3.1 Public power industry

Changes in emissions from the public power sector (GNFR_A) were assumed to follow the changes observed in the electricity demand data reported by the European Network of Transmission System Operators for Electricity (ENTSO-E) transparency platform (Hirth et al., 2018; ENTSO-E, 2021). For each country, we collected daily electricity demand data from January 2015 to December 2020. For Russia, Ukraine and Turkey we derived the electricity demand data from the corresponding national transmission system operators: SO-UPS (2021), UNEC (2021) and TEIAS (2021), respectively.

We first estimated the demand that would have occurred in the absence of COVID-19 under the same meteorological conditions, hereafter referred to as BAU. To estimate the BAU electricity demand we used gradient-boosting machine (GBM) models trained and tuned independently for each country using daily data from January 2015 to December 2019. As inputs, we considered the following features: country-level daily population-weighted temperature (T_pop(d)), date index (number of days since 1 January 2015), Julian date, day of week and a Boolean feature indicating the country-specific public holidays. The models also consider bridge weekends, in the sense that when there is a holiday on a Tuesday (Thursday), the Monday (Friday) of the same week is also set as a holiday. We replicated the GBM modelling and tuning strategy previously used in Guevara et al. (2021) with random search in the hyper-parameter space and rolling-origin cross-validation (appropriate for time series).

T_pop(d) is defined as follows (Eq. 2):

where T_2 m(x,d) is the daily mean 2 m outdoor temperature for grid cell x and day d [^∘C], Pop(x) is the quantity of the population included in grid cell x [no. of inhabitants], and n is the total number of grid cells that corresponds to a specific country. Outdoor temperature information was obtained from the ERA5 reanalysis dataset for the years 2015 to 2020 (C3S, 2017), while gridded population was derived from the Gridded Population of the World, Version 4 (GPWv4; CIESIN, 2016).

The difference between the daily BAU and measured 2020 electricity demand levels was used to derive country-dependent daily emission adjustment factors, as described in Eq. (2):

where EAF_{pub_pow}(d,c) is the final emission adjustment factor for the energy industry sector for day d and country c [%], ED_BAU(d,c) is the estimated BAU electricity demand for day d and country c [MW], and ED_measured(d,c) is the measured electricity demand for day d and country c [MW].

Figure 2 shows the daily adjustment factors obtained for the GNFR_A sector and selected countries (i.e. Spain, France, Germany, the UK and Sweden). The resulting trends are consistent with the national lockdown calendars and levels of restriction implemented in each country. During the strictest period of the first lockdown, Italy experienced the largest reductions (−30 %), followed by Spain (−25 %) and France (−20 %). For Sweden, positive values are observed during the same period, in line with the results reported by Le Quéré et al. (2020). It is likely that in this country electricity demand from commercial services remained unperturbed as no national lockdowns were enforced. We also hypothesize that a voluntary self-isolation of a fraction of the population may have increased household electricity consumption. When confinement was eased, electricity demand shows the first signs of recovering in all countries. This trend is confirmed in the summer as governments softened even more lockdown measures. The most pronounced recovery occurs in Italy, where emissions reach levels above BAU during August. A second significant drop of emissions is observed in France and the UK and, to a lesser extent, in Italy during November 2020, coinciding with the implementation of a second round of lockdowns. Emissions rebound sharply after that and are back to BAU levels or even above them during the Christmas holidays.

3.2 Manufacturing industry

The adjustment factors for the manufacturing industry (GNFR_B) are based on the monthly industrial production index (IPI) values reported by Eurostat (2021c). We considered the seasonally adjusted and calendar-adjusted data. Note that for the UK the IPI values for November and December 2020 were derived from ONS (2021) as Eurostat only reports information until October 2020 for this country. The original IPI values reported for each individual economic activity (NACE Rev. 2) were grouped and averaged into the three subcategories listed in Table 2, according to the impacts of the COVID-19 restrictions observed on their activity (Fig. S2):

• GNFR_B1 – manufacture of petroleum refining products. This industrial branch was considered to be essential and therefore was less affected than other industries during the full-lockdown phase. However, due to the large decrease in the demand for finished petroleum products (e.g. jet fuel, motor gasoline), the recovery of its activity was lower than in other sectors during the lockdown exit process.

• GNFR_B2 – manufacture of pharmaceutical, chemistry, and food and beverage products. These industrial branches were also considered to be essential during the full-lockdown phase, but in contrast to the petroleum industry, the demand associated with their products barely decreased or even increased during or after the lockdown, which translates to a low decrease (slight increase) in their activity.

• GNFR_B3 – manufacture of other products (i.e. non-metallic mineral products, basic metals, paper and paper products, and machinery and equipment). These industries were considered non-essential and therefore were heavily affected during the lockdown period as in the majority of cases they were forced to close. Nevertheless, a sharp recovery is observed with the easing of lockdowns.

For the manufacturing industrial subcategories GNFR_B2 and GNFR_B3, the averaging of the IPI values was done considering the share of each industrial branch (i.e. pharmaceutical, chemistry, and food and beverage products for GNFR_B2 and non-metallic mineral products, basic metals, paper and paper products, and machinery and equipment for GNFR_B3) in the total fossil energy final consumption as reported by the Eurostat (2021a) energy balances. For GNFR_B3, the manufacture of basic metals and non-metallic mineral products comprises the largest energy-intensive activities (almost 70 % of total energy consumption), whereas manufacturing of paper and machinery and equipment represent approximately 30 % of total energy consumption (Fig. S3). Note that other industrial branches originally included in GNFR_B3 (i.e. manufacture of wood, textiles and leather) were not considered in the final calculations since the Eurostat IPI statistics for these industrial categories are incomplete. It is expected that the removal of these industrial branches will not have a major impact on final results as their total fossil fuel consumption is not predominant (i.e. 12 % in total according to Fig. S3).

For each manufacturing industry subgroup, we computed monthly and country-specific adjustment factors from a baseline taken as the average value over the 2 months prior to the lockdown (January and February 2020). The computed monthly adjustment factors were translated into daily adjustment factors by considering the Oxford COVID-19 Government Response Tracker dataset (OxCGRT; Hale et al., 2021). OxCGRT provides a systematic cross-national, cross-temporal measure to understand how government responses have evolved over the full period of the COVID-19 spread. We considered the indicator “workplace closing”, which records the closings of workplaces according to four different scales of intensity: 0 – no measures, 1 – recommend closing, 2 – require closing (or work from home) for some sectors or categories of workers, and 3 – require closing (or work from home) for all but essential workplaces. We assumed that changes in industrial emissions during March started to happen in each country once the corresponding indicator reached a value of 2 or more.

