Characterizing methane emissions on oil and gas facilities 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 emissions monitoring systems (CEMS) 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, with the former assessing the fit of pattern and the latter the fit of amplitude. 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 also investigate how the frequency at which puffs are generated affects the accuracy and computational cost of the Gaussian puff and propose guidelines for choosing an appropriate value. Finally, we provide open-source implementations of the Gaussian puff model in Python and R that are tailored for use on oil and gas facilities.
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.