the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Apr 2025
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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Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-1371', Anonymous Referee #1, 13 May 2025
- AC1: 'Reply on RC1', W. Nie, 31 Jul 2025
-
RC2: 'Comment on egusphere-2025-1371', Anonymous Referee #2, 22 May 2025
General comments:
PMF analysis is a widely used receptor model for source apportionment. Running bin-PMF for subranges is an interesting combination to extract more detailed information from the CIMS dataset, where both the sources and sinks can vary greatly. However, the interpretation of the solutions requires great efforts and experience. Although the manuscript is well-structured, several parts of the manuscript need improvement and more detailed clarification. Therefore, I recommend a major revision before the manuscript can be considered for acceptance.
Specific/technical comments:
- Line 145. The assumption that “OOMs detected have the same ionization efficiency as H2SO4” may not be valid. Previous quantum chemical computations (Hyttinen et al., 2015) have shown that in order to be detected effectively by NO3, the highly oxygenated organic molecules need to contain two H-donor functional groups to reach collision-limited detection (typically, at least 7-8 O atoms). However, you “observed OOMs include 3–6 effective oxygen atoms, accounting for 85% of the total 210 signals ” (line 206). Can you estimate, at least, the measurement uncertainty introduced by your assumption?
- Page 4, Section 2.2 instrumentation. Did you use an Eisle NO3 inlet for NO3-CIMS? What was the time resolution of the CIMS dataset used for the PMF analysis? And what are the flow settings for the instruments (e.g., NO3-CIMS, PTR, TOF-ACSM…, maybe summarize the details in a table in the supplementary)? It could also be useful if you could include some of the results/figures on your SA calibration and transmission tests in the supplementary. All these experimental details are important for data quality assessment and inter-study comparisons.
- Section 3.2.2. For factor D2-AVOC-II, the diurnal variation is bimodal. Why do you think PFM analysis failed to separate this factor into two different factors, one multi-generational oxidation product peaking around 13:00, and one NO3 oxidation of CHNO compounds peaking around 19:00? Since they have different time variations, PMF should, in principle, be able to distinguish them, right?
- Lines 326-327. The statement “higher nighttime values are observed, suggesting some transport influence” is unclear to me. Why does nighttime indicate transport? You've identified a transport factor, so did you find some common compounds? Nighttime concentrations could also result from some sources or compounds with relatively high volatility that linger after daytime formation.
- Line 345. Could these C10 compounds be partly formed through C5-RO2 + C5-RO2 rather than from monoterpene, because they are grouped in this Isoprene-related factor by PMF? Similarly, for section 3.3.4, factor N4-SQT, could there be C15 compounds formed from C5-RO2 + C10-RO2 dimer?
- Lines 360-361. A repeated sentence, “This RO2 360 radical originates from NO3-initiated oxidation of isoprene.”
- Figure 2. It would be helpful for readers if you could label each factor with the molecular formulae of their highest peaks for quick visual reference (e.g., a quick glance at the C numbers). Also, a pie chart showing the contribution of each factor to the total signal intensity could be informative.
- Lines 396-397. Can you provide plots for the diurnal variation of dinitrates C10H16,18O8-13N2 and trinitrates C10H17O10-13N3? The diurnal plot of this factor will be the average of all organic nitrate, while the dinitrates (NO3-RO2 + NO or monoterpenes contain two C=C bonds that reacted twice with NO3?) and trinitrates (further oxidation of dinitrates?) should only appear in the early morning, based on your hypothesis, right?
- Lines 464-467. I don’t understand why the sub-range PMF “reveals how chemical conditions and processing pathways evolve over time, reflected by temporal variations in the relative contributions of spectral sub-ranges to individual factors”. Like traditional PMF, your sub-range PMF should also generate static mass spectra for each factor, right? Then, how were you able to obtain mass spectra for factors D3-AVOC-III and D1-AVOC-I under different conditions? Please explain this in your method part as well.
