Evolution and chemical characteristics of organic aerosols during wintertime PM<sub>2.5</sub> episodes in Shanghai, China: Insights gained from online measurements of organic molecular markers

Zhu, Shuhui; Zhou, Min; Qiao, Liping; Huang, Dan Dan; Wang, Qiongqiong; Wang, Shan; Gao, Yaqin; Jing, Shengao; Wang, Qian; Wang, Hongli; Chen, Changhong; Huang, Cheng; Yu, Jian Zhen

doi:https://doi.org/10.5194/acp-2022-813

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https://doi.org/10.5194/acp-2022-813

Preprints

24 Jan 2023

| 24 Jan 2023

Status: a revised version of this preprint is currently under review for the journal ACP.

Evolution and chemical characteristics of organic aerosols during wintertime PM_2.5 episodes in Shanghai, China: Insights gained from online measurements of organic molecular markers

Shuhui Zhu, Min Zhou, Liping Qiao, Dan Dan Huang, Qiongqiong Wang, Shan Wang, Yaqin Gao, Shengao Jing, Qian Wang, Hongli Wang, Changhong Chen, Cheng Huang, and Jian Zhen Yu

Abstract. Organic aerosol (OA) is a significant part of urban fine particulate matter (PM_2.5) and a lack of detailed knowledge of their sources has increasingly hindered the improvement of air quality in China in recent years as significant reductions have been achieved in inorganic ion constituents. In this study, a wide range of organic molecular markers in PM_2.5 were monitored with a bihourly time resolution using a Thermal desorption Aerosol Gas chromatograph system (TAG) in urban Shanghai in winter 2019. The molecular marker data have provided a unique source tracking ability in characterizing the evolution of organic aerosols during nine wintertime episodic events. Episodes primarily influenced by local air masses were characterized with higher proportions and mass increments of secondary OA. Rapid elevation in both absolute mass concentration and relative proportion was observed for primary and secondary OA markers indicative of vehicle emissions (e.g., alkanes, hopanes, and 2,3-dihydroxy-4-oxopentanoic acid), as well as cooking activities (e.g., saturated and unsaturated fatty acids, and C9 acids). In comparison, episodes under significant influences of transported air mass were typically associated with a predominant PM_2.5 contribution from secondary inorganic aerosols and enhanced OA contribution from biomass burning activities. The latter was evident from the tracer data (e.g., levoglucosan, aromatic polycarboxylic acids, and nitro-aromatic compounds). Secondary OA markers associated with later generation products of hydrocarbon oxidation process, such as C_3–5 dicarboxylic acids, were the most deficient during local episodes while notably enhanced during the episodes under influence of transported air masses, reflecting different extent and pathways of atmospheric aging processing. The ability of distinguishing the variations of OA evolution during different types of episodes demonstrates the value of online organic molecular measurements to episodic analysis. The results indicate that control of local urban sources such as vehicular and cooking emissions would lessen severity of local episodes while regional control of precursors for secondary inorganic aerosols and biomass burning activities would reduce PM_2.5 episodes under synoptic conditions conducive for regional transport.

Received: 02 Dec 2022 – Discussion started: 24 Jan 2023

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Shuhui Zhu et al.

Status: final response (author comments only)

