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

A. V. Gochakov, A. V. Penenko, P. N. Antokhin, A. B. Kolker

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number012050
JournalIOP Conference Series: Earth and Environmental Science
Volume211
Issue number1
DOIs
Publication statusPublished - 17 Dec 2018
EventInternational Conference and Early Career Scientists School on Environmental Observations, Modeling and Information Systems, ENVIROMIS 2018 - Tomsk, Russian Federation
Duration: 5 Jul 201811 Jul 2018

Fingerprint

Dive into the research topics of 'Air pollution modelling in urban environment based on a priori and reconstructed data'. Together they form a unique fingerprint.

Cite this