Daily emission adjustment factors were computed as a weighted average of the adjustment factors obtained for each industrial subcategory (Eq. 1), taking into account their relative contribution to total GNFR_B emissions (Fig. 3).

Figure 3Average contribution of each GNFR subcategory (see definitions in Table 2) to total annual emissions for selected pollutants per country (EU27 + UK) for the year 2020. Country abbreviations follow the ISO 3166-1 alpha-3 code standard (https://www.iban.com/country-codes, last access: May 2022).

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Figure 2 illustrates the resulting adjustment factors proposed for NO_x and NMVOC emissions. A common pattern is observed for the two pollutants, with the largest reductions occurring during April, when the restrictions were at their maximum and a large number of facilities were not allowed to operate. A pronounced recovery is observed from May onwards, coinciding with the easing of the lockdowns and the recovery of industrial activity. For NO_x, the computed reductions are larger than for NMVOCs, with Italy, France and Spain presenting the largest decrease (between −35 % and −40 % during April). Low reductions are observed for Sweden, where emissions never decreased more than −20 %. Emission reductions reached levels close to BAU by the end of the year in almost all countries as the new curfews adopted around October, November and December did not affect the manufacturing industry. In the case of NMVOCs, a general lower reduction than for NO_x emissions is observed, with most countries presenting a maximum decrease below −30 % during April. It is worth noting that some countries even experienced an increase in emissions during the beginning of the first lockdowns (up to 10 %). The adjustments computed for NMVOCs are different relative to NO_x as its emissions are related to food, beverage, pharmaceutical and chemical industry branches (Fig. 3), which were less affected by the COVID-19 restrictions or even had to increase their productivity due to an increase in demand. The largest emission reductions are reported for Italy and the lowest ones for the UK and Sweden, with the latter even showing emission values above BAU levels (i.e. up to 5 %) during the second half of 2020.

3.3 Other stationary combustion activities

This sector includes emissions from stationary combustion activities related to the commercial and institutional sector; the residential sector; and other stationary sectors such as the agriculture, forestry, fishing and military sectors.

Our emission adjustment assumes that the COVID-19 restrictions only affected the combustion activities in the commercial–institutional and residential sectors. In the first case, significant emission reductions are expected as a result of the closure of schools, universities, public buildings, restaurants and other non-essential businesses. In the second case, emission increases are expected due to the required household confinement during the lockdown period. Regarding the agriculture, forestry and fishing sectors, we assumed no changes occurred as they were considered to be essential.

The emission adjustment factors considered for this sector are based on Google COVID-19 Community Mobility Reports (Google LLC, 2021). The Google dataset reports daily movement trends over time by geography (country and region) across different categories of places (i.e. groceries and pharmacies, parks, transit stations, retail and recreation, residential, and workplaces) based on aggregated and anonymized sets of data from users who have turned on the Location History setting for their Google Account on their mobile devices. Reductions for each day are calculated by Google from a baseline taken as the median value, for the corresponding day of the week, over a 5-week period prior to the lockdowns (3 January to 6 February). For this sector, we used the mobility trends reported for the following categories:

• retail and recreation – mobility trends for places like restaurants, cafes, shopping centres, theme parks, museums, libraries and cinemas;

• grocery and pharmacy – mobility trends for places like grocery markets, food warehouses, farmers' markets, specialty food shops, drug stores and pharmacies;

• workplaces – mobility trends for places of work;

• residential – mobility trends for places of residence.

The mobility trends for retail and recreation, grocery and pharmacy, and workplaces were used to derive an average trend for the commercial and institutional sector, while the mobility trends for places of residence were used for the residential sector.

These Google trends report changes in movements, which do not necessarily represent changes in energy consumption (i.e. fossil fuels and biomass) and associated emissions. The increases in residential activity reported by Google are significantly larger than the ones reported in Le Quéré et al. (2020), which indicate an average increase of 5 %, and a maximum increase of 10 % during the most restrictive lockdown phase. The results reported in Le Quéré et al. (2020) inferred from UK smart meter data are consistent with the ones reported by the thermostat maker Tado (Tado, 2020), which indicate an average increase of 14 % in home heating consumption in Europe during March 2020 compared to March 2019. Considering the aforementioned results, the original Google trend values for the residential sector were scaled down for countries to have a maximum daily relative change of 10 %. Our approach is limited by data availability, and further constraints will require more data on residential energy consumption.

In the case of the commercial and institutional sector, we also adjusted the original daily decrease trends reported by Google making use of energy consumption statistics. We used information provided by IDAE (2018) on the energy consumption in the Spanish commercial and institutional sector. As shown in Table S2, Spanish commercial buildings represent more than 40 % of the total energy consumption (fossil fuels and biomass) in the commercial and institutional sector, followed by workplaces (26.5 %), hospitals (11.6 %), other buildings (8.8 %, e.g. museums, public buildings and religious buildings), schools and universities (7.8 %), and restaurants and hotels (4.3 %). We hypothesized that the Spanish national lockdown restrictions implied a change in the energy consumption of (i) −100 % in schools and universities (all buildings were closed); (ii) −90 % in hotels and restaurants (certain hotels were converted into temporary medical facilities); (iii) −80 % in workplaces, commercial buildings and other buildings (supermarkets and other grocery stores remained opened during the entire lockdown, as well as certain workplaces that were considered to be essential); and (iv) +50 % in hospitals (due to the increase in the number of patients). We combined the aforementioned information and derived an overall maximum reduction in energy consumption across Spanish commercial and institutional buildings of −66.9 %. Following the approach applied for the residential sector, we scaled up the original Google trend values for the commercial and institutional sector to set this minimum value.

Daily emission adjustment factors for the other stationary combustion sector were computed as a weighted average of the adjustment factors obtained for each GNFR_C subcategory (Eq. 1), taking into account their relative contribution to total emissions (Fig. 3).

