evaluation of air pollution control policies in mexico city using finite markov chain observation model

This paper proposes a Markov observation based model, where the transition matrix is formulated using air quality monitoring data for specific pollutant emissions, with the primary objective to analyze the corresponding stationary distributions and evaluate sceneries for the air quality impact of pollution control policies. The model is non predictive and could be applied to every source of pollutant emissions included in air monitoring data. Two cases of study are presented, ozone and sulfur, over central zone of Mexico City for a seven years span from 2000 to 2006. For presentation purposes each year were divided in two semesters. In ozone case, the stationary distribution for both semesters shows a probability diminution of the higher ozone concentrate levels, with tendency to ”piston effect”. In the sulfur case, the first semester displays an oscillatory behavior with a little tendency to diminution of the higher sulfur concentrate levels, the second semester had decreasing probabilities of the higher sulfur levels. The results support an small improvement of air quality and then a favorable evaluation of the diverse pollution control policies that had been implemented in Mexico City over the last several years.