the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
25 Feb 2021
25 Feb 2021
OMI-observed HCHO in Shanghai, China during 2010–2019 and ozone sensitivity inferred by improved HCHO / NO2 ratio
- 1Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, China
- 2Institute of Eco-Chongming (IEC), No. 20 Cuiniao Road, Shanghai 202162, China
- 3Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, China
- 1Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai, China
- 2Institute of Eco-Chongming (IEC), No. 20 Cuiniao Road, Shanghai 202162, China
- 3Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, China
Abstract. In recent years, satellite remote sensing has been increasingly used in the long-term observation of ozone (O3) precursors and its formation regime. In this work, formaldehyde (HCHO) data from Ozone Monitoring Instrument (OMI) were used to analyse the temporal and spatial distribution of HCHO vertical column densities (VCDs) in Shanghai from 2010 to 2019. HCHO VCDs exhibited the highest value in summer and the lowest in winter, the high-VCD concentrated in western Shanghai. Temperature largely influence HCHO by affecting the biogenic emissions and photochemical reactions, and industry was the major anthropogenic source. The satellite observed formaldehyde to nitrogen dioxide ratio (FNRSAT) reflects that the O3 formation regime had significant seasonal characteristics and gradually manifested as transitional ozone formation regime dominated in Shanghai. The uneven distribution in space was mainly reflected as the higher FNRSAT and surface O3 concentration in rural area. To compensate the shortcoming of FNRSAT that it can only characterize O3 formation around satellite overpass time, correction of FNRSAT was implemented with hourly surface FNR and O3 data. After correction, O3 formation regime showed the trend moving towards VOC-limited in both time and space, and regime indicated by FNRSAT can better reflect O3 formation for a day. This study can help us better understand HCHO characteristics and O3 formation regime in Shanghai, and also provide a method to improve FNRSAT for characterizing O3 formation in a day, which will be significant for developing O3 prevention and control strategies.
Danran Li et al.
Status: closed
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RC1: 'Comment on acp-2020-1298', Anonymous Referee #2, 05 Apr 2021
This paper uses satellite HCHO/NO2 to investigate the ozone sensitivity in Shanghai, China. While the linkage with surface observations is novel, there are several issues with the manuscript:
- The novelty of this paper is the inclusion of ground-based observations, but I think the ground-based measurements are underused in this study. Since a main part of this study is on HCHO, I don’t see how the authors use ground-based measurements of HCHO to support satellite HCHO. Do you see similar temporal patterns from ground vs. space? This may also help understand the difference between column vs. surface HCHO.
- There seems to be some artificial strip patterns with HCHO (Figure 2), which looks like due to the influence of OMI swath changes. It is not clear how the authors process OMI HCHO data. The authors mentioned they re-grid the data to 0.01Ë x 0.01Ë, which is much finer than the resolution of OMI. No details are provided in terms of spatial downscaling. In general, spatial oversampling is used to process OMI data to achieve better resolution (e.g. Zhu et al., 2014). I suggest the authors consider following such procedure.
- It’s not clear to me how the authors explore the impacts of anthropogenic emissions on HCHO. There seems to be several issues. First, the authors only consider the primary emissions of HCHO, but a lot of HCHO is produced secondarily from other VOCs like alkene. The HCHO yield should also vary with VOC species, and also meteorology. Second, as I pointed out earlier, the authors did not consider the role of biogenic emissions especially isoprene. Without secondary HCHO, there is little we can learn about the driven factors of HCHO from this paper.
- Recent literature report there is uncertainty with the regime threshold for HCHO/NO2. The authors consider the uncertainty with diurnal cycle, but even at the overpass time, the regime threshold may also vary (Shroeder et al., 2017; Jin et al., 2020; Souri et al., 2020). I suggest the authors be more cautionary about applying the thresholds to separate regimes. More validation analysis is needed to support their regime classification.
- As I commented previously, the correction for diurnal variation doesn’t make sense to me. First, the authors did not consider the difference between column-based satellite HCHO/NO2 vs. surface observed HCHO/NO2. Given the variation of the boundary layer height, the relationship between surface and column HCHO/NO2 should also vary with time. Second, it’s not clear to me why the authors use âO3 to weight FNR. If the authors are only interested in the time when ozone production is most efficient, wouldn’t it be easier to look the 1-hour maximum ozone? Third, there is no evidence supports whether such changes actually improved the regime classification.
Minor Comments:
- Lines 116 to 120: Do you see similar seasonal cycle of HCHO from ground?
- Figure 1: Please define season here.
- Figure 1: I’d suggest include error bars to indicate spatial variation.
- Figure 3: Why did you choose to show seasonal cycle only? I think it will be more interesting if you can show the time series from 2010 to 2018, and see how HCHO is correlated with each factor. This may also help explain the inter-annual variability of HCHO.
