the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Jul 2024
Formation drivers and photochemical effects of ClNO2 in a coastal city of Southeast China
Abstract. Nitryl chloride (ClNO2) is an important precursor of chlorine (Cl) radical, significantly affecting ozone (O3) formation and photochemical oxidation. However, the key drivers of ClNO2 production are not fully understood. In this study, the field observations of ClNO2 and related parameters were conducted in a coastal city of Southeast China during the autumn of 2022, combining with machine learning and model simulations to elucidate its key influencing factors and atmospheric impacts. Elevated concentrations of ClNO2 (> 500 ppt) were notably observed during nighttime in late autumn, accompanied by increased levels of dinitrogen pentoxide (N2O5) and nitrate (NO3−). Nighttime concentrations of ClNO2 peaked at 3.4 ppb, while its daytime levels remained significant, reaching up to 100 ppt and sustaining at approximately 40 ppt at noon. Machine learning and field observations identified nighttime N2O5 heterogeneous uptake as the predominant pathway for ClNO2 production, whereas NO3− photolysis contributed to its daytime generation. Additionally, ambient temperature (T) and relative humidity (RH) emerged as primary meteorological factors affecting ClNO2 formation, mainly through their effects on thermal equilibrium and N2O5 hydrolysis processes, respectively. Ultraviolet (UV) radiation was found to play a dual role in ClNO2 concentrations around noon. Box model simulations showed that under high ClNO2 conditions, the rates of alkane oxidation by Cl radical in the early morning exceeded those by OH radical. Consequently, VOC oxidation by Cl radical contributed ~ 19 % to ROx production rates, thereby significantly impacting O3 formation and atmospheric oxidation capacity. This research enriched the understanding of ClNO2 generation and loss pathways, providing valuable insights for the regulation of photochemical pollution in coastal regions.
- Preprint
(1833 KB) - Metadata XML
-
Supplement
(926 KB) - BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2024-1638', Anonymous Referee #1, 31 Jul 2024
General Comments
The work by Gaojie Chen et al. is a well written study presenting two months of ambient observations in Southeast China and has two main components. First, the work introduces interesting evidence for the formation of ClNO2 during the daytime by a recently suggested particulate nitrate mechanism. Second, the work discusses the implications for Cl radical production from ClNO2 photolysis.
The first component has significant implications for the understanding of ClNO2 formation globally. However, a discussion of the traditional metrics of ClNO2 formation, the N2O5 uptake rate and ClNO2 yield, are completely absent from the paper. Without a discussion on this topic, the authors’ conclusion that “NO3– photolysis contributed to daytime generation” is severely weakened. In fact, it is based only a machine learning output which gauges the “importance” of NO3– influence on ClNO2 as well as a linear regression of ClNO2 with NO3–×jNO2×aerosol Sa. In this joint correlation, insufficient evidence is provided to suggest that the photolysis component improves the correlation. As such, I request major revisions in which the authors justify their conclusion by demonstrating that the daytime observations of ClNO2 cannot be explained by traditional N2O5 and ClNO2 chemistry.
The second component is based on box modeling from the master chemical mechanism. Aside from a lack of detail on the parametrization used for N2O5 uptake and ClNO2 yield, the results presented are generally sound and informative. I request that the authors include their choice of parametrization in the main text.
Specific Comments
- Section 2: A description on the handling of N2O5 uptake and ClNO2 yield is absent from the methods. A list of previous papers is provided but it is not clear how these two parameters are handled. Both N2O5 uptake and ClNO2 yield will vary with the parameters investigated here (T, RH, etc.). See McDuffie et al.
