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

Abstract

A B S T R A C TIn this work, we present combined statistical indexes for evaluating air quality monitoring networks based on concepts derived from the information theory and KullbackÁLiebler 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.

Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

“…Many attractive criteria have been proposed: root mean square errors of network-based estimation of the field, information-theoretical based criteria, etc. Such criteria have been investigated in atmospheric chemistry in many studies conducted by environmental statisticians, more recently for instance by Fuentes et al (2007) and Osses et al (2013). Nowadays, the network design issue also concerns the sparse ground networks of greenhouse gas monitoring at mesoscales and global scales (Rayner, 2004;Lauvaux et al, 2012), which in our context can be seen mostly as tracers of atmospheric transport.…”

Analysis and optimal design of air quality monitoring networks using a variational approach

“…All in all, our results for summer conditions suggest that downtown stations are important from the point of view of total information content regarding CO. A previous study that considered multiple species using statistical information indexes by Osses et al (2013) also suggested retention of downtown stations. These stations were in fact deemed to be the most representative of dispersion patterns for the Santiago basin, considering all species and all hours of the day, and all seasons of the year.…”

“…We further address the evolution of the air quality network in terms of its information gain by taking into account both the number and the location of CO stations. We discuss the pros and cons of the variational approach introduced here and compare it with the results of a statistical approach previously presented by Osses et al (2013).…”

“…An alternative but equivalent definition uses the KullbackLiebler distance (Kullback, 1959) as described in Osses et al (2013). Since m ponders the relative importance of the errors in observations and emissions, the extreme cases correspond to no knowledge of observations relative to emissions, that is, m0' so i g 00, and perfect knowledge of observations relative to emissions, that is, m00 so i g 0'.…”

“…Of course, many of these methodologies allow network reduction or extension and weighted criteria under suitable social, economic or technical restrictions (e.g. Langstaff et al, 1967;Husain and Khan, 1983;Shindo et al, 1990;TrujilloÁVentura and Ellis, 1991;Nychka and Saltzman, 1998;Tseng and Chang, 2001;Bocquet, 2005aBocquet, , 2005bKanaroglou et al, 2005;Kao and Hsieh, 2006;Abida et al, 2008;Elkamel et al, 2008;Abida and Bocquet, 2009;Ainslie et al, 2009;Mofarrah and Husain, 2010;Wu et al, 2010;Romary et al, 2011;Saunier et al, 2011;Sturman et al, 2011;Wu and Bocquet, 2011;Ruiz-Ca´rdenas et al, 2012;Osses et al, 2013;Singh et al, 2013;Wang et al, 2013;Cioaca and Sandu, 2014;Go´mez-Losada et al, 2014;Romary et al, 2014;Zidek et al, 2000Zidek et al, , 2014.…”