MAX-DOAS observations of formaldehyde and nitrogen dioxide at three sites in Asia and comparison with the global chemistry transport model CHASER

Hoque, Hossain M. S.; Sudo, Kengo; Irie, Hitoshi; Damiani, Alessandro; Fatmi, Al Mashroor

doi:https://doi.org/10.5194/acp-2021-815

Preprints

https://doi.org/10.5194/acp-2021-815

Preprints

25 Oct 2021

| 25 Oct 2021

Status: this preprint was under review for the journal ACP but the revision was not accepted.

MAX-DOAS observations of formaldehyde and nitrogen dioxide at three sites in Asia and comparison with the global chemistry transport model CHASER

Hossain M. S. Hoque, Kengo Sudo, Hitoshi Irie, Alessandro Damiani, and Al Mashroor Fatmi

Abstract. Formaldehyde (HCHO) and nitrogen dioxide (NO₂) concentrations and profiles were retrieved from ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) observation during January 2017 through December 2018 at three sites in Asia: (1) Phimai in Thailand (15.18° N, 102.5° E); (2) Pantnagar (29° N, 78.90° E) in the Indo Gangetic plain (IGP) in India; and (3) Chiba (35.62° N, 140.10° E) in Japan. The NO₂ and HCHO partial columns (< 4 km) and profiles simulated using the global chemistry transport model (CTM) and CHASER were compared to those of MAX-DOAS. The vertical sensitivity of the datasets was elucidated using the averaging kernel (AK) information from the MAX-DOAS retrievals. The NO₂ and HCHO concentrations at all three sites showed consistent seasonal variation throughout the investigated period. Biomass burning affected the HCHO and NO₂ variation in Phimai during the dry season and in Pantnagar during spring (March–May) and the post-monsoon (September–November) season. High NO₂ concentrations in Phimai during the wet season (June–September) are attributed to soil emissions of nitrogen oxides (NO_x), confirmed from satellite observations and CHASER simulations. Comparison with CHASER shows that the seasonal variations in the HCHO and NO₂ abundances at Phimai and Chiba agree well, with a correlation coefficient (R) of 0.80. Results agree with the variation, ranging mainly within the one sigma standard deviation of the observations. At Phimai, pyrogenic emissions contribute to the HCHO and NO₂ concentrations up to ~50 and ~35 %, respectively. CHASER showed limited skills in reproducing the NO₂ and HCHO variability at Pantnagar. However, the CHASER simulations in the IGP region agreed well with the reported results. Sensitivity studies showed that anthropogenic emissions affected the seasonal variation of NO₂ and HCHO concentrations in the IGP region.

Received: 30 Sep 2021 – Discussion started: 25 Oct 2021

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Hossain M. S. Hoque, Kengo Sudo, Hitoshi Irie, Alessandro Damiani, and Al Mashroor Fatmi

Status: closed

CC1:
'Comment on acp-2021-815', Vinod Kumar, 29 Oct 2021
The study by Hoque et al., 2021 presents MAX-DOAS measurements of NO₂ and HCHO at three different sites in south-Asia and compareS the vertical distributions with simulations from the chemical transport model CHASER. I have a few comments regarding the study.
In the introduction (lines 88-93), the authors mention that it is the first study to evaluate the CHASER model simulated NO₂ and HCHO profiles using MAX-DOAS observations in three atmospheric environments. They also mention that no relevant literature in the past has described the use of MAX-DOAS datasets to evaluate global CTMs in southern and southeastern Asian regions. In this context, the study by Kumar et al. (2021) should be mentioned, which proposed a robust method to evaluate the vertical distribution of trace gases in an atmospheric chemistry model using MAX-DOAS measurements.

In lines 145-146, the authors mention that the reference spectra were recorded at elevation angle of 70° instead of 90° to minimize variations in the signals measured at each elevation angle. I find it difficult to understand. How would a reference at 70° minimize the variation in signals and what advantage does it have?

In lines 147-148, the authors mention that the off-axis elevation angles were limited to < 10° to reduce the systematic error in the in-oxygen collision complex (O₄) fitting results. This statement is not so clear and should be explained.

The authors mention that they have used the anthropogenic emissions from HTAP_v2.2 for 2008, while the model simulations were performed for almost a decade later. I wonder why EDGAR v5AP (https://edgar.jrc.ec.europa.eu/emissions_data_and_maps) was not used, which includes the anthropogenic emissions for up to 2015.

