Air pollution modelling in urban environment based on a priori and reconstructed data

International Conference and Early Career Scientists School on Environmental Observations, Modeling and Information Systems: ENVIROMIS-2018

生态环境科学;计算机科学

Gochakov, A.V.^1 ; Penenko, A.V.^2,3 ; Antokhin, P.N.^4 ; Kolker, A.B.^1,5

Siberian Regional Research Hydrometeorological Institute, Sovetskaya, 30, Novosibirsk

630099, Russia^1

Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Akademika Lavrentieva, 6, Novosibirsk

630090, Russia^2

Novosibirsk National Research State University, Pirogova, 2, Novosibirsk

630090, Russia^3

Institute of Atmospheric Optics SB RAS, 1 Akademika Zueva, Tomsk

634055, Russia^4

Novosibirsk State Technical University, Prospekt K. Marksa, 20, Novosibirsk

630073, Russia^5

关键词: Air pollution modelling;    Air quality forecasting;    Air quality forecasts;    Concentration distributions;    Distribution of emissions;    Effectiveness analysis;    Initial information;    Numerical experiments;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/211/1/012050/pdf
DOI  :  10.1088/1755-1315/211/1/012050

来源: IOP

【 摘 要 】

This paper presents preliminary results of the effectiveness analysis of an air quality forecasting system for the city of Novosibirsk with replenishment of the missing information on emission sources by solving an inverse problem with urban monitoring network data. In solving the inverse problem, a priori information about the location and mode of the sources is used. To simulate concentration distributions, the WRF-Chem model is used, and a simplified model of chemical transport is applied to solving the inverse problem. These models are offline coupled in a hybrid forecast system in order to improve the initial information about the spatial distribution of emission intensity and air quality forecast, respectively. The results of numerical experiments and their analysis are presented. The influence of an urban parameterization on the results of the forecast is shown.