the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Satellite-based, top-down approach for the adjustment of aerosol precursor emissions over East Asia: The Geostationary Environment Monitoring Spectrometer (GEMS) data fusion product and its proxies
Jincheol Park
Jia Jung
Yunsoo Choi
Hyunkwang Lim
Minseok Kim
Kyunghwa Lee
Yungon Lee
Jhoon Kim
Abstract. In response to the need for securing a spatiotemporally more up-to-date emissions inventory and the impending release of new geostationary platform-derived observational data generated by the Geostationary Environment Monitoring Spectrometer (GEMS) and its sister instruments, this study, using a series of GEMS data fusion product and its proxy data and CTM-based inverse modeling techniques, aims to establish a top-down approach for adjusting aerosol precursor emissions over East Asia. We begin by sequentially adjusting bottom-up estimates of nitrogen oxides (NOx) and primary particulate matter (PM) emissions, both of which significantly contribute to aerosol loadings over East Asia, to reduce model biases in aerosol optical depth (AOD) simulations during the year 2019. While the model initially underestimates AOD by 50.73 % on average, the sequential emissions adjustments that led to overall increases in the amounts of NOx emissions by 122.79 % and of primary PM emissions by 76.68 % and 114.63 % (single- and multiple-instrument-derived emissions adjustments, respectively), reduce the extent of AOD underestimation to 33.84 % and 19.60 %, respectively. We consider the outperformance of the model using the emissions constrained by the data fusion product the result of the improvement in the quantity of available data. Taking advantage of the data fusion product, we perform sequential emissions adjustments during the spring of 2022, the period during which the substantial reductions in anthropogenic emissions took place accompanied by the COVID-19 pandemic lockdowns over highly industrialized and urbanized regions in China. While the model initially overestimates surface PM2.5 concentrations by 47.58 % and 20.60 % in the North China Plain (NCP) region and Korea, the sequential emissions adjustments that led to overall decreases in NOx and primary PM emissions by 7.84 % and 9.03 %, respectively, substantially reduce the extent of PM2.5 underestimation to 19.58 % and 6.81 %, respectively. These findings indicate that the series of emissions adjustments performed in this study are generally effective at reducing model biases in simulations of aerosol loading over East Asia; in particular, the model performance tends to improve to a greater extent on the condition that spatiotemporally more continuous and frequent observational references are used to capture variations in bottom-up estimates of emissions. In addition to reconfirming the close association between aerosol precursor emissions and AOD as well as surface PM2.5 concentrations, the findings of this study could provide a useful basis for how to most effectively exploit multi-source top-down information for capturing highly varying anthropogenic emissions.
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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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Supplement
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- Final revised paper
Journal article(s) based on this preprint
Jincheol Park et al.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-87', Anonymous Referee #1, 13 Feb 2023
- AC1: 'Reply on RC1', Jincheol Park, 04 Apr 2023
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RC2: 'Comment on egusphere-2023-87', Anonymous Referee #2, 24 Feb 2023
The manuscript presents a study of where emissions from NOx and primary aerosols are modified sequentially to improve AOD predictions over eastern Asia using TROPOMI NO2 data and AOD products from multiple geostationary satellites (including GEMS). This is done for two periods, one with resulting increasing of emissions and another with decreases due to COVID lock-down conditions. This study represents a great contribution to the field and it’s within the scope of the journal. The manuscript is well written and referenced.
One of my major concerns is that I think organic aerosols are being treated as primary aerosols which is a misconception. This likely results in an overprediction of the contribution of primary aerosols. More discussion on the topic and caution on how this data might be used needs to be included as is likely that changes attributed to primary PM emissions should really be attributed to changes to precursor gases other than NOx. This needs to be addressed throughout the manuscript.