Figure 2 illustrates the resulting adjustment factors proposed for NO_x and PM₁₀ emissions, respectively. For NO_x, major reductions are observed for the United Kingdom, France and Italy. In these three countries, maximum reductions between −15 % and −20 % were reached during the strictest lockdown period. On the contrary, despite being under similar lockdown measures, in Spain the maximum relative reduction during the same period was only −10 %. This is explained by the different contributions of agriculture, forestry and fishing subcategories (GNFR_C3) to the total GNFR_C NO_x emissions. While in Spain this category represents around 40 % of total NO_x emissions, in France, Italy and the United Kingdom the contribution is lower than 10 % (Fig. 3). Assuming that this category was not affected by the COVID-19 restrictions implies a lower overall emission reduction in Spain. In the case of Sweden, a slight emission increase is observed until the end of August. We hypothesize that this is a consequence of the likely small perturbation in the public and commercial service activity (i.e. non-essential businesses were not forced to close) and a slight increase in the residential activity as a consequence of the voluntary self-isolation of a fraction of the population. By the end of August most countries reached or were about to reach their BAU levels, except for the United Kingdom, where emissions were still −10 % below pre-lockdown values. A second significant drop in emissions is observed in France, the United Kingdom and Italy during November, which is related to the forced closure of non-essential business under the second epidemic wave.

For PM₁₀, an increase in the business-as-usual levels is observed for all selected countries. This is explained by the fact that a majority of total emissions are driven by changes in the residential sector (Fig. 3), which increased its activity due to the enforced confinement. Germany is the country that registered the lowest increase in total emissions (maximum increase of approximately 2.5 %) compared to the other countries. This is again explained by the different contributions of subcategories to total GNFR_C emissions. In this particular case, the German commercial/service subcategory represents around 10 % of total emissions, while in the other countries the contribution for this subcategory is less than 5 % (Fig. 3). By the end of August, all countries were close to reaching the BAU levels again, and in some countries like Italy emission levels even reached values below BAU as people started to spend more time outdoors. A slight increase in emissions is observed during November, coincident with the introduction of new additional mobility restrictions to curb the high incidence during the second wave of COVID-19 spread.

3.4 Fugitive emissions

This sector covers the release of emissions during the extraction and processing of fossil fuels along with their delivery to the point of final use. The activities selected for the development of specific COVID-19-related emission adjustment factors were as follows: (1) coal mining and handling, (2) refining/storage and venting and flaring, and (3) distribution of oil products (gasoline). Other subcategories included in this sector were assumed to be unaffected by lockdowns and mobility restrictions.

The following sources of information were used to derive the adjustment factors:

• GNFR_D1 – coal mining and handling. Monthly indigenous production of hard and brown coal per country reported by Eurostat (2021b) is used. We computed monthly and country-specific adjustment factors from a baseline taken as the average value over the 2 months prior to the lockdown (January and February 2020). We then averaged the resulting monthly factors per month and country and derived daily adjustment factors using the workplace closing indicator reported by OxCGRT, as detailed in Sect. 3.1.2.

• GNFR_D2 – refining/storage and venting and flaring. Monthly IPI values related to the manufacture of petroleum refining products (Eurostat, 2021c) are used. For this subcategory, we used the same adjustment factors as for GNFR_B1 of the manufacturing industry (see Sect. 3.1.2).

• GNFR_D3 – distribution of oil products (gasoline). We assumed that changes in this activity can be represented by changes in road fuel sales in filling stations, which at the same time can be linked to changes in road traffic activity. This hypothesis is illustrated in Fig. S4, which shows the relationship between monthly/weekly changes in petrol sales and traffic activity for selected countries. In all cases the Pearson correlation coefficient (PCC) is larger than 0.9, with the intensity in the drop of petrol sales during the lockdown periods generally coinciding with the decrease in traffic activity. Considering these results, for this activity we used the same emission adjustment factors for road transport gasoline exhaust emissions (see Sect. 3.1.6).

GNFR sector-level daily emission adjustment factors were computed as a weighted average of the adjustment factors obtained for each subcategory (Eq. 1), taking into account their relative contribution to total GNFR_D emissions (Fig. 3).

Figure 2 shows the adjustment factors for NMVOC fugitive emissions from fossil fuels. The pattern of emission decreases is significantly different from one country to another, mainly because of the effect of the individual subcategory that dominates total emissions in each country and to a lesser extent due to the different levels and types of restrictions implemented. For instance, in the UK almost 40 % of total NMVOC emissions come from refining activities (storage, flaring), and therefore the decrease in emissions is largely driven by their decrease (Fig. 3). On the other hand, approximately 50 % of total NMVOC emissions in France come from the distribution of oil products, and subsequently the drop in emissions is similar to that of road traffic emissions, with two significant drops corresponding to the lockdowns implemented during the spring and autumn epidemic waves.

3.5 Use of solvents

The GNFR_E category includes NMVOC emissions from the residential/commercial and industrial use of solvents. Our assumption for this sector is that the COVID-19 restrictions only affected certain industrial subcategories, including (i) GNFR_E1 – the use of organic solvents to remove grease, fats, oils, wax or soil from metal products – and (ii) GNFR_E2 – the use of inks in the printing industry. Other industrial activities that involve the use of solvents (e.g. manufacturing of pharmaceutical products or automobiles) could not be considered as they are not individually distinguished in the official nomenclature for reporting (NFR) system, but rather they are reported as part of broader categories (e.g. 2.D.3.g – chemical products, 2.D.3.i – other solvent use, 2.G – other product use). Emissions from domestic and commercial solvent use were assumed to remain constant due to the lack of specific activity data to compute the adjustment factors and the limited number of categories considered in the NFR system. We hypothesize that the potential increase in the use of certain products containing solvents, such as cleaning products, was compensated for by the potential decrease in the use of other products, such as car products or cosmetics for personal care. We are aware that this hypothesis may be limited by the increased use of the so-called “pandemic products” triggered by COVID-19 (Steinemann et al., 2021), which include products intended to clean and disinfect, such as hand sanitizers or surface cleaners. However, the lack of specific information does not allow us to compute associated adjustment factors.

