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 PM10 concentrations. These results may provide helpful insights for understanding air pollution dynamics and support policymakers in identifying intervention areas.

Aiello, L., Legramanti, S., Paci, L., A spatial product partition model for PM10 data, in Methodological and Applied Statistics and Demography III, (Bari, 17-20 June 2024), Springer, Cham 2024:<<ITALIAN STATISTICAL SOCIETY SERIES ON ADVANCES IN STATISTICS>>, 8-13. [10.1007/978-3-031-64431-3] [https://hdl.handle.net/10807/310417]

A spatial product partition model for PM10 data

Paci, Lucia
2024

Abstract

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 PM10 concentrations. These results may provide helpful insights for understanding air pollution dynamics and support policymakers in identifying intervention areas.
2024
Inglese
Methodological and Applied Statistics and Demography III
SIS 2024
Bari
17-giu-2024
20-giu-2024
9783031644306
Springer
Aiello, L., Legramanti, S., Paci, L., A spatial product partition model for PM10 data, in Methodological and Applied Statistics and Demography III, (Bari, 17-20 June 2024), Springer, Cham 2024:<<ITALIAN STATISTICAL SOCIETY SERIES ON ADVANCES IN STATISTICS>>, 8-13. [10.1007/978-3-031-64431-3] [https://hdl.handle.net/10807/310417]
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/310417
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact