№76-25
Predictive model for assessment of atmospheric air pollution by car transport
T. Rusakova1, Y. Voitenko1
1Oles Honchar Dnipro National University, Dnipro, Ukraine
Coll.res.pap.nat.min.univ. 2023, 75:292–302
Full text (PDF)
https://doi.org/10.33271/crpnmu/76.292
ABSTRACT
Purpose. To investigate the dynamics of changes in the volume of pollutants entering the atmospheric air from stationary and mobile sources of pollution. To build a predictive model that links the amount of atmospheric air pollution with a number of factors affecting their level.
Research methodology is based on the results of the analysis of correlations between the factor variables and the resulting variable to reveal the degree of their dependence and mutual influence, as well as the extent to which the regression model will explain a significant part of the variations of the resulting variable. The use of variance analysis allows to determine the probability of maintaining the null hypothesis and is a strong evidence for accepting the application of the regression model.
Research results. On the basis of descriptive statistics, an analysis of the dynamics of changes in the volume of emissions into the atmosphere from stationary and mobile sources of emissions for the period from 2016 to 2021, taking into account the forecast until 2023, was carried out. A methodological approach has been developed for estimating the volume of atmospheric air pollution by vehicle emissions. An average and strong connection between the amount of atmospheric air pollution, the number of registered first and new vehicles, the number of electric cars and the amount of investments and expenses for environmental protection activities was revealed. Based on the correlation-regression analysis, a predictive model was obtained, on the basis of which the analysis of the received calculation data was carried out, its adequacy was checked and it was shown that the average relative error of the calculation data was 0.11%, the maximum relative error was 0.23%.
Scientific novelty. A multifactor predictive model has been developed, which reflects the relationships between various factor variables and the total volume of atmospheric air pollution by emissions from mobile sources of pollution.
Practical significance. The forecast calculations of the volumes of harmful emissions from mobile sources of pollution can be useful in creating long-term plans and measures to reduce the impact on the environment in the transport strategy management system.
Keywords: predictive model, correlation-regression methods, atmospheric air pollution, motor vehicles.
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