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arXiv · 1909.11484

Analysis of air pollution time series using complexity-invariant distance and information measures

Federico Amato·Mohamed Laib·Fabian Guignard·Mikhail Kanevski
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Abstract

Air pollution is known to be a major threat for human and ecosystem health. A proper understanding of the factors generating pollution and of the behavior of air pollution in time is crucial to support the development of effective policies aiming at the reduction of pollutant concentration. This paper considers the hourly time series of three pollutants, namely NO$_2$, O$_3$ and PM$_{2.5}$, collected on sixteen measurement stations in Switzerland. The air pollution patterns due to the location of measurement stations and their relationship with anthropogenic activities, and specifically land use, are studied using two approaches: Fisher-Shannon information plane and complexity-invariant distance between time series. A clustering analysis is used to recognize within the measurements of a same pollutant group of stations behaving in a similar way. The results clearly demonstrate the relationship between the air pollution probability densities and land use activities.

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Federico Amato, Mohamed Laib, Fabian Guignard, Mikhail Kanevski. 2019-09-21. Analysis of air pollution time series using complexity-invariant distance and information measures. https://doi.org/10.1016/j.physa.2020.124391

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