Multi-Source Emission Determination Using an Inverse-Dispersion Technique

Flesch, Thomas K.; Harper, Lowry A.; Desjardins, Raymond L.; Gao, Zhiling; Crenna, Brian P.

doi:10.1007/s10546-009-9387-1

Cited by 33 publications

(53 citation statements)

References 15 publications

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“…Just as with the calculations using data pairs, there was no evidence that ill-conditioning was a problem in this study. Even with very large κ values approaching 200 in this dataset, there were no large errors in emission rate calculations as documented by Flesch et al [17].…”

Section: Two Emission Sourcessupporting

confidence: 66%

“…In our calculations with the PIC pairs, κ ranged from 1.5 to 150. While Flesch et al [17] concluded that accurate emission inferences required κ below approximately 20, in this study two emission rates were calculated to within 35% of their correct value even with κ greater than 100.…”

Section: Two Emission Sourcesmentioning

confidence: 59%

“…The work of Crenna et al [16] and Flesch et al [17] showed the tendency of "multi-source" problems like the ones of this study to be ill-conditioned and prone to large errors depending on the geometry of the source and sensor layout. The degree of ill-conditioning can be quantified by the condition number (κ).…”

Section: Two Emission Sourcesmentioning

confidence: 63%

“…A particularly important finding of this study was insensitivity to the problem of ill-conditioning in dual source calculations. Earlier work by Flesch et al [17] suggested ill-conditioning was a serious concern in multi-source applications. However, our results suggest greater robustness in bLS for these applications (at least for two-source problems).…”

Section: Discussionmentioning

confidence: 99%

“…WindTrax allows more concentration information to be used in an inference. However, the calculation becomes a statistical best-fit problem, i.e., the equation set is over-determined [17]. A three-PIC dataset was created to calculate the two emission rates using the same earlier data pool for 3/10/2009; additionally, M9 and M10 PIC data were added for 4/8/2009.…”

Section: Two Emission Sourcesmentioning

confidence: 99%

See 4 more Smart Citations

Measuring Trace Gas Emission from Multi-Distributed Sources Using Vertical Radial Plume Mapping (VRPM) and Backward Lagrangian Stochastic (bLS) Techniques

Ro¹,

Johnson²,

Hunt³

et al. 2011

Atmosphere

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Two micrometeorological techniques for measuring trace gas emission rates from distributed area sources were evaluated using a variety of synthetic area sources. The vertical radial plume mapping (VRPM) and the backward Lagrangian stochastic (bLS) techniques with an open-path optical spectroscopic sensor were evaluated for relative accuracy for multiple emission-source and sensor configurations. The relative accuracy was calculated by dividing the measured emission rate by the actual emission rate; thus, a relative accuracy of 1.0 represents a perfect measure. For a single area emission source, the VRPM technique yielded a somewhat high relative accuracy of 1.38 ± 0.28. The bLS technique resulted in a relative accuracy close to unity, 0.98 ± 0.24. Relative accuracies for dual source emissions for the VRPM and bLS techniques were somewhat similar to single source emissions, 1.23 ± 0.17 and 0.94 ± 0.24, respectively. When the bLS technique was used with vertical point concentrations, the relative accuracy was unacceptably low, <0.62. However, when using multiple point sensors in a horizontal line, the bLS technique yielded a relative accuracy of 1.08 ± 0.44. Altogether, these results suggest that the bLS technique has significant potential for measuring trace gas emissions from distributed agricultural systems.

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Section: Two Emission Sourcessupporting

confidence: 66%

Section: Two Emission Sourcesmentioning

confidence: 59%

Section: Two Emission Sourcesmentioning

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Section: Two Emission Sourcesmentioning

confidence: 99%

See 3 more Smart Citations

Measuring Trace Gas Emission from Multi-Distributed Sources Using Vertical Radial Plume Mapping (VRPM) and Backward Lagrangian Stochastic (bLS) Techniques

Ro¹,

Johnson²,

Hunt³

et al. 2011

Atmosphere

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Probability Theory as Logic: Data Assimilation for Multiple Source Reconstruction

Yee

2011

Pure Appl. Geophys.

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Probability theory as logic (or Bayesian probability theory) is a rational inferential methodology that provides a natural and logically consistent framework for source reconstruction. This methodology fully utilizes the information provided by a limited number of noisy concentration data obtained from a network of sensors and combines it in a consistent manner with the available prior knowledge (mathematical representation of relevant physical laws), hence providing a rigorous basis for the assimilation of this data into models of atmospheric dispersion for the purpose of contaminant source reconstruction. This paper addresses the application of this framework to the reconstruction of contaminant source distributions consisting of an unknown number of localized sources, using concentration measurements obtained from a sensor array. To this purpose, Bayesian probability theory is used to formulate the full joint posterior probability density function for the parameters of the unknown source distribution. A simulated annealing algorithm, applied in conjunction with a reversible-jump Markov chain Monte Carlo technique, is used to draw random samples of source distribution models from the posterior probability density function. The methodology is validated against a real (full-scale) atmospheric dispersion experiment involving a multiple point source release.

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Multisource emission retrieval within a biogas plant based on inverse dispersion calculations—a real-life example

Hrad

Piringer

Kamarád

et al. 2014

Environ Monit Assess

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Open digestate storage tanks were identified as one of the main methane (CH4) emitters of a biogas plant. The main purpose of this paper is to determine these emission rates using an inverse dispersion technique in conjunction with open-path tunable diode laser spectroscopy (OP-TDLS) concentration measurements for multisource reconstruction. Since the condition number, a measure of "ill-conditioned" matrices, strongly influences the accuracy of source reconstruction, it is used as a diagnostic of error sensitivity. The investigations demonstrate that the condition number for a given source-sensor configuration in the highly disturbed flow field within the plant significantly depends on the meteorological conditions (e.g., wind speed, stratification, wind direction, etc.). The CH₄ emissions are retrieved by removing unrepresentative periods with high condition numbers, which indicate uncertainty in recovering the individual sources. In a final step, the CH₄ emissions are compared with the maximum biological methane potential (BMP) in the digestate analyzed under laboratory conditions. The retrieved methane emission rates represent an average of 50% of the maximum BMP of the stored digestate in the winter months, while they comprised an average of 85% during the measurement campaigns in the summer months. The results indicate that the open tanks have the potential to represent a substantial emission source even during colder periods.

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Multi-Source Emission Determination Using an Inverse-Dispersion Technique

Cited by 33 publications

References 15 publications

Measuring Trace Gas Emission from Multi-Distributed Sources Using Vertical Radial Plume Mapping (VRPM) and Backward Lagrangian Stochastic (bLS) Techniques

Measuring Trace Gas Emission from Multi-Distributed Sources Using Vertical Radial Plume Mapping (VRPM) and Backward Lagrangian Stochastic (bLS) Techniques

Probability Theory as Logic: Data Assimilation for Multiple Source Reconstruction

Multisource emission retrieval within a biogas plant based on inverse dispersion calculations—a real-life example

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