RC1:
'Comment on acp-2022-813', Anonymous Referee #1, 03 Mar 2023

The manuscript prepared by Zhu et al titled “Evolution and chemical characteristics of organic aerosols during wintertime PM_2.5episodes in Shanghai, China: Insights gained from online measurements of organic molecular markers” reported an online bi-hourly molecular tracer dataset collected by Thermal desorption Aerosol Gas chromatography system (TAG) in winter 2019, Shanghai. Combining with analytical results from other online instruments such as AMS and MARGA, the authors analyzed molecular markers (levoglucosan, C3-5 organic acids, C9 acids, DHOPA, phthalic acid, uFAs, sFAs etc.) in 9 episodes during their observation and found that vehicle and cooking emission were the two local sources for fine particulate matter while biomass burning was the main OA source contributed via regional transportation. Their results indicated that control of local urban sources such as vehicular, cooking, and biomass burning emissions would help alleviating the winter haze episodes in Shanghai. The paper is well prepared, but I found some of the conclusions are contradictory with each other and the current version lacks novelty in general out of a scientific perspective. The overall language needs to be improved. I sincerely hope the authors explain the following major concerns before the manuscript could be published.
Major concerns:
1. Can the authors explain more about why they picked 2019 winter as astudying period for investigating the OA composition and evolution in Shanghai. How representative it is? Will the final conclusion of this work about the importance of control primary emissions such as cooking, vehicle, and biomass burning emissions change if the author changed the year of study?
2. Section 3.1: The authors analyzed 9 episodes in total and divided them into three categories, namely, transport episodes, local episodes, and mixed-influence episodes, but in Line 154-155 the author stated that the haze episodes were under the impacts from local emission and clean episodes were influenced by long range transportation of air mass. Such claims also seem contradictory to the following statement in Line 159-161, where the author again mentioned that episodes with high average PM2.5 level were found during transport episodes. The definitions and explanations for each types of episode should definitely be clearer in this section.
3. OC/EC method was used by the authors for SOM and POM estimation. Can the authors add uncertainty analysis for the SOM and POM estimations in this study? SOM as well as a variety of primary emissions were found to be dominant in the local episodes. Was the SOM partially influenced by primary emissions since primary factors were commonly strong during the local episodes? How accurate the estimation of SOM can be in this study?
4. C9 produced from ozonolysis of fatty acids were not detected to increase in their mass content during the local episode and the author then explain this was because of the low O₃ mixing ratio. Did the author just mentioned the local episode was largely influenced by SOA formation based on Figure 2? Does this mean that most of SOA in the local episodes were formed form pathways other than ozonolysis, can the authors provide further evidence for this point?
5. From discussions of Figure 2, the authors claimed that the local episodes were significantly influenced by SOA. But in line 268, the author mentioned that AMS data indicated higher MO-OOA and LO-OOA were observed for the mixed-influence and transport episodes, indicating that OA in these two episodes were more aged. Should there at least be some assumptions given in the manuscript on how the PM1 data from AMS and the PM2.5 data from TAG were compared? The logic in terms of which episode underwent more profound secondary OA formation process is a bit mess in the current manuscript. The authors should be more explicit on which episode is more aged and has a higher formation of SOA under which specific conditions.
6. The hC4/C4 ratio was used as an indicator for aqueous phase secondary OH oxidation. However, as indicated in Figure 6e, the positive correlation between hC4/C4 and RH was found to be more significant for the non-episodic period compared to the episodic period. I’m afraid the explanation of impact from marine aerosol should not be persuasive as hC4 should be secondary formed according to the statement of the authors. More discussion needed to explain why the hC4/C4 ratio climbed up more significantly with the increase of RH during the non-episodic period.

Minor points:
Line 41: sentence needs to be rephrased
Line 54-56: references needed here
Line 58-60: This sentence is hard to understand. Please provide evidence in more detail
Line 67: while studying evolution processes…
Line 105: it is very abrupt to bring up residual oil combustion here
Line 180: SOA has already been defined previously no need to have SOM here
Line 184: SIA has already been defined
Line 224: what does “sizable” mean
Line 226-227: should be local episode with high SOM contribution that have high secondary molecular tracers?
Line 296-298: I am not sure if it is appropriate to just simply define aromatic SOA compounds into these two categories---they might have overlapped zones
Line 397: oxidation degree

Citation: https://doi.org/10.5194/acp-2022-813-RC1
- AC1: 'Reply on RC1', Jian Zhen Yu, 27 Mar 2023
  