McDuffie, E. E., Fibiger, D. L., Dubé, W. P., Lopez Hilfiker, F., Lee, B. H., Jaeglé, L., et al. (2018a). ClNO2 yields from aircraft measurements during the 2015 WINTER campaign and critical evaluation of the current parameterization. Journal of Geophysical Research: Atmospheres, 123(22), 12994–13015. https://doi.org/10.1029/2018JD029358
McDuffie, E. E., Fibiger, D. L., Dubé, W. P., Lopez-Hilfiker, F., Lee, B. H., Thornton, J. A., et al. (2018b). Heterogeneous N2O5 uptake during winter: Aircraft measurements during the 2015 WINTER campaign and critical evaluation of current parameterizations. Journal of Geophysical Research: Atmospheres, 123(8), 4345–4372. https://doi.org/10.1002/2018JD028336 - Section 3.1: There is no uncertainty presented with the observations in the main text. Please include the uncertainties as the uncertainties in the SI are non-negligible (~20 %).
- Figure 5: What is the interpretation of negative “importance factors”? During the daytime, N2O5 is a negative importance factor. Please discuss this in the main text.
- Section 3.2: A discussion on the changes in aerosol content (particulate nitrate) and the effect on N2O5 uptake and ClNO2 yield is absent. Such a discussion is critical here. Traditionally, one expects nitrate to reduce N2O5 uptake (the nitrate effect) which would limit the production of ClNO2. Even so, ClNO2 could be enhanced in a high nitrate case if the N2O5 uptake and ClNO2 yield are substantially greater than low nitrate air masses. According to Figure 1, there are concurrent enhancements of pCl and pNO3 during some time periods. As pCl increases the ClNO2 yield will also increase which would then be (coincidentally?) concurrent with high pNO3 Even more, these periods of concurrent pCl and pNO3 appear to correlate with enhanced PM2.5 and thus, I assume, aerosol surface area. Increases in surface area would then increase N2O5 uptake further promoting ClNO2 and pNO3 production. Lastly, Figure 6 suggests that the correlation between ClNO2 mixing ratio and pNO3xjNO2xSa is driven by pNO3xSa while jNO2 has a limited or no correlation (panel d). In other words, photolysis appears to have a limited role in the production of ClNO2.
While the above may be speculative, it is an example of why a lack of discussion on the ClNO2 yield and N2O5 uptake significantly weakens the arguments made by the authors. As written, I believe there is insufficient evidence to conclude that “NO3– photolysis contributed to daytime [ClNO2] generation”.
Technical Comments
Line 76: tenths : tens
Figure 3, 5 and 6: Please change the color scale to a colorblind friendly version.
Line 215: averagely : average
Line 224: corrected : correlated
Citation: https://doi.org/10.5194/egusphere-2024-1638-RC1 -
AC1: 'Reply on RC1', Jinsheng Chen, 07 Oct 2024
We appreciate the reviewers for the constructive and helpful comments, the incorporation of which has led to an improved manuscript. We have revised the manuscript appropriately and addressed the reviewer’s comments in our point-by-point responses. Please check the attachment.
- Section 2: A description on the handling of N2O5 uptake and ClNO2 yield is absent from the methods. A list of previous papers is provided but it is not clear how these two parameters are handled. Both N2O5 uptake and ClNO2 yield will vary with the parameters investigated here (T, RH, etc.). See McDuffie et al.
-
RC2: 'Comment on egusphere-2024-1638', Anonymous Referee #3, 03 Jan 2025
In this manuscript, the authors present a study that investigates key factors driving the production of ClNO2 based on field observations and XGBoost-SHAP model. Furthermore, the authors evaluated the potential impact of ClNO2 photolysis on the formation of RO2 and hence, the atmospheric oxidative capacity.
Overall, I found this manuscript interesting and well-constructed. Although the conclusion drawn for the nighttime ClNO2 formation has been well recognized for two decades, the contribution of NO3- photolysis to daytime ClNO2 is confirmed by the authors, which brings sufficient novelty to this manuscript.
Despite this, I do have some comments, particularly on the interpretation of the machine learning results, which need to be fully addressed before this manuscript can be accepted for publication.
