the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Nov 2024
Monitoring of total and off-road NOx emissions from Canadian oil sands surface mining using the Ozone Monitoring Instrument
Abstract. The oil sands in Alberta, Canada is a significant source of air pollution. Observations from the Ozone Monitoring Instrument (OMI) on the NASA Aura satellite have been used to quantify NOx emissions from the surface mining region of the oil sands. Two related emissions methods were utilized, one for point and one for area sources, where OMI vertical columns densities of NO2 were combined with winds from a meteorological reanalysis and a two-dimensional exponentially-modified Gaussian (EMG) plume model. This work better connects the two (point and area) emissions methods, discusses the interpretation of fit parameters, and the ability of OMI (and other sensors) to resolve emissions between neighbouring sources.
The two methods employed, in good agreement with each other, indicated an increase in emissions from about 55 to 80 kt[NO2]/yr between 2005–2011, and flat thereafter. Reported emissions were typically 0–15 % smaller, consistent to within uncertainties. In an extension of this methodology, OMI observations were combined with reported point source emissions to derive the more uncertain emissions component from the large off-road mining fleet. These were found to make up about 60 % of total NOx emissions, also consistent with reported emissions. The OMI-derived 1.3 %/year increase in fleet emissions and the 5.9 %/year increase in bitumen mined, generally a good proxy for fleet emissions, can be reconciled by considering the evolution of the mine fleet over this period. OMI is therefore able to track the transition from US EPA Tier 1 standards, through Tier 4 standards, to the present, and in so doing demonstrates the efficacy of this policy. Furthermore, this analysis shows that had the fleet remained at Tier 1 this source would currently be emitting an additional 25 kt/yr.
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RC1: 'Comment on egusphere-2024-2856', Anonymous Referee #1, 05 Dec 2024
Review of “ Monitoring of total and off-road NOx emissions from Canadian oil sands surface mining using the Ozone Monitoring Instrument”
Using OMI observations, this study quantifies the point and area NOx emissions from the oil sands mining and fleet in Canada with two existing methods based on exponentially-modified Gaussian (EMG) plume model. The paper is well constructed and comprehensive. I recommend it to be published after adding some more details about the methods used including also its limitations.
Detailed list of comments
Line 30: Consider adding CH4 in the list of pollutants (see https://doi.org/10.1029/2021GL094151)
Figure 1: (1) the dark blue lines are difficult to distinguish from the dark background. Consider using “light blue”.
(2) Please, add the locations of Fort Mckay and Fort Chipewtan on the map.Line 89-90: Different units are used for the emissions, which makes it difficult to compare. Please, use one type of unit throughout the paper.
Line 122-126: For the OMI instrument the row anomaly plays an important role. Especially for the trend analysis, it is important to know what the number of observations are that are used for each year. In addition, in order to understand how representative the measurements are I would like to know what are the number of observations per month that are used in the annual averages (or 3-year averages). Please, add this information to the paper.
The row anomaly is also affecting the pixels in nadir-viewing, and therefore the resolution is changing during the time series, which is also worthwhile to discuss here.Line 138: What is the reason of partly using ERA-Interim instead of using ERA-5 for the whole time period?
Line 158: This reference to equation (B4) in the appendix, makes it hard to read this analysis. Please include equation (B4) in the main text.
Line 166: Figure F1 is often referred to in the text. Why not place in the main text?
Line 181: The “single, average plume” is an composition of plumes with various wind speeds. What does this mean for the definition of the wind speed s in equation (B1)?
Section 2.4.1. Important feature of this method is that it assumes a known location of the sources. I think this is important to mention here.
Section 2.4.2 Here the assumption is made that the lifetime is constant over the whole region and independent on the strength of source. In strong sources the lifetime can become longer inside the plume (due to OH depletion).
The lifetime is also an average of the whole year, while the number of observations are changing over the year (less observations in the winter time) and change from year-to-year. I wonder how this affect the uncertainty of the lifetime used in this method. Please, add more discussion to the text.Line 213: “Observations of of “. One “of” too many.
Line 214: How can the authors draw this conclusion that the plume resides between100 and 800 m, if the altitudes below 100 m are not sampled?
Line 220: “the the oil sand”, one “the” should be removed.
Line 225-235: this NOx/NO2 ratio seems also dependent on the season and location, so I wonder if the 8% uncertainty is maybe an underestimation. Can you specify what the variation is over the year based on the GEM-MACH model ?
Figure 2: (1) A discrete number of colors is shown in the Figure, while the legend shows a continuous color bar. Please adapt this, also for the other Figures in the manuscript with similar mismatch between Figure and legend.
(2) I assume the triangle is at (0,0) for the rotated VCD figures. It will help if the triangle is shown at this location.
(3) In Figure (c), showing the reconstructed VCD, the distribution looks very symmetrical. Why does it not look more like Figure (a) with dominating winds from the South ?Figure 3: (1) The figure captions are often difficult to understand without reading the text. For example, in Figure 3, it is not clear what the “VMR: and “Effective pixel size” mean.
(2) The exceptional year 2005 is explained in the caption. What about 2022? Is that also for two years?Line 317-318: A large lifetime variation has been found within the individual NOx plume from Krol et al. (2024). Therefore, it might be useful also to refer to this more recent paper: https://doi.org/10.5194/acp-24-8243-2024.
Line 344: Change the Roman “tau“ into the Greek τ.
Line 376: How was the 22 km derived. Can you add a reference?
Figure 7. Results are shown for species SO2 and NH3, which are not discussed in the paper. I suggest to remove these points.
Figure 10: These trends may be affected by the different sampling of OMI due to the row anomaly. This may explain the changes around the year 2007/2008. For a trend analysis it would be better to use exactly the same sampling in all years, as if the row anomaly already existed in 2005. This would give a better trend estimate.
Line 577: “A correction factor…..”: This sentence is difficult to understand. Please, explain better what steps you took here.Figure E2, Page 41: “See section XX”. Specify the section number.
Citation: https://doi.org/10.5194/egusphere-2024-2856-RC1 -
RC2: 'Comment on egusphere-2024-2856', Anonymous Referee #2, 15 Dec 2024
This study applies EMG approach to estimating NOx emissions from oil sands in Alberta, Canada. The authors further derived NOx emissions from large off-road mining fleets. Overall, the manuscript is well structured, and the results are robust with clear discussions of the uncertainties of the methods. I think this manuscript is almost ready for publication. I only have a few minor comments.
- Line 565: It’s not clear to me how the EMG method applies to multiple sources. My understanding is that the x,y,s are specific to each plume, but it’s unclear to me how the x,y,s are defined with respect to multiple source locations. As the authors mentioned, one source location may affect the other, especially when they are nearby. It’s unclear to me how the proposed methods could separate the influence from nearby sources. Maybe it’d be better if the authors could present a figure of several example plumes to explain the methods.
- Figure 2: Since the trend analysis is based annual emissions, I’d suggest the authors present a figure for a single year. Same for other figures. I think the novel part of this manuscript is the long-term trend, but the figures presented are mostly for multi-year average. It’d be useful if the authors could show the contrast between 2005 and 2022 to highlight the changes occurred.
- Line 400: The emissions are reconstructed from NPRI emissions averaged from 2005 to 2020, but it seems that NPRI emissions vary yearly. How would this affect the derived trends?
- Figure 3: Lifetime should be hours, not years.
Citation: https://doi.org/10.5194/egusphere-2024-2856-RC2
Data sets
OMI-ECCC NO2 over the Canadian Oil Sands C. McLinden and D. Griffin https://collaboration.cmc.ec.gc.ca/cmc/arqi/OilSands_satellite_NO2data/
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