Rainwater quality is influenced by air pollutants and can affect sensitive ecosystems. This study was conducted to identify the sources of rainwater contamination in a receptor investigated in the southern part of Brazil. A total of 22 rainwater samples were collected at Florianópolis, Brazil. The sampling station is influenced by continental emissions (soil resuspension, traffic emissions and combustion) and marine aerosols. Over the sampling period, the average pH and electrical conductivity (EC) of the precipitation was found to be 4.97±0.41 and 14.68 μS cm⁻¹±13.47, respectively. In addition to pH and EC, ions and trace metals in the collected rainwater were quantified. The results were investigated by a combination of techniques including principal component analysis (PCA), a back trajectory model and other statistical and graphical interpretation methodologies. A PCA showed that Cl⁻, Na⁺, Mg²⁺ and part of the K⁺ and content were mainly contributed by marine aerosols, whereas the contribution from continental sources (combustion, traffic emissions and other urban activities) was dominant in the content of NO ₃⁻ and part of the and Mn content. Soil resuspension was responsible for the concentrations of most of the trace metals (apart from Mn) and Ca²⁺ in the rainwater. An inverse correlation among the elemental concentrations, amount of rainfall and wind speed was observed. The northern transport pathway was identified as being associated with high concentrations of NO ₃⁻ and slightly decreased pH values. However, the low standard deviation observed for the pH values during the sampling campaign also showed a small variation in the data, suggesting that the acidity is most probably being constantly sourced from a natural origin, such as organic acids.

Developing air quality management systems to control the impacts of air pollution requires reliable data. However, current initiatives do not provide datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Here, we introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database of air quality and its drivers in Brazil. BRAIN encompasses hourly datasets of meteorology, emissions, and air quality. The emissions dataset includes vehicular emissions derived from the Brazilian Vehicular Emissions Inventory Software (BRAVES), industrial emissions produced with local data from the Brazilian environmental agencies, biomass burning emissions from FINN – Fire INventory from the National Center for Atmospheric Research (NCAR), and biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (https://doi.org/10.57760/sciencedb.09858, Hoinaski et al., 2023a; https://doi.org/10.57760/sciencedb.09886, Hoinaski et al., 2023b). The meteorology dataset has been derived from the Weather Research and Forecasting Model (WRF) (https://doi.org/10.57760/sciencedb.09857, Hoinaski and Will, 2023a; https://doi.org/10.57760/sciencedb.09885, Hoinaski and Will, 2023c). The air quality dataset contains the surface concentration of 216 air pollutants produced from coupling meteorological and emissions datasets with the Community Multiscale Air Quality Modeling System (CMAQ) (https://doi.org/10.57760/sciencedb.09859, Hoinaski and Will, 2023b; https://doi.org/10.57760/sciencedb.09884, Hoinaski and Will, 2023d). We provide gridded data in two domains, one covering the Brazilian territory with 20×20 km spatial resolution and another covering southern Brazil with 4×4 km spatial resolution. This paper describes how the datasets were produced, their limitations, and their spatiotemporal features. To evaluate the quality of the database, we compare the air quality dataset with 244 air quality monitoring stations, providing the model's performance for each pollutant measured by the monitoring stations. We present a sample of the spatial variability of emissions, meteorology, and air quality in Brazil from 2019, revealing the hotspots of emissions and air pollution issues. By making BRAIN publicly available, we aim to provide the required data for developing air quality policies on municipal and state scales, especially for under-developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights into and opportunities for air pollution research in Brazil.​​​​​​​

In this article, we present the BRAzilian Vehicular Emissions inventory Software (BRAVES) database, a multispecies and high-spatiotemporal-resolution database of vehicular emissions in Brazil. We provide this database using spatial disaggregation based on road density, temporal disaggregation using vehicular flow profiles, and chemical speciation based on local studies and the SPECIATE 5.1 database from the United States Environmental Protection Agency (US EPA). Our BRAVES database provides hourly and annual emissions of 41 gaseous and particle pollutants, where users can define the spatial resolution, which ranges from a coarse to a very refined scale. Spatial correlation analysis reveals that the BRAVES database reaches better performance than the vehicular emissions inventory from the Emissions Database for Global Atmospheric Research (EDGAR). A comparison with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) surface concentration confirms the consistency and reliability of the BRAVES database in representing the spatial pattern of vehicular emissions. Compared to EDGAR, the BRAVES database brings more spatial, temporal, and chemical details. These additional features are crucial to understanding important atmospheric chemistry processes in Brazil. All codes and inputs are freely available, and the outputs are compatible with the input requirements of sophisticated chemical transport models. We envision that our database will enable the scientific and environmental community to gain new insights into vehicular emissions and their effects in Brazil, where emissions inventories are scarce and urgently needed. The BRAVES database is freely available at https://doi.org/10.5281/zenodo.6588692 (Hoinaski et al., 2022).