Search

We have analyzed TROPOMI NO2 data over the Copperbelt, a mining region which straddles the Democratic Republic of Congo and Zambia. While the ore mined there is primarily copper, this region is currently of great strategic interest because it is the world’s biggest producer of cobalt. Demand for cobalt, key to clean energy technologies (e.g., electric car batteries), is increasing worldwide and cobalt control is becoming a matter of national and global energy security. The impact of increasing mining-related activities on local air quality (high NOx is harmful to respiratory systems and crops) is unknown.TROPOMI, onboard ESA’s Sentinel-5 Precursor, is an imaging spectrometer in a sun-synchronous orbit at 824 km of altitude which measures concentrations of relevant atmospheric species (trace gases, aerosols, cloud) with quasi-global daily coverage and at high spatial resolution (~ 3.5 x 5.5 km2 in the case of NO2).We show that mining-related activities (such as extraction, smelting, and refining) can be remotely detected based on their TROPOMI NO2 signature, even in the presence of high background NO2 from biomass burning. Annual TROPOMI NO2 means for 2019, 2020, and 2021 show local enrichments consistent with point sources spatially collocated with both mines and large cities where mining-related activities take place. We have identified temporal trends in NO2 from these point sources and, when possible, we have compared those to production figures from the mining companies involved. We have quantified top-down annual NOx (NO+NO2) emissions for each of the point sources identified by applying the divergence method to the TROPOMI retrievals, using ancillary ERA5 meteorological data. Because in situ NOx measurements are not available, we contrast our emission results with emissions from the CAMS-GLOB-ANT v5.1 inventory.Our results show that NOx emissions from mining-related activities can be quantified remotely, which is important in the absence of local air quality monitoring. They also demonstrate that NO2 trend analysis can be a good indicator of mine production. This is particularly relevant for non-publicly traded mining companies, which are not required to publish their production figures. Lack of TROPOMI SO2 enhancements colocated with our NO2 point sources is consistent with SO2 capture and transformation into H2SO4, which is then used in mining-related processes or commercialized.

We present two inventories of anthropogenic emissions as well as an air quality atlas linked to Copernicus, the European earth observation system. CAMS-GLOB-ANTv5.3, a global anthropogenic emissions inventory, has been developed and provides emission data for the major atmospheric pollutants and greenhouse gases, as well as 25 speciated Volatile Organic Compounds (VOCs), for the period 2000-2023. This inventory is used as input for the Copernicus Atmosphere Monitoring System (CAMS) atmospheric reanalysis and forecasts.A mosaic inventory of anthropogenic emissions was also developed as part of the Copernicus CO2 project (CoCO2) focusing on greenhouse gases emissions. This mosaic uses the CAMS-GLOB-ANTv5.3 inventory as a global basis, and the DACCIWA2 (Dynamics-Aerosol-Chemistry-Cloud interactions in West Africa) inventory, a regional African emissions inventory. DACCIWA2 provides 0.1x0.1 degree resolution emissions of greenhouse gases and atmospheric pollutants for the period 2010-2018.The CAMS reanalysis is being used to develop an atlas of air quality called AQWA (Air Quality Worldwide Atlas). The AQWA atlas gives precious information about historical, climatological and current atmospheric composition, as well as air quality indexes. These statistics are available for all countries around the world, as well as states/provinces for the United States and China.This presentation describes the methodology used to develop and evaluate the two inventories and shows a few visualization examples from the atlas, which is still in development.

We have analyzed TROPOMI data over the Copperbelt mining region (Democratic Republic of Congo and Zambia). Despite high background values, we find that annual 2019-2022 means of TROPOMI NO2 show local enhancements consistent with six point sources (mines and cities) where high-emission industrial activities take place. We have quantified annual NO2 emissions for the six sources, identified temporal trends in these emissions, and found strong correlations with mine/refinery production data. CAMS-GLOB-ANT v5 inventory emissions are lower than TROPOMI-derived emissions by 61-96 % and lack the temporal trends observed in TROPOMI and mine/oil refinery production. Lack of TROPOMI SO2 enhancements over the point sources analyzed indicates SO2 capture and transformation into sulfuric acid, a profitable byproduct. These results demonstrate the potential for satellite monitoring of mining/oil refining activity which impacts the air quality of local communities. This is particularly important for Africa, where mining is increasing aggressively.

