European primary emissions of criteria pollutants and greenhouse gases in 2020 modulated by the COVID-19 pandemic disruptions
- 1Barcelona Supercomputing Center, Barcelona, Spain
- 2TNO, Department of Climate, Air and Sustainability, Utrecht, the Netherland
- 3Atmospheric Composition Research, Finnish Meteorological Institute, 00560 Helsinki, Finland
- 4European Centre for Medium-Range Weather Forecasts, Reading, UK
- 5ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain
- 1Barcelona Supercomputing Center, Barcelona, Spain
- 2TNO, Department of Climate, Air and Sustainability, Utrecht, the Netherland
- 3Atmospheric Composition Research, Finnish Meteorological Institute, 00560 Helsinki, Finland
- 4European Centre for Medium-Range Weather Forecasts, Reading, UK
- 5ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Spain
Abstract. We present a European dataset of daily-, sector-, pollutant- and country-dependent emission adjustment factors associated to the COVID-19 mobility restrictions for the year 2020. The resulting dataset covers a total of nine emission sectors, including road transport, energy industry, manufacturing industry, residential and commercial combustion, aviation, shipping, off-road transport, use of solvents, and fugitive emissions from transportation and distribution of fossil fuels. The dataset was produced to be combined with the Copernicus CAMS-REG_v5.1 2020 business-as-usual (BAU) inventory, which provides high resolution (0.1 × 0.05 deg.) emission estimates for 2020 omitting the impact of the COVID-19 restrictions. The combination of both datasets allows quantifying spatially- and temporally-resolved reductions in primary emissions from both criteria pollutants (NOx, SO2, NMVOC, NH3, CO, PM10 and PM2.5) and greenhouse gases (CO2 fossil fuel, CO2 biofuel and CH4), as well as assessing the contribution of each emission sector and European country to the overall emission changes. Estimated overall emission changes in 2020 relative to BAU emissions were as follows: −10.5 % for NOx (−602 kt), −7.8 % (−260.2 Mt) for CO2 from fossil fuels, −4.7 % (−808.5 kt) for CO, −4.6 % (−80 kt) for SO2, −3.3 % (−19.1 Mt) for CO2 from biofuels, −3.0 % (−56.3 kt) for PM10, −2.5 % (−173.3 kt) for NMVOC, −2.1 % (−24.3 kt) for PM2.5, −0.9 % (−156.1 kt) for CH4 and −0.2 % (−8.6 kt) for NH3. The most pronounced drop in emissions occurred in April (up to −32.8 % on average for NOx) when mobility restrictions were at their maxima. The emission reductions during the second epidemic wave between October and December, were three to four times lower than those occurred during the Spring lockdown, as mobility restrictions were generally softer (e.g., curfews, limited social gatherings). Italy, France, Spain, the United Kingdom and Germany were, together, the largest contributors to the total EU27 + UK absolute emission decreases. At the sectoral level, the largest emission declines were found for aviation (−51 to −56 %), followed by road transport (−15.5 % to −18.8 %), the latter being the main driver of the estimated reductions for the majority of pollutants. The collection of COVID-19 emission adjustment factors (https://doi.org/10.24380/k966-3957, Guevara et al., 2022) and the CAMS-REG_v5.1 2020 BAU gridded inventory (https://doi.org/10.24380/eptm-kn40, Kuenen et al., 2022) have been produced in support of air quality modelling studies.
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Marc Guevara et al.
Status: closed
- RC1: 'Comment on essd-2022-31', Anonymous Referee #1, 26 Jan 2022
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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?
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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.
Status: closed
- RC1: 'Comment on essd-2022-31', Anonymous Referee #1, 26 Jan 2022
-
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?
-
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.
Marc Guevara et al.
Data sets
CAMS-REG-v5.1 BAU 2020 emission inventory Jeroen Kuenen, Stijn Dellaert, Antoon Visschedijk, Jukka-Pekka Jalkanen, Ingrid Super, and Hugo Denier van der Gon https://doi.org/10.24380/eptm-kn40
CAMS-REG_EAF-COVID19 emission adjustment factors Marc Guevara, Hervé Petetin, Oriol Jorba, and Carlos Pérez García-Pando https://doi.org/10.24380/k966-3957
Marc Guevara et al.
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