Measurement report: Characterizing O<sub>3</sub>-NOₓ-VOC sensitivity and O<sub>3</sub> formation in a heavily polluted central China megacity using multi-methods during 2019&ndash;2021 warm seasons

Yu, Shijie; Liu, Hongyu; Wang, Hui; Su, Fangcheng; Yuan, Minghao; Zhang, Ruiqin

doi:10.5194/egusphere-2025-4519

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https://doi.org/10.5194/egusphere-2025-4519

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23 Oct 2025

| 23 Oct 2025

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Measurement report: Characterizing O₃-NOₓ-VOC sensitivity and O₃ formation in a heavily polluted central China megacity using multi-methods during 2019–2021 warm seasons

Shijie Yu, Hongyu Liu, Hui Wang, Fangcheng Su, Minghao Yuan, and Ruiqin Zhang

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 reducing vehicle emissions should be prioritized to mitigate ozone pollution in Zhengzhou, as ransportation emissions accounted for 64 % and 31 % of ozone and VOC emissions, respectively. In addition, local ozone production rates and HOx base budgets were calculated using an observation-based model (OBM). 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.

Received: 14 Sep 2025 – Discussion started: 23 Oct 2025

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Shijie Yu, Hongyu Liu, Hui Wang, Fangcheng Su, Minghao Yuan, and Ruiqin Zhang

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RC1:
'Comment on egusphere-2025-4519', Anonymous Referee #1, 27 Nov 2025 reply
This manuscript investigates O₃–NOx–VOC sensitivity and O₃formation mechanisms in Zhengzhou (2019–2021) using a combination of online VOC measurements, OBM, CMAQ-DDM, source apportionment, PMF, and machine-learning (ML)/SHAP interpretation. While the dataset is valuable and the research direction is meaningful, the manuscript suffers from inconsistent methodology, unclear descriptions of model configurations, uncertainties and machine learning, no comparisons between different analytical models for the O₃ formation mechanism, and very vague data interpretation. The manuscript is long and unreadable. Therefore, I suggest the manuscript be rejected.
Major Comment:
In Section 3, “Results and Discussion”, there is a substantial focus on O₃ source apportionment (CMAQ) and VOCs source apportionment (PMF). Unfortunately, how these contents are linked to the research question “O₃-NO_x-VOC sensitivity” is weak. For example, the authors claim that traffic and industry dominate both O₃ and VOCs. But there is no justification for “X% of O₃ from traffic” and “how O₃ responds if traffic NO_x/VOCs are reduced”.

In addition, there is no cross-method comparison. A combined table is highly recommended to show the section contributions across PMF, CMAQ-DDM, and OBM RIR/EKMA. As a result, the manuscript reads like a report by stacking results (sensitivity diagnostics + VOC and O₃ source apportionment + ML/SHAP), but with limited discussion.
The CMAQ simulation suggests that transportation and industry dominate MDA8 O₃ contributions, while the PMF result indicates that vehicle, solvent, and industrial sources contribute most VOCs. The current discussion is very vague: “transportation should be prioritized,” “more aggressive control is required,” etc. However, what is the take-home message for policy translation? There are no recommended emission-reduction scenarios (e.g. -30% traffic VOCs, -30% industrial NO_x, different VOC/NO_x ratios by sector) and no quantitative estimate of the expected O₃ under those scenarios.

Machine-learning setup and data leakage risk are not clearly addressed. The authors use XGBoost and RF with grid search and cross-validation (CV), and report training/test metrics in Table S4. However, essential methodological details are missing. How are training and test sets defined—random split or chronological split for this time-series dataset? What CV scheme is used (k-fold, blocked time-series CV)? Does CV account for temporal ordering?

What is the inherent relationship between the VOCs/NO_x ratio method for determining O₃sensitivity, modelling results from OBM and CMAQ, and radical budgets?

How do the results of XGBoost relate to those of other methods? Why is XGBoost analysis not mentioned at all in the Section 4 Summary and Conclusions? How do the authors come up with the claim that the ML method should be advised in the future in the subsection Limitations and future research directions in Line 986?

