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Article  

• Volume 16, issue 5
• ESSD, 16, 2385–2405, 2024
• Peer review
• Related articles

Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil

Leonardo hoinaski, robson will, camilo bastos ribeiro.

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.​​​​​​​

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Hoinaski, L., Will, R., and Ribeiro, C. B.: Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil, Earth Syst. Sci. Data, 16, 2385–2405, https://doi.org/10.5194/essd-16-2385-2024, 2024.

It is consensus that air pollution threatens public health (OECD, 2024), economic progress (OECD, 2016), and climate (US EPA, 2023a). The negative outcomes associated with air pollution are not uniform within populations, and the impacts may be greater for more susceptible and exposed individuals (Makri and Stilianakis, 2008). Due to their social vulnerability and increasing emissions, developing countries urgently require reliable databases to provide information for designing air quality management systems to control air pollution (Sant'Anna et al., 2021).

Brazil has continental dimensions, is the seventh most populous country in the world, and has the 12th largest gross domestic product (IBGE, 2024). The combination of poorly planned development and the huge socioeconomic discrepancy has led to air quality impacts in Brazil. Air-pollution-related problems are not only restricted to great Brazilian cities and industrialized areas. Vehicular fleets and fuel consumption have also increased in small municipalities (CEIC, 2021; MME, 2023), posing a challenge to controlling vehicular emissions. In preserved and rural areas, large fire emissions have occurred due to illegal deforestation and poor soil management (Escobar, 2019; Rajão et al., 2020).

Following the practices of developed countries, Brazilian air quality policies have been enforced through legislative laws, using air quality standards as key components. However, the air quality management system remains incomplete in Brazil, with policies falling short of effectiveness due to a lack of air quality monitoring data across much of the country, primarily limited to well-developed areas (Sant'Anna et al., 2021). Moreover, Brazilian environmental agencies have not provided enough data and guidance to permit progress. Air quality consultants are still struggling to find mandatory inputs to understand and predict air quality for regulatory purposes. Efforts toward the permanent improvement of high-spatiotemporal-resolution emissions inventories and of meteorological and air quality data are needed.

An effective air quality management system must provide data to determine what emission reductions are needed to achieve the air quality standards (US EPA, 2023b). It requires air quality monitoring, a robust and detailed emissions inventory, reliable meteorological datasets, and methodologies to adapt the state-of-the-art air quality models to Brazil's reality. Moreover, it is crucial to undertake ongoing evaluation and to fully understand the air quality problem to design and implement the programs for pollution control. Currently, available initiatives including reanalysis and satellite products are still not providing datasets with large spatial and temporal resolutions for developing air pollution policies in Brazil. Global reanalyses such as the Copernicus Atmospheric Monitoring Service (CAMS) (Inness et al., 2019) and the second version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) (GMAO, 2015a, b) can provide estimates of air pollutants by combining chemical transport models (CTMs) with satellite and ground-based observations and physical information, assimilating data to constrain the results. However, the currently available reanalysis products do not provide data with high spatial resolution (up to 0.75°  ×  0.75° and 0.5°  ×  0.625°) and could be biased toward representing local and regional air quality (Arfan Ali et al., 2022). Moreover, they provide data for only a small list of air pollutants. Satellite-based products such as Sentinel-5P TROPOMI (Veefkind et al., 2012) and the Moderate-Resolution Imaging Spectroradiometer (MODIS) (Levy et al., 2015; Platnick et al., 2015) are still challenging due to their low temporal resolution, data gaps due to cloud coverage, and uncertainties (Shin et al., 2020). Besides, satellites rely on total tropospheric column measurements, which do not represent surface concentrations (Shin et al., 2019).

In this article, we present the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database to elaborate upon air quality management systems in Brazil. BRAIN combines local inventories, consolidated datasets, and the usage of internationally recommended models to provide hourly emissions and meteorological and air quality data covering the entire country.

Figure 1 Spatial distribution of CO emissions from (a) vehicles, (b) industries, (c) biomass burning, and (d) biogenic sources, as provided by BRAIN.

BRAIN contains three types of hourly datasets: emissions, meteorology, and air quality. The emissions inventories include vehicular, industrial, biogenic, and biomass burning emissions. We provide meteorological data derived from the Weather Research and Forecasting (WRF) model. Coupling emissions, WRF, and the Community Multiscale Air Quality Modeling System (CMAQ) version 5.3.2, we provide air quality gridded data. All datasets are available on two spatial resolutions: the largest (Fig. S1 in the Supplement – d01) covers the entire country, while the smallest covers southern Brazil (Fig. S1 – d02). The BRAIN datasets in d01 are freely available at https://doi.org/10.57760/sciencedb.09858 (Hoinaski et al., 2023a), https://doi.org/10.57760/sciencedb.09857 (Hoinaski and Will, 2023a), and https://doi.org/10.57760/sciencedb.09859 (Hoinaski and Will, 2023b). The BRAIN datasets in d02 are available at https://doi.org/10.57760/sciencedb.09886 (Hoinaski et al., 2023b), https://doi.org/10.57760/sciencedb.09885 (Hoinaski and Will, 2023c), and https://doi.org/10.57760/sciencedb.09884 (Hoinaski and Will, 2023d). The Federal University of Santa Catarina (UFSC) institutional repository, https://brain.ens.ufsc.br/ (last access: 8 May 2024​​​​​​​), and the web platform, https://hoinaski.prof.ufsc.br/BRAIN/ (last access: 8 May 2024​​​​​​​), have served the BRAIN database since 2019. We envision making available more detailed datasets for other Brazilian regions, especially in the southeast, where the anthropogenic emission effects are more prominent. Future versions will also provide more detailed modeling outputs to properly cover medium- and small-sized cities.

BRAIN is intended to fill the gaps in those cases where adequately representative monitoring data to characterize the air quality are not available. BRAIN would be useful in providing background concentrations for a good procedure for licensing new sources of air pollution.

2.1  Emissions inventory

The BRAIN emissions inventory allows the spatiotemporal analysis of vehicular, industrial, biomass burning, and biogenic emissions in Brazil. The present version of this database does not account for other South American countries' emissions, apart from biomass burning and biogenic sources. We envision implementing other sources and a more detailed emissions inventory from other South American countries in a future version. Figure 1 presents a sample of the inventory, showing the annual carbon monoxide (CO) emissions by source. Section S2 in the Supplement (Table S1) summarizes the species in each emission source inventory. More information on each emissions dataset can be found in Sect. 2.1.1 to 2.1.5.

We observed higher vehicular emission rates of CO in urban areas with large population and vehicle fleet densities, mainly in the south and southeast (Fig. 1a). High industrial emission rates have been detected in the Brazilian regions, with large stationary sources such as refining units, thermoelectric power plants, and cement and paper industries (Fig. 1b) (Kawashima et al., 2020). In general, the north shows a higher concentration of biogenic and fire emissions. While the hotspots of biogenic emissions are predominately in the extreme north, the hotspots of fire emissions turn up in the midwestern, northern, and southern regions, as well as on the Brazilian western border (Fig. 1c–d).

2.1.1  Vehicular emissions

BRAIN uses the multispecies and high-spatiotemporal-resolution database of vehicular emissions from the Brazilian Vehicular Emission Inventory Software (BRAVES) (Hoinaski et al., 2022; Vasques and Hoinaski, 2021). The BRAVES database disaggregates municipality-aggregated emissions using the road density approach and temporal disaggregation based on vehicular flow profiles. SPECIATE 5.1 (Eyth et al., 2020) from the United States Environmental Protection Agency (USEPA, https://www.epa.gov/air-emissions-modeling/speciate , last access: 8 May 2024) speciates conventional pollutants in 41 species. BRAVES considers local studies (Nogueira et al., 2015) and data from Companhia Ambiental do Estado de São Paulo (CETESB) ( https://cetesb.sp.gov.br/veicular/relatorios-e-publicacoes/ , last access: 8 May 2024) to speciate acetaldehydes, formaldehyde, ethanol, and aldehydes in order to account for biofuel particularities in Brazil.

In BRAVES, vehicular activity is defined by fuel consumption in each municipality using data provided by the Brazilian National Agency for Oil, Natural Gas and Biofuel (ANP) ( https://www.gov.br/anp/pt-br/centrais-de-conteudo/dados-abertos/vendas-de-derivados-de-petroleo-e-biocombustiveis , last access: 8 May 2024). A fraction of fuel consumed by road transportation is based on data from the National Energy Balance (BEN) ( https://www.epe.gov.br/pt/publicacoes-dados-abertos/publicacoes/balanco-energetico-nacional-ben , last access: 8 May 2024), and MMA (2014). BRAVES calculates the weighted emission factor (EF) to address the effect of the fleet composition in terms of category, model year, and fuel utilization.

Vasques and Hoinaski (2021) compared BRAVES with different vehicular emission inventories from a local to national scale. On a national scale, vehicular emission rates from BRAVES underestimate the Emission Database for Global Atmospheric Research (EDGAR) and are slightly higher for CO (14 %) and non-methane volatile organic compounds (NMVOCs) (9 %) compared with the national inventory from Ministério do Meio Ambiente (MMA). The differences between estimates from BRAVES and from well-developed state inventories vary from −1  % to 35 % in São Paulo and from −2  % to 52 % in Minas Gerais. In addition, a relatively small bias between BRAVES and the Vehicular Emission Inventory (VEIN) was observed in São Paulo and Vale do Paraiba (Vasques and Hoinaski, 2021).

2.1.2  Industrial emissions

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 , last access: 8 May 2024) and Fundação Estadual de Meio Ambiente (FEAM) ( http://www.feam.br/qualidade-do-ar/emissao-de-fontes-fixas , last access: 8 May 2024). Data from IEMA-ES contain emissions from the metropolitan region of Vitória from 2015, compiling measurements from regulatory procedures and emissions estimates. We did not convert the emissions inventory to the current modeling year since the data are not continuously updated. Therefore, we assumed that all emissions from these sources occurred in 2019.

In Santa Catarina, industrial emission data have been provided by Instituto de Meio Ambiente (IMA) ( https://www.ima.sc.gov.br/index.php , last access: 8 May 2024). These data are collected in the licensing process of potentially polluting industries. The base year of emission rates varies according to the availability. Summary information about the industrial sector types, the number of industries, and the respective emission rates in Santa Catarina can be found in Hoinaski et al. (2020) and at https://github.com/leohoinaski/IND_Inventory/blob/main/Inputs/BR_Ind.xlsx (last access: 8 May 2024​​​​​​​). Emissions from large stationary sources (refining units, thermoelectric power plants, cement, and paper industries) provided by Kawashima et al. (2020) have been included when not encountered in the environmental agencies' inventories.

