dataesr/scientific-paragraphs-categorization huggingface dataset:
This dataset provides, for the January-August 2020 period, gridded AFs at a 0. 1 × 0. 1 latitude-longitude degree resolution on a daily or monthly basis for the transportation (road, air and ship traffic), power generation, industry and residential sectors. The quantification of AFs is based on activity data collected from different databases and previously published studies. A range of AFs are provided at each grid point for model sensitivity studies. The emission AFs developed in this study are applied to the CAMS global inventory (CAMS-GLOB-ANT_v4. 2_R1. 1), and the changes in emissions of the main pollutants are discussed for different regions of the world and the first 6 months of 2020. Maximum decreases in the total emissions are found in February in eastern China, with an average reduction of 20 %-30 % in NO x , NMVOCs (non-methane volatile organic compounds) and SO 2 relative to the reference emissions.
In the other regions, the maximum changes occur in April, with average reductions of 20 %-30 % for NO x , NMVOCs and CO in Europe and North America and larger decreases (30 %-50 %) in South America. In India and African regions, NO x and NMVOC emissions are reduced on average by 15 %-30 %. For the other species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC (black carbon) are estimated. As discussed in the paper, reductions vary highly across regions and sectors due to the differences in the duration of the lockdowns before partial or complete recovery. The dataset providing a range of AFs (average and average ± standard deviation) is called.
g. ,  Shi and Brasseur, 2020; Lee et al. , 2020; Kim et al. , 2021)  and satellite retrievals (e. g. ,  Bauwens et al. , 2020; Zhang et al. , 2020; Diamond et al. , 2020; Biswal et al. , 2021). Modeling studies have been initiated to investigate the changes in the global and regional distributions of tropospheric chemical compounds during the pandemic  (Gaubert et al. , 2021; Venter et al. , 2020; Xing et al. , 2020; Keller et al. , 2021; Barré et al. , 2021). These modeling studies are based on estimates of emissions for primary species and on consistent changes in the emissions during the COVID-19 lockdown periods. Here we present a global gridded dataset of emission adjustment factors (AFs) at a 0. 1 • × 0. 1 • resolution and on a daily or monthly basis based on available activity data.
Recent studies (Le  Quéré et al. , 2020; Forster et al. , 2020; Liu et al. , 2020)  have discussed the changes in emissions at the global scale for several chemical compounds. In this paper, we propose a dataset of AFs for the individual sectors related to transportation (including road, air and ship traffic), industry, residential activities and power generation. The advantage of such a dataset, which provides AFs for assessments of the impact of COVID-19 restrictions on pollutants emitted into the atmosphere, is that it can be applied directly to most global and regional inventories used in chemistry-climate and transport models in a flexible way. The inventories commonly used in these models (Emission Database for Global Atmospheric Research, EDGAR,  Crippa et al. , 2018;  Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants, ECLIPSE,  Klimont et al.
, 2017;  Copernicus Atmosphere Monitoring Service, CAMS,  Granier et al. , 2019;  Multi-resolution Emission Inventory for China, MEIC,  Li et al. , 2017; etc. ) include the sectors already mentioned (industrial processes, road transportation, power generation, resi- dential, aviation and shipping). The emissions of these sectors are developed on the basis of a combination of several sub-sectors that are not provided separately in the emission inventories. Section 2 describes the general methodology used in the development of the AFs. The following sub-sections analyze the sectoral activity data used to determine these factors. The availability of activity data depends strongly on the regions under consideration, and this might often lead to large uncertainties. We are therefore providing for each location and sector an estimated average, low and high values of the AFs.
In Sect. 3, we present and discuss the sectoral changes in emission AFs over selected regions as a function of time. This paper discusses the dataset developed for the January-August 2020 period: the dataset is called CON-FORM (COvid-19 adjustmeNt Factors fOR eMissions) and will be updated to account for the latest available activity data information. The impact of the AFs on the emissions developed as part of the CAMS global anthropogenic emissions  (Granier et al. , 2019; Elguindi et al. , 2020)  is discussed for the NMVOC (non-methane volatile organic compound), CO, NO x , BC (black carbon), OC and SO 2 species in Sect. 3. 5.
