Comparing airborne algorithms for greenhouse gas flux measurements over the Alberta oil sands

Abstract

Abstract. To combat global warming, Canada has committed to reducing greenhouse gases to be (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's (SciAv) Gaussian algorithm and the Environment and Climate Change Canada's 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 uncertainties (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 atmospheric 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. In addition, hourly on-site activity data would provide insight to the observed temporal variability in emissions and make a comparison to reported emissions more straightforward.

Recent Advances Toward Transparent Methane Emissions Monitoring: A Review

“…Airborne measurements are used to characterize and validate site, area, and regional methane emissions . They provide an intermediary sampling scale that can attain detailed quantification of emission budgets from sites and sample large regional areas. , Methods for sampling atmospheric GHGs from the air tend to fall into two major categories: (i) remote spectral imaging methods and (ii) mass-balance spectroscopic methods. Figure illustrates the difference between the sampling schemes and imagery produced from the two approaches.…”

“…Top-down and bottom-up methods of measuring anthropogenic GHGs can complement each other and reduce uncertainty in estimating methane emissions. Top-down methods have been significantly improved over the past decade, and there is increasing confidence in these methods as comparisons between and among satellite and airborne methods show general agreement. ,,− This recent development and ongoing refinement of top-down methods make them an increasingly popular tool for independent confirmation of anthropogenic carbon dioxide and methane emissions. On the other hand, bottom-up methods, while useful for identifying mechanisms and processes leading to emissions, often suffer from a number of potential sampling biases resulting from nonrepresentative sampling in time and space.…”

“…Airborne methods are limited by airspace restrictions and can be costly and time intensive, which makes them ineffective as continuous monitoring methods, but they provide in depth emissions estimates for supplementing and auditing emissions inventories, as well as follow up to emission hotspots flagged by satellite data . Because of the high cost of repeat visits, airborne methods are not always able to assess the temporal variability of emissions and rely on ground-based or satellite measurements to upscale hourly emission rates to monthly and yearly estimates for inventories. ,, …”

“…Mass-balance methods most commonly capture point and area source plumes by flying in transects, screens, or box-flights that constrain the emission plume as it traverses the flight path around the source. ,,, Airborne mass-balance flights can sample following several sampling schemes; the simplest are single transects and screens which are faster, but have large errors (25–60%), or there are more complex time-intensive box-flights which surround an emissions source and attain a lower estimate error (∼2%) . Two box-flight mass-balance algorithms, the Top-down Emission Rate Retrieval Algorithm (TERRA) and SciAv (Scientific Aviation) model, have recently been applied to box-flight patterns when an aircraft encircles a source to estimate emissions by calculating the flux through the boxed-in source. ,,,,,,, Full capture at the top of the box is often attained by operators flying laps up to the top of the (stable) atmospheric boundary layer, which typically caps the top of an emission plume . Due to minimum flight height restrictions, a gap between the surface and the flight box is inevitable.…”

“… 73 Because of the high cost of repeat visits, airborne methods are not always able to assess the temporal variability of emissions and rely on ground-based or satellite measurements to upscale hourly emission rates to monthly and yearly estimates for inventories. 31 , 49 , 74 …”