Diffusion Modeling of Carbon Dioxide Concentration from Stationary Sources with Improved Gaussian Plume Modeling

To achieve the precise quantification and real-time monitoring of CO2 emissions from stationary sources, this study developed a Gaussian plume model-based dispersion framework incorporating emission characteristics. Critical factors affecting CO2 dispersion were systematically analyzed, with model optimization conducted through plume rise height adjustments and reflection coefficient calibrations. MATLAB-based simulations on an industrial park case study demonstrated that wind speed, atmospheric stability, and effective release height constituted pivotal determinants for enhancing CO2 dispersion modeling accuracy. Furthermore, the inverse estimation of source strength at emission terminals was implemented via particle swarm optimization, establishing both theoretical foundations and methodological frameworks for the precision monitoring and predictive dispersion analysis of stationary-source CO2 emissions.

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Publication Details

Journal
Energies
Published
2025-05-29
DOI
https://doi.org/10.3390/en18112827
Citations
1
Primary Topic
Wind and Air Flow Studies
Type
article
Field-Weighted Citation Impact
0.80

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Diffusion Modeling of Carbon Dioxide Concentration from Stationary Sources with Improved Gaussian Plume Modeling

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Diffusion Modeling of Carbon Dioxide Concentration from Stationary Sources with Improved Gaussian Plume Modeling

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article en
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Abstract

To achieve the precise quantification and real-time monitoring of CO2 emissions from stationary sources, this study developed a Gaussian plume model-based dispersion framework incorporating emission characteristics. Critical factors affecting CO2 dispersion were systematically analyzed, with model optimization conducted through plume rise height adjustments and reflection coefficient calibrations. MATLAB-based simulations on an industrial park case study demonstrated that wind speed, atmospheric stability, and effective release height constituted pivotal determinants for enhancing CO2 dispersion modeling accuracy. Furthermore, the inverse estimation of source strength at emission terminals was implemented via particle swarm optimization, establishing both theoretical foundations and methodological frameworks for the precision monitoring and predictive dispersion analysis of stationary-source CO2 emissions.

EnergiesVol. 18(11)
Southwest Petroleum University (CN), Science and Technology Department of Sichuan Province (CN)
Science and Technology Project of State Grid
Openalex Percentile: Top 29%
Wind and Air Flow Studies
0.80
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