Air quality assessment via functional principal component analysis

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 main pollutants. Such data need to be aggregated according to different dimensions, such as time, space and type of pollutant, in order to provide a synthetic air quality index which takes into account interactions among pollutants and correlation among monitoring sites.This paper focuses on Functional Principal Component techniques for the statistical analysis of a set of environmental data x(spt), where s stands for the monitoring site, p for the pollutant and t for time, usually days (after the aggregation according to national agency guidelines). This approach could highlight some relevant statistical features of time series from an explorative point of view, and, consequently, new opportunities to obtain a synthetic AQI. The analysis will be illustrated by considering the data concerning the daily values of the 5 main pollutants collected in Palermo during 2006.