China was the first country to undergo large-scale lockdowns in response to the pandemic in early 2020 and a progressive return to normalization after April 2020. Spaceborne observations of atmospheric nitrogen dioxide (NO2) and oxygenated volatile organic compounds (OVOCs), including formaldehyde (HCHO), glyoxal (CHOCHO), and peroxyacetyl nitrate (PAN), reveal important changes over China in 2020, relative to 2019, in response to the pandemic-induced shutdown and the subsequent drop in pollutant emissions. In February, at the peak of the shutdown, the observed declines in OVOC levels were generally weaker (less than 20%) compared to the observed NO2 reductions (-40%). In May 2020, the observations reveal moderate decreases in NO2 (-15%) and PAN (-21%), small changes in CHOCHO (-3%) and HCHO (6%). Model simulations using the regional model MAGRITTEv1.1 with anthropogenic emissions accounting for the reductions due to the pandemic explain to a large extent the observed changes in lockdown-affected regions. The model results suggest that meteorological variability accounts for a minor but non-negligible part (~-5%) of the observed changes for NO2, whereas it is negligible for CHOCHO but plays a more substantial role for HCHO and PAN, especially in May. The interannual variability of biogenic and biomass burning emissions also contribute to the observed variations, explaining e.g. the important column increases of NO2 and OVOCs in February 2020, relative to 2019. These changes are well captured by the model simulations., SCOPUS: ar.j, info:eu-repo/semantics/published

We present a dataset of global soil NO emissions comprising gridded monthly data and the corresponding 3-hourly weight factors, suitable for atmospheric chemistry modelling. Data are provided globally at 0.5° × 0.5° degrees horizontal resolution, and with monthly time resolution over the period 2000–2018. Emissions are provided as total values and also with separate data for soil NO emissions from background biome values, and those induced by fertilizers/manure, pulsing effects, and atmospheric deposition, so that users can include, exclude or modify each component if wanted. This paper presents the emission algorithms and their data-sources, some comments on the availability of soil NO emissions in other inventories (and how to avoid double-counting), and finally some preliminary modelling results and comparison with observed data. This dataset was constructed as part of the Copernicus Atmosphere Monitoring Service (CAMS), with the dataset referred to as CAMS-GLOB-SOIL v2.2. These data are available through the Copernicus Atmosphere Data Store (ADS) system, (https://doi.org/10.24380/kz2r-fe18, last access June 2021, Simpson 2021a) or through the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access June 2021). For review purposes, ECCAD has set up an anonymous repository where a subset of the CAMS-GLOB-SOIL v2.2 data can be accessed directly (https://eccad.aeris-data.fr/essd-surf-emis-cams-soil/, Last access July 2021, Simpson 2021b).

