Anthropogenic emissions of carbon dioxide (CO2) and methane (CH4) have resulted in positive radiative forcing and increased global temperature. For both atmospheric trace gases, point source emissions…

This federal contract award is a $159,968.00 grant for research to the University of Utah Office of Sponsored Projects Division. The award, issued by the National Aeronautics and Space Administration (NASA) Shared Services Center, is focused on developing new algorithms for detecting and quantifying carbon dioxide (CO2) and methane (CH4) emissions from point sources using airborne spectroscopic imaging data. The research aims to improve upon current satellite-based methods by incorporating spatial correlations within gas plumes to enhance detection sensitivity. No set-aside designation was specified. The University of Utah is a leading research institution that has received numerous federal research grants and contracts across disciplines like genomics, transportation, and environmental studies.

Anthropogenic emissions of carbon dioxide (CO2) and methane (CH4) have resulted in positive radiative forcing and increased global temperature. For both atmospheric trace gases, point source emissions represent an important contribution to total emissions. A small number of point sources contribute a large percentage of point source emissions, with EPA data pointing to less than 1.5% of facilities being responsible for over a third of total reported point source emissions in the US. Unintended CH4 emissions, known as fugitive emissions, have also been shown to be regionally significant sources. Detecting and quantifying point source emissions is critically important for reducing anthropogenic greenhouse gas emissions, but current and planned satellite missions for retrieving column CO2 and CH4 concentrations are not capable of resolving point source emitters. Recent work has demonstrated that airborne visible infrared imaging spectrometer-next generation (aviris-NG) data are capable of detecting trace gas plumes and even estimating emission rates. Matched filter algorithms developed for trace gas plume detection have shown some success, but they ignore spatial structure in background reflectance and in the plume itself. Taking this spatial structure into account will reduce false positive plume detections and improve sensitivity to trace gas absorption features in the shortwave infrared. The primary objective of the proposed research is to develop new iterative algorithms for CO2 and CH4 plume detection in avirisng data using data collected during the india campaign. These algorithms will explicitly take advantage of spatial correlation in trace gas concentrations within plumes to improve detection, applying methods developed within the field of biomedical image reconstruction. The algorithms will rely on maximum a posteriori (map) parametric estimation and markov random field modeling to estimate trace gas mixing ratio length, the product of concentration and path length. Template spectra required for detection will be generated by radiative transfer modeling.