This work presents the integration of a gas-phase and particulate atmospheric emission inventory (AEI) for Argentina in high spatial resolution ($\mathrm{0.025}{}^{\circ }\times \mathrm{0.025}{}^{\circ }$ ; approx. 2.5 km×2.5 km) considering monthly variability from 1995 to 2020. The new inventory, called GEAA-AEIv3.0M, includes the following activities: energy production, fugitive emissions from oil and gas production, industrial fuel consumption and production, transport (road, maritime, and air), agriculture, livestock production, manufacturing, residential, commercial, and biomass and agricultural waste burning. The following species, grouped by atmospheric reactivity, are considered: (i) greenhouse gases (GHGs) – CO₂, CH₄, and N₂O; (ii) ozone precursors – CO, NO_x (NO+NO₂), and non-methane volatile organic compounds (NMVOCs); (iii) acidifying gases – NH₃ and SO₂; and (iv) particulate matter (PM) – PM₁₀, PM_2.5, total suspended particles (TSPs), and black carbon (BC). The main objective of the GEAA-AEIv3.0M high-resolution emission inventory is to provide temporally resolved emission maps to support air quality and climate modeling oriented to evaluate pollutant mitigation strategies by local governments. This is of major concern, especially in countries where air quality monitoring networks are scarce, and the development of regional and seasonal emissions inventories would result in remarkable improvements in the time and space chemical prediction achieved by air quality models. Despite distinguishing among different sectoral and activity databases as well as introducing a novel spatial distribution approach based on census radii, our high-resolution GEAA-AEIv3.0M shows equivalent national-wide total emissions compared to the Third National Communication of Argentina (TNCA), which compiles annual GHG emissions from 1990 through 2014 (agreement within ±7.5 %). However, the GEAA-AEIv3.0M includes acidifying gases and PM species not considered in TNCA. Temporal comparisons were also performed against two international databases: Community Emissions Data System (CEDS) and EDGAR HTAPv5.0 for several pollutants; for EDGAR it also includes a spatial comparison. The agreement was acceptable within less than 30 % for most of the pollutants and activities, although a >90 % discrepancy was obtained for methane from fuel production and fugitive emissions and >120 % for biomass burning. Finally, the updated seasonal series clearly showed the pollution reduction due to the COVID-19 lockdown during the first quarter of year 2020 with respect to same months in previous years. Through an open-access data repository, we present the GEAA-AEIv3.0M inventory as the largest and more detailed spatial resolution dataset for the Argentine Republic, which includes monthly gridded emissions for 12 species and 15 stors between 1995 and 2020. The datasets are available at https://doi.org/10.17632/d6xrhpmzdp.2 (Puliafito et al., 2021), under a CC-BY 4 license.

This work studied the spread of COVID-19, the meteorological conditions and the air quality in a megacity from two viewpoints: (1) the correlation between meteorological and air quality (PM₁₀ and NO₂) variables with infections and deaths due COVID-19, and (2) the improvement in air quality. Both analyses were performed for the pandemic lockdown due to COVID-19 in the City of Buenos Aires (CABA), the capital and the largest city in Argentina. Daily data from temperature, rainfall, average relative humidity, wind speed, PM₁₀, NO₂, new cases and deaths due COVID-19 were analyzed. Our findings showed a significant correlation of meteorological and air quality variables with COVID-19 cases. The highest temperature correlation occurred before the confirmation day of new cases. PM₁₀ presented the highest correlation within 13 to 15 days lag, while NO₂ within 3 to 6 days lag. Also, reductions in PM₁₀ and NO₂ were observed. This study shows that exposure to air pollution was significantly correlated with an increased risk of becoming infected and dying due to COVID-19. Thus, these results show that the NO₂ and PM₁₀ levels in CABA can serve as one of the indicators to assess vulnerability to COVID-19. In addition, decision-makers can use this information to adopt strategies to restrict human mobility during the COVID-19 pandemic and future outbreaks of similar diseases in CABA.

Gridded bottom-up inventories of CO₂ emissions are needed in global CO₂ inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO₂ emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1^∘ × 0.1^∘) monthly emissions fluxes of CO₂ fossil fuel (CO₂ff, long cycle) and CO₂ biofuel (CO₂bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO₂ff (36.7 Gt) and CO₂bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations. CoCO2-MOSAIC 1.0 is freely available at zenodo (https://doi.org/10.5281/zenodo.7092358; Urraca et al., 2023) and at the JRC Data Catalogue (https://data.jrc.ec.europa.eu/dataset/6c8f9148-ce09-4dca-a4d5-422fb3682389, last access: 15 May 2023; Urraca Valle et al., 2023).

This work studied the emission changes and their economic effects during the Argentina’s COVID-19 pandemic lockdown. We have analyzed the atmospheric emissions of the main greenhouse gases (GHG: CO₂, CH₄, and N₂O) and other pollutants (NOx, CO, NMVOC, SO₂, PM₁₀, PM_2.5, and BC) from various sectors such as private road transport, freight, public transport, agriculture machines, thermal power plants, residential, commercial, and governmental from January 2005 to April 2020. We focused on the months with the greatest restrictions of COVID-19 pandemic in Argentina (March and April 2020). The results show emissions reduction up to 37% for PM₁₀, PM_2.5, and BC, consistent with observed from satellite images and up to 160% for NOx, CO, NMVOC, and SOx. However, the residential sector has increased their emissions by 8% for the same period. As a consequence, 3337 Gg of CO_2eq of GHG emissions were reduced, corresponding to a 20% reduction compared to the same period in 2019. Besides, a 26% reduction in gross domestic product (GDP) was observed due to the COVID-19 pandemic. Our results show that each Tg of GHG reduction was associated to a 0.16% reduction of the GDP from the analyzed sectors. Thus, without a voluntary reduction in consumption associated to significant cultural and technological changes, reduction in GHG would still be associated with deepening inequalities and asymmetries between high and low consumption sectors (i.e., with better (lesser) education, health, and job opportunities), even within countries and cities.