Sections 3.1.1 and 3.1.2 show the seasonal variability of HCHO and NO₂, respectively, in two different vertical layers at Pantnagar located in the Indo-Gangetic plain and discuss the impact of crop residue burning in this region. The Study by Kumar et al. (2020) have also shown more than four years long vertically resolved measurements of the same species from a regionally representative site in the Indo-Gangetic plain and highlighted the impact of crop residue burning in the pre-monsoon and post-monsoon period. It is unfortunate to have no mention of the findings of Kumar et al., 2020 in this context.

Again, in section 3.1.3, HCHO/NO₂ ratios are investigated to determine the ozone production sensitivity for the site Pantnagar in the Indo-Gangetic plain. Yet, there is no mention of two very relevant studies (Kumar et al., 2020; Kumar and Sinha, 2021) in this context that discusses the ozone production sensitivity on VOC and NO_x using various indicators, including HCHO/NO₂. Here, I would like to point out that the threshold values used for HCHO/NO₂ ratios are valid for tropospheric columns and using the same for concentrations might lead to inappropriate inferences (Martin et al., 2004). This aspect should be discussed by the authors.

References
Kumar, V. and Sinha, V.: Season-wise analyses of VOCs, hydroxyl radicals and ozone formation chemistry over north-west India reveal isoprene and acetaldehyde as the most potent ozone precursors throughout the year, Chemosphere, 131184, 10.1016/j.chemosphere.2021.131184, 2021.
Kumar, V., Beirle, S., Dörner, S., Mishra, A. K., Donner, S., Wang, Y., Sinha, V., and Wagner, T.: Long-term MAX-DOAS measurements of NO2, HCHO, and aerosols and evaluation of corresponding satellite data products over Mohali in the Indo-Gangetic Plain, Atmos. Chem. Phys., 20, 14183-14235, 10.5194/acp-20-14183-2020, 2020.
Kumar, V., Remmers, J., Beirle, S., Fallmann, J., Kerkweg, A., Lelieveld, J., Mertens, M., Pozzer, A., Steil, B., Barra, M., Tost, H., and Wagner, T.: Evaluation of the coupled high-resolution atmospheric chemistry model system MECO(n) using in situ and MAX-DOAS NO2 measurements, Atmos. Meas. Tech., 14, 5241-5269, 10.5194/amt-14-5241-2021, 2021.
Martin, R. V., Fiore, A. M., and Van Donkelaar, A.: Space-based diagnosis of surface ozone sensitivity to anthropogenic emissions, Geophysical Research Letters, 31, 10.1029/2004gl019416, 2004.
Citation: https://doi.org/10.5194/acp-2021-815-CC1
- AC1:
  'Reply on CC1', H.M.S. Hoque, 01 Nov 2021
  We thank Mr. Vinod Kumar for his comments on the article. It has indicated more scope of discussion in some sections of the manuscript. We are providing our responses to the comments. The responses the marked in bold alphabets.
  The study by Hoque et al., 2021 presents MAX-DOAS measurements of NO₂ and HCHO at three different sites in south-Asia and compareS the vertical distributions with simulations from the chemical transport model CHASER. I have a few comments regarding the study.
  In the introduction (lines 88-93), the authors mention that it is the first study to evaluate the CHASER model simulated NO₂ and HCHO profiles using MAX-DOAS observations in three atmospheric environments. They also mention that no relevant literature in the past has described the use of MAX-DOAS datasets to evaluate global CTMs in southern and southeastern Asian regions. In this context, the study by Kumar et al. (2021) should be mentioned, which proposed a robust method to evaluate the vertical distribution of trace gases in an atmospheric chemistry model using MAX-DOAS measurements.
  
  Response: Thank you very for notifying the article. We will include this relevant information in the revision.
  In lines 145-146, the authors mention that the reference spectra were recorded at elevation angle of 70° instead of 90° to minimize variations in the signals measured at each elevation angle. I find it difficult to understand. How would a reference at 70° minimize the variation in signals and what advantage does it have?
  