- Figures 6 and S6 (and also Figures 7 and S7), what is the unit for “difference of profiles”? Using relative difference (e.g., normalized to “Low T/NOx” or “Low CS”) might make the comparison clearer and easier to understand.
- Line 497. What exactly is the “T/NOx ratio”? They have different units, so, the meaning of this ratio is unclear. What does a high or low value indicate chemically or physically? Why not directly use T or NOx, or make one plot vs T but colored by NOx, for example?
- Lines 510-512. Do you have references to support this argument that “elevated temperatures favor the RO + NO2 channel, reducing the formation of RONO2 from the RO2 + NO reaction”? Should the product of RO+NO2 also be thermally unstable? Why does higher T favor RO + NO2 than RO2 + NO?
- Line 554. What is the typical range of CS observed at your site? Is 0.05 s-1 considered extremely high in your context?
- Lines 558-559. The sentence “condensation processes under high CS conditions act as a controlling mechanism for species partitioning” is very confusing? Should volatility and OA mass always control the gas-particle partitioning? And CS means condensation sink, of course under higher CS one would expect higher rate of condensation? Please clarify.
- Figures 6d and 7d. What does this fractional contribution analysis mean? Because at each CS bin, the sum of R1, R2, and R3 seems to be larger than 1, which is confusing. Also, your explanation in lines 561-564 is misleading. With the increases in CS, all mass ranges should decrease because of enhanced condensation, right? It is because R3 and R2 condensed more than R1, making the relative fraction of R1 increase. Therefore, I think absolute concentrations would be more informative than fraction contributions here.
Citation: https://doi.org/10.5194/egusphere-2025-1371-RC2 - AC2: 'Reply on RC2', W. Nie, 31 Jul 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-1371', Anonymous Referee #1, 13 May 2025
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 'Arom×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 - AC1: 'Reply on RC1', W. Nie, 31 Jul 2025
-
RC2: 'Comment on egusphere-2025-1371', Anonymous Referee #2, 22 May 2025
General comments:
PMF analysis is a widely used receptor model for source apportionment. Running bin-PMF for subranges is an interesting combination to extract more detailed information from the CIMS dataset, where both the sources and sinks can vary greatly. However, the interpretation of the solutions requires great efforts and experience. Although the manuscript is well-structured, several parts of the manuscript need improvement and more detailed clarification. Therefore, I recommend a major revision before the manuscript can be considered for acceptance.
Specific/technical comments:
- Line 145. The assumption that “OOMs detected have the same ionization efficiency as H2SO4” may not be valid. Previous quantum chemical computations (Hyttinen et al., 2015) have shown that in order to be detected effectively by NO3, the highly oxygenated organic molecules need to contain two H-donor functional groups to reach collision-limited detection (typically, at least 7-8 O atoms). However, you “observed OOMs include 3–6 effective oxygen atoms, accounting for 85% of the total 210 signals ” (line 206). Can you estimate, at least, the measurement uncertainty introduced by your assumption?
- Page 4, Section 2.2 instrumentation. Did you use an Eisle NO3 inlet for NO3-CIMS? What was the time resolution of the CIMS dataset used for the PMF analysis? And what are the flow settings for the instruments (e.g., NO3-CIMS, PTR, TOF-ACSM…, maybe summarize the details in a table in the supplementary)? It could also be useful if you could include some of the results/figures on your SA calibration and transmission tests in the supplementary. All these experimental details are important for data quality assessment and inter-study comparisons.
- Section 3.2.2. For factor D2-AVOC-II, the diurnal variation is bimodal. Why do you think PFM analysis failed to separate this factor into two different factors, one multi-generational oxidation product peaking around 13:00, and one NO3 oxidation of CHNO compounds peaking around 19:00? Since they have different time variations, PMF should, in principle, be able to distinguish them, right?
