Identification of air quality redundant stations through a clustering ensemble method

Abstract

The main purpose of this study is to assess the performance of three air quality monitoring networks of Mexico. Emphasis is placed on an ensemble method to combine the results of the different clustering techniques: Principle Component Analysis, Hierarchical Clustering and k-means. The specific objectives of this paper are: (i) finding similar and redundant stations using the ensemble method and (ii) giving a physical meaning to groups of similar stations by evaluating additional information like emission sources, meteorology and topography of the area of interest. The study was applied on time series data of particulates that have aerodynamic diameters less than or equal to 10 μm (PM10) and ozone (O3), acquired from the air pollutant monitoring systems in the metropolitan areas of Mexico City (MCMA), Monterrey (MMA) and Guadalajara (GMA). These three conurbations are characterized by diverse meteorological and geographical conditions. The findings show that the GMA has a well distributed air quality network with the fewest number of similar stations. The MMA presents the same clusters of stations for PM10 and O3, while in the MCMA a cluster of possible redundant stations is found. Results confirm that the clustering ensemble method is a confidence tool to identify similar stations.