We chemically speciated the industrial emission rates by adopting the following steps: (i) grouping each point source using the same categories as in the Emission Database for Global Atmospheric Research (EDGAR) (Crippa et al., 2018) and the Intergovernmental Panel on Climate Change (IPCC) industrial segments, (ii) selecting compatible profiles in SPECIATE 5.1 for each group (Eyth et al., 2020), (iii) averaging the speciation factor by group and pollutant, and (iv) applying the speciation factor for the targeted pollutant (PM, NO x , VOCs). The SPECIATE 5.1 profiles used in this work are listed at https://github.com/leohoinaski/IND_Inventory/tree/main/IndustrialSpeciation (last access: 8 May 2024​​​​​​​). The speciation factors by industrial group and pollutant are available at https://github.com/leohoinaski/IND_Inventory/blob/main/IndustrialSpeciation/IND_speciation.csv (last access: 8 May 2024​​​​​​​).

We also vertically allocate the industrial emissions according to the plume's effective height, estimated by the sum of the geometric height and the superelevation of the plume. The plume superelevation was estimated by the Briggs method (Briggs, 1975, 1969). The initial vertical distribution of the plume has been estimated by disaggregating the emissions using a Gaussian approach, as proposed in the Sparse Matrix Operator Kernel Emissions (SMOKE) model (Bieser et al., 2011; Gordon et al., 2018; Guevara et al., 2014). Python code to estimate the plume's effective height and the initial vertical disaggregation of industrial emissions is available at https://doi.org/10.5281/zenodo.11167115 (Hoinaski, 2024a​​​​​​​).

Figure 2 Annual average of meteorological variables in 2019, simulated by the WRF with 20  ×  20 km resolution. (a) Atmospheric pressure, (b) planetary boundary layer height, (c) specific humidity, (d) annual accumulated precipitation, (e) temperature, (f) wind intensity and direction. All variables are annual averages except for precipitation, which represents the annual accumulated total.

2.1.3  Biomass burning emissions

The Fire INventory from NCAR (FINN) version 1.5 (Wiedinmyer et al., 2011) provides data on 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 a 1 km spatial resolution and are available at https://www.acom.ucar.edu/Data/fire/ (last access: 8 May 2024​​​​​​​).

Since CMAQ requires hourly emissions, a Python code ( https://github.com/barronh/finn2cmaq , last access: 8 May 2024) 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 (Henderson, 2022), converting text files into hourly 3D netCDF files.

Pereira et al. (2016) suggest that fire emissions estimated by FINN are strongly related to deforestation in many Brazilian regions. FINN estimates have a high correlation with both the Brazilian Biomass Burning Emission Model (3BEM) (0.86) and the Global Fire Assimilation System (GFAS) (0.84). The emissions estimated from FINN are commonly overestimated in comparison to other biomass burning emission inventories. An overestimation also occurs when FINN is used in air quality models and compared with observations. However, the use of FINN as input in air quality models can capture the temporal variability of pollutants emitted by biomass burning (Vongruang et al., 2017).

We have implemented the FINN v1.5 in this first version of BRAIN. However, FINN version 2.5 (Wiedinmyer et al., 2023) will be included in our emissions inventory in future work; this version uses an updated algorithm for determining fire size based on MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite instruments. We also provide data from 2020 with the same modeling grid upgraded to FINN v2.5.

Figure 3 Spatial distribution of air pollutant concentration (a, c, e) and number of violations of air quality standards (b, d, f) for NO 2 (a–b) , O 3 (c–d) , and PM 10 (e–f) .

2.1.4  Biogenic emissions

We derived the biogenic emissions using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 3.2 (Guenther et al., 2012; Silva et al., 2020). 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 (Guenther et al., 2012). 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 (Silva et al., 2020). Input datasets, emission factor processors, and emission estimation modules are available at https://bai.ess.uci.edu/megan/data-and-code (last access: 8 May 2024​​​​​​​). Data from WRF and the Meteorology-Chemistry Interface Processor (MCIP) have been used in MEGAN simulations.

MEGAN is commonly adopted to estimate emissions from biogenic fluxes, which constitute an important input for air quality modeling in many regions worldwide (Hogrefe et al., 2011; Kitagawa et al., 2022; Kota et al., 2015). Although MEGAN overestimates nighttime biogenic fluxes, the modeled emissions are correlated with measurements in the Amazon during both wet and dry seasons. The model is capable of capturing relatively well the seasonal variability of important organic pollutants in tropical forests (Sindelarova et al., 2014).

2.1.5  Sea spray aerosol emissions

Sea spray aerosol (SSA) is an important source of particles in the atmosphere. Due to its properties, SSA influences gas–particle partitioning in coastal environments (Gantt et al., 2015). SSA has been implemented in CMAQ as an inline source and requires the input of an ocean mask file (OCEAN) to identify the fractional coverage in each model grid cell allocated to the open ocean (OPEN) or surf zone (SURF). CMAQ uses this coverage information to calculate sea spray emission fluxes from the model's grid cells (US EPA, 2022). Detailed information on the mechanism of sea spray aerosol emissions and its implementation in CMAQ can be found in Gantt et al. (2015).

We provide a Python code ( https://github.com/leohoinaski/CMAQrunner/blob/master/hoinaskiSURFZONEv2.py , last access: 8 May 2024) to reproduce the OCEAN time-independent Input/Output Applications Programming Interface (I/O API) ( https://www.cmascenter.org/ioapi/ , last access: 8 May 2024) file so that it is ready to use in CMAQ. This code uses a shoreline Environmental Systems Research Institute (ESRI) shapefile from the National Oceanic and Atmospheric Administration (NOAA), available at https://www.ngdc.noaa.gov/mgg/shorelines/ (last access: 8 May 2024​​​​​​​).

Figure 4 Spearman rank, bias, root mean squared error (RMSE), and mean absolute error (MAE) of O 3 dataset of BRAIN vs. observed values. Box plots of statistical metric by Brazilian state (considering only states with monitoring stations with representative data in 2019).

2.2  Meteorology

The WRF model has been used in this work to produce inputs for CMAQ and for meteorology characterization in Brazil. We provide hourly simulations in netCDF files. WRF has been set up to reproduce 36 h simulations, where the initial 12 h have been dedicated to model stabilization; these are excluded from the analysis. Thirty-three vertical levels have been employed, spaced at 50 hPa intervals. The parameterizations used in this work are described in Sect. S3. The remaining vertical levels followed a hybrid modeling scheme, accounting for terrain in the lower layers and gradually minimizing its influence at the higher levels. Details of WRF outputs can be found in Sect. S4 (Table S2).

The Global Forecast System (GFS) from the National Center for Atmospheric Research (NCAR) provided inputs with a spatial resolution of 0.25°  ×  0.25° and a temporal resolution of 6 h for the WRF simulations (Skamarock et al., 2008). Land use data and classification parameters are from the United States Geological Survey's (USGS) Moderate Resolution Imaging Spectroradiometer (MODIS).

Figure 5 Time series of O 3 and PM 10 modeled and measured at Limeira  (a, c, e) and CIPP  (b, d, f) monitoring stations.

Figure 6 Annual average concentration of CO from BRAIN with its original resolution (a) , from BRAIN regridded to MERRA-2 resolution (b) , and from MERRA-2 (c) and the difference between MERRA-2 and BRAIN (d) .

Table 1 BRAIN datasets freely available.

Download Print Version | Download XLSX

The Brazilian regions (north, northeast, midwest, southeast, and south) encompass three distinct climatic zones, namely the equatorial, tropical, and subtropical zones. The climatic diversity in Brazil is also shaped by topographical variations, landscape or vegetation, and the coastal areas. The temperature in Brazil follows a latitudinal pattern, increasing from south to north (Fig. 2e). The highest average temperatures are observed in the Amazon region, matching the historic data (Cavalcanti, 2016). The southern region exhibits the lowest average temperatures, which is also consistent with historical data (Cavalcanti, 2016).

The highest values of atmospheric pressure occurred in the northern region and in the extreme south of the country, and the lowest values were between the southeastern and southern regions (Fig. 2a). The planetary boundary layer height (PBLH) reaches the highest levels in the northeastern region and the lowest levels at the southern and southeastern coasts (Fig. 2b). The highest values of wind speed occurred in part of the northern and southern region. The Amazon region presented the lowest values of surface wind speed (Fig. 2f).

Humidity and precipitation exhibit similar patterns in the northern and northeastern regions (Fig. 2c, d) due to the trade winds that transport moisture from the tropical Atlantic (Mendonça and Danni-Oliveira, 2017). Except for the coast, the northeastern region is characterized by low humidity and drought during half of the year. The southern and southeastern regions have well-distributed rainfall throughout the year, as well as intermediate levels of humidity, except for the northern coast of the southern region, which has an elevated level of precipitation and humidity throughout the year.

The WRF model demonstrated the ability to reproduce diurnal and seasonal variability in winds in the Brazilian northeastern region (Souza et al., 2022a), although it underestimated the height of the planetary boundary layer (PBLH) by up to 20 %, as well as the temperature and humidity at 4 °C and 15 %, respectively. Pedruzzi et al. (2022) tested several model configurations, including an alternative land use scheme, and found a WRF tendency to overestimate temperature and humidity in the Brazilian southeastern region. Macedo et al. (2016) also evaluated the model's ability to predict extreme precipitation events. Although the WRF reasonably predicts the main meteorological aspects of the Brazilian southern region, the precipitation extremes were underestimated. A wind mapping study (Souza et al., 2022b) using WRF indicated that the average errors presented by the model in Brazil are minor, with an average bias of 2 m s −1 at 200 m in terms of wind intensity and errors at temperatures of 2 °C and humidity of approximately 10 %. Winds at lower levels tended to be overestimated, whereas PBLH was generally underestimated during the day.

Figure 7 Concentration of CO from BRAIN vs. MERRA-2 in Brazil (a) , northern Brazil (b) , northeastern Brazil (c) , midwestern Brazil (d) , southeastern Brazil (e) , and southern Brazil (f) .

2.3  Air quality

We coupled emissions inventories, WRF, and CMAQ to produce the BRAIN air quality dataset for Brazil. CMAQ version 5.3.2 was set up using the third version of the Carbon Bond 6 chemical mechanism (cb6r3_ae7_aq) (Yarwood et al., 2010; Emery et al., 2015) with AERO7 treatment of secondary organic aerosol for standard cloud chemistry (Appel et al., 2021). The other model configurations used in this work can be found in Sect. S5 and at https://github.com/leohoinaski/CMAQrunner (last access: 8 May 2024​​​​​​​). The pollutant list in CMAQ outputs, containing 216 species, can be found in Sect. S6 (Table S3).

The CMAQ standard profile of boundary conditions is used in the larger domain (d01), which provides the boundary conditions for the smaller one (d02). Further improvements to the database could include the boundary conditions derived from the GEOS-Chem model (Bey et al., 2001) ( https://geoschem.github.io/ , last access: 8 May 2024) or other better alternatives for the largest domain. The simulations have 24 h length and a time step interval of 1 h. The last hour of the previous simulation has been set up as the initial condition of the next one. We used the standard profile for the first hour of the first simulation (00:00:00 GMT on 1 January 2019). The figures with the spatial distribution and violations of criteria pollutants can be found in Sect. S7. Section S8 also presents the time series of criteria pollutants in Brazilian cities.