Some of these data are used to support our analysis and contribute to the estimation of uncertainties in the AFs; (2) the collected data are then analyzed, and an intercomparison of the changes in activity data of datasets providing similar or equivalent parameters is performed. The dataset that provides the most detailed information and which better meets our needs is then chosen. The non-gridded AFs are calculated for each country and state/province according to the availability of activity data, as detailed in the following sections; (3) the gridded daily and monthly NetCDF files developed for each sector are obtained by assigning to each cell the value of the AFs in the whole country or state/province level corresponding to this grid cell: this is based on the fact that the lockdowns and restrictions have been generally taken at national or state level.
For several sectors (road transport, industry and residential) the AF at the country or state/province level represents an average value calculated from several individual cities or locations; and (4) finally, a comparison of the AFs derived from this study with other published data is performed, together with an evaluation of their impact on emissions, using the CAMS-GLOB-ANT_v4. 2_R1. 1 inventory as a basis. The dataset discussed in this paper covers the period from 1 January to 31 August 2020. It will be regularly updated to account for the latest available activity data information. The AFs are calculated as the ratio between the activity data for a given sector and day or month and the median value of the activity data over the 5-week period starting on 1 January 2020. This corresponds to the baseline used in Google mobility trends.
Road transportation Several studies related to the COVID-19 pandemic have used the Google COVID-19 Community Mobility data (https:// www. google. com/covid19/mobility/, last access: 12 August 2021)  (Forster et al. , 2020; Guevara et al. , 2021). This dataset provides the number of visitors to specific locations based on mobile phone locations (e. g. , parks, grocery and pharmacy stores, workplaces, retail and recreation, train stations, and residential) every day relative to a baseline value. This baseline is calculated as the median value over the 5-week period from 3 January to 6 February 2020. We use measurements up to 31 August 2020 for all countries for which data are available (about 133 countries). For the USA, the analysis was performed for each state. Google daily data are provided at country and state level and generally cover several local areas (i.
e. , subregion or city). For example, in the USA, Google spans the 50 states and the District of Columbia as well as several cities in each state. The AFs for the road transportation sector are derived from the estimation of transit usage (i. e. , public transportation including train stations, buses and subways) made by Google. In order to make the calculated AFs comparable with those derived using the other data sources considered in this study, the AFs for Google's categories are scaled to 1 using the following formula: AF = 1 + Google / 100. In this way their values are less than 1 for a reduction in activity and above 1 otherwise. It should be noted that Google mobility data are not available for all countries (e. g. , China and for nearly twothirds of African countries). Other datasets are providing mobility trends, such as the Apple mobility trend reports (https://covid19.
apple. com/mobility, last access: 12 August 2021) and the TomTom (https://www. tomtom. com/en_gb/ traffic-index/ranking/, last access: 12 August 2021) traffic congestion index. Apple measures the number of requests for directions scaled relative to 13 January 2020, while Tom-Tom provides the percentage of the differences in time spent on a trip compared to uncongested conditions. Google provides a better spatial coverage than the other two datasets. In Africa for example, Apple and TomTom data are available for 3 and 2 countries, respectively, while Google provides data for 26 African countries. In China, TomTom reports measurements only for some cities. Unlike Google and Apple, TomTom markets its data, which are therefore not openaccess. In order to evaluate the Google dataset, a regional comparison with Apple mobility data was performed (Supplement, Figs.
A comparison between Google's monthly nonresidential (i. e. , combining grocery, workplaces, transit and retail) and Apple driving data, which can be considered to be equivalent categories, displays high correlation coefficients varying from 0. 79 to 0. 97 depending on the region (Fig. S2 ). The data of these two categories agree relatively well, particularly during the first months of the COVID-19 pandemic (February to May), when lockdowns were strict in most regions. The largest discrepancies are observed from June to August, especially in Europe and the USA. These differences are likely due to the fact that the two datasets do not represent the same parameters: Apple bases their values on the volume of direction requests on phone applications, while Google uses mobile phone locations. These differences can be attributed to a combination of several factors including the spatial coverage, the mode and category of transportation considered, and the location of the measurements within the country or state/province.
