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The present paper discusses a novel methodology based on neural network to determine air pollutants’ correlation with life expectancy in European countries. The models were developed using historical data from the period 1992–2016, for a set of 20 European countries. The subject of the analysis included the input variables of the following air pollutants: sulphur oxides, nitrogen oxides, carbon monoxide, particulate matters, polycyclic aromatic hydrocarbons and non-methane volatile organic compounds. Our main findings indicate that all the variables significantly affect life expectancy. Sensitivity of constructed neural networks to pollutants proved to be particularly important in the case of changes in the value of particulate matters, sulphur oxides and non-methane volatile organic compounds. The most frequent association was found for fine particle. Modelled courses of changes in the variable under study coincide with the actual data, which confirms that the proposed models generalize acquired knowledge well.

Air pollution in European countries and life expectancy—modelling with the use of neural network