Biogenic volatile organic compounds (BVOCs) emitted from the terrestrial vegetation into the Earth’s atmosphere play an important role in atmospheric chemical processes. A gridded information of their temporal and spatial distribution is therefore needed for proper representation of the atmospheric composition by the air quality models. Here we present three newly developed high-resolution global emission inventories of the main BVOC species including isoprene, monoterpenes, sesquiterpenes, methanol, acetone and ethene. Monthly mean and monthly averaged daily profile emissions were calculated by the Model of Emission of Gases and Aerosols from Nature (MEGANv2.1) driven by meteorological reanalyzes of the European Centre for Medium-Range Weather Forecasts for the period of 2000–2019. The dataset CAMS-GLOB-BIOv1.2 is based on ERA-Interim meteorology, datasets CAMS-GLOB-BIOv3.0 and v3.1 were calculated with ERA5. Furthermore, European isoprene emission potential data were updated using high-resolution land cover maps and detailed information of tree species composition and emission factors from the EMEP MSC-W model system. Updated isoprene emissions are included in CAMS-GLOB-BIOv3.1 dataset. The effect of annually changing land cover on BVOC emissions is captured by the CAMS-GLOB-BIOv3.0 as it was calculated with land cover data provided by the Climate Change Initiative of the European Space Agency (ESA-CCI). The global total annual BVOC emissions averaged over the simulated period vary between the datasets from 424 to 591 Tg(C) yr−1, with isoprene emissions from 299.1 to 440.5 Tg(isoprene) yr−1. Differences between the datasets and variation in their emission estimates suggests the emission uncertainty range and the main sources of uncertainty, i.e. meteorological inputs, emission potential data and land cover description. The CAMS-GLOB-BIO time series of isoprene and monoterpenes were compared to other available data. There is a general agreement in an inter-annual variability of the emission estimates and the values fall within the uncertainty range. The CAMS-GLOB-BIO datasets (CAMS-GLOB-BIOv1.2, https://doi.org/10.24380/t53a-qw03, Sindelarova et al., 2021a; CAMS-GLOB-BIOv3.0, https://doi.org/10.24380/xs64-gj42, Sindelarova et al., 2021b; CAMS-GLOB-BIOv3.1, https://doi.org/10.24380/cv4p-5f79, Sindelarova et al., 2021c) are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: June 2021).

During the COVID-19 pandemic, the first lockdown period (March-May 2020) has led to an unprecedented reduction in pollutant emissions. For 3⁄4 of the more than 1,100 available monitoring stations in Europe, the average NO2 concentrations decreased by at least 25% (2.7 μg.m-3) compared to the average concentrations recorded during the same period of the previous seven years. The relative reduction was of similar magnitude in both urban and rural areas.We further investigate the spatial distribution of the O3 change. Relative to the seven years average, positive anomalies were observed in northern Europe and negative anomalies in southwestern Europe. However, the level of total oxidant (Ox = O3 + NO2) remained unchanged except in southwestern Europe where it decreased.At the global scale, the ozone concentration increased only in a few NOx-saturated regions. After presenting data from monitoring stations in Europe, we analyze the drivers of the change in surface ozone concentrations using the global Community Earth System Model. We contrast global simulations of the atmospheric composition with and without lockdown adjusted anthropogenic emissions for the COVID-19 period.By comparing the situation in Europe with that of the United States and China, we show that the reduced cloudiness in northern Europe played a significant role by shifting the photochemical partitioning between NO2 and O3 toward more ozone, while in the North China Plain, enhanced ozone concentrations resulted primarily from reduced emissions of primary pollutants.These results illustrate the complexity of the processes affecting ozone in the troposphere and hence the difficulty of implementing efficient regulations targeting air quality impacts.

We use the global Community Earth System Model to investigate the response of secondary pollutants (ozone O3, secondary organic aerosols SOA) in different parts of the world in response to modified emissions of primary pollutants during the COVID‐19 pandemic. We quantify the respective effects of the reductions in anthropogenic emissions and meteorological anomalies, including a discussion on long-term changes from the chemical climatology. We show that the level of NOx has been reduced by typically 40 % in China during February 2020 and by similar amounts in many areas of Europe and North America in mid-March to mid-April 2020. Relative to a situation in which the emission reductions are ignored and despite the calculated increase in hydroxyl and peroxy radicals, the ozone concentration increased only in a few NOx‐saturated regions during the winter months of the pandemic when the titration of this molecule by NOx was reduced. In other regions, where ozone is NOx‐controlled, the concentration of ozone decreased. Zonally averaged ozone concentrations in the free troposphere during Northern Hemisphere spring and summer were 5 to 15 % lower than 19-year climatological values, in good quantitative agreement with observations from ozonesondes and ground-based remote sensing from the Network for the Detection of Atmospheric Composition Change (NDACC). About one third of this anomaly is attributed to the drastic reduction in air traffic during the pandemic, another third to reductions in surface emissions, the remainder to 2020 meteorological conditions, including the exceptional springtime Arctic stratospheric ozone depletion. The overall COVID-19 reduction in mean northern hemisphere tropospheric ozone in June is less than 5 ppb below 400 hPa, but reaches 8 ppb at 250 hPa.

