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: final response (author comments only)
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RC1: 'Comment on egusphere-2024-3044', Pete D. Akers, 21 Nov 2024
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 -
AC1: 'Reply on RC1', Jianhua Qi, 25 Mar 2025
The comment was uploaded in the form of a supplement.
Citation: https://doi.org/10.5194/egusphere-2024-3044-AC1 -
AC3: 'Reply on RC1', Jianhua Qi, 25 Mar 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3044/egusphere-2024-3044-AC3-supplement.pdf
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AC1: 'Reply on RC1', Jianhua Qi, 25 Mar 2025
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RC2: 'Comment on egusphere-2024-3044', Anonymous Referee #2, 27 Jan 2025
Summary: The authors present an interesting study on wintertime atmospheric nitrate formation in the North China Region for 2013 and 2018, using model simulations aimed at validating the model against isotope observations. This research is highly valuable given the increasing contribution of nitrate to particulate matter, especially during the winter months. The authors have conducted detailed work on the topic; however, the presentation of their findings appears somewhat unfocused. Much of the model simulations and interpretations regarding nitrate changes reiterate findings from previous studies. The novel aspect of this research lies in the validation of model chemistry through comparison with isotope observations. However, most of the isotope data and modeling results are included in the supplementary material, which was not accessible for review. This component is critical for the interpretation of the work and requires thorough examination. The comparisons between model simulations and isotope observations were presented in broad terms, raising concerns about the reliability of using d18O and d15N values to determine oxidation pathways, given that the d18O values of atmospheric oxidants remain poorly constrained and recent documentation of potential d18O source effects from nighttime NO emissions. Moreover, the study lacked discussion on uncertainties in the isotope data and their potential impact on the interpretation within the modeling framework. Additionally, the model constrained nitrate production to a single grid cell, while the nitrate observations were derived from field samples that likely included contributions from long-range transport of nitrate produced upwind of the grid cell. Addressing this discrepancy is crucial for robust interpretation. Overall, while I appreciate the detailed efforts of the authors, the study appears unfocused due to the lack of integration and discussion of the model outputs, particularly the concentration and isotope comparisons. I recommend revisiting and refocusing the work to strengthen its coherence and clarity before it can be considered suitable for publication in ACP.
Comments:
Lines 60 – 64: How was this determined?
Line 64 – 66: This sentence doesn’t make sense to me.
Lines 55 – 98: This appears to be a block of text and clear paragraph breaks are not apparent. This makes it hard for the reader to follow the main points in the introduction section.
Lines 78 – 79: The motivation for the work should be stronger than pointed out in this line. It is unclear to the readers how the nitrate formation cited in the works of this paragraph are from model studies or from some other mechanistic constraint. Further, there have been global model studies of nitrate formation that would enable some insight into the land-ocean influence on nitrate formation.
Lines 82 – 98: The jump from a discussion of nitrate formation (prior to lines 82) to the role of nitrate during haze events (Lines 82-98), back to nitrate formation mechanisms (Lines 99-108), is hard for the reader to follow.
Lines 99 – 101: Do coastal cities have a nitrate concentration change that is different compared to the inland cities? I am still unclear the motivation to explore mechanism differences between inland and coastal cities as it relates to nitrate concentration changes. Do we expect potential differences in chemistry to influence the rate of nitrate concentration change from these types of locations?
Lines 104: I think finishing off the introduction with a statement of goals or objectives of the study would be important so readers know what to anticipate from this study, since the introduction was very broad.
Lines 126: What is MEIC?
Lines 129: I think MEGAN needs a citation (rather than a link).
Lines 144-150: What layer height is the IRR calculations used for interpreting nitrate formation? The layer closest to the surface? An integrated column from the surface to some layer height? Further, the IRR will calculate the nitrate production within a grid cell while not including the influence of transported nitrate. This could bias the interpretation if compared to isotope data. The readers should check out previous CMAQ work that have used IRR for simulation of nitrate formation (Walters et al., Modeling the Oxygen Isotope Anomaly (Δ17O) of Reactive Nitrogen in the Community Multiscale Air Quality Model: Insights into Nitrogen Oxide Chemistry in the Northeastern United States, ES&T-Air, 1(6), 451-463, 2024).
Lines 162-163: Are these observations compared to near surface simulations or with a different layer height?
Lines 173: Where is the supplement?
Lines 178 – 180: This sentence reads a bit odd to me. The way it is worded, it seems like the Thermo Scientific IC system extracted the samples in ultrapure water; however, I think the authors mean the IC system was used to measure ion concentrations.
Lines 180 – 181: What were the blank values? Also, it is mentioned that the ionic concentrations were blank corrected, but blanks would also impact the isotope data. Are blanks corrected for in the isotope data? If so, how?
Lines 191-202: Where is this information at? This is critical to be reviewed since the calculation will have a major influence on the interpretation of the results.
Lines 208- 2010: What are these benchmarks?
Table 1: I don’t know what the benchmark section of the table means.
Lines 257-265: Are there regional differences in the model efficacy of nitrate concentrations?
Lines 266- 298: This is interesting but it appears to me that a lot of these results and implications have already been published in previous works. Can the authors focus on this section to have more of a focus on what information is new compared to what has already been published?
Lines 312-313: I am unsure what is meant by “reduced by -2.1% to 7.8%”. Did some site reduce and some increased?
Lines 382- 385: Are there major takeaways or implication for this finding? Does this imply that NOx oxidation is more efficient at the coastal site compared to urban or vice versa? Will this impact the change in nitrate concentrations due to emission regulations?
Lines 394-396: the d18O value of atmospheric oxidants can vary widely. What values were chosen and are there temperature dependence factors that need to be accounted for? Further, recent work has shown that nighttime emissions of NO can carry over the emission d18O value, lowering d18O and Δ17O compared to the assumption of complete photochemical cycle (Albertin et al., Measurement report: Nitrogen isotopes (d15N) and first quantification of oxygen isotope anomalies (Δ17O, d18O) in atmospheric nitrogen dioxide, Atmos, Chem. Phys, 21, 10477-10497, 2021). This would be very important for urban areas with large nighttime NO emissions. Was this accounted for?
Lines 405-406: What were the “Other” pathways? Do you have an idea if their d18O values of the formed nitrate are close to the het pathway? If not then the model compared to the observations would appear biased towards accurately getting the het reaction pathway correct.
Lines 415-416: It would be important for the partitioning of HNO3 to pNO3, but it wouldn’t have a major impact on the formation of nitrate. Though it could influence the aerosol properties that could influence N2O5 reactions on aerosol surface for HNO3 production.
Lines 449-450: I think it might be better worded to say NH3 plays a critical role in influence particulate nitrate concentrations. Using “formation” is slightly confusing for this work, because some much time and effort was devoted to talking about nitrate formation via oxidation chemistry, which isn’t the same use of formation in this context.
Lines 458 – 459: The rate formation of HNO3 from the NO2 + OH reaction: d[HNO3]/dt = k(NO2+OH)[NO2][OH] shows that it depends on both [NO2] and [OH]
Lines 501-505: Why did [O3] increase during this period?
Line 510-512: “uptake” coefficient?
Lines 617-618: I think this is an inadequate Data availability statement.
Citation: https://doi.org/10.5194/egusphere-2024-3044-RC2 - AC2: 'Reply on RC2', Jianhua Qi, 25 Mar 2025
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