the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Oct 2025
Driving Factors of Oxalic Acid and Enhanced Role of Gas-Phase Oxidation under Cleaner Conditions: Insights from 2007–2018 Field Observations in the Pearl River Delta
Abstract. Secondary organic aerosol (SOA) is a dominant constituent of fine particulate matter, exerting significant impacts on both climate and human health. Oxalic acid (C2), a key end-product formed from the oxidation of volatile organic compounds, can provide insights into the formation mechanism of SOA. Thus, long-term measurements of C2 and related compounds help understand the changes in SOA formation with decreasing pollutant levels. In this study, C2 and its homologs, along with five primary anthropogenic source markers and three SOA markers, were measured in the Pearl River Delta (PRD) during 2007–2018. The concentrations of C2 and its homologs did not exhibit significant downward trends, despite substantial reductions in anthropogenic emissions, for example, biomass burning (−11 % yr−1), vehicle emissions (−17 % yr−1), and cooking emissions (−7 % yr−1). Correlation analysis revealed that aerosol liquid water content (ALWC) and Ox (O3 + NO2) were the main drivers of C2 variation. Moreover, the relative contribution of biogenic SOA increased under cleaner conditions. A machine learning model was applied to quantify the contributions of anthropogenic precursors emission, biogenic precursors emission, aqueous-phase oxidation processes, and gas-phase oxidation processes to C2 variability. As pollution levels declined, the contribution of gas-phase oxidation increased from 24 % to 48 %, whereas that of aqueous-phase oxidation declined from 35 % to 20 %. This shift indicated a transition from aqueous-phase to gas-phase pathways in C2 and SOA formation. Our findings highlight the increasing importance of gas-phase oxidation and underscore the need for effective ozone control strategies to further reduce SOA in the future.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-4624', Anonymous Referee #1, 18 Nov 2025
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RC2: 'Comment on egusphere-2025-4624', Anonymous Referee #2, 26 Nov 2025
This work analyzed a long-term variation of oxalic acid in atmospheric aerosols in the Pearl River Delta from 2007 to 2018. Aerosol liquid water and Ox are the driving factors of C2 formation, and gas-phase oxidation would play a more important role than aqueous-phase oxidation as air pollution decreases. The long-term data is valuable and informative. However, the data interpretation needs to be more rigorous. I may suggest a major revision before publication.
Major comments:
The ALWC and Ox are identified as the drivers of C2 variation. The authors also highlight the increased contribution of gas-phase oxidation and decreased contribution of aqueous-phase oxidation to C2 formation as pollution levels declined. However, high air pollution level is usually accompanied by high humidity and low Ox.
How did the authors exclude the impacts of different emission levels when addressing the influence of RH and Ox on C2 variation?
In Fig. 5, the correlation between C2 and Ox is always higher than that between C2 and ALWC under IT1, 2, 3, and 4. The correlation between C2 and ALWC is weak under any IT condition.
Section 3.4: For the machine learning analysis, the authors quantify the contributions of different sources using some input parameters. The rationality of this approach needs to be elaborated. For example, air temperature, solar radiation, and relative humidity are used to represent the emission of biogenic precursors (lines 318-319). Do all these meteorological factors promote the emission of biogenic precursors? For biogenic emissions, is there a synergistic or antagonistic mechanism between these factors? Please explain in detail. The reasonability of using the input parameters to represent other sources also needs to be elaborated.
For the contribution of gas-phase oxidation versus aqueous-phase oxidation, is the result here obtained based on machine learning comparable to those reported in published literature?
Figures 2 and 3: I am curious about the high levels since 2013. Please explain the reasons.
Lines 250-252: “Meanwhile, the correlations between C2 and ASOA markers became weaker. These results suggested ….” Please explain this statement. In Table 1, I did not find an obvious decreasing trend for the correlation coefficients between C2 and Phthalic acid (changing from 0.28 under IT1 to 0.31 under IT4) or DHOPA (changing from 0.49 under IT1 to 0.32 under IT4) from IT1 to IT4.
Lines 287-290: I did not see an obvious difference in the correlation efficiency between C2 and Ox or between C2 and ALWC from IT1 to IT4. The change of Pearson r values seems small.
The dataset collected during 2007-2018 is valuable and informative. My concern is the uncertainty caused by long-term storage. How long after sampling were these samples analyzed? How much of the C2 organic acid could change during storage?
In addition, in lines 115-116, I may suggest adding a table in the supplementary to detail the sample information.
Specific comments:
Please specify the data source of solar radiation in the method section.
In Figure 2, 3, or other similar figures, modify the name of the y-axis to the corresponding species. It would be easier for readers.
Figure captions need to be revised. For example, “The concentrations decreased from 864 ± 283 ng m-3 (2007) to 307 ± 122 ng m-3 (2018), ….”, “Pearson’s r values between C2 and ALWC decreased from 0.43 to 0.15, while those between C2 and Ox increased from 0.28 to 0.68.” or similar statements should not be described in the figure caption.
Line 225 and Table 1: Change “IT4 (25 ug/m3 >PM2.5)” to “PM2.5 < 25 ug/m3”.
Lines 244-245: Please show the data or other evidence on the higher temperature, solar radiation, or humidity in PRD.
Lines 330-332: Please show evidence on the statement that lower ALWC favors the C2 compounds from the particle-phase to the gas-phase.
Citation: https://doi.org/10.5194/egusphere-2025-4624-RC2
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General Comments:
The manuscript reports long-term field observations of di-acids and its related primary and secondary markers from anthropogenic and biogenic sources at a site in the PRD region, China. It also combines these observations with machine-learning methods to investigate and quantify potential contributions of major drivers to the variation of oxalic acid. Their major findings highlight the increasing importance of gas-phase oxidation in forming SOA. Overall, the topic is valuable with good-quality datasets, but the manuscript needs clearer methodological descriptions, stronger validation of the machine-learning attribution, and more mechanistic and systematic support before publication. For the machine learning methodology part, the attribution is potentially interesting, but I am concerned about robustness given the relatively small dataset (~400 observations) and 11 features. With this sample size there is a substantial risk of overfitting and unstable feature attributions, especially if the data are temporally autocorrelated. I would suggest a major revision.
Specific Comments: