been in the range 2-3 Gg yr-1 since 2019 and reflect the near-total destruction of emissions from HCFC-22 production reported by the countries with major HCFC-22 manufacturers, such as China and India. However, atmospheric observations show that, whilst emissions fell from their maximum in 2019 of 17.3 ± 0.8 Gg yr-1 to 14.0 ± 0.9 Gg yr-1 in 2023, they remain many times higher than reported. In addition, regional inverse modelling was performed based on measurements from the AGAGE site at Gosan, South Korea, using three different Bayesian inverse models (FLEXINVERT+, InTEM and RHIME) to estimate emissions from eastern China. These inversions use the same observational data, but different transport models, baselines, priors and uncertainties. Results are compared to better quantify regional emissions and their uncertainties. The results suggest that emissions from eastern China are four to six times higher than reported for the whole of China.
In addition, we examine the emission of HFC-23 as a by-product during the production of other hydrofluorocarbons and fluorochemicals. In-atmosphere HFC-23 production (from the breakdown of certain hydrofluoroolefins used as replacements for HFCs) is also investigated further using a 3D chemical transport model incorporating photolysis and ozonolysis reactions. Our results indicate that, based on currently available information, these potential alternative sources contribute less than 2.0 Gg yr-1 to global emissions. This suggests that HFC-23 emissions from HCFC-22 production have been consistently under-reported since the implementation of the Kigali Amendment. It therefore appears likely that abatement of HFC-23 emissions has not occurred to the extent reported in this period. Improved monitoring and verification of HFC-23 emissions from industrial sources is essential to the continued success and efficacy of the Kigali Amendment.
How to cite: Adam, B., Western, L., Muhle, J., Choi, H., Krummel, P., O'Doherty, S., Young, D., Stanley, K., Fraser, P., Harth, C., Salameh, P., Weiss, R., Prinn, R., Kim, J., Park, H., Park, S., Manning, A., Khan, A., Shallcross, D., and Rigby, M.: Emissions of the powerful greenhouse gas HFC-23 suggest significant under-reporting since the implementation of the Kigali Amendment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6956, https://doi.org/10.5194/egusphere-egu25-6956, 2025.
Hydrofluorocarbons (HFCs) are potent greenhouse gases that contribute substantially to climate change. Their emissions are rapidly evolving due to changes in production and use that are driven by the Kigali Amendment to the Montreal Protocol and regional regulations. Atmospheric data and inverse modelling systems can be valuable for evaluating the effectiveness of these controls and the emissions reported to the United Nations Framework Convention on Climate Change (UNFCCC). Currently in Europe, the United Kingdom and Switzerland include atmospheric top-down emission estimates as part of their National Inventory Reports to the UNFCCC, and now the Horizon Europe project Process Attribution of Regional emISsions (PARIS) aims to expand similar inventory evaluation to several additional European countries.
In this PARIS study, we derived HFC emissions for north-western Europe from 2012 to 2023 using the NAME transport model and three Bayesian inversion systems (InTEM, ELRIS, RHIME), focusing on HFC-134a, HFC-143a, HFC-32, HFC-125, HFC-23, HFC-152a, HFC-227ea, HFC-236fa, HFC-245fa, HFC-365mfc, and HFC-4310mee. Our results indicate an overall decline in HFC emissions in north-western Europe, broadly consistent with European F-gas regulations. Derived emissions trends are compared with National Inventory Reports, highlighting discrepancies. Moreover, we explore the driving factors behind these trends. These findings contribute to understanding emissions trends and improving inventory evaluations in Europe.
How to cite: De Longueville, H., Brito Melo, D., Ramsden, A., Redington, A., Danjou, A., Andrews, P., Pitt, J., Murphy, B., Rigby, M., Henne, S., Manning, A., and Ganesan, A. and the other members of the PARIS team: Assessing European HFC Emissions Using Inverse Modelling Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15759, https://doi.org/10.5194/egusphere-egu25-15759, 2025.
Sulfur hexafluoride (SF6), nitrogen trifluoride (NF3), and three types of perfluorocarbons (PFCs; PFC-14, PFC-116, and PFC-318) are perfluorinated greenhouse gases (PF-GHGs). PF-GHGs have long atmospheric lifetimes and global warming potentials thousands of times greater than carbon dioxide (CO2). Using high-frequency continuous in situ observations from the Xichong Monitoring station at Shenzhen, China and a Bayesian inversion framework, we assess the 2021-2023 PF-GHG emissions in southeastern China, a region featuring substantial growth in population and industries. We find a continued increase in emissions of all PF-GHGs. During 2021-2023, NF3 emissions show the highest annual growth rate of 40.38% yr-1, likely linked with the increasing demand in semiconductor industries in this region, while PFC-14 has the lowest of 5.87% yr-1. Regarding CO2-equivalent emissions, SF6 contributes the most to total PF-GHG growth (51.75%), followed by NF3 (30.86%). As for the seasonality in PF-GHG emissions in southeastern China, SF6 and PFC-116 emissions show significant seasonal variation. The seasonal variabilities in SF6 are likely associated with the high winter electricity demand, while the winter peaks in PFC-116 emissions may tie with semiconductor manufacturing. PFC-318 exhibits the largest seasonal variation, with a winter-to-spring and autumn emissions ratio of 5.10. The increased PFC-318 emissions in winter might be due to heightened HCFC-22 feedstock uses. The findings provide guidance for targeted mitigation strategies to address the rising emissions.
How to cite: Chen, Y., Yao, B., An, M., Ding, A., Liu, S., Li, X., Li, Y., O'Doherty, S., Krummel, P., and Zhu, L.: Inverse Modeling of High Global Warming Potential Perfluorinated Greenhouse Gases in Southeastern China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5703, https://doi.org/10.5194/egusphere-egu25-5703, 2025.
Posters on site: Wed, 30 Apr, 16:15–18:00 | Hall X5
Traffic congestion remains one of the biggest environmental and social issues in urban cities. Insights from traffic reports, modelling results, and real-world measurements show that traffic congestion would exacerbate vehicular emissions of up to 55%, compared to optimal driving conditions in highly congested urban areas.
To capture the dynamics of traffic patterns, we built our geospatial framework by utilising multiple sources of traffic data: traffic counts and speeds by local in-situ traffic counters, open-access aggregated floating car data (TomTom and Google Traffic), and a standardised functional road classification. The framework also incorporates meteorological parameters that affect the traffic capacity of urban road network to calculate the traffic density. Together with a projected fleet composition and its corresponding speed-dependent traffic emission factors, we computed the resulting dynamic traffic emissions of greenhouse gases (e.g. carbon dioxide CO2) and air pollutants (e.g. carbon monoxide CO and nitrogen oxides NOx) in gridded format. These can then be deployed in existing urban climate models to quantify climatic effects and air pollutant exposure induced by road transportation, and in particular congestion.
We applied the framework in two cities in Europe with distinct traffic behaviour: Helsinki and Munich. The preliminary results show relatively good performance in capturing the dynamics of traffic density in both cities (R2 = 0.78–0.88). The framework was further evaluated against their local emission inventory. However, this gave varying results for different emittants for different road classes in both cities. Beyond local applicability, we also explored the scalabilty of the framework. Applying the calibration coefficients trained in one city and testing in another, we found that road classes such as local connecting roads behaved similarly in both places (r = 0.70–0.96 ) while some others did not.
