Abstract
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.
Highlights
Air pollution is a very important environmental factor
The goal of this study was to develop a new approach to modelling of life expectancy (LE) depending on air pollutants using artificial neural networks (ANN)
Our findings suggested that air quality plays an important role in LE of the residents of European countries
Summary
Air pollution is a very important environmental factor. In order to take effective actions aimed at reducing the impact of air pollution on people, ecosystems and climate, it is necessary to understand the causes of its formation, transportation and transformation in the atmosphere (European Environment Agency). Pollutants such as particulate matters (PM10, PM2.5), sulphur dioxide (SO2), volatile organic compound (VOC), carbon monoxide (CO) and nitrogen oxides (NOx) are mainly released from gasoline used in diesel-run vehicles, industrial plants and heating processes due to anthropogenic activities (Ashraf et al 2019). It is of vital importance to use ambient air quality measurements in order to get an insight into the air quality of a given region (Cacciotolo et al 2017)
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