Using the BRAIN air quality dataset, we can observe the highest concentrations of NO 2 (Fig. 3a–b), O 3 (Fig. 3c–d), and PM 10 (Fig. 3e–f) in southeastern and southern Brazil. The concentration of O 3 violates the World Health Organization (WHO) air quality standards in multiple locations all over the country, while for NO 2 and PM 10 , this occurred mostly in southeastern and southern Brazil.

Figure 8 Annual average concentration of CO and NO 2 from BRAIN at its original resolution (a, c) and from Sentinel-5P TROPOMI spatially aligned to the BRAIN resolution (b, d) .

2.3.1  Models' performance

We sampled pixels around the monitoring station using a buffer of 0.5° to calculate the Spearman rank, bias, root mean squared error (RMSE), and mean absolute error (MAE) of the sampled pixels. We selected the highest Spearman rank of each pixel to demonstrate the model's performance in Figs. 4 and 5. Section S10 presents the box plots with overall statistical metrics for all stations. Section S11 presents statistical metrics by means of a monitoring station and pollutant, considering the pixel with the highest Spearman rank around each monitoring station. Section S12 presents the scatter plots comparing the BRAIN air quality dataset and the observations of each monitoring station. We used the simulations with domain d01 in the statistical analysis.

We observed the highest Spearman rank (0.72) in the state of São Paulo for O 3 concentration. Bias analysis revealed an underestimation in the São Paulo metropolitan area, while an overestimation occurred in Minas Gerais, Santa Catarina, Rio Grande do Sul, and the interior of São Paulo. In the northeast and in the state of Espírito Santo, bias is closer to zero. In Rio de Janeiro, the model over- and underestimated the observations. Regarding RMSE and MAE, the model performed better in coastal areas (maps in Fig. 4).

Comparing the states with air quality monitoring stations, the Spearman correlation of the O 3 dataset of BRAIN is higher in São Paulo, Minas Gerais, and Rio de Janeiro. However, these states also have a higher range of bias values, which could be negative and positive in São Paulo and Rio de Janeiro and are only positive in Minas Gerais (box plots in Fig. 4).

The heterogeneity in the stations' types and the insufficient spatial representativeness of observations in the Brazilian states must be considered while evaluating the model performance. According to the IEMA (2022), the strategic planning for the implementation of air quality monitoring stations, the financing and political efforts, and the technical characteristics (from installation to calibration and maintenance) vary significantly between Brazilian states. The lack of data quality assurance may compromise the credibility of the available air quality observations in Brazil.

BRAIN reproduced well the concentrations in moderately urbanized areas, such as Limeira and Piracicaba (Sect. S12). The database reached moderate performance in highly urbanized areas such as Copacabana and Rio de Janeiro (RJ) and at Marginal Tietê in the megacity of São Paulo (Sect. S12). Regarding the temporal profiles of O 3 and PM 10 , the seasonal and daily profiles are captured for both modeled pollutants, showing a suitable fit with the observations at the Limeira and Pecém Industrial and Port Complex (CIPP) air quality monitoring stations (Fig. 5). This reveals that the database can capture temporal patterns of air pollutant concentrations in urbanized and industrialized areas.

Figures with statistical metrics for other pollutants can be found in Sect. S13. Figures of modeled and observed time series for all monitoring stations can be found in Sect. S14.

Figure 9 Concentration of CO from BRAIN vs. Sentinel-5P TROPOMI in Brazil (a) , northern Brazil (b) , northeastern Brazil (c) , midwestern Brazil (d) , southeastern Brazil (e) , and southern Brazil (f) .

Overall, the average concentrations are well simulated by CMAQ in BRAIN, with fair to good correlations (up to ∼  0.7) between modeling and local measurements in São Paulo. Similar results have been reported by Albuquerque et al. (2018). Kitagawa et al. (2021) simulated PM 2.5 in a Brazilian coastal–urban area and showed that the CMAQ results commonly overestimated the observations, which agrees with the BRAIN air quality dataset. In another comparison between observations and CMAQ simulations (Kitagawa et al., 2022), the model overestimated the PM and NO 2 concentrations in the metropolitan region of Vitória (MRV) and underestimated O 3 . The authors suggest that the CMAQ simulations are suitable over the MRV, even though the model could not capture some local variabilities in air pollutant concentrations. It is already reported that the short-time abrupt variations are difficult to reproduce by means of air quality models (Albuquerque et al., 2018). The complex task of predicting air quality is associated with multiple error factors, including the lack of an emissions inventory, meteorology parameterizations, initial and boundary conditions, chemical mechanisms, numerical routines, etc. (Cheng et al., 2019; Albuquerque et al., 2018; Park et al., 2006; Pedruzzi et al., 2019).

Figure 10 Annual average and hourly time series of CO from BRAIN (a) , MERRA-2 (b) , and Sentinel-5P TROPOMI (daily averages) (c)  in Porto Velho, Brazil.

We analyzed the performances of 4×4  km simulations for CO, NO 2 , O 3 , and SO 2 , drawing a buffer of 0.5° degrees around monitoring station positions in southern Brazil. Our findings indicated higher Spearman values for the spatial resolution of 20×20  km for CO, O 3 , and SO 2 . Specifically, for O 3 , the best result at 20×20  km was 0.76, whereas the same point at 4×4  km resolution showed a correlation of 0.46. This pattern was also observed for CO, with the best result at 20×20  km being a Spearman value of 0.47 and 0.23 at the same point at 4×4  km resolution. The smallest differences in Spearman rank were observed for SO 2 (0.22: 20×20 , 0.19: 4×4 ). Even though improving the spatial resolution did not increase the correlation with measured data, we found the best results for bias, RMSE, and MAE for almost all pollutants at a 4×4  km resolution, except for CO. Please refer to Sect. S15 for the complete statistical analysis of 4×4  km simulations.

BRAIN captures seasonal patterns and the absolute magnitude of PM 2.5 in the northwest of the Amazonas state (near the Amazon Tall Tower Observatory – ATTO), as presented by Artaxo et al. (2013). This shows that our database can reproduce the concentrations in background areas (far from highly urbanized centers). Comparing BRAIN with observations at heavily biomass-burning-impacted sites in southwestern Amazonia (Porto Velho) (Artaxo et al., 2013) revealed that BRAIN can capture seasonal variations caused by wet and dry seasons, as well as the magnitude of average and peak concentrations. However, BRAIN PM 2.5 estimates are closer to the coarse mode of the time series rather than the fine mode shown in Artaxo et al. (2013). Even though BRAIN has captured the O 3 pattern observed by Artaxo et al. (2013), the estimates are around 2.7 times higher than the observations in the dry season and a factor of 2 higher for the wet season. It is worth mentioning that BRAIN uses 2019 data, while the study by Artaxo et al. (2013) consists of a sampling campaign from 2008 to 2012.

Figure 11 Scatter plot and daily time series of CO (a) , O 3 (b) , and NO 2 (c) from BRAIN and Sentinel-5P TROPOMI at T0a (GoAmazon reference). Values extracted using a buffer of 0.2° around the site.

Figure 12 Scatterplot and daily time series of CO, O 3 , and NO 2 from BRAIN and Sentinel-5P TROPOMI at T0t/TT34 (GoAmazon reference). Values extracted using a buffer of 0.2° around the site.

BRAIN has a similar spatial pattern compared with MERRA-2 (GMAO, 2015a b), capturing hotspots in higher populated areas located in the southeast, south, and midwest. In the Amazon region, BRAIN can also capture hotspots similarly to MERRA-2 (Fig. 6). BRAIN estimates for carbon monoxide are lower than those of MERRA-2, except in the southern region and in some urban centers in the southeast and midwest (Fig. 6). Carbon monoxide concentrations estimated by BRAIN are moderately correlated with MERRA-2, mainly in the south (0.57) and southeast (0.55), while in the midwest, north, and northeast, the correlation is weaker (Fig. 7). Compared with the consolidated MERRA-2 database, BRAIN has the advantage since it uses local and more refined information and provides data at a higher spatial resolution for multiple species. We provide a detailed comparison between the MERRA-2 and BRAIN datasets for PM 2.5 , SO 2 , O 3 , and CO in Sect. S16.

We also compare our database with Sentinel-5P TROPOMI (Veefkind et al., 2012) data to demonstrate BRAIN's ability to capture the spatiotemporal variability of air pollutants in unmonitored areas (Fig. 8). We spatially realign Sentinel-5P TROPOMI products to the BRAIN resolution ( 20×20  km) using data from the NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC) ( https://disc.gsfc.nasa.gov/ , last access: 8 May 2024). We merged all layers of the same day and interpolated them to match the BRAIN resolution. We computed the daily averages for both datasets. In this evaluation, we must consider the differences between the datasets since Sentinel-5P TROPOMI relies on tropospheric column measurements and BRAIN surface concentrations. BRAIN captured the hotspots of CO and NO 2 similarly to Sentinel-5P TROPOMI products, especially in southeastern Brazil. However, the hotspots of CO are dislocated towards the ocean in Sentinel-5P TROPOMI. NO 2 estimates from BRAIN present a higher number of hotspots. We emphasize that surface concentration data are more suitable than tropospheric column data in representing air quality. In this analysis, we removed negative values from Sentinel-5P TROPOMI products since they represent low-quality measurements (Eskes et al., 2022).

When we compared CO daily datasets from BRAIN and Sentinel-5P TROPOMI by Brazilian regions, we observed a moderate correlation in the north (0.41), midwest (0.32), and south (0.3). This analysis shows that BRAIN can reasonably detect temporal and spatial patterns of air pollutants. The complete comparison of CO and NO 2 from Sentinel-5P TROPOMI and BRAIN can be found in Sect. S17.

Figure 13 Scatter plot and daily time series of CO (a) , O 3 (b) , and NO 2 (c) from BRAIN and Sentinel-5P TROPOMI at T1 (GoAmazon reference). Values extracted using a buffer of 0.2° around the site.

We highlight that BRAIN, MERRA-2, and Sentinel-5P TROPOMI can capture similar temporal patterns of air pollutant concentrations in heavily biomass-burning-impacted sites such as Porto Velho in Rondônia (Fig. 10) and in urban areas such as São Paulo. We provide time series (minimum–maximum and average) of BRAIN, MERRA-2, and Sentinel-5P TROPOMI data spatially averaged within Brazilian capitals in Sect. S18. Section S19 contains time series (only average) of BRAIN data in Brazilian capitals.

To analyze BRAIN's performance in background regions (with low anthropogenic influence), we extracted data from two forested sites in the Brazilian northern region. As reference, we used the sampling sites of the GoAmazon experiment (Martin et al., 2016), named T0a (forested site situated 154.1 km from the Manaus urban area) and T0t/TT34 (a broken-canopy forested site situated 60.9 km from the city of Manaus). Sentinel-5P TROPOMI data spatially aligned to the BRAIN resolution were also extracted for comparison. A buffer of 0.2° around the sites selected the data of CO, O 3 , and NO 2 from both datasets. The results revealed that BRAIN captured the seasonal profile at T0a (Fig. 11), showing a moderate correlation with the tropospheric column measurements of Sentinel-5P TROPOMI, especially for CO and O 3 .

