Preprint Article Version 1 This version is not peer-reviewed

Air Pollution Dispersion Modelling Using Spatial Analyses

Version 1 : Received: 8 October 2018 / Approved: 8 October 2018 / Online: 8 October 2018 (16:18:22 CEST)

A peer-reviewed article of this Preprint also exists.

Bitta, J.; Pavlíková, I.; Svozilík, V.; Jančík, P. Air Pollution Dispersion Modelling Using Spatial Analyses. ISPRS Int. J. Geo-Inf. 2018, 7, 489. Bitta, J.; Pavlíková, I.; Svozilík, V.; Jančík, P. Air Pollution Dispersion Modelling Using Spatial Analyses. ISPRS Int. J. Geo-Inf. 2018, 7, 489.

Journal reference: ISPRS Int. J. Geo-Inf. 2018, 7, 489
DOI: 10.3390/ijgi7120489

Abstract

Abstract: The air pollution dispersion modelling via spatial analyses (Land Use Regression – LUR) is an alternative approach to the air quality assessment to the standard air pollution dispersion modelling techniques. Its advantages are mainly much simpler mathematical apparatus, quicker and simpler calculations and a possibility to incorporate other factors affecting pollutant’s concentration. The goal of the study was to model the PM10 particles dispersion modelling via spatial analyses v in Czech-Polish border area of Upper Silesian industrial agglomeration and compare results with results of the standard Gaussian dispersion model SYMOS’97. 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 and were included into the LUR model, the LUR model results were significantly improved (65% determination coefficient) to the level comparable with Gaussian model. The hybrid approach combining the Gaussian model with the LUR gives superior quality of results (65% determination coefficient).

Subject Areas

Pollution dispersion; PM10; air quality; Land Use Regression; Symos’97

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