The knowledge of the urban air quality represents the first step to face air pollution issues. For the last decades many cities can rely on a network of monitoring stations recording concentration values for the main pollutants. This paper focuses on functional principal component analysis (FPCA) to investigate multiple pollutant datasets measured over time at multiple sites within a given urban area. Our purpose is to extend what has been proposed in the literature to data that are multisite and multivariate at the same time. The approach results to be effective to highlight some relevant statistical features of the time series, giving the opportunity to identify significant pollutants and to know the evolution of their variability along time. The paper also deals with missing value issue. As it is known, very long gap sequences can often occur in air quality datasets, due to long time failures not easily solvable or to data coming from a mobile monitoring station. In the considered dataset, large and continuous gaps are imputed by empirical orthogonal function procedure, after denoising raw data by functional data analysis and before performing FPCA, in order to further improve the reconstruction.

Ruggieri, M., Plaia, A., Di Salvo, F., Agrò, G. (2013). Functional Principal Component Analysis for the explorative analysis of multisite-multivariate air pollution time series with long gaps. JOURNAL OF APPLIED STATISTICS, 40, 795-807 [10.1080/02664763.2012.754852].

Functional Principal Component Analysis for the explorative analysis of multisite-multivariate air pollution time series with long gaps

RUGGIERI, Mariantonietta;PLAIA, Antonella;DI SALVO, Francesca;AGRO', Gianna
2013-01-01

Abstract

The knowledge of the urban air quality represents the first step to face air pollution issues. For the last decades many cities can rely on a network of monitoring stations recording concentration values for the main pollutants. This paper focuses on functional principal component analysis (FPCA) to investigate multiple pollutant datasets measured over time at multiple sites within a given urban area. Our purpose is to extend what has been proposed in the literature to data that are multisite and multivariate at the same time. The approach results to be effective to highlight some relevant statistical features of the time series, giving the opportunity to identify significant pollutants and to know the evolution of their variability along time. The paper also deals with missing value issue. As it is known, very long gap sequences can often occur in air quality datasets, due to long time failures not easily solvable or to data coming from a mobile monitoring station. In the considered dataset, large and continuous gaps are imputed by empirical orthogonal function procedure, after denoising raw data by functional data analysis and before performing FPCA, in order to further improve the reconstruction.
2013
Settore SECS-S/01 - Statistica
Ruggieri, M., Plaia, A., Di Salvo, F., Agrò, G. (2013). Functional Principal Component Analysis for the explorative analysis of multisite-multivariate air pollution time series with long gaps. JOURNAL OF APPLIED STATISTICS, 40, 795-807 [10.1080/02664763.2012.754852].
File in questo prodotto:
File Dimensione Formato  
02664763.2012.pdf

Solo gestori archvio

Descrizione: Articolo principale
Dimensione 510.65 kB
Formato Adobe PDF
510.65 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/78417
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 6
social impact