Kristian D. Hajny, David R. Lyon, Austin Armstrong, Cody R. Floerchinger, Thilina Jayarathne, Robert Kaeser, Tegan Lavoie, Olivia E. Salmon, Brian H. Stirm, Andrew A. Stuff, Jay M. Tomlin, Bernard Wulle, Israel Lopez-Coto, Paul B. Shepson

Assessing the bias and uncertainties in the aircraft mass balance technique for the determination of carbon dioxide emission rates

  • Atmospheric Science
  • Geology
  • Geotechnical Engineering and Engineering Geology
  • Ecology
  • Environmental Engineering
  • Oceanography
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Urban areas are the major sources of greenhouse gas emissions but also leaders in emission reduction efforts. Appropriate techniques to quantify emissions and any potential reductions over time are necessary to effectively inform these mitigation efforts. The aircraft mass balance experiment (MBE) is an established technique used for such a purpose. In this work, we use a series of 55 MBEs downwind of power plants to assess the technique’s bias and precision. In addition, we investigate what factors drive the absolute error, determined as the absolute difference between observed and reported emission rates, in individual experiments using multilinear regressions. Power plants are required to monitor their carbon dioxide emissions with an hourly resolution, and these publicly available reported emissions can be directly compared to the mass balance estimates as a pseudo-known release. To quantify the bias we calculated the mean error, which was 10 ± 240 Mg·h−1 (1σ), regressed mass balance emission rates against reported emission rates to yield a slope of 0.967 ± 0.062, and compared the sum across all mass balance emission rates, 31,000 ± 1,000 Mg·h−1, to the sum across all reported emissions, 30,660 ± 740 Mg·h−1. All three of these approaches suggest no systematic bias. Then to quantify the precision for individual determinations we calculated the slope of a regression between the standard deviation across repeated MBEs and the corresponding average emission rate, which is 30.7% ± 6.7%. The main drivers of the absolute error were sparse sampling of the plume, poor horizontal and vertical mixing of the plume, and smaller signal-to-noise ratios. Quantifying the capabilities of this technique provides context for previous analyses and allows stakeholders and researchers to make informed decisions when choosing quantification methods. Identifying the factors that drive the absolute error also allows us to adjust flight design to minimize it and potentially improve uncertainty estimates.

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