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the Creative Commons Attribution 4.0 License.
| 14 Jan 2025
How COVID-19 related policies reshaped organic aerosol source contributions in central London
Abstract. Particulate matter (PM) poses both health and climate risks. Understanding pollution sources is therefore crucial for effective mitigation. Positive Matrix Factorization (PMF) of Aerosol Chemical Speciation Monitor (ACSM) data is a powerful tool to quantify organic aerosol (OA) sources. A year-long study of ACSM data from London's Marylebone Road monitoring station during the COVID-19 pandemic provides insights into the impact of lockdown and the Eat Out To Help Out (EOTHO) scheme, which offered support to the hospitality during the pandemic, on PM composition and OA sources. Five OA sources were identified including hydrocarbon-like OA (HOA, traffic-related, 11 % to OA), cooking OA (COA, 20 %), biomass burning OA (BBOA, 12 %), more-oxidized oxygenated OA (MO-OOA, 38 %), and less-oxidized oxygenated OA (LO-OOA, 21 %). Lockdown significantly reduced HOA (-52 %), COA (-67 %), and BBOA (-41 %) compared to their pre-COVID levels, while EOTHO increased COA (+38 %) significantly compared to the post-lockdown period. However, MO-OOA and LO-OOA were less affected, as these primarily originated from long-range transport. This research has highlighted the importance of commercial cooking as a significant source of OA (20 %) and PM1 (9 %) in urban areas. The co-emission of BBOA with COA observed in Central London demonstrates a similar diurnal cycle and response to the EOTHO policy, indicating that cooking activities might be currently underestimated and contribute to urban BBOA. Therefore, more effort is required to quantify this source and develop targeted abatement policies to mitigate emissions as currently limited regulation is in force.
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RC1: 'Comment on egusphere-2024-4041', Anonymous Referee #1, 21 Feb 2025
Chen et al. investigated the effect of COVID-19-related policies on aerosol composition in London, particularly how the source apportionment of organic aerosol varied before and after the implementation of different policies (lockdown and Eat Out to Help Out (EOTHO)). They found that the lockdown due to COVID-19 substantially reduced levels of primary organic aerosol (POA = hydrogen-like + cooking + biomass burning OAs (HOA + COA + BBOA)), lowering them by approximately 50% compared to pre-COVID levels. In contrast, oxygenated organic aerosols (OOA = less-oxidized OOA (LO-OOA) + more-oxidized OOA (MO-OOA)) were not as significantly affected. However, when EOTHO was introduced, POA levels—especially COA—increased, highlighting cooking activities as an important source of urban air pollution.
This study is a compelling example of why policymakers should consider unintended consequences when implementing policies. However, several aspects need improvement before publication. My primary recommendation is for the authors to reorganize the results and discussion sections to create a smoother flow that readers can easily follow and ensure the content aligns with the title. I suggest restructuring sections to first introduce the regional characteristics, then clearly describe the similarities and differences in OA characteristics during the pre-COVID, COVID, and EOTHO periods, while avoiding a time series-based presentation of the results.
That said, the authors present an excellent dataset that clearly illustrates changes in the characteristics of OA across the pre-COVID, COVID, and EOTHO periods. Accordingly, I suggest a major revision before publication in ACP. Additional comments are listed below.
Major comments:
- The ME-2 solution selection process should be described in greater detail. I expect to see more information in the SI on how the current solution differs from those obtained using a lower or higher number of factors. Additionally, it would be beneficial for the authors to explain how varying the a-value influences the source apportionment solutions. Lastly, I noticed that the a-value in Figure 2 is presented in a 0.## format, whereas the measurement section states that the a-value was adjusted in increments of 0.1. The authors should provide a comprehensive description of the source apportionment analysis, including the aspects mentioned above.
- I expect to see a more detailed description in Section 3.1, not just the changes in mass concentration. For example, how did the fraction of non-refractory species vary by season or implementation of policies? How was this particular period different from the past PM variation from literature? Also, when comparing the PM characteristics between pre-lockdown vs. post-lockdown, I think it should be more clearly compared by season.
