New insight into the formation and aging processes of organic aerosol from positive matrix factorization (PMF) analysis of ambient FIGAERO-CIMS thermograms

Cai, Mingfu; Yuan, Bin; Hu, Weiwei; Ye, Chenshuo; Huang, Shan; Yang, Suxia; Chen, Wei; Peng, Yuwen; Deng, Zhaoxiong; Zhao, Jun; Chen, Duohong; Sun, Jiaren; Shao, Min

doi:10.5194/egusphere-2025-4597

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https://doi.org/10.5194/egusphere-2025-4597

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05 Oct 2025

| 05 Oct 2025

New insight into the formation and aging processes of organic aerosol from positive matrix factorization (PMF) analysis of ambient FIGAERO-CIMS thermograms

Mingfu Cai, Bin Yuan, Weiwei Hu, Chenshuo Ye, Shan Huang, Suxia Yang, Wei Chen, Yuwen Peng, Zhaoxiong Deng, Jun Zhao, Duohong Chen, Jiaren Sun, and Min Shao

Abstract. Secondary organic aerosol (SOA) is an important component of organic aerosol (OA), yet its evolution of volatility remains unclear. We investigated SOA volatility at a downwind site of the Pearl River Delta (PRD) region in the fall of 2019, using a time-of-flight chemical ionization mass spectrometer coupled with a Filter Inlet for Gases and Aerosol (FIGAERO-CIMS). Positive matrix factorization (PMF) analysis was performed on the thermogram data of organic compounds measured by the FIGAERO-CIMS. Eight factors were resolved, including six daytime chemistry related factors, a biomass burning related factor (BB-LVOA, 10 %), and a nighttime chemistry related factor (Night-LVOA, 15 %) along with their corresponding volatility. Day-HNO_x-LVOA (12 %) and Day-LNO_x-LVOA (11 %) were mainly formed through gas-particle partitioning, with higher NO_x promoting more volatile OA. Two aged OA factors (Day-aged-LVOA, 16 %; Day-aged-ELVOA, 11 %) reflected daytime photochemical aging, while Day-urban-LVOA (16 %) and Day-urban-ELVOA (7 %) were linked to urban plumes. Results show that both gas-particle partitioning (36 %) and photochemical aging (30 %) accounted for a major fraction in the afternoon during the urban air masses period, especially for high-NO_x-like pathway (~21 %). In general, the six daytime OA factors collectively explain the majority (82 %) of daytime SOA identified by an aerosol mass spectrometer (AMS), while the highly oxygenated OA and hydrocarbon-like OA cannot be identified with FIGAERO-CIMS in this study. In summary, our results show that the volatility of OA is strongly governed by its formation pathways and subsequent atmospheric aging processes.

Received: 18 Sep 2025 – Discussion started: 05 Oct 2025

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Mingfu Cai, Bin Yuan, Weiwei Hu, Chenshuo Ye, Shan Huang, Suxia Yang, Wei Chen, Yuwen Peng, Zhaoxiong Deng, Jun Zhao, Duohong Chen, Jiaren Sun, and Min Shao

Status: final response (author comments only)

RC1:
'Comment on egusphere-2025-4597', Anonymous Referee #1, 21 Oct 2025
The paper provides a comprehensive analysis of SOA formation and aging processes in the PRD region, using advanced measurements from a FIGAERO-CIMS coupled with PMF analysis. The study identifies and characterizes different SOA factors based on their volatility and formation pathways. The results highlight the significant role of gas-particle partitioning and photochemical aging in SOA formation, with variations driven by environmental factors such as NOx levels. The authors also compare these findings with data from AMS and discuss the limitations of FIGAERO-CIMS in detecting certain OA components. This manuscript is suitable for publication in ACP and I recommend it for publication after the following comments have been addressed.
The author mentioned that six daytime FIGAERO factors were positively correlated with LOOA in AMS OA. I wonder whether the relationship between FIGAERO factors and LOOA varies across different periods.

The calibration experiment regarding the relationship between Tmax and saturation vapor concentration is important. Could the authors provide more details about this calibration experiment in the main text? Specifically, why were the fitting parameters chosen?

The study finds discrepancies between nighttime SOA measured by AMS and that characterized by FIGAERO-CIMS, suggesting that the nighttime processes may not be fully captured by the FIGAERO-CIMS thermogram data. Is there any evidence about the low volatility of nighttime OA？

The authors suggest that an increase in NOx levels could enhance the volatility of SOA. I recommend that the authors compare this finding with other studies on the impact of NOx on OA volatility.

The paper finds that FIGAERO-OA cannot explain MO-OOA and HOA in AMS, but it does not further analyze the reasons. For MO-OOA, it is unclear whether it is a "very low-volatility species not desorbed by heating”. For HOA, the undetection may be due to the low response efficiency of the ionization method (I⁻ reagent) in FIGAERO-CIMS, which weakens the discussion on the data complementarity of the two instruments.

