A quantitative comparison of methods used to measure smaller methane emissions typically observed from superannuated oil and gas infrastructure

Author:

Riddick Stuart N.,Ancona Riley,Mbua Mercy,Bell Clay S.,Duggan Aidan,Vaughn Timothy L.,Bennett Kristine,Zimmerle Daniel J.ORCID

Abstract

Abstract. Recent interest in measuring methane (CH4) emissions from abandoned oil and gas infrastructure has resulted in several methods being continually used to quantify point source emissions less than 200 g CH4 h−1. The choice of measurement approach depends on how close observers can come to the source, the instruments available, and the meteorological/micrometeorological conditions. As such, static chambers, dynamic chambers, Bacharach Hi Flow Sampler (BHFS) measurements, Gaussian plume (GP) modeling, and backward Lagrangian stochastic (bLs) models have all been used, but there is no clear understanding of the accuracy or precision of each method. To address this, we copy the experimental design for each of the measurement methods to make single field measurements of a known source, to simulate single measurement field protocol, and then make repeat measurements to generate an understanding of the accuracy and precision of each method. Here, we present estimates for the average percentage difference between the measured emission and the known emission for three repeat measurements, Ar, for emissions of 40 to 200 g CH4 h−1. The static chamber data were not presented because of safety concerns during the experiments. Both the dynamic chamber (Ar = −10 %, −8 %, and −10 % at emission rates of 40, 100, and 200 g CH4 h−1, respectively) and BHFS (Ar = −18 %, −16 %, and −18 %) repeatedly underestimate the emissions, but the dynamic chamber had better accuracy. The standard deviation of emissions from these direct measurement methods remained relatively constant for emissions between 40 and 200 g CH4 h−1. For the far-field methods, the bLs method generally underestimated emissions (Ar = +6 %, −6 %, and −7 %) while the GP method significantly overestimated the emissions (Ar = +86 %, +57 %, and +29 %) despite using the same meteorological and concentration data as input. Variability in wind speed, wind direction, and atmospheric stability over the 20 min averaging period are likely to propagate through to large variability in the emission estimate, making these methods less precise than the direct measurement methods. To our knowledge, this is the first time that methods for measuring CH4 emissions from point sources between 40 and 200 g CH4 h−1 have been quantitatively assessed against a known reference source and against each other.

Funder

Colorado State University

Publisher

Copernicus GmbH

Subject

Atmospheric Science

Reference54 articles.

1. Allen, D. T., Torres, V. M., Thomas, J., Sullivan, D. W., Harrison, M., Hendler, A., Herndon, S. C., Kolb, C. E., Fraser, M. P., Hill, A. D., Lamb, B. K., Miskimins, J., Sawyer, R. F., and Seinfeld, J. H.: Measurements of methane emissions at natural gas production sites in the United States, P. Natl. Acad. Sci. USA, 110, 17768–17773, https://doi.org/10.1073/pnas.1304880110, 2013.

2. Aneja, V. P., Blunden, J., Claiborn, C. S., and Rogers, H. H.: Dynamic Chamber System to Measure Gaseous Compounds Emissions and Atmospheric-Biospheric Interactions, in: Environmental Simulation Chambers: Application to Atmospheric Chemical Processes, vol. 62, edited by: Barnes, I. and Rudzinski, K. J., Kluwer Academic Publishers, Dordrecht, 97–109, https://doi.org/10.1007/1-4020-4232-9_7, 2006.

3. Baillie, J., Risk, D., Atherton, E., O'Connell, E., Fougère, C., Bourlon, E., and MacKay, K.: Methane emissions from conventional and unconventional oil and gas production sites in southeastern Saskatchewan, Canada, Environ. Res. Commun., 1, 011003, https://doi.org/10.1088/2515-7620/ab01f2, 2019.

4. Bell, C. S., Vaughn, T. L., Zimmerle, D., Herndon, S. C., Yacovitch, T. I., Heath, G. A., Pétron, G., Edie, R., Field, R. A., Murphy, S. M., Robertson, A. M., and Soltis, J.: Comparison of methane emission estimates from multiple measurement techniques at natural gas production pads, Elem. Sci. Anth., 5, 79, https://doi.org/10.1525/elementa.266, 2017.

5. Bonifacio, H. F., Maghirang, R. G., Razote, E. B., Trabue, S. L., and Prueger, J. H.: Comparison of AERMOD and WindTrax dispersion models in determining PM 10 emission rates from a beef cattle feedlot, J. Air Waste Manage., 63, 545–556, https://doi.org/10.1080/10962247.2013.768311, 2013.

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