- I recommend the authors revisit the discussion regarding OA factors because I see multiple points that are difficult to agree with the authors' perspectives. For example, it is hard for me to recognize a strong seasonality in Figure 3 while only the effects of COVID-related policies are noticeable. In addition, I think there is a small lunchtime peak in the diurnal of COA if you zoom in. Additionally, the authors should add more detailed discussion by comparing with previous studies that were conducted in other major cities around the world. I added further details in the specific comments below.
- In some sections, it was hard to follow the authors' discussion due to the lack of results or references that support authors' opinions. A couple of examples can be found in Section 3.2. If the authors can provide any results (or references) that show changes in temperature, GDP changes, travel, economic activities, vehicle mileage, etc., it would be much more supportive of the authors' discussion in Section 3.2.3 and 3.2.4, where focused on policy-related effects on OA.
- I recommend that the authors add a separate section at the beginning of Section 3.2 to describe the typical OA characteristics in the region. This would help guide readers and establish a clearer baseline for understanding the impact of the policies on OA characteristics. Additionally, I suggest restructuring the paper as follows: (1) general OA characteristics of the region, (2) pre-lockdown, (3) lockdown, and (4) EOTHO. From my perspective, the current structure, which includes an overall time series and diurnal section, weakens the paper’s main argument and leads to repetitive statements (e.g., long-range transport, seasonality of photochemistry).
Specific comments:
- Line 119: Is this ACSM deployed with a standard vaporizer? Or a capture vaporizer?
- Line 218: What kind of activities are related to elevated PM1 events? Did airmass originate from northern continental Europe and also be related to agricultural activities? Please specify.
- Line 220: Would only volatility be related to lower NO3 concentration in summer? How about NOx/NH3 emission or other factors that can affect the formation of inorganic NO3?
- Line 221: Please add a citation and describe briefly how SO4 is formed via photochemistry. Also, how did SO2 change throughout the measurement period?
- Line 222: Please refer to which figure describes long-range transport. In addition, I would like to see if SO4 or other pollutants are indeed related to long-range transport via CWT or PSCF backtrajectory analysis.
- Line 229: Wouldn’t the reduced BC concentration from winter to spring be related to less heating or energy consumption?
- Line 241: I find it odd that the weekday comparison of BC concentration appears suddenly in this section. Since it is not a critical discussion point of the paper, I suggest removing it.
- Line 256: From my perspective, strong seasonality in OA factors is not as evident as the differences between the pre-COVID and lockdown periods. This is because the total OOA did not change significantly, and the PMF OOA separation carries more uncertainties compared to POA-related factors.
- Line 258: Would there be any relationship between the decrease in heating and energy consumption?
- Line 261: Wouldn’t photolysis affect the evaporation of some semi-volatile components in OOA? How about the f44 & f43 comparison by season?
- Line 261: Where can I find increased VOC emissions in summer? Please provide any related references or data.
- Figure 2 right panel: I suggest having an independent axis for the OA data of the lockdown period.
- Line 275-276: What does the smaller diurnal variation of MO-OOA compared to LO-OOA suggest? Could LO-OOA be associated with a specific source? Please provide a more detailed discussion.
- Line 279: If you zoom in, there could be a small COA peak during lunchtime. Please see Specific Comments #12.
- Line 285-289: I believe this part does not fit well in the “Diurnal Cycle” section. Instead, it would be more appropriate in the next section, where the impact of the lockdown is discussed.
- Line 289: Figure S3: What was the backtrajectory like before the lockdown? I believe this information should be included to confirm whether the change in PM composition is indeed attributable to the lockdown policy.
- Line 293-296: Does the data used to generate Figure 4 encompass the entire period? If so, I believe this information represents a key OA characteristic of central London and should be moved to the beginning of Section 3.2, rather than placed under the “Diurnal” section, to emphasize its significance. Additionally, making comparisons with other major cities, such as New York and Beijing, would be beneficial.
- Line 307: Please add a figure in SI that shows a temperature variation.
- Line 313: Please include any results that allow readers to determine whether photochemistry was increased in pre-lockdown spring compared to March 2020.
- Section 3.2.4: I suggest relocating the POA part of this section to the front to emphasize the impact of EOTHO.
