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Lagona, F., Maruotti, A. and Picone, M. (2011) A Non-Homogeneous Hidden Markov Model for Analysis of Multi-Pollutant Exceedances Data. In: Dymarski, P., Ed., Hidden Markov Models: Theory and Applications, InTech, Croatia, 207-222.
https://doi.org/10.5772/14749

has been cited by the following article:

• TITLE: Air Quality Estimation Using Nonhomogeneous Markov Chains: A Case Study Comparing Two Rules Applied to Mexico City Data

  AUTHORS: Eliane R. Rodrigues, Juan A. Cruz-Juárez, Hortensia J. Reyes-Cervantes, Guadalupe Tzintzun

  KEYWORDS: Air Quality Index, Air Pollution, Mexico City, Nonhomogeneous Markov Chains, Bayesian Inference

  JOURNAL NAME: Journal of Environmental Protection, Vol.14 No.7, July 28, 2023

  ABSTRACT: A nonhomogeneous Markov chain is applied to the study of the air quality classification in Mexico City when the so-called criterion pollutants are used. We consider the indices associated with air quality using two regulations where different ways of classification are taken into account. Parameters of the model are the initial and transition probabilities of the chain. They are estimated under the Bayesian point of view through samples generated directly from the corresponding posterior distributions. Using the estimated parameters, the probability of having an air quality index in a given hour of the day is obtained.