The calculation methods are also very different from one dataset to another. Comparisons of the changes in the ground transportation sector for the different mobility datasets have been performed in recent studies  (Forster et al. , 2020; Le Quéré et al. , 2020). The correlation coefficients between Google and Apple transit categories calculated in our study are in line with the value of 0. 8 reported by  Forster et al. (2020)  for the February to June period. Liu et al. (2020)  show trends from both Google and TomTom mobility datasets with the same order of magnitude during the first quarter of 2020. This analysis highlights the importance in the choice of the data to be considered in the estimation of changes based on activity data, especially for the more recent months as well as for the future, when the dataset will be extended.
Based on this analysis, we have used the Google mobility data for estimating the AFs in regions where data are available. In China, the AFs for road transportation were calculated based on the Baidu migration scale index (https:// qianxi. baidu. com/, last access: 12 August 2021; available for China only). These indices are aggregated from migration flows within China based on the positioning requests on Baidu Maps services. The index indicates the ratio between the number of people traveling in a city and the population of this city. The data are available only from 1 January to 2 May 2020 and cover about 343 Chinese cities in all provinces. Baidu officially stopped updating the dataset on its platform on 8 May 2020. We compared the changes in the Baidu migration scale index with the relative difference in TomTom congestion levels for the Beijing, Tianjin, Chongqing and Shanghai Chinese areas.
TomTomarchived data are not freely available, but they can be re-trieved from published graphs (https://www. tomtom. com/ en_gb/traffic-index/ranking, last access: 12 August 2021) providing the weekly changes in 2020 relative to the same periods in 2019. The results show a strong similarity between changes given by these two datasets for the period covered by the Baidu dataset (January to April), with a correlation coefficient of 0. 9 (Fig. S3 ). For the period from May to August 2020,  Liu et al. (2020)  showed that the difference in estimated CO 2 emissions from road transport between 2020 and 2019 is on average about 4 % for May-November, with larger values for some days. Based on these analyses and in order to cover the whole period of our study (January to August), we assume that changes in road traffic in China after May 2020 are relatively low and close to those before the spread of the COVID-19 virus.
S4 ). The average change in the first 8 months of 2020 relative to the same periods in 2019 in crude steel production for the 24 countries shown in Fig. S4  is 17 %, while the corresponding value using Google's workplace measures is 27 %. This indicates a fair agreement between the two datasets. However, there are large differences in some countries between these data. For example, in Europe the greatest change in crude steel production is 24 % in comparison to 59 % estimated using Google's workplace category, indicating a high level of uncertainty in the AFs for the industry sector.
We also used the local electricity demand data in Singapore. For Canada, we assumed that the data in Ontario are representative of power generation trends (Table  1 ). It should be noted that we did not apply any temperature correction to the electricity load. Liu et al. (2020)  indicated that COVID-19 and related restrictions explain about 85 % of the reduction in the power sector from January to March, and only 15 % is attributed to the temperature effect. For the rest of the world where data on electricity demand or thermal production are not publicly available, we estimate the AFs based on the data published by Le  Quéré et al. (2020).
The results indicated that, when considering the global average, trends from both datasets are relatively similar, with a significant decrease of up to 65 % for OAG and 80 % for KCDM in April, May and June compared to the reference value in January 2020. However, since July there has been a slow recovery towards the pre-lockdown values (Fig. S5 , Supplement). EUROC-L seems to overestimate the changes during the period when restrictions were the most severe, especially in China, Japan and South Korea. In contrast, there is an underestimation of trends since July 2020 in most of the countries as shown in Fig. S5. A lockdown for the whole of China was declared on 10 February 2020, and the country had its air traffic heavily impacted on the following days. This disruption does not appear in the KCDM data.
