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Abstract Details

Activity Number: 109
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #306173
Title: Locating and Quantifying Gas Emission Rates Using Remotely Obtained Concentration Data
Author(s): Philip Jonathan*+ and Bill Hirst and David Randell and Fernando Gonzalez del Cueto and Oliver Kosut
Companies: Shell Projects & Technology and Shell Projects & Technology and Shell Projects & Technology and Shell Projects & Technology and MIT Laboratory for Information and Decision Systems
Address: P.O. Box 1, Chester, CH1 3SH, , United Kingdom
Keywords: remote sensing ; inversion ; mixture model ; MCMC ; compressed sensing ; methane
Abstract:

We describe a method for detecting, locating and quantifying sources of gas emissions to the atmosphere using remotely obtained gas concentration data; the method is applicable to gases of environmental concern. We demonstrate its performance using methane data collected from aircraft. Atmospheric point concentration measurements are modelled as the sum of a spatially and temporally smooth atmospheric background concentration, augmented by concentrations due to local sources. We model source emission rates with a Gaussian mixture model and use a Markov random field to represent the atmospheric background concentration component of the measurements. A Gaussian plume atmospheric eddy dispersion model represents gas dispersion between sources and measurement locations. Initial point estimates of background concentrations and source emission rates are obtained using mixed L1-L2 optimisation over a discretised grid of potential source locations. Subsequent reversible jump Markov chain Monte Carlo inference provides estimated values and uncertainties for the number, emission rates and locations of sources unconstrained by a grid. Source area, atmospheric background concentrations and other model parameters are also estimated. We investigate the performance of the approach first using a synthetic problem, then apply the method to real airborne data from a 1600 square km area containing two landfills, then a 225 square km area containing a gas flare stack.


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