the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Oct 2024
Exploring Atmospheric Nitrate Formation Mechanisms during the Winters of 2013 and 2018 in the North China Region via Modeling and Isotopic Analysis
Abstract. Nitrate (NO3-) has surpassed sulfate as the dominant secondary inorganic ion, posing a significant challenge to air quality improvement in China. We utilized the WRF-CMAQ model and isotopic analysis to investigate nitrate formation mechanisms in inland and coastal cities in North China during the winters of 2013 and 2018. Among the seven nitrate formation pathways, the oxidation reaction of OH radicals with NO2 (OH + NO2) and the heterogeneous reaction of N2O5 (hetN2O5) were dominant pathways (88 %–95.5 % NO3-), while others contributed less than 12.4 %. In inland cities, 63.7 %–85.6 % of nitrate formed via OH + NO2, and 8.3 %–27.7 % from hetN2O5. In coastal cities, about half of nitrate (48.2 %–56.5 %) was produced from OH + NO2, while hetN2O5 contributed 37.0 %–45.7 % due to higher N2O5 concentrations and longer NO3 radical lifetimes. Compared with 2013, the OH + NO2 contribution in 2018 increased by 7.6 % in inland cities and 3.6 % in coastal cities, driven by greater atmospheric oxidizing capacity. Scenario simulations showed that a 60 % reduction in NOx emissions could lower nitrate levels by 38.4 %, while combined reductions in NH3, NOx, and VOCs led to a 59.8 % decrease, from 14.6 μg/m3 to 5.9 μg/m3. These results highlight the need for comprehensive strategies targeting NH3, NOx, and VOCs to reduce nitrate pollution.
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Status: open (until 08 Jan 2025)
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RC1: 'Comment on egusphere-2024-3044', Pete D. Akers, 21 Nov 2024
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Comments on Liu et al., 2024
General summary:
Overall, I think there is clear evidence that a lot of work has been done and much data produced. The writing is clear. The technical analysis seems logical and results reported well, although I cannot speak much to the actual modelling technical components as that is outside my expertise.
My primary critique revolves around the structure and narrative of the paper. Namely, there is so much information presented that it is sometimes difficult to keep track of what the overall main point is that the authors are trying to get across. I think this is exemplified in two ways: first, the title itself about “Exploring…mechanisms”, while accurate to the content, highlights that there is less focus on a definitive point/conclusion than just summarizing and overviewing a lot of results from modelling. Second, there is a massive Results section, but no Discussion. There is some discussion happening within the Results, but the paper would likely improve by having less space dedicated to describing every result from the modelling and more space on what those results mean for things like policy implications, the need to treat interior cities differently from coastal ones in regulations, etc. Overall, I would suggest that the authors take a fresh look at the content of the paper as a whole narrative and re-evaluate if everything in the results needs to be included and described at the detail it currently is at.
That said, I do think that there is a good information here, and a valuable contribution. And I have largely minor critiques on the technical side and scientific content. But for the authors’ sake, I think the paper’s eventual impact could be greatly improved by focusing the narrative, simplifying/summarizing some of the base results further, and speaking more to the broader implications of this work.
Major points:
I cannot see a supplemental section on the Preprint review page? This made it impossible to examine things referenced in the methods. (Apologies if this was a mistake on my part).
Results: There is a huge amount of information and data both presented and discussed. While I commend the authors for being upfront with their data, it can be a bit overwhelming at times and causes some of the focus to be lost. I would recommend looking back over this section to determine what exactly are the main points and stories you are aiming to get across, and pare down any information and number discussion that distracts away from those points. Perhaps greater summarization of regional trends (e.g., inland vs. coastal) rather than relaying data from multiple cities would help focus the section, too. You do this already some by focusing on Beijing vs. Qingdao, but even further summarization/simplification could help in some spots.
There are a lot of figures, and many of them are similar in theme (e.g., comparing an atmospheric chemical in 2013 and in 2018 and their difference). Perhaps combining many of these into a single, larger figure would be more effective as the reader could cross compare more easily and not hit figure fatigue.
Data availability: This is an unacceptable statement for data availability, as per ACP standards. Data are to be hosted in a publicly accessible location. See further guidance from https://www.atmospheric-chemistry-and-physics.net/policies/data_policy.html:
If the data are not publicly accessible at the time of final publication, the data statement should describe where and when they will appear, and provide information on how readers can obtain the data until then. Nevertheless, authors should make such embargoed data available to reviewers during the review process in order to foster reproducibility. The Copernicus review system allows to define such assets as 'access limited to reviewers' and reviewers must then sign that they will use such data only for the purpose of reviewing without making copies, sharing, or reusing.
