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Atmospheric Plume Dispersion Modeling for Methane Quantification Under Variable Conditions
Subject area: Science,Engineering and Technology · Area of research: Methane Quantification
Abstract
Atmospheric plume dispersion modeling is critical for accurately quantifying methane emissions, a potent greenhouse gas contributing significantly to global warming. This paper presents a comprehensive synthesis of theoretical principles and modeling approaches for methane plume dispersion under variable environmental conditions, emphasizing the influence of meteorological variables, terrain complexity, and temporal atmospheric dynamics. The physical processes governing plume behavior, advection, and turbulent diffusion, are examined alongside methane-specific characteristics such as buoyancy and chemical stability. Analytical Gaussian models and advanced computational fluid dynamics techniques are critically reviewed, highlighting their respective strengths and limitations in capturing dispersion in diverse settings. The study underscores how fluctuating wind patterns, atmospheric stability, temperature gradients, and surface heterogeneity shape plume transport and dilution, affecting emission quantification accuracy. It also discusses the importance of selecting appropriate models tailored to specific environmental contexts and recognizes inherent assumptions that may constrain model applicability. The paper concludes by outlining theoretical implications for improving methane quantification methodologies and proposes future directions focused on integrating high-resolution data, hybrid modeling approaches, and real-time adaptive frameworks. These insights aim to support enhanced methane monitoring strategies vital for regulatory compliance and global climate mitigation efforts.
Keywords
Methane quantification, atmospheric plume dispersion, Gaussian models, computational fluid dynamics, meteorological variability, terrain effects
How to cite this paper
@article{1709943,
author = {Semiu Temidayo Fasasi, Oluwapelumi Joseph Adebowale, Abdulmaliq Abdulsalam, Zamathula Queen Sikhakhane Nwokediegwu},
title = {Atmospheric Plume Dispersion Modeling for Methane Quantification Under Variable Conditions},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {3},
number = {8},
pages = {353-362},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709943.pdf},
abstract = {Atmospheric plume dispersion modeling is critical for accurately quantifying methane emissions, a potent greenhouse gas contributing significantly to global warming. This paper presents a comprehensive synthesis of theoretical principles and modeling approaches for methane plume dispersion under variable environmental conditions, emphasizing the influence of meteorological variables, terrain complexity, and temporal atmospheric dynamics. The physical processes governing plume behavior, advection, and turbulent diffusion, are examined alongside methane-specific characteristics such as buoyancy and chemical stability. Analytical Gaussian models and advanced computational fluid dynamics techniques are critically reviewed, highlighting their respective strengths and limitations in capturing dispersion in diverse settings. The study underscores how fluctuating wind patterns, atmospheric stability, temperature gradients, and surface heterogeneity shape plume transport and dilution, affecting emission quantification accuracy. It also discusses the importance of selecting appropriate models tailored to specific environmental contexts and recognizes inherent assumptions that may constrain model applicability. The paper concludes by outlining theoretical implications for improving methane quantification methodologies and proposes future directions focused on integrating high-resolution data, hybrid modeling approaches, and real-time adaptive frameworks. These insights aim to support enhanced methane monitoring strategies vital for regulatory compliance and global climate mitigation efforts.},
keywords = {Methane quantification, atmospheric plume dispersion, Gaussian models, computational fluid dynamics, meteorological variability, terrain effects},
month = {February},
}