the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2024-4041', Anonymous Referee #1, 21 Feb 2025
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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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AC1: 'Reply on RC1', Gang I. Chen, 14 Jun 2025
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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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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:
Specific comments:
Minor comments: