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Air Quality, Atmosphere & Health

, Volume 9, Issue 8, pp 847–859 | Cite as

A wavelet-based approach applied to suspended particulate matter time series in Portugal

  • Ana M. J. CruzEmail author
  • Célia Alves
  • Sónia Gouveia
  • Manuel G. Scotto
  • Maria do Carmo Freitas
  • Hubert Th Wolterbeek
Article

Abstract

This study intends to analyse the particulate matter (PM) levels in Portugal (mainland and islands) throughout a 3-year period. Although a decreasing trend has been observed, the WHO guidelines for the PM10 and PM2.5 annual mean concentrations have been exceeded in all monitoring stations. Most inland urban, rural and suburban sites follow a pronounced seasonal variation with much higher values in winter than in summer. Lower levels and a weak seasonal variability were registered in the two urban background stations of Madeira Island, which are permanently under the influence of clean air masses over the Atlantic. Receiving long-range transported pollution, rural stations located in mountain sites presented an opposite seasonal pattern, with higher levels in summer. Diurnal profiles were also analysed and compared between stations. A mining process was also carried out, consisting in the application of multi-scale wavelet transforms, data pattern identification using cluster analysis and examination of the contribution to the total variance/covariance of the time series per wavelet scale for all stations. Groups of stations exhibiting similar variance/covariance profiles were identified. One group contains urban and rural stations with diurnal and daily time scales. Urban background stations located in the island of Madeira constitute another cluster, corresponding to higher wavelet scales (lower periodicity phenomena). One traffic station in the Oporto metropolitan area was grouped with a suburban/industrial station of central Portugal, suggesting the need for reclassification in what concerns the type of environmental influence.

Keywords

Air quality monitoring stations PM Air mass trajectories Wavelets Classification Clustering 

Notes

Acknowledgments

This work was partially supported by the Portuguese Foundation for Science and Technology (FCT), with national (MEC) and European structural funds through the programmes FEDER, under the partnership agreement PT2020—within IEETA/UA project UID/CEC/00127/2013 (Instituto de Engenharia Eletrónica e Informática de Aveiro, IEETA/UA, Aveiro, www.ieeta.pt) and CIDMA/UA project UID/MAT/04106/2013 (Centro de Investigação e Desenvolvimento em Matemática e Aplicações, CIDMA/UA, Aveiro, www.cidma.mat.ua.pt). S. Gouveia acknowledges the postdoctoral grant by FCT (ref. SFRH/BPD/87037/2012).

Supplementary material

11869_2016_393_MOESM1_ESM.docx (190 kb)
ESM. 1 (DOCX 189 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Ana M. J. Cruz
    • 1
    • 2
    Email author
  • Célia Alves
    • 3
  • Sónia Gouveia
    • 4
  • Manuel G. Scotto
    • 5
  • Maria do Carmo Freitas
    • 6
  • Hubert Th Wolterbeek
    • 2
  1. 1.Oliveira do Hospital, Escola Superior de Tecnologia e Gestão de Oliveira do HospitalInstituto Politécnico de CoimbraOliveira do HospitalPortugal
  2. 2.College of Applied Sciences, Department of Radiation, Radionuclides and Reactors, Section RIHTechnical University of DelftDelftThe Netherlands
  3. 3.CESAM, Department of EnvironmentUniversity of AveiroAveiroPortugal
  4. 4.Institute of Electronics and Informatics Engineering of Aveiro (IEETA) and Center for Research & Development in Mathematics and Applications (CIDMA)University of AveiroAveiroPortugal
  5. 5.CEMAT, Department of Mathematics, Instituto Superior TécnicoUniversity of LisbonLisbonPortugal
  6. 6.Centre for Nuclear Sciences and Technologies, Superior Technical InstituteUniversity of LisbonBobadela LRSPortugal

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