A Geospatial Analytical Framework for Understanding Methane Emissions in California

Methane (CH4), an important greenhouse gas and pollutant, has been targeted for mitigation. Our recent California airborne survey identified >500 CH4 point sources, which accounted for 34-46% of the statewide CH4 emissions inventory for 2016. Individual plumes were observed in close proximity to expected CH4 emitting infrastructure. In order to systematically attribute these plumes to their sources, we developed Vista-CA a geospatial database, that contains more than 900,000 validated CH4 infrastructure elements in the state of California. In parallel, we developed a complimentary algorithm that attributes any individual CH4 plume observation to high confidence Vista-CA source with 99% accuracy. This research illustrates the capabilities of the Vista-CA CH4 database along with Airborne Visible/Infrared Imaging Spectrometer – Next Generation’s (AVIRIS-NG) airborne CH4 retrievals to locate and attribute CH4 point sources to specific economic sectors. Additionally, this research delivers two emissions products for Kern County: a top-down estimate called the AVIRIS-NG Source Data product and the Vista-CA Bottom-Up emissions dataset. We found general agreement in the source apportionment and magnitudes between these datasets. Moreover, due to current CH4 inventories having large uncertainties in emissions from the energy processing and production sectors where fugitive emissions predominate, we used airborne CH4 imaging survey data to show that CH4 emissions from power plants in California are underestimated by current CH4 inventory approaches. We developed process-based bottom-up emission estimates for over 300 power plants in California using Intergovernmental Panel on Climate Change (IPCC) methods. We used airborne CH4 imaging to attribute CH4 observations to over 250 California power plants and characterize the frequency and persistency of top-down CH4 emissions. We found that fugitive emissions constitute 90% of total observed emissions from power plants with the remainder derived from process-driven activity while bottom-up emissions are 28 – 54 times smaller than top-down observations. Comparing the inventory-based estimates with observations, the data show “super-emitter” behavior with 60% of total power plant emissions coming from a handful of facilities, likely due to fugitive CH4 emissions. Future inventories should take advantage of emission observations to quantify CH4 from these sources to improve the state CH4 budget and identify mitigation targets