The worldwide spread of Covid-19 pandemic caused a dramatic cutback of human activities and triggered a large-scale atmospheric composition experiment. This unfortunate situation provides the opportunity to investigate the response of atmospheric composition to the shutdown measures. Our focus will be on China, where the pandemic emerged in January 2020, and thence strict lockdowns were enforced. Substantial, large-scale decreases in pollutants levels over China and subsequent recovery were revealed by spaceborne observations from TROPOMI instrument on board Sentinel-5 Precursor, as well as by in situ measurements. Most published work on this topic relied on observed changes in column abundances of nitrogen dioxide (NO2), a predominantly anthropogenic compound and an important precursor for ozone production and secondary aerosol formation. Our work adds to this picture by studing the evolution of two other satellite-derived compounds, formaldehyde (HCHO) and peroxyacylnitrate (PAN), observed by TROPOMI and IASI, respectively. HCHO is an intermediate product in the chemical processing of volatile organic compounds (VOCs) of anthropogenic and natural origin. PAN is formed in the oxidation of anthropogenic and biogenic VOCs, and constitute the principal tropospheric NOx reservoir, enabling the transport and release of NOx away from the sources. Chemistry-transport simulations of PAN are challenging due to large uncertainties in formation mechanisms and precursor emissions. We will evaluate and analyze the observed variability of NO2, HCHO, and PAN columns using model simulations with the MAGRITTE v1.1 regional CTM run at 0.5ox0.5o resolution over China for 2019 and 2020. The model uses updated anthropogenic emissions from the CONFORM dataset, which takes into account the reductions during the shutdowns based on traffic and other economic activity data.

In order to fight the spread of the global COVID-19 pandemic, most of the world countries have taken control measures such as lockdowns during a few weeks to a few months. These lockdowns had significant impacts on economic and personal activities in many countries. Several studies using satellite and surface observations have reported important changes in the spatial and temporal distributions of atmospheric pollutants and greenhouse gases. Global and regional chemistry-transport model studies are being performed in order to analyze the impact of these lockdowns on the distribution of atmospheric compounds. These modeling studies aim at evaluating the impact of the regional lockdowns at the global scale. In order to provide input for the global and regional model simulations, a dataset providing adjustment factors (AFs) that can easily be applied to global and regional emission inventories has been developed. 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 is 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 six months of 2020. Maximum decreases in the emissions are found in February in Eastern China, with an average reduction of 20–30 % in NOx, NMVOCs and SO2 relative to the reference emissions. In the other regions, the maximum changes occur in April, with average reductions of 20–30 % for NOx, NMVOCs and CO in Europe and North America and larger decreases (30–50 %) in South America. In India and African regions, NOx and NMVOCs emissions are reduced by 15–30 %. For the others species, the maximum reductions are generally less than 15 %, except in South America, where large decreases in CO and BC 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 CONFORM (COvid adjustmeNt Factor fOR eMissions) (https://doi.org/10.25326/88). It is distributed by the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) database (https://eccad.aeris-data.fr/).

We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx ; SOx ; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1 x 0.1 degrees and regional European 0.1 x 0.05 degrees grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS- TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021). The present work was funded through the CAMS_81 (CAMS global and regional emissions) contract, coordinated by the Centre National de la Recherche Scientifique (CNRS, Claire Granier). The Copernicus Atmosphere Monitoring Service (CAMS, https://atmosphere.copernicus.eu/) is operated by the European Centre for Medium-Range Weather Forecasts on behalf of the European Commission as part of the Copernicus Programme. The study has also received support from the Ministerio de Ciencia, Innovación y Universidades (MICINN) as part of the BROWNING project (grant no. RTI2018-099894-B-I00) and NUTRIENT project (grant no. CGL2017-88911-R) and from the Agencia Estatal de Investigación (AEI) as part of the VITALISE project (grant no. PID2019-108086RA-I00 / AEI / 10.13039/501100011033). Carlos Pérez García-Pando acknowledges the European Research Council (grant no. 773051, FRAGMENT), the long-term support from the AXA Research Fund, and the support received through the Ramón y Cajal programme (grant no. RYC-2015-18690) of the MICINN. The authors acknowledge PRACE and RES for awarding access to MareNostrum at Barcelona Supercomputing Center. The authors are thankful to the Spanish Research Centre for Energy, Environment and Technology (CIEMAT) for sharing the databases of power plant emissions.

