Preprints
https://doi.org/10.5194/amt-2022-120
https://doi.org/10.5194/amt-2022-120
 
04 May 2022
04 May 2022
Status: this preprint is currently under review for the journal AMT.

Comparing Airborne Algorithms for Greenhouse Gas Flux Measurements over the Alberta Oil Sands

Broghan M. Erland1, Cristen Adams2, Andrea Darlington3, Mackenzie L. Smith4, Andrew K. Thorpe5, Gregory R. Wentworth2, Steve Conley4, John Liggio3, Shao-Meng Li3, Charles E. Miller5, and John A. Gamon1,6 Broghan M. Erland et al.
  • 1Department of Earth and Atmospheric Sciences; & Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2R3, Canada
  • 2Resource Stewardship Division, Alberta Environment and Parks, Edmonton, AB, T5J 5C6, Canada
  • 3Air Quality Research Division, Environment and Climate Change Canada, Toronto, M3H 5T4, Canada
  • 4Scientific Aviation, Inc., Boulder, CO, 80301, USA
  • 5Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, 91109, USA
  • 6School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, 68583, USA

Abstract. To combat global warming, Canada has committed to reducing greenhouse gases (GHGs) 40–45 % below 2005 emission levels by 2025. Monitoring emissions and deriving accurate inventories are essential to reaching these goals. Airborne methods can provide regional and area source measurements with small error if ideal conditions for sampling are met. In this study, two airborne mass-balance box-flight algorithms were compared to assess the extent of their agreement and their performance under various conditions. The Scientific Aviation, SciAv Gaussian algorithm and the Environment and Climate Change Canada Top-down Emission Rate Retrieval Algorithm (TERRA) were applied to data from five samples. Estimates were compared using standard procedures, by systematically testing other method fits, and by investigating the effects on the estimates when method assumptions were not met. Results indicate that in standard scenarios the SciAv and TERRA mass-balance, box-flight methods produce similar estimates that agree (3–25 %) within algorithm errors (4–34 %). Implementing a sample-specific surface extrapolation procedure for the SciAv algorithm may improve emission estimation. Algorithms disagreed when non-ideal conditions occurred (i.e., under non-stationary conditions). Overall, the results provide confidence in the box-flight methods, and indicate that emissions estimates are not overly sensitive to the choice of algorithm, but demonstrate that fundamental algorithm assumptions should be assessed for each flight. Using a different method, the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG), independently mapped individual plumes with emissions 5 times larger than the source SciAv sampled three days later. The range in estimates highlights the utility of increased sampling to get a more complete understanding of the temporal variability of emissions and to identify emission sources within facilities.

Broghan M. Erland et al.

Status: open (until 09 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Broghan M. Erland et al.

Broghan M. Erland et al.

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Short summary
Accurately estimating greenhouse gas (GHG) emissions is essential to reaching net-zero goals to combat the climate crisis. Airborne box-flights are ideal for assessing regional GHG emissions as they can attain small error. We compare two box-flight algorithms and found they produce similar results, but daily variability must be considered when deriving emissions inventories. Increasing the consistency, and agreement between airborne methods moves us closer to achieving more accurate estimates.