A New Approach to Solving the Problem of Atmospheric Air Pollution in the Industrial City

Zhanar Oralbekova,Alma Zakirova,Kazizat Iskakov,Ainur Kussainova,Tamara Zhukabayeva,Makpal Zhartybayeva,Nargiz Yessimova,Zhu Xiao

doi:10.1155/2021/8970949

Abstract

In order to ensure optimal operation of the existing environmental monitoring information system, it has become essential to use mathematical modeling based on the data assimilation algorithm. In this paper, a data assimilation algorithm has been designed and implemented. An algorithmic approach was tested for the assimilation of city atmosphere monitoring data from an industrial area. An industrial district of Karaganda city was selected for the investigation of the algorithm. The industrial district of Karaganda was taken as a research object due to the high level of atmospheric air pollution in industrial cities in the Republic of Kazakhstan. The result of our research and testing of the algorithm showed the effectiveness of the data assimilation algorithm for monitoring the atmosphere of the selected city. The practical value of the work lies on the fact that the presented results can be used to assess the state of atmospheric air in real time, to model the state of atmospheric air at each point of the city, and to determine the zone of increased environmental risk in an industrial city.

Highlights

Nowadays, the environmental monitoring problems have received considerable attention due to the high level of atmospheric air pollution in industrial cities of many countries [1,2,3,4,5,6]
For the effective operation of the existing information system for monitoring the atmosphere for pollution by heavy metals, it has become essential to use mathematical modeling based on the data assimilation algorithm
146 posts and 14 mobile laboratories located in the largest cities and national industrial centers of Kazakhstan are engaged in the analysis of the state of atmospheric air pollution

Summary

Introduction

The environmental monitoring problems have received considerable attention due to the high level of atmospheric air pollution in industrial cities of many countries [1,2,3,4,5,6]. For the effective operation of the existing information system for monitoring the atmosphere for pollution by heavy metals, it has become essential to use mathematical modeling based on the data assimilation algorithm. Data assimilation technology is used to improve forecasts of air quality in atmospheric chemistry, as well as to perform a reanalysis of three-dimensional chemical (including aerosol) concentrations and determine the values of input variables (parameters) of the inverse simulation model (for example, emissions). Combines a sequence of operations starting with observations of the system and ending with the assessment of its state based on additional statistical and dynamic information. Data assimilation technology is widely used in the fields of modeling the atmosphere, climate, ocean, and environment under any conditions, if it is necessary to assess the state of a large dynamic system based on limited information. Kalman demonstrated an optimization method for linear filtering, and this filter is named

Methods

Results

Conclusion