Our study, based on Google's residential measures reflecting time spent by people at their home, leads to increased emissions from the residential sector. However,  Liu et al. (2020)  reported a global decrease of about -2 % during the first 7 months, with an uncertainty of about 40 % using fuel consumption data in residential buildings from 2019 that was scaled to 2020 based on the population-weighted heating degree days variation. In   this study, the estimated uncertainty range in the emission AFs for the residential sector reached ±20 %. Our global estimate is consistent with an average increase of around 5 % in activity data from the residential sector, mainly during the strict lockdown period, estimated in Le  Quéré et al. (2020).
Adjustment factors for the power generation sector The COVID-19 pandemic has implications on the share of energy use in industry, commercial and domestic operations. In order to quantify the impact of the lockdowns in this sector, we used the data of electricity demand. The global demand experienced a maximum average decrease of 20 % (AF = 0. 8) between late March and early April, when the restrictions were most stringent in Europe, USA, Africa and many other countries (Fig. 6 ). From mid-June, there is an increase in the demand for electricity consumption in the USA, which reaches a maximum value of about 20 % at the end of July and beginning of August but seems to be fol-lowed by a slow decrease. This can possibly be explained by an important demand in the commercial and residential sectors.
Impact on surface emissions The estimations of emission AFs for six economic activity sectors (road transportation, industry, power generation, residential, shipping and aviation) discussed in the previous sections are provided by the CONFORM dataset on a daily basis -except for aviation and shipping, which are calculated on a monthly basis -for the period from January to August 2020 at a 0. 1 • × 0. 1 • grid resolution. This dataset was also used in the  Gaubert et al. (2021)  paper, which examined changes in secondary atmospheric pollutants during the 2020 COVID-19 pandemic using the Community Atmosphere Model (CAM-Chem) at the global scale. More evaluations of the CONFORM dataset have been performed in analyses of simulated and observed (in situ and satellite measurements) atmospheric surface ozone, nitrogen dioxide and oxygenated volatile organic compounds across China by  Liu et al.
(2021)  and  Stavrakou et al. (2021). These studies demonstrate how reductions in anthropogenic emissions of ozone precursors (NO x and volatile organic compounds, VOCs) as proposed by the CONFORM dataset contributed to the observed changes in the regions affected by the COVID-19 lockdowns. The impact of the AF changes on the total emissions (sum of emissions from transportation (road and non-road traffic), industry, residential, power and shipping) has been analyzed for different compounds and selected regions using the anthropogenic CAMS-GLOB-ANT_v4. 2_R. 1. 1 emission inventory  (Granier et al. , 2019; Elguindi et al. , 2020). This dataset provides daily emissions of the main atmospheric compounds, including speciated volatile organic compounds at a 0. 1 • × 0. 1 • resolution, from 2000 to 2020.
Version R. 1 of the CAMS-GLOB-ANT_v4. 2 dataset incorporates the MEIC1. 3 regional inventory for China described by  Zheng et al. (2018). The percentage of global change in emissions during COVID-19 compared to the reference 2020 emissions is shown in Fig. 7  for the main pollutants (NO x , CO, SO 2 , BC, OC and NMVOCs) from January to June 2020. The vertical bars indicate the estimated lower and upper limits of the changes in emissions due to the restrictions. Figure  7  shows that the changes depend on the chemical species, with decreases in monthly global emissions of 22 % (11 %-26 %) for NMVOCs, 17 % (12 %-24 %) for NO x , 14 % (7 %-18 %) for CO, 10 % (6 %-18 %) for SO 2 and 9 % (3 %-13 %) for BC in April 2020 compared to a non-COVID-19 scenario. The global changes in OC emissions are different from the other species, with an increase of 3 % (0 %-7 %).