Another concern is that when reading the title and abstract it gives the impression this study is using GEMS trace gas data which is not the case as the only GEMS product being used is the AOD one after being fused with a few other datasets. I would encourage the authors to rephrase the title and abstract to avoid giving these expectations, as there are high expectations from the community about studies assimilating trace gas retrievals from GEMS.
Additional comments line by line can be found below
Comments by line:
238-241. Please provide additional information with respect to iteration procedure. Is this iterating from month to month? Or is this an iteration within the same month to find convergence? Also clarify if F and the jacobian matrices are recomputed after each iteration. Eqn 3 can generate negative values, so also provide information on how that was handled.
242-256. What prior emissions are used when doing the primary PM emission estimations? Line 251 says that PM adjustments are applied to the NOx-constrained emissions, but if not clear if the NOx constrained emissions are used as the prior for the PM emission estimation or not.
242-256. Aerosols in east Asia are mostly secondary unless coming from biomass burning or dust events. But this approach is scaling primary PM emissions. This caveat and discussion on the limitations of this approach needs to be discussed in the text. If NOx constrained emissions were used as prior for the PM emission constraints this reduces the problem only partially as discrepancies in AOD could be attributed to emissions from other precursors such as VOCs, SO2 and NH3.
250-252 If I’m reading the text correctly, NOx emissions were estimated at a monthly scale and primary PM emissions at a daily scale? Can you elaborate these different timescales were chosen?
Section 2.5.1. Can you clarify how NOx and primary aerosol emissions are scaled spatially? Are different correction factors derived for each gridcell? Is there any spatial correlation used within neighboring cells?
Section 2.5.2. Why not apply the same approach as in section 2.5.1 for NOx emissions on 2022? Eqn 5 might only be valid is meteorological conditions were consistent for both years. Unless there is a very good reason for doing this, I would suggest using the same approach for consistency.
- Are AERONET sites considered over the whole domain or only over Korea?
- How is organic aerosol included in this summation of lumped species? Organic aerosols are a mixture or primary and secondary aerosols, with a big fraction of it being secondary for anthropogenic pollution other than biomass burning (e.g., see papers from Jose Jimenez group at CU-Boulder), and thus if organic aerosol is being considered as primary this is a strong misconception that needs to be addressed. Additionally, sampling of organic aerosol is a difficult undertaking, and it is been found that routine measurements as those used in the Korean sites might underpredict organic aerosol as compared to the more research grade measurements (like those from an High res -time of flight – aerosol mass spectrometer). You can refer to KORUS-AQ measurements for insights on this.
- How is dust being measured? If it’s through ions, generally only a small fraction of the total mass concentration is captured.
- It would be great if the emission changes could be aggregated on a per country or per region basis, as emissions generally are based on what’s reported by each country, which will help inform the teams producing those emissions. Also, evaluation against NO2 surface measurements is only done in Korea, so knowing what emissions changes were found here would help the interpretation.
Figure 2 and 3. Shouldn’t columns b) and c) be the same plots in both figures as is the same base year and same emissions? They look quite different in both figures.
Figure 2-4. There still seems to be a substantial gap for AOD after the inversions. Thus, I would encourage the authors to discuss potential reasons for this behavior. One might be related to the approach of only scaling primary PM, while most of the aerosol might be from secondary origin. It was not clear to me how emissions were modified spatially, so depending on how’s that done that could be another potential reason.
- As mentioned above, it looks like organic aerosol is being considered as primary aerosol which is generally not the case. Thus some of the conclusions derived here might not be accurate. I think there needs to be text suggesting that is likely that the corrections to primary PM emissions might be overpredicted as they are compensating for changes that might need to be made to precursor gases other than NOx.
44-445. This is stating that things improved due to GEMS, which in my opinion is not clear from these results as multiple other datasets are being used. To make this point more clear you would have to add an additional test where GEMS is not used and compare it to the one with GEMS for the same period.