  Response: Our spectrometer operated on a fixed integration time. The intensity of the measured radiation varies with the elevation angle(EL). Therefore, significant variability in the light intensity can potentially occur at all ELs within 15 minutes, which corresponds to the time for one complete scan, which can lead to saturation at the reference EL. To avoid such saturation, the reference EL at 70˚ were preferred to 90˚. Notably, information on the EL settings was fully considered during the differential air mass factor computation. Thus, the sensitivity of the retrieved profile on the choice of reference EL is minimal. The profile retrieval is explicitly explained in the work of Irie et al. 2011, 2015.
  In lines 147-148, the authors mention that the off-axis elevation angles were limited to < 10° to reduce the systematic error in the in-oxygen collision complex (O₄) fitting results. This statement is not so clear and should be explained.
  
  Response: The study by Irie et al. (2015), reported the following findings:
  (a) measurements at off-axis ELs < 10˚ and adopting an EL-dependent correction factor for oxygen collision complexes minimized the effects of temperature-dependent O4 cross-sections and subsequently reduced the uncertainty in the DOAS-fit.
  (b) The aerosol profiles retrieved on the conditions (a), showed better agreement with the coincident cavity ring-down spectrometer (CRDS), LIDAR, and sky radiometer observations.
  The current study has adopted the findings. In addition, the clarity of the sentences will be improved in the revised version.
  The authors mention that they have used the anthropogenic emissions from HTAP_v2.2 for 2008, while the model simulations were performed almost a decade later. I wonder why EDGAR v5AP (https://edgar.jrc.ec.europa.eu/emissions_data_and_maps) was not used, which includes the anthropogenic emissions for up to 2015.
  
  Response: In the standard CHASER simulations, the anthropogenic emissions are kept constant to the 2008 values for consistency in the simulations period. General bottom-up inventories provide data for intermediate years. However, using such inventories, dealing with uncertainty becomes a complex issue. CHASER NO₂ simulations based on the inventories used in the current study have been validated in the work of Sekiya et al. (2018). However, we will discuss this issue in the revised manuscript.
  Sections 3.1.1 and 3.1.2 show the seasonal variability of HCHO and NO₂, respectively, in two different vertical layers at Pantnagar located in the Indo-Gangetic plain and discuss the impact of crop residue burning in this region. The Study by Kumar et al. (2020) have also shown more than four years long vertically resolved measurements of the same species from a regionally representative site in the Indo-Gangetic plain and highlighted the impact of crop residue burning in the pre-monsoon and post-monsoon period. It is unfortunate to have no mention of the findings of Kumar et al., 2020 in this context.
  
  Response: Thank you very for notifying the article. We will include this relevant information in the revision.
  
  Again, in section 3.1.3, HCHO/NO₂ ratios are investigated to determine the ozone production sensitivity for the site Pantnagar in the Indo-Gangetic plain. Yet, there is no mention of two very relevant studies (Kumar et al., 2020; Kumar and Sinha, 2021) in this context that discusses the ozone production sensitivity on VOC and NO_x using various indicators, including HCHO/NO₂. Here, I would like to point out that the threshold values used for HCHO/NO₂ ratios are valid for tropospheric columns and using the same for concentrations might lead to inappropriate inferences (Martin et al., 2004). This aspect should be discussed by the authors.
  
  Response: Thank you very for notifying the article. We will include this relevant information in the revision. Yes, the threshold value can affect the HCHO/NO2 ratio, estimated from the concentration values. We will conduct a sensitivity study and include the discussion in the revised manuscript.
  
  References
  Irie, H., Takashima, H., Kanaya, Y., Boersma, K., Gast, L., Wittrock, F., Brunner, D., Zhou, Y., Roozendael, M. V. : Eight-component retrievals from ground-based MAX-DOAS observations. Atmos. Meas. Tech., 4(6), 1027-1044, https://doi.org/10.5194/amt-4-1027-2011, 2011
  
  Irie, H., Nakayama, T., Shimizu, A., Yamazaki, A., Nagai, T., Uchiyama, A., Zaizen, Y., Kagamitani, S., and Matsumi, Y. : Evaluation of MAX-DOAS aerosol retrievals by coincident observations using CRDS, lidar, and sky radiometer inTsukuba, Japan. Atmos. Meas.Tech., 8(7), 2775-2788, https://doi.org/10.5194/amt-8-2775-2015, 2015
  