  We sincerely thank the referee for the comments concerning our manuscript. They are valuable in helping us improve our manuscript. Our point-by-point response is provided below.
  1. The main reason that we chose 2019 winter to study OA episodic evolutions is because online measurement data from other instruments (e.g., OA in PM₁ from AMS, water-soluble ions in PM_2.5 from MARGA, OC and EC in PM_2.5 from OC/EC analyzer) were also available during this period, which enable us to have a more unambiguous understanding of OA formations in Shanghai. Although we did not discuss OA evolution in other years, several of our previously published papers (He et al., 2020; Huang et al., 2021; Wang et al., 2020; Zhu et al., 2019) have discussed the formation and sources of OA observed in 2018 and 2020. These studies invariably show that cooking, vehicular, and biomass burning emissions are major OA sources and have significant impacts on PM pollution in Shanghai. In this study, we take one step further to look deeper into differences of OA evolutions during different episodic events and find that local OA was more impacted by cooking and vehicle emissions while transported OA contained more biomass burning related organic compounds. Insights learned from this work, despite the measurements conducted in 2019, will inform present and near future policymaking in improving air quality, as the general landscape of major emission sources has not changed.
  2. The transport episodes in this study refers to air masses transported from YRD region while the “long-range” in the sentence “the clean periods were characterized by prevailing air masses that were transported long-range” (Line 155) refers to air masses transported from much farther areas (i.e., the north China). When we submit the revised manuscript, we will rephrase the sentence in Line 154-156 to avoid ambiguity.
  3. The SOM estimated in this study exhibited strong correlations with summed concentrations of SOA source factors derived from AMS measurements. Therefore, we think primary emissions had negligible impacts on the SOM estimation. In submitting a revised manuscript, we will add scatter plots in supporting material to compare the estimated SOM and POM concentrations with source factors derived from AMS measurements to support this statement.
  4. Firstly, from Table 3, Figure 3, and Figure S6 in our manuscript, we can see that both the absolute concentration of C₉ acids and their mass percentage in total TAG-measured OA increased during local episodes. However, their increases were insignificant compared with their primary precursors (i.e., fatty acids). We think the suppression of O3 oxidation by the high NO_x emissions is a major reason, since NO_x concentrations and NO/NO₂ mass ratios were substantially higher during local episodes (Table 2). Additionally, the more drastic increase in the mass percentage of DHOPA during local episodes, which is a typical SOA product of monoaromatics with OH radicals, may further suggest that SOA formed during local episodes were more influenced by pathways other than ozonolysis (e.g., OH oxidation). We will add these explanations in the manuscript to further clarify the variations of C₉ acids observed in this study.
  5. The claim that local episodes were significantly influenced by SOA did not contradict with the observation that SOA compositions (e.g., fresh vs. aged SOA) were different during different episodes. That is SOA overall had higher proportions in PM_2.5 during local episodes compared with mix-influenced and transport episodes. However, when we further investigate their OA compositions, we find that local SOA was more dominant by less-oxidized molecular groups while transported SOA contained significant higher proportions of more oxidized molecular groups (e.g., C_3-5 DCAs, C_3-5 hDCAs). The measurement data from TAG were generally in consistent with those from AMS. As shown in Figure S2, the TAG-measured SOA tracers produced in early generations (e.g., DHOPA, phthalic acid, pinic acid) correlated well with LO-OOA derived from AMS while those associated with later generation products (e.g., C_3-5 DCAs, C_3-5 hDCAs) had stronger correlations with MO-OOA. We will give more explicit explanations in the revised manuscript to reveal the diverse SOA formations during different episodes.
  6. We also observed higher concentrations of O₃ during non-episodic periods in reference to episodic events under the same RH level bins, which appeared to support that the formation of hC₄ was largely facilitated by aqueous phase OH oxidation during non-episodic periods. We will also add more discussions about this in the revised manuscript.
  7. We will rephrase the sentence in Line 41.
  8. We will add references to support the statement in Line 54-56.
  9. We will rephrase the sentence in Line 58-60 to make the statement more clear.
  10. We will rephrase the sentence in Line 67.
  11. We will delete this sentence (Line 105).
  12. We will rephrase the sentence in Line 180.
  13. We will delete the definition in Line 184.
  14. The word “sizable” in the sentence (LIne 224) means “fairly large” (see dictionary.com for meanings of “sizable”). Here we mean to state that those primary and secondary anthropogenic organic molecules had much higher proportions in TAG-measured OA during local episodes.
  15. Yes, the local episodes with high SOM contributions also are characterized with high concentrations of secondary molecular tracers of anthropogenic origins (see Table 3). However, POM also showed higher mass concentrations during local episodes, resulting in that SOM (or SOA) did not account for a larger fraction in total OM (or total OA) during local episodes compared with mix-influenced and transport episodes. In Line 226-227”, we meant to say, “On the contrary, the TAG-measured OA during transport episodes (average: 1164 ng/m³) was dominated by secondary organic molecular groups when examining the relative proportion of primary and secondary organic molecules.” We’ll revise the two sentences in Lines 225-227 to improve clarity.
  16. Since we deduct semi-volatile aromatic SOA from total aromatic SOA to obtain the more-oxidized aromatic SOA, thus semi-volatile and more-oxidized aromatic SOA should have no overlapped zones. This classification has also been applied in our previously published papers (Gao et al., 2019; Zhang et al., 2021).
  17. We will rephrase the sentence in Line 397.
  
