Air quality assessment via functional principal component analysis

Research output: Contribution to conferenceOther

Abstract

The knowledge of the global urban air quality situation represents the first step to face air pollution issues.For the last decades many urban areas can rely on a monitoring network, recording hourly data for the mainpollutants. Such data need to be aggregated according to different dimensions, such as time, space and typeof pollutant, in order to provide a synthetic air quality index which takes into account interactions amongpollutants and correlation among monitoring sites.This paper focuses on Functional Principal Componenttechniques for the statistical analysis of a set of environmental data x(spt), where s stands for the monitoringsite, p for the pollutant and t for time, usually days (after the aggregation according to national agencyguidelines). This approach could highlight some relevant statistical features of time series from an explorativepoint of view, and, consequently, new opportunities to obtain a synthetic AQI. The analysis will be illustratedby considering the data concerning the daily values of the 5 main pollutants collected in Palermo during 2006.
Original languageEnglish
Number of pages0
Publication statusPublished - 2009

Fingerprint Dive into the research topics of 'Air quality assessment via functional principal component analysis'. Together they form a unique fingerprint.

Cite this