  Sekiya, T., Miyazaki, K., Ogochi, K., Sudo, K., & Takigawa, M. : Global high-resolution simulations of tropospheric nitrogen dioxide using CHASER V4.0. Geosci. Model Dev., 11(3), 959-988. http://doi.org/10.5194/gmd-11-959-2018, 2018
  
  Citation: https://doi.org/10.5194/acp-2021-815-AC1
RC1: 'Comment on acp-2021-815', Anonymous Referee #1, 19 Nov 2021

This paper provides an analysis of HCHO and NO2 across three diverse sites. While the data themselves are very interesting and important, the analysis lacks insight and the model used to aid interpretation is inappropriate leading to a paper that is fundamentally flawed in several respects. These include, but are not limited to, the following:

1) Authors posit a change in NO2 behavior at Phimai between this dataset and a previously published data set by the same authors (Hoque et al. 2018) with the difference being that the current dataset shows high NO2 concentrations in the wet season while the previous 2015-2016 data did not. Frankly, I do not see this difference. Looking at figure 3 from Hoque et al. 2018, the 2015-2016 NO2 data are greater than the more recent 2017-2018 data for all months. The trends are also similar, with even higher NO2 during wet season in the earlier 2015-2016 data. This leads to a somewhat unnecessary and lengthy discussion of soil moisture and associated NOx emissions that is not compelling. Even the model simulations show that month-to-month soil emissions only range from 18-24%, such that, even if soil emissions maximize in July, it is only a 5% effect overall compared to the annual average.

2) The treatment of the HCHO to NO2 ratio is not well posed. Looking at Figure 4, it is clear that the vertical gradient in NO2 and HCHO between 0-1 and 1-2 km are quite different. This difference in gradient with much larger decreases in NO2 with altitude fundamentally undermines the use of column values of the ratio as outlined in Schroeder et al. (2017; https://doi.org/10.1002/2017JD026781). Rather than following an old and flawed recipe from the Martin et al. and Duncan et al. papers, the authors would be better served to ask how their data challenges the use of the fixed range of values (VOC-limited for <1 and NOx limited for >2) that Schroeder clearly shows are not viable when more detailed information on vertical gradients is known.

3) The use of the CHASER model, given its resolution on 2.8x2.8 degrees is entirely inappropriate for this analysis. While model suitability for all three sites is problematic, it is greatest for Pantnagar, which is located near the Himalayan foothills where large changes in elevation occur well within the local grid resolution of CHASER. While the problem is obvious from the start, the authors go through an awkward analysis of all possibilities for why the model fails, only to come to the conclusion that problems “are expected to decrease with high resolution (improved spatial resolution) simulations.” This was already a foregone conclusion. In the end, the comparison between CHASER and the MAX-DOAS data yields no useful insights in my opinion.

4) There are red flags in the analysis with regard to proper methods. One example is the comparisons between CHASER model results compared to CHASER when smoothed by application of the averaging kernel. This smoothing should not generate wholesale shifts in the data. For example, in Figure 10, the Phimai (top left) seasonal average NO2 columns from CHASER not only change by as much as a factor of three when smoothed, they also show anticorrelation in the seasonal trend. I don’t know how this is possible, and I have to assume that this is a mistake. Likewise, in Figure 8, sometimes the smoothing has almost no effect (e.g., Post-Monsoon and Winter in Pantnagar) and at other times there are dramatic changes (e.g., Winter in Chiba).

5) Finally, the language issues in the paper are simply too comprehensive to recount here. This goes beyond grammatical issues and affects clarity, especially when trying to understand how data was treated statistically. I would suggest that the authors invite a colleague to help with this as a simple language editor would almost certainly miss the nuanced language problems.

While I would normally provide a thorough line-by-line review, such detail is not warranted given the major flaws in the paper. Given the importance of the data, I suggest that the authors go back and reconsider their choice of model and approach to investigating metrics like HCHO:NO2 ratios. This data set could well challenge current thought on the value of such metrics.

Citation: https://doi.org/10.5194/acp-2021-815-RC1
RC2: 'Comment on acp-2021-815', Anonymous Referee #2, 21 Nov 2021

This manuscript presents observations of HCHO and NO2 at three sites over a multi-year period. The dataset has the potential to be useful, if errors in the data identified by Reviewer 1 are addressed. Presentation of a corrected dataset alone may merit a “Measurement Report” manuscript. However, the analysis component must be significantly strengthened for inclusion in ACP. Specifically:

1.) The HCHO/NO2 ratio discussion is not particularly useful. The vertical profile arguments highlighted by Reviewer 1 are a major concern. Additionally, the Souri et al. (2020) reference cautions against using the ratio in the way that is used here, suggesting instead the use of a slope/intercept parametrization with fitting coefficients that are dependent on local chemistry. The known limitations of the ratio method are not treated seriously in this manuscript. The comparison with modeled ratios is problematic due to choice of model.

