Wavelets-based clustering of air quality monitoring sites

Abstract

This paper aims at providing a variance/ covariance profile of a set of 36 monitoring stations measuring ozone (O3) and nitrogen dioxide (NO2) hourly concentrations, collected over the period 2005–2013, in Portugal mainland. The resulting individual profiles are embedded in a wavelet decomposition-based clustering algorithm in order to identify groups of stations exhibiting similar profiles. The results of the cluster analysis identify three groups of stations, namely urban, suburban/urban/rural, and a third group containing all but one rural stations. The results clearly indicate a geographical pattern among urban stations, distinguishing those located in Lisbon area from those located in Oporto/North. Furthermore, for urban stations, intra-diurnal and daily time scales exhibit the highest variance. This is due to the more relevant chemical activity occurring in high NO2 emissions areas which are responsible for high variability on daily profiles. These chemical processes also explain the reason for NO2 and O3 being highly negatively cross-correlated in suburban and urban sites as compared with rural stations. Finally, the clustering analysis also identifies sites which need revision concerning classification according to environment/ influence type.