Resumen

BAENA-SALAZAR,, Daniela; JIMENEZ,, José F.; ZAPATA, Carmen E.  y  RAMIREZ-CARDONA, Álvaro. Artificial neural network applied for the forecast of critical PM2.5 events in the Aburra Valley. Dyna rev.fac.nac.minas [online]. 2019, vol.86, n.209, pp.347-356. ISSN 0012-7353.  https://doi.org/10.15446/dyna.v86n209.63228.

The great human health implications of exposure to atmospheric pollution events can have repercussions on the quality of life, economy, and the quality of city’s ecosystems. With the possibility of predicting a critical event, the option of taking adequate measures for mitigation or even prevention of these impacts is enabled. In this paper, an Artificial Neural Networks (RNA) model was developed and tested to predict the daily concentration of particulate matter less than 2.5 microns (PM2.5) in the Aburrá Valley (Colombia), with a day of anticipation, based on information from three stations of the Metropolitan Area Air Quality Monitoring Network.

Palabras clave : Keywords: air pollution; PM2.5 forecast; artificial neural network; meteorological data..

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