the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Oct 2023
Brazilian Atmospheric Inventories – BRAIN: A comprehensive database of air quality in Brazil
Leonardo Hoinaski
Robson Will
Camilo Bastos Ribeiro
Abstract. 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. We provide gridded data in two domains, covering the Brazilian territory with 20x20 km of spatial resolution and another covering Southern Brazil with 4x4 km. 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). The meteorology dataset has been derived from Weather Research and Forecasting Model (WRF). 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). 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 measured pollutant 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 municipality and state scales, especially for not developed and data-scarce municipalities. We also envision that BRAIN has the potential to create new insights and opportunities for air pollution research in Brazil.
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Leonardo Hoinaski et al.
Status: open (until 15 Dec 2023)
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CC1: 'Air Quality Database', Taciana Toledo, 17 Oct 2023
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It is a valuable contribution to air quality management in Brazil. Thanks to the authors for making this data set available. This initiative will help to fill the data gaps and improve our understanding of the air pollution process in Brazil.
Citation: https://doi.org/10.5194/essd-2023-305-CC1 -
RC1: 'Comment on essd-2023-305', Anonymous Referee #1, 25 Nov 2023
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General comment:
The manuscript shows the development and analysis of an air quality management system to help air quality policies and management in Brazil. The paper brings a very interesting topic, specfically due to: i) the current lack of numerical simulated dataset needed to work with air quality in Brazil; ii) Not everyone that works with meteorological and/or air quality in knows how to produce and work with atmospheric models. Thus, having the possibility to use a platform, as proposed by the authors for a specific region, is an indubitable advantage. I would recommend the paper for publishing after addressing all the following comments.
My major concern is on creating numerical simulation for a country with such a large size as Brazil. For that, a massive statistic evaluation is mandatory and not only in cities with large amount of pollutants emissions but also in background regions, such as the Northwest of the Amazonas state (near to the Amazon Tall Tower Observatory -ATTO). In cases where a monitoring network is not available, the authors should use remote sensing to evaluate the numerical experiments. There is a good amount of satellite products to use in this case: MODIS to evaluate AOD; TROPOMI to evaluate NO2 and HCHO; IASI+GOME2 to evaluate O3 (on the 20km grid). If the authors are proposing to make available numerical simulations for the entire country, it requires evaluation for the whole domain or at least an evaluation scale where it shows a confiability level of the data. In order to assume the premises of good modeling results for regions without validation, the authors need to build an underlying analysis by showing how the model performs on key scenarios, such as:
i) Background regions.
ii) Under anthropogenic emission effects (also analyze the model’s abilities with plume transport).
ii) Long range transport (specially in the Amazon region during the wet season with (between January and May), when the Intertropical Convergence Zone (ITCZ) is more intense in the south, allowing the long transport of BC (biomass burning emissions) and dust from Africa (Sahara desert) (Artaxo et al., 2013; Martin et al., 2016; Pöhlker et al., 2018, 2019). With the availability of several years of BC background measurements at the ATTO tower, the authors could separate African episodic events from the rather constant regional BC concentrations that are relevant when comparing with modeled values not under anthropogenic influence.
Once it is shown that the model is capable of representing key scenarios, the idea of using modeling data for regions not fully evaluated is more reasonable.
Specific comments:
- I would strongly recommend the authors to put more focus on the states with the most extensive networks of AQS such as SP, RJ and MG, in addition of course to PR, SC and RS (I am suspicious it was done that way because the authors are based in SC).
- The authors should have a modeling strategy component focused on the States with AQS to allow the increase of the spatial resolution, and thus, get a more robust statistical evaluation for medium and small-sized cities that, otherwise, would not be properly covered by coarse resolution model simulations (10 km or more). I also recommend the authors to do a full evaluation by selecting a region with AQS available and combine observational data from satellites and field campaigns (if available). The idea here is to try to have a case where the model can be massive evaluated vertically and horizontally.
- If there is no observations to compare with in States other than those with AQS, I do not think a country-scale simulation is really worthwhile here. Maybe the authors should provide a strategy/approach for validade/evaluate regions without AQS (remote sensing?).
- As most of the air quality stations used for model evaluation are placed in southeastern Brazil, why not focusing on the largest metropolitan areas of the country, such as, the metropolitan areas of São Paulo, Rio de janeiro, Belo horizonte, etc?. Or maybe use passed campaigns such as The Green Ocean Amazon experiment (GoAmazon2014/5) or The Regional Carbon Balance in Amazonia (BARCA) to evaluate the chemistry component of the model in different vertical levels and with high spatial resolution.
- If 2019 is just a sampling year, what is the base period?