Citation: https://doi.org/10.5194/egusphere-2023-87-RC2 - AC2: 'Reply on RC2', Jincheol Park, 04 Apr 2023
-
RC3: 'Comment on egusphere-2023-87', Anonymous Referee #3, 28 Feb 2023
The manuscript delivers informative methodology and results to improve a bottom-up emissions inventory to better simulate AOD and surface PM2.5 concentrations. Authors utilized AOD and NO2 products, and photochemical air quality modeling to show their concepts, examples, and results step by step. Some minor revisions would be necessary before its publication.
1) the manuscript includes too much information; two episodes, different satellite products, AOD, NO2, AERONET, surface observations, two regions (NCP and South Korea). A schematic diagram would be helpful to understand the overall scope of the study.
2) Figures 2 and 3, Table 1: NOx-constrained emissions were updated based on TROPOMI, not AHI nor GOCI as I understand, more clear explanation would be helpful.
3) Table S5: After NOx emission adjustment, model bias increases are observed for certain seasons. Authors need to discuss how this will have influence on modeled AOD, especially during cold season when nitrate concentration increases in NE Asia.
4) Line 370: the remaining portion may include 'unknown' species which is not always primary.
Citation: https://doi.org/10.5194/egusphere-2023-87-RC3 - AC3: 'Reply on RC3', Jincheol Park, 04 Apr 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-87', Anonymous Referee #1, 13 Feb 2023
- AC1: 'Reply on RC1', Jincheol Park, 04 Apr 2023
-
RC2: 'Comment on egusphere-2023-87', Anonymous Referee #2, 24 Feb 2023
The manuscript presents a study of where emissions from NOx and primary aerosols are modified sequentially to improve AOD predictions over eastern Asia using TROPOMI NO2 data and AOD products from multiple geostationary satellites (including GEMS). This is done for two periods, one with resulting increasing of emissions and another with decreases due to COVID lock-down conditions. This study represents a great contribution to the field and it’s within the scope of the journal. The manuscript is well written and referenced.
One of my major concerns is that I think organic aerosols are being treated as primary aerosols which is a misconception. This likely results in an overprediction of the contribution of primary aerosols. More discussion on the topic and caution on how this data might be used needs to be included as is likely that changes attributed to primary PM emissions should really be attributed to changes to precursor gases other than NOx. This needs to be addressed throughout the manuscript.
Another concern is that when reading the title and abstract it gives the impression this study is using GEMS trace gas data which is not the case as the only GEMS product being used is the AOD one after being fused with a few other datasets. I would encourage the authors to rephrase the title and abstract to avoid giving these expectations, as there are high expectations from the community about studies assimilating trace gas retrievals from GEMS.
Additional comments line by line can be found below
Comments by line:
238-241. Please provide additional information with respect to iteration procedure. Is this iterating from month to month? Or is this an iteration within the same month to find convergence? Also clarify if F and the jacobian matrices are recomputed after each iteration. Eqn 3 can generate negative values, so also provide information on how that was handled.
242-256. What prior emissions are used when doing the primary PM emission estimations? Line 251 says that PM adjustments are applied to the NOx-constrained emissions, but if not clear if the NOx constrained emissions are used as the prior for the PM emission estimation or not.
242-256. Aerosols in east Asia are mostly secondary unless coming from biomass burning or dust events. But this approach is scaling primary PM emissions. This caveat and discussion on the limitations of this approach needs to be discussed in the text. If NOx constrained emissions were used as prior for the PM emission constraints this reduces the problem only partially as discrepancies in AOD could be attributed to emissions from other precursors such as VOCs, SO2 and NH3.
250-252 If I’m reading the text correctly, NOx emissions were estimated at a monthly scale and primary PM emissions at a daily scale? Can you elaborate these different timescales were chosen?
Section 2.5.1. Can you clarify how NOx and primary aerosol emissions are scaled spatially? Are different correction factors derived for each gridcell? Is there any spatial correlation used within neighboring cells?