BRAIN estimates are slightly higher than the observed concentrations in background areas of CO, O 3 , and NO 2 in TT34 (Fig. 12) and T0a (Fig. 11). While O 3 concentrations simulated by BRAIN range around 18 ppb (average in 2019) at the TT34 site, observed concentrations in 2013 (Artaxo et al., 2013) were around 8.5 ppb  ±  1.9 ppb. In T0a, BRAIN simulated concentrations around 16 ppb, overestimating the observations (7 ppb  ±  2 ppb during the wet season from March to April 2013–2020) (Nascimento et al., 2022). Concerning CO, the concentrations simulated by BRAIN are slightly lower, ranging around 73 ppb (average) at TT34 compared to the 130 ppb observed during the GoAmazon experiment from 2010 to 2011 (Artaxo et al., 2013). We emphasize that the BRAIN and GoAmazon datasets are reported in different periods and, consequently, are influenced by different emission rates. For instance, fire emissions have changed significantly since 2011 in the Amazon (Copernicus, 2022; Naus et al., 2022).

We also analyzed BRAIN results in the Manaus urban area. We adopted the sampling site of the GoAmazon experiment (Martin et al., 2016) named T1 (INPA campus in Manaus). Compared with Sentinel-5P TROPOMI data, BRAIN reproduced fairly the temporal pattern of CO, O 3 , and NO 2 in the T1 site (Fig. 13). Abou Rafee et al. (2017) reported mean concentrations of 88.7 ppb for NO x and 382.6 pbb for CO in the Manaus urban area, while BRAIN reached 79 and 99 ppb (maximum of 383 ppb), revealing an underestimation in this area. Again, the sampling campaign presented by Abou Rafee et al. (2017) and the BRAIN simulations use different base years. Comparing BRAIN at T0a/TT34 (background sites) and T1 (urbanized), the database has reached consistent results, with lower concentration levels in preserved areas.

The inability to better predict the observations is mostly due to the quality of the emissions inventory. The lack of information on industrial emissions and their temporal variability is an important source of errors. Moreover, the vehicular emissions inventory also needs improvements to properly disaggregate the emissions in high-flow roads. Future versions of BRAIN could address these issues and incorporate other emission sources.

Emission data are available at https://doi.org/10.57760/sciencedb.09858 (Hoinaski et al., 2023a) and https://doi.org/10.57760/sciencedb.09886 (Hoinaski et al., 2023b). Meteorology data are available at https://doi.org/10.57760/sciencedb.09857 (Hoinaski and Will, 2023a) and https://doi.org/10.57760/sciencedb.09885 (Hoinaski and Will, 2023c). Air quality data are available at https://doi.org/10.57760/sciencedb.09859 (Hoinaski and Will, 2023b) and https://doi.org/10.57760/sciencedb.09884 (Hoinaski and Will, 2023d).

Code to generate the database, statistics, and figures is available at https://github.com/leohoinaski/CMAQrunner (last access: 8 May 2024) or https://doi.org/10.5281/zenodo.11166975 (Hoinaski, 2024b) and https://github.com/leohoinaski/IND_Inventory (last access: 8 May 2024) or https://doi.org/10.5281/zenodo.11167115 (Hoinaski, 2024a).

In this paper, we present BRAIN, the first comprehensive database for air quality management in Brazil. BRAIN provides emissions, meteorology, and air quality datasets for the entire country at a reliable spatiotemporal resolution. The BRAIN database covers a wide range of pollutant species (emissions and ambient concentrations) and atmospheric variables. So far, Brazil has lacked a comprehensive and easily accessible database for developing air quality management systems in urbanized and rural areas. This work contributes to overcoming this gap. BRAIN is a step forward toward a good procedure for licensing new sources of air pollution in Brazil.

Using a sample of BRAIN, we observed several violations of WHO air quality recommendations. The violations are not restricted to densely populated areas but also occur in rural ones. This reinforces the need for better air quality policies and a deep restructuring of the environmental agencies' procedures and data management in Brazil.

Compared with observations, the BRAIN air quality dataset has achieved good overall performance in predicting the criteria pollutants. However, there is plenty of room for improvement, mainly in relation to the quality of the emissions inventory. The lack of information on industrial emissions and their temporal variability is an important source of error. Moreover, the vehicular emissions inventory also needs improvements to properly disaggregate the emissions in high-flow roads. Improvements in boundary conditions and the inclusion of emission sources from other Latin American countries could also enhance the CMAQ performance. The influence of long-range transport will be addressed in a future version of the database by implementing boundary contributions from GEOSCHEM and other tools. Future versions of BRAIN could address these issues, incorporate other emission sources, and provide CMAQ outputs using different chemical mechanisms. We envision providing enough data to reproduce the historical pattern and future scenarios of air pollution in Brazil through a web platform to facilitate the access and usage of our database. We believe in an ongoing process that will improve the database.

The supplement related to this article is available online at:  https://doi.org/10.5194/essd-16-2385-2024-supplement .

LH designed the methodology and developed the software. LH, RW, and CBR processed the data curation, conducted the formal analysis, and created the figures. LH, RW, and CBR prepared the original draft and revised the paper. LH is the project administrator and laboratory supervisor.

The contact author has declared that none of the authors has any competing interests.

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

The authors would like to thank the Secretaria de Estado do Desenvolvimento Econômico Sustentável do governo de Santa Catarina. The authors are grateful for the doctoral scholarships provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES).

This research has been supported by the Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (grant no. 2018/TR/499; “Avaliação do impacto das emissões veiculares, queimadas, industriais e naturais na qualidade do ar em Santa Catarina”).

This paper was edited by Jing Wei and reviewed by two anonymous referees.

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• Introduction
• BRAIN database
• Data availability
• Code availability
• Author contributions
• Competing interests
• Acknowledgements
• Financial support
• Review statement

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• Published: 17 April 2020

High resolution temporal profiles in the Emissions Database for Global Atmospheric Research

• Monica Crippa 1 ,
• Efisio Solazzo 1 ,
• Ganlin Huang 2 ,
• Diego Guizzardi 1 ,
• Ernest Koffi 1 ,
• Marilena Muntean 1 ,
• Christian Schieberle 2 ,
• Rainer Friedrich 2 &
• Greet Janssens-Maenhout   ORCID: orcid.org/0000-0002-9335-0709 1  

Scientific Data volume  7 , Article number:  121 ( 2020 ) Cite this article

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Emissions into the atmosphere from human activities show marked temporal variations, from inter-annual to hourly levels. The consolidated practice of calculating yearly emissions follows the same temporal allocation of the underlying annual statistics. However, yearly emissions might not reflect heavy pollution episodes, seasonal trends, or any time-dependant atmospheric process. This study develops high-time resolution profiles for air pollutants and greenhouse gases co- emitted by anthropogenic sources in support of atmospheric modelling, Earth observation communities and decision makers. The key novelties of the Emissions Database for Global Atmospheric Research (EDGAR) temporal profiles are the development of (i) country/region- and sector- specific yearly profiles for all sources, (ii) time dependent yearly profiles for sources with inter-annual variability of their seasonal pattern, (iii) country- specific weekly and daily profiles to represent hourly emissions, (iv) a flexible system to compute hourly emissions including input from different users. This work creates a harmonized emission temporal distribution to be applied to any emission database as input for atmospheric models, thus promoting homogeneity in inter-comparison exercises.

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12052887

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Background & summary.

Owing to the increasing need of integrated climate and air quality policies, implementation of mitigation and adaptation measures and the application of top-down approaches using Earth observations to address environmental issues, real-time mapping of human emissions of greenhouse gases (GHGs) and air pollutants is becoming of high relevance 1 , 2 , 3 . In this respect, annual emission estimates might be unable to reflect acute heavy pollution episodes 4 , and to model the dynamics of atmospheric formation of pollution loadings during different periods of the year and different hours of the day 5 . Even moderate total annual emissions for a certain region can be affected by periodic intensive emissions. Temporally disaggregated emissions are also essential to estimate surface emission fluxes of atmospheric composition (including reactive gases and greenhouse gases), and are a required input for advanced chemical transport models (CTMs) 6 , 7 , which simulate hourly concentrations of air pollutants 8 , 9 , 10 and are used in support of the legislation.

Human activities emit greenhouse gases and air pollutants with different temporal variation, depending on the type of activity. A review of temporal profiles from literature and profiles used by atmospheric models is conducted, despite the relatively limited amount of studies in this field. Few studies focus on the monthly variations of the emissions 4 , 11 , 12 , while little attention has been paid to daily and hourly variations. In addition, the spatial and sectorial resolutions of relevant works are also limited, often covering only a specific region or sector 8 , 13 , 14 . Until recently, global emission inventories mainly provide anthropogenic emissions on annual or monthly time series 15 , 16 , 17 , 18 , 19 .

In this study, sector- and country-specific temporal profiles (EDGAR_temporal_profiles_r1, available at figshare) 20 are developed and integrated into the EDGAR database to produce monthly and hourly emission time series and gridmaps. The novelty of this work relies on the development of (i) country/region- and sector- specific yearly splitting profiles for all EDGAR emissions, (ii) time dependent yearly profiles for emission sectors with inter-annual variability of their seasonal patterns, (iii) country-specific weekly and daily profiles to represent the hourly variations of the emissions, taking into account country specific holidays and weekends definition, (iv) a flexible system to compute hourly emissions including input from different users. The EDGAR temporal profiles can be applied to any air pollutant (SO 2 , NOx, CO, NMVOC, NH 3 , PM 10 , PM 2.5 , BC, OC) and GHG (CO 2 , CH 4 , N 2 O) since they have been developed for all anthropogenic emissions sources which often co-emit a variety of pollutants. As an exemplification, in this work we also compute monthly emission time series of EDGARv4.3.2 for all pollutants (CO 2 , CH 4 , N 2 O, SO 2 , NOx, CO, NMVOC, NH 3 , PM 10 , PM 2.5 , BC, OC). The EDGAR temporal profiles system is easily updated when new information is available and can be applied to any global and regional database.

As applications to modelling and policy related fields, we would like to point out that:

EDGAR is used as default emission inventory in air quality modelling (e.g. Hemispheric Transport of Air Pollution (HTAP_v1 and HTAP_v2) 21 , FP7 PEGASOS 15 , etc.) and model intercomparisons (e.g. HTAP, AQMEII, EURODELTA, etc. 7 ). In particular, global and regional models employ their own emission time distribution, giving rise to heterogeneity of results and difficult interpretation of model differences. Using a common emission temporal distribution could promote homogeneity in intercomparison exercises.

EDGAR emission grids are used by the Global Carbon Project as a-priori fluxes to run inverse atmospheric modeling 22 . With the increased interest in the monitoring and verification of GHGs using top-down measurements temporal profiles are equally needed.

EDGAR can support analysis in the agricultural sector providing more accurate information to assess impacts on crops 23 .

EDGAR is widely used in support of policy design, treaty compliances, Intergovernamental Panel on Climate Change (IPCC) and emission verification ( http://verify.lsce.ipsl.fr/ ). High time resolution emissions enhance the monitoring capability of EDGAR by adding seasonal variability to identify more targeted intervention at regional and global scale 8 , 24 .

