Developing a New Air Pollution Dispersion Model with Chemical Reactions Based on Multiple Cell Approach

2009 ◽  
Author(s):  
Ali Alizadeh Osalu ◽  
Mohammad Ali Kaynejad ◽  
Esmaeil Fatehifar ◽  
Ali Elkamel
Keyword(s):  
Air Pollution ◽  
Chemical Reactions ◽  
Dispersion Model ◽  
Pollution Dispersion ◽  
Multiple Cell ◽  
Air Pollution Dispersion

A numerical air pollution dispersion model for central Stockholm

1974 ◽  
Vol 8 (2) ◽  
pp. 131-148 ◽  
Author(s):  
Björn Bringfelt ◽  
Thomas Hjorth ◽  
Sture Ring
Keyword(s):  
Air Pollution ◽  
Dispersion Model ◽  
Pollution Dispersion ◽  
Air Pollution Dispersion

A Fuzzy Modified Gaussian Air Pollution Dispersion Model

2008 ◽  
Vol 2 (3) ◽  
pp. 156-169 ◽  
Author(s):  
M. Saeedi . ◽  
H. Fakhraee . ◽  
M. Rezaei Sadrabadi .
Keyword(s):  
Air Pollution ◽  
Dispersion Model ◽  
Pollution Dispersion ◽  
Air Pollution Dispersion

Comparison of methods for assessment of children exposure to air pollution: dispersion model, ambient monitoring, and personal samplers

2021 ◽  
Author(s):  
Davi de Ferreyro Monticelli ◽  
Jane Meri Santos ◽  
Elisa Valentim Goulart ◽  
José Geraldo Mill ◽  
Jeferson da Silva Corrêa ◽  
...  
Keyword(s):  
Air Pollution ◽  
Dispersion Model ◽  
Comparison Of Methods ◽  
Pollution Dispersion ◽  
Ambient Monitoring ◽  
Air Pollution Dispersion ◽  
Children Exposure

scholarly journals Air Pollution Dispersion Modelling Using Spatial Analyses

2018 ◽  
Vol 7 (12) ◽  
pp. 489 ◽  
Author(s):  
Jan Bitta ◽  
Irena Pavlíková ◽  
Vladislav Svozilík ◽  
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

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).

scholarly journals Air Pollution Dispersion Modelling Using Spatial Analyses

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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