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

Federico Amato, Mohamed Laib, Fabian Guignard and Mikhail Kanevski

Physica A: Statistical Mechanics and its Applications, 2020, vol. 547, issue C

Abstract: Air pollution is known to be a major threat to 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 NO2, O3 and PM2.5, 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. Clustering analysis is used to recognize within the measurements of same pollutant groups of stations behaving in a similar way. The results clearly demonstrate the relationship between air pollution probability densities and land use activities.

Keywords: Air pollution; Fisher information measure; Shannon entropy; Time series clustering; Complexity-invariant distance (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:547:y:2020:i:c:s0378437120301497

DOI: 10.1016/j.physa.2020.124391

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