The adjustment factors for industrial solvent use are based on the monthly IPI values adjusted for seasonal and calendar effects (Eurostat, 2021c). As already mentioned in Sect. 3.1.2, for the UK the IPI values for November and December 2020 were derived from ONS (2021). The “Manufacture of fabricated metal products, except machinery and equipment” and “Manufacture of computer, electronic and optical products”, on the one hand, and the “Printing and reproduction of recorded media”, on the other hand, were the industrial branches considered to quantify the impacts of restrictions on each of the two subcategories considered. For each subcategory, we computed monthly and country-specific adjustment factors from a baseline taken as the average value over the 2 months prior to the lockdown (January and February 2020). The computed monthly adjustment factors were translated into daily adjustment factors by considering the workplace closing indicator reported by OxCGRT, as detailed in Sect. 3.1.2.

Daily emission adjustment factors for the use of the solvents sector were computed as a weighted average of the adjustment factors obtained for each subcategory (Eq. 1), taking into account their relative contribution to total GNFR_E emissions (Fig. 3).

Figure 2 illustrates the resulting adjustment factors proposed for NMVOC emissions. The decrease in emissions is generally low (i.e. below −10 %) and mainly occurs during the spring lockdowns. The small reductions are due to the limited contribution of metal cleaning and printing industrial activities to the overall emissions from this sector (Fig. 3). A pronounced recovery is observed from May onwards, coinciding with the easing of the lockdowns and the recovery of industrial activity.

3.6 Road transport

The emission adjustment factors considered for this sector are based on Google COVID-19 Community Mobility Reports (Google LLC, 2021). We used the mobility trends reported for the transit station category, which includes places like public transport hubs such as subway, bus and train stations. We compared the Google movement trends against trends derived from measured traffic counts reported by 18 European national road administrations. Table A1 summarizes the countries covered, sources of information and characteristics of the traffic count datasets considered, as well as the baseline considered to derive traffic activity trends.

Figure 4Comparison of traffic movement trends derived from Google COVID-19 Community Mobility Reports (Google LLC, 2021) and measured traffic counts for selected countries (see Table A1 for references), the latter kind being distinguished by type of vehicle (i.e. heavy-duty vehicles, HDVs; light-duty vehicles and cars, LDVs), for the period 21 February to 31 December 2020.

Figure 4 shows the results of the intercomparison at the country level for selected countries. Black lines represent the Google mobility trends, while red and blue lines represent the measurement-based trends computed for light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs). Similar patterns are observed for all cases as a function of the period of study:

• First COVID-19 lockdown period (mid-March until mid-May). The Google dataset is capable of reproducing the LDV measurement-based trends. Overall, the average reductions reported by each of the two datasets are fairly similar, with Google reporting in some cases reductions slightly larger than the measured ones, particularly in Scandinavian countries (e.g. Finland, Sweden, Norway). On the other hand, a large discrepancy is observed between Google results and the HDV measurement-based trends, with the former presenting larger reductions. In the UK for instance, the average reduction for HDVs was −35.6 % between March and 26 April, almost 2 times lower than the one reported by Google (−69 %).

• COVID-19 lockdown exit process (mid-May until the end of September). Differences between LDV and Google trends become larger, showing different rates of recovery. Google tends to underestimate the observed recovery of traffic activity. The discrepancies between measured trends and the Google dataset become larger with time. During summer (i.e. July, August), the LDV trends in the majority of countries are close to or even above business-as-usual levels (e.g. the Netherlands, Ireland), yet Google continues to report mobility values that are below business-as-usual levels. In the case of HDV trends, discrepancies with Google trends are reduced but still significant.

• Second COVID-19 lockdown period (beginning of October until the end of December). Discrepancies between Google trends and LDV/HDV measurement-based trends remain almost unchanged. Google trends are, qualitatively speaking, capable of reproducing the drops in traffic activity observed in the LDV measurement-based trends during November and the Christmas season but not quantitatively speaking, as reductions are systematically larger than the observed ones.

A comparison between averaged monthly adjustment factors reported by Google LLC (2021) and LDV measurement-based trends per each of the countries listed in Table A1 shows results in line with the patterns described above (Fig. S5). The differences observed between measurement-based trends and the Google trends are mainly related to the fact that Google data refer to mobility trends in public transport hubs. As a result of COVID-19, people now avoid public transport as it is associated with places where it might be difficult to avoid contact with other passengers (De Vos, 2020). The adjustment factors proposed by Google during the lockdown exit process are affected by this factor and therefore underestimate the observed changes in traffic activity during the lockdown exit process. This hypothesis is illustrated in Fig. S6, where the traffic movement trends obtained in Rome are compared to the evolution of access to subway stations. The recovery of mobility in the subway system during the lockdown exit process is very much in line with the Google trend and much lower than the one observed for the private transport sector. On the other hand, the lower reduction observed in HDV activity when compared to Google is because these vehicles supported the delivery of essential goods and products during confinement (e.g. food, medical supplies), and subsequently their use decreased much less than that of LDVs.

In order to overcome the identified limitations of the original Google trends, we used the LDV and HDV measurement-based trends compiled for the different countries to produce two sets of European correction factors: (i) HDV correction factors and (ii) LDV correction factors. In both cases, the correction factors were computed as the ratio between the weekly average changes in traffic activity reported by the measured trends and the weekly average changes in mobility reported by Google. The resulting country-level weekly correction factors were then averaged to obtain a set of European weekly correction factors. The countries considered to develop the European average weekly correction factors were the ones listed in Table A1 except Poland and Estonia as the number of traffic stations used to derive measurement-based trends for these two countries was small.

The two sets of correction factors were applied to the original Google mobility trends in order to derive two new sets of adjustment factors for LDV and HDV emissions. Note that for those countries for which we had daily traffic count datasets available (i.e. the United Kingdom, Norway, France, Spain, Finland, Ireland, the Netherlands and Switzerland), we directly substitute the original Google trends for the ones derived from traffic counts. Similarly, for countries with weekly and monthly traffic count datasets, adjustments of the original Google trends were made by considering only the correction factors of the corresponding country.

We applied the adjusted Google transit mobility trends with the LDV factors to the GNFR_F1 (exhaust gasoline) and GNFR_F3 (exhaust LPG gas) sectors, as the contribution of HDVs to their emissions is null or almost residual. However, for the GNFR_F2 (exhaust diesel) and GNFR_F4 (non-exhaust) sectors, the final emission adjustment factors were computed as a weighted average of the adjustment factors obtained for LDV (GNFR_F21 and GNFR_F41) and HDV (GNFR_F22 and GNFR_F42) vehicle categories following Eq. (1) and considering their relative contribution to total corresponding emissions (Fig. 3).

Figure 2 illustrates the adjustment factors for road transport diesel exhaust (GNFR_F2) NO_x and CO₂_ff emissions. The patterns of the emission adjustment factors for the two species are very close. However, the reductions reported during the spring lockdowns (March and April 2020) are slightly lower for CO₂_ff, especially in countries where the HDV emissions have a larger contribution to total emissions such as Spain, Italy and France (Fig. 3). The decrease in the traffic activity in Italy started 2 d after the implementation of the localized lockdown (23 February) and intensified once the national lockdown was imposed on 12 March, reaching reductions of about −80 %. In the case of Spain and France, similar traffic reduction levels were reached just 3 d after the beginning of the corresponding national lockdowns. For the UK and Germany, the largest reductions are around −70 % and −50 %, respectively. The smaller reductions in Sweden (around −40 %) are consistent with the lack of enforced mobility restrictions in this country at any point. In all cases, the activity started recovering during the last week of April, coinciding with the relaxation of the mobility restrictions. This trend is confirmed between May and August, with a steady recovery observed in all countries except for Spain, where a slight decrease occurs during July. This abrupt change in the upward trend corresponds to a sudden increase in infections in this country and the subsequent implementation of additional measures to restrict mobility. In contrast, large recovery rates were observed in Italy, Germany and the UK, where values even exceeded BAU levels during certain days in July and August. However, the introduction of new restrictions measures continued to curb traffic activity in October. Strengthening measures caused a second significant drop in emissions during November, although it was ∼50 % lower than that of April (e.g. the UK, Italy). The first weeks of December were marked by a relaxation of the second lockdown measures and a subsequent recovery of the traffic emissions. However, a third drop in emissions was observed during the Christmas season as additional measures were implemented to restrict social gatherings.

3.7 Aviation

We derived the adjustment factors related to air traffic emissions during landing and take-off cycles (LTOs) in airports from statistics provided by EUROCONTROL (2021), which reports daily arrivals and departures by airport from January 2016 to December 2020. We computed day- and country-specific flight operation reductions from a baseline taken as the average value for the corresponding day of the week (Monday to Sunday and national holidays) and month of the year from 2019.

Figure 2 illustrates the resulting emission adjustment factors for selected countries. For most countries the reductions in flight activity started some days before the implementation of the national lockdowns as certain international flights (especially the ones coming from and going to Asia) were already being cancelled. It is observed that in almost all countries, the reduction levels reached values of −90 % or more before the beginning of April. In contrast to road transport, the signs of recovery during May and June are very weak as the movements between countries were still restricted at that time. On the contrary, a general more pronounced recovery was observed during July and August as a consequence of the beginning of the summer holidays and the lifting of restrictions to travel. This recovery was especially significant in Spain and France. However, most of the countries still presented reductions larger than −50 % during the summer. Strengthening measures linked to the second wave of infections negatively impacted European air traffic in November, when new drops were observed, especially in the UK and France. The end of the year, however, was marked by a recovery in air traffic operations, similar to the one observed during summer, that can be attributed to the Christmas holidays.

3.8 Shipping

Emission adjustment factors for the shipping sector were based on the automatic identification system (AIS)-based gridded emissions computed by STEAM (Jalkanen et al., 2012, 2016) under CAMS (Granier et al., 2019). Weekly and sea-region-dependent adjustment factors were derived as the ratio between the shipping emissions reported for a given week in 2020 and the emissions reported by the equivalent week in 2019. Estimated CO₂ emissions were used as a proxy to compute the adjustment factors, as this pollutant can give a more direct indication of the changes in the fuel used. The use of other pollutants such as SO₂ or PM would mask the impact of COVID-19 on 2020 emissions as they were affected by the implementation of the global 0.5 % sulfur cap, as discussed in Sect. 2.

Figure 2 illustrates the adjustment factors produced for selected sea regions (i.e. the Atlantic Ocean, ATL; Baltic Sea, BAS; English Channel, ENC; Mediterranean Sea, MED; North Sea, NOS; and Norwegian Sea, NWS). In general terms, the decrease in shipping emissions began in week 12 (i.e. 16–22 March) and followed a downward trend until mid-June. From that point, a slight constant recovery was observed in most sea regions, with sporadic ups and downs (e.g. NWS). By the end of the year, some sea regions were already close to BAU levels (e.g. BAS, −5 %). Overall, MED and NOS were the sea regions presenting the largest (i.e. −17 %) and lowest reductions (i.e. −3 %), respectively. The contrast in the results obtained for these two sea regions is very much related to the different contribution of passenger ships to total shipping traffic, which is larger in MED than in NOS. As reported by EMSA (2021), cruise ships and ro-ro/passenger ships were the ship types mostly affected by COVID-19, showing reductions in 2020 ship calls in EU ports of −85 % and −39 % when compared to 2019 levels. These reductions were significantly larger than the ones reported for cargo ships (between −7 % and −2 %), which are dominant in NOS.

3.9 Off-road transport

The GNFR_I category reports emissions from non-road mobile machinery that is used in several sectors, including (i) commercial (e.g. transportable equipment); (ii) residential (e.g. gardening and handheld equipment); (iii) agriculture, forestry and fishing (e.g. harvesters, cultivators); (iv) manufacturing industries and construction (e.g. excavators, loaders, bulldozers); and (v) other categories including military, land-based railways and recreational boats. In the present work, the impact of COVID-19 restrictions was quantified for emissions related to mobile machines in the manufacturing industry and construction sector (GNFR_I1), while emissions from the other subcategories (GNFR_I2) were assumed to remain unaffected.

The adjustment factors are based on seasonally adjusted and calendar-adjusted monthly IPI values reported by Eurostat (2021c). We considered the IPI values reported for the general manufacturing and construction categories. As for the manufacturing industry, monthly and country-specific adjustment factors were computed taking as a baseline the average value over January and February 2020. The translation from monthly to daily factors was performed considering the evolution of the workplace closing indicator reported by OxCGRT.