This initiative sheds light on the feasibility of translating the framework to a larger scale beyond a few cities in Europe. Our future step is to improve the scalability of the framework by including existing large-scale multi-city traffic datasets on urban roads worldwide to better model the heterogeneity of the traffic patterns and emissions in the world.
How to cite: Fung, P. L., Kühbacher, D., Hohenberger, T., Chen, J., and Järvi, L.: Capturing and translating the dynamics of traffic emissions using a congestion-based framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1198, https://doi.org/10.5194/egusphere-egu25-1198, 2025.
Radon (Rn-222) is a unique atmospheric tracer, since it is an inert gaseous radionuclide with a predominantly terrestrial source and a short half-life (3.8232 (8) d), enabling quantification of the relative degree of recent (< 21 d) terrestrial influences on marine air masses. High quality measurements of atmospheric radon activity concentration in remote oceanic locations enable the most accurate identification of baseline conditions. Observations of GHGs under baseline conditions, representative of hemispheric background values, are essential to characterise long-term changes in hemispheric-mean GHG concentrations, differentiate between natural and anthropogenic GHG sources, and improve understanding of the global carbon budget.
The EU-funded project NuClim (Nuclear observations to improve Climate research and GHG emission estimates) will establish world-leading high-quality atmospheric measurements of radon activity concentration and of selected GHG concentrations (CO2, and CH4) at a remote oceanic location, the Eastern North Atlantic (ENA) facility, managed by the Atmospheric Radiation Measurement (ARM) programme (Office of Science from the U.S. Department of Energy), located on Graciosa Island (Azores archipelago), near the middle of the north Atlantic Ocean. These observations will provide an accurate, time-varying atmospheric baseline reference for European greenhouse gas (GHG) levels, enabling a clearer distinction between anthropogenic emissions and slowly changing background levels. NuClim will also enhance measurement of atmospheric radon activity concentration at the Mace Head Station, allowing the identification of latitudinal gradients in baseline atmospheric composition, and supporting the evaluation of the performance of GHG mitigation measures for countries in the northern hemisphere.
The high-quality nuclear and GHG observations from NuClim, and the resulting classification of terrestrial influences on marine air masses, will assist diverse climate and environmental studies, including the study of pollution events, characterisation of marine boundary layer clouds and aerosols, and exploration of the impact of natural planktonic communities on GHG emissions. This poster presents an overview of NuClim, outlines the project objectives and methodologies, and summarises the relevant data products that will be made available to the climate community.
Project NuClim received funding from the EURATOM research and training program 2023-2025 under Grant Agreement No 101166515.
How to cite: Barbosa, S. and Chambers, S. and the NuClim Team: Improving GHG emissions estimates and multidisciplinary climate research using nuclear observations: the NuClim project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4446, https://doi.org/10.5194/egusphere-egu25-4446, 2025.
Thermoelectric coolers (TECs) differ from conventional cooling devices that use refrigerants in that they utilize the Peltier effect to convert electrical energy into thermal energy, generating a cooling effect [1]. Therefore, unlike conventional cooling devices that use refrigerants such as CFCs, which have a high global warming potential and emit greenhouse gases, thermoelectric coolers have a different environmental impact. Particularly during the usage phase, since electrical energy is converted into thermal energy during operation, it is important to assess the emissions during electrical energy generation. Particularly, the COP of thermoelectric coolers is currently much lower compared to conventional coolers [2], meaning that a greater amount of electrical power is required to achieve the same cooling amount.
Additionally, during the production phase, the manufacturing of alumina plates generates 90.7% of greenhouse gas emissions, and the sintering process involved in the production of alumina plates contributes 87.3% of the emissions. The primary cause of greenhouse gas emissions during the sintering process is the high temperature and pressure, and the large amount of power used to compact the powder. Therefore, methods to reduce energy consumption should be considered to address the hotspots of the sintering process and reduce the greenhouse gases associated with alumina plates production phase.
Consequently, possible methods and quantities of greenhouse gas reduction were aimed to be identified by improving the process to reduce energy consumption in the sintering process. In addition, since the main input material is electricity, there is a way for the grid mix to become more eco-friendly. For this purpose, a method of adding sintering aids and applying eco-friendly grid mix is considered. Sintering aids can reduce energy consumption by up to 1.4 times [3], resulting in 28.6% reduction in emissions during the sintering process, from 466.1 kg CO2-eq to 333.0 kg CO2-eq. Additionally, producing with the 2030 power grid mix, which reduces fossil fuel use and increases renewable energy, results in a reduction of 80.0kg CO2-eq, leading to a 38.6% decrease in emissions during sintering process.
Reference
[1] Newby, S., Mirihanage, W., Fernando, A., 2025. Body heat energy deriven knitted thermoelectric garments with personal cooling. Applied Thermal Engineering, 258 (A)., pp. 124546.
[2] Tian, M., Aldawi, F., Anqi, A.E., Moria, H., Dizaji, H.S., Wae-hayee, M., 2021. Cost-effective and performance analysis of thermoelectricity as a building cooling system; experimental case study based on a single TEC-12706 commercial module. Case Studies in Thermal Engineering, 27, pp. 101366.
[3] Heidary, D. S. B., Lanagan, M., and Randall, C. A., 2018. Contrasting energy efficiency in various ceramic sintering processes. Journal of the European Ceramic Society 38(4), 1018-1029.
How to cite: Kim, H. Y. and Wee, D.: Analysis on greenhouse gas reduction strategies for thermoelectric coolers using cradle-to-gate life cycle assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4694, https://doi.org/10.5194/egusphere-egu25-4694, 2025.
Urban areas account for more than 70% of fossil fuel carbon dioxide (CO2) emissions worldwide. Recent (OCO-3 released in 2019) and forthcoming (CO2M, TANSAT-2, and GOSAT-GW) greenhouse gas satellites can observe wide area column average dry air mole fraction of carbon dioxide (XCO2) of entire urban areas. Although top-down urban emission monitoring has improved in terms of spatial coverage and frequency, the challenge remains in how to utilize space-based observations to perform accurate inversion of source area’s emission. The high uncertainty mainly arises from XCO2 observations’ low signal-to-noise ratio due to non-anthropogenic fluxes and missing data due to sophisticated atmospheric conditions.
To achieve accurate urban emission estimation from space, we propose a deep learning (DL) framework which can intelligently capture XCO2 patterns from wide area XCO2 observations. The synthetic CO2M dataset serves as model pre-training materials for its ideal XCO2 observations given by chemical transport model. Transformer is selected as the architecture of DL model for its ability to model global dependency across wide area observations. The proposed model has been validated on the Berlin city’s synthetic CO2M dataset and OCO-3 snapshot area map (SAM) mode observations. In both cases, the pre-trained DL model effectively interpolated missing XCO2 values throughout the XCO2 snapshot, and showed outperformance on urban plume signal identification compared to conventional algorithms. Furthermore, by incorporating DL model’s prediction results with inversion methods, we performed emission estimates for Berlin city on synthetic CO2M data and multiple cities globally on OCO-3 SAMs. Our top-down emission estimation results showed high consistency with prior bottom-up inventories. This study provides valuable insights into advancing intelligent methodologies for urban emission inversion from wide area satellite observations.
