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Active CONTINUING GRANT National Science Foundation (US)

Collaborative Research: Geologic Methane Emissions to the Atmosphere--Improving the Bottom-Up Estimates of Microseepage

$6.03M USD

Funder National Science Foundation (US)
Recipient Organization University of Rochester
Country United States
Start Date Feb 15, 2021
End Date Jan 31, 2026
Duration 1,811 days
Number of Grantees 2
Roles Principal Investigator; Co-Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2039234
Grant Description

Methane (CH4) is a powerful greenhouse gas that contributes to warming of the atmosphere and climate change and has significant societal implications. Nevertheless, relative magnitudes of its numerous sources remain ill-constrained. In the present study, field observations are collected of CH4 that is emitted from natural geological features and represent one of the most uncertain sources of CH4 to the atmosphere.

Machine learning techniques are then used to increase predictive power of these emissions across the United States. Undergraduate and graduate students are an integral part of the field sampling and data analysis components of the work, and educational outreach activities are intended through established programs with the local school district and Rochester Museum & Science Center.

Microseepage is a low-intensity flux of natural geological CH4 to the atmosphere that is postulated to be the single largest, yet poorly constrained component of geological emissions. The research seeks to improve bottom-up estimates of microseepage from large areas of hydrocarbon-rich sedimentary basins, with the overarching hypothesis that microseepage emissions in existing inventories are overestimated.

CH4 fluxes are determined during summer and winter in eight different US basins to cover a wide area of varying characteristics in gas reservoir depth, structure/tectonics, and climate/ecosystem. A field deployable closed dynamic flux chamber technique is used for these measurements. The relationship between microseepage flux magnitude and a set of hypothesized flux-enhancing parameters is then investigated and used to develop a machine-learning approach for upscaling microseepage fluxes.

Resulting new estimates of microseepage CH4 emissions from the contiguous US will improve our understanding of natural emissions of this important greenhouse gas that is also a key player in atmospheric chemistry and global climate.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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University of Rochester

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