General comments:
- Machine learning, especially SHAP value, starts to be widely used in atmospheric research very recently, but many readers may not be sufficiently familiar with it. To improve the readability, I believe the way of interpreting SHAP values must be fully informed in the manuscript. E.g., what do the negative and positive SHAP values stand for? Should the contribution be evaluated by the true value or absolute value.
- I am not fully convinced by the way of performing SHAP model and its interpretation.
1) why does the aerosol surface, as a known important factor for N2O5 uptake, not used as an input of SHAP model?
2) ClNO2 has a rather long nighttime lifetime, which means ClNO2 could be accumulated during airmass transport. Meanwhile, N2O5 could both form and loss through the transport, leading to varying patterns of its concentration. In fact, this can be testified by calculating the maximal ClNO2 production through N2O5 uptake by, e.g., assuming gamma=0.1 and ClNO2 yield =1. Given this assumption, I didn’t see any model input that could represent the influence of airmass transport. I suggest to reconsider their model input and incorporate certain transport parameters.
3) As this study suggested, daytime and nighttime ClNO2 are driven by different processes, which however, were affected by similar parameters (in different ways). For instance, NO3- is a co-product with ClNO2 at nighttime, but a precursor of ClNO2 in the daytime. I suggest to consider conducting SHAP models daytime and nighttime data sets separately, so that the exact role of these parameters can be better revealed.
Detailed comments:
Line 64 “were” could be replaced by “are”, as this is common case.
Line 99-100 “our research integrated….” This sentence has grammatic error, please rephrase.
Line 141-143. The statement of JClNO2 calculation is not clear, please consider to rephrase.
Line 167-168 “Simultaneously, …” I think the high correlation between ClNO2 and N2O5 (and NO3-) does not mean simultaneous peaking. From Fig.1, I can clearly see that their concentration do not reach the maxima at exactly the same time.
Line 203-204 the authors first indicate NO3- could affect the formation of ClNO2; but afterwards, the authors say that the high NO3- and ClNO2 together were caused by the simultaneous formation. Please improve the logic of this part.
Line 221 “did not promoted…” should be “did not promote”.
Line 222 “A recent study declared that…”. Please use “suggested” or “argued” instead of “declared”.
Line 236-237. I am not convinced by the discussion about the role of temperature. The authors suggested that N2O5 is not important for ClNO2 in the daytime. Then how can temperature affect ClNO2 through the thermal equilibrium of N2O5? Also, N2O5 is a measured quantity. Such a temperature impact should be already reflected by the connection between daytime N2O5 and ClNO2.
Line 243 I suggest the subtitle of “Impact of ClNO2 photolysis on ROx budget”
Figure 2: the N2O5 in the lowest panel is barely seen. Please consider to show the pattern by perhaps N2O5*5.
Figure 4. the division of x ticks looks strange. Please modify.
Citation: https://doi.org/10.5194/egusphere-2024-1638-RC2 -
AC2: 'Reply on RC2', Jinsheng Chen, 12 Jan 2025
We sincerely appreciate the reviewer for their constructive and insightful comments, which are of great benefit to improve the quality of the manuscript. In response, we have carefully revised the manuscript and addressed each comment in a point-by-point manner. For clarity, the reviewer’ comments are presented in black, our responses in blue, and the added or revised sections of the manuscript are highlighted in red. Please refer to the PDF file for specific responses.
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 425 | 139 | 233 | 797 | 65 | 20 | 17 |
- HTML: 425
- PDF: 139
- XML: 233
- Total: 797
- Supplement: 65
- BibTeX: 20
- EndNote: 17
Viewed (geographical distribution)
| Country | # | Views | % |
|---|---|---|---|
| United States of America | 1 | 213 | 26 |
| China | 2 | 178 | 22 |
| undefined | 3 | 47 | 5 |
| Germany | 4 | 37 | 4 |
| Romania | 5 | 25 | 3 |
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
- 213