In this paper, we present the application of statistical tools for data optimization in air quality monitoring net-works, particularly analysing data correlation structure with multivariate statistical techniques and applying a method based on the Shannon index to evaluate the possible exclu-sion of monitoring stations or measured pollutants appear-ing as “the least informative”. Our goal is the definition of a simple procedure for identifying the redundancy in air quality data sets. The procedure results may be useful both to evaluate effectiveness and efficiency of existing net-works, and to select the data sub-sets more suitable for analysing, modelling and reporting AQM data.
Statistical tools for data optimization in air quality monitoring networks
RAGOSTA, Maria
2007-01-01
Abstract
In this paper, we present the application of statistical tools for data optimization in air quality monitoring net-works, particularly analysing data correlation structure with multivariate statistical techniques and applying a method based on the Shannon index to evaluate the possible exclu-sion of monitoring stations or measured pollutants appear-ing as “the least informative”. Our goal is the definition of a simple procedure for identifying the redundancy in air quality data sets. The procedure results may be useful both to evaluate effectiveness and efficiency of existing net-works, and to select the data sub-sets more suitable for analysing, modelling and reporting AQM data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