How to cite: Wang, Z. and Zhang, F.: Leveraging wide area XCO2 deep learning in estimating urban CO2 emissions from space, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5294, https://doi.org/10.5194/egusphere-egu25-5294, 2025.
Nitrous oxide (N2O) is a kind of long-lived greenhouse gas. Since the Industrial Revolution, increasing atmospheric N2O concentrations have contributed to the depletion of the stratospheric ozone layer and climate change. China has been a hot spot for global N2O emissions, with a rapid growth. However, estimates of N2O emissions from China’s ecosystem remain largely uncertain. Therefore, here we provide a multi-method estimates (inventory, process-based model and atmospheric inversion) of terrestrial ecosystem N2O emissions in China. The process-based models were further modified based on observational datasets. Finally, we provide a comprehensive quantification of China's N2O emissions caused by natural and anthropogenic ecosystems from 1980 to 2023.
How to cite: Sun, Z., Li, Z., and Hong, S.: The nitrous oxide budget in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5388, https://doi.org/10.5194/egusphere-egu25-5388, 2025.
We determined global emissions of the perfluorocarbons (PFC) tetrafluoromethane (CF4) and hexafluoroethane (C2F6) from 2004 to 2023 using an inverse modeling approach. These PFCs are characterised by their exceptionally long atmospheric lifetimes (~50.000y for CF4 and ~10.000y for C2F6) and strong infrared absorption, making them some of the most potent greenhouse gases. Emissions of these gases are almost entirely anthropogenic, originating primarily from aluminium smelting and semiconductor manufacturing. Previous studies have highlighted significant discrepancies between bottom-up inventories, based on activity and industry data, and top-down estimates derived from atmospheric measurements. In this study, we use continuous and flask measurements combined the Bayesian inversion algorithm FLEXINVERT+ and the Lagrangian particle dispersion model FLEXPART to estimate global emissions of CF4 and C2F6 and their regional distribution. Our findings indicate a decline in emissions until approximately 2009, followed by an increase in subsequent years, contrasting with bottom-up inventories, which show a steady decrease over the study period. The largest emissions are located primarily in East Asia, with substantial potential emissions in South and Southeast Asia, followed by North America and Europe. India and Malaysia, with their growing aluminium (India) and semiconductor (Malaysia) industries, emerge as significant sources of uncertainty in our emission estimates due to limited observational coverage in these regions. While emission reduction measures in the aluminium industry appear to be effective, the impact of mitigation efforts by semiconductor manufacturers are likely overestimated.
How to cite: Püschel, B., Kandler, L., Vojta, M., and Stohl, A.: Global Emissions of Tetrafluoromethane (CF4) Hexafluoroethane (C2F6) Determined by Inverse Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6791, https://doi.org/10.5194/egusphere-egu25-6791, 2025.
Bayesian inverse modeling is a widely used approach for estimating greenhouse gas (GHG) emissions from atmospheric measurements. However, this method is subject to various uncertainties, including errors in the transport model, inaccuracies in baseline mole fractions, and uncertainties associated with the parameters of the Bayesian inversion framework.
In this study, we investigated the impact of these uncertainties on the Bayesian inversion of a key hydrofluorocarbon contributing to climate change, HFC-134a. We first conducted a grid search to refine the nudging parameters for simulating three-dimensional initial HFC fields using the FLEXible PARTicle-Linear Chemistry Module (FLEXPART-LCM). Subsequently, we employed Latin Hypercube Sampling (LHS) to explore inversion uncertainties by sampling a broad parameter space within the Bayesian inverse modeling framework FLEXINVERT.
Through over 250 ensemble simulations for initial fields and 15,000 ensemble inversion runs, we identified the most influential parameters and optimized configurations for the inverse modeling of HFC-134a. These findings improve the reliability of HFC-134a emission estimates and provide insights into the role of inversion parameters, applicable to the inversion of other greenhouse gases.
How to cite: Nabavi, S. O., Vojta, M., Raju, A., Wittig, S., and Stohl, A.: Enhancing top-down HFC-134a emission estimates through parameter space exploration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8609, https://doi.org/10.5194/egusphere-egu25-8609, 2025.
The isotopic composition of CO can be used to detect enhanced oxidation of methane by atomic chlorine due to the strong kinetic isotope effect related to this reaction (KIECH4+Cl = 66 per mil). Importantly, this detection method has demonstrated the presence of a large ground-level North Atlantic chlorine source for the years 1996-1997, linked to the geographic distribution of iron-rich Sahara dust within the marine boundary layer (Mak et al., 2003; van Herpen et al., 2023). Here, we present 2023-2024 d13CCO and d18OCO data from an air sampling network established across the low-latitude Atlantic Ocean, including bi-weekly measurements from Tenerife (IEO and IZO), Cape Verde (CVAO), Barbados (RPB), and northern Brazil (ATTO). In addition, the network includes intermittent flask samples taken aboard commercial shipping vessels as they complete trans-Atlantic routes. Our analysis supports the existence of a large chlorine sink of methane in dust-associated regions, which varies seasonally. Underestimates in the occurrence of chlorine oxidation propagate to isotope-constrained top-down global methane models, shifting predicted contributions away from fossil fuels and towards biological sources. Ultimately, our results provide an opportunity to reconcile missing chlorine sources, which may have significant implications for global methane source estimations.
How to cite: Brashear, C., van Herpen, M., van de Kraats, B., Johnson, M., Pennacchio, L., Mikkelsen, M., Saiz-Lopez, A., Meidan, D., and Röckmann, T.: Utilizing tropospheric CO isotope observations from a low-latitude Atlantic sampling network to constrain the oxidative chlorine sink , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10322, https://doi.org/10.5194/egusphere-egu25-10322, 2025.
Cities have a great responsibility to mitigate CO2 emissions, as they contribute substantially to global anthropogenic CO2. To assist cities in efficient mitigation planning an independent data-driven approach to monitor emissions within urban areas is required.
The Rhine-Neckar area comprises the cities of Mannheim and Heidelberg and is characterized by large emissions due to significant energy production and industry on the one hand, and by ambitious climate goals on the other hand. To monitor and support mitigation efforts of these cities, we are developing an urban monitoring network using mid-cost CO2 and air quality sensors for Heidelberg and Mannheim. The network will consist of 18 sensor nodes provided by the University of California, Berkeley. Each node is identical in construction to the sensors in the Berkeley Air Quality and CO2 Network (BEACO2N) (Shusterman et al., 2016) and measures CO2, CO, PM2.5 and NO2.
In conjunction with the measurement network, we use GRAMM/GRAL to model atmospheric transport in the domain on high resolution. GRAMM/GRAL is run following a catalog approach, in which hourly steady-state wind conditions are assumed. This way the computational costs can be reduced enabling the simulation of longer time scales on street canyon resolving spatial resolution (Berchet et al., 2017, May et al., 2024). We feed the model with high-resolution inventories of fossil fuel and biogenic emissions and compare the simulated enhancements to the measurements of the first deployed nodes. We discuss the capability of the conjunction of high-resolution modeling and mid-cost observations to detect emission patterns within the Rhine-Neckar area.