2.) The CHOCHO/HCHO ratio is also not particularly useful. There is insufficient discussion of uncertainties in the ratio. There are no uncertainties shown in Figure S1, or otherwise stated in the text. It is unclear what the reader is supposed to glean from the analysis. The conclusion of this section states that detailed analysis is needed – this reviewer agrees.

3.) The usefulness of the comparison with the CHASER model is very limited, given (a) the coarse resolution of the model (b) the outdated emissions inventories.

Citation: https://doi.org/10.5194/acp-2021-815-RC2

Status: closed

CC1:
'Comment on acp-2021-815', Vinod Kumar, 29 Oct 2021
The study by Hoque et al., 2021 presents MAX-DOAS measurements of NO₂ and HCHO at three different sites in south-Asia and compareS the vertical distributions with simulations from the chemical transport model CHASER. I have a few comments regarding the study.
In the introduction (lines 88-93), the authors mention that it is the first study to evaluate the CHASER model simulated NO₂ and HCHO profiles using MAX-DOAS observations in three atmospheric environments. They also mention that no relevant literature in the past has described the use of MAX-DOAS datasets to evaluate global CTMs in southern and southeastern Asian regions. In this context, the study by Kumar et al. (2021) should be mentioned, which proposed a robust method to evaluate the vertical distribution of trace gases in an atmospheric chemistry model using MAX-DOAS measurements.

In lines 145-146, the authors mention that the reference spectra were recorded at elevation angle of 70° instead of 90° to minimize variations in the signals measured at each elevation angle. I find it difficult to understand. How would a reference at 70° minimize the variation in signals and what advantage does it have?

In lines 147-148, the authors mention that the off-axis elevation angles were limited to < 10° to reduce the systematic error in the in-oxygen collision complex (O₄) fitting results. This statement is not so clear and should be explained.

The authors mention that they have used the anthropogenic emissions from HTAP_v2.2 for 2008, while the model simulations were performed for almost a decade later. I wonder why EDGAR v5AP (https://edgar.jrc.ec.europa.eu/emissions_data_and_maps) was not used, which includes the anthropogenic emissions for up to 2015.

Sections 3.1.1 and 3.1.2 show the seasonal variability of HCHO and NO₂, respectively, in two different vertical layers at Pantnagar located in the Indo-Gangetic plain and discuss the impact of crop residue burning in this region. The Study by Kumar et al. (2020) have also shown more than four years long vertically resolved measurements of the same species from a regionally representative site in the Indo-Gangetic plain and highlighted the impact of crop residue burning in the pre-monsoon and post-monsoon period. It is unfortunate to have no mention of the findings of Kumar et al., 2020 in this context.

Again, in section 3.1.3, HCHO/NO₂ ratios are investigated to determine the ozone production sensitivity for the site Pantnagar in the Indo-Gangetic plain. Yet, there is no mention of two very relevant studies (Kumar et al., 2020; Kumar and Sinha, 2021) in this context that discusses the ozone production sensitivity on VOC and NO_x using various indicators, including HCHO/NO₂. Here, I would like to point out that the threshold values used for HCHO/NO₂ ratios are valid for tropospheric columns and using the same for concentrations might lead to inappropriate inferences (Martin et al., 2004). This aspect should be discussed by the authors.

