Volume 6, Issue 3 (autum 2020)                   J Health Res Commun 2020, 6(3): 55-64 | Back to browse issues page

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Darvishi G, Yousefi Kebria D, Ehteshami M, Asadi-Ghalhari M, Golbabaei Kootenaei F. Prediction of Traffic-induced Air Pollution in Suburban Roads using an Ozone Pollutant Modeling with a Regression Method. J Health Res Commun 2020; 6 (3) :55-64
URL: http://jhc.mazums.ac.ir/article-1-508-en.html
Postdoc Researcher, Department of Environmental Engineering, Faculty of Environment, Campus of Engineering, University of Tehran
Abstract:   (2222 Views)
Introduction and purpose: Nowadays, traffic-induced air pollution factors are classified as destructive to the environment. Regarding an increase in urbanization and the number of cars, transportation, and movement of passengers and citizens between cities, transportation systems should utilize mathematics scientifically and intelligent systems practically to move towards sustainable development and benefit from healthy air and transportation. This study aimed to investigate the effect of traffic parameters on air pollution of the suburban route of Sari-Qaemshahr road in Mazandaran province, Iran, regarding the atmospheric variables and ozone pollutants.
Methods: This study analyzed and modeled the ozone pollutant concentrations in the suburban route of Sari-Qaemshahr road. Moreover, the factors affecting the concentration of pollutants based on traffic and climate statistics were determined in this study. Additionally, it was attempted to investigate the relationship of air pollution with traffic variables, average speed, rainfall, temperature, humidity, and wind speed. Subsequently, SPSS software (version 16) and regression method were used to present a model that will be able to estimate the concentration of ozone pollutants on suburban roads with appropriate accuracy for the coming years.
Results: According to the proposed model for ozone pollutants, among the available variables, temperature, traffic volume, and wind speed had the greatest impact on ozone pollutants. Moreover, the results obtained from the validation showed the success rate of the proposed model in estimating pollution. In this study, the level of regression significance was above 95%. In addition, the 90% data contribution rate for ozone pollutants in the model has been satisfying.
Conclusion: According to the results, the modeling by a regression method and SPSS software is a suitable method for estimating ozone pollutants. The proposed model can control and manage pollution emissions in road design and construction.
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Type of Study: Research(Original) | Subject: Environmental Health

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