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

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