  Citation: https://doi.org/10.5194/acp-2022-813-AC1
RC2:
'Comment on acp-2022-813', Anonymous Referee #2, 04 Apr 2023
This manuscript describes an experimental field study of the speciation of organic molecular markers with a bihourly time resolution using a Thermal desorption Aerosol Gas chromatograph system (TAG) in an urban city, Shanghai, China. Additional compounds were also reported and discussed in this study including inorganic species and gas-phase VOCs. The field study was conducted during the winter (December-January) of 2019. The evolution of organic aerosols during nine wintertime episodic events was evaluated and discussed using organic molecular markers (C3-5 organic acids, C9 acids, levoglucosan, DHOPA, phthalic acid, uFAs, sFAs…) to track the sources of organic aerosol. The markers were able to track several sources in this winter period distributed in 9 episodes including: (1) episodes influenced by local air masses were dominated by secondary organic aerosol; (2) episodes influenced by transported air mass typically associated with a predominant PM2.5 contribution from secondary inorganic aerosols and enhanced OA contribution from biomass burning activities; (3) mixed-influence episodes (local and transport influenced episodes). The main conclusion the authors report is that control of local urban sources such as biomass burning, cooking, and vehicular emissions will alleviate pollution during winter in Shanghai.
The data and field of organic speciation of ambient OA are very important to the scientific atmospheric community interested in ambient OA. Although the manuscript and the analytical work as a whole are important and provide useful information on secondary and primary species and their evolution during winter months and link sources to OA during wintertime. Using the analytical methods mainly TAG, the authors track the evolution of POA and SOA species and their association with OA aging processes. The interpretation of the data and conclusions should be more carefully presented, and I feel that additional consistent discussion mainly for the interpretation of the data needs to be incorporated before the paper is accepted for publication.

The authors should discuss more HOMs compounds as well as their analytical analysis. Is this analytical method appropriate for their analysis? Recently offline techniques were reported to characterize several HOMs compounds in field samples including work from this group.

Winter pollution is associated with nitrogen-containing compounds (mainly nitrates). The authors should discuss if nitrates were observed. Are they detected using the TAG method?

The authors report aging process occurs during these episodes based on the structure of the markers observed by the authors. The authors should discuss how they distinguished between aging OA and non-aging OA (specify the representative chemicals responsible for aging for example)? The interpretation of the markers based on the structure (more oxygenated) is presented and is a bit tedious but is important for the authors to clarify this issue.

The authors should clarify how the AMS and TAG DATA were used and interconnected in this study. Mainly for differentiating between the 3 categories/groups of episodes, the aging process, and SOA vs POA. They are areas in the manuscript where these processes need to be carefully and explicitly discussed and more cautious about the reconciliation between the two methods AMS and TAG (PM1 and PM2.5 analyzed by AMS and TAG respectively).

There are instances where I feel confused when the authors refer to SOA and POA to link to sources of OA in the different episodes reported in this study (for example the role of O3).

Specific Comments
Lines 16-20: Please clarify if secondary and primary are dominating the OA. It seems to me that (line 17): secondary sources were important and in the next few lines the authors report that primary also are important sources!

Is sampling done every 2 hours (see abstract) or every one hour as mentioned in the text (Table 1/line 63 etc.)? Please clarify.

Line 17: suggests replacing “elevation” with “increase” or clarifying the sentence!

Line 35. Please correct the references (e.g., “L. Chen et al., 2017” should be “Chen et al. 2017”). Please check this throughout the manuscript.

Line 54: Add reference(s) to Recent studies….end of the sentence.

Table 1. Please clarify which parameters were measured in the gas or particle phase and for how long?

