the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Apr 2024
Ammonia emission estimates using CrIS satellite observations over Europe
Abstract. Over the past century ammonia (NH3) emissions have increased with the growth of livestock and fertilizer usage. The abundant NH3 emissions lead to secondary fine particulate matter (PM2.5) pollution, climate change, reduction in biodiversity and affects human health. Up-to-date and spatially and temporally resolved information of NH3 emissions is essential to better quantify its impact. In this study we applied the existing DECSO (Daily Emissions Constrained by Satellite Observations) algorithm to NH3 observations from the Cross-track Infrared Sounder (CrIS) to estimate NH3 emissions. Because NH3 in the atmosphere is influenced by Nitrogen Oxides (NOx), we implemented DECSO to estimate NOx and NH3 emissions simultaneously. The emissions are derived over Europe for 2020 on a spatial resolution of 0.2° x 0.2° using daily observations from both CrIS and TROPOMI (on the Sentinel 5p satellite). Due to the sparseness of daily satellite observations of NH3, monthly emissions of NH3 are reported. The total NH3 emissions derived from observations are about 8 Tg/year with a precision of about 0.2 % over the European domain. The comparison of the satellite-derived NH3 emissions from DECSO with independent bottom-up inventories and in-situ observations indicates a consistency in terms of magnitude on the country totals, the results also being comparable regarding the temporal and spatial distributions.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1073', Anonymous Referee #1, 13 May 2024
General comments:
Ding et al. presented a comparison of NH3 emissions in Europe derived from the CrIS satellite instruments and existing emission inventories. The authors included NO2 observations from the TROPOMI instrument in the emission calculation as NH3 emissions were found to be sensitive to NOX. They also compared satellite and inventory derived NH3 surface concentrations to the Dutch monitoring networks and found those using satellites had least bias against in-situ observations. This work makes a good example of why we need accurate emission data for policy interventions as bottom-up inventories often fall behind real-world changes. In this regard, satellites that offer daily global coverage are an excellent tool. Overall, I am curious as to why the authors only studied 2020 despite that CrIS and TROPOMI have data records that can be jointly studied beginning in 2018 (first year of TROPOMI operational product) until current day. We know 2020 was a special year in recent decades as anthropogenic activities and emissions were largely altered by the pandemic. As the authors mentioned, NOx emissions decreased in 2020. Meanwhile, NH3 was found to have an “anomalous increase” during the lockdowns (Kuttippurath et al., 2024). It is not surprising that inventories did not capture these sudden changes as observations (satellite and in-situ) did. In fact, the authors clearly stated one of the inventories was the 2018 data, which does not make a very fair comparison. Since DECSO is not computationally demanding to run, I would be curious to see at least one more year of results, otherwise it is difficult to comment on the general applicability of DECSO.
Specific comments:
Line 28: Is the 0.2% precision the uncertainty of derived emissions or the agreement between the two satellites? Also please include the uncertainty of other emission estimates, such as Line 305-306.
Line 83: You should clarify that JPSS-1 was renamed as NOAA-20, which is the name being used later in the text. Also clarify JPSS-2 (NOAA-21) was launched much later and therefore not used in the study.
Line 174: Can you expand on what you mean by superobservations and how is it different from the original observations?
Line 187: How large is the uncertainty in the in-situ data? This is an important part that should be discussed since you are using in-situ data as the reference for comparing satellites and inventories.
Line 298: Is this DECSO-parallel version using the setting of multi-species DECSO or DECSO-NH3 only?
Line 315: Do you know if the underestimation of emissions in East Europe by inventories is due to inaccurate information collected in those countries or other reasons? A little more insight on this would be helpful.
Line 373: Can you explain more specifically what caused the displacements of emissions? Is it intrinsic to the data, or does it have something to do with the way you aggregate the emissions (Line 213)?
Line 387: Please explain how you define and compare the biases here, on Line 392, and in Table 2 (assuming they mean the same concept). I can see from Figure 7 that HTAP is underestimating and CAMS is overestimating. However DECSO also has many extreme values indicating it can overshoot either way, so I’m not sure if it really is the better option, especially if you look at Table 2 the spatial correlations and biases of DECSO and CAMS aren’t that different (with CAMS having a much better temporal correlation). And please include the uncertainties of these numbers too.
Line 417: I would include the correlation coefficients and biases here or show them on Figure S9.
Line 480: Given that your derived emissions (8 Tg/year) are almost twice as high as inventories (4.0-5.9 Tg/year), how much of this difference do you think may be attributed to the fact that the inversion is solely based on satellite overpasses in the afternoon when NH3 concentration is usually higher than rest of the day (in other words, not incorporating diurnal changes)?
