Thesis Details

Air pollution is a current monitored problem in areas with high population density such as big cities. In this sense, the models should be accurate in order to generate better air quality evaluations; but in consequence they are complex. Nowadays, the artificial intelligence based on heuristic methods allow evaluating air quality parameters, providing a partial solution to this problem. Accordingly, this paper proposes a new evaluation model using fuzzy inferences combined with analytical hierarchy process providing an air quality index. Each environmental parameter is evaluated according to a toxicological level and then a fuzzy reasoning process, assesses different air quality situations. Additionally to each parameter is assigned a weight according to its importance on the air pollution impact. Finally, the model proposed considers into air quality, to five score stages: excellent, good, regular, bad and dangerous based on data from the Mexico City Atmospheric Monitoring System. The results are compared against conventional air quality models in the local and international order, showing a good performance. Therefore, it is a new tool for the air quality diagnosis for urban areas in where population health is carefully monitored.

Honorable Mention - Given by the academic comitee evaluating the thesis.