Shusterman, A. A., et al., (2016). The BErkeley Atmospheric CO2 Observation Network: initial evaluation. Atmos. Chem. Phys., 16, 13449–13463., https://doi.org/10.5194/acp-16-13449-2016
Berchet, A., et al., (2017). A cost-effective method for simulating city-wide air flow and pollutant dispersion at building resolving scale. Atmospheric Environment, 158, 181-196., https://doi.org/10.1016/j.atmosenv.2017.03.030
May, M., et al., (2024). Evaluation of the GRAMM/GRAL Model for High-Resolution Wind Fields in Heidelberg, Germany. Atmospheric Research, 300, 107207., https://doi.org/10.1016/j.atmosres.2023.107207
How to cite: Murai von Buenau, K., Haas, P., Maiwald, R., Ulrich, V., Block, S., Butz, A., and Vardag, S. N.: Towards an urban CO2 and air pollution network in Heidelberg-Mannheim, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10965, https://doi.org/10.5194/egusphere-egu25-10965, 2025.
Universities and other institutions are currently faced with the goal of utilizing responsible practices regarding carbon dioxide (CO2) emissions. At the University of Zurich, the initiative to start real-world laboratories inspired innovations and science-based studies to explore additional options for mitigating CO2 emissions from operations through both direct and indirect vegetation processes, i.e. direct sequestration and reduction in emissions from reduced energy consumption of buildings due to the shading and cooling effect of trees, respectively. As of now, it is unknown how much CO2 is sequestered by the vegetation on the University of Zurich Irchel campus and also which vegetated areas are possibly emitting CO2 (through soil respiration, decomposition, etc.). Within this initiative, the Green4Clim project monitors and quantifies the current CO2 sources and sinks on the Irchel campus and, in close collaboration with campus gardeners,determines options to optimize CO2 sequestration and cooling on campus. In this presentation, we present the preliminary results on (i) establishing a protocol for measuring direct and indirect effects of trees and other vegetation carbon sequestration, shading and cooling effects, and (ii) the measurements obtained on CO2 sources and sinks of natural areas on Irchel campus. Our measurements were taken at the leaf and soil level with a portable photosynthesis system and soil chamber systems to create an inventory of measurements, focusing on the four dominant tree species, green roofs, and the three dominant land cover types on campus (shrubs/bushes, short grass, and tall grass). Through this experiment we will identify the most suitable places and the most efficient plant species and communities to sequester CO2 on Irchel campus and suggest a management strategy that maximizes the CO2 reduction of the University of Zurich Irchel campus.
How to cite: Grysko, R., Santos, M., and Paul-Limoges, E.: Green4Clim: Making the University of Zurich a real-world laboratory for climate change mitigation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11062, https://doi.org/10.5194/egusphere-egu25-11062, 2025.
As the U.S. Metrology Institute, the National Institute of Standards and Technology (NIST) has responded to the measurements and standards challenge of monitoring, reporting, and verifying greenhouse gas (GHG) emissions from a broad range of sources, with an emphasis on urban environments, to: a) improve U.S. capabilities to measure GHG emissions accurately; b) demonstrate the capabilities of atmospheric urban monitoring networks (top-down or atmospheric measurement approaches) to determine quantitatively GHG fluxes from industrial, residential, transportation, power generation and other activities; c) complement such measurements with spatially explicit emissions modeling (bottom-up or emissions modeling) approaches based on socio-economic data; and d) demonstrate that the combination improves confidence in emission estimates while identifying areas of improvement.
As part of these efforts, NIST established its first Urban Test Bed in Indianapolis, Indiana (the INFLUX Project) in 2010 with Purdue University, NOAA, and Penn State University collaborators. Additional testbeds were established in Los Angeles (2012) and the Northeast Corridor (2014) to test applicability of methodologies over a range of meteorological conditions and emissions profiles. In this talk, we summarize some of the results obtained where we demonstrated methodologies for biogenic emission and uptake processes estimation, network design and emissions quantification from dense tower-networks and aircraft measurements. In addition, we highlight current efforts to transfer the research to operations, facilitate the adoption of the techniques by developing lower cost monitoring stations, and promote transparency by consolidating the methods in open-source computational tools.
How to cite: Lopez-Coto, I., Boyle, T., Marrs, J., Karion, A., Mueller, K., Eldering, A., Semerjian, H., and Whetstone, J.: Enhancing Urban GHG Monitoring: Progress of the NIST test-bed system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21706, https://doi.org/10.5194/egusphere-egu25-21706, 2025.
Top-down verification methods are crucial for ensuring confidence in the bottom-up approaches used to report greenhouse gas emissions. These methods are reliant on robust baseline estimates, which can be calculated via several methods using a combination of meteorological data, transport models, and tracers such as CO and radon-222. In particular, high-quality radon measurements have been shown to reliably and consistently identify baseline airmasses across the globe, due to radon’s unique properties as a terrestrial tracer. However, the methodology used in this process differs between studies, as a result of variations in the location (e.g. remote, coastal, terrestrial), altitude, and atmospheric features of each observation site, as well as the sensitivity of the instruments available at the time/location.
This study aims to provide a universal procedure with which to calculate baseline estimates of greenhouse gases using radon, accounting for differences between stations. To evaluate and adjust this procedure, data from the Kennaook/Cape Grim (Tasmania), Mauna Loa (Hawaii), Jungfraujoch (Switzerland), Mace Head (Ireland) and Monte Cimone (Italy) observatories will be assessed, encompassing a range of locations and altitudes. This will include analysis of a variety of greenhouse gases, to understand whether alterations in the technique are required when estimating baselines of different gases and highlight how features such as low pollution spikes of N2O or sudden pollution events of SF6 influence our ability to estimate their baseline levels. Furthermore, using back trajectories obtained from the FLEXPART atmospheric dispersion model and high-frequency trace gas observations at each site, modelled baseline estimates will be derived to provide a direct comparison to the radon methodology. In doing so, this research will provide an unambiguous procedure for future baseline estimates, increasing the accessibility of this technique and improving comparability between studies.
How to cite: Lils, C., Kikaj, D., Chung, E., Chambers, S., Griffiths, A., Conen, F., Fukumura-Sawada, P., and Krummel, P.: A Standardised Procedure for Estimating Greenhouse Gas Baselines Using Radon-222, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18706, https://doi.org/10.5194/egusphere-egu25-18706, 2025.
Methane (CH₄), a potent greenhouse gas, is a key player in atmospheric chemistry and climate forcing. Its spatial and temporal variability is driven by emissions, atmospheric transport, and chemical loss processes. Accurate modelling of CH₄ is essential for understanding its sources, sinks, and role in Earth’s energy budget. In this study, we evaluate the skill of forward methane simulations of ICON-ART (ICOsahedral Nonhydrostatic - Aerosols and Reactive Trace gases) implementation established at Max Planck Institute for Biogeochemistry in Jena. The ICON-ART model represents a cutting-edge atmospheric modelling system jointly developed by the consortium of German and Swiss institutes. Its proven capability to realistically simulate trace gases, aerosols, and chemical interactions makes it a versatile tool for regional-to-global atmospheric studies focusing on improving flux estimates of a variety of atmospheric compounds, including methane. This work was conducted within the framework of the ITMS project (Integrated Greenhouse Gas Monitoring System for Germany), designed to enable Germany to operationally monitor the source and sinks of the most import long-lived greenhouse gases.