Figure 2 shows the emission adjustment factors for NO_x emissions. The decrease in emissions is generally low, with a maximum reduction of less than −15 % in the UK during April and reductions between −2.5 % and −5 % in Germany and Spain during the same period. As shown in Fig. 3, the contribution of the manufacturing industry and construction machinery subcategory to total emissions is rather low (30 % on average at the EU27 + UK level), which explains why reductions are not as large as the ones shown in, for example, the GNFR_B manufacturing industry sector. Emissions reach levels close to BAU by the end of the year in almost all countries as the new virus-related curfews adopted during the second wave did not affect industrial manufacturing and construction activities.

4.1 European and country-level analysis

Figure 5 illustrates the COVID-related changes in the EU27 + UK daily emissions for criteria pollutants and GHGs between 1 January and 31 December 2020 as compared to the BAU scenario. Dotted and solid lines represent the BAU and COVID-19 daily emissions, respectively, and differences between them are illustrated with the shaded areas.

Figure 5Daily emissions [t d⁻¹] by pollutant computed for the 2020 business-as-usual (BAU) (dotted lines) and COVID-19 (solid lines) scenarios between 1 January and 31 December 2020 for EU27 + UK. The areas highlighted between the two lines represent the emission differences between scenarios.

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For all pollutants, the decrease in emissions started to occur during the first weeks of March, coinciding with the implementation of localized and national lockdowns to reduce mobility and social interactions. The greatest reductions are observed at the end of March and beginning of April, when the restrictions were at their maximum. During late April and the beginning of May, emissions began to recover in a persistent and continuous way, as national governments started to roll back COVID-19 measures and the different economic activities resumed. By mid-September emissions of all pollutants were close to reaching pre-lockdown levels again. However, a second drop in emissions similar to that of June is observed during the end of October and beginning of November, coinciding with the implementation of a new round of mobility restrictions to break the second wave of COVID-19 infections. Reductions in emissions remained almost unchanged until the end of the year as restrictions were kept in place with a few exceptions during the Christmas holidays. It is important to note that the daily evolution of the emissions plotted in the charts is affected not only by the COVID-19 restrictions but also by the inherent seasonality associated with emissions from each pollutant sector. For instance, emissions from other stationary combustion activities are mainly related to the combustion of fuels in households and commercial buildings for space heating, and therefore they decrease as winter ends and outdoor temperatures start to be higher. This fact can be observed with the daily evolution of PM_2.5 and CO₂_bf emissions, as they are mainly driven by residential wood combustion emissions.

In the aggregate, a reduction of −10.5 % (−602 kt) was seen in NO_x emissions, followed by −7.8 % (−260.2 Mt) in CO₂_ff, −4.7 % (−808.5 kt) in CO, −4.6 % (−80 kt) in SO₂, −3.3 % (−19.1 Mt) in CO₂_bf, −3.0 % (−56.3 kt) in PM₁₀, −2.5 % (−173.3 kt) in NMVOCs, −2.1 % (−24.3 kt) in PM_2.5, −0.9 % (−156.1 kt) in CH₄ and −0.2 % (−8.6 kt) in NH₃. The largest decline in European emissions was observed during the month of April for all pollutants, with an abrupt −32.8 % and −25.5 % decrease in total NO_x and CO₂_ff emissions, which corresponds to −157.3 kt and −70.2 Mt, respectively (Fig. S7). Around 25 % of the total drop in emissions that occurred in 2020 took place during the month of April. As mentioned before, emission levels in September were already close to the pre-lockdown levels, although still presenting a slight decrease when compared to the BAU scenario (−4.8 % and −3.9 % for NO_x and CO₂_ff, respectively). The emission reductions observed during November and December (i.e. up to −10.5 % for NO_x and −6.5 % for CO₂_ff) were lower than those that occurred during the first epidemic wave because mobility restrictions implemented by governments were generally slower and softer (e.g. curfews, limited social gatherings, early closing times for restaurants and bars) and only had to be toughened in those countries affected by a new and more contagious variant of the COVID-19 such as France, Germany, the UK and the Netherlands.

Results shown in Figs. 5 and S7 allow illustrating the heterogeneous impact of the COVID-19 restrictions on total emission changes across pollutants. Worth noting is the large contrast between decreases in NO_x (−10.5 %) and PM₁₀ and PM_2.5 (−3 % and −2.1 %) emissions (see Sect. 4.1.2 for further discussions). The almost null reduction reported for NH₃ and CH₄ emissions is linked to the fact that a large majority of these emissions are related to agricultural and waste management activities (e.g. use of fertilizers, manure management and livestock), which in the present work were assumed to remain unaffected during the COVID-19 restrictions.

Figures 6 and 7 show the relative decline (%) in total emissions per country and species for criteria pollutants and GHGs, respectively. Vertical lines indicate the average relative declines computed at the EU27 + UK level for each species. Non-shaded marks highlight those countries/species where reductions were larger than the ones observed at the EU27 + UK level. A large variation in the relative declines in emissions is observed between countries due to (1) the heterogeneous levels and types of restrictions implemented across countries and (2) the different contributions of each pollutant sector, particularly of the road transport sector and other stationary combustion activities, to total emissions in each country.

Figure 6Relative decline in emissions of criteria pollutants [%] per species and country in 2020. The vertical lines indicate the average relative declines at the EU27 + UK level. Non-shaded marks highlight those countries/species for which reductions are larger than the ones computed at the EU27 + UK level.

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Figure 7Same as Fig. 6 for greenhouse gases.