This work is mainly relevant for policy makers looking at the hemispheric transport of air pollution where the global and regional picture of the emissions is needed to model heavy pollution events not only affected by local sources but also by transported pollution. Local authorities focusing on local pollution events might complement the EDGAR data with local (city or province scale) emission inventories.

This section describes (i) how higher temporally distributed emissions (total country and sectorial emissions as well as spatially distributed emissions) are derived from annual emissions, (ii) how yearly, weekly and daily profiles (in terms of weighting factors) are developed.

General approach to distribute annual emissions to high time resolution data

The general approach to account for the temporal variation of emissions is to distribute the annual total to monthly, daily and hourly emissions using yearly (12 coefficients for monthly variation in a year), weekly (7 coefficients for daily variation in a week), and daily (24 coefficients for hourly variation in a day) profiles. Temporal disaggregation of emission data may increase the range of data uncertainty as precise estimate of monthly, daily, and hourly distributions of emissions is quite complex and heterogeneous, in particular when operating at the global scale 25 . However, most of these studies are limited in scope with regard to the coverage in emission sources, time frames, and geographical regions.

Till present, the EDGAR database provides annual and monthly sector- and country- specific emission time series and maps but with some limitations that we are overtaking with this study. The ‘Online-only Table  1 ’ provides an overview of sector specific yearly profiles previously used by EDGAR 16 and the current work (EDGAR_temporal_profiles_r1 20 ). In Janssens-Maenhout et al . 16 the distribution of yearly emissions to monthly data is mainly based on regional seasonal factors obtained from scientific literature (see ‘Online-only Table  1 ’) which are applied to all world countries based on their regional belonging (the Northern and Southern Hemispheres and the Equator). For the Southern Hemisphere, the Northern Hemisphere profiles were assumed shifted by six months, and for the countries in the equatorial region no seasonality was included (scaling factors of 1).

In this study, the most appropriate temporal profile for each EDGAR process is identified through a quality assessment procedure. All EDGAR processes (e.g. energy, industry, residential, transport, agriculture, etc., for a total number of 227 processes) and all countries (226 countries over the globe) are covered and allow the disaggregation of annual emissions over time for all co-emitted air pollutants and greenhouse gases by the same sources. The temporal profiles developed in this work can be applied to any greenhouse gas or air pollutant primarily co-emitted in the atmosphere by any IPCC emission reporting category (with the exception of Land Use, Land Use Change and Forestry (LULUCF) which is currently not included in EDGAR and in the temporal profiles). Particulate matter emissions from e.g. fireworks, wind erosion in agriculture, resuspension, etc. are not included in the EDGAR emission estimates and their seasonal pattern might need to be described through atmospheric models.

Combining annual emissions and temporal profiles, monthly and hourly disaggregated emission data for different sources and countries are generated for a representative year 2005 when no information over historic time series is available. The 2005 year is selected for this purpose because it is a relatively recent year without anomalies (e.g. in terms of climate, economy, etc.) and is a base year for many mitigation measures (e.g. Nationally Determined Contributions under the Paris Agreement).

Yearly, weekly, and daily profiles are integrated into the EDGAR database in order to disaggregate annual emissions into finer data; to distribute the annual emissions to hourly emissions per grid the following relationship is used:

E = Emissions;

x = Country, sector, year and month specific activity;

y = Country, sector and day specific activity;

z = Country, sector, day, hour and time zone specific activity;

n = month- and year- specific number of days;

i = grid code (lon/lat);

s = Sector;

h = hour (from 1 to 24);

c = Country;

m = Month (from 1 to 12);

d = Weekday;

t = Time zone

Computing monthly emissions: region and country mapping

In order to distribute yearly emissions to monthly data, all world countries are grouped into 23 regions for which region-specific yearly profiles are defined. Regional yearly profiles are defined mainly based on three parameters: i) climate zones, ii) heating degree days (HDD), and iii) ecological zones, defined as following:

Seasonal cycles are different for the different climate zones (e.g. equator (band between ±30°N), Northern (above 30°N) and Southern Hemispheres (below 30°S)), consequently affecting the seasonal variation of the activities and emissions.

Weather conditions strongly affect the energy consumption and emissions, especially extremes of temperatures. HDD is the cumulative number of degrees by which the mean daily temperature falls below a given temperature called the “reference temperature” (usually 18 °C or 19 °C which is adequate for human comfort). A “degree day” is calculated as the difference between the reference temperature and the average of the maximum and minimum temperature over the day. HDD is regarded as a reliable indicator for appropriately accounting for the effect of weather on energy demand. Based on a review of HDD data sources, HDD data are collected from the CMCC-KAPSARC degree days database 26 , which provides average HDD over the last decades for 147 countries. For countries which are not included in the CMCC-KAPSARC database, extrapolation is made considering mainly geographical proximity.

Temporal variation of activities and emissions can also be different among various ecological zones, especially for agriculture and biomass burning. The Food and Agriculture Organization of the United Nations (FAO) defines ecological zones considering climatic variables such as mean 24-hour temperature, diurnal temperature range, sunshine fraction, wind speed, relative humidity, wet day frequency and precipitation 27 .

Based on the above three parameters, all countries are grouped into 23 regions for yearly profiles mapping, as shown in Fig.  1 . The definition of the regions is also listed in the file EDGAR_temporal_profiles_r1.xls.

Regional aggregation of world countries for yearly profiles mapping.

Computing weekly, daily and hourly emissions

The final stage of the methodology to disaggregate emissions in time is the production of hourly emissions at the global scale in form of total and/or sector specific emissions and gridded data. Weekend days, holidays, and time zone offset can be different for different countries and have relatively big influence on weekly and daily profiles. Therefore, to integrate weekly (daily share) and daily profiles (hourly share), the information on the day type (as defined in Table  1 ) of each day should be considered, since the hourly variation of the activities for certain sectors (e.g. transport) can be different on a weekday and on a holiday in different countries.

The definition of the weekend days is also different among countries. Weekend types with different weekend days are defined for all world countries and mapped into EDGAR. Globally, there are six weekend types which are included in the EDGAR model, as specified by Table  2 . Fixed and variable holidays are also compiled for all the countries over the 1970-nowadays time series. Coupling all these information with country- specific weekly and daily profiles into EDGAR, hourly emission time series and grid maps are produced.

In addition, to integrate the time zone information into EDGAR, time zone boundaries are extracted from time zone boundary builder ( https://timezonedb.com/download ). Country code, offsets from UTC, and summertime shift (from 1970 till nowadays, https://github.com/evansiroky/timezone-boundary-builder , https://www.timeanddate.com/time/dst/2005.html ) are identified and compiled for all time zone regions. The development of such a system allows the representation of the hourly emissions during a specific day or heavy pollution episode in one global map.

Development of temporal profiles for anthropogenic emissions

The basis of our work is the IER (Institute of Energy Economics and Rational Energy Use) database of temporal profiles to distribute the annual emissions from EDGAR, since it includes a large number of source- and country- specific temporal profiles developed within different studies 28 , 29 , 30 , 31 , 32 , 33 and applied in several projects 34 , 35 , 36 , 37 , 38 . A review of temporal profiles from other studies and used by atmospheric models was also conduced. There are relatively limited amount of studies in this field. Studies have more focus on the monthly variations of emissions 4 , 11 , 12 , while little attention has been paid to daily and hourly variations 39 , 40 . The spatial and sectoral resolutions of relevant studies are also limited, often only for a specific region or sector 8 , 13 , 41 . Comparison of the IER database with the temporal profiles used by certain atmospheric models and other emission inventories shows good agreement across sectors, and a higher sector and region resolution in the IER database 42 .

The approach of deriving temporal profiles is based on statistical data sets (e.g. Eurostat, ENTSO-E, UN monthly bulletin, etc.) that can be used as proxy data for the temporal profile computation. The IER temporal profiles database covers the following main sectors: energy industry, fuels transformation/non-energy use, combustion in manufacturing industry, non-metallic mineral processes, chemical processes, metal processes, international and domestic aviation (distinguishing between cruise, climb and descent, take-off and landing), road transportation, non-road ground transport, international and domestic shipping; residential combustion, oil production and refineries, solvent use, agriculture, solid waste disposal, fossil fuel fires, large scale biomass burning.

Table  3 provides an overview of the indicator data used to represent the drivers of the temporal variations of activities and emissions of significant sources in the IER database. For example, fuel use and temperature are the main indicators for monthly variations of activities in power plants, industrial and small combustion plants. Daily and hourly variations of industrial activities are indicated by working times, time shifts and holidays. Temporal variations of transport activities are represented by traffic counts data.

In addition to the information provided by the IER database, for the power generation, residential combustion and agriculture sectors, further developments of the yearly profiles have been included in the current work, as discussed in the next paragraphs. As major improvement compared to the IER database is the generation of time dependent temporal profiles for these sectors, reflecting the inter-annual variability of the key indicators used to temporarily distribute the annual emissions into monthly data. The ‘Online-only Table  1 ’ compares the data sources used to derive the yearly profiles in EDGAR_temporal_profiles_r1 and in the former version of the EDGAR database (EDGARv4.3.2 16 ).

Due to the limited data availability for all countries, it is not always possible to consider regional or local characteristics for each country. Country-specific temporal profiles are extrapolated to the globe using information on climate zone, seasonal variation, average temperature, and other socio-economic parameters. Yearly, weekly, and daily profiles expressing the temporal variation of activities and emissions are then compiled in the database for all countries.

Concerning the hourly profiles development, the aim of this study is to build a system within the EDGAR database allowing the downscale of annual emissions to hourly data at gridcell level. The hourly profiles implemented in the current work do not always represent the best profile for each sector and country combination. However, having rather generic profiles reduces the discontinuity from cell to cell due to the application of the same hourly profile through all the countries. The end-users of the EDGAR data can however provide ad hoc temporal profiles to be implemented in the EDGAR system. Moreover, EDGAR data are often used in global chemical transport models which do not always require hourly emission data as input.

Temporal profiles for power generation

Electricity production and fuel consumption in the power sector coupled with ambient temperature are considered as the indicators of the yearly variation of power generation emissions. In order to develop monthly profiles to be applied to the EDGAR power generation emissions, monthly electricity statistics are gathered from IEA ( https://www.iea.org/statistics/monthly/#electricity ) and other national statistics. IEA provides monthly statistics for electricity production (in GWh/month) for different type of fuels (natural gas, oil, coal, biofuels, other) for all OECD countries from January 2016 till 2018, while from January 2000 onwards the monthly statistics of produced electricity by country are not provided by fuel category. OECD countries include: Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Latvia, Lithuania, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States. Therefore the same monthly profile is assumed for all fuels over those years. For the years before 2000, the same monthly profile as in 2000 is assumed. For China, information on the monthly GWh produced for all fuels is obtained from the National Bureau of Statistics of China ( http://www.stats.gov.cn/english/ ), covering the 1990–2017 period. Regional averages are also computed using the country specific yearly profiles and applied to the remaining world countries consistently with the regional aggregation shown in Fig.  1 . Emissions from the different power plant types included in the EDGAR database (i.e. auto producers electricity plants, auto producers heat plants, auto produced cogeneration, public cogeneration, public district heating, public electricity production, own use of electricity and heat) are distributed assuming the same temporal pattern of the electricity production, being the latter the major contributor of the emissions of the whole energy sector (e.g. in 2018, 76% of global fossil CO 2 emissions from power generation are produced by public electricity generation). For completeness, profiles for nuclear power plants and pumped storage of electricity are estimated under the assumption of continuum operation (although they have a yearly shutdown for at least 3 weeks).

