the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 Jun 2023
Detecting nitrogen oxide emissions in Qatar and quantifying emission factors of gas-fired power plants - A four-years study
Anthony Rey-Pommier
Frédéric Chevallier
Philippe Ciais
Jonilda Kushta
Theodoros Christoudias
I. Safak Bayram
Jean Sciare
Abstract. Nitrogen oxides (NOx = NO + NO2), produced in urban areas and industrial facilities (particularly in fossil fuel-fired power plants), are major sources of air pollutants, with implications for human health, leading local and national authorities to estimate their emissions using inventories. In Qatar, these inventories are not systematically updated, while the country is experiencing fast economic growth. Here, we use spaceborne retrievals of nitrogen dioxide (NO2) columns at high spatial resolution from the TROPOMI instrument to estimate NOx emissions in Qatar from 2019 to 2022 with a flux-divergence scheme, according to which emissions are calculated as the sum of a transport term and a sink term representing the three-body reaction comprising NO2 and hydroxyl radical (OH). Our results highlight emissions from gas power plants in the north-east of the country, and from the urban area of the capital Doha. The emissions from cement plants in the west and different industrial facilities in the south-east are under-estimated, due to frequent low-quality measurements of NO2 columns in these areas. Our top-down model estimates a weekly cycle with lower emissions on Fridays compared to the rest of the week, which is consistent with social norms in the country, and an annual cycle with mean emissions of 9.56 kt per month for the four-year period. These monthly emissions differ from CAMS-GLOB-ANT_v5.3 and EDGARv6.1 global inventories, for which the annual cycle is less marked and the average emissions are respectively 1.44 and 1.68 times higher. Our emission estimates are correlated with local electricity generation, and allow to infer a mean NOx emission factor of 0.557 tNOx.GWh−1 for the three gas power plants in the Ras Laffan area.
-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(1307 KB)
-
Supplement
(1497 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1307 KB) - Metadata XML
-
Supplement
(1497 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Anthony Rey-Pommier et al.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1024', Anonymous Referee #1, 04 Jul 2023
This manuscript applies the flux divergence method to estimate NOx emissions over Qatar using TROPOMI NO2 retrievals. It represents an incremental development on the author’s previous paper for emissions in Egypt. The paper is clearly written and appears to be thorough and sound. I am happy to recommend it for publication.
General Comments:
Urban emissions: as you note, Doha coincides with 5 gas power plants, making it difficult to separate emissions. However, it would be interesting to show estimated emissions of the urban and residential sectors versus the power and industrial sectors. These are readily available for EDGAR and CAMS. They would also improve the discussion of seasonal and day-of-week variability below.
Fig. 7: This makes me wonder if a coarse land-use mask is used. Work with the TROPOMI methane product found that a new high resolution water mask had to be used for coastal areas (de Foy et al., 2023). This problem is much more acute for methane than for NOx, but still it might have an effect here.
Fig. 8: I wonder if you could show boxplots here to get a sense of the difference as a function of the variability. I think you did a sum of the flux divergence over the whole of Qatar? What happens if you look at different areas? I would expect a stronger weekday effect over residential area, and a weaker one over power plants and industrial facilities. As a check, I think it would be good to show the weekly cycle in VCD as well as in flux divergence.
Fig. 10: Maybe in SI you could show the monthly variation, or at least put color bars over the summer months to help see the annual cycle. In the text you say there is no seasonal signal in the VCD. I think it would be good to show the cycle in VCD as well as flux divergence side by side (as for the weekly cycle). Given the large seasonal cycle in electricity cycle, a lack of cycle in the TROPOMI results suggests that something else is going on. For example transport and industrial emission may be stable throughout the year.
Getting actual emission totals from the flux divergence method involves uncertainties, especially due to lifetime as you note. It would be interesting to see how your method compares to the values reported in the global catalog (Beirle et al., 2021). It would also be interesting to see how your evaluation of TROPOMI and EDGAR sources compares with that reported for large point sources and urban areas in South Asia (de Foy et al., 2022).
Minor Comments:
Fig. 12: I think you are plotting one point per month, with total NOx emissions and Electricity generation over the whole of Qatar? I think the explanation could be clearer to help the casual reader.
Line 311: replace “estimaed” with “estimated”.
References:
Beirle, Steffen, Christian Borger, Steffen Dörner, Henk Eskes, Vinod Kumar, Adrianus de Laat, and Thomas Wagner. "Catalog of NO x emissions from point sources as derived from the divergence of the NO 2 flux for TROPOMI." Earth System Science Data 13, no. 6 (2021): 2995-3012.
de Foy, Benjamin, and James J. Schauer. "An improved understanding of NOx emissions in South Asian megacities using TROPOMI NO2 retrievals." Environmental Research Letters 17, no. 2 (2022): 024006.
de Foy, Benjamin, James J. Schauer, Alba Lorente, and Tobias Borsdorff. "Investigating high methane emissions from urban areas detected by TROPOMI and their association with untreated wastewater." Environmental Research Letters 18, no. 4 (2023): 044004.
Citation: https://doi.org/10.5194/egusphere-2023-1024-RC1 - AC1: 'Reply on RC1', Anthony Rey-Pommier, 05 Sep 2023
-
RC2: 'Comment on egusphere-2023-1024', Anonymous Referee #2, 01 Aug 2023
The authors infer NOx emissions in Qatar using satellite NO2 observations and compare it with the bottom-up inventories. It is well written. The results look sound. I recommend publication after minor revision.
General comments:
- Section 4.2. The divergence method used here has been proposed by existing studies, e.g., Beirle et al. (2011). I think the authors shall give the credit to those studies by clarifying that this study is an application of an existing method. How the method is different (if any) from existing studies shall be highlighted.
