2013
DOI: 10.1016/j.atmosenv.2013.03.044
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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 wit… Show more

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Cited by 46 publications
(47 citation statements)
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“…Hirst et al [2012] describe a problem in which methane concentration data obtained from an aircraft-mounted sensor is available for a known set of locations and times; the authors wish to use this data to infer the properties (spatial location, emission rate and half-width) of a set of gas sources at ground level, about which they have prior beliefs. This is a typical Bayesian inverse problem; in order to infer these source properties, a model must be chosen to describe the transport of methane from sources (with particular properties) to sensors at particular locations and times, as well as the natural background concentration of methane at the same points.…”
Section: Motivating Examplementioning
confidence: 99%
“…Hirst et al [2012] describe a problem in which methane concentration data obtained from an aircraft-mounted sensor is available for a known set of locations and times; the authors wish to use this data to infer the properties (spatial location, emission rate and half-width) of a set of gas sources at ground level, about which they have prior beliefs. This is a typical Bayesian inverse problem; in order to infer these source properties, a model must be chosen to describe the transport of methane from sources (with particular properties) to sensors at particular locations and times, as well as the natural background concentration of methane at the same points.…”
Section: Motivating Examplementioning
confidence: 99%
“…The non-negativity enforcement mechanism uses StOMP but does not use the MsRF model. The imposition of non-negativity in the sparse reconstruction of an emission field has never been explored before; for example, in Hirst et al (2013), the non-negativity constraint was not applied to CH 4 emissions from landfills.…”
Section: Introductionmentioning
confidence: 99%
“…Most of these inverse problems have been in the estimation of log-transformed permeability fields (Li and Jafarpour, 2010;Jafarpour, 2013), seismic tomography (Loris et al, 2007;Simons et al, 2011;Gholami and Siahkoohi, 2010) and estimation of point and distributed emissions (Hirst et al, 2013;Martinez-Camara et al, 2013). A more detailed review of the sparse reconstruction methods can be found in our previous paper .…”
Section: Introductionmentioning
confidence: 99%
“…Yang and Huang [12] utilized a sensory system based on an unmanned helicopter to monitor the 2 SO , NO , and CO in a chemical industry park. The UAV has been also used in the source term estimation and boundary tracking of the atmospheric dispersion [13,14].…”
Section: Introductionmentioning
confidence: 99%