Skip to main content Skip to secondary navigation

Natural Gas Initiative is a cross-campus effort of the Precourt Institute for Energy.

Main content start

Seongeun Jeong: An Artificial Intelligence Approach for Estimating Dairy Methane Emissions

Event Details:

Thursday, June 16, 2022
12:30pm - 1:30pm PDT

Location

Online

This event is open to:

Alumni/Friends
Faculty/Staff
General Public
Members
Students

View recording: https://www.youtube.com/watch?v=cHmzME2vxaY

California's dairy sector accounts for ~50% of anthropogenic CH4 emissions in the state's greenhouse gas (GHG) emission inventory. Although California dairy facilities' location and herd size vary over time, atmospheric inverse modeling studies rely on decade-old facility-scale geospatial information. For the first time, we apply artificial intelligence (AI) to aerial imagery to estimate dairy CH4 emissions from California's San Joaquin Valley (SJV), a region with ~90% of the state's dairy population. Using an AI method, we process 316,882 images to estimate the facility-scale herd size across SJV. The AI approach predicts herd size that strongly (> 95%) correlates with that made by human visual inspection, providing a low-cost alternative to the labor-intensive inventory development process. We estimate SJV's dairy enteric and manure CH4 emissions for 2018 to be 496 - 763 Gg/yr (mean = 624; 95% confidence) using the predicted herd size. We also apply our AI approach to estimate CH4 emission reduction from anaerobic digester deployment. We identify 162 large (90th percentile) farms and estimate a CH4 reduction potential of 83 Gg CH4/yr for these large facilities from anaerobic digester adoption. The results indicate that our AI approach can be applied to characterize the manure system (e.g., use of anaerobic lagoon) and estimate GHG emissions for other sectors. 

Bio

Dr. Seongeun Jeong is a Research Scientist at LBNL. Dr. Jeong conducts research to quantify emissions for major greenhouse gases (GHGs) using atmospheric measurements, transport models, and statistical methods. With a background in developing a land surface model, he focuses on improving atmospheric transport simulations to reduce uncertainty in model predictions. Dr. Jeong is also interested in applying robust statistical methods to GHG-related research, including hierarchical Bayesian methods. While working for more than a decade at LBNL, he focused on quantifying CO2, CH4, and N2O emissions to help guide policymakers. Dr. Jeong is currently the principal investigator for a NASA Carbon Cycle Science project to estimate California's 2020 CO2 emission budget. He also leads the atmospheric transport simulation and inverse modeling team in a California Energy Commission project to quantify CH4 emissions from California's San Joaquin Valley. He has recently developed a spatial inventory for California's dairy CH4 emissions using artificial intelligence (AI) and remotely sensed imagery and continues to apply AI technology to earth sciences. In addition, for the past several years, Dr. Jeong has been working on simulating wind and solar energy generation, utilizing his expertise in meteorological modeling. He earned his M.S. and Ph.D. degrees in Environmental Engineering from the University of California, Berkeley.

Related Topics

Explore More Events