Abstract
Dust is among the atmospheric phenomena with negative environmental effects and consequences for urban areas' long-term viability. In recent years, due to the lack of rainfall and central potholes, Sistan and Baluchestan province has formed several centers of dust in these areas, leading to destructive effects on cities. Therefore, dust scattered in the air of Sistan and Baluchestan province's urban areas is an important type of atmospheric dust that affects public health, economy, agriculture and community migration, and climate change. The final goal of the present study is the impact of dust in Sistan and Baluchestan provinces for the period 2018 to 2020 using Sentinel-5P NRTI AER AI products: Near Real-time UV Aerosol Index in the Google Earth Engine system. First, in the Google Earth engine system, the process of identifying the amount of dust in urban areas using five statistical parameters (average, average, minimum, maximum, total) was shown. Meanwhile, the statistical parameter (sum) showed that the amount of dust in the northeastern areas (Zabol city), southwest and south of Chabahar city was more prevalent, showing 7.41%. The results showed that the four indicators of urban sustainability (economy, agriculture, migration, and health) affected this phenomenon for the four indicators of 6.80, 47.50, 15, and 40.51, respectively showed the percentage. The results of this study can directly contribute to the decision-making process by the local authorities to understand the environmental issues across urban Sistan and Baluchestan province and even suburban areas, which are at severe risk and deteriorating faster.
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Vahid Isazade designed this research, wrote this paper, collected the necessary data, and analyzed the data.
Rebwar Dara and Esmail Isazade participated in drafting the manuscript and he contributed to the collection of data and interpretation of data and edited the format of the paper under the manuscript style.
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Isazade, V., Qasimi, A.B. & Isazade, E. Environmental dust effect phenomenon on the sustainability of urban areas using remote sensing data in GEE. Saf. Extreme Environ. 5, 59–67 (2023). https://doi.org/10.1007/s42797-022-00067-z
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DOI: https://doi.org/10.1007/s42797-022-00067-z