European primary emissions of criteria pollutants and greenhouse gases in 2020 modulated by the COVID-19 pandemic disruptions

Guevara, Marc; Petetin, Hervé; Jorba, Oriol; Denier van der Gon, Hugo; Kuenen, Jeroen; Super, Ingrid; Jalkanen, Jukka-Pekka; Majamäki, Elisa; Johansson, Lasse; Peuch, Vincent-Henri; Pérez García-Pando, Carlos

doi:https://doi.org/10.5194/essd-14-2521-2022

Articles | Volume 14, issue 6

https://doi.org/10.5194/essd-14-2521-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-14-2521-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 6

Data description paper

|

02 Jun 2022

Data description paper |

| 02 Jun 2022

European primary emissions of criteria pollutants and greenhouse gases in 2020 modulated by the COVID-19 pandemic disruptions

Marc Guevara, Hervé Petetin, Oriol Jorba, Hugo Denier van der Gon, Jeroen Kuenen, Ingrid Super, Jukka-Pekka Jalkanen, Elisa Majamäki, Lasse Johansson, Vincent-Henri Peuch, and Carlos Pérez García-Pando

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Final revised paper (published on 02 Jun 2022)
Supplement to the final revised paper
Preprint (discussion started on 25 Jan 2022)
Supplement to the preprint

Interactive discussion

Status: closed

RC1: 'Comment on essd-2022-31', Anonymous Referee #1, 26 Jan 2022

See report as attached file

Citation: https://doi.org/10.5194/essd-2022-31-RC1
RC2: 'Comment on essd-2022-31', Anonymous Referee #2, 24 Feb 2022

The manuscript developed a comprehensive European dataset of emission adjustment factors due to COVID-19 at daily basis for each country in 2020. A total of nine sectors is included in the dataset. Combining the emission adjustment factors, as well as the basic pre-COVID emission inventory, the dataset can serve the atmospheric modeling community on the analyses regarding the emission contributions by countries, sectors, pollutants and days during the pandemic. The manuscript is generally clearly organized, thorough discussions are provided, and dataset is publically available. I just have several technical comments on the data sources in deriving the emission changes. Below are my detailed comments.

Line 17: You didn’t mention the methodology and dataset used in this work. Can you summarize the key method and dataset in deriving the emission adjustment factors for key sectors in one sentence or two in the abstract?

Line 33: -51%

Line 35-36: I don’t think a reference or doi here is appropriate in the abstract. The same for the BAU gridded inventory.

Line 39: There are 17 figures in the paper… too many for the readers to follow the key analyses. Can you simplify them or moving some of them to supporting information?

Line 192: For the power industry, do you have statistics of the power generation? I understand the power generation can be related to the outdoor temperature (for AC), but there are lots of other electricity needs which are not directly related to temperature (like cooking). It’s more straightforward and reliable to use the power production or fuel consumption statistics to derive the emissions.

Line 337: I’m confused for the usage of Google Mobility Reports in estimating the emissions of “other stationary combustion activities”. Only mobility trends can be reflected, so even they appear in places like restaurants, they can not represent the emissions emitted by the restaurants.

Line 499, Line 550: How about the gasoline-fueled vehicles?

Line 957: Where are the machine learning techniques used? Anything I missed?

Citation: https://doi.org/10.5194/essd-2022-31-RC2
AC1: 'Response to referee comments', Marc Guevara, 25 Mar 2022

We would like to thank the reviewers for their positive and constructive feedback, which helped improve the quality of the paper. The review comments have been helpful in pointing out parts that required further improvements. In the supplement to this comment we address specific issues mentioned by the reviewers point by point.

Citation: https://doi.org/10.5194/essd-2022-31-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Marc Guevara on behalf of the Authors (06 Apr 2022) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (07 Apr 2022) by Bo Zheng

RR by Anonymous Referee #2 (20 Apr 2022)

ED: Publish as is (28 Apr 2022) by Bo Zheng

AR by Marc Guevara on behalf of the Authors (03 May 2022)

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Short summary

To control the spread of the COVID-19 disease, European governments implemented mobility restriction measures that resulted in an unprecedented drop in anthropogenic emissions. This work presents a dataset of emission adjustment factors that allows quantifying changes in 2020 European primary emissions per country and pollutant sector at the daily scale. The resulting dataset can be used as input in modelling studies aiming at quantifying the impact of COVID-19 on air quality levels.