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Gaussian puff atmospheric dispersion model: an analysis at low wind speeds
Basanese, Michael Sorensen, Troy Daniels, William Hammerling, Dorit
Basanese, Michael
Sorensen, Troy
Daniels, William
Hammerling, Dorit
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2025-04
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Abstract
Monitoring methane emissions from the oil and gas sector is essential for emissions reduction. To do so, we use an inversion algorithm that converts methane concentration observations from in-situ sensors placed around the perimeter of oil and gas sites into estimates of emission source and rate. One step in this inversion involves simulating methane transport from potential emission sources to sensor locations using the Gaussian Puff atmospheric dispersion model. Wind speed and direction are critical inputs to this model, as they primarily control methane movement through the atmosphere. The Gaussian Puff model is known to perform poorly at low wind speeds, but the threshold at which its performance begins to decline remains unknown. This study analyzes limitations of the model at low wind speeds, identifying the exact wind regimes where it begins to underperform and investigating the underlying phenomena leading to this underperformance. For this analysis, we use data from an experiment during which methane releases were conducted from structures at an emissions testing facility at known emission rates. We obtain simulated concentrations from the Gaussian Puff model at each sensor location on the site and compare them to observed concentrations from the sensors. By analyzing discrepancies at different wind speeds, we find that the model begins underperforming at about 3 m/s. This underperformance often occurs because the model fails to capture spikes in observed concentration data. Incorporating these findings allows for omission of times when the model performs unreliably, improving methane emissions understanding and enabling more effective reduction strategies.
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