EmissV: A preprocessor for WRF-Chem model

Authors

  • Daniel Schuch Department Atmospheric sciences, University of são Paulo, são Paulo, 05508-090, Brazil
  • Sergio Ibarra Department Atmospheric sciences, University of são Paulo, são Paulo, 05508-090, Brazil
  • Edmilson Dias de Freitas Department Atmospheric sciences, University of são Paulo, são Paulo, 05508-090, Brazil
  • Maria de Fátima Andrade Department Atmospheric sciences, University of são Paulo, são Paulo, 05508-090, Brazil

DOI:

https://doi.org/10.30564/jasr.v1i1.347

Abstract

Air quality models are tools capable to predict the physical and chemical processes that affect air pollutants as they disperse and reacts in the atmosphere. These models need input containing meteorological data, terrestrial data and emissions. Meteorological and terrestrial data comes from different sources such as meteorological stations and satellite images which are important to represent the current state of the atmosphere and are available at least on a daily frequency. On the other hand, the emission data comes from pollution inventories generated mainly from governmental reports, this data needs to be processed by various reasons such as the correction of outdated emissions, for combining inventories or to speciate the emitted pollutants to different chemical mechanisms. EmissV is a code written into a high-level programming language to create emissions input for these atmospheric models. The emissions from EmissV are coherent with the total and the spatial distribution of emissions obtained from other preprocessors.

Keywords:

Emissions, Air Quality Model, Numerical Models, Preprocessor, Air Pollution Inventories

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How to Cite

Schuch, D., Ibarra, S., Freitas, E. D. de, & Andrade, M. de F. (2019). EmissV: A preprocessor for WRF-Chem model. Journal of Atmospheric Science Research, 1(1), 1–5. https://doi.org/10.30564/jasr.v1i1.347

Issue

Vol. 1 , Iss. 1 (October 2018)

Article Type

Article

License

Copyright © 2018 Author(s)

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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