Study the effects of Traffic Conditions on the PM2.5 emission Geographically Weighted Regression model (case study: Isfahan city)

PM2.5 concentration has become a public concern in cities due to its harmful effects on human health. In this study, due to the importance of air pollution, the effect of urban traffic conditions on the emission of urban air pollutants (PM2.5) was studied using geographic weight regression model (GWR) and IDW interpolation method.

For this purpose, concentration of PM2.5 in 2019 was collected from 9 air pollution monitoring stations of Isfahan Municipality and population data and traffic in the city were collected and entered into the model. Interpolation IDW method was used for preparing seasonal air pollutants dispersion maps. After performing geographical weight regression on the model parameters and in order to evaluate the validity of the model, the RMSE parameter was used, which is obtained from the difference between the actual value of the concentration and the predicted value and indicates the predictive power of the model. Finally, R2 values ​​were calculated and Moran's index was used to examine the spatial autocorrelation test.

After performing geographical weight regression on the model parameters, in order to evaluate the validity of the model, the value of R2 was calculated and the Moran index was used to examine the spatial autocorrelation test.

According to the amount of R2=0.75 for PM2.5, a direct correlation has been shown between this pollutant and independent variables, especially in the summer. The Moran index results showed that the GWR model was a good model for investigating the spatial temporal pattern of suspended particles.