Technical corrections:
Line 18: “2.5” in PM2.5 should be subscripted throughout the text.
Line 54: Studies show
Line 122: to validate emissions
Line 151: The NH3 profiles
Line 268: there are almost no
Line 331: Figure 3 caption says LRTAP 2018 but figure legend says LRTAP 2020.
Line 336: is closer to
Line 345: Spring does not need to be capitalized here.
Line 462: redundant “of”
References:
Kuttippurath, J., Patel, V. K., Kashyap, R., Singh, A., & Clerbaux, C. (2024). Anomalous increase in global atmospheric ammonia during COVID-19 lockdown: Need policies to curb agricultural emissions. Journal of Cleaner Production, 434, 140424. https://doi.org/10.1016/j.jclepro.2023.140424
Citation: https://doi.org/10.5194/egusphere-2024-1073-RC1 -
RC2: 'Comment on egusphere-2024-1073', Anonymous Referee #2, 30 Jun 2024
This MS deals with the ammonia emission estimates for Europe using satellite observations. As there are not many studies like this, the MS has new information and thus, can be considered for publication. However, there are certain things to be clarified before the MS can be accepted.
Minor:
- Neither the Abstract nor Conclusions provides a clear message about this study. What is the main conclusion from this study? Just the comparison of different estimates? How we can improve the emission estimates? What are current uncertainties? What uncertainty is addressed in this study? This has to be clearly mentioned in the Abstract and Conclusion, and the problem must be properly discussed in the Introduction too. Now this is just a comparison paper, as you stated in the last sentence of the Abstract.
- Please state why the year 2020 is selected for this particular study.
- When you add NOx, how much the emission estimates improved in percent? Please state this.
- There is a clear seasonal difference in emission estimates. This suggests that the meteorology plays a big part in these calculations. So if you change meteorological input, how much will be the difference? If you consider the uncertainties in meteorological data, what would be the uncertainty or bias in the estimated emissions?
- Conclusion is almost two pages, but I do not see that much discussion in the paper. So please make it short. A 350-word Conclusion would be much more effective than this.
- I find some language issues in the MS, please pay attention to that.
technical:
L19: affect
L26: limited daily observation
L28: be specific about the region you mentioned here
L52: citation is not correct
L66: too many “type” here. Please rephrase
L270: this aspect is discussed in this paper https://doi.org/10.1016/j.jclepro.2023.140424
L301: How small is this “small”? Please specify the value
L385, L388: Please state the reasons for the differences
L410: If you look at the table, the comparison shows better for DESCO, but Figure 7 shows the least bias for HTAP?
L420: Figure 7 shows the smallest bias for HTAP
L465: “For Europe”
---
Citation: https://doi.org/10.5194/egusphere-2024-1073-RC2 - AC1: 'Comment on egusphere-2024-1073', Jieying Ding, 02 Aug 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1073', Anonymous Referee #1, 13 May 2024
General comments:
Ding et al. presented a comparison of NH3 emissions in Europe derived from the CrIS satellite instruments and existing emission inventories. The authors included NO2 observations from the TROPOMI instrument in the emission calculation as NH3 emissions were found to be sensitive to NOX. They also compared satellite and inventory derived NH3 surface concentrations to the Dutch monitoring networks and found those using satellites had least bias against in-situ observations. This work makes a good example of why we need accurate emission data for policy interventions as bottom-up inventories often fall behind real-world changes. In this regard, satellites that offer daily global coverage are an excellent tool. Overall, I am curious as to why the authors only studied 2020 despite that CrIS and TROPOMI have data records that can be jointly studied beginning in 2018 (first year of TROPOMI operational product) until current day. We know 2020 was a special year in recent decades as anthropogenic activities and emissions were largely altered by the pandemic. As the authors mentioned, NOx emissions decreased in 2020. Meanwhile, NH3 was found to have an “anomalous increase” during the lockdowns (Kuttippurath et al., 2024). It is not surprising that inventories did not capture these sudden changes as observations (satellite and in-situ) did. In fact, the authors clearly stated one of the inventories was the 2018 data, which does not make a very fair comparison. Since DECSO is not computationally demanding to run, I would be curious to see at least one more year of results, otherwise it is difficult to comment on the general applicability of DECSO.
Specific comments:
Line 28: Is the 0.2% precision the uncertainty of derived emissions or the agreement between the two satellites? Also please include the uncertainty of other emission estimates, such as Line 305-306.
Line 83: You should clarify that JPSS-1 was renamed as NOAA-20, which is the name being used later in the text. Also clarify JPSS-2 (NOAA-21) was launched much later and therefore not used in the study.
