Influence of source–sensor geometry on multi-source emission rate estimates

Crenna, Brian P.; Flesch, Thomas K.; Wilson, John D.

doi:10.1016/j.atmosenv.2008.06.019

Cited by 24 publications

(36 citation statements)

References 17 publications

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“…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: 99%

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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 Sourcesmentioning

confidence: 99%

“…To make this calculation for two sources requires at least two concentration sensors [16]. In principle it is possible to deduce the emission rate from n sources if there are at least n sensors.…”

Section: Two Emission Sourcesmentioning

confidence: 99%

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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“…where Δ signifies a change or error, the operator I · I represents a norm (usually the Euclidean or spectral norm), and κ indicates the condition number [10]. If G is square (i.e., m = n) and non-singular, the condition number κ may be calculated as IGI/IG −1 I.…”

Section: Upper Bound On Uncertainty In Source Strengthmentioning

confidence: 99%

“…Equation 9 provides an upper bound on the relative error in the estimated source strength. This relative error is proportional to the condition number κ, as well as the relative error in estimating the coefficient matrix, the measured concentrations and background concentrations [10] [25]. Therefore, a low value of κ implies a low sensitivity to error, but a large value does not necessarily imply a high sensitivity.…”

Section: Upper Bound On Uncertainty In Source Strengthmentioning

confidence: 99%

“…Typical examples include measurements made with instruments mounted inside ground-based vehicles operating on roads close to a possible source. For such scales atmospheric inversions are usually done using plume dispersion and surface layer models [10], [11], [12]. In this report, we are primarily interested in measurements that are made relatively close to the source and restrict ourselves to studying the uncertainty in inverse modeling related to plume dispersion models.…”

Section: Introductionmentioning

confidence: 99%

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Greenhouse Gas Emissions and Dispersion 3. Reducing Uncertainty in Estimating Source Strength and Location through Plume Inversion Models

Prasad¹,

Pintar²,

Hu³

et al. 2015

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Recent development of accurate instruments for measuring greenhouse gas concentrations and the ability to mount them in ground-based vehicles has provided an opportunity to make temporally and spatially resolved measurements in the vicinity of suspected source locations, and for subsequently estimating the source location and strength. The basic approach of us ing downwind atmospheric measurements in an inversion methodology to predict the source strength and location is an ill-posed problem and results in large uncertainty. In this report, we present a new measurement methodology for reducing the uncertainty in predicting source strength from downwind measurements associated with inverse modeling. In order to demon strate the approach, an inversion methodology built around a plume dispersion model is de veloped. Synthetic data derived from an assumed source distribution is used to compare and contrast the predicted source strength and location. The effect of introducing various levels of noise in the synthetic data or uncertainty in meteorological variables on the inversion method ology is studied. Results indicate that the use of noisy measurement data had a small effect on the total predicted source strength, but gave rise to several spurious sources (in many cases 8-10 sources were detected, while the assumed source distribution only consisted of 2 sources). Use of noisy measurement data for inversion also introduced large uncertainty in the location of the predicted sources. A mathematical model for estimating an upper bound on the un certainty, and a bootstrap statistical approach for determining the variability in the predicted source distribution is demonstrated. The new measurement methodology, which involves using measurement data from two or more wind directions, combined together as part of a single inversion process is presented. Results of the bootstrap process indicated that the uncertainty in locating sources reduced significantly when measurements are made using the new proposed measurement approach. The proposed measurement system can be significant in determining emission inventories in urban domains at a high level of reliability, and for studying the role of remediation measures.

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Effects of atmospheric and manure surface conditions on H₂S emissions from an in‐ground finisher hog manure slurry tank

Grant

Boehm

2023

J of Env Quality

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Hydrogen sulfide (H 2 S) emitted by livestock operations can be detrimental to human health. The storage of hog manure is a significant agricultural source of H 2 S emissions. H 2 S emissions from a ground-level Midwestern hog finisher manure tank were measured for 8-20 days each quarter over a 15-month period. After excluding 4 days with outlier emissions the mean daily emission was 1.89 g H 2 S m −2 day −1 . Mean daily emission was 1.39 g H 2 S m −2 day −1 when the slurry surface was liquid and 3.00 g H 2 S m −2 day −1 when crusted. Emissions however were not significantly different whether the surface was liquid or crusted when differences in temperature were considered. Diurnal variation in emissions was not correlated with air temperature, water vapor saturation deficit, or wind speed when the manure surface was crusted but was positively correlated with these variables when the surface was not crusted. Daily H 2 S emissions were modeled according to two-film theory incorporating resistance approach with limited success. Additional emissions measurements with greater documentation of the manure liquid composition and crust characteristics are needed to assess the component transport resistances in the emissions model.

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Influence of source–sensor geometry on multi-source emission rate estimates

Cited by 24 publications

References 17 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

Greenhouse Gas Emissions and Dispersion 3. Reducing Uncertainty in Estimating Source Strength and Location through Plume Inversion Models

Effects of atmospheric and manure surface conditions on H₂S emissions from an in‐ground finisher hog manure slurry tank

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Influence of source–sensor geometry on multi-source emission rate estimates

Cited by 24 publications

References 17 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

Greenhouse Gas Emissions and Dispersion 3. Reducing Uncertainty in Estimating Source Strength and Location through Plume Inversion Models

Effects of atmospheric and manure surface conditions on H2S emissions from an in‐ground finisher hog manure slurry tank

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Product

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Effects of atmospheric and manure surface conditions on H₂S emissions from an in‐ground finisher hog manure slurry tank