- The uncertainty of using 5 percentiles as background shall be discussed.
- Section 6.1. I understand the correlation between emissions and generation data is relatively low for monthly data. How is the correlation compared with that between bottom-up estimates and generation data? The comparison could help explain the inconsistency between TROPOMI-derived emissions and generation data.
Specific comments:
- line 13. Regularly updated.
- Line 19. No dash in under-estimated.
- Line 65. The sentence is too long to read.
- Line 169. Covered?
- Is there any specific reason for choosing 30 km/h as the criteria to remove high-wind speed days?
Citation: https://doi.org/10.5194/egusphere-2023-1024-RC2 - AC2: 'Reply on RC2', Anthony Rey-Pommier, 05 Sep 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1024', Anonymous Referee #1, 04 Jul 2023
This manuscript applies the flux divergence method to estimate NOx emissions over Qatar using TROPOMI NO2 retrievals. It represents an incremental development on the author’s previous paper for emissions in Egypt. The paper is clearly written and appears to be thorough and sound. I am happy to recommend it for publication.
General Comments:
Urban emissions: as you note, Doha coincides with 5 gas power plants, making it difficult to separate emissions. However, it would be interesting to show estimated emissions of the urban and residential sectors versus the power and industrial sectors. These are readily available for EDGAR and CAMS. They would also improve the discussion of seasonal and day-of-week variability below.
Fig. 7: This makes me wonder if a coarse land-use mask is used. Work with the TROPOMI methane product found that a new high resolution water mask had to be used for coastal areas (de Foy et al., 2023). This problem is much more acute for methane than for NOx, but still it might have an effect here.
Fig. 8: I wonder if you could show boxplots here to get a sense of the difference as a function of the variability. I think you did a sum of the flux divergence over the whole of Qatar? What happens if you look at different areas? I would expect a stronger weekday effect over residential area, and a weaker one over power plants and industrial facilities. As a check, I think it would be good to show the weekly cycle in VCD as well as in flux divergence.
Fig. 10: Maybe in SI you could show the monthly variation, or at least put color bars over the summer months to help see the annual cycle. In the text you say there is no seasonal signal in the VCD. I think it would be good to show the cycle in VCD as well as flux divergence side by side (as for the weekly cycle). Given the large seasonal cycle in electricity cycle, a lack of cycle in the TROPOMI results suggests that something else is going on. For example transport and industrial emission may be stable throughout the year.
Getting actual emission totals from the flux divergence method involves uncertainties, especially due to lifetime as you note. It would be interesting to see how your method compares to the values reported in the global catalog (Beirle et al., 2021). It would also be interesting to see how your evaluation of TROPOMI and EDGAR sources compares with that reported for large point sources and urban areas in South Asia (de Foy et al., 2022).
Minor Comments:
Fig. 12: I think you are plotting one point per month, with total NOx emissions and Electricity generation over the whole of Qatar? I think the explanation could be clearer to help the casual reader.
Line 311: replace “estimaed” with “estimated”.
References:
Beirle, Steffen, Christian Borger, Steffen Dörner, Henk Eskes, Vinod Kumar, Adrianus de Laat, and Thomas Wagner. "Catalog of NO x emissions from point sources as derived from the divergence of the NO 2 flux for TROPOMI." Earth System Science Data 13, no. 6 (2021): 2995-3012.
de Foy, Benjamin, and James J. Schauer. "An improved understanding of NOx emissions in South Asian megacities using TROPOMI NO2 retrievals." Environmental Research Letters 17, no. 2 (2022): 024006.
de Foy, Benjamin, James J. Schauer, Alba Lorente, and Tobias Borsdorff. "Investigating high methane emissions from urban areas detected by TROPOMI and their association with untreated wastewater." Environmental Research Letters 18, no. 4 (2023): 044004.
Citation: https://doi.org/10.5194/egusphere-2023-1024-RC1 - AC1: 'Reply on RC1', Anthony Rey-Pommier, 05 Sep 2023
-
RC2: 'Comment on egusphere-2023-1024', Anonymous Referee #2, 01 Aug 2023
The authors infer NOx emissions in Qatar using satellite NO2 observations and compare it with the bottom-up inventories. It is well written. The results look sound. I recommend publication after minor revision.
General comments:
- Section 4.2. The divergence method used here has been proposed by existing studies, e.g., Beirle et al. (2011). I think the authors shall give the credit to those studies by clarifying that this study is an application of an existing method. How the method is different (if any) from existing studies shall be highlighted.
- The uncertainty of using 5 percentiles as background shall be discussed.
- Section 6.1. I understand the correlation between emissions and generation data is relatively low for monthly data. How is the correlation compared with that between bottom-up estimates and generation data? The comparison could help explain the inconsistency between TROPOMI-derived emissions and generation data.
Specific comments:
- line 13. Regularly updated.
- Line 19. No dash in under-estimated.
- Line 65. The sentence is too long to read.
- Line 169. Covered?
- Is there any specific reason for choosing 30 km/h as the criteria to remove high-wind speed days?
Citation: https://doi.org/10.5194/egusphere-2023-1024-RC2 - AC2: 'Reply on RC2', Anthony Rey-Pommier, 05 Sep 2023
Peer review completion
Journal article(s) based on this preprint
Anthony Rey-Pommier et al.
Anthony Rey-Pommier et al.
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 423 | 149 | 23 | 595 | 35 | 10 | 8 |
- HTML: 423
- PDF: 149
- XML: 23
- Total: 595
- Supplement: 35
- BibTeX: 10
- EndNote: 8
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1307 KB) - Metadata XML
-
Supplement
(1497 KB) - BibTeX
- EndNote
- Final revised paper