Maximum a posteriori estimation of the daily ozone peaks in Mexico City

Exposure to high levels of pollution is a persistent problem in large cities throughout the world. The ability to predict the occurrence of a high level of a pollutant allows environmental authorities to take preventive measures, such as controlling the emission of pollution. Communities and officials can also take actions to reduce the exposure of susceptible groups in the population. Therefore, being able to estimate the behavior of a given pollutant is of great importance. In this article we use a Markov chain model to study this behavior. In order to do so, we consider the sequence of the daily maximum measurements of a pollutant and let successive intervals containing them follow a Markov chain of order K>-0. The novelty here is that we allow K to be a random variable and estimate it and the corresponding transition probabilities using a maximum a posteriori method. The results are used to perform estimations about the behavior of ozone levels in Mexico City.