A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina

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 NOx 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).Fil: Diaz Resquin, Melisa. Centro de Ciencia del Clima y la Resiliencia; Chile. Comisión Nacional de Energía Atómica; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Lichtig, Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; ArgentinaFil: Alessandrello, Diego. Comisión Nacional de Energía Atómica; ArgentinaFil: De Oto, Marcelo. Comisión Nacional de Energía Atómica; ArgentinaFil: Gómez, Darío. Comisión Nacional de Energía Atómica; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Rossler, Cristina Elena. Comisión Nacional de Energía Atómica; Argentina. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; ArgentinaFil: Castesana, Paula Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica; Argentina. YPF - Tecnología; ArgentinaFil: Dawidowski, Laura Elena. Comisión Nacional de Energía Atómica; Argentina. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; Argentin

PAPILA dataset: A regional emission inventory of reactive gases for South America based on the combination of local and global information

The multidisciplinary project Prediction of Air Pollution in Latin America and the Caribbean (PAPILA) is dedicated to the development and implementation of an air quality analysis and forecasting system to assess pollution impacts on human health and economy. In this context, a comprehensive emission inventory for South America was developed on the basis of the existing data on the global dataset CAMS-GLOB-ANT v4.1 (developed by joining CEDS trends and EDGAR v4.3.2 historical data), enriching it with data derived from locally available emission inventories for Argentina, Chile, and Colombia. This work presents the results of the first joint effort of South American researchers and European colleagues to generate regional maps of emissions, together with a methodological approach to continue incorporating information into future versions of the dataset. This version of the PAPILA dataset includes CO, NOx, NMVOCs, NH3, and SO2 annual emissions from anthropogenic sources for the period 2014-2016, with a spatial resolution of 0.1gg×g0.1gover a domain that covers 32-120ggW and 34ggN-58ggS. The PAPILA dataset is presented as netCDF4 files and is available in an open-Access data repository under a CC-BY 4 license: 10.17632/btf2mz4fhf.3 . A comparative assessment of PAPILA-CAMS datasets was carried out for (i) the South American region, (ii) the countries with local data (Argentina, Colombia, and Chile), and (iii) downscaled emission maps for urban domains with different environmental and anthropogenic factors. Relevant differences were found at both country and urban levels for all the compounds analyzed. Among them, we found that when comparing PAPILA total emissions versus CAMS datasets at the national level, higher levels of NOx and considerably lower levels of the other species were obtained for Argentina, higher levels of SO2 and lower levels of CO and NOx for Colombia, and considerably higher levels of CO, NMVOCs, and SO2 for Chile. These discrepancies are mainly related to the representativeness of local practices in the local emission estimates, to the improvements made in the spatial distribution of the locally estimated emissions, or to both. Both datasets were evaluated against surface concentrations of CO and NOx by using them as input data to the WRF-Chem model for one of the analyzed domains, the metropolitan area of Buenos Aires, for summer and winter of 2015. PAPILA-based modeling results had a smaller bias for CO and NOx concentrations in winter while CAMS-based results for the same period tended to deliver an underestimation of these concentrations. Both inventories exhibited similar performances for CO in summer, while the PAPILA simulation outperformed CAMS for NOx concentrations. These results highlight the importance of refining global inventories with local data to obtain accurate results with high-resolution air quality models.Fil: Castesana, Paula Soledad. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; Argentina. Comisión Nacional de Energía Atómica; ArgentinaFil: Diaz Resquin, Melisa. Comisión Nacional de Energía Atómica; Argentina. Universidad de Buenos Aires; ArgentinaFil: Huneeus, Nicolás. Universidad de Chile; ChileFil: Puliafito, Salvador Enrique. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Darras, Sabine. Instituto Polytechnique de Toulouse; FranciaFil: Gómez, Darío. Comisión Nacional de Energía Atómica; Argentina. Universidad de Buenos Aires; ArgentinaFil: Granier, Claire. University of Colorado; Estados UnidosFil: Osses Alvarado, Mauricio. Universidad Técnica Federico Santa María; ChileFil: Rojas, Néstor. Universidad Nacional de Colombia; ColombiaFil: Dawidowski, Laura Elena. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación e Ingeniería Ambiental; Argentina. Comisión Nacional de Energía Atómica; Argentin