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