Using the CAM-chem Model, we simulate the response of chemical species in the free troposphere to scenarios of primary pollutant emission reductions during the COVID-19 pandemic. Zonally averaged ozone in the free troposphere during Northern Hemisphere spring and summer is found to be 5%–15% lower than 19-yr climatological values, in good agreement with observations. About one third of this anomaly is attributed to the reduction scenario of air traffic during the pandemic, another third to the reduction scenario of surface emissions, the remainder to 2020 meteorological conditions, including the exceptional springtime Arctic stratospheric ozone depletion. For the combined emission reductions, the overall COVID-19 reduction in northern hemisphere tropospheric ozone in June is less than 5ppb below 400hPa, but reaches 8ppb at 250hPa. In the Southern Hemisphere, COVID-19 related ozone reductions by 4%–6% were masked by comparable ozone increases due to other changes in 2020.

We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx, SOx, NMVOC, NH3, CO, PM10, PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1 × 0.1 deg and regional European 0.1 × 0.05 deg grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS program. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production, traffic counts) at national and local levels were collected and combined with existing meteorological-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily, hourly) the resulting profiles are pollutant-dependent, yearly-dependent (i.e. time series from 2010 to 2017) and/or spatially-dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/). For review purposes, ECCAD has set up an anonymous repository where subsets of the CAMS-GLOB-TEMPOv2.1 and CAMS-REG-TEMPOv2.1data can be accessed directly (https://www7.obs-mip.fr/eccad/essd-surf-emis-cams-tempo/).; We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx, SOx, NMVOC, NH3, CO, PM10, PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air 15 traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1x0.1 deg and regional European 0.1x0.05 deg grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS program. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production, traffic counts) at national and local levels were collected and combined with existing meteorological-dependent parametrizations to account for the 20 influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily, hourly) the resulting profiles are pollutant-dependent, yearly-dependent (i.e. time series from 2010 to 2017) and/or spatially-dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when 25 comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.

We use the global Community Earth System Model to investigate the response of secondary pollutants (ozone O3, secondary organic aerosols SOA) in different parts of the world in response to modified emissions of primary pollutants during the COVID‐19 pandemic. We quantify the respective effects of the reductions in NOx and in volatile organic carbon (VOC) emissions, which, in most cases, affect oxidants in opposite ways. Using model simulations, we show that the level of NOx has been reduced by typically 40% in China during February 2020 and by similar amounts in many areas of Europe and North America in mid‐March to mid‐April 2020, in good agreement with space and surface observations. We show that, relative to a situation in which the emission reductions are ignored and despite the calculated increase in hydroxyl and peroxy radicals, the ozone concentration increased only in a few NOx‐saturated regions (northern China, northern Europe, and the US) during the winter months of the pandemic when the titration of this molecule by NOx was reduced. In other regions, where ozone is NOx‐controlled, the concentration of ozone decreased. SOA concentrations decrease in response to the concurrent reduction in the NOx and VOC emissions. The model also shows that atmospheric meteorological anomalies produced substantial variations in the concentrations of chemical species during the pandemic. In Europe, for example, a large fraction of the ozone increase in February 2020 was associated with meteorological anomalies, while in the North China Plain, enhanced ozone concentrations resulted primarily from reduced emissions of primary pollutants., Plain Language Summary: With the reduction in economic activities following the COVID‐19 pandemic outbreak in early 2020, most emissions of air pollutants (i.e., nitrogen oxides [NOx], carbon monoxide [CO], sulfur dioxide [SO2], volatile organic carbon [VOC], black carbon [BC], organic carbon [OC]) have decreased substantially during several months in different regions of the world. This unintended global experiment offered a glimpse into a potential future in which air quality would be improved. Here, a global atmospheric model is used to assess the changes in the chemical composition of the atmosphere during the pandemic period and in the related chemical processes that lead to the formation of ozone (O3) and secondary organic aerosols (SOA). The study illustrates the nonlinearity of the air quality response to reduced NOx and VOC emissions, which depends on the chemical environment including the background level of nitrogen oxides. Meteorological variability can lead to anomalies in the concentration of chemical species with magnitudes that are as large or even larger than the perturbations due to COVID‐induced changes in the emissions., Key Points: During the COVID‐19 lockdown, the atmospheric concentration of primary pollutants (NOx, VOCs, CO, SO2) was considerably reduced The concentration of secondary pollutants increased in NOx‐saturated areas and decreased in NOx‐limited areas The response of the chemical system depends on the relative changes in NOx and VOC emissions, and is affected by weather variability, AQ‐WATCH European project, HORIZON 2020 Research and Innovation Action, Hong Kong Research Grants Council