- Line 341 & 345: The fact that EOTHO operated only from Monday to Wednesday may explain why COA did not return to pre-COVID levels. Therefore, Figure 6 should include a pre-COVID weekday plot and discuss if the difference in COA for the rest of the week would be responsible for the COA not being returned to pre-COVID levels.
- Line 342: From Figure 5, it is hard to recognize a 10% increase in COA from EOTHO to post EOTHO. Please show a figure that clearly shows such a difference or remove this statement.
- Line 370: Which cities do you refer to? Please specify and add citations.
Minor comments:
- Line 47: Please add references that show PM2.5 composition-dependent health effects and hospitalization:
- Pye, H. O. T., Ward-Caviness, C. K., Murphy, B. N., Appel, K. W., and Seltzer, K. M.: Secondary organic aerosol association with cardiorespiratory disease mortality in the United States, Nature Communications, 12, 7215, 10.1038/s41467-021-27484-1, 2021.
- Joo, T., Rogers, M. J., Soong, C., Hass-Mitchell, T., Heo, S., Bell, M. L., Ng, N. L., and Gentner, D. R.: Aged and Obscured Wildfire Smoke Associated with Downwind Health Risks, Environmental Science & Technology Letters, 11, 1340-1347, 10.1021/acs.estlett.4c00785, 2024.
- Line 49: Please add references that show source apportioned PM association with health effects owing to oxidative stresses:
- Vasilakopoulou, C. N., Matrali, A., Skyllakou, K., Georgopoulou, M., Aktypis, A., Florou, K., Kaltsonoudis, C., Siouti, E., Kostenidou, E., Błaziak, A., Nenes, A., Papagiannis, S., Eleftheriadis, K., Patoulias, D., Kioutsioukis, I., and Pandis, S. N.: Rapid transformation of wildfire emissions to harmful background aerosol, npj Climate and Atmospheric Science, 6, 218, 10.1038/s41612-023-00544-7, 2023.
- Liu, F., Joo, T., Ditto, J. C., Saavedra, M. G., Takeuchi, M., Boris, A. J., Yang, Y., Weber, R. J., Dillner, A. M., Gentner, D. R., and Ng, N. L.: Oxidized and Unsaturated: Key Organic Aerosol Traits Associated with Cellular Reactive Oxygen Species Production in the Southeastern United States, Environmental Science & Technology, 57, 14150-14161, 10.1021/acs.est.3c03641, 2023.
- Daellenbach, K. R., Uzu, G., Jiang, J., Cassagnes, L.-E., Leni, Z., Vlachou, A., Stefenelli, G., Canonaco, F., Weber, S., Segers, A., Kuenen, J. J. P., Schaap, M., Favez, O., Albinet, A., Aksoyoglu, S., Dommen, J., Baltensperger, U., Geiser, M., El Haddad, I., Jaffrezo, J.-L., and Prévôt, A. S. H.: Sources of particulate-matter air pollution and its oxidative potential in Europe, Nature, 587, 414-419, 10.1038/s41586-020-2902-8, 2020.
- Line 56: Please add references related to PMF analysis on OA:
- Jimenez, J. L., Canagaratna, M. R., Donahue, N. M., Prevot, A. S. H., Zhang, Q., Kroll, J. H., DeCarlo, P. F., Allan, J. D., Coe, H., Ng, N. L., Aiken, A. C., Docherty, K. S., Ulbrich, I. M., Grieshop, A. P., Robinson, A. L., Duplissy, J., Smith, J. D., Wilson, K. R., Lanz, V. A., Hueglin, C., Sun, Y. L., Tian, J., Laaksonen, A., Raatikainen, T., Rautiainen, J., Vaattovaara, P., Ehn, M., Kulmala, M., Tomlinson, J. M., Collins, D. R., Cubison, M. J., Dunlea, J., Huffman, J. A., Onasch, T. B., Alfarra, M. R., Williams, P. I., Bower, K., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K., Salcedo, D., Cottrell, L., Griffin, R., Takami, A., Miyoshi, T., Hatakeyama, S., Shimono, A., Sun, J. Y., Zhang, Y. M., Dzepina, K., Kimmel, J. R., Sueper, D., Jayne, J. T., Herndon, S. C., Trimborn, A. M., Williams, L. R., Wood, E. C., Middlebrook, A. M., Kolb, C. E., Baltensperger, U., and Worsnop, D. R.: Evolution of Organic Aerosols in the Atmosphere, Science, 326, 1525-1529, 10.1126/science.1180353, 2009.
