the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Feb 2025
Diagnosing O3 formation and O3-NOX-VOC sensitivity in a heavily polluted megacity of central China: A multi-method systematic evaluation over the warm seasons from 2019 to 2021
Abstract. This study investigated the high ozone pollution in Zhengzhou City from 2019 to 2021 using observational data and model simulations, focusing on volatile organic compound (VOC) pollution and its impact on ozone formation. Using online VOC data and statistical analyses, the results showed that VOC concentration increased with ozone pollution level, with average values of 84.7±51.0, 96.6±53.4 and 105.3±59.4 µg/m³ for non-pollution, mildly polluted and moderately polluted periods, respectively. Source apportionment of ozone and its precursor VOCs was performed using CMAQ and PMF models. The results demonstrated that the reduction of vehicle emissions should be prioritized to mitigate ozone pollution in Zhengzhou, since transportation emissions respectively accounted for 64 % and 31 % of ozone and VOC precursor emissions. In addition, local ozone production rates and HOx base budgets were calculated using an observation-based (OBM) model. The ozone production rates on non-pollution, mildly polluted, and moderately polluted days were respectively 2.0, 4.5, and 6.9 ppbv/h on average. The HOx radical concentration on polluted days was 1.5–6.4 times higher than that on non-pollution days, which is indicative of more efficient radical cycling during photochemical pollution. The O3-NOx-VOC sensitivity was analyzed using the OBM model, CMAQ model and ratio method. The results showed that ozone generation in Zhengzhou was mainly limited by VOCs, suggesting that the reduction of VOCs should be focused on aromatic hydrocarbons and olefins. The optimal reduction ratio of anthropogenic VOCs to NOx was about 2.9:1. This study will offer deeper insights for formulating effective ozone pollution prevention and control strategies.
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RC1: 'Comment on egusphere-2024-4178', Anonymous Referee #1, 13 Feb 2025
Yu et al. analyzed the two-year routine hourly measurement data from a municipal environmental monitoring station in Zhengzhou City, China. Using PMF and CMAQ, they found that O3 formation was primarily driven by VOC emissions from the transportation sectors. Furthermore, they deployed an OBM model to provide insights into the equilibrium of free radicals. Based on the analysis, the authors claim that the O3 formation is limited by VOCs.
The current version is long to read and poorly written. Some terminologies are badly defined or not consistent throughout the manuscript. The PMF analysis is not technically sound, and the study reads like a measurement report overall. In addition, the comparison between studies is limited to other Chinese studies. I don’t think readers from countries other than China could benefit from the findings. Considering the work lacks a broad readership, I suggest rejecting the manuscript.
Major Comments:
- Introduction is long and reads very descriptive. It is hard to see the novelty and importance of the work. I will suggest the authors make it compact and concise and, at the same time, highlight the novelty and importance of the study.
- Lines 316 – 324: The way how VOC species were included in the PMF analysis seems subjective. How did the author define the high frequency of concentrations below MDL? What are local emissions? How do they contribute to abnormally high concentrations? Which VOC compounds had abnormally high concentrations? Do they always exhibit abnormally high concentrations? Why is the signal-to-noise threshold defined as 5?
- Lines 325 – 332: The current description for selecting PMF factor solution is vague. Why was Q/Qexp not chosen to find the optimal factor solution? This is a well-established variable for searching for the factor solution. The ideal solution should provide a Q/Qexp close to 1.0. The change in Q/Qexp as a function of the number of factors should be presented as a figure in the Supplement. In addition, no uncertainty estimation method (e.g., bootstrap, displacement) is mentioned to examine the solution's robustness. Is the chosen PMF solution for presentation robust enough?
- Lines 336 – 346: Several terminologies are badly defined. What concentrations are considered high O3 values? What concentration is the national secondary standard limit? This needs to be highlighted as a horizontal line in Figure 1. What is defined as moderate, high, or severe pollution days? Is it based on the concentration of O3 or PM2.5? Was the downward trend statistically significant?
- Lines 347 – 363: The Pearson correlation coefficient is a correlation coefficient that measures the linear correlation between two variables. Therefore, a low Pearson correlation coefficient indicates that the linear correlation is weak, but it does not necessarily imply that the non-linear correlation is also weak. In addition, the correlation between O3 and its precursors only makes sense if the meteorological conditions have been normalized. I highly recommend the authors choose a proper statistical method to analyze the relationships between variables.
Minor Comments:
- Lines 73 – 79: The authors listed two studies from Taichung and Wuhan as examples. Readers not familiar with Chinese geography have no clue about these two cities. Proper descriptions need to be provided for these two example cities.
- Lines 91 – 93: Some descriptions need to be provided for Henan here.
- Line 100: What are the transition zones?
- Lines 171 – 172: How often was the particulate removal device cleaned? I am concerned that during high pollution periods in China, the removal device can be saturated with high aerosol mass loading very quickly.
- Section 2.1: How many VOC species were identified?
- Lines 367 – 369: “Nighttime short-term… the next day (Du et al., 2024).” are redundant.
- Table 1: How many hours or days are defined as non-pollution, lightly pollution, and moderately pollution periods? What are the definitions of non-pollution, lightly pollution, and moderately pollution periods?