Figures  2 – 5 show the mean seasonal variations of the yearly profiles (i.e., monthly scale factors) obtained from the IEA monthly electricity statistics over the time period 2000–2017 for the 35 OECD countries and obtained from the National Bureau of Statistics of China over the time 1990–2017. This analysis allows us to evaluate the representativeness and stability of each profile for a given country over the years and to identify specific features of the monthly variation of the energy production depending on the geographical location of each country. Larger standard deviations occur for Lithuania, Latvia, Finland, Iceland, Norway and Sweden, meaning that for these countries larger variations in the power generation happen from one year to another probably due to changes in economic variables, meteorological conditions as well as electricity trade. The EDGAR database is not an input-output model and currently it does not include trade information which might have some effects also on the electricity patterns described in this work. In addition, data availability might influence the different standard deviations represented in Figs.  2 – 5 . Higher contributions of the energy sector are expected over the cold months, while lower contributions are found over the warmer months (see as an example the seasonal pattern of Northern European countries represented in Fig.  4 ). A particular feature of the Southern European countries characterized by very hot summer periods is the presence of local peaks also over the summer months due to the energy production for cooling (see Fig.  5 ).

Inter-annual variability of monthly scaling factors over the 2000–2017 time series for the power generation sector for Asian countries (red), North America (light blue), Oceania (dark green) and Latin America (blue). Mean values ± one standard deviation are represented.

Inter-annual variability of monthly scaling factors over the 2000–2017 time series for the power generation sector for Central European countries (light green) and Turkey (grey). Mean values ± one standard deviation are represented.

Inter-annual variability of monthly scaling factors over the 2000–2017 time series for the power generation sector for Northern European countries (pink). Mean values ± one standard deviation are represented.

Inter-annual variability of monthly scaling factors over the 2000–2017 time series for the power generation sector for Southern European countries (yellow) and Western European countries (grey). Mean values ± one standard deviation are represented.

To calculate the daily variation, the influence of weekdays is taken into account, while to resolve hourly emissions typical national heat and electricity demand profiles are implemented accordingly with the methodology developed by Adolph 29 , showing lower contributions at night and characteristic peaks during the day time.

Temporal profiles small scale combustion

Emissions from small scale combustion (e.g. the residential sector) show a significant seasonal variability, reflecting the climatic conditions of each country. In order to account for the ambient temperature dependence of the heating system in the residential sector, heating degree days (HDD) have been computed using the 2 meters temperature data with hourly resolution obtained from the ERA5 atmospheric reanalysis of global climate produced by ECMWF within the Copernicus frame over the years 1980 till 2017 ( https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview ). Hourly averages are computed using the temperature data available for each gridcell at 0.25° × 0.25° within each country, thus allowing the computing of country specific yearly profiles for this sector based on HDD (assuming a reference temperature of 18 °C for all countries to allow comparability amongst them). In addition to the seasonal variability associated with the HDD metric, a proportion of energy consumption is assumed for each of the 23 regions to be constantly used for the production of hot water in the residential sector based on the IER dataset 42 . EDGAR emissions from the residential sector are related with the combustion of fuels for heating and cooking in household/commercial/other purposes. Therefore, HDD is an indicator that suites this purposes globally (northern and southern hemispheres) and this is also the reason why the temporal profiles do not incorporate Cooling Degree Days (CDD) since mainly related with electricity consumption.”

To calculate hourly emissions for the residential sector, hourly patterns of fuel use for heating purposes and hourly patterns of production related fuel use following the typical daily working times are considered within the IER database. However, hourly variation of heating related fuel use depends on heating technology, insulation standards and climatic conditions, variables that are partly included in the current work. For central-heating, we assume a reduction of residential emissions at high ambient temperature, showing a reduction of residential emissions at night time. For coal or wood stoves, two daily peaks are observed, corresponding to the morning and late-afternoon or evening fueling of the stoves in the early morning and after returning home from work. In the IER database 42 , hourly patterns for households are derived from an evaluation of a comprehensive survey in Germany in VDI (Verein Deutscher Ingenieure) guideline 2067 43 . For commercial and institutional heat plants a similar hourly pattern of the household combustion activities is assumed due to the lack of information. Additional information from surveys in different small consumers groups conducted by Seier 32 is also included.

Temporal profiles for agriculture

Monthly variation of agricultural emissions can be quite different among different sub-sectors (e.g. animal emissions, manure emissions) and pollutants (e.g. CO 2 , CH 4 , NH 3 ). Moreover, temporal variations of agricultural emissions can vary from one year to another as they are related to meteorological conditions, e.g. temperature, wind speed (Friedrich and Reis, 2004). Owing to the lack of data and studies and the complexity of the problem, yearly profiles for agriculture sector are associated with relatively higher uncertainties 36 . Atmospheric modelers often use as reference for the seasonality, the monthly CH 4 emissions of rice 44 ( https://data.giss.nasa.gov/ch4_fung/ ). Among all cultivation activities, we dedicated special effort in including seasonal profiles for the rice cultivation sector, representing a relevant source of CH 4 emissions in the world (ca 11% in 2012 16 ). Emission from rice cultivation in top methane emitters countries like China and India ( ∼ 20% and ∼ 10% of global share) accounts for over 4% and 1% of total methane emission, respectively. For the other crop related emissions (wheat, maize, etc.), the assumption of constant production over the year is made, leaving developments for this subsectors to a future release.

The recent RiceAtlas 45 produced by the International Rice Research Institute (IRRI) provides a comprehensive rice calendar with monthly specification at country to sub-country level, covering the whole world with monthly cultivated area and production data. The split between the different agro-ecological ecosystem types is already taken into account by EDGAR, in that four specific emission factors are implemented depending on the fraction of cultivated area: rainfed, deep water, irrigated, and upland (never flooded for a significant period of time).

The month-varying weights in Fig.  6 represent the share of the area cultivated to rice as average in the years 2010–2012, as provided by the RiceAtlas, and aggregated for the sub-regions of Fig.  6 . The seasonality reflects the multiple crop patterns during the majority of the year (e.g. regions 7, 8 and 9, including India and Indonesia), or concentrated during half year (typically from early/late spring to early/late autumn), as for example in region 16.

Monthly weights for rice-area and for the 23 world regions.

Laborte et al . 45 explain the limitations of other existing rice calendars datasets, which include poor or outdates coverage, missing information about multiple crop per year. The RiceAtlas fits the purpose of providing EDGAR with the most updated and comprehensive monthly disaggregation available. Early studies by e.g. Matthews et al . 44 , based on FAO statistics for annual production and on a variety of sources to derive monthly splitting, provided an exhaustive compilation of monthly cultivated rice area and associated latitudinal methane emission, which has been used as reference for emission calculation by a number of studies (e.g. Bergamaschi et al . 46 ). Comparing the time series of monthly rice calendar predicted by Matthews et al . 44 and by the RiceAtlas for four among the top ten world rice producers, the profiles for China and India do not show any significant offset although a weak deviation in magnitude is observed. For USA, RiceAtlas predicts an earlier cultivation and quicker ending respect to Matthews et al . 44 , peaking in April-May rather than from May to September and terminating in October rather than November. For Indonesia, the difference between the two monthly series is quite substantial, with seasonal offset and difference in the peaking period, possibly due to change of practice or difficulty in retrieving monthly splitting for this country. In this sense, the consistency of the recent RiceAtlas datasets can be thought as a reliable update for compilation of emission inventories.

Animal related emissions estimates do not show a yearly pattern, since intensive production activities in this sector are kept rather constant. However, some variations in the emissions over the course of the year might be found in countries where animals are grazing in summer months and kept in stables in winter periods (or rainfall periods) and where animal feed varies considerably throughout the seasons. In this work we rely on the assumption of intensive animal production with no seasonality for indoor husbandry (mainly cattle, pigs and poultry), although representing a simplification of country specific practices which a global database like EDGAR cannot modulate.

Hourly emission patterns follow animal activity, which is mainly influenced by times of feeding, drinking and resting and thus shows a clear day/night-rhythm. Typical courses of emissions are examined in the work of Comberg & Wolfermann 47 , Hahne et al . 48 , Hinz & Linke 49 , Kaiser 50 and Mayer 51 .

The temporal variability of emissions from manure management is mainly dependent on temperature and grazing periods, although the amplitude of this seasonality is rather uncertain. No detailed information is available about the hourly pattern of manure management, therefore a day/night-rhythm determined by the influence of temperature is assumed within the IER database 36 .

To our knowledge, no global data is available regarding enteric fermentation in ruminants and manure management (only few local measurements could be found 52 , 53 to better describe the seasonal variation of animal related emissions.

Temporal profiles for road transport

Road transport emissions are characterized by very strong seasonal and hourly variations. Temporal profiles included in this work are entirely based on the IER database 36 , whose methodology and assumptions are briefly summarized below.

Monthly variations of road transport emissions are gathered from monthly national energy statistics available from EUROSTAT 54 for Europe. Additional information is also included from traffic flow data (e.g. traffic counts) although appropriate traffic counts data are often not available. While different vehicle types show a similar temporal behavior, different road types (e.g. motorways, rural and urban roads) show different temporal patterns. Stronger seasonal variation is found for motorways than for rural and urban roads, and a stronger seasonal variation in rural areas than in urban areas. Hourly variations, instead, are quite similar for different road types and regions. Whereas evaporation emissions mainly depend on temperature, exhaust emissions are connected with the variation of traffic volume. Road transport emissions are characterized by a very strong hourly variation, with daytime peaks up to 7 times higher than the lowest emissions happening at night time.

Temporal profiles for other mobile sources

Other mobile sources include air, ship and railway traffic, and off-road vehicles. Landing and take-off cycles (LTO cycles), passenger numbers and freight statistics available from airports or the International Air Traffic Association (IATA) are used to generate profiles for aircraft emissions in the EDGAR database. However, in the EDGAR_temporal_profiles_r1 20 , only one yearly profile is defined for the flight components included in the EDGAR emissions (landing and take-off, cruise and climb-out and descent) since no differentiation of the monthly variation of these components is present. Modelers interested in low- and high-altitude emissions can directly use the EDGAR aviation emission gridmaps disaggregated for the different flight components or apply the same yearly profile to disaggregate their own emission data of each flight phase. Hourly emissions from air transport are assumed to be distributed over the day without strong variations.

To distribute shipping emissions, the number of ships per hour, day, week, or months in harbours or on ship routes could be used. However in this work, an equal distribution over the year and over the day is assumed due to the lack of appropriate data.

For off-road vehicles (e.g. construction machinery, agricultural machinery) a similar temporal variation of production processes in small enterprises (night/day, working day/weekend) is assumed. A strong seasonal variation is taken into account for activities of agricultural machinery 36 .

