Investigating the Relationship between Air Pollutants and Remote Sensing Indices (NDVI, NDBI, LST, and ATI) in Tehran

Intra-urban air pollution is a major environmental and public health concern worldwide. As the capital of Iran, Tehran is increasing its population day by day, and it has caused problems such as air pollution, and its severity is subject to change every year. The aim of this study was to find a relationship between Remotely Sensed Indices with pollutants and air pollution modeling in Tehran. Pearson correlation coefficient was used to investigate the relationship between independent variables and pollutants and multivariate linear regression model was used to model it. Independent variables in this study are Land Surface Temperature (LST), Normalized Vegetation Difference Index (NDVI), Apparent Thermal Inertia (ATI), and Normalized Difference Built-Up Index (NDBI). The data obtained from Landsat 8 satellite images and MODIS sensor were used. Moreover, air pollution parameters were CO, NO2 and SO2, which were prepared in two spring (April) and summer (August July) of 2017. ENVI, ArcGIS and SPSS software were used to analyze the images and evaluate the relationship between the Remotely Sensed Indices and air pollutants and modeling. The results showed that the highest correlation between LST and SO2 is in the spring (0.24), NDVI and SO2 is in the spring (-0.36), ATI and CO is in the summer, and between NDBI and SO2 is in the summer. Also, the results of the model for the statistical coefficient of determination (R2) showed a value between 0.33 and 0.8, and for the statistical root-mean-square error (RMSE) showed a value of 5.05-47.47. It was also found that NDVI and LST are inversely related, and that NDBI was indirectly related to air pollutants.