Bromine released from the decomposition of short-lived brominated source gases contributes as a sink of ozone in the lower stratosphere. The two major contributors are CH₂Br₂ and CHBr₃. In this study, we investigate the global seasonal distribution of these two substances, based on four High Altitude and Long Range Research Aircraft (HALO) missions, the HIAPER Pole-to-Pole Observations (HIPPO) mission, and the Atmospheric Tomography (ATom) mission. Observations of CH₂Br₂ in the free and upper troposphere indicate a pronounced seasonality in both hemispheres, with slightly larger mixing ratios in the Northern Hemisphere (NH). Compared to CH₂Br₂, CHBr₃ in these regions shows larger variability and less clear seasonality, presenting larger mixing ratios in winter and autumn in NH midlatitudes to high latitudes. The lowermost stratosphere of SH and NH shows a very similar distribution of CH₂Br₂ in hemispheric spring with differences well below 0.1 ppt, while the differences in hemispheric autumn are much larger with substantially smaller values in the SH than in the NH. This suggests that transport processes may be different in both hemispheric autumn seasons, which implies that the influx of tropospheric air (“flushing”) into the NH lowermost stratosphere is more efficient than in the SH. The observations of CHBr₃ support the suggestion, with a steeper vertical gradient in the upper troposphere and lower stratosphere in SH autumn than in NH autumn. However, the SH database is insufficient to quantify this difference. We further compare the observations to model estimates of TOMCAT (Toulouse Off-line Model of Chemistry And Transport) and CAM-Chem (Community Atmosphere Model with Chemistry, version 4), both using the same emission inventory of Ordóñez et al. (2012). The pronounced tropospheric seasonality of CH₂Br₂ in the SH is not reproduced by the models, presumably due to erroneous seasonal emissions or atmospheric photochemical decomposition efficiencies. In contrast, model simulations of CHBr₃ show a pronounced seasonality in both hemispheres, which is not confirmed by observations. The distributions of both species in the lowermost stratosphere of the Northern and Southern hemispheres are overall well captured by the models with the exception of southern hemispheric autumn, where both models present a bias that maximizes in the lowest 40 K above the tropopause, with considerably lower mixing ratios in the observations. Thus, both models reproduce equivalent flushing in both hemispheres, which is not confirmed by the limited available observations. Our study emphasizes the need for more extensive observations in the SH to fully understand the impact of CH₂Br₂ and CHBr₃ on lowermost-stratospheric ozone loss and to help constrain emissions.

This work presents the integration of a gas-phase and particulate atmospheric emission inventory (AEI) for Argentina in high spatial resolution (0.025° × 0.025°; approx. 2.5 km × 2.5 km) considering monthly variability from 1995 to 2020. The new inventory, called GEAA-AEIv3.0M, includes the following activities: energy production, fugitive emissions from oil and gas production, industrial fuel consumption and production, transport -road, maritime and air-, agriculture, livestock production, manufacturing, residential, commercial and biomass + agricultural-waste burning. The following species, grouped by atmospheric reactivity, are considered: i) Greenhouse Gases (GHG): CO₂, CH₄ and N₂O; ii) Ozone Precursors: CO, NO_x (NO + NO₂) and Non-Methane Volatile Organic Compounds (NMVOC); iii) Acidifying Gases: NH₃ and SO₂; and iv) Particulate Matter (PM): PM₁₀, PM_2.5, Total Suspended Particle (TSP) and Black-Carbon (BC). The main objective of the GEAA-AEIv3.0M high-resolution emission inventory is to provide temporal resolved emission maps to support air quality and climate modeling oriented to evaluate pollutant mitigation strategies by local governments. This is of major concern especially in countries where air quality monitoring networks are scarce, and the development of regional and seasonal emissions inventories would result in remarkable improvements in the time + space chemical prediction achieved by air quality models. Despite distinguishing among different sectoral and activity databases as well as introducing a novel spatial distribution approach based on census radii, our high-resolution GEAA-AEIv3.0M show equivalent national-wide total emissions compared to the Third National Communication of Argentina (TNCA), which compiles annual GHG emissions from 1990 through 2014 (agreement within ±4 %). However, the GEAA-AEIv3.0M includes acidifying gases and PM species not considered in TNCA. Spatial and temporal comparisons were also performed against EDGAR HTAPv5.0 inventory for several pollutants. The agreement was acceptable within less than 30 % for most of the pollutants and activities, although a > 90 % discrepancy was obtained for methane from fuel production and fugitive emissions and > 120 % for biomass burning. Finally, the updated seasonal series clearly showed the pollution reduction due to the COVID-19 lockdown during the first quarter of year 2020 with respect to same months in previous years. Through an open access data repository, we present the GEAA-AEIv3.0M inventory, as the largest and more detailed spatial resolution dataset for the Argentine Republic, which includes monthly gridded emissions for 12 species and 15 sectors between 1995 and 2020. The datasets are available at http://dx.doi.org/10.17632/d6xrhpmzdp.1, under a CC-BY 4 license (Puliafito et al., 2021).