Temporal profiles for waste

The temporal distribution applied to the waste sector in this work is based on the IER database 36 . Emissions from the waste sector include landfills, incineration plants and sewage treatment plants which are assumed to be constant during the year and with no significant weekly and daily patterns. Also for landfills, the seasonal changes in ambient temperature do not affect waste temperature in layers deeper than two meters below surface.

Temporal profiles for other sources

The temporal patterns (monthly and hourly) of all remaining emission sources (e.g. refineries, industrial processes, fuel extraction, transformation, etc.) are taken from the IER database 36 .

Data Records

The EDGAR temporal profiles library (EDGAR_temporal_profiles_r1 20 ) and the emission time series of EDGARv4.3.2 with the new monthly resolution are available for all pollutants (CO 2 , CH 4 , N 2 O, SO 2 , NOx, CO, NMVOC, NH 3 , PM 10 , PM 2.5 , BC, OC) as Microsoft Excel files and can be freely accessed via the EDGAR website at http://edgar.jrc.ec.europa.eu/overview.php?v=temp_profile (last update: March 2020). We have applied the EDGAR_temporal_profiles_r1 to the EDGARv4.3.2 emission data since the latest EDGARv5.0 version currently covers only the GHGs and to allow EDGAR users to compare the monthly emission time series of EDGARv4.3.2 obtained with the former EDGAR temporal profiles (available at https://edgar.jrc.ec.europa.eu/overview.php?v=432_AP and https://edgar.jrc.ec.europa.eu/overview.php?v=432_GHG ) with the new ones.

Data are registered at figshare 20 and available also at https://data.europa.eu/doi/10.2904/JRC_DATASET_EDGAR  55 . Monthly temporal profiles are downloadable online for each country (226 countries plus international shipping and aviation) and source category (e.g. energy, industry, residential, transport, agriculture, etc., for a total number of 227 processes). The EDGAR_temporal_profiles_r1 20 are reported in the EDGAR_temporal_profiles_r1.xls file including the following information: region/country, activity sector description, IPCC_1996_source_category, IPCC_2006_source_category, year, yearly temporal profiles. The EDGAR temporal profiles library will be further improved for specific sectors or countries when new data will become available. New versions will be periodically uploaded to the JRC EDGAR Repository.

Technical Validation

All datasets used to compile the temporal profiles database EDGAR_temporal_profiles_r1 20 for global anthropogenic emissions are entirely based on official international statistics (e.g. IEA, NBSC, EUROSTAT, etc.), reputable data sources (ECMWF ERA5 atmospheric reanalysis of global climate, etc.) and relevant scientific literature works, which guarantee the robustness and validity of the underlying data used in this research.

Temporal allocation of emissions is often based on expert opinions as well as on the use of surrogate data, therefore limiting the information about the accuracy and the quality of the data. It is challenging to quantify the impact on uncertainty of some of the data handling steps, e.g. expert judgement in allocation of temporal profiles to a particular EDGAR sector. The results from such approach would be greatly influenced by the assumptions incorporated into the analysis. It is therefore considered that a qualitative analysis is the most appropriate method for expressing uncertainties, by using “quality scores” (Table  4 ). The use of “quality scores” is an approach commonly used when uncertainties are particularly large or difficult to quantify (as used in other studies, e.g. Huang et al . 56 ). In this case, quality scores are assigned to an EDGAR source group to represent the quality (accuracy/appropriateness and completeness) of the temporal profiles that are assigned to the EDGAR sector. A quality assessment of the generated datasets is performed, showing the reliability of the temporal disaggregation in particular for combustion related sectors (e.g. power plants, transportation, residential combustion, etc.), while anticipating the need for further development for some fuel transformation activities, some agriculture sub-sectors (e.g. manure management, enteric fermentation, and agricultural waste burning) and waste management.

The EDGAR emission process code consists of five 3-letter codes which identify the sector, sub-sector, fuel type, technology and end-of-pipe measures related to the emissions. The temporal profiles mapping is performed mainly at sector and sub-sector level (first 2 levels of EDGAR code), considering that technology and end-of-pipe measures have little impacts on the temporal variation of the activities, and on the other hand the lack of fuel type specific temporal profiles. Table  5 lists the aggregated sectors of the EDGAR database for which different types of temporal profiles are assigned, together with representative quality scores.

In order to show how well a temporal profile qualitatively matches the corresponding EDGAR process, quality scores indicating the level of the matching quality are assigned (see Table  5 ). Four levels of mapping quality scores, from 1 to 4, are defined to give an indication on how well a matching is and on priorities for further improvement for the yearly profiles. Quality scores 1 and 2 are considered to be a relatively good match and representative of the EDGAR sector. Quality scores 3 and 4 represent fuzzy matches due to the lack of process-specific temporal profiles and are considered to be the priority areas for further development. A quality code of 1 is assumed for oil, 2 for gas and 3 for coal related fuels for the fuel production sector (PRO). A quality code of 1 is assumed for combustion activities in the transformation industry sector, 2 for transformation in gas to liquids plants, chemical heat for electricity production, fuel transformation in gas works and non-specified transformation activity and 3 for blast furnaces, electric boilers, blended natural gas, heat pumps, gasification plants for biogas, charcoal production plants, coke ovens, transformation in liquefaction/regasification plants in gas to liquids plants, in coal liquefaction plants, in patent fuel plants, distribution losses in transformation processes, petrochemical industry.

Table  4 also presents the percentages of global CO 2 emissions in 2005 associated with each quality code. 33% of CO 2 emissions in 2005 are attributable to sources for which temporal profiles with quality code 1 (well matched) are mapped. 38% of CO 2 emissions are associated with temporal profiles that are considered to be sector specific without fully differentiation of sub-sectors (i.e. quality code 2). 29% of CO 2 emissions are attributable to the sources to which a general temporal profile is assigned (quality code 3 and 4). Lower mapping qualities are found for some fuel transformation activities and some agriculture sub-sectors (e.g. manure management, enteric fermentation, and agricultural waste burning), owing mainly to the fact that temporal variation of activities and emissions from these sectors are quite diverse and to lack of knowledge. Further improvements are therefore needed to develop more representative temporal profiles for these sources.

To further address the quality of the produced data set per sector and region, the assigned temporal profiles are compared with the temporal profiles used by common atmospheric models as discussed in the following section.

Results comparison

Temporal profiles are commonly required by CTMs to distribute annual emissions to monthly and hourly emissions. To conduct a comprehensive comparison between the temporal profiles assigned to the EDGAR database in this study with other existing data sets, temporal profiles used by the CHIMERE 57 and LOTOS-EUROS 58 models are reviewed and collected. The monthly profiles used in the EDGAR v4.3.2 database, the Hemispheric Transport of Air Pollution v2 (HTAP) Task Force 59 and the Community Emissions Data System (CEDS) 60 are also included in the comparison.

Table  6 summarizes the main characteristics of the six data sets considered in the comparison regarding spatial, sectorial and temporal resolution. CHIMERE and LOTOS-EUROS profiles cover the 28 European countries. HTAP temporal profiles were developed for Europe, the United States of America (USA), and Canada. Temporal profiles from EDGAR v4.3.2 are global; however, they only distinguish three geo-regions, i.e. the Northern temperate zone, equator, and the Southern temperate zone. This study (226 countries) and the CEDS (222 countries) inventory provide country-specific temporal profiles for global emission databases. However, the CEDS inventory, similar to HTAP and EDGAR v4.3.2, only has yearly profiles.

The CHIMERE and LOTOS-EUROS temporal profiles apply Standardized Nomenclature for Air Pollutants (SNAP) sector categorization, and differentiate 11 SNAP sectors. The HTAP and CEDS data sets have a relatively rougher sector resolution (6 and 7 main sectors, respectively). The EDGAR v4.3.2 data set integrates temporal profiles for 15 main sectors. In this study, temporal profiles covering 20 main sectors are mapped to all EDGAR processes.

Regarding temporal resolution, the EDGAR v4.3.2, CEDS, and HTAP data sets have only yearly profiles with monthly variations. CHIMERE, LOTOS-EUROS and this study employ yearly, weekly and daily profiles, and therefore enable emission distribution not only to monthly scale but also to hourly values.

In addition, a very important novelty of our work is the development of year dependent monthly scaling factors for activities with strong inter-annual variability. This information is absent in the aforementioned literature datasets.

Global monthly emission pattern

When applying the EDGAR_temporal_profiles_r1 20 to the EDGARv5.0 emissions 61 , https://edgar.jrc.ec.europa.eu/overview.php?v=booklet2019 ), the corresponding monthly data successfully reproduce the major seasonal patterns that are expected, as shown in Figs.  7 – 9 .

Time series (2000–2018) of monthly fossil CO 2 emissions by sector in the world.

Seasonality of regional fossil CO 2 emissions in 2015 (expressed in Mt/month).

Seasonality of regional CH 4 emissions in 2015 (expressed in Mt/month).

Figure  7 shows the time series of monthly fossil CO 2 emissions from the year 2000 to 2018 for sectors with a strong temporal variability over the course of the year 61 . As top CO 2 emitting countries are located in the Northern Hemisphere (e.g. China, USA, Europe, Russia), the monthly variations of global CO 2 emissions are dominated by the seasonal variations of the Northern Hemisphere countries. Therefore the annual variability of global CO 2 emission is strongly influenced by the power generation and residential combustion sectors, with higher emissions during the winter months and lower emissions from May to August. The residential sector is the one showing the strongest monthly variation, with emission peaks during cold months more than 3 times higher than the summer time peaks. Emissions from the agricultural sectors have an anti-correlated seasonal cycle compared with combustion related emissions due to the occurrence of higher agricultural emissions over the warmer months. This pattern is also enhanced when looking at the seasonality of CH 4 emissions (Fig.  9 ), showing strong temporal variability in the agricultural sector anti-correlated with the residential combustion activities. As global average, agricultural activities are characterized by emission peaks during warmer months 1.5 times higher than during cold months.

Figure  8 represents the seasonality of fossil CO 2 and Fig.  9 of CH 4 emissions in 2015 by sector for different regions in the world. The highest contributions to CO 2 emissions happen in the Northern Hemisphere during cold months mainly due to the combustion of fuels in the power and residential sectors. Specific seasonality is observed in Brazil and Latin America for the agricultural sector mainly from agricultural waste burning activities and agricultural soil emissions. The seasonality of CH 4 emissions is mostly dominated by agricultural activities, in particular in countries with high emissions from rice cultivation which reflect the rice cultivation calendar (e.g. China, India, Japan, Rest of Asia).

Usage Notes

The unique feature of the EDGAR_temporal_profiles_r1 20 library relies on the possibility to use the newly developed sector- and country- specific temporal profiles i) as EDGAR application using data from in any EDGAR release, ii) by global and regional atmospheric modelers and iii) by emission inventory developers. The aim of this work is not only to improve the current knowledge of highly time resolved emissions, but in particular to allow any modeler to implement these new temporal profiles in any model, thus allowing the analysis of the impact of different emission temporal disaggregation methods on the model output. In addition, this work provides the basis for emission inventory developers aiming at disaggregating their annual emissions into higher resolution data.

