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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Autores principales:	Alicja Kolasa-Więcek, Dariusz Suszanowicz, Agnieszka A. Pilarska, Krzysztof Pilarski
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	air pollution fuel combustion hard coal energy industry transportation emissions Technology T
Acceso en línea:	https://doaj.org/article/22936efe9aa0469d9bac575b12dec9ab
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spelling	oai:doaj.org-article:22936efe9aa0469d9bac575b12dec9ab2021-11-11T15:44:09ZModelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland10.3390/en142168911996-1073https://doaj.org/article/22936efe9aa0469d9bac575b12dec9ab2021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/6891https://doaj.org/toc/1996-1073The 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.Alicja Kolasa-WięcekDariusz SuszanowiczAgnieszka A. PilarskaKrzysztof PilarskiMDPI AGarticleair pollutionfuel combustionhard coalenergy industrytransportationemissionsTechnologyTENEnergies, Vol 14, Iss 6891, p 6891 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	air pollution fuel combustion hard coal energy industry transportation emissions Technology T
spellingShingle	air pollution fuel combustion hard coal energy industry transportation emissions Technology T Alicja Kolasa-Więcek Dariusz Suszanowicz Agnieszka A. Pilarska Krzysztof Pilarski Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland
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.
format	article
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
title	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_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_sort	modelling the interaction between air pollutant emissions and their key sources in poland
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/22936efe9aa0469d9bac575b12dec9ab
work_keys_str_mv	AT alicjakolasawiecek modellingtheinteractionbetweenairpollutantemissionsandtheirkeysourcesinpoland AT dariuszsuszanowicz modellingtheinteractionbetweenairpollutantemissionsandtheirkeysourcesinpoland AT agnieszkaapilarska modellingtheinteractionbetweenairpollutantemissionsandtheirkeysourcesinpoland AT krzysztofpilarski modellingtheinteractionbetweenairpollutantemissionsandtheirkeysourcesinpoland
_version_	1718434072490934272

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

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