Title: Mobile sensing of point-source gas emissions using Bayesian inference: An empirical examination of the likelihood function

Abstract

This paper evaluates likelihood function forms for Bayesian inference of point-source gas emissions using a mobile sensor. Whereas Bayesian inference has been successfully used to estimate emission rates from time-averaged concentration data measured by stationary sensors, data collected by mobile sensors do not represent ensemble or time-averaged conditions. To examine the potential impact of this contrast, controlled release experiments were conducted with a mobile sensor measuring concentrations repeatedly along transverse cross sections of the downwind plumes. Experiments were conducted with measurements made at different downwind distances, different sensor heights, and with different obstacle states. An examination is made between two commonly-used likelihood functions, the Gaussian and the log-normal. For experiments conducted in the absence of obstacles, the Bayesian estimates using the log-normal likelihood function yield a much smaller bias than those based on the Gaussian likelihood function. This finding is consistent with the non-Gaussian nature of concentration fluctuations near a point-source. For experiments conducted in the presence of obstacles, the Bayesian inference based on the Gaussian likelihood function exhibits a better performance. This can be explained by the enhanced turbulent mixing due to the obstacle-introduced wake eddies. Overall, we find that the selection of the likelihood function can be physicallymore » related to the underlying conditions, and the proper selection is critical to ensure the performance of the Bayesian inference for source characterization using mobile sensing data.« less

Authors:

Zhou, Xiaochi  ^[1];

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Montazeri, Amir ^[1]; Albertson, John D. ^[1]

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1. Cornell Univ., Ithaca, NY (United States)

Publication Date:

Thu Sep 19 00:00:00 EDT 2019

Research Org.:

Cornell Univ., Ithaca, NY (United States)

Sponsoring Org.:

USDOE Advanced Research Projects Agency - Energy (ARPA-E)

OSTI Identifier:

1799067

Alternate Identifier(s):

OSTI ID: 1569489

Grant/Contract Number:  

AR0000749

Resource Type:

Accepted Manuscript

Journal Name:

Atmospheric Environment (1994)

Additional Journal Information:

Journal Name: Atmospheric Environment (1994); Journal Volume: 218; Journal ID: ISSN 1352-2310

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES; Bayesian inference; environmental sensing; mobile monitoring; methane