In order to allow a straightforward implementation of the EDGAR_temporal_profiles_r1 20 in any other system (emission database or model), each sector specific temporal profile has been mapped with a sector description and the standard Intergovernamental Panel on Climate Change (IPCC) 1996 ( https://www.ipcc-nggip.iges.or.jp/public/gl/guidelin/ch1ri.pdf ) and 2006 ( https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/1_Volume1/V1_4_Ch4_MethodChoice.pdf ) classification and definition of source categories. Similarly, countries are identified with their name, regional belonging and International Organization for Standardization (ISO 3166-1 alpha-3 standard) codes in order to allow a clear and unique identification by any user.

Country names are consistent with the Interinstitutional Style Guide of the European Commission available at http://publications.europa.eu/code/en/en-370100.htm , the “Short name” definition listed in the “List of countries, territories and currencies” table at http://publications.europa.eu/code/en/en-5000500.htm has been used (updated at 16/07/2019).

Code availability

Most of the temporal profiles data processing has been done using the software R version 3.5 and Python version 3.6. The computation of heating degree days maps was based on the 2 m air temperature of ECMWF ERA5 re-analysis 62 and produced by using IDL8.6 programming software. Further computations, such as mapping sectors and countries have been performed using Microsoft Access 2010.

The implementation of the EDGAR_temporal_profiles_r1 library into the Emissions Database for Global Atmospheric Research has been developed using a dedicated EDGAR development tool of the Joint Research Centre named EOLO based on Php and Microsoft SQL Server. This system cannot be accessed outside the institution but further details can be provided upon request. All scripts related with this work are available at figshare 20 .

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Acknowledgements

The authors are further grateful to Peter Bergamaschi and Gabriel Oreggioni (JRC) for inspiring scientific discussions on CH 4 emissions and temporal profiles for combustion related activities, to Marlene Duerr (JRC) for her help in gathering literature information for the seasonal profiles of rice cultivation and summertime shift for all countries, and to Julian Wilson (JRC) for the thorough review and English proofreading.

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M.C. worked on the temporal profiles development (power generation and residential combustion sectors) together with G.H. (integration of the IER database within the EDGAR system) and E.S. (rice cultivation sector). E.N.K. processed the ERA5 atmospheric reanalysis of global climate to compute heating degree days. M.C. prepared the manuscript with contributions from E.S. and G.H. G.J.M., M.M. and C.S. supervised the scientific content and development of the temporal profiles. R.F. helped in the development and provision of the methodology for the temporal resolution of emission data. D.G. worked on the implementation of the temporal profiles in the EDGAR system developing a new methodology in particular to integrate hourly profiles.

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DOI : https://doi.org/10.1038/s41597-020-0462-2

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Application Of Cold Atmospheric Plasma In The Treatment Of Viruses And Decontamination

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During the SARS-CoV-2 pandemic, there is an urgent need to develop better treatment methods to decontaminate and inactivate the virus from hospital personal protective equipment (PPE). [1,2]. In the past decade, cold atmospheric plasma (CAP) has garnered a lot of interest due to the simplicity of its decontamination/ sterilization properties and has been used for the deactivation of many viruses [4,5] which may be more convenient than. the current chemical and physical methods using in disinfection/sterilization, include hydrogen peroxide, 70% ethanol, detergents, steam sterilization, ozone and UV exposure. [3]. CAP is a non-thermal partly ionized gas formed at atmospheric pressure. It contains a cocktail of reactive oxygen and nitrogen species (OH, O, H2O2, O3, NO, NO2, etc.), ions, molecules, electromagnetic waves, and UV photons collectively termed reactive agents (RAs) [6,7]. Due to a limited understanding of viral decontamination and a high demand for treatment modality, we tested the effects of CAP as a potential method for decontamination against influenza (H1N1) and the SARS-CoV-2 virus (on inoculated plates/PPE). CAP exposure treatment of the viral particles was carried out for 30, 60, and 120 seconds. For samples of the influenza virus, we observed a significant ~2.83 TCID50/mL (tissue culture infectious dose) log unit reduction following 120 seconds of CAP treatment. Whereas our preliminary results for SARS-CoV-2 deactivation with a TCID50/mL show ~ 0.72 log reduction at 120 secs in PBS. Hence, we our new CAP method of PPE decontamination could have clinical utility as a more practical and rapid viral inactivation method.

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Some results uranium dioxide powder structure investigation

• Processes of Obtaining and Properties of Powders
• Published: 28 June 2009
• Volume 50 , pages 281–285, ( 2009 )

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• E. I. Andreev 1 ,
• K. V. Glavin 2 ,
• A. V. Ivanov 3 ,
• V. V. Malovik 3 ,
• V. V. Martynov 3 &
• V. S. Panov 2  

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Features of the macrostructure and microstructure of uranium dioxide powders are considered. Assumptions are made on the mechanisms of the behavior of powders of various natures during pelletizing. Experimental data that reflect the effect of these powders on the quality of fuel pellets, which is evaluated by modern procedures, are presented. To investigate the structure of the powders, modern methods of electron microscopy, helium pycnometry, etc., are used. The presented results indicate the disadvantages of wet methods for obtaining the starting UO 2 powders by the ammonium diuranate (ADU) flow sheet because strong agglomerates and conglomerates, which complicate the process of pelletizing, are formed. The main directions of investigation that can lead to understanding the regularities of formation of the structure of starting UO 2 powders, which will allow one to control the process of their fabrication and stabilize the properties of powders and pellets, are emphasized.

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Investigation of the Properties of Uranium-Molybdenum Pellet Fuel for VVER

Investigation of the Influence of the Energy of Thermal Plasma on the Morphology and Phase Composition of Aluminosilicate Microspheres

Evaluation of the possibility of fabricating uranium-molybdenum fuel for vver by powder metallurgy methods.

Patlazhan, S.A., Poristost’ i mikrostruktura sluchainykh upakovok tverdykh sharov raznykh razmerov (Porosity and Microstructure of Chaotic Packings of Solid Spheres of Different Sizes), Chernogolovka: IKhF RAN, 1993.

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Andreev, E.I., Bocharov, A.S., Ivanov, A.V., et al., Izv. Vyssh. Uchebn. Zaved., Tsvetn. Metall. , 2003, no. 1, p. 48.

Assmann, H., Dörr, W., and Peehs, M., “Control of HO 2 Microstructure by Oxidative Sintering,” J. Nucl. Mater. , 1986, vol. 140,issue 1, pp. 1–6.

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Elektrostal’ Polytechnical Institute (Branch), Moscow Institute of Steel and Alloys, ul. Pervomaiskaya 7, Elektrostal’, Moscow oblast, 144000, Russia

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Andreev, E.I., Glavin, K.V., Ivanov, A.V. et al. Some results uranium dioxide powder structure investigation. Russ. J. Non-ferrous Metals 50 , 281–285 (2009). https://doi.org/10.3103/S1067821209030183

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Posts falsely blame HAARP research project for atmospheric auroras

A powerful solar storm triggered spectacular atmospheric auroras seen around the world in may 2024, but widely shared social media posts claim the light displays resulted from a research project formerly operated by the us military. this is false; the geomagnetic phenomenon was well documented, and scientists say the facility known as haarp is incapable of producing anything of this magnitude..

"HAARP CREATED THE AURORAS! They may have been beautiful but they were not natural," said an Instagram user on May 11, 2024, referencing the High-frequency Active Auroral Research Program , which studies the ionosphere and is the target of numerous conspiracy theories .

"Did y'all enjoy the fabricated light show?" said a Facebook post, also on May 11, with thousands of interactions. "Stop giving them so much credit.  This was NOT the Aurora Borealis."

Similar claims were shared on  TikTok , where one clip garnered more than one million views, as well as on  Threads , X , Bitchute , in online  articles and in posts in French , Spanish and German .

Spectacular auroras dominated the sky in many parts of the world May 10 and 11, resulting from the most powerful solar storm in more than two decades. The first of several coronal mass ejections (CMEs) -- expulsions of plasma and magnetic fields from the Sun -- came just after 1600 GMT Friday, according to the US-based  National Oceanic and Atmospheric Administration (NOAA).

Fluctuating magnetic fields associated with geomagnetic storms induce currents in long wires, including power lines, which can potentially lead to blackouts. Long pipelines can also become electrified, leading to engineering problems.

But the phenomenon seen worldwide is unrelated to HAARP, a former US military initiative now housed at the University of Alaska Fairbanks that conducts research on the ionosphere using a high-energy transmitter. 

While HAARP research can lead to "weak luminous  aurora-like glows ," according to its website, its impact cannot extend beyond a small area, scientists say (archived here ).

Dennis Papadopoulos , a professor specializing in plasma physics at the University of Maryland (archived here ) and one of the scientists involved in conceiving HAARP, said the claims are unfounded.

"While we have in the past generated artificial aurora it is confined within the area around Gakona and orders of magnitude weaker than what is observed," he said in a May 13 email. "HAARP cannot drive global effects."

Experiment fuels claims

Fueling the claims was the news that HAARP conducted research May 8-10 (archived here), and several posts used a genuine notice of the project as purported evidence that the auroras were manipulated.

But a spokesperson for the Alaska-based facility told AFP on May 13 the experiment "studied mechanisms for the detection of orbiting space debris," not anything related to the geomagnetic storm.

The university issued a statement later the same day with additional details on its research from Jessica Matthews, HAARP director (archived here ).

"We have been responding to many enquiries from the media and the public," Matthews said in the statement. "The HAARP scientific experiments were in no way linked to the solar storm or high auroral activity seen around the globe."

Jeffrey Hughes , a professor of astronomy specializing in space physics at Boston University (archived here ), agreed, telling AFP in a May 13 email that the radio waves emitted by HAARP "can modify the local ionosphere (over a region perhaps 100 miles wide) but not any further away. It could not cause airglow away from Alaska."

Stanford University professor emeritus Umran Inan (archived here ), who specializes in the ionosphere, said the claims make little sense.

"The electromagnetic power delivered to the ionosphere by the HAARP facility is minuscule compared to that delivered by intense lightning flashes, which occur approximately 50 to 60 times a second on our planet," he said in an email . "Accordingly, any suggestion that a global event like the one that occurred is related to HAARP is truly ludicrous."

Tuija Pulkkinen , chair of climate and space sciences and engineering at the University of Michigan (archived here ), similarly dismissed the claims.

"HAARP is a radio transmitter, which sends signals to the upper atmosphere creating electron heating. It has the ability to create artificial airglow that could resemble auroras, but only in a very local region," she told AFP. "The solar storm occurrence was verified by solar observations (sunspot activity, strong flare activity, solar energetic particles, coronal mass ejections seen leaving the solar corona), solar wind observations 1.5 million km from the Earth towards the Sun."

Plasma physicist Jan Egedal of the University of Wisconsin (archived here ) called the claims "nonsense" and added: "The HAARP facility has nowhere near the energy output required to create the aurora borealis."

HAARP has also been  falsely blamed for weather manipulation , earthquakes , and other phenomena.