Abstract. Air pollution is one of the main problems in human and environmental health in big cities and in developing countries. The precise monitoring and predicting of air quality and the assessing of the amount of contaminants will reduce the risks to human and the environment health. The particulate materials in the atmosphere are divided into two PM2.5 (particulate materials with a diameter of less than 2.5 μm) and PM10 (particulate materials with a diameter of less than 10 μm) groups, the

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... main contributor to the air pollution associated with these pollutants. In this study, an experimental relationship is established between in-situ values of PM2.5 and PM10 with satellite images and a high-precision air pollution model is produced. Also, the impact of some urban parameters such as vegetation on air pollution have been the objectives of this research. Using four year course (From the beginning of 2015 until the end of 2018 from Tehran) from Landsat-8, OLI images and receiving ground data, at the same time, air pollution rates in different parts of Tehran have been investigated. For this purpose, 23 air quality control stations in Tehran have been used. The study suggests that the study of atmospheric reflectance from Landsat-8, OLI is a good alternative to monitoring the quality of air on Earth. The feasibility of the proposed algorithms was investigated based on the correlation coefficient (R) and root mean-square error (RMSE) and normalize root mean-square error (NRMSE) compared with the PM2.5 and PM10 in-situ measurement data. A choice of our proposed multispectral model was founded on the highest value R and lowest value of the RMSE and so and lowest value of NRMSE with PM10 in-situ data. The outcomes of this research showed that visible bands of Landsat 8 OLI were capable of calculating PM2.5 and PM10 concentration to an acceptable level of accuracy.