Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland
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 pollu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/21/6891 |
| _version_ | 1797512619442044928 |
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| author | Alicja Kolasa-Więcek Dariusz Suszanowicz Agnieszka A. Pilarska Krzysztof Pilarski |
| author_facet | Alicja Kolasa-Więcek Dariusz Suszanowicz Agnieszka A. Pilarska Krzysztof Pilarski |
| author_sort | Alicja Kolasa-Więcek |
| collection | DOAJ |
| description | 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<sub>2</sub>, NO<sub>x</sub>, 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<sub>x</sub>, CO and PM pollutants indicates the significance of all input variables. For SO<sub>2</sub>, 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. |
| first_indexed | 2024-03-10T06:04:21Z |
| format | Article |
| id | doaj.art-22936efe9aa0469d9bac575b12dec9ab |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T06:04:21Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-22936efe9aa0469d9bac575b12dec9ab2023-11-22T20:40:27ZengMDPI AGEnergies1996-10732021-10-011421689110.3390/en14216891Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in PolandAlicja Kolasa-Więcek0Dariusz Suszanowicz1Agnieszka A. Pilarska2Krzysztof Pilarski3Institute of Environmental Engineering and Biotechnology, Faculty of Natural Sciences and Technology, University of Opole, Kominka 6, 46-020 Opole, PolandInstitute of Environmental Engineering and Biotechnology, Faculty of Natural Sciences and Technology, University of Opole, Kominka 6, 46-020 Opole, PolandDepartment of Dairy and Process Engineering, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznań, PolandDepartment of Biosystems Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, PolandThe 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<sub>2</sub>, NO<sub>x</sub>, 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<sub>x</sub>, CO and PM pollutants indicates the significance of all input variables. For SO<sub>2</sub>, 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.https://www.mdpi.com/1996-1073/14/21/6891air pollutionfuel combustionhard coalenergy industrytransportationemissions |
| spellingShingle | Alicja Kolasa-Więcek Dariusz Suszanowicz Agnieszka A. Pilarska Krzysztof Pilarski Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland Energies air pollution fuel combustion hard coal energy industry transportation emissions |
| title | Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland |
| title_full | Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland |
| title_fullStr | Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland |
| title_full_unstemmed | Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland |
| title_short | Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland |
| title_sort | modelling the interaction between air pollutant emissions and their key sources in poland |
| topic | air pollution fuel combustion hard coal energy industry transportation emissions |
| url | https://www.mdpi.com/1996-1073/14/21/6891 |
| work_keys_str_mv | AT alicjakolasawiecek modellingtheinteractionbetweenairpollutantemissionsandtheirkeysourcesinpoland AT dariuszsuszanowicz modellingtheinteractionbetweenairpollutantemissionsandtheirkeysourcesinpoland AT agnieszkaapilarska modellingtheinteractionbetweenairpollutantemissionsandtheirkeysourcesinpoland AT krzysztofpilarski modellingtheinteractionbetweenairpollutantemissionsandtheirkeysourcesinpoland |