Line 94: Table S1…to the end of the sentence. It seems to me that IS were also measured. The quantification of the 98 cpds was done using IS!
Citation: https://doi.org/10.5194/acp-2022-813-RC2
- AC2: 'Reply on RC2', Jian Zhen Yu, 17 Apr 2023
  
  We sincerely thank the referee for the comments concerning our manuscript. They are valuable in helping us improve our manuscript. Our point-by-point response is provided below and also listed in the attached file.
  1. In general, highly oxygenated organic molecules (HOMs) refers to a group of organic compounds which are formed in the atmosphere via autoxidation involving peroxy radicals and their chemical structures contain six or more oxygen atoms, many of which are incorporated as hydroperoxide (-OOH) functional group (Ehn et al., 2014, 2017; Bianchi et al., 2019). The -OOH functional group renders HOMs thermally liable, thus not amenable for analysis by gas chromatography (GC) techniques. Here in our study, majority of the organic compounds reported (e.g., DCAs, hDCAs, αPinT) contain less than six oxygen atoms. Two exceptions are 3-MBTCA (3-methyl-1,2,3-butanetricarboxylic acid, C₈H₁₂O₆) and mannitol (C₆H₁₄O₆), both containing six oxygen atoms. However, they are not regarded as HOMs since neither of them is formed via autoxidation or contain hydroperoxide functional group. Given that the organic compounds reported in this study are not HOMs and the TAG instrument, incorporating GC as part of its instrument component to achieve separation of organic mixture, is not designed for analyzing HOMs, we feel it is outside the scope of this work to discuss more about HOMs. The reviewer mentioned that offline techniques have been developed by our group to quantify HOMs in field samples. We guess the reviewer may be referring to the papers by Nie et al. (2022) and Lu et al. (2023), which adopted CIMS and chemical-ionization orbitrap mass spectrometry to measure oxygenated organic molecules (OOMs) including several HOMs compounds. However, these instruments were not deployed during this campaign.
  2. We agree with the reviewer regarding the importance of organic nitrates. Organic nitrates can play an important role in PM_2.5 pollution, especially in urban areas with high NO_x emissions. However, the TAG method, which thermally desorbs the organic compounds in particle samples, is not suitable for analyzing organic nitrates due to the inherent instability of their chemical structures (the -ONO₂ function group). For example, peroxy nitrates (RO₂NO₂) will dissociate when temperature raises to ~150℃ and alkyl nitrates (ANs, RONO₂) are found to dissociate around 200~250 ℃ (Hao et al., 1994; Keehan et al., 2020). Among the nitrogen-containing compounds, only four nitro-substituted aromatic compounds (i.e., 4-nitrophenol, 4-nitrocatechol, 3-methyl-5-nitrocatechol, and 4-methyl-5-nitrocatechol) were quantified during this field campaign, as these compounds have sufficient thermal stability, with higher dissociation temperature (> 350 ℃) (Hao et al., 1994; Jaoui et al., 2018).
  3. The unambiguous molecular information offered by the TAG system enables us to interpret OA aging processes through specific SOA tracers and their formation chemistry established in controlled chamber experiments. For example, a number of chamber studies have confirmed that pinic acid and pinonic acid are early generation SOA products of α-pinene ozonolysis while 3-MBTCA is a later generation product (kristensen et al., 2014; Ma et al., 2008; Szmigielski et al., 2007). Several studies have also shown that L_DCAs and L_hDCAs are aging SOA tracers, the formation of which require multiple oxidation steps (Ervens et al., 2004; Yang et al., 2008). Also, as shown in Figure S2, DHOPA and pinic acid showed stronger correlations with LO-OOA derived from AMS measurements while L_DCAs and L_hDCAs showed stronger correlations with MO-OOA. This further supports our interpretations of aging and non-aging SOA. Better clarification will be provided in the revised manuscript.
  4. The measurement data from TAG were generally consistent with those from AMS. As shown in Figure S2, the TAG-measured SOA tracers produced in early generations (e.g., DHOPA, phthalic acid, pinic acid) correlated well with LO-OOA derived from AMS while those associated with later generation products (e.g., C_3-5 DCAs, C_3-5 hDCAs) had stronger correlations with MO-OOA. After further investigating OA compositions in PM₁ during different episodes, we also find that local episodes were characterized by higher mass proportions of POA and less aged SOA (LO-OOA) while mix-influenced and transport episodes were associated with higher mass proportions of more aged SOA (MO-OOA), which is consistent with the observations from TAG. We will add a figure in the revised manuscript to present OA compositions in PM₁ during different episodes to clarify how the AMS and TAG DATA were used and interconnected for differentiating episodic events.