Data availability The average estimated daily and monthly gridded AFs for the sectors considered in this study, i. e. , transportation (including air traffic and shipping), industries, residential and power generation, are available as NetCDF files for the global domain at a resolution of 0. 1 • × 0. 1 •. A range of AF is provided at each grid point as the average ± standard deviation. The acronym of the dataset is CONFORM (COvid-19 adjustmeNt Factors fOR eMissions), and it is available at https://doi. org/10. 25326/88 (Doumbia et al. , 2020). The files (which have been updated to December 2020) can be openly accessed through the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database with a login account (https://eccad. aeris-data. fr/, last access: 23 August 2021).
Conclusions The restrictions and lockdowns resulting from the COVID-19 pandemic since the end of January 2020 have had important social, economic and environmental consequences. In this study, we have provided an estimate of adjustment factors (AFs) to quantify the changes in global emissions of major atmospheric pollutants during the lockdown periods. This dataset can be easily applied to the emissions used in global and regional models to simulate the impacts of the reduced human activity on the atmospheric composition and climate. To this purpose, we analyzed activity data from various sectors representative of transportation (road, air and ship), industry, residential and power generation. The resulting dataset provides daily or monthly sectoral AFs on a 0. 1 • ×0. 1 • resolution over the globe and can be used to quantify regional patterns in the distributions of emissions during the COVID-19 pandemic.
When applied to the CAMS-GLOB-ANT_v4. 2_R1. 1 emissions dataset, large changes are estimated, with maximum decreases in the total emissions in February in eastern China, with an average reduction of 20 %-30 % in NO x , NMVOC and SO 2 emissions. In other regions, the maximum changes occur in April, with average reductions of 20 %-30 % for NO x , NMVOCs and CO in Europe and North America and larger decreases (30 %-50 %) in South America. In both India and Africa, NO x emissions decline by 10 % to 40 %, while NMVOC emissions decrease by 2 % to 20 %, with larger uncertainties compared to other regions. For the other species, the maximum average reduc-tions are generally less than 15 %, except in the South American countries, where large decreases in CO and BC are estimated. These changes in the total emissions are related to the different changes in the individual sectors.
The patterns of the reductions also show large differences at the regional level. We acknowledge that the lack of data for some activity sectors in several regions and the absence of accurate information on all sectors might cause significant uncertainties in the estimation of the AFs. For that reason, besides average values, the dataset also includes low and high estimation of the AFs for the period over January-August 2020. This should give an estimate of the uncertainties in the distribution of chemical species in models. The AFs obtained in this study will be extended at least until the end of the year 2020 and will be revised and updated as new or improved information on economic and mobility activity becomes available. Competing interests. The authors declare that they have no conflict of interest.
The supplement related to this article is available online at: https://doi. org/10. 5194/essd-13-4191-2021-supplement. Author contributions. TD and CG designed the study. TD, SD, NE and CG produced emission data. TD, YL, XS and TW collected the datasets. TD, IB, ST, BG and TS analyzed the developed adjustment factors (AFs). BG, AD, FL, ST, IB, YL, TW and TS tested the AFs in atmospheric models. TD, CG and GB wrote the original draft. All authors provided critical feedback and helped shape the research, analysis and manuscript.
We acknowledge the AQ-WATCH (Air Quality -Worldwide Analysis and Forecasting of Atmospheric Composition for Health) program, the support of the ESA (European Space Agency) project and the SEEDS (South-Eastern European Data Services) European project. The CAMS-GLOB-ANT dataset has been developed with the support of CAMS (Copernicus Atmosphere Monitoring Service), operated by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission as part of the Copernicus program. The EC-CAD (Emissions of atmospheric Compounds and Compilation of Ancillary Data) database is supported by the French AERIS (Data.
and Service for the Atmosphere). This material is also based upon work supported by the  National Center for Atmospheric Research (NCAR) , which is a major facility sponsored by the  US National Science Foundation  under cooperative agreement no. 1852977. Tao Wang  and  Yiming Liu  acknowledge support by the  Hong Kong Research Grants Council  ( T24-504/17-N  and  A-PolyU502/16 ).