In the study, we evaluate the performance of the ICON-ART simulations set over the ICON-EU domain at 7 km horizontal resolution and compare their results other, more established modelling systems, including CAMS (Copernicus Atmosphere Monitoring Service) inversion optimized product (v21r1), CAMS reanalysis (EGG4) and the WRF-GHG (Weather Research and Forecasting with GHG module) model run at 5 km horizontal resolution. Both ICON-ART and other models include realistic realizations of anthropogenic emissions, natural fluxes, and boundary conditions that allow for realistic representation of atmospheric methane. We further compare all model results to in-situ airborne observations performed with HALO (High Altitude and LOng Range) during CoMet Campaign in May-June 2018, providing high-resolution CH₄ measurements, including vertical profiles spanning from the planetary boundary layer (PBL) to the low stratosphere (LS). The comparability of the models was ensured through collocated data analysis and performance metrics. These methodological frameworks minimize biases arising from resolution differences, enabling a fair assessment of the models’ capabilities.
The results reveal that ICON-ART is able to capture uplift transport and strong vertical mixing processes with remarkable fidelity. Displaying only 1.8 ppb mean bias error (MBE) for CH4, it outperforms both WRF and global CAMS products, across the used metrics. In the PBL, ICON-ART resolves small-scale CH₄ variability better than CAMS and WRF. Similarly, in the free troposphere, ICON-ART successfully simulates CH₄ transport and mixing, aligning closely with aircraft observations. Notably, ICON-ART shows better agreement in the LS, which is linked to improved stratosphere-troposphere exchange processes, but also underlines the importance of realistic lateral boundary conditions.
How to cite: Custódio, D., Ho, D., Gałkowski, M., and Gerbig, C.: Evaluating ICON-ART’s Performance in Simulating Methane: A Benchmark Against aircraft observations, CAMS, and WRF Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13030, https://doi.org/10.5194/egusphere-egu25-13030, 2025.
On-road transportation is one of the major contributors to energy consumption and CO2 emissions in global megacities, and high-resolution CO2 traffic emission maps are needed to analyze emission patterns. In this study, commercial GPS-based data provides hourly, road-specific information on vehicle speed and traffic volume, and machine learning models are employed to address data gaps and transform sample counts into real traffic flux. Combined with COPERT, we developed on-road transportation CO2 emission maps for 10 selected cities in France, Germany, and the Netherlands. Our analysis offered insights into annual, per capita, and area-specific emissions for each city. Spatial emission patterns reflect urban structures and commuting behaviors, with cities such as Paris exhibiting concentrated hotspots along its ring road, whereas Berlin demonstrates a more uniform spatial distribution. Temporal variations reveal distinct weekly and seasonal trends, with more significant reductions during holidays and summer in French cities compared to German and Dutch cities. This approach enhances the spatial and temporal characterization of CO2 emissions in on-road transportation compared to the previous method used in Carbon Monitor, indicating the potential of GPS-based data for supporting future efforts in emission monitoring and developing emission reduction policies.
How to cite: Shi, Q., Ciais, P., Bonnemazion, X., Mittakola, R. T., Megal, N., and Zhou, C.: Near-real-time CO2 traffic emission maps of 10 European cities based on high-resolution GPS-based data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11782, https://doi.org/10.5194/egusphere-egu25-11782, 2025.
Greenhouse gases (GHGs) are the main cause of climate change, and their concentrations are steadily increasing due to continuous emissions from anthropogenic activities. To establish effective carbon emission reduction policies and mitigate climate change, monitoring changes in atmospheric GHG concentrations and identifying their origin regions is essential. In this study, we analyzed the regional characteristics of carbon dioxide (CO2) and methane (CH4) at the Global Atmospheric Watch (GAW) stations (AMY, GSN, and ULD) in South Korea and investigated regional differences in the relationship between the two substances. We also explored the relationship between the regional differences and the source regions of greenhouse gases. The STILT mode (a Lagrangian dispersion model) and the EDGAR (an anthropogenic emission dataset) were used to identify the source regions of GHGs. The relationships (correlation coefficient and ratio) between CO2 and CH4 at three stations showed regional differences (GSN > ULD > AMY). It was investigated that these differences were caused by differences in the characteristics of major airflow patterns and emission sources that affect CO2 and CH4 concentration changes in the corresponding regions. The results of this study can help identify the causes of regional greenhouse gas concentration changes.
How to cite: Kim, J., Lee, Y. G., Park, S., and Shin, H.-Y.: Study on the influence of the origin region on the relationship between carbon dioxide and methane concentrations in South Korea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15002, https://doi.org/10.5194/egusphere-egu25-15002, 2025.
Monitoring and verifying anthropogenic greenhouse gases (GHG) Emissions at high spatiotemporal resolution with observation-based evidence is desirable for climate policymakers. A multiple-scale nested GHG assimilation system, named GCASv3, was developed for quantifying anthropogenic GHG emissions at high spatiotemporal resolution. GCASv3 uses a four level nested scheme and consists of one global module and one regional module. The global model is capable of assimilating XCO2 and XCH4 data to infer global CO2 flux and CH4 emissions at 10x10 resolution, while the regional module is able to assimilate ground and satellite GHG observations to quantify anthropogenic GHG emissions on national, regional and urban scales at 27km, 9km and 1km resolution respectively. This presentation describes briefly the framework and the major components of GCASv3. Anthropogenic CO2 emissions and CH4 emissions inferred by GCASv3 at different scales are presented and discussed.
How to cite: Wang, H., Jiang, F., and Feng, S.: Toward Monitoring Greenhouse Gas Emissions from National to Regional and Urban Scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15020, https://doi.org/10.5194/egusphere-egu25-15020, 2025.
China, as the largest contributor to global anthropogenic methane (CH4) emissions, has pledged to reduce its global CH4 emissions by 30% in 2030 compared to 2022 levels. Accurate estimation of CH4 emissions is crucial for climate prediction and mitigation policies but poses a significant challenge for methods relying solely on economic statistics and emission factors. In this study, we developed a regional carbon assimilation system (RegGCAS) to integrate TROPOMI XCH4 observations for inferring daily CH4 emissions across China. Our estimated national total CH4 emission for 2022 was 45 Tg·yr⁻¹, approximately 35% lower than the widely used EDGARv8 inventory (prior estimate). Notable reductions were primarily observed in Northern China, with only sporadic increases in Shanxi Province, which contributes one-third of China's coal production. Increases were primarily concentrated in the Sichuan Basin, the southeast coastal provinces, and Heilongjiang Province in Northeast China. The optimized CH4 emission estimate exhibited more pronounced seasonal variations, with a significant decrease in emissions during winter. However, constraints on emissions in summer were limited due to the lack of observational data. Posterior simulations demonstrated better consistency with both TROPOMI XCH4 observations and ground-based observations. These findings enhance our understanding of the spatiotemporal patterns of CH4 emissions in China.
