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Journal of Electrical and Computer Engineering
Volume 2017, Article ID 5106045, 14 pages
Research Article

Modeling PM2.5 Urban Pollution Using Machine Learning and Selected Meteorological Parameters

1University of Twente, Enschede, Netherlands
2Intelligent & Interactive Systems Lab (SI2 Lab), FICA, Universidad de Las Américas, Quito, Ecuador
3DEE, Nova University of Lisbon and CTS, UNINOVA, Monte de Caparica, Portugal

Correspondence should be addressed to Yves Rybarczyk; tp.lnu.tcf@kyzcrabyr.y

Received 24 February 2017; Revised 23 April 2017; Accepted 11 May 2017; Published 18 June 2017

Academic Editor: Lei Zhang

Copyright © 2017 Jan Kleine Deters et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Outdoor air pollution costs millions of premature deaths annually, mostly due to anthropogenic fine particulate matter (or PM2.5). Quito, the capital city of Ecuador, is no exception in exceeding the healthy levels of pollution. In addition to the impact of urbanization, motorization, and rapid population growth, particulate pollution is modulated by meteorological factors and geophysical characteristics, which complicate the implementation of the most advanced models of weather forecast. Thus, this paper proposes a machine learning approach based on six years of meteorological and pollution data analyses to predict the concentrations of PM2.5 from wind (speed and direction) and precipitation levels. The results of the classification model show a high reliability in the classification of low (<10 µg/m3) versus high (>25 µg/m3) and low (<10 µg/m3) versus moderate (10–25 µg/m3) concentrations of PM2.5. A regression analysis suggests a better prediction of PM2.5 when the climatic conditions are getting more extreme (strong winds or high levels of precipitation). The high correlation between estimated and real data for a time series analysis during the wet season confirms this finding. The study demonstrates that the use of statistical models based on machine learning is relevant to predict PM2.5 concentrations from meteorological data.