This work illustrates a model-based clustering method for analyzing PM10 measurements over time. In particular, we develop a Bayesian dynamic linear model coupled with a spatial product partition model for clustering monitoring stations that exhibit similar persistence and variability of the PM10 concentrations over time. The model integrates spatial information (the locations of the considered monitoring stations) into the clustering process in order to increase the probability that neighboring stations will be assigned to the same cluster. This methodology is applied to the time series of daily PM10 measurements recorded by 110 monitoring stations in Austria. Our analysis reveals three spatially cohesive clusters characterized by different levels of persistence and variability of the PM \(_{10}\) concentrations. These results may provide helpful insights for understanding air pollution dynamics and support policymakers in identifying intervention areas.