How to cite: Feng, S., Jiang, F., Wang, H., and Zhang, Y.: Significant Overestimation in Anthropogenic Methane Emissions in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16572, https://doi.org/10.5194/egusphere-egu25-16572, 2025.
The correlation between estimated national GHG emissions and uncertainty is generally known. The causes and sources of the uncertainties are diverse and relate to source activity (field studies and research, census data), methodologies, variations in emission factors, and scientific studies/publications. Uncertainty has occurred due to a lack of knowledge of true values, in which uncertainty is assessed by the probability density function. Uncertainty analysis helps identify and prioritize activities (monitoring, inventory, evaluation methods, etc.) to improve the evaluation and reduce uncertainty. A quantitative uncertainty analysis is often performed for a 95% confidence interval.
Different calculation methodologies are used in each sector (waste, energy, LULUCF, industry, transport, and agriculture); the mechanisms for producing emissions of emitted substances are complex and variable and require scientifically based research to update them. Regional differences are also essential, as climate, access to technologies, the possibility of introducing them, and other physio-geographic conditions have a significant impact. One of the challenging issues in the GHG emissions assessment relates to future emission projections related to future unpredictability due to climate change; changes in economic growth plans also create a lot of uncertainty. For example, in the forest management and land use sectors, the intensity of CO2 sequestration in the ecosystem must be assessed. Recent studies, including those informing Latvia's LULUCF emission factors, reveal significant uncertainties in estimating GHG emissions from organic-rich soils due to short-term measurements, limited sampling, and neglect of long-term soil carbon dynamics. Another relatively more straightforward source of data uncertainty is identified in the waste management sector. In this sector, the analysis of methane emissions from landfills from disposed solid municipal waste requires a precise morphological composition of the waste, as the result of the calculation depends not only on the amount of waste but also on the content of organic matter and the intensity of aerobic or anaerobic degradation. It is self-evident that the composition of waste can be variable and monitored effectively today. Still, different waste fractions are characterized by different degradation intensities, and according to the assessment method, degradation should be assessed over a period of 100 years, which means that the historical morphological composition of waste is required. The lack of such data often leads to up to 150 % uncertainty.
When analyzing the national reports of the Northeastern Region of Europe (Latvia, Finland, Estonia, and Lithuania), the most considerable uncertainties can be found in the LULUCF sector, which, in view of these countries' economic activities, is very substantial in the overall assessment. Reducing uncertainty in this area is of the utmost importance as it is closely linked to national climate plans and the measures taken to achieve climate neutrality.
How to cite: Steinberga, I., Vinogradovs, I., Briede, A., Peneze, Z., and Putnina, K. K.: Diversity and uncertainty in the assessment of GHG emissions in national inventories: a sectoral analysis of Northeastern European countries, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10688, https://doi.org/10.5194/egusphere-egu25-10688, 2025.
Scientific monitoring, verification and reporting (MRV) is necessary to independently examine the quality of national greenhouse gas (GHG) inventories as assessment methods are inherently uncertain including systematic effects from biased input information and lack of knowledge. The atmospheric research community develops observation systems to monitor the large-scale net GHG exchange with remote sensing and tall tower based concentration field measurements and atmospheric transport model inversion techniques. Because the spatial and temporal scales of these approaches are too coarse for small nations and even more so for local government districts, we used an alternative direct method to estimate the GHG budget of an agricultural landscape in Denmark, the tall tower eddy covariance method. In the presentation, we will use this case study to illustrate the strengths and limitations of net GHG flux measurements to test against GHG inventories.
We compared our one year’s data set of continuous GHG (CO2, N2O and CH4) flux measurements with the estimates from IPCC based emission methods that were refined for the Danish agricultural landscape. We calculated GHG emissions and their uncertainties using the IPCC methods and propagated those to annual estimates. Likewise, we estimated the uncertainty for annual budgets from turbulent flux measurements including a number of factors that are deemed most important for the quality of net flux estimates.
While the emission estimates for the non-CO2 GHG were at least similar, the IPCC inventory characterized the area as a net GHG source, whereas the measured fluxes determined a large GHG sink, owing to an overwhelming CO2 uptake.
In our presentation, we will resolve this apparent contradiction and conclude on the strengths and limitation of MRV from scientific net GHG exchange approaches.
Acknowledgement:
We acknowledge funding by the Free Danish Research Council (DFF, grant number 1127-00308B) and the contribution of MSc. Victoria Abelenda and MSc. Isabel Lopez in their MSc. Project “Comprehensive Assessment of Greenhouse Gas Emissions (N2O, CO2, CH4) in Agricultural Practices: A Case Study from a Rural Area in Denmark”, Inst. of Resouce and Environmental Engineering, Technical University of Denmark (DTU) Kgs. Lyngby, Denmark (2024).
How to cite: Ibrom, A., Kissas, K., Gorlenko, A., Wamg, Z., Wiesner, S., and Scheutz, C.: Prospects of scientific monitoring ,verification and reporting to support national and subnational GHG inventories, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14671, https://doi.org/10.5194/egusphere-egu25-14671, 2025.
The presence of CO2 in the atmosphere facilitates the maintenance of adequate levels of heat, which is essential for the establishment and sustenance of life on Earth. Although its concentration has varied dramatically throughout the planet's history, recent levels of atmospheric CO₂ are the result from a delicate balance among processes such as volcanism, weathering, photosynthesis, respiration and combustion. However, extensive use of fossil fuels has altered this balance causing atmospheric CO2 concentrations to rise, thereby intensifying global warming and accelerating climate change
While relatively few countries in the intertropical region release substantial amounts of CO2, nations in the northern hemisphere have been the primary contributors to CO₂ emissions over the past centuries, largely due to industrialization. Since the Industrial Revolution, urban development has concentrated several people around heavily industrialized cities, which have become central drivers of climate change. Global atmospheric circulation facilitates the rapid dispersion of CO₂ emissions originating from tropical latitudes throughout the troposphere. In contrast, emissions from mid- to high-latitude regions persist longer on a regional scale. Consequently, the latitude of CO₂ emissions significantly influences their climatic effects, with high-latitude emissions remaining in the atmosphere for longer time. On the other hand, growing urban areas in the transitional mid-latitude regions are particularly vulnerable to the impacts of climate change, with Mediterranean cities being especially susceptible to extreme events. These include more frequent heatwaves, rising sea levels, droughts, and intense rainfall, all of which pose significant threats to infrastructure, public health, and urban ecosystems. Moreover, rising temperatures enhance social and economic inequalities, underscoring the urgent need for resilient and sustainable adaptation strategies.
This study addresses the rationale for and development of a research infrastructure aimed at monitoring atmospheric CO₂ and its latitudinal variation within the Mediterranean region. The objective is to assess the impacts of actions taken to reduce anthropogenic CO₂ emissions as outlined in the European Green Deal.
The proposed infrastructure is designed to collect and disseminate data for a comprehensive examination of the causes of latitudinal and temporal variations in atmospheric CO₂ across a north-south transect from the Alpine glaciers in Valle d’Aosta to the island of Lampedusa, both located in Italy. This system includes 12 automatic monitoring stations equipped to measure the concentration and isotopic composition of carbon and oxygen in atmospheric CO₂. Extensive research highlights the importance of monitoring carbon isotopes (e.g., ¹³C, ¹²C) to identify emission sources, as well as triple oxygen isotope ratios (¹⁶O, ¹⁸O, and ¹⁷O) to trace the fate of CO₂ within the interconnected carbon and water biogeochemical cycles.