- Figure 4: Need to include secondary HCHO from both anthropogenic and biogenic VOCs.
- Figure 7: How do you define urban vs. rural areas?
- Figure 8: It’s unclear whether you’re showing FNR and ozone for one site or three sites together? If one site, which site?
Zhu, L. et al. Anthropogenic emissions of highly reactive volatile organic compounds in eastern Texas inferred from oversampling of satellite (OMI) measurements of HCHO columns. Environ Res Lett 9, 114004 (2014).
Schroeder, J. R. et al. New insights into the column CH2O/NO2 ratio as an indicator of near-surface ozone sensitivity. Journal of Geophysical Research: Atmospheres 122, 8885–8907 (2017).
Souri, A. H. et al. Revisiting the effectiveness of HCHO/NO2 ratios for inferring ozone sensitivity to its precursors using high resolution airborne remote sensing observations in a high ozone episode during the KORUS-AQ campaign. Atmospheric Environment 224, 117341–12 (2020).
Jin, X., Fiore, A., Boersma, K. F., Smedt, I. D. & Valin, L. Inferring changes in summertime surface ozone-NOx -VOC chemistry over U.S. urban areas from two decades of satellite and ground-based observations. Environ Sci Technol (2020) doi:10.1021/acs.est.9b07785.
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AC1: 'Reply on RC1', Shanshan Wang, 18 Jun 2021
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2020-1298/acp-2020-1298-AC1-supplement.pdf
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RC2: 'Comment on acp-2020-1298', Anonymous Referee #1, 09 May 2021
This study examined long-term HCHO columns in Shanghai with OMI and ground-based remote sensing observations. The author also studied the ozone sensitivity in Shanghai and proposed a correction factor to satellite HCHO to NO2 ratio. The authors found that applying such a correction results in ozone formation is more likely to be under the VOC-limited regime. In my view, this work is among few studies exclusively focusing on HCHO observations and ozone sensitivity in China, thus is appropriate for publication at ACP subject to the following concerns.
The authors examined monthly averaged columns at a spatial resolution of 0.01x0.01 degree. I am not sure whether there would be enough pixels in each grid cell to reduce the uncertainty in mean HCHO column to a much lower and acceptable level. This could be a potential issue in the winter seasons when SZA is high and the light path is long.
Did the authors correct the well-known drift in OMI SAO HCHO product? If not, please do so and update the results accordingly.
In page 7, please clarify how to get the HCHO emission rate? Is HCHO mainly secondary? If so, how to determine HCHO yields from NMVOCs? The authors may also want to consider the lower HCHO yields from NMVOCs as NOx emissions drop in the study period.
Minor comments:
Page 2, line 39-40, please include GOME-2 A, B, and C
Page 3, line 76, please change “in a day” to “in the daytime”
Page 3, line 80, please change “pixels” to “rows”
Page 3, line 81, here and elsewhere, please change “~” to “-”. “~” means “approximately”
Page 3, line 92-93, I think by using MainQualityFlag=0 as the criterion, you have filtered out pixels affected by row anomalies already.
Page 5, line 127-129, could you please explain the spatial variations in HCHO columns. Is the spatial pattern consistent with emissions?
-
AC2: 'Reply on RC2', Shanshan Wang, 18 Jun 2021
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2020-1298/acp-2020-1298-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Shanshan Wang, 18 Jun 2021
Status: closed
-
RC1: 'Comment on acp-2020-1298', Anonymous Referee #2, 05 Apr 2021
This paper uses satellite HCHO/NO2 to investigate the ozone sensitivity in Shanghai, China. While the linkage with surface observations is novel, there are several issues with the manuscript:
- The novelty of this paper is the inclusion of ground-based observations, but I think the ground-based measurements are underused in this study. Since a main part of this study is on HCHO, I don’t see how the authors use ground-based measurements of HCHO to support satellite HCHO. Do you see similar temporal patterns from ground vs. space? This may also help understand the difference between column vs. surface HCHO.
- There seems to be some artificial strip patterns with HCHO (Figure 2), which looks like due to the influence of OMI swath changes. It is not clear how the authors process OMI HCHO data. The authors mentioned they re-grid the data to 0.01Ë x 0.01Ë, which is much finer than the resolution of OMI. No details are provided in terms of spatial downscaling. In general, spatial oversampling is used to process OMI data to achieve better resolution (e.g. Zhu et al., 2014). I suggest the authors consider following such procedure.
- It’s not clear to me how the authors explore the impacts of anthropogenic emissions on HCHO. There seems to be several issues. First, the authors only consider the primary emissions of HCHO, but a lot of HCHO is produced secondarily from other VOCs like alkene. The HCHO yield should also vary with VOC species, and also meteorology. Second, as I pointed out earlier, the authors did not consider the role of biogenic emissions especially isoprene. Without secondary HCHO, there is little we can learn about the driven factors of HCHO from this paper.