- Lines 326-327. The statement “higher nighttime values are observed, suggesting some transport influence” is unclear to me. Why does nighttime indicate transport? You've identified a transport factor, so did you find some common compounds? Nighttime concentrations could also result from some sources or compounds with relatively high volatility that linger after daytime formation.
- Line 345. Could these C10 compounds be partly formed through C5-RO2 + C5-RO2 rather than from monoterpene, because they are grouped in this Isoprene-related factor by PMF? Similarly, for section 3.3.4, factor N4-SQT, could there be C15 compounds formed from C5-RO2 + C10-RO2 dimer?
- Lines 360-361. A repeated sentence, “This RO2 360 radical originates from NO3-initiated oxidation of isoprene.”
- Figure 2. It would be helpful for readers if you could label each factor with the molecular formulae of their highest peaks for quick visual reference (e.g., a quick glance at the C numbers). Also, a pie chart showing the contribution of each factor to the total signal intensity could be informative.
- Lines 396-397. Can you provide plots for the diurnal variation of dinitrates C10H16,18O8-13N2 and trinitrates C10H17O10-13N3? The diurnal plot of this factor will be the average of all organic nitrate, while the dinitrates (NO3-RO2 + NO or monoterpenes contain two C=C bonds that reacted twice with NO3?) and trinitrates (further oxidation of dinitrates?) should only appear in the early morning, based on your hypothesis, right?
- Lines 464-467. I don’t understand why the sub-range PMF “reveals how chemical conditions and processing pathways evolve over time, reflected by temporal variations in the relative contributions of spectral sub-ranges to individual factors”. Like traditional PMF, your sub-range PMF should also generate static mass spectra for each factor, right? Then, how were you able to obtain mass spectra for factors D3-AVOC-III and D1-AVOC-I under different conditions? Please explain this in your method part as well.
- Figures 6 and S6 (and also Figures 7 and S7), what is the unit for “difference of profiles”? Using relative difference (e.g., normalized to “Low T/NOx” or “Low CS”) might make the comparison clearer and easier to understand.
- Line 497. What exactly is the “T/NOx ratio”? They have different units, so, the meaning of this ratio is unclear. What does a high or low value indicate chemically or physically? Why not directly use T or NOx, or make one plot vs T but colored by NOx, for example?
- Lines 510-512. Do you have references to support this argument that “elevated temperatures favor the RO + NO2 channel, reducing the formation of RONO2 from the RO2 + NO reaction”? Should the product of RO+NO2 also be thermally unstable? Why does higher T favor RO + NO2 than RO2 + NO?
- Line 554. What is the typical range of CS observed at your site? Is 0.05 s-1 considered extremely high in your context?
- Lines 558-559. The sentence “condensation processes under high CS conditions act as a controlling mechanism for species partitioning” is very confusing? Should volatility and OA mass always control the gas-particle partitioning? And CS means condensation sink, of course under higher CS one would expect higher rate of condensation? Please clarify.
- Figures 6d and 7d. What does this fractional contribution analysis mean? Because at each CS bin, the sum of R1, R2, and R3 seems to be larger than 1, which is confusing. Also, your explanation in lines 561-564 is misleading. With the increases in CS, all mass ranges should decrease because of enhanced condensation, right? It is because R3 and R2 condensed more than R1, making the relative fraction of R1 increase. Therefore, I think absolute concentrations would be more informative than fraction contributions here.
Citation: https://doi.org/10.5194/egusphere-2025-1371-RC2 - AC2: 'Reply on RC2', W. Nie, 31 Jul 2025
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Junchao Yin
Yuliang Liu
Qiaozhi Zha
Yuanyuan Li
Dafeng Ge
Chong Liu
Caijun Zhu
Xuguang Chi
Aijun Ding
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1590 KB) - Metadata XML
-
Supplement
(1745 KB) - BibTeX
- EndNote
- Final revised paper
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 'Arom×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.