The network’s high-frequency acquisition capability (minute intervals) enables near real-time evaluation, facilitating the identification and characterization of diverse CO₂ sources and the apportionment of their emissions.
How to cite: Di Martino, R. M. R. and Gurrieri, S.: Monitoring Atmospheric CO₂ in the Mediterranean: A Strategic Infrastructure for Climate Action and Latitudinal Impact Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16829, https://doi.org/10.5194/egusphere-egu25-16829, 2025.
As part of the current international effort to limit global warming, signatories to the Paris Agreement are required to quantify their greenhouse gas (GHG) emissions. Former Kyoto Annex I countries thus report their emissions annually to the United Nations Framework Convention on Climate Change (UNFCCC) . This assessment allows countries to evaluate their progress in reducing GHG emissions and their compliance with existing agreements.
The general approach to quantifying GHG emissions at the national level is to use activity data and emission factors (bottom-up method). An independent quantification can be achieved with inverse modelling, which makes use of an a priori estimate, atmospheric transport models (ATM), and atmospheric measurements of GHG concentrations (top-down method). However, the accuracy and uncertainty of inverse estimates are highly dependent on several parameters and modelling choices. Consequently, inter-model variability can be significant, potentially limiting the use of this technique in policy-relevant discussions.
A representative quantification of GHG emissions based on inverse modelling requires an in-depth understanding of different inverse model estimates, their uncertainties and model limitations. An intercomparison of three inverse methods and a suite of sensitivity tests were performed. This exercise considered two fluorinated gases (HFC-143a and PFC-218), which are potent GHGs with very different emission characteristics (diffuse versus point source). Both are covered under the European F-gas regulation. Additionally, HFC-143a is expected to be phased-down under the Kigali Amendment to the Montreal Protocol.
We found that top-down estimates for Central and Western European countries are most sensitive to the ATM used. For gases with localised emission sources, such as PFC-218, the choice of a priori emissions and assigned model-data mismatch uncertainty are particularly relevant. For gases with widely distributed emission sources, such as HFC-143a, the emission estimates are more consistent and less sensitive to modelling choices. This detailed understanding of uncertainties in top-down estimates is then used to inform how inverse modelling can be used to support the reporting of halogenated GHG emissions at the national and European level.
How to cite: Brito Melo, D., Ramsden, A., De Longueville, H., Redington, A., Danjou, A., Andrews, P., Murphy, B., Pitt, J., Saboya, E., Rigby, M., Emmenegger, L., Manning, A., Henne, S., and Ganesan, A.: Addressing uncertainties in top-down estimates of national-scale greenhouse gas emissions across different inversion systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17986, https://doi.org/10.5194/egusphere-egu25-17986, 2025.
Targeting and tracking climate change mitigation efforts requires accurate bottom-up inventories of GHG emissions, verified by independent atmospheric measurements. So far, most policy decisions have been based on annual emission inventories at national and city scales. Inventories with higher resolution in both space (sub-city) and time (daily to hourly), while generally more uncertain, have major advantages. First, they are a key input to inverse modelling of emission sources from atmospheric measurements, which offers a semi-independent approach to verify bottom-up estimates. Second, they can serve as simulation tools to assess the impact of specific interventions (from policy to industrial standards and household behavior) on GHG emissions and measured atmospheric concentrations. Third, by offering more localized emission estimates almost in real time, they may act as more powerful motivators of behavioral and policy change when used to communicate and track climate action.
Here we present a simple approach to develop bottom-up inventories of carbon dioxide emissions from road traffic (at street level) and residential space heating (in a 100-m grid) using crowd-sourced data from OpenStreetMap and other publicly available data sources. Our approach can be easily scaled to all of Germany and, with some modifications, can be tailored to a wide range of contexts and applications. We demonstrate the approach for the cities of Mannheim and Heidelberg, in the Rhine-Neckar Metropolitan Area in Germany.
Emissions from road traffic are derived from multiplying estimates of average daily traffic volume – based on road type information, number of lanes, and population density – by speed- and fuel-dependent emission factors and data about the national vehicle fleet composition. Space heating emissions rely primarily on gridded data from the 2022 German census on population density, living space per capita, heating energy carriers, and building age.
We validate our traffic volume estimates with independent traffic count data and compare our emission estimates to available inventories. Road traffic emissions in the Rhine-Neckar region were 1.6% higher than TNO estimates for the region (Super et al. 2021), a widely used inventory of disaggregated emission in Europe. Our residential space heating emissions estimates were slightly lower than estimates from emissions inventories for the cities of Mannheim and Heidelberg (12% and 8%, respectively), largely attributable to the type of emission factors used in the calculations.
How to cite: Block, S., Ulrich, V., Martin, M., von Elverfeldt, K., Murai von Buenau, K., Haas, P., Maiwald, R., Butz, A., and Vardag, S. N.: A scalable approach to high-resolution, bottom-up GHG emission inventories using open data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17019, https://doi.org/10.5194/egusphere-egu25-17019, 2025.
The city of Paris aims to reach net zero emissions by 2050, an ambitious target whose achievement will need to be verified. Atmospheric measurements of CO2 can provide independent information on the city emissions and therefore, play an important role in monitoring the effectiveness of emission reduction plans.
To derive emissions from measured concentrations, an atmospheric transport model is needed. This model should cover long time periods to detect trends and emission patterns, and run at high-resolution to simulate the air flow around urban structures. We use GRAMM/GRAL to model CO2 transport over Paris at 10m resolution with a catalogue approach. The hourly occurring meteorological situation and its respective concentration field is selected from a catalogue of around 1000 precomputed meteorological conditions, which are representative of wind situations over Paris. The selection of the appropriate catalogue entry is based on minimizing differences to wind measurements in the modelling domain. Thus, long time series of concentration enhancement maps can be calculated with low computational costs. Our setup for Paris includes anthropogenic fluxes, biogenic fluxes from Sentinel-2-based VPRM, and boundary conditions derived from in-situ measurements to allow a direct comparison to the observed concentrations in the city.
We compare the simulated CO2 concentrations to measurements for 2023 from the ICOS Cities project. The modelled signals generally capture the diurnal dynamics and agree with the measured CO2. There are certain meteorological conditions where GRAMM/GRAL fails to capture the measured signal. GRAMM/GRAL does not accurately capture meteorological situations with lower boundary layer heights which most often occur during nighttime and in winter. However, we present a method of estimating a time-dependent uncertainty using concentration distribution from multiple catalogue entries. This uncertainty can be used in an inversion.
We determine the underlying emission patterns and analyse the importance of the resolution of the emission inventory for emission quantification and emission sector disaggregation. Such detailed sector-specific information can help to inform policymakers about progress towards reduction goals and the effectiveness of specific reduction measures.
