Mining Temporal Associations Between Air Pollution and Effects on the Human Health

The task of monitoring and improving the urban air quality has attracted a great deal of interest both from national governments and scientific communities. In order to implement policies for the environmental protection, the recent urban planning decisions are often based on the results produced by several research fields. An important research direction aims at understanding the pollution phenomenon by means of data mining approaches, which support decision makers with information extracted directly from data. In this work we investigate the effect of air pollution on human health by taking into account the temporal variability of environmental data. Since the repercussions of air pollution on humans are perceived only after a certain lapse of time, we propose to discover temporal associations which relate a change at time tj of the population health conditions with a change at time ti (tj>ti) of the polluting emissions. Information conveyed by discovered temporal associations could be exploited both to support policies for environmental protection and to adopt strategies for the reduction of human health risks.