Analysis and evolution of air quality monitoring networks using combined statistical information indexes

Abstract

In this work, we present combined statistical indexes for evaluating air quality monitoring networks based on concepts derived from the information theory and Kullback Liebler divergence. More precisely, we introduce: (1) the standard measure of complementary mutual information or ‘specificity’ index; (2) a new measure of information gain or ‘representativity’ index; (3) the information gaps associated with the evolution of a network and (4) the normalised information distance used in clustering analysis. All these information concepts are illustrated by applying them to 14 yr of data collected by the air quality monitoring network in Santiago de Chile (33.5 S, 70.5 W, 500 m a.s.l.). We find that downtown stations, located in a relatively flat area of the Santiago basin, generally show high ‘representativity’ and low ‘specificity’, whereas the contrary is found for a station located in a canyon to the east of the basin, consistently with known emission and circulation patterns of Santiago. We also show interesting applications of information gain to the analysis of the evolution of a network, where the choice of background information is also discussed, and of mutual information distance to the classifications of stations. Our analyses show that information as those presented here should of course be used in a complementary way when addressing the analysis of an air quality network for planning and evaluation purposes.