Line 174: Can you expand on what you mean by superobservations and how is it different from the original observations?
Line 187: How large is the uncertainty in the in-situ data? This is an important part that should be discussed since you are using in-situ data as the reference for comparing satellites and inventories.
Line 298: Is this DECSO-parallel version using the setting of multi-species DECSO or DECSO-NH3 only?
Line 315: Do you know if the underestimation of emissions in East Europe by inventories is due to inaccurate information collected in those countries or other reasons? A little more insight on this would be helpful.
Line 373: Can you explain more specifically what caused the displacements of emissions? Is it intrinsic to the data, or does it have something to do with the way you aggregate the emissions (Line 213)?
Line 387: Please explain how you define and compare the biases here, on Line 392, and in Table 2 (assuming they mean the same concept). I can see from Figure 7 that HTAP is underestimating and CAMS is overestimating. However DECSO also has many extreme values indicating it can overshoot either way, so I’m not sure if it really is the better option, especially if you look at Table 2 the spatial correlations and biases of DECSO and CAMS aren’t that different (with CAMS having a much better temporal correlation). And please include the uncertainties of these numbers too.
Line 417: I would include the correlation coefficients and biases here or show them on Figure S9.
Line 480: Given that your derived emissions (8 Tg/year) are almost twice as high as inventories (4.0-5.9 Tg/year), how much of this difference do you think may be attributed to the fact that the inversion is solely based on satellite overpasses in the afternoon when NH3 concentration is usually higher than rest of the day (in other words, not incorporating diurnal changes)?
Technical corrections:
Line 18: “2.5” in PM2.5 should be subscripted throughout the text.
Line 54: Studies show
Line 122: to validate emissions
Line 151: The NH3 profiles
Line 268: there are almost no
Line 331: Figure 3 caption says LRTAP 2018 but figure legend says LRTAP 2020.
Line 336: is closer to
Line 345: Spring does not need to be capitalized here.
Line 462: redundant “of”
References:
Kuttippurath, J., Patel, V. K., Kashyap, R., Singh, A., & Clerbaux, C. (2024). Anomalous increase in global atmospheric ammonia during COVID-19 lockdown: Need policies to curb agricultural emissions. Journal of Cleaner Production, 434, 140424. https://doi.org/10.1016/j.jclepro.2023.140424
Citation: https://doi.org/10.5194/egusphere-2024-1073-RC1 -
RC2: 'Comment on egusphere-2024-1073', Anonymous Referee #2, 30 Jun 2024
This MS deals with the ammonia emission estimates for Europe using satellite observations. As there are not many studies like this, the MS has new information and thus, can be considered for publication. However, there are certain things to be clarified before the MS can be accepted.
Minor:
- Neither the Abstract nor Conclusions provides a clear message about this study. What is the main conclusion from this study? Just the comparison of different estimates? How we can improve the emission estimates? What are current uncertainties? What uncertainty is addressed in this study? This has to be clearly mentioned in the Abstract and Conclusion, and the problem must be properly discussed in the Introduction too. Now this is just a comparison paper, as you stated in the last sentence of the Abstract.
- Please state why the year 2020 is selected for this particular study.
- When you add NOx, how much the emission estimates improved in percent? Please state this.
- There is a clear seasonal difference in emission estimates. This suggests that the meteorology plays a big part in these calculations. So if you change meteorological input, how much will be the difference? If you consider the uncertainties in meteorological data, what would be the uncertainty or bias in the estimated emissions?
- Conclusion is almost two pages, but I do not see that much discussion in the paper. So please make it short. A 350-word Conclusion would be much more effective than this.
- I find some language issues in the MS, please pay attention to that.
technical:
L19: affect
L26: limited daily observation
L28: be specific about the region you mentioned here
L52: citation is not correct
L66: too many “type” here. Please rephrase
L270: this aspect is discussed in this paper https://doi.org/10.1016/j.jclepro.2023.140424
L301: How small is this “small”? Please specify the value
L385, L388: Please state the reasons for the differences
L410: If you look at the table, the comparison shows better for DESCO, but Figure 7 shows the least bias for HTAP?
L420: Figure 7 shows the smallest bias for HTAP
L465: “For Europe”
---
Citation: https://doi.org/10.5194/egusphere-2024-1073-RC2 - AC1: 'Comment on egusphere-2024-1073', Jieying Ding, 02 Aug 2024
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Ronald van der A
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Leonor Tarrason
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(4031 KB) - Metadata XML
-
Supplement
(1857 KB) - BibTeX
- EndNote
- Final revised paper