the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Unveiling the Formation of Atmospheric Oxygenated Organic Molecules under Anthropogenic-Biogenic Interactions: Insights from Binned Positive Matrix Factorization on Multi-Subrange Mass Spectra
Abstract. Oxygenated organic molecules (OOMs), which are low-volatility intermediates produced via volatile organic compound (VOC) oxidation, play a critical role in secondary organic aerosol (SOA) formation through gas-to-particle conversion. Despite recent advancements in OOM characterization, the high complexity of OOM spectra poses a significant challenge in the interpretation of their sources. This study investigates OOM formation in a Chinese megacity using an improved analytical strategy that integrates binned Positive Matrix Factorization on multiple sub-range mass spectral analysis. Unlike traditional approaches that handle mass spectral peak identification and chemical interpretation sequentially, our method simultaneously optimizes both, reducing uncertainties associated with peak assignment and chemical analysis. The method successfully identified 2571 OOM molecules and systematically revealed major OOM formation pathways through 11 distinct factors: five daytime photochemical processes, four nighttime NO3-driven oxidation processes, and two regional mixed sources. Notably, this approach enabled the successful separation of sesquiterpene oxidation products in ambient measurements—compounds previously unidentified by traditional full-mass-range analysis due to their weak signals. The method captured dynamic changes in OOM composition under varying environmental conditions, demonstrating the influence of temperature and NOx levels on OOM formation, as well as the volatility-dependent patterns influenced by condensation sink. This improved analytical strategy provides new insights into atmospheric OOM chemistry and establishes a robust foundation for future studies of VOCs-OOMs-SOA conversion mechanisms.
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RC1: 'Comment on egusphere-2025-1371', Anonymous Referee #1, 13 May 2025
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This paper presents the ambient OOMs measurement in a complex urban environment in China. By combining binPMF with multiple sub-range spectral analysis, 2571 OOMs were successfully identified, 11 distinct factors were used to explain major OOM formation pathways: five daytime photochemical processes, four nighttime NO3-driven oxidation processes, and two regional mixed sources. This analysis achieved the first successful separation of sesquiterpene oxidation products in environmental measurements. In previous studies, these compounds were indistinguishable in traditional full-spectrum analyses due to their weak signals and overlapping temporal patterns with other nocturnal factors. In general, I think this paper is well-structured and easy to follow. However, I do have some concerns that need to be addressed before it can be accepted for publication.
Major Comments:
1. Clarifications on Factor Analysis in R1, R2, and R3
In Figure 3, R1, R2, and R3 correspond to 6, 8, and 9 factors, respectively, whereas Figures S2-S4 in the SI indicates that 12 factors are required to explain the N2-MT-I factors in R1, and 11 factors are needed for both R2 and R3.
(a) Figure S2 shows 5 NP-related factors, and Figure S3 shows 2 NP-related factors. Since the formation pathways of these ions were not discussed in the final analysis, would it be possible to re-perform the factor analysis after removing the NP-related ions?
(b) Could you provide a detailed explanation of the contamination factors present in R1, R2, and R3?
(c) In R3, the factors D3-AVOC-III-1 and D3-AVOC-III-2 were merged before conducting correlation analysis with factors in the first two ranges. Could you elaborate on how this merging was specifically performed?
2. Interpreting OOM Factors Based on Precursor Compounds
CIMS data typically utilizes fingerprint molecules to characterize formation pathways. However, in complex atmospheric environments, naming factors based on their precursors (e.g., AVOC, isoprene, monoterpene) introduces significant interpretation challenges. For instance, regarding the D1-AVOC-I factor, the currently presented evidence collectively supports its interpretation:
It correlates relatively well with the 'AromĂ—OH' proxy.
This factor exhibits the highest average double bond equivalent (DBE).
The tracer molecules show comparability with existing laboratory studies.
For other factors, could additional discussion of results be incorporated in Sections 3.2 and 3.3? Specific comments follow:
(a) [D2-AVOC-II] Lines 291-292: “The first series represents typical aliphatic products, while the latter corresponds to second-generation aromatic products observed in laboratory studies.” Please provide the reference/supporting evidence for this statement. Furthermore, it cannot be denied that CxH2x-2O8N2 (e.g., C=10) may also originate from terpene oxidation (Luo et al., 2023).
(b) [D3-AVOC-III] Line 306: “These compounds are typical aromatic oxidation products.” This conclusion appears overly assertive, as these products—CxH2x-4O5 (7.3% abundance) and CxH2x-2O5 (6.0% abundance)—could also potentially originate from isoprene and monoterpene oxidation.
(c) [D4-AVOC-IV] The fingerprint molecules CxH2x-2O4 and CxH2x-1,2x-3O6N are currently grouped within the same factor. However, are there laboratory studies showing shared precursors for these compounds or similar formation pathway?
(d) Line351: What is the relative importance of ozonolysis for these nighttime factors?
(e) [N1-IP] Given that the RO2 radical C5H8NO5 accounts for 57.4% of the total factor intensity, while no higher-oxygen-number isoprene-RO2 radicals were detected, here recommend to plot the time series of C5H8NO5 and demonstrate its correlation with the factor.
(f) [N3-MT-II] From the diurnal pattern, the formation of this factor can be affected by O3 oxidation.
(g) [Mixed-MT] The current characterization of this factor appears incomplete and need additional explanation.
3. Mixed-Precursor Effects on Volatility Estimation
In the discussion of OOM volatility, the authors state: "The identification of monoterpene-related compounds was based on the approach proposed by Nie et al. (2022), where OOMs with DBE=2 that appeared in the PMF monoterpene-related factors were classified as monoterpene OOMs." This precursor-dependent classification approach introduces additional uncertainty to the volatility distribution shown in Figure 5, particularly for factors like Mixed-MT where precursors are not exclusively monoterpenes.
Minor Comment:
Line 192: C6H5OHNO3- is incorrect.
Line 306: should be Table S3.
Reference:
Luo, H., et al., Formation of highly oxygenated organic molecules from the oxidation of limonene by OH radical: significant contribution of H-abstraction pathway. Atmos. Chem. Phys. 2023, 23, (13), 7297-7319.
Citation: https://doi.org/10.5194/egusphere-2025-1371-RC1
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