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The most pronounced declines occur for NO_x and CO₂ fossil fuel emissions, Italy being the country where these two pollutants suffered the largest relative reduction (i.e. −15.1 % and −11.4 %, respectively). On the other hand, Malta presents the largest relative reductions in SO₂, CO, NMVOC and CO₂ biofuel emissions (between −17.2 % and −6.8 %). Despite not being among the countries where the strictest lockdowns and containment strategies took place, the contribution of road transport to total CO, NMVOC and CO₂ biofuel emissions in this country is significantly larger than what it is reported at the EU27 + UK level (i.e. 54.1 % versus 14.8 %, 87 % versus 21.1 %, 40.3 % versus 7.5 % and 69 % versus 10.5 %, respectively). A similar situation is observed in Cyprus, which presents the largest relative reduction in total PM_2.5 emissions (−6.2 %). This country reports the lowest fraction of PM_2.5 emissions from other stationary combustion activities (4.9 % versus 52.1 % at the EU27 + UK level), a sector that experienced an increase in emissions during lockdown restrictions (see Sect. 4.1.2). For PM₁₀ emissions, the largest relative drop occurs in the UK (−6.5 %), which is among the countries that imposed the strictest restrictions. In the case of CH₄ the largest reduction is observed in Romania (−4.1 %) mainly due to the decrease in emissions from coal mining activities. Finally, for NH₃ most of the EU countries present relative reductions close to the average value that are almost negligible (between −0.56 % and −0.03 %) as in all of them agricultural activities, which remained unaffected by COVID-19 restrictions, represent more than 90 % of total NH₃ emissions. Results also show that for certain countries and species, emissions not only decreased but also, in some cases, slightly increased due to the COVID-19 restrictions. This is the case, for instance, of PM_2.5 emissions in Hungary and CO₂ biofuel emissions in Croatia (i.e. 0.4 % in both cases). The observed increase in these two countries is a direct consequence of the large contribution of the other stationary combustion activities to total PM_2.5 and CO₂ biofuel emissions, respectively. In Hungary, this sector represents 81.3 % of total PM_2.5 emissions, whereas in Croatia it represents 79.9 % of total CO₂ biofuel emissions. These values are much larger than the average contribution observed at the EU27 + UK level, which is 52.1 % and 39.1 %, respectively.

4.2 Sectoral analysis

Figures 8 and 9 show the relative decline in emissions of criteria pollutants and GHGs by sector and species in 2020 for EU27 + UK, while Fig. 13 illustrates the daily evolution of NO_x emission differences per sector between 1 January and 31 December 2020.

Figure 8Relative decline in emissions of criteria pollutants [%] by sector and species between 1 January and 31 December 2020 for EU27 + UK. For the shipping sector the relative differences consider both inland and sea shipping sectors.

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Figure 9Same as Fig. 8 for greenhouse gases. Note that for aviation, shipping, use of solvents and fugitives, no emissions are reported for CO₂ biofuel.

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The aviation sector presents the largest drop among all sectors, with a reduction of between −51 % and −56 % in emissions during 2020. The second most affected sector is road transport, which presents a decline in emissions between −15.5 % and −18.8 %, depending on the pollutant. These two are by far the sectors affected the most by the COVID-19 restrictions, with NO_x emission declines reaching approximately −90 % and −60 %, respectively, during April (Fig. 10). Despite showing drops of similar intensity, the recovery of emissions differs significantly between these two sectors. For road transport, emissions started to gradually and steadily recover during late April and almost reached BAU levels again by September (i.e. approximately −5 % for NO_x). On the other hand, the drop in emissions from aviation remained almost unchanged until the beginning of July, when a modest rebound is observed coinciding with the beginning of the summer holidays. The introduction of new restriction measures continued to curb road traffic activity in October. Strengthening measures caused a second important drop in November, although a ∼50 % lower one than in April. Strengthening measures linked to the second wave of infections also impacted the European air traffic emissions in November, when new drops are observed. The end of the year, however, was marked by a slight new recovery in emissions, similar to the one observed during summer, that can be attributed to the Christmas holidays.

Figure 10Daily NO_x emissions [t d⁻¹] by sector computed for the 2020 business-as-usual (dotted line) and COVID-19 (solid line) scenarios between 1 January and 31 December 2020 for EU27 + UK. For the shipping sector the relative differences consider both inland and sea shipping sectors. The areas highlighted between the two lines represent the emission differences between the two scenarios.

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For the manufacturing industry and other stationary combustion activity sectors, a heterogeneous impact of the COVID-19 restrictions is observed across the different pollutants. For the first sector, a lower reduction is observed for NMVOCs and NH₃ (between −2.8 % and −3.5 %) when compared to the other pollutants (between −6.8 % and −7.2 %). This is due to NMVOC and NH₃ emissions being mostly driven by processes occurring in the food–beverage and chemistry industries, which were considered essential during the lockdown phase and were therefore less affected than other industry branches, such as the manufacturing of basic metals or mineral products (see Sect. 3.1.2). Similarly to road transport, the largest drop in industrial emissions was reported during April (i.e. −25 % for NO_x), when a significant number of facilities were not allowed to operate. However, emissions began to recover in late April and May, as industrial activities fully resumed in large parts of Europe. As shown in Fig. 10, emissions from this sector quickly picked up again, approaching their pre-pandemic levels of activity during November (i.e. −1.1 % for NO_x). The reason for this rapid recovery is the fact that, unlike other sectors such as road transport that were more limited by the measures to curb the second wave of infections, since spring there had been hardly any restrictions directly affecting manufacturing industrial activities.

For the other stationary combustion activities, the pollutants that are mainly related to residential wood combustion processes (i.e. PM₁₀, PM_2.5, NH₃, NMVOCs, CO, CO₂_bf and CH₄) experienced a slight increase (between 1.1 % and 1.7 %), while the rest of the pollutants (i.e. NO_x, SO₂ and CO₂_ff) showed a modest decrease (between −0.4 % and −2.9 %). In both cases, the cumulative changes were not substantial, and after the lockdowns in spring, emissions were practically back to BAU levels by the end of July 2020 (Fig. 10). A new decrease in emissions is observed during November and December, coinciding with the new round of restrictions and the closure or limitation of working hours of non-essential commercial business such as restaurants or shopping stores.

In the public energy sector, the overall relative reduction in emissions during 2020 was approximately −3.3 %. As for the previous sectors, large differences are observed between months: in September, public energy emissions in the COVID-19 scenario were only −2.5 % lower than in the BAU scenario, compared to being −12 % lower in April. As in the case of the manufacturing industry sector, emissions were barely affected during the autumn restrictions and were almost back to BAU levels during December.

The shipping sector experienced a decrease in emissions of around −9.5 % for all pollutants. The evolution of daily emissions in this sector indicates a slow recovery of the activity, which is partially linked to the slow recovery of maritime passenger services. Decreases in emissions from off-road transport emissions were between −3 % and −1.8 %. More than 50 % of the total drop in emissions from this sector happened between April and May, when restrictions were at their maximum. After this period, a rapid recovery is observed, emissions being only −1 % below BAU by the end of the year. Fugitive emissions from fossil fuel production and transportation show decreases of up to −10 % for NMVOCs and −6.7 % for CH₄. Finally, the decrease in NMVOC emissions from use of solvents is very limited (−1.3 %) as only metal cleaning and printing industrial activities were considered to be affected by COVID-19 restrictions.

