Analysis of air quality monitoring networks by functional clustering

Air quality monitoring networks are important tools in management and evaluation of air quality. Classifying monitoring stations via homogeneous clusters allows identification of similarities in pollution, of representative sites, and of spatial patterns. Instead of summaries by statistical indicators, we propose to consider the air pollutant concentrations as functional data. We then classify using functional cluster analysis, where Partitioning Around Medoids (PAM) algorithm is embedded. The proposed data analysis approach is applied to the air quality monitoring network in Piemonte (Northern Italy); we consider the three more critical pollutants: NO2 , PM10, and O3.