We present a European dataset of daily sector-, pollutant- and country-dependent emission adjustment factors associated with the COVID-19 mobility restrictions for the year 2020. We considered metrics traditionally used to estimate emissions, such as energy statistics or traffic counts, as well as information derived from new mobility indicators and machine learning techniques. The resulting dataset covers a total of nine emission sectors, including road transport, the energy industry, the 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 ($\mathrm{0.1}{}^{\circ }\times \mathrm{0.05}{}^{\circ }$) 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 (NO_x, SO₂, non-methane volatile organic compounds – NMVOCs, NH₃, CO, PM₁₀ and PM_2.5) 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 NO_x (−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 NMVOCs, −2.1 % (−24.3 kt) for PM_2.5, −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 NO_x) when mobility restrictions were at their maxima. The emission reductions during the second epidemic wave between October and December were 3 to 4 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 (27 member states of the European Union and the 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., 2022b) have been produced in support of air quality modelling studies.

中文翻译：

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受 COVID-19 大流行中断影响的 2020 年欧洲标准污染物和温室气体的主要排放量

我们提供了一个欧洲数据集，其中包含与 2020 年 COVID-19 出行限制相关的每日行业、污染物和国家相关排放调整因子。我们考虑了传统上用于估计排放的指标，例如能源统计或交通计数，以及来自新的移动性指标和机器学习技术的信息。生成的数据集共涵盖九个排放部门，包括道路运输、能源工业、制造业、住宅和商业燃烧、航空、航运、越野运输、溶剂的使用以及化石运输和分销的逃逸排放燃料。生成该数据集是为了与 Copernicus CAMS-REG_v5.1 2020 常规业务 (BAU) 库存相结合，该库存提供高分辨率 ($\mathrm{0.1}{}^{\circ }\times \mathrm{0.05}{}^{\circ }$) 2020 年的排放估算，忽略了 COVID-19 限制的影响。两个数据集的组合允许量化两种标准污染物（NO _x、SO ₂、非甲烷挥发性有机化合物 - NMVOC、NH ₃、CO、PM ₁₀和 PM _2.5）和温室气体（CO ₂化石燃料、CO ₂生物燃料和 CH ₄)，以及评估每个排放部门和欧洲国家对总体排放变化的贡献。2020 年相对于 BAU 排放的估计总体排放变化如下：-来自化石燃料的NO _x为 10.5 % ( - 602 kt)，-来自化石燃料 的CO ₂为7.8 % ( - 260.2 Mt) ， - 4.7 % ( - 808.5 kt) CO, − 4.6 % ( − 80 kt) 用于SO ₂ , − 3.3 % ( − 19.1 Mt) 用于来自生物燃料的CO ₂ , − 3.0 % ( −_{PM 10}为 56.3 kt)，NMVOC为- 2.5 % ( - 173.3 kt) ， PM _2.5为- 2.1 % ( - 24.3 kt) ，CH ₄为- 0.9 % ( - 156.1 kt)和- 0.2 % ( - 8.6 kt)对于NH ₃。排放量下降最显着发生在 4 月（ NO _x平均 下降高达− 32.8 %) 当流动性限制达到最大时。10 月至 12 月的第二波疫情期间的减排量比春季封锁期间的减排量低 3 至 4 倍，因为流动限制普遍较宽松（例如宵禁、限制社交聚会）。意大利、法国、西班牙、英国和德国是欧盟 27 国+英国（欧盟和英国的 27 个成员国）绝对排放减少总量的最大贡献者。在部门层面，航空业的排放量下降幅度最大（- 51 % 至- 56 %），其次是公路运输（- 15.5 % 至-18.8 %），后者是大多数污染物估计减少的主要驱动力。COVID-19 排放调整因子的收集（https://doi.org/10.24380/k966-3957，Guevara 等人，2022）和 CAMS-REG_v5.1 2020 BAU 网格化清单（https://doi.org /10.24380/eptm-kn40, Kuenen et al., 2022b) 已生成以支持空气质量建模研究。