  5. Our data indicate O₃ oxidation played a relatively limited role in SOA formation during local episodes. We’d like to make a few points related to this. First, the high NO_x concentrations as well as the high mass ratios of NO/NO2 during local episodes likely have kept O₃ low and thus suppressed O₃ oxidation pathway (Table 2). Consequently, we observed more significant increases in mass concentrations of SOA markers formed via OH oxidation pathway compared with those formed via O₃ oxidation pathway. For example, DHOPA, which is a typical SOA product of monoaromatics with OH radicals, showed drastic increase in the mass concentration by 777%, in reference to non-episodic periods. In comparison, C₉ acids, which are typical oxidation products of fatty acids with O₃, their mass concentrations increased by 326% during local episodes in reference to non-episodic periods. And the mass concentrations of αPinT and βCaryT, which are oxidation products of biogenic VOCs with O₃, increased by 393% and 276% during local episodes in reference to non-episodic periods, respectively. Such contrasts between SOA products from OH-initiated vs O₃-initiated oxidation pathways appear to suggest that SOA formed during local episodes were more influenced by pathways other than ozonolysis (e.g., OH oxidation). We will add these explanations in the manuscript to further clarify SOA formation during different episodes.
  6. The claim in Line 17 that local episodes were significantly influenced by SOA is deduced from the mass variations of major chemical components in PM_2.5. That is, SOA overall had higher percentage proportions in PM_2.5 during local episodes compared with mix-influenced and transport episodes, while the latter two were characterized by significant higher mass incrementations in secondary inorganic ions (e.g., nitrate) in PM_2.5. When we further investigated OA compositions with the measurement data obtained from the TAG system, we find that SOA enhancements during local episodes were associated with sources from vehicle and cooking emissions. In other words, abundant precursors from local vehicle and cooking emissions greatly contributed to the formation of local SOA. Therefore, it is not odd that we also observed mass incrementations in hopanes, alkanes, and fatty acids during local episodes, which are typical POA markers for vehicle and cooking emissions. In this case, the claims in Lines 16-20 do not contradict each other.
  7. The time resolution of TAG system is 2-hour. We will correct related information in Table 1.
  8. We will rephrase the sentence.
  9. We will correct the references.
  10. We will add references to support the statement.
  11. All parameters listed in Table 1 were measured from 25^th November 2019 to 23^rd January 2020 as stated in Line 75. In the column “parameter” of Table 1, we have stated that organic molecular markers, inorganic water-soluble ions, OC, EC, and 15 trace elements were measured in PM_2.5, and organics were measured in PM₁. In other words, these parameters were measured in the particle phase. For the parameter “C₂ - C₁₂ VOCs”, they were measured in the gas phase and we will clarify this in the revised manuscript.
  12. We apologize that our wording was unclear. Below is a detailed explanation of how we quantify the 98 compounds and the role of internal standards (IS). We added a series of deuterated ISs in each sample introduced to the TAG system to compensate the matrix effects and other injection-to-injection variations (Gosetti et al., 2010; Wang et al., 2020). In our study, calibration curves were first established before using the TAG system to measure ambient samples. To be specific, 5 µL of ISs was mixed with 0-5 loops (5 uL/loop) of external standards and co-injected into CTD cell for GC-MS quantification. This yielded a five-point calibration curve for each analyte. Calibration curves were established by fitting the normalized peak areas of external standards to their corresponding IS with respective concentrations. During the ambient measurements, we also introduced 1 loop (5 μL) of IS in each aerosol sample. Then we calculated peak area ratios of target organic compounds against their corresponding IS (listed in Table S1) for each ambient sample and used the above-mentioned calibration curves to quantify their masses in real aerosol samples. The detail descriptions of the TAG calibration and quantification method have been given in several of our previously published papers (He et al., 2020; Wang et al., 2020).
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  Szmigielski, R., Surratt, J. D., Gómez-González, Y., Van der Veken, P., Kourtchev, I., Vermeylen, R., Blockhuys, F., Jaoui, M., Kleindienst, T. E., Lewandowski, M., Offenberg, J. H., Edney, E. O., Seinfeld, J. H., Maenhaut, W. and Claeys, M.: 3-methyl-1,2,3-butanetricarboxylic acid: An atmospheric tracer for terpene secondary organic aerosol, Geophys. Res. Lett., 34, L24811-n/a, 10.1029/2007GL031338, 2007.
  