The role of biogenic VOCs is mentioned but not fully clarified in terms of O₃ formation and control. Biogenic VOCs are explicitly identified as a separate PMF factor with isoprene as tracer, and OBM RIR shows that biogenic VOCs have non-negligible reactivity, especially on polluted days. Emissions used in CMAQ also include biogenic VOCs via MEGAN. On the one hand, the authors claim that the contribution of biogenic sources to VOC is small (Line 724). On the other hand, they claim that the contribution of biogenic VOCs to local O₃ is high (Lines 840-850). So, I wonder what the overall role of biogenic VOCs in the O₃–NO_x–VOC sensitivity and O₃ formation mechanism is.

Minor Comment:
The period simulated by the CMAQ model was not mentioned in the methodology.

What is the rationale for studying warm seasons? Could you provide any justification and supporting references?

Lines 141-143: Could you provide any reference for the study city of Zhengzhou?

Lines 212-215: “PO₃^S(X) and PO₃^S (X-DX) refer to the simulated O₃ yields…” Are you sure “yield” is the right terminology here? Why does the author later state that “The net O₃ production rate (PO₃^S)” in Lines 215-216?

The CMAQ result shows transportation can contribute 64% to O₃(Fig.4), but the PMF result shows vehicles contribute only 31% to VOCs. This apparent discrepancy needs justification. Presumably, VOCs and NOₓ emissions from transportation are equally important for O₃ formation. Therefore, the role of NO_x emissions needs to be discussed.

Why does Table S5 show a negative correlation (Kendall’s, -0.305) between VOCs and O3, while the RIR indicates a positive correlation (Fig.9)? Shouldn’t this discrepancy require some explanations?

Table S4: The rationale for selecting XGBoost over RF is well justified.

A strong positive correlation between O₃ and alkenes is observed in SHAP (Fig.2), whereas Spearman's analysis reveals the opposite relationship (Table S6, -0.054**). Please standardise the terminology for “olefins” (Fig.2) or “Alkene” (Table S6)!

Line 427: “160 160 µg/m³ and 160 µg/m³ are categorised as light pollution and moderate pollution”? Could you please carefully check if this is CORRECT?

9: What are “AVOCs”, “ALKA”, “ALKE”, “ARO”? What is the relationship between them? Which subsets of compounds belong to AVOCs? Why is there no further discussion of the contribution and sensitivity of VOCs from different sources to O₃?

Technical Comment:
2 The font is too small to read.

Line 351: What is the full name of “XGBoost”?

Line 386: Should it be “SHAP” instead of “Shapley”?

Table S3: What do “DISP” and “BS” mean? What is the meaning of BS mapping＜80%?

S3: A full description needs to be provided for “E/X” in the manuscript for the first time and figure captions.

Line 712: “Zhengzhou comprises vehicle emissions (31%), solvent use (24%), and industrial processes (21%)”. But in Table S8, the corresponding values are 32.4%, 24.8% and 18.3%.

Lines 1135-1137: Make sure the font style is consistent throughout.

Please be consistent in the use of subscripts and full names or abbreviations for proper nouns.

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Citation: https://doi.org/10.5194/egusphere-2025-4519-RC1

Shijie Yu, Hongyu Liu, Hui Wang, Fangcheng Su, Minghao Yuan, and Ruiqin Zhang

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https://doi.org/10.5194/egusphere-2025-4519-supplement

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VOCs Dataset S. Yu et al. https://doi.org/10.5281/zenodo.17214861

Shijie Yu, Hongyu Liu, Hui Wang, Fangcheng Su, Minghao Yuan, and Ruiqin Zhang

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Short summary

As ozone (O₃) pollution remains a major concern, this study draws on long-term observational data to thoroughly analyze O₃ and its precursor substances. This study examines VOC pollution characteristics, explores local O₃ formation mechanisms, and assesses O₃ sensitivity. Our key finding is that motor vehicles are main sources of VOCs and O₃; local O₃ is more sensitive to VOCs. These guide targeted pollution cuts and protect the public environment.


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Measurement report: Characterizing O3-NOₓ-VOC sensitivity and O3 formation in a heavily polluted central China megacity using multi-methods during 2019–2021 warm seasons

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Measurement report: Characterizing O₃-NOₓ-VOC sensitivity and O₃ formation in a heavily polluted central China megacity using multi-methods during 2019–2021 warm seasons