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 ma-trix is formulated using air quality monitoring data for specific pollutant emissions,with the primary objective to analyze the corresponding stationary distributions andevaluate sceneries for the air quality impact of...

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Autores Principales: Hoyos, Luis, Lara Velázquez, Pedro, Ortiz, Elba, López Bracho, Rafael, González, Jesús
Formato: Artículo
Idioma: Español
Publicado: 2015
Acceso en línea: http://revistas.ucr.ac.cr/index.php/matematica/article/view/305
http://hdl.handle.net/10669/12962
Sumario:     This paper proposes a Markov observation based model, where the transition ma-trix is formulated using air quality monitoring data for specific pollutant emissions,with the primary objective to analyze the corresponding stationary distributions andevaluate sceneries for the air quality impact of pollution control policies. The modelis non predictive and could be applied to every source of pollutant emissions includedin air monitoring data. Two cases of study are presented, ozone and sulfur, overcentral zone of Mexico City for a seven years span from 2000 to 2006. For presenta-tion purposes each year were divided in two semesters. In ozone case, the stationarydistribution for both semesters shows a probability diminution of the higher ozone con-centrate levels, with tendency to ”piston effect”. In the sulfur case, the first semesterdisplays an oscillatory behavior with a little tendency to diminution of the higher sul-fur concentrate levels, the second semester had decreasing probabilities of the highersulfur levels. The results support an small improvement of air quality and then a fa-vorable evaluation of the diverse pollution control policies that had been implementedin Mexico City over the last several years.Keywords: Markov chain, stationary distribution, air pollution, sulfur emissions,ozone emissions, Mexico City.