This study compares recent CO, NO, NMVOC, SO, BC and OC anthropogenic emissions from several state-of-the-art top-down estimates to global and regional bottom-up inventories and projections from fi...

International audience; This study compares recent CO, NOx, NMVOC, SO2, BC, and OC anthropogenic emissions from several state-of-the-art top-down estimates to global and regional bottom-up inventories and projections from five Shared Socioeconomic Pathways (SSPs) in several regions. Results show that top-down emissions derived in several recent studies exhibit similar uncertainty as bottom-up inventories in some regions for certain species and even less in the case of Chinese CO emissions. In general, the largest discrepancies are found outside of regions such as the United States, Europe, and Japan where the most accurate and detailed information on emissions is available. In some regions such as China, which has recently undergone dynamical economic growth and changes in air quality regulations, the top-down estimates better capture recent emission trends than global bottom-up inventories. These results show the potential of top-down estimates to complement bottom-up inventories and to aide in the development of emission scenarios, particularly in regions where global inventories lack the necessary up-to-date and accurate information regarding regional activity data and emission factors such as Africa and India. Areas of future work aimed at quantifying and reducing uncertainty are also highlighted. A regional comparison of recent CO and NOx trends in the five SSPs indicate that SSP126, a strong pollution control scenario, best represents the trends from the top-down and regional bottom-up inventories in the United States, Europe, and China, while SSP460, a low-pollution control scenario, lies closest to actual trends in West Africa. This analysis can be useful for air quality forecasting and near-future pollution control/mitigation policy studies.

International audience

International audience; The ECCAD database (Emissions of atmospheric Compounds and Compilation of Ancillary Data) provides a user-friendly access to global and regional surface emissions for a large set of chemical compounds and ancillary data (land use, active fires, burned areas, population,etc). The emissions inventories are time series gridded data at spatial resolution from 1x1 to 0.1x0.1 degrees. ECCAD is the emissions database of the GEIA (Global Emissions InitiAtive) project and a sub-project of the French Atmospheric Data Center AERIS (http://www.aeris-data.fr). ECCAD has currently more than 2200 users originating from more than 80 countries. The project benefits from this large international community of users to expand the number of emission datasets made available. ECCAD provides detailed metadata for each of the datasets and various tools for data visualization, for computing global and regional totals and for interactive spatial and temporal analysis. The data can be downloaded as interoperable NetCDF CF-compliant files, i.e. the data are compatible with many other client interfaces. The presentation will provide information on the datasets available within ECCAD, as well as examples of the analysis work that can be done online through the website: http://eccad.aeris-data.fr.