- Lines 374 – 376: “The daily average… non-pollution days”. Is it the difference statistically significant?
- Lines 382 – 383: How did the author determine the atmospheric oxidation capacity and free radical concentrations? I find the statement is very speculative.
- Lines 386 – 387: What is the contribution of the top 20 substances to the total VOC concentrations?
- Lines 402 – 405: Could you label P1, P2, and P3 in Figure S5? Same for Figure 2.
- Lines 409 – 412: How did the author determine the correlation between age indicator and O3 as strong?
- Lines 621: Why was the HONO mixing ratio underestimated? Can it be overestimated? Same question for the HOX.
- Figure 9: Could the authors label NOx and VOC control zones in the plot? Where is the transition regime?
Technical Comments:
- Make consistent formatting for “NOx” and “HOx”.
- Lines 57 – 58: Provide a few references to support the sentence
- Line 97: Use another word to replace “escalating”.
- Line 136: The abbreviation “VOC” can be used here.
- Line 201: Should j be in italic?
- Lines 213 – 224: They symbols should be consistent in both paragraphs and equations.
- Line 223: Should k be in italic?
- Figure S1: What does the small red star stand for? Also, the star is too small to read.
- Figure S2: What are d01, d02, d03 and d04? I could not find them in the figure.
- Line 264: more accurate than what?
- Line 291: What does “quality of species” mean?
- Line 314: Is it supposed to be “ErrorFraction” instead of “Error”?
- Line 372: similar pattern to what?
- Figure 2 and Figure S5: Please use the interquartile range as the error bars.
- Line 416: What concentration decreased to a minimum?
- Line 453: What is a coordinated control zone?
- Line 497: I am confused with the part “in order to mitigate O3 pollution under unfavorable conditions.”
- Figure 4: What do those dots and bars stand for?
- Line 595: It should be “cm3”
- Table S2: Where is the data for highly polluted days?
- Line 638: Is it “moderately/highly polluted days” or “moderately and highly polluted days”?
Citation: https://doi.org/10.5194/egusphere-2024-4178-RC1 -
RC2: 'Comment on egusphere-2024-4178', Anonymous Referee #2, 06 Mar 2025
This manuscript presents a comprehensive investigation of O3 pollution in Zhengzhou over the warm seasons from 2019 to 2021, utilizing observational data and model simulations (CMAQ, PMF, and OBM) to provide insights into the O3-NOx-VOC sensitivity and to propose effective control strategies. Significant improvements are needed in terms of novelty, logistics, and writing. Below are my main comments.
Major comments
- More comprehensive analysis of O3 pollution characteristics in Zhengzhou. The title describes Zhengzhou as "a heavily polluted megacity of central China," yet the manuscript does not provide sufficient evidence to justify this statement. Additionally, comparisons with other urban areas should be incorporated into the results discussion.
- More details on the CMAQ model configuration. The current version lacks a detailed description of the CMAQ model configuration. Essential aspects such as horizontal and vertical resolution, meteorological condition, chemical mechanisms, emission inventories, and boundary conditions should be explicitly stated. Additionally, Fig.S2 is too blurred, and the information it expresses should be described in detail in manuscript.
- Different methods were included, including CMAQ, PMF and OBM. However, no clear connections and intercomparison were introduced for these methods. Were these methods really necessary?
- Describe the main improvement or innovation compared with your previous study.
Wang, X. D., Yin, S. S., Zhang, R. Q., Yuan, M. H., and Ying, Q.: Assessment of summertime O3 formation and the O3-NOx-VOC sensitivity in Zhengzhou, China using an observation-based model, Sci. Total Environ., 813, 152449, 2022.
- In referencing previous studies, should write as Huang et al. (2019) instead of Huang (2019) (line 73). Do this for the other references.
Detailed comments:
- Line 40-41: The phrase "precursor emissions" is inaccurate, please revise for clarity.
- Line 42: Should be "observation-based model (OBM)".
- Line 59: "Continue to increase" should be revised to "continue increasing."
- Line 61: Clarify the distinction between "VOC" and "VOCs" and explain why this differentiation is necessary.
- Lines 71–72: The sentence structure is overly complex.
- Lines 83: can provide abbreviation for “Yangtze River Delta” here and then use it later.
- Lines 264-265: I do not understand the logic here. It’s not surprising that the model has a better performing in simulating NO2 since it was directly emitted. The logic does not make sense at all here.
- Line 325: "specie" should be corrected to "species".
- Line 338: Please define MDA8 correctly.
- Line 384: Clearly define "non-polluted," "lightly polluted," and "moderately polluted" periods.
- Line 402: Mark the P1–P4 phases in Fig. S5 to improve readability.
- Line 474: How did the authors conclude that "cross-regional mitigation measures" are required? Please provide a clear rationale.
- Line 496: Clarify whether the "power sector" is equivalent to "electricity".
- Lines 713–714: Please label the relevant regions in Fig. 10 to enhance readability.
- Conduct a thorough grammatical revision and refine sentence structures throughout the manuscript.
Citation: https://doi.org/10.5194/egusphere-2024-4178-RC2
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