Abstract
The main challenge regarding multivariate data is to transform and integrate this information into useful knowledge. In the case of the emerging vehicle as a sensor concept and its application on environmental assessment, the challenge arises from the possibility to board several types of sensors cheaply and, consequently, to gather information in high spatiotemporal frequency. In this sense, the main objective of this article is to categorize urban locations due to pollutants concentration, based on the spatial dependence of regionalized variables into the n-dimensional space, through self-organizing maps (SOM) technique. For this goal, we use a pollution dataset gathered by equipped vehicles in five trips in São Paulo, Brazil, comprising the following variables: four gas concentration parameters (carbon monoxide—CO, carbon dioxide—CO2, nitrogen dioxide—NO2, and ozone—O3), meteorological parameters (temperature and humidity), altitude and speed. As a first result, we obtain the correlation among considered variables. Afterward, we assess the average behavior of each variable considering only the pollutants, interpreted as a pollution signature, for each cluster obtained by SOM technique. Thus, each cluster is classified according to the air pollution signature. As the main result, by visualizing this proposed classification in a Geographic Information System, we relate the air quality to the urban landscape on the surroundings of the samples and to the time of the day when the sample was gathered. Concretely, for the dataset collected in São Paulo, the worst air conditions are related to non-green urban areas, large traffic corridors and traffic peak hours.
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Acknowledgements
The authors would like to thank Professor Mariana Abrantes Giannotti for valuable remarks on the manuscript and the Brazilian National Council for Scientific and Technological Development (CNPq) for the support to this project.
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de Oliveira, R.H., Carneiro, C.d., de Almeida, F.G.V. et al. Multivariate air pollution classification in urban areas using mobile sensors and self-organizing maps. Int. J. Environ. Sci. Technol. 16, 5475–5488 (2019). https://doi.org/10.1007/s13762-018-2060-9
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DOI: https://doi.org/10.1007/s13762-018-2060-9