Small-Particle Pollution Modeling Using Fuzzy Approaches

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 256)

Abstract

Air pollution caused by small particles is a major public health problem in many cities of the world. One of the most contaminated cities is Mexico City. The fact that it is located in a volcanic crater surrounded by mountains helps thermal inversion and imply a huge pollution problem by trapping a thick layer of smog that float over the city. Modeling air pollution is a political and administrative important issue due to the fact that the prediction of critical events should guide decision making. The need for countermeasures against such episodes requires predicting with accuracy and in advance relevant indicators of air pollution, such are particles smaller than 2.5 microns (PM2.5). In this work two different fuzzy approaches for modeling PM2.5 concentrations in Mexico City metropolitan area are compared with respect the simple persistence method.

Keywords

Air Pollution Modeling PM2.5 Pollution Fuzzy Inductive Reasoning ANFIS Persistence Time Series Analysis 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Soft Computing Research GroupTechnical University of CataloniaBarcelonaSpain

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