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Article

Air Pollution Dispersion Modelling Using Spatial Analyses

1
Department of Environmental Protection in Industry, Faculty of Metallurgy and Material Engineering, VSB—Technical University of Ostrava, 17.listopadu 15/2172, 708 33 Ostrava, Czech Republic
2
Institute of Environmental Technology (IET), VSB—Technical University of Ostrava, 17.listopadu 15/2172, 708 33 Ostrava, Czech Republic
3
Joint Institute for Nuclear Research (JINR), Joliot-Curie 6, 141980 Dubna, Moscow Region, Russia
4
Institute of Geoinformatics, Faculty of Mining and Geology, VSB—Technical University of Ostrava, 17.listopadu 15/2172, 708 33 Ostrava, Czech Republic
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(12), 489; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120489
Received: 8 October 2018 / Revised: 18 November 2018 / Accepted: 15 December 2018 / Published: 19 December 2018
(This article belongs to the Special Issue GIS for Safety & Security Management)
Air pollution dispersion modelling via spatial analyses (Land Use Regression—LUR) is an alternative approach to the standard air pollution dispersion modelling techniques in air quality assessment. Its advantages are mainly a much simpler mathematical apparatus, quicker and simpler calculations and a possibility to incorporate more factors affecting pollutant’s concentration than standard dispersion models. The goal of the study was to model the PM10 particles dispersion via spatial analyses in the Czech–Polish border area of the Upper Silesian industrial agglomeration and compare the results with the results of the standard Gaussian dispersion model SYMOS’97. The results show that standard Gaussian model with the same data as the LUR model gives better results (determination coefficient 71% for Gaussian model to 48% for LUR model). When factors of the land cover were included in the LUR model, the LUR model results improved significantly (65% determination coefficient) to a level comparable with the Gaussian model. A hybrid approach of combining the Gaussian model with the LUR gives superior quality of results (86% determination coefficient). View Full-Text
Keywords: pollution dispersion; air quality; land use regression; Symos’97 pollution dispersion; air quality; land use regression; Symos’97
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MDPI and ACS Style

Bitta, J.; Pavlíková, I.; Svozilík, V.; Jančík, P. Air Pollution Dispersion Modelling Using Spatial Analyses. ISPRS Int. J. Geo-Inf. 2018, 7, 489. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120489

AMA Style

Bitta J, Pavlíková I, Svozilík V, Jančík P. Air Pollution Dispersion Modelling Using Spatial Analyses. ISPRS International Journal of Geo-Information. 2018; 7(12):489. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120489

Chicago/Turabian Style

Bitta, Jan, Irena Pavlíková, Vladislav Svozilík, and Petr Jančík. 2018. "Air Pollution Dispersion Modelling Using Spatial Analyses" ISPRS International Journal of Geo-Information 7, no. 12: 489. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120489

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