the Impact of Traffic Emissions on Human Health

Abstract. Artificial Neural Networks (ANN) have been essentially used as re-gression models to predict the concentration of one or more pollutants usually requiring information collected from air quality stations. In this work we con-sider a Multilayer Perceptron (MLP) with one hidden layer as a classifier of the impact of air quality on human health, using only traffic and meteorological da-ta as inputs. Our data was obtained from a specific urban area and constitutes a 2-class problem: above or below the legal limits of specific pollutant concentra-tions. The results show that an MLP with 40 to 50 hidden neurons and trained with the cross-entropy cost function, is able to achieve a mean error around 11%, meaning that air quality impacts can be predicted with good accuracy us-ing only traffic and meteorological data. The use of an ANN without air quality inputs constitutes a significant achievement because governments may therefore minimize the use of such expensive stations