Spatial Analysis of PM2.5 and Its Statistical Correlation with Meteorological Parameters in Tehran

Air pollution has become one of the major challenges associated with urbanization and industrialization. This study aims to conduct a spatial analysis of PM2.5 pollution and examine its statistical relationship with meteorological parameters in the metropolitan area of Tehran. To achieve these objectives, two data sets were utilized: (1) daily PM2.5 concentration data obtained from the Air Quality Control Company and (2) daily meteorological data (including rainfall, evaporation, maximum wind speed, minimum and maximum relative humidity, sunshine hours, and minimum and maximum temperatures) provided by the Meteorological Organization. The inverse distance weighting (IDW) method was used to map the spatial distribution of PM2.5 concentrations. The IDW results revealed that regions 16, 19, 20, and 9 had the highest levels of particulate matter pollution, while regions 8, 22, and 15 had the lowest concentrations. Pearson correlation and multiple linear regression analyses were used to investigate the statistical relationship between PM2.5 levels and meteorological parameters. The Pearson correlation results indicated a positive correlation between PM2.5 and minimum/maximum temperature, as well as sunshine hours, and a negative correlation with maximum wind speed and rainfall. Two linear regression models (Enter and Stepwise) were employed to establish the relationship between PM2.5 (dependent variable) and meteorological parameters (independent variables). The results showed regression coefficients of 0.6 for the Enter model and 0.565 for the Stepwise model, indicating that both models performed well in predicting PM2.5 concentrations. To determine the more suitable model, the estimated standard error was compared, with the Stepwise model showing a lower error (5.84) than the Enter model (6.0089). Therefore, the Stepwise model was deemed more appropriate for predicting PM2.5 concentrations.