  Wang, Q., He, X., Zhou, M., Huang, D. D., Qiao, L., Zhu, S., Ma, Y., Wang, H., Li, L., Huang, C., Huang, X. H. H., Xu, W., Worsnop, D., Goldstein, A. H., Guo, H. and Yu, J. Z.: Hourly Measurements of Organic Molecular Markers in Urban Shanghai, China: Primary Organic Aerosol Source Identification and Observation of Cooking Aerosol Aging, ACS earth and space chemistry; ACS Earth Space Chem, 4, 1670-1685, 10.1021/acsearthspacechem.0c00205, 2020.
  
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  Citation: https://doi.org/10.5194/acp-2022-813-AC2
RC3:
'Comment on acp-2022-813', Anonymous Referee #3, 22 Apr 2023

The study investigates the impact of organic aerosol (OA) components in PM2.5 on air quality in China, focusing on Shanghai during winter 2019. Using a Thermal desorption Aerosol Gas chromatograph system (TAG), researchers analyzed OA molecular markers with bihourly time resolution, which allowed for unique source tracking. They found that local air mass episodes had higher secondary OA, with elevated primary and secondary OA markers linked to vehicle emissions and cooking activities. Episodes influenced by transported air mass had higher PM2.5 contributions from secondary inorganic aerosols and increased OA from biomass burning. The study highlights the importance of online organic molecular measurements for episodic analysis and suggests controlling local urban sources and regional precursors to reduce PM2.5 episodes.
The data for this field observation are quite comprehensive. There are not many TAG-related measurements conducted in Shanghai. Overall, this dataset is interesting. However, some major concerns need to be addressed.
1. I think the introduction part is not sufficient to raise the scientific question. Numerous field studies have been conducted in Shanghai over the past decades. The limitations of these previous studies should be clearly discussed, and the ways in which this study can address these limitations need to be stated.
2. TAG is the key instrument of this paper. However, I did not see much information about its operation in this paper. For example, how much was the TD temperature? Was the derivatization agents used in TAG? If so, what are the agents?
3. The same comments also apply to other instruments. The method section is too brief.
4. Table S1 lists the range and average concentrations of the 98 quantified organic compounds. How did the authors quantify their concentration? Through standard injection? How were these compounds identified? Is the identification method very reliable? Or it is just a search through NIST MS database? Such information should be clearly presented.
5. TAG can operate in one-hour resolution. Why it was operated with a time resolution of 2 hours (Line 80)? In Table 2, it stated that TAG’s time resolution was 1 hour. Which number is correct?
6. Line 265: it is stated that “transported PM1 contained higher proportions of more-oxidized organic aerosol (MO-OOA) while less oxidized organic aerosol (LO-OOA) accounted for more PM1 mass during local episodes.” Can this statement also be supported by the AMS f44 trace?
7. This paper uses a very traditional way to separate POC and SOC. But this study has AMS data. Why not just do a PMF analysis for AMS data and separate a SOM factor? I think this way is more accurate.
8. It seems that AMS PMF analysis was done and the results were included in Figure 3. But what are the details of this analysis. How many factors were used? How about other key parameters used in PMF? I would not report the PMF results without showing the key PMF parameters.
Overall, I believe the dataset presented in this paper is interesting. However, there seems to be a lack of some crucial technical details regarding these measurements.

Citation: https://doi.org/10.5194/acp-2022-813-RC3
- AC3: 'Reply on RC3', Jian Zhen Yu, 09 May 2023
  
  We sincerely thank the referee for the comments concerning our manuscript. They are valuable in helping us improve our manuscript. Our point-by-point response is provided below and also listed in the attached file.
  1. Thanks for your suggestion. We will give more detail discussions on the findings and limitations of previous field studies conducted in Shanghai in the revised manuscript.
  2. Although the operation of the TAG instrument was described in detail in our earlier publications, we agree with the reviewer that more essential operation information needs to be included in the current manuscript to facilitate readers’ comprehension of our work.
  
  In brief, the CTD temperature program was set to be firstly held at 45 °C for 2 min, then increased to 330 °C in 6 min, and lastly held at 330 °C for 12 min. During this thermal desorption step, polar organic compound in PM_2.5 deposit on the CTD underwent in-situ derivatization under a helium stream saturated with N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA). MSTFA was the derivatization agent used in this study. More detailed information related to our TAG system is provided in our previously published papers (He et al., 2020; Wang et al., 2020; Zhu et al., 2021).
  
  3. Other instruments are more commonly deployed in field studies and better known to the atmospheric measurement community. The working principles of these instruments mentioned have been described in our previously published papers. For example, the performance of AMS during this field campaign is reported in Huang et al. (2021). Qiao et al. (2014) and Zhou et al. (2016) have described the working principles of OC/EC analyzer and MARGA adopted in this study and their performances during this field campaign is available in Zhou et al. (2022). In addition, detailed information related to the VOC measurements by the GC-FID adopted in this study is given in Liu et al. (2019) and Wang et al. (2015). We will add these references in the revised manuscript.
  4. Below is a detailed explanation of how we identify and quantify the 98 compounds and this information will be added to the supplementary information document. In our study, calibration curves were first established before using the TAG system to measure ambient samples. To be specific, 5 µL of ISs was mixed with 0-5 loops (5 uL/loop) of external standards and co-injected into CTD cell for GC-MS identification and quantification. This yielded a five-point calibration curve for each analyte. Calibration curves were established by fitting the normalized peak areas of external standards to their corresponding IS with respective concentrations. During the ambient measurements, 1 loop (5 μL) of IS was also injected into each aerosol sample by the auto-injection system equipped in the TAG. The target organic compounds in aerosol samples were identified by the retention time and mass spectrum, which were obtained from their authentic standards. Then we calculated peak area ratios of target organic compounds against their corresponding IS (listed in Table S1) for each ambient sample and used the above-mentioned calibration curves to quantify their masses in real aerosol samples. The details of the TAG calibration and quantification method have been given in several of our previously published papers (He et al., 2020; Wang et al., 2020).
  5. Yes, the TAG system can be operated in one-hour resolution as reported by other papers (Isaacman et al., 2014), if a short GC program is adopted. Figure R1 (see the attached file) shows the temperature status of three components in the TAG system. Note that during part of sample analysis stage, the CTD is not ready for sample collection mode. Sampling can only start when the CTD temperature has dropped to 35°C. As such, the time allocated to sample collection would be less than 1 hour if an hourly time resolution is adopted, such as in previous studies by Goldstein and coworkers. In our work, we have adopted a longer GC program and a full hour for sample collection. The combined time for each cycle of sample collection and analysis is 2 hours. We will correct the related information in Table 1.
  6. Yes, this statement is also supported by the AMS f44 tracer. We will add Figure R2 (see the attached file) in the revised manuscript.
  7. We agree with the reviewer that AMS PMF is capable of separating POC and SOC using characteristic ion fragments. The reason why we use the OC/EC ratio method to estimate SOM and POM is that OC and EC were measured in PM_2.5 while AMS provides PM₁ measurements. Using POC and SOC derived from PM₁ to approximate those in PM_2.5 would bring in an uncertainty that is not straightforward in quantifying. We note that the AMS-derived source factors and POM, SOM estimated by OC/EC ratio method were well correlated, indicating that the OC/EC ratio method applied in this work overall gave reasonable estimations of POM and SOM.
  8. Our recently published paper (Huang et al., 2021) has described the AMS PMF analysis in detail. In brief, a total of seven source factors were resolved by the PMF model based on AMS data collected during this period (see Figure R3 in the attached file). We will add this reference in the manuscript so that interested readers can get more detail information about the AMS PMF analysis.
  References:
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  Citation: https://doi.org/10.5194/acp-2022-813-AC3

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Organic aerosol (OA) becomes increasingly important in urban PM_2.5 pollution as inorganic ions are getting lower. We investigated chemical characteristics of OA during nine episodes in Shanghai. The unique availability of bi-hourly measured molecular markers revealed that control of local urban sources such as vehicular and cooking emissions would lessen severity of local episodes. Regional control of precursors and biomass burning would reduce PM_2.5 episodes influenced by regional transport.


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Evolution and chemical characteristics of organic aerosols during wintertime PM2.5 episodes in Shanghai, China: Insights gained from online measurements of organic molecular markers

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Evolution and chemical characteristics of organic aerosols during wintertime PM_2.5 episodes in Shanghai, China: Insights gained from online measurements of organic molecular markers