Ambient pollution manage through data mining tools

Article Sidebar

PDF (Español (España)) HTML (Español (España)) XML-JATS (Español (España)) EPUB (Español (España))
Published: Nov 24, 2018
Keywords:
Fixed sources, pollution, emissions, data mining

Main Article Content

Dianelis Portal-Castillo
Centro Meteorológico Provincial

Abstract

The emissions of polluting substances into the atmosphere and the magnitude of them are the cause of many environmental problems at present. The present work focused on air pollution in the province of Sancti Spíritus, where it was sought to find relationships between climatic variables and pollutant emissions through data mining techniques that can be defined as the process of extracting valid, useful knowledge and understandable that it is found in large data sets.

Downloads

Download data is not yet available.

Article Details

How to Cite
Portal-CastilloD. (2018). Ambient pollution manage through data mining tools. Revista Cubana De Meteorología, 24, 349-355. Retrieved from http://rcm.insmet.cu/index.php/rcm/article/view/440
Issue
Vol 24 (2018): Especial
Section
Original Articles

Those authors who have publications with this journal accept the following terms of the License Attribution-NonCommercial 4.0 International (CC BY-NC 4.0):

You are free to:

  • Share — copy and redistribute the material in any medium or format
  • Adapt — remix, transform, and build upon the material

The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  • Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  • NonCommercial — You may not use the material for commercial purposes.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

The journal is not responsible for the opinions and concepts expressed in the works, they are the sole responsibility of the authors. The Editor, with the assistance of the Editorial Committee, reserves the right to suggest or request advisable or necessary modifications. They are accepted to publish original scientific papers, research results of interest that have not been published or sent to another journal for the same purpose.

The mention of trademarks of equipment, instruments or specific materials is for identification purposes, and there is no promotional commitment in relation to them, neither by the authors nor by the publisher.

References

Carslaw, D.C. and Kart Ropkins, (2012) openair-an R package for air quality data analysis. Environmental Modelling & Software. Volume 27-28, 52-61.
Carslaw, D.C . and K. Ropkins, (2015): openair: Open-Source tools for the analysis of air pollution data. R package version 1.6, http://CRAN.Rproject.org/package=openair.
Carslaw, D.C . (2017). The openair manual - open-source tools for analyzing air pollution data. Manual for version 2.1-6, University of York.
Cimorelli, A. et. al. «AERMOD: A Dispersion Model for Industrial Source Applications. Part I: General Model Formulation and Boundary Layer Characterization». Journal of Applied Meteorology, 44(5): 682-693, 2005.
Oficina Nacional de Estadística e Información 2016b. Anuario Estadístico de Sancti Spíritus 2015. La Habana, Cuba: Oficina Nacional de Estadística e Información (ONEI).
Pliego, F. F. 2012. Lenguaje R aplicado al análisis de datos de Calidad del Aire - Manual de uso de R y Openair.
PNUMA. Perspectivas de medio ambiente mundial GEO-3. Grupo Mundi-Prensa. España. 2002.
R Developement Core Team (2012). R: A lenguaje and environment for statistical computing. R Foundation for Statistical Computing,Viena, Austria. ISBN 3-900051-07-0.