Comparison of the Gaussian plume and puff atmospheric dispersion models for methane modeling on oil and gas sites

03 May 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Characterizing methane emissions on oil and gas sites often relies on a forward model to describe the atmospheric transport of methane. Here we compare two forward models: the Gaussian plume, a commonly used steady-state dispersion model, and the Gaussian puff, a time varying dispersion model that approximates a continuous release as a sum over many small “puffs”. We compare model predictions to observations from a network of point-in-space continuous monitoring systems (CMS) collected during a series of controlled releases. Specifically, we use the Pearson correlation coefficient and mean absolute error (MAE) as metrics to assess the fit of the model predictions to the observed concentrations in terms of pattern and amplitude, respectively. The Gaussian puff outperforms the Gaussian plume using both metrics with average correlation coefficients of 0.38 and 0.31 and average MAEs of 0.70 and 0.74, respectively. We provide computationally efficient and scalable implementations of the Gaussian puff model. Compared to regulatory-grade, Gaussian puff-based models like CALPUFF, our implementations have higher spatial and temporal resolution and require only essential and practically available meteorological information. These features enable near real-time methane mitigation applications on oil and gas sites and might be useful for near-field atmospheric transport modeling applications more broadly.


Gaussian plume
Gaussian puff
methane emission
oil and gas
continuous monitoring systems
code implementations

Supplementary materials

Supporting Information for: Comparison of the Gaussian plume and puff atmospheric dispersion models on oil and gas facilities - supporting information
This document contains additional details on the implementation of the Gaussian puff model in Python and R.


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