- Zhang, Q., Jimenez, J. L., Canagaratna, M. R., Allan, J. D., Coe, H., Ulbrich, I., Alfarra, M. R., Takami, A., Middlebrook, A. M., Sun, Y. L., Dzepina, K., Dunlea, E., Docherty, K., DeCarlo, P. F., Salcedo, D., Onasch, T., Jayne, J. T., Miyoshi, T., Shimono, A., Hatakeyama, S., Takegawa, N., Kondo, Y., Schneider, J., Drewnick, F., Borrmann, S., Weimer, S., Demerjian, K., Williams, P., Bower, K., Bahreini, R., Cottrell, L., Griffin, R. J., Rautiainen, J., Sun, J. Y., Zhang, Y. M., and Worsnop, D. R.: Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically-influenced Northern Hemisphere midlatitudes, Geophysical Research Letters, 34, https://doi.org/10.1029/2007GL029979, 2007.
- Line 62 and 63: Please add references related to ACTRIS & ASCENT:
- Laj, P., Lund Myhre, C., Riffault, V., Amiridis, V., Fuchs, H., Eleftheriadis, K., Petäjä, T., Salameh, T., Kivekäs, N., Juurola, E., Saponaro, G., Philippin, S., Cornacchia, C., Alados Arboledas, L., Baars, H., Claude, A., De Mazière, M., Dils, B., Dufresne, M., Evangeliou, N., Favez, O., Fiebig, M., Haeffelin, M., Herrmann, H., Höhler, K., Illmann, N., Kreuter, A., Ludewig, E., Marinou, E., Möhler, O., Mona, L., Eder Murberg, L., Nicolae, D., Novelli, A., O’Connor, E., Ohneiser, K., Petracca Altieri, R. M., Picquet-Varrault, B., van Pinxteren, D., Pospichal, B., Putaud, J.-P., Reimann, S., Siomos, N., Stachlewska, I., Tillmann, R., Voudouri, K. A., Wandinger, U., Wiedensohler, A., Apituley, A., Comerón, A., Gysel-Beer, M., Mihalopoulos, N., Nikolova, N., Pietruczuk, A., Sauvage, S., Sciare, J., Skov, H., Svendby, T., Swietlicki, E., Tonev, D., Vaughan, G., Zdimal, V., Baltensperger, U., Doussin, J.-F., Kulmala, M., Pappalardo, G., Sorvari Sundet, S., and Vana, M.: Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS): The European Research Infrastructure Supporting Atmospheric Science, Bulletin of the American Meteorological Society, 105, E1098-E1136, https://doi.org/10.1175/BAMS-D-23-0064.1, 2024.
- Hass-Mitchell, T., Joo, T., Rogers, M., Nault, B. A., Soong, C., Tran, M., Seo, M., Machesky, J. E., Canagaratna, M., Roscioli, J., Claflin, M. S., Lerner, B. M., Blomdahl, D. C., Misztal, P. K., Ng, N. L., Dillner, A. M., Bahreini, R., Russell, A., Krechmer, J. E., Lambe, A., and Gentner, D. R.: Increasing Contributions of Temperature-Dependent Oxygenated Organic Aerosol to Summertime Particulate Matter in New York City, ACS ES&T Air, 1, 113-128, 10.1021/acsestair.3c00037, 2024.
- Joo, T., Rogers, M. J., Soong, C., Hass-Mitchell, T., Heo, S., Bell, M. L., Ng, N. L., and Gentner, D. R.: Aged and Obscured Wildfire Smoke Associated with Downwind Health Risks, Environmental Science & Technology Letters, 11, 1340-1347, 10.1021/acs.estlett.4c00785, 2024.
- Line 248: Please add “tracers” after “m/z”.
- Line 253: relocate “respectively” to Line 254, after “59% (26% to PM1)”
- Line 299-302: I believe this should be combined into one sentence. Please revise.