In rare cases where the data cannot be deposited publicly (e.g., because of commercial constraints), a detailed explanation of why this is the case is required. The data needed to replicate figures in a paper should in any case be publicly available, either in a public database (strongly recommended), or in a supplement to the paper.
Specific points:
71: Is this coastal or inland Greenland?
73: What is it about the air mass origin that affects the nitrate formation? Or why is this being set apart and discussed here after the review of the coastal vs. inland cities? Isn’t air mass origin also the primary reason for those differences? The structure of the paragraph is just confusing me a little bit here.
Fig. 2: Data source for terrain heights should be cited
165: Was there a specific data network that you were sourcing within that website? For example, that website is just a portal to access many different data networks, such as WMO and GHCN, and if you know the exact data source network, that could be cited here and be more clear.
168: A little more information about these 68 stations would be beneficial, such as are they all within a specific region/geographic bounds? Were there any selection criteria applied to choose the stations?
185: Just to confirm, are all the instrumentation specifics the same that you used here as in this cited paper? You might add a brief line or addition to the end of the sentence currently ending in “denitrifier method” to add the instrumentation used, so that the reader doesn’t have to go look that basic information up in another paper.
204: I think that some more information needs to be given here on how you used these indicators to evaluate the simulation effect. You have cited some proposed benchmarks, but it isn’t clear to me readily how you will be using this information in your paper. In a very soon following section (3.1) about model evaluation where you present simulated values and some of the benchmarks, I was able to eventually infer how you were doing the evaluation, but it should really be more explicitly clear in the methodology.
220: I’m unclear exactly how the numbers being discussed here from the 68 sites were gathered and compared. Are these pairwise calculations, or overall means, or involving some sort of spatial dimension, etc? Are the comparisons all at hourly resolution, or aggregated to daily or something else? There needs to be more clarity on this, likely in the methodology of the 2.5 section. Also, how did you evaluate parameters that lacked cited benchmarks (perhaps something else that could be included in the methodology?)? For example, some of the Pearson correlation coefficients are somewhat low, for wind especially.
Section 3.4: The model has output for “Others” but your isotopic method doesn’t. However, I don’t see any discussion of this in this section, but I feel it needs addressed in some form. If 5-8% of reactions are “others” in the model, but you don’t distinguish those in the isotopic method, does that mean that you assume you are attributing those “others” reactions to either OH+NO2 or hetN2O5? Is that baked into the uncertainties in any way, or handled specifically?
Technical points:
45 : Do you mean “adsorbed” here rather than “absorbed”? The use of “onto” makes it seem like you might be referring to adsorption rather than absorption.
65: This paragraph is excessively long and should be broken up by paragraph breaks to aid readability.
133: I think the use of a colon (:) here is more appropriate than “i.e.”
182: Perhaps refer to it as the “bacterial denitrifier method” just to be explicitly clear.
Figure 3: The star symbol is used four times in total, but I’m guess you are only referring to the two times it is used for the R value? Perhaps just state in the caption that the R value is significant at a p <0.05 level.
Figure 4: Maybe consider putting a larger label on the vertical left side for PM2.5, NO3−, NH4+, and SO4 to make it more clear what each row of data is representing.
Fig 5: Period missing at end of caption. This diverging color scheme is also a bit confusing as used here, because it is the same color scheme used in Fig 4 to show representative change (pos = red, neg = green), but here it is a unidirectional scale. I’d recommend a different color scheme to avoid confusion or unintentional misleading.
Figure 6: Humans are pretty bad at estimating angular areas. You might consider alternatives such as treemaps or waffle charts. Not required from me, but just put here for consideration. This is also a pretty simple figure, and since you have so many figures, you might consider merging it with another or whether it is necessary.
Fig 9: The color choices could be changed to improve the visual story. For example, the OH +NO2 on both sides would ideally both be blue, or shades of blue. And hetN2O5 both be orange or shades of orange. That would make it more clear that we should be directly relating them.
Fig 10: The legend for the dot looks like it is just connected to OH Pathway, and it should be labelled ast NO3− concentration or [NO3−] not just NO3−. Missing a period at end of caption. Subfigures should probably either be all in one column OR the 2018/19 Qingdao be under the 2018 Beijing chart.
432: There is a comma splice in this sentence.
Fig 13: The scaling seems poor or wrong in the difference map. The HONO concentrations only cover <2 ppb but the scaling on the difference is ±50.
Citation: https://doi.org/10.5194/egusphere-2024-3044-RC1
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