After stabilizing in the mid-2000s, atmospheric methane (CH₄) levels have accelerated over the past decade. In response, satellite-based inversion techniques have been employed to meet the increasing demands of the climate community. In this study, the Integrated Methane Inversion (IMI) model, a novel approach based on the TROPOspheric Monitoring Instrument (TROPOMI), is used to quantify CH₄ emissions across Denmark. Over 900,000 TROPOMI observations from spring to early autumn of 2018–2022 were used to inform the inversions. Overall, TROPOMI CH₄ concentrations within the inversion domain showed an upward trend of approximately 12.71 ppb per year, reflecting the global trend. Excluding 2022, which included only four months of data, the inversions suggest an underestimation of emissions by 190(160–215) × 10³ tonnes, or 66(56–75)% of prior estimates. Northern and southern Jutland, along with the Copenhagen metropolitan area, were identified as key sources of CH₄ emissions. Additionally, the inversions indicated a decline in emissions during the COVID-19 pandemic, despite stable activity data. This study demonstrates the feasibility of using the IMI model to monitor CH₄ emissions in small countries like Denmark, offering a satellite-based perspective to better identify and mitigate these emissions.

Atmospheric CO₂ concentrations in urban areas reflect a combination of fossil fuel emissions and biogenic fluxes, offering a potential approach to assess city climate policies. However, atmospheric models used to simulate urban CO₂ plumes face significant uncertainties, particularly in complex urban environments with dense populations and vegetation. This study addresses these challenges by analyzing CO₂ dynamics in the Metropolitan Area of São Paulo (MASP) using the Weather Research and Forecasting model with Chemistry (WRF-Chem). Simulations were evaluated against ground-based observations from the METROCLIMA network, the first greenhouse gas monitoring network in South America, and column concentrations (XCO₂) from the OCO-2 satellite spanning February to August 2019. To improve biogenic fluxes, we optimized parameters in the Vegetation Photosynthesis and Respiration Model (VPRM) using eddy covariance flux measurements for key vegetation types, including the Atlantic Forest, Cerrado, and sugarcane. Results show that at the urban site (IAG), the model consistently underestimated CO₂ concentrations, with a negative mean bias of −9 ppm throughout the simulation period, likely due to the complexity of vehicular emissions and urban dynamics. In contrast, at the vegetated site (PDJ), simulations showed a consistent positive mean bias of 5 ppm and closely matched observations. Seasonal analyses revealed higher CO₂ concentrations in winter, driven by greater atmospheric stability and reduced vegetation uptake estimated by VPRM, while summer exhibited lower levels due to increased mixing and higher agricultural productivity. A comparison of biogenic and anthropogenic scenarios highlights the need for integrated emission modeling and improved representation of biogenic fluxes, anthropogenic emissions, and boundary conditions for high-resolution modeling in tropical regions.

Carbon capture, utilisation and storage technologies are increasingly recognised as critical components of global climate mitigation strategies. However, the effective monitoring and verification of greenhouse gas emission reductions from carbon capture, utilisation and storage facilities remain significant challenges. This review synthesises current monitoring methods, including in situ sensing, drone-based observations and satellite remote sensing, and critically evaluates their strengths, limitations and applicability to various carbon capture, utilisation and storage contexts. We analyse the regulatory frameworks that govern monitoring practices across jurisdictions, identify methodological gaps and assess the performance of existing technologies with respect to detection thresholds, the integration of multiple data sources and the requirements for long-term verification. Particular emphasis is placed on the role of data assimilation and inversion modelling in interpreting measurements and quantifying emissions. Based on this synthesis, we recommend a more harmonised, concentration-based approach to monitoring that combines diverse observation platforms to enhance the accuracy, transparency and cost-effectiveness of verification efforts. This review aims to support the development of best practices for environmental monitoring and assessment in the context of carbon capture, utilisation and storage deployment.

Atmospheric CO₂ concentrations over urban areas indirectly reflect local fossil fuel emissions and biogenic fluxes, offering a potential approach to assess city climate policies. However, atmospheric models used to simulate urban CO₂ plumes face significant uncertainties, particularly in complex urban environments with dense populations and vegetation. This study aims to address these challenges and fill the research gap regarding such vegetated and urbanized areas by conducting a comprehensive analysis of atmospheric CO₂ dynamics in the Metropolitan Area of São Paulo, Brazil, and its surroundings, using the WRF-GHG atmospheric model. The simulations are evaluated using observations from ground stations collected across the METROCLIMA GHG network, the first greenhouse gas monitoring network in South America, and column concentrations (XCO₂) from the OCO-2 satellite spanning February to August 2019. We also assess and improve the performances of the biospheric model Vegetation Photosynthesis and Respiration Model (VPRM) by optimizing the model parameters of the dominant vegetation types (Atlantic forest, cerrado, sugarcane) using flux measurements from multiple eddy-covariance flux towers. We evaluate the atmospheric model's ability to replicate seasonal variations in CO₂ concentrations by comparing the simulations with measurements from two sites part of the GHG network in São Paulo. We conclude here that atmospheric concentrations over metropolitan areas located in tropical areas largely depend on our ability to represent the biogenic contribution from the surrounding vegetation, the large-scale contribution in global models, and the model’s ability to represent the local atmospheric dynamics.

Atmospheric CO₂ concentrations over urban areas indirectly reflect local fossil fuel emissions and biogenic fluxes, offering a potential approach to assess city climate policies. However, atmospheric models used to simulate urban CO₂ plumes face significant uncertainties, particularly in complex urban environments with dense populations and vegetation. This study aims to address these challenges and fill the research gap regarding such vegetated and urbanized areas by conducting a comprehensive analysis of atmospheric CO₂ dynamics in the Metropolitan Area of São Paulo, Brazil, and its surroundings, using the WRF-GHG atmospheric model. The simulations are evaluated using observations from ground stations collected across the METROCLIMA GHG network, the first greenhouse gas monitoring network in South America, and column concentrations (XCO₂) from the OCO-2 satellite spanning February to August 2019. We also assess and improve the performances of the biospheric model Vegetation Photosynthesis and Respiration Model (VPRM) by optimizing the model parameters of the dominant vegetation types (Atlantic forest, cerrado, sugarcane) using flux measurements from multiple eddy-covariance flux towers. We evaluate the atmospheric model's ability to replicate seasonal variations in CO₂ concentrations by comparing the simulations with measurements from two sites part of the GHG network in São Paulo. We conclude here that atmospheric concentrations over metropolitan areas located in tropical areas largely depend on our ability to represent the biogenic contribution from the surrounding vegetation, the large-scale contribution in global models, and the model’s ability to represent the local atmospheric dynamics.

Carbon capture, utilisation and storage technologies are increasingly recognized as crucial components in the global strategy to mitigate climate change. As the deployment of carbon capture, utilisation and storage systems expands, there is a growing need for rigorous monitoring and verification methods to ensure that these technologies achieve their intended reduction in greenhouse gas emissions. This paper explores the current state of monitoring techniques for carbon capture, utilisation and storage facilities, highlighting the challenges and opportunities associated with accurately quantifying net greenhouse gas emissions. We analyse both traditional bookkeeping methods and direct measurement techniques. Our analysis reveals the limitations of current monitoring frameworks and underscores the importance of integrating advanced measurement technologies, such as in-situ sensors, drones and satellite observations, to enhance the accuracy and reliability of greenhouse gas reporting. By proposing a comprehensive monitoring strategy that combines these methods, we aim to provide a roadmap for more effective oversight of carbon capture, utilisation and storage facilities, thereby contributing to the broader goal of reducing global carbon emissions.