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Estimation and Applications of Uncertainty in Methane Emissions Quantification Technologies: A Bayesian Approach.
Wigle, Augustine; Béliveau, Audrey; Blackmore, Daniel; Lapeyre, Paule; Osadetz, Kirk; Lemieux, Christiane; Daun, Kyle J.
Afiliação
  • Wigle A; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
  • Béliveau A; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
  • Blackmore D; Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
  • Lapeyre P; Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
  • Osadetz K; Carbon Management Canada, Bow City, Alberta T0J 2M0, Canada.
  • Lemieux C; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
  • Daun KJ; Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
ACS EST Air ; 1(9): 1000-1014, 2024 Sep 13.
Article em En | MEDLINE | ID: mdl-39295738
ABSTRACT
An accurate understanding of uncertainty is needed to properly interpret methane emission estimates from upstream oil and gas sources in a variety of contexts, from component-level measurements to yearly jurisdiction-wide inventories. To characterize measurement uncertainty, we examine controlled release (CR) data from five different technology providers including quantitative gas imaging (QOGI), tunable diode laser-absorption spectroscopy (TDLAS); and airborne near-infrared hyperspectral (NIR HS) imaging. We introduce a novel empirical method to develop probability distributions of measurements given a true emission rate using the CR data. The approach includes flexible likelihoods which capture complex relationships in the data. An algorithm which provides the distribution of the true emission rate given a measurement is also developed, which synthesizes the measurement with the CR data and external information about the possible true emission rate. The results show that flexible models that accommodate complex nonlinear behavior are needed to adequately model measurement error. We also show that measurement error can vary under different conditions. We demonstrate that measurement uncertainty can be reduced by performing repeated measurements. A limitation of the study is that the collected CR data is collected under controlled conditions that may differ from those in industrial settings. As new CR data become available, the models presented in this paper can be refit to consider more diverse scenarios. The methodology can be extended to explicitly model different conditions to improve performance.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: ACS EST Air Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Canadá País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: ACS EST Air Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Canadá País de publicação: Estados Unidos