Locating and quantifying gas emission sources using remotely obtained concentration data

Bill Hirst, Philip Jonathan, Fernando González del Cueto, David Randell, Oliver Kosut

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

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 ℓ2-ℓ1 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, including plume model spreading and Lagrangian turbulence time scale, 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 1600km2 area containing two landfills, then a 225km2 area containing a gas flare stack.

Original languageEnglish (US)
Pages (from-to)141-158
Number of pages18
JournalAtmospheric Environment
Volume74
DOIs
StatePublished - Aug 1 2013
Externally publishedYes

Keywords

  • Atmospheric background gas
  • Bayesian inversion
  • Gaseous emissions
  • Gaussian mixture model
  • Random field modelling
  • Remote sensing
  • Reversible jump MCMC

ASJC Scopus subject areas

  • Environmental Science(all)
  • Atmospheric Science

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