Literature DB >> 22191809

Atmospheric tomography: a Bayesian inversion technique for determining the rate and location of fugitive emissions.

Ruhi Humphries1, Charles Jenkins, Ray Leuning, Steve Zegelin, David Griffith, Christopher Caldow, Henry Berko, Andrew Feitz.   

Abstract

A Bayesian inversion technique to determine the location and strength of trace gas emissions from a point source in open air is presented. It was tested using atmospheric measurements of N(2)O and CO(2) released at known rates from a source located within an array of eight evenly spaced sampling points on a 20-m radius circle. The analysis requires knowledge of concentration enhancement downwind of the source and the normalized, three-dimensional distribution (shape) of concentration in the dispersion plume. The influence of varying background concentrations of ∼1% for N(2)O and ∼10% for CO(2) was removed by subtracting upwind concentrations from those downwind of the source to yield only concentration enhancements. Continuous measurements of turbulent wind and temperature statistics were used to model the dispersion plume. The analysis localized the source to within 0.8 m of the true position and the emission rates were determined to better than 3% accuracy. This technique will be useful in assurance monitoring for geological storage of CO(2) and for applications requiring knowledge of the location and rate of fugitive emissions.

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Year:  2012        PMID: 22191809     DOI: 10.1021/es202807s

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  2 in total

1.  The detection of carbon dioxide leaks using quasi-tomographic laser absorption spectroscopy measurements in variable wind.

Authors:  Zachary H Levine; Adam L Pintar; Jeremy T Dobler; Nathan Blume; Michael Braun; T Scott Zaccheo; Timothy G Pernini
Journal:  Atmos Meas Tech       Date:  2016-04-13       Impact factor: 4.176

2.  Locating and Quantifying Methane Emissions by Inverse Analysis of Path-Integrated Concentration Data Using a Markov-Chain Monte Carlo Approach.

Authors:  Damien Weidmann; Bill Hirst; Matthew Jones; Rutger Ijzermans; David Randell; Neil Macleod; Arun Kannath; Johnny Chu; Marcella Dean
Journal:  ACS Earth Space Chem       Date:  2022-07-08       Impact factor: 3.556

  2 in total

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