References
Kumar, V. and Sinha, V.: Season-wise analyses of VOCs, hydroxyl radicals and ozone formation chemistry over north-west India reveal isoprene and acetaldehyde as the most potent ozone precursors throughout the year, Chemosphere, 131184, 10.1016/j.chemosphere.2021.131184, 2021.
Kumar, V., Beirle, S., Dörner, S., Mishra, A. K., Donner, S., Wang, Y., Sinha, V., and Wagner, T.: Long-term MAX-DOAS measurements of NO2, HCHO, and aerosols and evaluation of corresponding satellite data products over Mohali in the Indo-Gangetic Plain, Atmos. Chem. Phys., 20, 14183-14235, 10.5194/acp-20-14183-2020, 2020.
Kumar, V., Remmers, J., Beirle, S., Fallmann, J., Kerkweg, A., Lelieveld, J., Mertens, M., Pozzer, A., Steil, B., Barra, M., Tost, H., and Wagner, T.: Evaluation of the coupled high-resolution atmospheric chemistry model system MECO(n) using in situ and MAX-DOAS NO2 measurements, Atmos. Meas. Tech., 14, 5241-5269, 10.5194/amt-14-5241-2021, 2021.
Martin, R. V., Fiore, A. M., and Van Donkelaar, A.: Space-based diagnosis of surface ozone sensitivity to anthropogenic emissions, Geophysical Research Letters, 31, 10.1029/2004gl019416, 2004.
Citation: https://doi.org/10.5194/acp-2021-815-CC1
- AC1:
  'Reply on CC1', H.M.S. Hoque, 01 Nov 2021
  We thank Mr. Vinod Kumar for his comments on the article. It has indicated more scope of discussion in some sections of the manuscript. We are providing our responses to the comments. The responses the marked in bold alphabets.
  The study by Hoque et al., 2021 presents MAX-DOAS measurements of NO₂ and HCHO at three different sites in south-Asia and compareS the vertical distributions with simulations from the chemical transport model CHASER. I have a few comments regarding the study.
  In the introduction (lines 88-93), the authors mention that it is the first study to evaluate the CHASER model simulated NO₂ and HCHO profiles using MAX-DOAS observations in three atmospheric environments. They also mention that no relevant literature in the past has described the use of MAX-DOAS datasets to evaluate global CTMs in southern and southeastern Asian regions. In this context, the study by Kumar et al. (2021) should be mentioned, which proposed a robust method to evaluate the vertical distribution of trace gases in an atmospheric chemistry model using MAX-DOAS measurements.
  
  Response: Thank you very for notifying the article. We will include this relevant information in the revision.
  In lines 145-146, the authors mention that the reference spectra were recorded at elevation angle of 70° instead of 90° to minimize variations in the signals measured at each elevation angle. I find it difficult to understand. How would a reference at 70° minimize the variation in signals and what advantage does it have?
  
  Response: Our spectrometer operated on a fixed integration time. The intensity of the measured radiation varies with the elevation angle(EL). Therefore, significant variability in the light intensity can potentially occur at all ELs within 15 minutes, which corresponds to the time for one complete scan, which can lead to saturation at the reference EL. To avoid such saturation, the reference EL at 70˚ were preferred to 90˚. Notably, information on the EL settings was fully considered during the differential air mass factor computation. Thus, the sensitivity of the retrieved profile on the choice of reference EL is minimal. The profile retrieval is explicitly explained in the work of Irie et al. 2011, 2015.
  In lines 147-148, the authors mention that the off-axis elevation angles were limited to < 10° to reduce the systematic error in the in-oxygen collision complex (O₄) fitting results. This statement is not so clear and should be explained.
  
  Response: The study by Irie et al. (2015), reported the following findings:
  (a) measurements at off-axis ELs < 10˚ and adopting an EL-dependent correction factor for oxygen collision complexes minimized the effects of temperature-dependent O4 cross-sections and subsequently reduced the uncertainty in the DOAS-fit.
  (b) The aerosol profiles retrieved on the conditions (a), showed better agreement with the coincident cavity ring-down spectrometer (CRDS), LIDAR, and sky radiometer observations.
  The current study has adopted the findings. In addition, the clarity of the sentences will be improved in the revised version.
  The authors mention that they have used the anthropogenic emissions from HTAP_v2.2 for 2008, while the model simulations were performed almost a decade later. I wonder why EDGAR v5AP (https://edgar.jrc.ec.europa.eu/emissions_data_and_maps) was not used, which includes the anthropogenic emissions for up to 2015.
  
