عنوان مقاله [English]
Having an up-to-date, accurate, fast and comprehensive map of air quality can solve the problems of people and managers. In this study, Landsat 8 images in 1399 were used to monitor the PM10 in Tabriz neighborhoods. For this research; the images were taken in four seasons of the year and after initial preprocessing, from the available algorithms, the algorithm that had the highest correlation and lowest RMSE to estimate PM10 was selected and applied to the images. The results were normalized using the fuzzy linear function. The results showed that the air quality of Tabriz neighborhoods in the first three seasons of the year are very similar and in winter is very different from these three seasons. The two clean neighborhoods of the city in the first three seasons of the year were Vali-amar 2 and the airport, and the neighborhoods of Lilabad were 2 and 42 meters 1 In the annual average, Emma Vali Amar 2 and Rushdieh were the cleanest neighborhoods and Lilabad 2 and 42 meters 1 were the most polluted neighborhoods in terms of PM10.
10. Choung, Y. J., & Kim, J. M. (2019). Study of the Relationship between Urban Expansion and PM10 Concentration Using Multi-Temporal Spatial Datasets and the Machine Learning Technique: Case Study for Daegu, South Korea. Applied Sciences, 9(6), 1098.
11. Elliott P, Cuzick J, English D, Stern R (1996) Geographical and Environmental Epidemiology: Methods for Small-area Studies. Oxford: Oxford University Press, UK
12. Finzi, G., & Lechi, G. M. (1991). Landsat images of urban air pollution in stable meteorological conditions. IL Nuovo Cimento C, 14(5), 433-443.
13. Liu, Y., Wu, J., Yu, D., & Ma, Q. (2018). The relationship between urban form and air pollution depends on seasonality and city size. Environmental Science and Pollution Research, 25(16), 15554-15567.
14. POURAHMAD, A., BAGH, V. A., ZANGENEHE, S. S., & Givehchi, S. (2007). The impact of urban sprawl up on air pollution.
15. Retalis, A., & Sifakis, N. (2010). Urban aerosol mapping over Athens using the differential textural analysis (DTA) algorithm on MERIS-ENVISAT data. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 17-25.
16. Saleh, S. A. H., & Hasan, G. (2014). Estimation of PM10 concentration using ground measurements and Landsat 8 OLI satellite image. J Geophys Remote Sens, 3(120), 2169-0049.
18. Song, W. W., & Guan, D. S. (2008). The distribution of aerosol optical depth retrieved by TM imagery over Guangzhou, China. Acta Sci. Circumst, 8, 1638-1645.
19. Sotoudeheian, S., & Arhami, M. (2014). Estimating ground-level PM 10 using satellite remote sensing and ground-based meteorological measurements over Tehran. Journal of Environmental Health Science and Engineering, 12(1), 1-13.
20. Stone Jr, B. (2008). Urban sprawl and air quality in large US cities. Journal of environmental management, 86(4), 688-698.
21. Zaman, N. A. F. K., Kanniah, K. D., & Kaskaoutis, D. G. (2017). Estimating particulate matter using satellite based aerosol optical depth and meteorological variables in Malaysia. Atmospheric Research, 193, 142-162.
22. Zhong, B. (2011, July). Improved estimation of aerosol optical depth from Landsat TM/ETM+ imagery over land. In 2011 IEEE International Geoscience and Remote Sensing Symposium (pp. 3304-3307). IEEE.