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

TL;DR

This study analyzes hourly air pollution time series from Swiss stations using complexity-invariant distance and information measures to understand pollution patterns and their relation to land use activities.

Contribution

It introduces a combined approach using Fisher-Shannon information and complexity-invariant distance to analyze air pollution time series and their spatial relationships.

Findings

Pollution patterns are linked to land use activities.

Stations with similar pollution behavior form identifiable clusters.

Information measures reveal the influence of anthropogenic land use.

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.