Figure 2 a: please add “” to the x-axis label.

Table 1: It should be “average elemental composition” in the heading.

Eq (5) and (6) are missing in the main text. This is presumably a typesetting omission.
Citation: https://doi.org/10.5194/egusphere-2025-4597-RC1
- AC1: 'Reply on RC1', Mingfu Cai, 24 Dec 2025
  
  Please refer to the supplement for the comments.
  
  Citation: https://doi.org/10.5194/egusphere-2025-4597-AC1
RC2:
'Comment on egusphere-2025-4597', Anonymous Referee #2, 08 Dec 2025
This manuscript presents source apportionment of organic aerosol (OA) measured by a FIGAERO-CIMS at a coastal downwind receptor site and resolves eight organic aerosol factors using PMF, followed by a comparison with HR-AMS measurements. Eight factors include six daytime chemistry related factors, a biomass burning related factor (BB-LVOA), and a nighttime chemistry related factor (Night-LVOA). It was also found that increasing NOx levels mainly affected SOA formation via gas-particle partitioning, suppressing the formation of low-volatile organic vapors. Besides, two aged OA factors (Day-aged-LVOA and Day-aged-ELVOA) were mainly attributed to daytime photochemical aging of pre-existing OA.
The topic is scientifically relevant, particularly given the increasing interest in linking OA volatility, oxidation state, and formation pathways. The dataset is valuable, and the thermogram-based OA classification is potentially insightful. However, the attribution of the resolved factors (e.g., High-NOx LVOA, Urban-LVOA, Aged-LVOA) remains insufficiently supported by the current evidence. The manuscript would benefit from clearer methodological justification, more cautious interpretation of factor identities, and additional analyses to better constrain potential chemical and meteorological influences on factor behavior.

The authors should clearly state what scientific insights are genuinely new compared with previous FIGAERO-CIMS PMF studies.

In lines 192-193, the thermogram matrix was split into three segments for PMF due to computational limitations, but the implications for factor consistency and rotational ambiguity were not discussed. A justification and uncertainty evaluation are needed.

In line 194, the justification for selecting the 8-factor solution is insufficient. Standard PMF diagnostics (Q/Qexp, residuals, Fpeak sensitivity) should be provided.

In lines 214-220, the PEG-based calibration (PEG 5–8) may not be representative of nitrogen-containing or highly oxygenated organic species. Calibration uncertainties should also be discussed.

In lines 257-260, the manuscript acknowledges decomposition artifacts for some species (e.g., C2–C3), but does not systematically address pyrolysis across all factors. A more comprehensive evaluation is required.

The chemical characteristics of Day-urban-LVOA and Day-HNOx-LVOA overlap significantly. More evidence is needed to show they are not artifacts of factor splitting. (in lines 275-280 and Table 1)

In lines 314-316, the deviation of Day-urban-ELVOA from the expected relationship is attributed simply to “decomposition”. This may require a more rigorous discussion.

In lines 386-418, the interpretation of NOx effects is speculative without supporting evidence from highly oxygenated organic molecules or accretion reaction markers. Although the manuscript proposes that NOx suppresses autoxidation and shifts SOA formation toward more volatile and less oxygenated components, this conclusion is currently based primarily on correlations and factor behavior. To substantiate this mechanism, molecular-level evidence would be necessary. The authors should therefore adopt more cautious wording or provide additional analyses to better support their proposed NOx-driven interpretation.
Citation: https://doi.org/10.5194/egusphere-2025-4597-RC2
- AC2: 'Reply on RC2', Mingfu Cai, 24 Dec 2025
  
  Please refer to the supplement for the comments.
  
  Citation: https://doi.org/10.5194/egusphere-2025-4597-AC2

Mingfu Cai, Bin Yuan, Weiwei Hu, Chenshuo Ye, Shan Huang, Suxia Yang, Wei Chen, Yuwen Peng, Zhaoxiong Deng, Jun Zhao, Duohong Chen, Jiaren Sun, and Min Shao

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https://doi.org/10.5194/egusphere-2025-4597-supplement

Mingfu Cai, Bin Yuan, Weiwei Hu, Chenshuo Ye, Shan Huang, Suxia Yang, Wei Chen, Yuwen Peng, Zhaoxiong Deng, Jun Zhao, Duohong Chen, Jiaren Sun, and Min Shao

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Short summary

This study investigates how the formation and aging processes of secondary organic aerosol (SOA) influence the evolution of SOA volatility in downwind regions. Our results reveal that elevated NOₓ levels enhanced the daytime SOA volatility by modifying gas-particle partitioning, particularly through suppressing the production of low-volatility organic vapors. In contrast, photochemical aging was associated with reduced SOA volatility.


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