This study presents a 2014 high-resolution spatially disaggregated emission inventory (0.025° × 0.025° horizontal resolution), of the main activities in the energy sector in Argentina. The sub-sectors considered are public generation of electricity, oil refineries, cement production, transport (maritime, air, rail and road), residential and commercial. The following pollutants were included: greenhouse gases (CO2, CH4, N2O), ozone precursors (CO, NOx, VOC) and other specific air quality indicators such as SO2, PM10, and PM2.5. This work could contribute to a better geographical allocation of the pollutant sources through census based population maps. Considering the sources of greenhouse gas emissions, the total amount is 144 Tg CO2eq, from which the transportation sector emits 57.8 Tg (40%); followed by electricity generation, with 40.9 Tg (28%); residential + commercial, with 31.24 Tg (22%); and cement and refinery production, with 14.3 Tg (10%). This inventory shows that 49% of the total emissions occur in rural areas: 31% in rural areas of medium population density, 13% in intermediate urban areas and 7% in densely populated urban areas. However, if emissions are analyzed by extension (per square km), the largest impact is observed in medium and densely populated urban areas, reaching more than 20.3 Gg per square km of greenhouse gases, 297 Mg/km2 of ozone precursors gases and 11.5 Mg/km2 of other air quality emissions. A comparison with the EDGAR global emission database shows that, although the total country emissions are similar for several sub sectors and pollutants, its spatial distribution is not applicable to Argentina. The road and residential transport emissions represented by EDGAR result in an overestimation of emissions in rural areas and an underestimation in urban areas, especially in more densely populated areas. EDGAR underestimates 60 Gg of methane emissions from road transport sector and fugitive emissions from refining activities.

Having a prediction model for air quality at a low computational cost can be useful for research, forecasting, regulatory, and monitoring applications. This is of particular importance for Latin America, where rapid urbanization has imposed increasing stress on the air quality of almost all cities. In recent years, machine learning techniques have been increasingly accepted as a useful tool for air quality forecasting. Out of these, random forest has proven to be an approach that is both well-performing and computationally efficient while still providing key components reflecting the nonlinear relationships among emissions, chemical reactions, and meteorological effects. In this work, we employed the random forest methodology to build and test a forecasting model for the city of Buenos Aires. We used this model to study the deep decline in most pollutants during the lockdown imposed by the COVID-19 (COronaVIrus Disease 2019) pandemic by analyzing the effects of the change in emissions, while taking into account the changes in the meteorology, using two different approaches. First, we built random forest models trained with the data from before the beginning of the lockdown periods. We used the data to make predictions of the business-as-usual scenario during the lockdown periods and estimated the changes in concentrations by comparing the model results with the observations. This allowed us to assess the combined effects of the particular weather conditions and the reduction in emissions during the period when restrictions were in place. Second, we used random forest with meteorological normalization to compare the observational data from the lockdown periods with the data from the same dates in 2019, thus decoupling the effects of the meteorology from short-term emission changes. This allowed us to analyze the general effect that restrictions similar to those imposed during the pandemic could have on pollutant concentrations, and this information could be useful to design mitigation strategies. The results during testing showed that the model captured the observed hourly variations and the diurnal cycles of these pollutants with a normalized mean bias of less than 6 % and Pearson correlation coefficients of the diurnal variations between 0.64 and 0.91 for all the pollutants considered. Based on the random forest results, we estimated that the lockdown implied relative changes in concentration of up to −45 % for CO, −75 % for NO, −46 % for NO₂, −12 % for SO₂, and −33 % for PM₁₀ during the strictest mobility restrictions. O₃ had a positive relative change in concentration (up to an 80 %) that is consistent with the response in a volatile-organic-compound-limited chemical regime to the decline in NO_x emissions. The relative changes estimated using the meteorological normalization technique show mostly smaller changes than those obtained by the random forest predictive model. The relative changes were up to −26 % for CO, up to −47 % for NO, −36 % for NO₂, −20 % for PM₁₀, and up to 27 % for O₃. SO₂ is the only species that had a larger relative change when the meteorology was normalized (up to 20 %). This points out the need for accounting not only for differences in emissions but also in meteorological variables in order to evaluate the lockdown effects on air quality. The findings of this study may be valuable for formulating emission control strategies that do not disregard their implication on secondary pollutants. We believe that the model itself can also be a valuable contribution to a forecasting system in the city and that the general methodology could also be easily applied to other Latin American cities as well. We also provide the first O₃ and SO₂ observational dataset in more that a decade for a residential area in Buenos Aires, and it is openly available at https://doi.org/10.17632/h9y4hb8sf8.1 (Diaz Resquin et al., 2021).