The effect of meteorological parameters on PM2.5 concentration changes in 2018 (Case study: Tehran)

Studies in different parts of the world have shown that exposure to air pollutants has a negative effect on human health.

For statistical analysis between the dependent variable Particulate Matter less than 2.5 µm (PM2.5) concentration and independent variables (wind speed, precipitation, humidity, and temperature) R version 3.6.2 was used. Spearman correlation coefficient was used to determine the correlation between the parameters with the dependent variable. In addition, the multiple linear regression model was used to investigate the relationship and prediction between the independent variables and the dependent variable. In the present study, the effect of meteorological parameters on PM2.5 concentration in different seasons in Tehran in 2018 was studied using Pearson correlation and linear regression statistical analyzes.

Based on the results, it was found that PM2.5 had no significant relationship with meteorological parameters. Only in summer, there was a significant relationship between the dependent variable and the independent variable (wind speed) (p-value=0.01) and there was also an inverse relationship between these variables (r=-0.65). Multiple linear regression was also used to investigate the significant effect of independent variables on the dependent variable.

According to the coefficients of determination in this model, 47, 16, 45, and 20% of the dependent variable change in autumn, winter, spring, and summer, respectively, can be explained by meteorological parameters (independent). Due to the fact that the concentration of PM2.5 in Tehran is higher than the national standard, more attention of officials is necessary to improve air quality in Tehran.