Investigation of changes in air pollutants in major metropolises of Iran using the optical depth of satellite images

Document Type : Research Paper


Department of Meteorology, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran


Global analysis of particulate matter indicates the fifth highest risk of death in the world due to exposure to PM2.5. The purpose of this study is to analyze the monitoring and study of changes in air pollutants in the metropolises of Tehran, Karaj, Isfahan, Mashhad and Shiraz using a combination of optical depth of satellite images. Methods first, satellite images of MODIS, MISR and SeaWifs sensors were used, and the data of quality control stations of Iranian metropolises and statistical indicators were used for validation. Kendall and Sense slope methods were also used to analyze the trend. The results showed that the annual variability of suspended particles (PM2.5) during the statistical period of 2000-2000 has an increasing trend in all metropolises of Iran. The city of Tehran showed the highest PM2.5, which is more than other metropolises. After that, Karaj and Isfahan showed the maximum PM2.5. Also, the trend value and trend slope of PM2.5 trend is increasing in all metropolises; this value of trend is statistically significant for Tehran at the level of 0.05; So that the Z score of Man-Kendall test for Tehran is 1.998. The lowest value of suspended particles trend in Isfahan metropolis with Z-score of Mann-Kendall test is 0.02. The effects of urban areas and elevation changes show the most spatial variation of the estimated PM2.5. The amount of PM2.5 in all metropolises has a high spatial diversity; the reason for this variability is due to the proximity to the main sources of dust on the one hand and urban and industrial pollutants on the other.


  1. Ahmadi, M., Dadashi Rudbari, A.A., 2019. Temporal-spatial distribution of suspended particles (PM2.5) with environmental approach in western and southern Iran based on SeaWifs, MISR and MODIS sensors. Environmental Science 45(3), 379-394. (In Persian)

    Ahmadi, M., Dadashi Rudbari, A.A., 2020. Urban meteorological models and remote sensing techniques (theoretical-applied) Navid Mehr Publications, Tehran, 288 p. (In Persian)

    Ahmadi, M., Dadashi Roudbari, A.A., 2017.Identification of urban thermal islands based on environmental approach, case study (Isfahan metropolis). Geography and Environmental Planning 28(3): 1-20. (In Persian)

    Ahmadi, M., Dadashi Roudbari, A.A., Esfandiari, N., 2019 2017. Monitoring of urban thermal islands with special fractal evolution approach (FNEA) (Case study: Tehran metropolis), 11(1): 114-95. (In Persian)

    Khoshsima, M., Ali Akbari Bidokhti, A. 2015. Estimation of the concentration of suspended particles (PM10) in the atmosphere using satellite and ground-based sensing data and meteorological parameters: Application of artificial neural network. Earth and Space Physics 41, 499-510. (In Persian)

    Ghorbaniyan, A., Mohammadzadeh, A.2018. Use nonlinear regressions to estimate the concentration and generate a scatter plot of particles smaller than 10 microns using remote sensing images and ground measurements. Journal of Surveying Science and Technology, No. 2, 163-171. (In Persian)

    Nikkho, N., Ildermi, A., Nouri, H.2015. Land Use Developments in Malayer Using Remote Sensing, 8(30): 63-86. (In Persian)

    Conitz, M. 2000. GIS applications in Africa: Introduction. Photogrammetric Engineering and Remote Sensing, 66(6), 672-673.

    Frey, C. M., Rigo, G., Parlow, E. 2009. Investigation of the daily Urban Cooling Island (UCI) in two coastal cities in an arid environment: Dubai and Abu Dhabi (UAE). City 81-102.

    Kamusoko, C. 2017. Importance of Remote Sensing and Land Change Modeling for Urbanization Studies. In Urban Development in Asia and Africa (pp. 3-10). Springer. Singapore. PP.310-312.

    Sorek-Hamer, M., Kloog, I., Koutrakis, P., Strawa, A. W., Chatfield, R., Cohen, A., Broday, D. M. 2015. Assessment of PM2. 5 concentrations over bright surfaces using MODIS satellite observations. Remote Sensing of Environment 163, 180-185.

    Van Donkelaar, A., Martin, R. V., Brauer, M., Boys, B. L. 2014. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environmental Health Perspectives 123(2), 135-143.

    Van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C., Winker, D. M.

    1. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environmental Science & Technology 50(7), 3762-3772.

    Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. 2005. Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing. Remote sensing of Environment 98(2), 317-328.