  Response: In the standard CHASER simulations, the anthropogenic emissions are kept constant to the 2008 values for consistency in the simulations period. General bottom-up inventories provide data for intermediate years. However, using such inventories, dealing with uncertainty becomes a complex issue. CHASER NO₂ simulations based on the inventories used in the current study have been validated in the work of Sekiya et al. (2018). However, we will discuss this issue in the revised manuscript.
  Sections 3.1.1 and 3.1.2 show the seasonal variability of HCHO and NO₂, respectively, in two different vertical layers at Pantnagar located in the Indo-Gangetic plain and discuss the impact of crop residue burning in this region. The Study by Kumar et al. (2020) have also shown more than four years long vertically resolved measurements of the same species from a regionally representative site in the Indo-Gangetic plain and highlighted the impact of crop residue burning in the pre-monsoon and post-monsoon period. It is unfortunate to have no mention of the findings of Kumar et al., 2020 in this context.
  
  Response: Thank you very for notifying the article. We will include this relevant information in the revision.
  
  Again, in section 3.1.3, HCHO/NO₂ ratios are investigated to determine the ozone production sensitivity for the site Pantnagar in the Indo-Gangetic plain. Yet, there is no mention of two very relevant studies (Kumar et al., 2020; Kumar and Sinha, 2021) in this context that discusses the ozone production sensitivity on VOC and NO_x using various indicators, including HCHO/NO₂. Here, I would like to point out that the threshold values used for HCHO/NO₂ ratios are valid for tropospheric columns and using the same for concentrations might lead to inappropriate inferences (Martin et al., 2004). This aspect should be discussed by the authors.
  
  Response: Thank you very for notifying the article. We will include this relevant information in the revision. Yes, the threshold value can affect the HCHO/NO2 ratio, estimated from the concentration values. We will conduct a sensitivity study and include the discussion in the revised manuscript.
  
  References
  Irie, H., Takashima, H., Kanaya, Y., Boersma, K., Gast, L., Wittrock, F., Brunner, D., Zhou, Y., Roozendael, M. V. : Eight-component retrievals from ground-based MAX-DOAS observations. Atmos. Meas. Tech., 4(6), 1027-1044, https://doi.org/10.5194/amt-4-1027-2011, 2011
  
  Irie, H., Nakayama, T., Shimizu, A., Yamazaki, A., Nagai, T., Uchiyama, A., Zaizen, Y., Kagamitani, S., and Matsumi, Y. : Evaluation of MAX-DOAS aerosol retrievals by coincident observations using CRDS, lidar, and sky radiometer inTsukuba, Japan. Atmos. Meas.Tech., 8(7), 2775-2788, https://doi.org/10.5194/amt-8-2775-2015, 2015
  
  Sekiya, T., Miyazaki, K., Ogochi, K., Sudo, K., & Takigawa, M. : Global high-resolution simulations of tropospheric nitrogen dioxide using CHASER V4.0. Geosci. Model Dev., 11(3), 959-988. http://doi.org/10.5194/gmd-11-959-2018, 2018
  
  Citation: https://doi.org/10.5194/acp-2021-815-AC1
RC1: 'Comment on acp-2021-815', Anonymous Referee #1, 19 Nov 2021

This paper provides an analysis of HCHO and NO2 across three diverse sites. While the data themselves are very interesting and important, the analysis lacks insight and the model used to aid interpretation is inappropriate leading to a paper that is fundamentally flawed in several respects. These include, but are not limited to, the following:

1) Authors posit a change in NO2 behavior at Phimai between this dataset and a previously published data set by the same authors (Hoque et al. 2018) with the difference being that the current dataset shows high NO2 concentrations in the wet season while the previous 2015-2016 data did not. Frankly, I do not see this difference. Looking at figure 3 from Hoque et al. 2018, the 2015-2016 NO2 data are greater than the more recent 2017-2018 data for all months. The trends are also similar, with even higher NO2 during wet season in the earlier 2015-2016 data. This leads to a somewhat unnecessary and lengthy discussion of soil moisture and associated NOx emissions that is not compelling. Even the model simulations show that month-to-month soil emissions only range from 18-24%, such that, even if soil emissions maximize in July, it is only a 5% effect overall compared to the annual average.

2) The treatment of the HCHO to NO2 ratio is not well posed. Looking at Figure 4, it is clear that the vertical gradient in NO2 and HCHO between 0-1 and 1-2 km are quite different. This difference in gradient with much larger decreases in NO2 with altitude fundamentally undermines the use of column values of the ratio as outlined in Schroeder et al. (2017; https://doi.org/10.1002/2017JD026781). Rather than following an old and flawed recipe from the Martin et al. and Duncan et al. papers, the authors would be better served to ask how their data challenges the use of the fixed range of values (VOC-limited for <1 and NOx limited for >2) that Schroeder clearly shows are not viable when more detailed information on vertical gradients is known.

