Time series clustering of air quality monitoring stations – The study case of the Beijing municipality.

Since air pollution is a major public health concern, collecting and analyzing air quality indicators data is very important for monitoring pollution. Data on these indicators are generally collected through air quality monitoring stations located in specific areas. In this study, 12 monitoring sites belonging to the Beijing Municipal Environmental Monitoring Center air pollution monitoring network are clustered based on their similarity in terms of hourly or monthly concentrations of some air pollutants. The hourly data was collected from these stations between March 2013 and February 2017. The clustering procedure was performed through average linkage and partitioning around medoids algorithms. The preliminary results obtained from the hierarchical algorithm show the presence of two clusters that are well distinguished with meaningful interpretation while the two obtained from the partitional algorithm are not well distinguished. [ABSTRACT FROM AUTHOR]