Forecasting Air Pollution Contingencies Using Predictive Analytic Techniques

The proliferation of pollutants affects the world's population, mainly those who live in large cities. Neurological and cardiovascular dysfunctions have a correlation with air particulate matter concentration, among other chronic diseases. Therefore, it is important to utilize different methods of analysis to build predictive models that can identify possible concentrations of contaminants in metropolitan areas. This work presents a methodology that will enable the forecasting of severe pollution contingencies using weather measurements as input variables. This predictive analytical technique combines several mathematical and statistical tools, which we refer to as predictive factor association (PFA). We perform principal component analysis on the samples to determine possible causal relationships and reduce dimensionality, resulting in orthogonal linear combinations of the variables called sample scores. For categorical variables, each linear combination probability is estimated using a window-based estimation technique or a machine learning algorithm to determine the class of a particular sample. For continuous variables, regression over the scores is carried out. The technique is used to predict environmental contingencies in Monterrey's metropolitan area based on meteorological data with R2 between 0.7 and 0.8 and classification accuracies between 0.74 and 0.98.