The stacked area charts shown in Fig. 11 illustrate the changes on average NO_x and PM_2.5 weekly emissions [t per week] from individual sectors across time for EU27 + UK countries. The charts consist of multiple lines drawn to track the emission changes for various pollutant sectors, and the area below each line is coloured to represent the associated sector: road transport (equivalent to GNFR_F), other stationary combustion activities (equivalent to GNFR_C), public energy (equivalent to GNFR_A), industry (equivalent to GNFR_B), aviation (equivalent to GNFR_H) and others (sum of emissions from all other sectors). Note that shipping emissions are not included in the results as they are not linked to any specific EU27 + UK country. A solid black line is used to represent the evolution of total emissions during the COVID-19 pandemic, and a dashed grey line is used to represent the evolution of total emissions under the BAU scenario.

Figure 11Stacked area charts representing the evolution of the average weekly emissions of NO_x (a) and PM_2.5 (b) per pollutant sector in EU27 + UK during the COVID-19 pandemic.

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The comparison between the charts produced for NO_x and PM_2.5 allows understanding the heterogeneous impact of COVID-19 across pollutants presented in Sect. 4.1.1. As shown in the charts, these differences are mainly due to the fact that total emission changes were primarily driven by changes in road transport and other stationary combustion activities and the contribution of these two sectors to the total emissions of each pollutant. In the case of NO_x, road transport is the largest contributor to total emissions, and therefore the drop in total emissions is significant, while in the case of PM_2.5 the main contributor to total emissions is other stationary combustion activities, which were practically not affected by the COVID-19 restrictions. As a matter of fact, more than 70 % of the total drop in NO_x emissions that occurred at the EU27 + UK level comes from the road transport sector.

4.3 Spatial analysis

Figure 12 shows a map of cumulative NO_x emission declines [kg per cell] between 1 January and 31 December as compared to the BAU scenario. The gridded emission results are provided at the same resolution as the CAMS-REG_v5.1 BAU inventory (i.e. $\mathrm{0.1}{}^{\circ }\times \mathrm{0.05}{}^{\circ }$). The main reductions occurred in urban areas and on main interurban roads, especially within the most affected countries (i.e. Italy, Spain, France and the United Kingdom). The pattern of the spatial emission difference is in line with the fact that most of the NO_x emission reductions are related to road transport, as previously shown in Fig. 11. Isolated and large emission drops can also be distinguished in certain grid cells (e.g. northwest of Spain or in the North Sea), which correspond to the decrease in emissions from individual industrial point sources.

Figure 12Map of the absolute cumulative NO_x emission decline [kg per cell] in 2020 as compared to the business-as-usual scenario. Gridded emission changes are reported at a resolution of $\mathrm{0.1}{}^{\circ }\times \mathrm{0.05}{}^{\circ }$. Administrative boundaries are derived from the Micro World Data Bank (MWDB2, 2011) (top). Average (dark red) and 5th and 95th percentiles (light blue shading) of the relative changes [%] in gridded NO_x emissions in Germany (bottom left) and Italy (bottom right) for the period 1 January to 31 December 2020.​​​​​​​

Figure 12 illustrates the average and 5th and 95th percentiles (p05, p95) of the daily relative changes [%] in the gridded NO_x emissions for Italy and Germany, respectively. The results were computed considering all the grid cells within each of the countries. In Italy, the last 2 weeks of March and first 2 weeks of April show certain areas of the country reaching reductions up to −70 %, whereas in other areas less affected by anthropogenic (and particularly road transport) emissions the reductions were significantly lower (ca. −20 %). During summer the range of relative changes becomes much lower, with emissions ranging between −10 % below and 10 % above BAU levels as mobility restrictions were lifted and traffic activity reached values above BAU levels due to an increase in domestic tourism. This was also observed in France. The drop in emissions observed during November and associated with the second round of nationwide COVID-19 restrictions shows relative changes of between ca. −26.5 % and ca. −7.2 %, which are approximately 2 times lower than the ones observed during the first round of lockdowns. In the case of Germany, the relative changes during the lockdowns of spring ranged approximately between −40 % (p95) and −10 % (p05). Similarly to what is observed for Italy, during the summer the relative decline in NO_x emissions is considerably reduced, ranging between −10 % and 5 % below and above BAU levels, respectively. A second significant drop in emissions is observed during the second half of December, when Germany had to go into a new hard lockdown as the number of deaths and infections from COVID-19 reached record levels. During this period of time, average emission reductions reached values of between −4.5 % (p05) and −24.5 % (p95). As in the case of Italy, the reductions associated with the second round of restrictions is approximately 2 times lower than the ones observed during the spring wave.

Figure 13 illustrates the relative NO_x and NMVOC emission declines that occurred in European high-density clustered urban centres, which are defined as urban regions with a density of at least 1500 inhabitants km⁻² and a minimum population of 50 000. The discrimination of the CAMS-REG AP and GHG gridded domain between urban and rural areas was derived from the Global Human Settlement Layer (GHSL) project (Pesaresi et al., 2019). The decline in NO_x urban emissions was on average 3.4 times larger than the one obtained for NMVOCs (i.e. −11.3 % versus −3.3 %). These results coincide with the general increase in O₃ levels in urban areas observed during the spring COVID-19 lockdowns, which is attributed to the fact that O₃ production is largely volatile organic compound (VOC)-sensitive across European urban areas (Grange et al., 2021; Querol et al., 2021). The largest differences between the NO_x and NMVOC emission declines were found in Spain (−15.6 % versus −3.1 %) and Portugal (−17.1 % versus −3.9 %). These results are in line with the relative changes in O₃ concentrations in traffic stations reported by Grange et al. (2021), which show that the largest O₃ increases occurred in Spain (61.9 %) and Portugal (46.8 %).

Figure 13Relative NO_x and NMVOC emission declines [%] per country occurring in high-density urban areas between 1 January and 31 December 2020. High-density urban areas were defined according to the Global Human Settlement Layer (GHSL) project (Pesaresi et al., 2019). The solid blue and red lines represent the average NO_x and NMVOC emission declines at the EU27 + UK level.

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