A Teaching Tool for Air Pollution Dispersion Modelling

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).

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Air Pollution Dispersion Modelling Using Spatial Analyses

10.20944/preprints201810.0159.v1 ◽

2018 ◽

Author(s):

Jan Bitta ◽

Vladislav Svozilík ◽

Irena Pavlíková ◽

Petr Jančík

Keyword(s):

Air Pollution ◽

Dispersion Model ◽

Hybrid Approach ◽

Gaussian Model ◽

Dispersion Modelling ◽

Spatial Analyses ◽

Pollution Dispersion ◽

Determination Coefficient ◽

Air Pollution Dispersion ◽

Pollution Dispersion Modelling

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).

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Cumulative Impact Evaluation in Central Part of Liepaja with Comulative Pollution Index Method and Air Pollution Dispersion Modelling

Proceedings of the Latvia University of Agriculture ◽

10.1515/plua-2015-0001 ◽

2015 ◽

Vol 33 (1) ◽

pp. 2-7

Author(s):

Viesturs Kalniņš

Keyword(s):

Air Pollution ◽

Impact Evaluation ◽

Pollution Index ◽

Pollution Level ◽

Dispersion Modelling ◽

Index Method ◽

Cumulative Impact ◽

Pollution Dispersion ◽

Air Pollution Dispersion ◽

Pollution Dispersion Modelling

Abstract Cumulative impact evaluation is one of the most actual problems in air quality monitoring. At the same time, it is also the most problematic factor to evaluate due to lack of appropriate methodology. The aim of this study was to assess the opportunity to use a new method – Cumulative Pollution Index (CPI) in cumulative impact calculation from two different sets of data – bioindication survey with Index of Atmospheric Purity method and air pollution dispersion modelling. Results show that the usage of modelling data, instead of measurements, in cumulative impact evaluation can be quite difficult due to the fact that dispersion models not always give sufficiently accurate data. Despite the issues with modelling specifics, the use of dispersion modelling in CPI calculation shows that the use of this approach not only gives plausible data – obtained values correlate with pollution level and forming strong clustering in spatial distribution, but also reveals new facts about cumulative impact – demonstrates the city microclimate importance in forming of cumulative effect due to geometry of street canyons.

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Mesoscale air pollution dispersion modelling

Atmospheric Environment (1967) ◽

10.1016/0004-6981(84)90174-4 ◽

1984 ◽

Vol 18 (3) ◽

pp. 545-552

Author(s):

H. Van Dop ◽

B.J. De Haan

Keyword(s):

Air Pollution ◽

Dispersion Modelling ◽

Pollution Dispersion ◽

Air Pollution Dispersion ◽

Pollution Dispersion Modelling

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Air Pollution Dispersion Modelling Arround Thermal Power Plant Trbovlje in Complex Terrain – Model Verification and Regulatory Planning

Air Pollution Modeling and Its Application XIX - NATO Science for Peace and Security Series ◽

10.1007/978-1-4020-8453-9_92 ◽

2008 ◽

pp. 695-696

Author(s):

Marija Zlata Boz̆nar ◽

Primoz̆ Mlakar ◽

Boštjan Grašič ◽

Gianni Tinarelli

Keyword(s):

Air Pollution ◽

Power Plant ◽

Thermal Power Plant ◽

Thermal Power ◽

Model Verification ◽

Dispersion Modelling ◽

Pollution Dispersion ◽

Terrain Model ◽

Air Pollution Dispersion ◽

Pollution Dispersion Modelling

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Case Study of Nanjing Inner Ring on Air Pollution Dispersion from Roadway Tunnel Portals

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.610-613.1895 ◽

2012 ◽

Vol 610-613 ◽

pp. 1895-1900 ◽

Cited By ~ 3

Author(s):

Shu Jiang Miao ◽

Da Fang Fu

Keyword(s):

Air Pollution ◽

Pollutant Dispersion ◽

Air Pollutant ◽

Urban Traffic ◽

Lagrangian Model ◽

Pollution Dispersion ◽

Piecewise Function ◽

Air Pollutant Dispersion ◽

Air Pollution Dispersion

The tunnel module of a rather simple Lagrangian model GRAL (Grazer Langrange model) has been chosen to study air pollutant dispersion around tunnel portals in Nanjing inner ring. Two points have been made to popularize GRAL3.5TM (the tunnel module of a Lagrangian model GRAL; the update was in May 2003) and assure it more suitable for the actual situations in Nanjing. One is to derive a piecewise function of the intermediate parameter ‘stiffness’. Another is to take Romberg NOx-NO2 scheme into account. After these 2 works on GRAL3.5TM, NO2 dispersion from portals of all the 6 tunnels in Nanjing inner ring has been simulated. The importance of limiting urban traffic volume to control air quality around tunnel portals and roadways has been emphasized.

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