Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland

Alicja Kolasa-Więcek, Dariusz Suszanowicz, Agnieszka A. Pilarska and Krzysztof Pilarski
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Alicja Kolasa-Więcek: Institute of Environmental Engineering and Biotechnology, Faculty of Natural Sciences and Technology, University of Opole, Kominka 6, 46-020 Opole, Poland
Dariusz Suszanowicz: Institute of Environmental Engineering and Biotechnology, Faculty of Natural Sciences and Technology, University of Opole, Kominka 6, 46-020 Opole, Poland
Agnieszka A. Pilarska: Department of Dairy and Process Engineering, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, Poland
Krzysztof Pilarski: Department of Biosystems Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland

Energies, 2021, vol. 14, issue 21, 1-14

Abstract: The main purpose of this study is to investigate the relationships between key sources of air pollutant emissions (sources of energy production, factories which are particularly harmful to the environment, the fleets of cars, environmental protection expenditure) and the main environmental air pollution (SO 2 , NO x , CO and PM) in Poland. Models based on MLP neural networks were used as predictive models. Global sensitivity analysis was used to demonstrate the significant impact of individual network input variables on the output variable. To verify the effectiveness of the models created, the actual data were compared with the data obtained through modelling. Projected courses of changes in the variables under study correspond with the real data, which confirms that the proposed models generalize acquired knowledge well. The high MLP network quality parameters of 0.99–0.85 indicate that the network generalizes the acquired knowledge accurately. The sensitivity analysis for NO x , CO and PM pollutants indicates the significance of all input variables. For SO 2 , it showed significance for four of the six variables analysed. The predictions made by the neural models are not very different from the experimental values.

Keywords: air pollution; fuel combustion; hard coal; energy industry; transportation; emissions; modelling; neural networks; MLP (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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