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Air pollution is a critical global health concern, responsible for millions of premature deaths each year. Particulate matter (PM), a major pollutant, comprises airborne particles from both natural and anthropogenic sources. In particular, fine particles such as PM \(_{10}\) ( \(\le 10\,\upmu \) m) pose significant health risks. Hence, continuous monitoring of PM \(_{10}\) levels is essential for mitigating hazardous exposure. This study employs a Bayesian spatial product partition model to analyze geo-referenced PM \(_{10}\) data. An efficient Markov Chain Monte Carlo algorithm is implemented to make posterior inference about the clustering of PM \(_{10}\) monitoring stations. To speed up the computation, we exploit the fact that the precision matrices in the proposed model are tridiagonal. The method is illustrated by analyzing daily PM \(_{10}\) levels collected in 2018 over Austria.

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Computationally Efficient Clustering of PM \(_{10}\) Time Series Data

  • 会议论文

  • 发表日期：

    2025年

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  • Luca Aiello,
  • Raffaele Argiento,
  • Sirio Legramanti,
  • Lucia Paci

摘要

Air pollution is a critical global health concern, responsible for millions of premature deaths each year. Particulate matter (PM), a major pollutant, comprises airborne particles from both natural and anthropogenic sources. In particular, fine particles such as PM \(_{10}\) ( \(\le 10\,\upmu \) m) pose significant health risks. Hence, continuous monitoring of PM \(_{10}\) levels is essential for mitigating hazardous exposure. This study employs a Bayesian spatial product partition model to analyze geo-referenced PM \(_{10}\) data. An efficient Markov Chain Monte Carlo algorithm is implemented to make posterior inference about the clustering of PM \(_{10}\) monitoring stations. To speed up the computation, we exploit the fact that the precision matrices in the proposed model are tridiagonal. The method is illustrated by analyzing daily PM \(_{10}\) levels collected in 2018 over Austria.