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.

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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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.

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