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Locating and quantifying gas emission sources using remotely obtained concentration data

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Locating and quantifying gas emission sources using remotely obtained concentration data. / Hirst, B.; Jonathan, P.; González del Cueto, F.; Randell, D.; Kosut, O.

In: Atmospheric Environment, Vol. 74, 2013, p. 141-158.

Research output: Contribution to journalJournal article

Harvard

Hirst, B, Jonathan, P, González del Cueto, F, Randell, D & Kosut, O 2013, 'Locating and quantifying gas emission sources using remotely obtained concentration data', Atmospheric Environment, vol. 74, pp. 141-158. https://doi.org/10.1016/j.atmosenv.2013.03.044

APA

Hirst, B., Jonathan, P., González del Cueto, F., Randell, D., & Kosut, O. (2013). Locating and quantifying gas emission sources using remotely obtained concentration data. Atmospheric Environment, 74, 141-158. https://doi.org/10.1016/j.atmosenv.2013.03.044

Vancouver

Author

Hirst, B. ; Jonathan, P. ; González del Cueto, F. ; Randell, D. ; Kosut, O. / Locating and quantifying gas emission sources using remotely obtained concentration data. In: Atmospheric Environment. 2013 ; Vol. 74. pp. 141-158.

Bibtex

@article{b99aa9f65bad4663a0dca7b95c2e3ac4,
title = "Locating and quantifying gas emission sources using remotely obtained concentration data",
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. {\textcopyright} 2013 Elsevier Ltd.",
keywords = "Atmospheric background gas, Bayesian inversion, Gaseous emissions, Gaussian mixture model, Random field modelling, Remote sensing, Reversible jump MCMC, Background gas, Gaussian Mixture Model, Random fields, Atmospheric movements, Image segmentation, Markov processes, Methane, Uncertainty analysis, Gas emissions, methane, aircraft emission, atmospheric gas, atmospheric plume, Gaussian method, Lagrangian analysis, landfill, Markov chain, Monte Carlo analysis, numerical model, point source pollution, remote sensing, timescale, aircraft, article, atmosphere, Bayes theorem, gas, Monte Carlo method, plume, priority journal, temperature, velocity, wind",
author = "B. Hirst and P. Jonathan and {Gonz{\'a}lez del Cueto}, F. and D. Randell and O. Kosut",
year = "2013",
doi = "10.1016/j.atmosenv.2013.03.044",
language = "English",
volume = "74",
pages = "141--158",
journal = "Atmospheric Environment",
issn = "0004-6981",
publisher = "Pergamon Press Ltd.",

}

RIS

TY - JOUR

T1 - Locating and quantifying gas emission sources using remotely obtained concentration data

AU - Hirst, B.

AU - Jonathan, P.

AU - González del Cueto, F.

AU - Randell, D.

AU - Kosut, O.

PY - 2013

Y1 - 2013

N2 - 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. © 2013 Elsevier Ltd.

AB - 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. © 2013 Elsevier Ltd.

KW - Atmospheric background gas

KW - Bayesian inversion

KW - Gaseous emissions

KW - Gaussian mixture model

KW - Random field modelling

KW - Remote sensing

KW - Reversible jump MCMC

KW - Background gas

KW - Gaussian Mixture Model

KW - Random fields

KW - Atmospheric movements

KW - Image segmentation

KW - Markov processes

KW - Methane

KW - Uncertainty analysis

KW - Gas emissions

KW - methane

KW - aircraft emission

KW - atmospheric gas

KW - atmospheric plume

KW - Gaussian method

KW - Lagrangian analysis

KW - landfill

KW - Markov chain

KW - Monte Carlo analysis

KW - numerical model

KW - point source pollution

KW - remote sensing

KW - timescale

KW - aircraft

KW - article

KW - atmosphere

KW - Bayes theorem

KW - gas

KW - Monte Carlo method

KW - plume

KW - priority journal

KW - temperature

KW - velocity

KW - wind

U2 - 10.1016/j.atmosenv.2013.03.044

DO - 10.1016/j.atmosenv.2013.03.044

M3 - Journal article

VL - 74

SP - 141

EP - 158

JO - Atmospheric Environment

JF - Atmospheric Environment

SN - 0004-6981

ER -