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Performance evaluation of multi-source methane emission quantification models using fixed-point continuous monitoring systems

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Publicado en:Atmospheric Measurement Techniques vol. 18, no. 20 (2025), p. 5375-5392
Autor principal: Ball, David
Otros Autores: Ismail, Umair, Eichenlaub, Nathan, Metzger, Noah, Lashgari, Ali
Publicado:
Copernicus GmbH
Materias:
Environmental Protection Agency > EPA
Advection
Wind fields
Hydrodynamics
Performance evaluation
Fluid dynamics
Methane
Greenhouse gases
Risk assessment
Localization
Methane emissions
Monitoring systems
Momentum equation
Accuracy
Algorithms
Wind data
Carbon footprint
Pollutants
Markov chains
Advection-diffusion equation
Controlled release
Data processing
Monitoring
Wind measurement
Emissions
Emission inventories
Sensors
Plume models
Data collection
Computational fluid dynamics
Least squares
Environmental
Acceso en línea:Citation/Abstract
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Resumen:Quantifying methane emissions from oil and gas facilities is crucial for emissions management and accurate facility-level greenhouse gas (GHG) inventory development. This paper evaluates the performance of several multi-source methane emission quantification models using the data collected by fixed-point continuous monitoring systems as part of a controlled-release experiment. Two dispersion modeling approaches (Gaussian plume, Gaussian puff) and two inversion frameworks (least-squares optimization and Markov chain Monte Carlo) are applied to the measurement data. In addition, a subset of experiments are selected to showcase the application of computational fluid dynamics (CFD) informed calculations for direct solution of the advection–diffusion equation. This solution utilizes a three-dimensional wind field informed by solving the momentum equation with the appropriate external forcing to match on-site wind measurements. Results show that the Puff model, driven by high-frequency wind data, significantly improves localization and reduces bias and error variance compared to the Plume model. The Markov chain Monte Carlo (MCMC)-based inversion framework further enhances accuracy over least-squares fitting, with the Puff MCMC approach showing the best performance. The study highlights the importance of long-term integration for accurate total mass emission estimates and the detection of anomalous emission patterns. The findings of this study can help improve emissions management strategies, aid in facility-level emissions risk assessment, and enhance the accuracy of greenhouse gas inventories.
ISSN:1867-1381
1867-8548
DOI:10.5194/amt-18-5375-2025
Fuente:Advanced Technologies & Aerospace Database