3) The use of the CHASER model, given its resolution on 2.8x2.8 degrees is entirely inappropriate for this analysis. While model suitability for all three sites is problematic, it is greatest for Pantnagar, which is located near the Himalayan foothills where large changes in elevation occur well within the local grid resolution of CHASER. While the problem is obvious from the start, the authors go through an awkward analysis of all possibilities for why the model fails, only to come to the conclusion that problems “are expected to decrease with high resolution (improved spatial resolution) simulations.” This was already a foregone conclusion. In the end, the comparison between CHASER and the MAX-DOAS data yields no useful insights in my opinion.

4) There are red flags in the analysis with regard to proper methods. One example is the comparisons between CHASER model results compared to CHASER when smoothed by application of the averaging kernel. This smoothing should not generate wholesale shifts in the data. For example, in Figure 10, the Phimai (top left) seasonal average NO2 columns from CHASER not only change by as much as a factor of three when smoothed, they also show anticorrelation in the seasonal trend. I don’t know how this is possible, and I have to assume that this is a mistake. Likewise, in Figure 8, sometimes the smoothing has almost no effect (e.g., Post-Monsoon and Winter in Pantnagar) and at other times there are dramatic changes (e.g., Winter in Chiba).

5) Finally, the language issues in the paper are simply too comprehensive to recount here. This goes beyond grammatical issues and affects clarity, especially when trying to understand how data was treated statistically. I would suggest that the authors invite a colleague to help with this as a simple language editor would almost certainly miss the nuanced language problems.

While I would normally provide a thorough line-by-line review, such detail is not warranted given the major flaws in the paper. Given the importance of the data, I suggest that the authors go back and reconsider their choice of model and approach to investigating metrics like HCHO:NO2 ratios. This data set could well challenge current thought on the value of such metrics.

Citation: https://doi.org/10.5194/acp-2021-815-RC1
RC2: 'Comment on acp-2021-815', Anonymous Referee #2, 21 Nov 2021

This manuscript presents observations of HCHO and NO2 at three sites over a multi-year period. The dataset has the potential to be useful, if errors in the data identified by Reviewer 1 are addressed. Presentation of a corrected dataset alone may merit a “Measurement Report” manuscript. However, the analysis component must be significantly strengthened for inclusion in ACP. Specifically:

1.) The HCHO/NO2 ratio discussion is not particularly useful. The vertical profile arguments highlighted by Reviewer 1 are a major concern. Additionally, the Souri et al. (2020) reference cautions against using the ratio in the way that is used here, suggesting instead the use of a slope/intercept parametrization with fitting coefficients that are dependent on local chemistry. The known limitations of the ratio method are not treated seriously in this manuscript. The comparison with modeled ratios is problematic due to choice of model.

2.) The CHOCHO/HCHO ratio is also not particularly useful. There is insufficient discussion of uncertainties in the ratio. There are no uncertainties shown in Figure S1, or otherwise stated in the text. It is unclear what the reader is supposed to glean from the analysis. The conclusion of this section states that detailed analysis is needed – this reviewer agrees.

3.) The usefulness of the comparison with the CHASER model is very limited, given (a) the coarse resolution of the model (b) the outdated emissions inventories.

Citation: https://doi.org/10.5194/acp-2021-815-RC2

Hossain M. S. Hoque, Kengo Sudo, Hitoshi Irie, Alessandro Damiani, and Al Mashroor Fatmi

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Hossain M. S. Hoque, Kengo Sudo, Hitoshi Irie, Alessandro Damiani, and Al Mashroor Fatmi

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Nitrogen dioxide (NO₂) and formaldehyde (HCHO) profiles, retrieved from remote sensing observations, are used to evaluate the global chemistry transport model CHASER. Overall, CHASER has demonstrated good skills in reproducing the seasonal climatology of NO₂ and HCHO on a local scale at sites in South and East Asia. Around mountainous terrains, the model performs better on a regional scale. The improved spatial resolution of CHASER can likely reduce the observed discrepancies in the datasets.


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MAX-DOAS observations of formaldehyde and nitrogen dioxide at three sites in Asia and comparison with the global chemistry transport model CHASER

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