- The authors claimed that, 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. In the biomass emission perspective, have the authors checked the fire products from Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi NPP satellite - VIIRS (375 m resolution for fire activities). The paper Ferrada et al., 2022 (Introducing the VIIRS-based Fire Emission Inventory version 0 (VFEIv0)) shows a new open biomass burning inventory that relies on the fire radiative power (FRP) data from VIIRS.
- BRAVES uses activity data from field campaigns conducted in the metropolitan area of São Paulo. It really does not make any sense take this region out of a very high-resolution simulation design.
- NCAR (FINN) version 1.5 (Wiedinmyer et al., 2011). This version is pretty outdated. An updated version is now available https://egusphere.copernicus.org/preprints/2023/egusphere-2023-124/egusphere-2023-124.pdf.
- The authors mentioned that he WRF model demonstrated the ability to reproduce diurnal and seasonal variability of winds in the Brazilian North-East region (Souza et al., 2022a). Although, this resolution is slightly lower than the one used in this work for the parent domain. If wouldn't be better to just focus on the largest metropolitan areas of Brazil?. I am assuming that you have set up the model simulations at 20 km resolution in an attempt to avoid out-of-memory and space of storage issues, but if that is the case, why not just focusing on high densely areas?
- The authors mentioned that the lack of data quality assurance may compromise the credibility of the available air quality observations in Brazil. It is true, and consequently, this could potentially compromise any analysis conducted on these data sets. In my view, a modeling study that centers on the States with the most extensive networks of AQS would have had a more effective simulation strategy, as the one mentioned in the previous comments. This point also brings the importance of using data from previous campaigns with high quality assurance.
References:
Artaxo, P., Rizzo, L. V., Brito, J. F., Barbosa, H. M., Arana, A., Sena, E. T., Cirino, G. G., Bastos, W., Martin, S. T., and Andreae, M. O.: Atmospheric aerosols in Amazonia and land use change: from natural biogenic to biomass burning conditions, Faraday Discuss., 165, 203–235, https://doi.org/10.1039/C3FD00052D, 2013.
Martin, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R. A. F., Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H. M. J., Fan, J., Fisch, G., Goldstein, A. H., Guenther, A., Jimenez, J. L., Pöschl, U., Silva Dias, M. A., Smith, J. N., andWendisch, M.: Introduction: Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5), Atmos. Chem. Phys., 16, 4785– 4797, https://doi.org/10.5194/acp-16-4785-2016, 2016.
Pöhlker, C., Walter, D., Paulsen, H., Könemann, T., Rodríguez- Caballero, E., Moran-Zuloaga, D., Brito, J., Carbone, S., De- grendele, C., Després, V. R., Ditas, F., Holanda, B. A., Kaiser, J. W., Lammel, G., Lavriˇc, J. V., Ming, J., Pickersgill, D., Pöh- lker, M. L., Praß, M., Löbs, N., Saturno, J., Sörgel, M., Wang, Q., Weber, B., Wolff, S., Artaxo, P., Pöschl, U., and Andreae, M. O.: Land cover and its transformation in the backward trajec- tory footprint region of the Amazon Tall Tower Observatory, At- mos. Chem. Phys., 19, 8425–8470, https://doi.org/10.5194/acp- 19-8425-2019, 2019.
Pöhlker, M. L., Ditas, F., Saturno, J., Klimach, T., Hrabˇe de Ange- lis, I., Araùjo, A. C., Brito, J., Carbone, S., Cheng, Y., Chi, X., Ditz, R., Gunthe, S. S., Holanda, B. A., Kandler, K., Kesselmeier, J., Könemann, T., Krüger, O. O., Lavriˇc, J. V., Martin, S. T., Mikhailov, E., Moran-Zuloaga, D., Rizzo, L. V., Rose, D., Su, H., Thalman, R., Walter, D., Wang, J., Wolff, S., Barbosa, H. M. J., Artaxo, P., Andreae, M. O., Pöschl, U., and Pöh- lker, C.: Long-term observations of cloud condensation nuclei over the Amazon rain forest – Part 2: Variability and charac- teristics of biomass burning, long-range transport, and pristine rain forest aerosols, Atmos. Chem. Phys., 18, 10289–10331, https://doi.org/10.5194/acp-18-10289-2018, 2018.
Citation: https://doi.org/10.5194/essd-2023-305-RC1
Leonardo Hoinaski et al.
Data sets
Brazilian Atmospheric Inventories - BRAIN: A comprehensive database of air quality in Brazil version 1.0 Leonardo Hoinaski, Robson Will and Camilo Bastos Ribeiro https://brain.ens.ufsc.br/
Model code and software
BRAIN codes repository - CMAQ runner Leonardo Hoinaski https://github.com/leohoinaski/CMAQrunner
BRAIN codes repository - Industrial emissions Leonardo Hoinaski https://github.com/leohoinaski/IND_Inventory
Leonardo Hoinaski et al.
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