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Detecting emissions:

I am presently focusing on gas detection methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O) emissions from point sources in the Los Angeles Basin using hyperspectral data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). This research aims to improve methods for detecting absorption features of atmospheric trace gasses, thereby permitting high resolution mapping of local emissions. A cluster-tuned matched filter approach originally developed by Funk et al. (2001) was adapted to permit gas detection in AVIRIS imagery and applied to a diversity of locations known to emit CH4, CO2, and N2O.

Initial results indicate this technique appears effective for mapping CH4, CO2, and N2O anomalies in multiple AVIRIS scenes for concentrated marine and terrestrial emission sources. At the Coal Oil Point (COP) marine seeps offshore of Santa Barbara, CA, strong CH4 anomalies were detected and CO2 anomalies were observed for the Moss Landing Power Plant. Multiple N2O and CH4 anomalies were also present at the Hyperion wastewater treatment facility in Los Angeles and smaller CH4 anomalies were detected immediately downwind of hydrocarbon storage tanks and centered on a flaring stack at the Inglewood Gas Plant. These results are presented in Proceedings of SPIE, “Point source emissions mapping using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)” (Thorpe et al., 2012) and Remote Sensing of Environment, "High resolution mapping of methane emissions from marine and terrestrial sources using a Cluster-Tuned Matched Filter technique and imaging spectrometry" (Thorpe et al., 2013).

Estimating concentrations:

While matched filters are well suited for detecting anomalies, they do not provide concentrations necessary to calculate fluxes or generate maps of gas concentrations. To estimate concentrations, I have been working with Dr. Christian Frankenberg at the Jet Propulsion Laboratory to develop greenhouse gas retrieval algorithms for use with the AVIRIS and AVIRISng airborne imaging spectrometers.

Scientific importance:

This research could help to improve national greenhouse gas budgets and partitioning between anthropogenic and natural sources (Bovensmann et al., 2010). Further, greater understanding of the spatial and temporal variability of anthropogenic emissions will aid modeling efforts (Peylin et al., 2011) and could help to address discrepancies between top-down and bottom-up estimates of greenhouse gas emissions (Montzka et al., 2011). High resolution mapping might also permit emission monitoring from sources of increasing concern, including fugitive CH4 emissions from leaking natural gas pipelines (Murdock et al., 2008) and hydraulic fracturing (Howarth et al., 2011). This could improve efforts to measure emissions at local and regional scales while complimenting ongoing global monitoring efforts at coarser spatial resolutions.