|Remote Sensing of Gas Emission Sources|
Detecting and quantifying sources of gaseous emissions from observed concentration levels is a challenging problem that is well-suited to a remote sensing approach. For example, consider estimating the volume of some pollutant that a chemical refinery releases into the atmosphere from measurements of pollutant concentrations. Many existing methods assume a known number of emitters, low ambient concentrations, or measurements from a group of stationary sensors. In contrast, we use measurements from an airborne sensor to detect source contributions that are several orders of magnitude smaller than ambient concentrations. In this project we extend and analyze an existing method for detecting the contributing sources, along with properties such as location, emission rate, diffusivity. This project involves aspects of Bayesian non-parametric modeling, sampling methods, data fusion, experimental design, and flow modeling.
People Involved: Christopher L. Dean, Guy Rosman, John W. Fisher III