- Recent literature report there is uncertainty with the regime threshold for HCHO/NO2. The authors consider the uncertainty with diurnal cycle, but even at the overpass time, the regime threshold may also vary (Shroeder et al., 2017; Jin et al., 2020; Souri et al., 2020). I suggest the authors be more cautionary about applying the thresholds to separate regimes. More validation analysis is needed to support their regime classification.
- As I commented previously, the correction for diurnal variation doesn’t make sense to me. First, the authors did not consider the difference between column-based satellite HCHO/NO2 vs. surface observed HCHO/NO2. Given the variation of the boundary layer height, the relationship between surface and column HCHO/NO2 should also vary with time. Second, it’s not clear to me why the authors use âO3 to weight FNR. If the authors are only interested in the time when ozone production is most efficient, wouldn’t it be easier to look the 1-hour maximum ozone? Third, there is no evidence supports whether such changes actually improved the regime classification.
Minor Comments:
- Lines 116 to 120: Do you see similar seasonal cycle of HCHO from ground?
- Figure 1: Please define season here.
- Figure 1: I’d suggest include error bars to indicate spatial variation.
- Figure 3: Why did you choose to show seasonal cycle only? I think it will be more interesting if you can show the time series from 2010 to 2018, and see how HCHO is correlated with each factor. This may also help explain the inter-annual variability of HCHO.
- Figure 4: Need to include secondary HCHO from both anthropogenic and biogenic VOCs.
- Figure 7: How do you define urban vs. rural areas?
- Figure 8: It’s unclear whether you’re showing FNR and ozone for one site or three sites together? If one site, which site?
Zhu, L. et al. Anthropogenic emissions of highly reactive volatile organic compounds in eastern Texas inferred from oversampling of satellite (OMI) measurements of HCHO columns. Environ Res Lett 9, 114004 (2014).
Schroeder, J. R. et al. New insights into the column CH2O/NO2 ratio as an indicator of near-surface ozone sensitivity. Journal of Geophysical Research: Atmospheres 122, 8885–8907 (2017).
Souri, A. H. et al. Revisiting the effectiveness of HCHO/NO2 ratios for inferring ozone sensitivity to its precursors using high resolution airborne remote sensing observations in a high ozone episode during the KORUS-AQ campaign. Atmospheric Environment 224, 117341–12 (2020).
Jin, X., Fiore, A., Boersma, K. F., Smedt, I. D. & Valin, L. Inferring changes in summertime surface ozone-NOx -VOC chemistry over U.S. urban areas from two decades of satellite and ground-based observations. Environ Sci Technol (2020) doi:10.1021/acs.est.9b07785.
-
AC1: 'Reply on RC1', Shanshan Wang, 18 Jun 2021
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2020-1298/acp-2020-1298-AC1-supplement.pdf
-
RC2: 'Comment on acp-2020-1298', Anonymous Referee #1, 09 May 2021
This study examined long-term HCHO columns in Shanghai with OMI and ground-based remote sensing observations. The author also studied the ozone sensitivity in Shanghai and proposed a correction factor to satellite HCHO to NO2 ratio. The authors found that applying such a correction results in ozone formation is more likely to be under the VOC-limited regime. In my view, this work is among few studies exclusively focusing on HCHO observations and ozone sensitivity in China, thus is appropriate for publication at ACP subject to the following concerns.
The authors examined monthly averaged columns at a spatial resolution of 0.01x0.01 degree. I am not sure whether there would be enough pixels in each grid cell to reduce the uncertainty in mean HCHO column to a much lower and acceptable level. This could be a potential issue in the winter seasons when SZA is high and the light path is long.
Did the authors correct the well-known drift in OMI SAO HCHO product? If not, please do so and update the results accordingly.
In page 7, please clarify how to get the HCHO emission rate? Is HCHO mainly secondary? If so, how to determine HCHO yields from NMVOCs? The authors may also want to consider the lower HCHO yields from NMVOCs as NOx emissions drop in the study period.
Minor comments:
Page 2, line 39-40, please include GOME-2 A, B, and C
Page 3, line 76, please change “in a day” to “in the daytime”
Page 3, line 80, please change “pixels” to “rows”
Page 3, line 81, here and elsewhere, please change “~” to “-”. “~” means “approximately”
Page 3, line 92-93, I think by using MainQualityFlag=0 as the criterion, you have filtered out pixels affected by row anomalies already.
Page 5, line 127-129, could you please explain the spatial variations in HCHO columns. Is the spatial pattern consistent with emissions?
-
AC2: 'Reply on RC2', Shanshan Wang, 18 Jun 2021
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2020-1298/acp-2020-1298-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Shanshan Wang, 18 Jun 2021
Danran Li et al.
Danran Li et al.
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