Relating Multi-Scale Plume Detection and Area Estimates of Methane Emissions: A Theoretical and Empirical Analysis

This is a Preprint and has not been peer reviewed. This is version 4 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Authors

Sudhanshu Pandey , John Worden, Daniel H Cusworth, Daniel J. Varon , Matthew D Thill, Daniel J. Jacob, Kevin W. Bowman

Abstract

Methodologies for inferring surface emissions of atmospheric trace gases can be categorized into plume detection and area-scale estimation. Plume detections are observations of emissions from either individual or clustered point sources. Area estimates are derived from top-down atmospheric flux inversion models or bottom-up inventories, which infer mean emissions typically over spatial scales greater than 10 km and temporal scales greater than a week. Integrating information from these distinct methodologies can enhance our understanding of emission sources and improve the accuracy of emission estimates. However, such integration is challenging because plume-detecting instruments exhibit irregular and infrequent sampling, as well as varying detection sensitivities and spatial footprint sizes. In this study, we present a theoretical framework to relate plume and area estimates of dense point source methane emission fields. We show that the spatial footprint size of plume-detecting instruments impacts the emission rate distribution of plumes. In empirical tests, we find a robust linear relationship between the sums of gridded plume emission rates and area estimates for the Permian Basin’s oil and gas emissions. After accounting for the plume detectors' sampling of the Permian emission field, the weekly plume sums demonstrate a strong correlation with TROPOMI top-down area estimates (R2 > 0.94, P < 0.005). We assess the feasibility of using plume data to inform area estimates within a Bayesian assimilation framework. We perform two plume inversions using prior area estimates from (1) constant EDF bottom-up inventory and (2) weekly-updated TROPOMI inversion estimates. We find that the posterior estimate of the EDF plume inversion improves, bringing it in good agreement with independent TROPOMI estimates. In the TROPOMI plume inversion, the fine spatial resolution features of area estimates improve. Our analysis underscores that plume datasets obtained from aircraft, satellites, and in situ instruments can evaluate and improve area estimates of dense point source emission fields.

DOI

https://doi.org/10.31223/X52M54

Subjects

Biogeochemistry, Earth Sciences, Environmental Sciences, Oil, Gas, and Energy, Physical Sciences and Mathematics

Keywords

Methane emissions, plume detection, remote sensing, Super-emitters, greenhouse gases, Atmospheric Monitoring, Oil and gas emissions, Point sources, Methane flux inversion, Area emission estimates

Dates

Published: 2024-07-23 07:38

Last Updated: 2024-11-04 22:17

Older Versions
License

No Creative Commons license

Additional Metadata

Conflict of interest statement:
The authors declare that they have no conflict of interest.

Data Availability (Reason not available):
All the data used in this manuscript are from already published papers