How to cite: Maiwald, R., Utard, H., Ramonet, M., Laurent, O., Glauch, T., Denier van der Gon, H., Lauvaux, T., and Vardag, S. N.: High-resolution CO2 flux modelling on the building-scale using GRAMM/GRAL and in-situ measurements for the Paris metropolitan area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19189, https://doi.org/10.5194/egusphere-egu25-19189, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 5
EGU25-20229 | ECS | Posters virtual | VPS3
Life cycle assessment of milk production: integrating changes in soil carbon stock with eddy covariance and DNDC modelingWed, 30 Apr, 14:00–15:45 (CEST) | vP5.38
Background aims: Life cycle assessment (LCA) is widely used to evaluate the carbon footprint (CF) of milk production. Changes in soil organic carbon (SOC) stock play a vital role in agricultural greenhouse gas emissions. However, no consensus has been reached to incorporate SOC changes into agricultural LCA. This study aims to evaluate the CF of milk production using LCA methodology with integrating SOC balance based on data from Viikki Research Farm at Helsinki. Methods: The CF of milk production was analyzed for 2022 and 2023 using the Solagro Carbon Calculator. Furthermore, the study explored the soil carbon and nitrogen balances using the DNDC model, for a comparison with IPCC Tier 1 & Tier 2 methods and the real measurements. Results and conclusions: Real measurements demonstrated substantial SOC loss from grassland and subsequent annual cropland, which was 607 and 3939 kg C ha-1 in 2022 and 2023, respectively. Incorporation of those results increased the CF of milk production. Estimated based on DNDC modeling, the SOC loss exceeded the measured results in 2022 and was underestimated in 2023, while the IPCC method showed SOC sequestration in 2022. The observed emissions fluctuation between the two years was related to the rotation between perennial grass and annual crop, and harsh wintertime conditions affecting crop growth. This study underscores the importance of SOC change in agricultural LCAs. While direct measurements may have limitations, a more profound understanding of SOC dynamics and better calculation is crucial to minimize bias in CF estimations.
How to cite: Gao, Y., Hu, T., Roitto, M., Jokiniemi, T., Sandell, M., Pihlatie, M., and Tuomisto, H.: Life cycle assessment of milk production: integrating changes in soil carbon stock with eddy covariance and DNDC modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20229, https://doi.org/10.5194/egusphere-egu25-20229, 2025.
EGU25-19307 | ECS | Posters virtual | VPS3
Evaluation of Chamber-based soil greenhouse gas emissions in contrasted land use of the Sudanian savannaWed, 30 Apr, 14:00–15:45 (CEST) | vP5.39
The effects of major greenhouse gas (GHG) emissions in West Africa remain insufficiently documented. Over two consecutive years, we monitored soil GHG emissions using a chamber-based experimental setup across four contrasting land management conditions in the Sudanian savanna. The environmental drivers of the emissions were assessed through stepwise linear regression and ANOVA statistical tests. Our results show that, regardless of land management conditions, N2O release occurs at the highest rate in rice fields (4.29±2.9 µg N m-2 h-1). The soil acts as a sink for CH4 in the forest reserve (-1.09±7.67 µg C m-2 h-1), whereas degraded lands, such as cropland and rainfed rice farms, exhibit CH4 release at rates of 1.03±13.1 µg C m-2 h-1 and 5.93±12.28 µg C m-2 h-1, respectively. Livestock breeding contributes significantly to CH4 emissions in grasslands, where the annual mean CH4 flux is the highest (16.79±6.69 µg C m-2 h-1). The statistical analysis indicates that 53.8% and 50.2% variability in the CH4 flux is explained by soil moisture and soil temperature respectively in the grassland and rice field. Soil moisture is negatively correlated with N2O release, while the relationship with CH4 is positive in grassland and rice fields, where higher CH4 emissions are observed. N2O flux shows a positive correlation with soil temperature. These findings suggest that land degradation exacerbates CH4 emissions, and the effect of fertilizer use on biomass during the growing season increases CH4 release in rice fields by approximately threefold. At the peak of the raining season, the forest CH4 sink reaches the highest -6.08±14.7 µg C m-2 h-1 while the rainfed rice field releases 9.14±29.57 µg C m-2 h-1. Overall, there is intra annual variability of GHG fluxes with dry and wet years showing different magnitude of N2O and CH4 emissions. The patterns of GHG flux dynamics in this data-scarce region is better clarified through our investigation. We conclude that GHG emissions in response to land cover degradation and agricultural practices, such as fertilizer use, are significant in the Sudanian savanna and urgent decisions are needed to mitigate these effects.
How to cite: Oussou, F. E., Sy, S., Bliefernicht, J., Spangenberg, I., Guug, S. S., Steinbrecher, R., Schäffler-Schmidt, A., Kiese, R., Ayamba, M., Yalo, N., Asiwaju-Bello, A. Y., Sawadogo, W., Olusegun, C. F., Amekudzi, L. K., and Kunstmann, H.: Evaluation of Chamber-based soil greenhouse gas emissions in contrasted land use of the Sudanian savanna, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19307, https://doi.org/10.5194/egusphere-egu25-19307, 2025.
EGU25-19539 | ECS | Posters virtual | VPS3
Quantifying regional and temporal heterogeneity in greenhouse gas emissions from Indian dietsWed, 30 Apr, 14:00–15:45 (CEST) | vP5.40
Providing sufficient and nutritious food while reducing climate emissions footprints from food systems is a Grand Engineering Challenge for India. The increasing dietary emissions pose a serious threat to achieving the national net-zero goal by 2070, yet such emissions are not yet accounted for in India’s Climate Action Plans. Since the 1990s, India’s dietary transitions have been largely propelled by economic development and intensive urbanization, yet such transitions have occurred unequally between urban and rural regions across India.
The regional and temporal heterogeneity in dietary consumption patterns across different populations and the corresponding GHG emissions is not well known. Here, we apply a life-cycle approach to quantify the regional, demographical, and food commodity-specific GHG emissions (CO2, N2O, and CH4) based on detailed household-expenditure data across three national-scale censuses (1999, 2011, 2022). We differentiate such emissions across twelve major food groups that are typically consumed in 88 distinct NSSO regions with demographics (rural and urban) differentiated by income. Our findings suggest that between 1999-2022, the per capita consumption of animal-based products has increased by ~20% respectively, and a ~15% decrease in wholegrain intake. Emissions from dairy (34%), wholegrain (31%), and meat (18%) food groups contributed more than 80% of total dietary emissions for 2011.
The demographical analysis suggested that household expenditure directly influences GHG emissions. For example, the highest expenditure decile of the population was 2.2 kgCO2eq cap-1 day-1 with 0.7 kgCO2eq cap-1 day-1 for the lowest decile in 2011Both rural and urban regions have per capita GHG emissions similarly, but the total emissions and share of food groups varied extremely with the household expenditure. The disparities in total emissions remain as high as 65% among poor and rich households, with poor houses having wholegrain-dominated emissions and rich households having dairy-dominated emissions. The spatial examination further showed the high heterogeneity in emissions among and within Indian states. Our findings highlight the opportunities and challenges in using food consumption as a lever for climate change while also reducing food inequality by shifting to healthier diets. Such findings can help strengthen State Climate Action Plans to help towards green agriculture and sustainable consumption.
How to cite: Yadav, S. and Balasubramanian, S.: Quantifying regional and temporal heterogeneity in greenhouse gas emissions from Indian diets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19539, https://doi.org/10.5194/egusphere-egu25-19539, 2025.