- Line 301&312&320&333: Please refer to the figure that describes the long-range transport of different chemical species.
- Line 305: Please add a reference (or add a figure in SI) about the GDP variation.
- Line 326-327: Please add references about the changes in travel, economic activities, and vehicle mileage during the measurement period.
- Line 359-362: The statement is difficult to follow. Please revise.
Citation: https://doi.org/10.5194/egusphere-2024-4041-RC1 -
AC1: 'Reply on RC1', Gang I. Chen, 14 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2024-4041/egusphere-2024-4041-AC1-supplement.pdf
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RC2: 'Comment on egusphere-2024-4041', Anonymous Referee #2, 11 Mar 2025
Chen et al. present an original study on the impact of COVID-19 related social distancing policies in London, UK on the composition of atmospheric aerosols. Aerosol mass spectrometer measurements combined with source apportionment analysis allowed a dedicated focus on the different organic fractions. They highlight a sharp decrease in primary organic aerosols (eg traffic and biomass burning) during lockdown period (LD), but a significant increase of cooking organic aerosols during Eat Out To Help Out (EOTHO) policies.
In this study, the impacts of COVID-19 policies have been estimated by comparing LD and EOTHO periods to pre-periods, supposed to be representative of business as usual concentrations. After all the flourishing literature on this kind of study, I am rather concerned that the authors didn't take into account (nor at least discuss) the main limitation of this kind of analysis : the variability induced by meteorology. Did the authors check that their "business as usual" periods are representative, meteorologically speaking ? As examples, I don't think the pre-LD period is representative of the meteorological conditions during LD; conditons in June may also be different than in August. Precipitation, temperature, relative humidity, wind speed & direction, BLH (among others) can have a direct impact on the variability and the concentrations measured at a given site, and none of these are presented. This is especially dangerous when stating that component X has increased/decreased by Y% during lockdown (/EOTHO), because these results are very important for stakeholders, and can also be easily understood by non-expert citizens. To this regard, methodology is a critical aspect of the work, and must be as robust as possible. I am afraid that this is not quite the case here. As a main major revision, I suggest the authors to : investigate the meteorological representativeness of the different periods, and discuss the potential impacts on the results. If representativeness is not achieved, I suggest either to change methodology (by taking meteorology into account), or reshape the presentation of the results, by avoiding as much as possible to present numbered decrease or increase results.
Other major concerns :
- Introduction is not well structured. For instance, I don't know why the authors talk about aerosol mass spectrometers and source apportionment here, which is, in that case, more "material & methods" rather than an element of context justifying the interest of the study.
- Section 2.4 is way too long. We don't need a general description of how ME-2 works, the authors need instead to provide all valuable information showing how the final PMF solution was obtained. Additionnally, BBOA has a rather constant contribution to OA throughout the different seasons (around 10-12%), even in summer. I am guessing that this may priorily come from the use of the rolling PMF rather than barbecue-ing or meat cooking. Plus, it is not clear if the authors constrained BBOA in their previous "summer" PMF. Did the authors also unambiguously find a solution with BBOA during summer only (with a profile ressembling to winter BBOA ?) ? The authors may also check BC data (and Angstrom exponent) to support the discussion.
Minor comments:
- Figure S3 (b) and (c) don't quite support long range transport. Maybe trajectory analysis (CWT, PSCF or cluster) would better help. Much more discussion is anyway needed concerning NO3, because it may not only arise from long range transport. Trajectory analysis may also help to look at the occurence of air masses (through eg cluster) throughout the different periods of the study. It would contribute to appreciate their meteorological representativeness.
- Can the authors elaborate more on Figure S1, and especially how the different slopes obtained over time may impact the presented results. Is it an issue of ACSM calibration ? or FIDAS measurements ?
- Comparison with literature and previous results elsewhere is clearly missing in almost all result sections.
Citation: https://doi.org/10.5194/egusphere-2024-4041-RC2 -
AC2: 'Reply on RC2', Gang I. Chen, 14 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2024-4041/egusphere-2024-4041-AC2-supplement.pdf
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AC2: 'Reply on RC2', Gang I. Chen, 14 Jun 2025
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