Prediction of air pollution caused by traffic on suburban roads by modeling Ozone pollutant with regression method

Nowadays, traffic-induced air pollution factors are classified as destructive to the environment. Due to the increase in urbanization and the increasing growth of cars and transportation and movement of passengers and citizens between cities, scientific and practical studies on transportation systems with the intervention of mathematical science and intelligent systems, should move towards sustainable development and having a healthy air and transportation. The purpose of this article is to investigate the effect of traffic parameters on air pollution of suburban roads in the Sari-Qaemshahr road in Mazandaran province with respect to atmospheric variables and ozone pollution.

In this study, ozone pollutant concentrations were analyzed and modeled the Sari-Qaemshahr road. The study was conducted to determine the factors affecting the concentration of pollutants based on traffic and climate statistics and the relationship between traffic variables, average speed, rainfall, temperature, humidity, wind speed and finally their relationship with air pollution. Then, by using SPSS 16 software and based on the regression method, a model was presented that will be able to estimate the concentration of ozone pollutant on suburban roads with appropriate accuracy for the coming years.

According to the proposed model for ozone pollutants, among the available variables, temperature, traffic volume and wind speed have the greatest impact on ozone pollutants. Also, the results obtained from the validation show the success rate of the model in estimating pollution. In this study, the level of regression significance was above 95%. Also, the 90% data contribution rate for ozone pollutants in the model has been very favorable.

According to the results, modeling by regression method and using Spss software is a suitable method for estimating ozone pollutants. The proposed model can control and manage pollution emissions in road design and construction.