Analysis of Atmospheric Pollutant Data Using Self-Organizing Maps

Emanoel L. R. Costa, Taiane Braga, Leonardo A. Dias, Édler L. de Albuquerque () and Marcelo A. C. Fernandes ()
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Emanoel L. R. Costa: Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
Taiane Braga: Federal Institute of Education, Science, and Technology of Bahia, Salvador 40301-015, BA, Brazil
Leonardo A. Dias: Centre for Cyber Security and Privacy, School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK
Édler L. de Albuquerque: Department of Industrial Processes and Chemical Engineering, Federal Institute of Education, Science and Technology of Bahia, Salvador 40301-015, BA, Brazil
Marcelo A. C. Fernandes: Laboratory of Machine Learning and Intelligent Instrumentation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil

Sustainability, 2022, vol. 14, issue 16, 1-24

Abstract: Atmospheric pollution is a critical issue in our society due to the continuous development of countries. Therefore, studies concerning atmospheric pollutants using multivariate statistical methods are widely available in the literature. Furthermore, machine learning has proved a good alternative, providing techniques capable of dealing with problems of great complexity, such as pollution. Therefore, this work used the Self-Organizing Map (SOM) algorithm to explore and analyze atmospheric pollutants data from four air quality monitoring stations in Salvador-Bahia. The maps generated by the SOM allow identifying patterns between the air quality pollutants (CO, NO, NO 2 , SO 2 , PM 10 and O 3 ) and meteorological parameters (environment temperature, relative humidity, wind velocity and standard deviation of wind direction) and also observing the correlations among them. For example, the clusters obtained with the SOM pointed to characteristics of the monitoring stations’ data samples, such as the quantity and distribution of pollution concentration. Therefore, by analyzing the correlations presented by the SOM, it was possible to estimate the effect of the pollutants and their possible emission sources.

Keywords: machine learning; atmospheric pollution; self-organizing maps; Salvador-BA (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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