DOI: 10.57760/sciencedb.14560
title: Brazilian Atmospheric Inventories - BRAIN version 2: emission dataset in Brazil - 2020
title: Brazilian Atmospheric Inventories - BRAIN version 2: emission dataset in Brazil - 2020

description: BRAIN emissions dataset contains the emissions inventory from vehicles, industries, biomass burning, and biogenic emissions in Brazil from 2020. Full description of BRAIN database can be found in https://essd.copernicus.org/preprints/essd-2023-305/BRAIN uses the multispecies and high-spatiotemporal-resolution database vehicular emissions from Brazilian Vehicular Emission Inventory Software – BRAVES (Hoinaski et al., 2022; Vasques and Hoinaski, 2021). In BRAIN version 2, we used an updated version of BRAVES which properly disaggregate vehicular emissions in primary roads. Also, vehicular emissions database was derived using monthly fuel consuption by municipality to characterize the vehicular activity.We derived the industrial emissions inventory by combining data from the state environmental agencies of Espírito Santo, Minas Gerais, and Santa Catarina. The emission rates of point sources from Espírito Santo and Minas Gerais are publicly provided by Instituto de Meio Ambiente e Recursos Hídricos do Espírito Santo (IEMA-ES) (https://iema.es.gov.br/qualidadedoar/inventariodefontes) and Fundação Estadual de Meio Ambiente (FEAM) (http://www.feam.br/qualidade-do-ar/emissao-de-fontes-fixas). Data from IEMA-ES contains emissions from Vitória Metropolitan Region from 2015, compiling measurements from regulatory procedures and emissions estimates. In this version, we have also considered non-point sources emissions from IEMA-ES.Fire Inventory from NCAR (FINN) version 2.5 (Wiedinmyer et al., 2011) with GEOS-Chem chemical mechanisms provides data from biomass burning emissions in BRAIN. FINN outputs contain daily emissions of trace gas and particle emissions from wildfires, agricultural fires, and prescribed burnings and do not include biofuel use and trash burning. Datasets have 1km of spatial resolution and are available at https://www.acom.ucar.edu/acresp/MODELING/finn_emis_txt/. A Python code (https://github.com/barronh/finn2cmaq) temporally disaggregates daily emissions into hourly emissions. The same code vertically splits the fire emissions to consider the plume rise effect and represents the vertical distribution, converting text files into hourly 3D netCDF files.The Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 3.2 (Guenther et al., 2012) provides the biogenic emissions in BRAIN. MEGAN is based on the leaf area index and plant functional groups. The model estimates emissions of gases and aerosols for different meteorological conditions and land cover types. The leaf-level temperature and photosynthetically active radiation, as well as the vegetative stress conditions implemented in MEGAN, provide more physically realistic parameterizations for biosphere-atmosphere interactions. Input datasets, emission factor processors, and emission estimation modules are available at https://bai.ess.uci.edu/megan/data-and-code. Data from WRF and Meteorology-Chemistry Interface Processor (MCIP) have been used in MEGAN simulations.The present version of this database does not account for other South American countries' emissions, apart from biomass burning and biogenic sources.All files are in netCDF format with hourly temporal resolution and spatial resolution of 20x20km, covering the Brazilian territory.SM1 and SM2 contain details of modeling domains and outputs. In this dataset, we provide data from the d01 domain.Files are divided by simulation day and they are organized in the following folders:BRAVES = vehicular emissionsFINN = Fire/biomass burning emissionsIND = Industrial emissionsMEGAN = Biogenic emissions
description: BRAIN emissions dataset contains the emissions inventory from vehicles, industries, biomass burning, and biogenic emissions in Brazil from 2020. Full description of BRAIN database can be found in https://essd.copernicus.org/preprints/essd-2023-305/BRAIN uses the multispecies and high-spatiotemporal-resolution database vehicular emissions from Brazilian Vehicular Emission Inventory Software – BRAVES (Hoinaski et al., 2022; Vasques and Hoinaski, 2021). In BRAIN version 2, we used an updated version of BRAVES which properly disaggregate vehicular emissions in primary roads. Also, vehicular emissions database was derived using monthly fuel consuption by municipality to characterize the vehicular activity.We derived the industrial emissions inventory by combining data from the state environmental agencies of Espírito Santo, Minas Gerais, and Santa Catarina. The emission rates of point sources from Espírito Santo and Minas Gerais are publicly provided by Instituto de Meio Ambiente e Recursos Hídricos do Espírito Santo (IEMA-ES) (https://iema.es.gov.br/qualidadedoar/inventariodefontes) and Fundação Estadual de Meio Ambiente (FEAM) (http://www.feam.br/qualidade-do-ar/emissao-de-fontes-fixas). Data from IEMA-ES contains emissions from Vitória Metropolitan Region from 2015, compiling measurements from regulatory procedures and emissions estimates. In this version, we have also considered non-point sources emissions from IEMA-ES.Fire Inventory from NCAR (FINN) version 2.5 (Wiedinmyer et al., 2011) with GEOS-Chem chemical mechanisms provides data from biomass burning emissions in BRAIN. FINN outputs contain daily emissions of trace gas and particle emissions from wildfires, agricultural fires, and prescribed burnings and do not include biofuel use and trash burning. Datasets have 1km of spatial resolution and are available at https://www.acom.ucar.edu/acresp/MODELING/finn_emis_txt/. A Python code (https://github.com/barronh/finn2cmaq) temporally disaggregates daily emissions into hourly emissions. The same code vertically splits the fire emissions to consider the plume rise effect and represents the vertical distribution, converting text files into hourly 3D netCDF files.The Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 3.2 (Guenther et al., 2012) provides the biogenic emissions in BRAIN. MEGAN is based on the leaf area index and plant functional groups. The model estimates emissions of gases and aerosols for different meteorological conditions and land cover types. The leaf-level temperature and photosynthetically active radiation, as well as the vegetative stress conditions implemented in MEGAN, provide more physically realistic parameterizations for biosphere-atmosphere interactions. Input datasets, emission factor processors, and emission estimation modules are available at https://bai.ess.uci.edu/megan/data-and-code. Data from WRF and Meteorology-Chemistry Interface Processor (MCIP) have been used in MEGAN simulations.The present version of this database does not account for other South American countries' emissions, apart from biomass burning and biogenic sources.All files are in netCDF format with hourly temporal resolution and spatial resolution of 20x20km, covering the Brazilian territory.SM1 and SM2 contain details of modeling domains and outputs. In this dataset, we provide data from the d01 domain.Files are divided by simulation day and they are organized in the following folders:BRAVES = vehicular emissionsFINN = Fire/biomass burning emissionsIND = Industrial emissionsMEGAN = Biogenic emissions