Assessment of the Negative Impact of Urban Air Pollution on Population Health Using Machine Learning Method

Nurlan Temirbekov, Marzhan Temirbekova (), Dinara Tamabay, Syrym Kasenov, Seilkhan Askarov and Zulfiya Tukenova
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Nurlan Temirbekov: National Engineering Academy of RK, Almaty 050010, Kazakhstan
Marzhan Temirbekova: Almaty University of Power Engineering and Telecommunications Named after G. Daukeyev, Almaty 050013, Kazakhstan
Dinara Tamabay: National Engineering Academy of RK, Almaty 050010, Kazakhstan
Syrym Kasenov: National Engineering Academy of RK, Almaty 050010, Kazakhstan
Seilkhan Askarov: Ecoservice-S Limited Liability Partnership, Almaty 050009, Kazakhstan
Zulfiya Tukenova: Institute of Zoology of the Ministry of Higher Education and Science of the RK, Almaty 050060, Kazakhstan

IJERPH, 2023, vol. 20, issue 18, 1-15

Abstract: This study focuses on assessing the level of morbidity among the population of Almaty, Kazakhstan, and investigating its connection with atmospheric air pollution using machine learning algorithms. The use of these algorithms is aimed at analyzing the relationship between air pollution levels and the state of public health, as well as the correlations between COVID-19 infection and the development of respiratory diseases. This study analyzes the respiratory diseases of the population of Almaty and the level of air pollution as a result of suspended particles for the period of 2017–2022. The study includes recommendations to reduce harmful emissions into the atmosphere using machine learning methods. The results of the study show that air pollution is a critical factor affecting the increase in the number of diseases of the respiratory system. The study recommends taking measures to reduce air pollution and improve air quality in order to prevent the development of chronic respiratory diseases. The study offers recommendations to industrial enterprises, traffic management organizations, thermal power plants, the Department of Environmental Protection, and local executive bodies in order to reduce respiratory diseases among the population.

Keywords: respiratory diseases; air pollution; machine learning algorithms; random forest; recommendations (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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