Assessment of reduced respiratory diseases in the Moscow population morbidity as a result of the implementation of the best available technology

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Abstract

AIM: To examine the potential for lowering respiratory diseases in the Moscow population as a result of the implementation of the best available technology at objects of the I and II categories of negative environmental impact.

MATERIAL AND METHODS: The source data array was created by the authors using a parser script written in the Python computer language. The determination of the geographical coordinates of the location of industrial facilities and their territorial linkage to the administrative districts of the city of Moscow was carried out using the JavaScript API of the Yandex Geocoder. Regression and correlation analysis were used to determine the relationship between the morbidity indicators of the population of the city of Moscow and the indices of comparative non-carcinogenic danger. The mathematical processing of statistical data was carried out using the interpreted programming language R and the spatial territorial binding of negative impact on the environment objects was carried out using the ESRI ArcGIS Online geoinformation system.

RESULTS: A significant relationship was found between the values of the comparative non-carcinogenic danger indices to the respiratory system (arising from the volume of emissions from objects in the I and II categories of negative environmental impact) and respiratory diseases for various age groups of the population of the city of Moscow (children — up to 14 years old, adolescents — from 15 to 17 years old, adults — over 18 years old). The spearman's ρsp coefficient was 0.84 (p <0.05), indicating a significant correlation on the Chaddock scale. The Student's t-test index was higher than the critical one at the significance level α=0.05. This study identified the potential for lowering the number of respiratory diseases in the population of Moscow, which varies in the range of 1.1%–2.2% for children, 1.2%–2.5% for adolescents, and 1.0–2.0 for adults, depending on the scenario for the implementation of the best available technologies at the facilities of the I and II categories of the negative environmental impact.

CONCLUSION: As a result of the research, a mathematical model has been developed, which allows determining the values of the potential for reducing the incidence of the respiratory system with the implementation of the best available technologies. This can be used in the formation of regional and federal programs for socio-economic development.

About the authors

Natalya V. Zvonkova

National research university “Moscow power engineering institute”

Email: ZvonkovaNV@mpei.ru
ORCID iD: 0000-0003-0213-8313
SPIN-code: 5430-8935

senior lecturer

Russian Federation, 14/1 Krasnokazarmennaja street, 111250, Moscow

Oleg A. Loktionov

National research university “Moscow power engineering institute”

Email: LoktionovOA@mpei.ru
ORCID iD: 0000-0002-4669-8729
SPIN-code: 2883-3017

Cand. Sci (Tech.), assistant professor

Russian Federation, 14/1 Krasnokazarmennaja street, 111250, Moscow

Olga E. Kondrateva

National research university “Moscow power engineering institute”

Author for correspondence.
Email: KondratyevaOYe@mpei.ru
ORCID iD: 0000-0002-5462-3612
SPIN-code: 9205-9338

Dr. Sci. (Tech.), assistant professor

Russian Federation, 14/1 Krasnokazarmennaja street, 111250, Moscow

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Supplementary files

Supplementary Files
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1. JATS XML
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2. Fig. 1. Distribution: a — Moscow enterprises depending on the category of negative impact on the environment; b — total emissions of Moscow enterprises, tons per year.

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3. Fig. 2. Distribution of pollutant emissions from objects of category I–IV negative impact on the environment by administrative districts of the city of Moscow, tons per year. Here and in fig. 3, 5: ЦАО — Central autonomous district, САО — Northern autonomous district, СВАО — North-Eastern autonomous district, ВАО — Eastern autonomous district, ЮВАО — South-Eastern autonomous district, ЮАО — Southern autonomous district, ЮЗАО — South-West autonomous district, ЗАО — Western autonomous district, СЗАО — North-Western autonomous district, ЗелАО — Zelenograd autonomous district, ТиНАО — Troitsk and Novomoskovsky autonomous district.

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4. Fig. 3. Distribution of non-carcinogenic danger indices to the respiratory system and the graph of the distribution of the number of respiratory diseases by administrative districts of the city of Moscow; o.e. — relative units (dimensionless value) of non-carcinogenic danger indices.

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5. Fig. 4. Distribution of respiratory diseases number for different age groups of the population of the city of Moscow, depending on the levels of the index of non-carcinogenic danger to the respiratory system and their power approximation.

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6. Fig. 5. Potential reduction values of non-carcinogenic hazard indices for the respiratory system of the population of the city of Moscow by administrative districts, %.

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7. Fig. 6. Distribution of the potential respiratory diseases reduction of various age groups Moscow population with the best available technologies introduction at the objects of the I and II categories of negative impact on the environment according to three scenarios.

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Copyright (c) 2022 Zvonkova N.V., Loktionov O.A., Kondrateva O.E.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
 


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