Section 2.5.2. Why not apply the same approach as in section 2.5.1 for NOx emissions on 2022? Eqn 5 might only be valid is meteorological conditions were consistent for both years. Unless there is a very good reason for doing this, I would suggest using the same approach for consistency.
- Are AERONET sites considered over the whole domain or only over Korea?
- How is organic aerosol included in this summation of lumped species? Organic aerosols are a mixture or primary and secondary aerosols, with a big fraction of it being secondary for anthropogenic pollution other than biomass burning (e.g., see papers from Jose Jimenez group at CU-Boulder), and thus if organic aerosol is being considered as primary this is a strong misconception that needs to be addressed. Additionally, sampling of organic aerosol is a difficult undertaking, and it is been found that routine measurements as those used in the Korean sites might underpredict organic aerosol as compared to the more research grade measurements (like those from an High res -time of flight – aerosol mass spectrometer). You can refer to KORUS-AQ measurements for insights on this.
- How is dust being measured? If it’s through ions, generally only a small fraction of the total mass concentration is captured.
- It would be great if the emission changes could be aggregated on a per country or per region basis, as emissions generally are based on what’s reported by each country, which will help inform the teams producing those emissions. Also, evaluation against NO2 surface measurements is only done in Korea, so knowing what emissions changes were found here would help the interpretation.
Figure 2 and 3. Shouldn’t columns b) and c) be the same plots in both figures as is the same base year and same emissions? They look quite different in both figures.
Figure 2-4. There still seems to be a substantial gap for AOD after the inversions. Thus, I would encourage the authors to discuss potential reasons for this behavior. One might be related to the approach of only scaling primary PM, while most of the aerosol might be from secondary origin. It was not clear to me how emissions were modified spatially, so depending on how’s that done that could be another potential reason.
- As mentioned above, it looks like organic aerosol is being considered as primary aerosol which is generally not the case. Thus some of the conclusions derived here might not be accurate. I think there needs to be text suggesting that is likely that the corrections to primary PM emissions might be overpredicted as they are compensating for changes that might need to be made to precursor gases other than NOx.
44-445. This is stating that things improved due to GEMS, which in my opinion is not clear from these results as multiple other datasets are being used. To make this point more clear you would have to add an additional test where GEMS is not used and compare it to the one with GEMS for the same period.
Citation: https://doi.org/10.5194/egusphere-2023-87-RC2 - AC2: 'Reply on RC2', Jincheol Park, 04 Apr 2023
-
RC3: 'Comment on egusphere-2023-87', Anonymous Referee #3, 28 Feb 2023
The manuscript delivers informative methodology and results to improve a bottom-up emissions inventory to better simulate AOD and surface PM2.5 concentrations. Authors utilized AOD and NO2 products, and photochemical air quality modeling to show their concepts, examples, and results step by step. Some minor revisions would be necessary before its publication.
1) the manuscript includes too much information; two episodes, different satellite products, AOD, NO2, AERONET, surface observations, two regions (NCP and South Korea). A schematic diagram would be helpful to understand the overall scope of the study.
2) Figures 2 and 3, Table 1: NOx-constrained emissions were updated based on TROPOMI, not AHI nor GOCI as I understand, more clear explanation would be helpful.
3) Table S5: After NOx emission adjustment, model bias increases are observed for certain seasons. Authors need to discuss how this will have influence on modeled AOD, especially during cold season when nitrate concentration increases in NE Asia.
4) Line 370: the remaining portion may include 'unknown' species which is not always primary.
Citation: https://doi.org/10.5194/egusphere-2023-87-RC3 - AC3: 'Reply on RC3', Jincheol Park, 04 Apr 2023
Peer review completion
Journal article(s) based on this preprint
Jincheol Park et al.
Jincheol Park et al.
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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
(2372 KB) - Metadata XML
-
Supplement
(2830 KB) - BibTeX
- EndNote
- Final revised paper