Accurate information on surface emissions is critical for understanding the current chemical composition of the atmosphere and for forecast simulations. Many research teams worked during the past years to better quantify emissions in different parts of the world, and to develop inventories of anthropogenic emissions for different periods. An evaluation of the most recent datasets providing emissions for Asia for the 1960–2014 period is discussed in this chapter for different gaseous and particulate compounds, i.e. carbon monoxide, nitrogen oxides, non-methane volatile organic compounds, sulfur dioxide, ammonia, black carbon, organic carbon, and particulate matter PM10 and PM2.5. The comparison between the inventories is used to quantify the range of the emissions for several regions and different periods. Since very little information is available concerning the uncertainties on emissions in the different regions of the world, we use the comparison between the different datasets to provide some information on these uncertainties.

Since 1994, the In-service Aircraft for a Global Observing System (IAGOS) program has produced in-situ measurements of the atmospheric composition during more than 51000 commercial flights. In order to help analyzing these observations and understanding the processes driving the observed concentration distribution and variability, we developed the SOFT-IO tool to quantify source/receptor links for all measured data. Based on the FLEXPART particle dispersion model (Stohl et al., 2005), SOFT-IO simulates the contributions of anthropogenic and biomass burning emissions from the ECCAD emission inventory database for all locations and times corresponding to the measured carbon monoxide mixing ratios along each IAGOS flight. Contributions are simulated from emissions occurring during the last 20 days before an observation, separating individual contributions from the different source regions. The main goal is to supply added-value products to the IAGOS database by evincing the geographical origin and emission sources driving the CO enhancements observed in the troposphere and lower stratosphere. This requires a good match between observed and modeled CO enhancements. Indeed, SOFT-IO detects more than 95 % of the observed CO anomalies over most of the regions sampled by IAGOS in the troposphere. In the majority of cases, SOFT-IO simulates CO pollution plumes with biases lower than 10–15 ppbv. Differences between the model and observations are larger for very low or very high observed CO values. The added-value products will help in the understanding of the trace-gas distribution and seasonal variability. They are available in the IAGOS data base via http://www.iagos.org. The SOFT-IO tool could also be applied to similar data sets of CO observations (e.g. ground-based measurements, satellite observations). SOFT-IO could also be used for statistical validation as well as for inter-comparisons of emission inventories using large amounts of data.

During the past few years, the ECCAD (Emissions of atmospheric Compounds & Compilation of Ancillary Data) database was developed in order to provide a user-friendly access to surface emissions and ancillary data, i.e. data on land use, active fires, burned areas, population, etc. This database and the emissions portal of the GEIA (Global Emissions InitiAtive) project have been merged. ECCAD is a sub-project of the ETHER French Atmospheric Chemistry Data Centre (CNES and CNRS, http://www.pole-ether.fr). The ECCAD database includes currently a large diversity of datasets, which provide global and regional surface emissions for a large set of chemical compounds. All the data are provided at a 0.5x0.5 or 1x1 degree resolution. ECCAD provides detailed metadata on each of the datasets, including information on complete references and methodology, and links to the original inventories. Several tools are provided for the visualization of the data, for computing global and regional totals and for an interactive spatial and temporal analysis. The data can be downloaded as interoperable NetCDF CF-compliant files, i.e. the data are compatible with many other client interfaces and can be downloaded through requests as geographical coverage or geo-referenced maps. ECCAD has currently more than 700 users originating from more than 30 countries. ECCAD benefits from this large international community of users to expand the number of emission datasets made available. The ECCAD database and the web interface are in continuous development: new tools are being built to improve the analysis and comparison of emissions and ancillary data. These new tools include a regridding tool, arithmetic expressions to combine different maps, interactive selection of scale values, and new tools for temporal profiles analysis. Comparisons of data at different scales is also in development. An online module to calculate biomass burning emissions is being improved, and will also be extended to anthropogenic emissions. The presentation will provide information on all the datasets available within ECCAD, as well as examples of the analysis work that can be done online from the database. All the datasets, associated metadata, tools and download can be achieved from the ECCAD website: http://eccad.pole-ether.fr