Using Bayesian networks for environmental health risk assessment

International audienceThe study investigated the potential relationships between air pollution, socio-economy, and proven pathologies (e.g., respiratory, cardiovascular) within an industrial area in Southern France (Etang de Berre), gathering steel industries, oil refineries, shipping, road traffic and experiencing a Mediterranean climate. A total of 178 variables were simultaneously integrated within a Bayesian model at intra-urban scale. Various unsupervised and supervised algorithms (maximum spanning tree, tree-augmented naive classifier) as well as sensitivity analyses were used to better understand the links between all variables, and highlighted correlations between population exposure to air pollutants and some pathologies. Adverse health effects (bronchus and lung cancers for 15–65 years old people) were observed for hydrofluoric acid at low background concentration (