Modeling the transition behaviors of PM10 pollution index

Abstract

Modeling and evaluating the behavior of particulate matter (PM10) is an important step in obtaining valuable information that can serve as a basis for environmental risk management, planning, and controlling the adverse effects of air pollution. This study proposes the use of a Markov chain model as an alternative approach for deriving relevant insights and understanding of PM10 data. Using first- and higher-order Markov chains, we analyzed daily PM10 index data for the city of Klang, Malaysia and found the Markov chain model to fit the PM10 data well. Based on the fitted model, we comprehensively describe the stochastic behaviors in the PM10 index based on the properties of the Markov chain, including its states classification, ergodic properties, long-term behaviors, and mean return times. Overall, this study concludes that the Markov chain model provides a good alternative technique for obtaining valuable information from different perspectives for the analysis of PM10 data.

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Acknowledgments

The authors are indebted to the staff of Malaysian Department of Environment for providing PM10 index data. This research would not have been possible without the sponsorship of Universiti Kebangsaan Malaysia (grant no. DIP-2018-038).

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Correspondence to Nurulkamal Masseran.

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Masseran, N., Safari, M.A.M. Modeling the transition behaviors of PM10 pollution index. Environ Monit Assess 192, 441 (2020). https://doi.org/10.1007/s10661-020-08376-1

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Keywords

  • Air pollution modeling and assessment
  • Markov chain
  • PM10 behaviors