Integration of Google Earth Engine and Aggregated Air Quality Index for Monitoring and Mapping the Spatio-Temporal Air Quality to Improve Environmental Sustainability in Arid Regions

Abdel-rahman A. Mustafa, Mohamed S. Shokr (), Talal Alharbi, Elsayed A. Abdelsamie, Abdelbaset S. El-Sorogy and Jose Emilio Meroño de Larriva ()
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Abdel-rahman A. Mustafa: Soil and Water Department, Faculty of Agriculture, Sohag University, Sohag 82524, Egypt
Mohamed S. Shokr: Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt
Talal Alharbi: Geology and Geophysics Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Elsayed A. Abdelsamie: National Authority for Remote Sensing and Space Sciences, Cairo 1564, Egypt
Abdelbaset S. El-Sorogy: Geology and Geophysics Department, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Jose Emilio Meroño de Larriva: Department of Graphic Engineering and Geomatics, Campus de Rabanales, University of Cordoba, 14071 Cordoba, Spain

Sustainability, 2025, vol. 17, issue 8, 1-30

Abstract: Egypt must present a more thorough and accurate picture of the state of the air, as this can contribute to better environmental and public health results. Hence, the goal of the current study is to map and track the spatiotemporal air quality over Egypt’s Qena Governorate using remote sensing data. The current investigation is considered a pioneering study and the first attempt to map the air quality index in the studied area. Multisource remote sensing data sets from the Google Earth Engine (GEE) were used to achieve this. The first is Sentinel-5P’s average annual satellite image data, which were gathered for four important pollutants: carbon monoxide (CO), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) over a six year period from 2019 to 2024. The second is the MODIS aerosol optical density (AOD) product satellite image data from the GEE platform, which calculate the average annual particulate matter (PM 2.5 and PM 10 ). All mentioned pollutant images were used to calculate the air quality index (AQI) and aggregated air quality index (AAQI). Lastly, we used Landsat’s average yearly land surface temperature (LST) retrieval (OLI/TIRS). The aggregated air quality index (AAQI) was computed, and the U.S. Environmental Protection Agency’s (USEPA) air quality index (AQI) was created for each pollutant. According to the data, the AQI for CO, PM 2.5 , and PM 10 in the research region ranged from hazardous to unhealthy; at the same time, the AQI for NO 2 varied between harmful and unhealthy for sensitive groups, with values ranging from 135 to 165. The annual average of the AQI for SO 2 throughout the studied period ranged from 29 to 339, with the categories ranging from good to hazardous. The constant AQI for ozone in the study area indicates that the ozone doses in Qena are surprisingly stable. Lastly, with a minimum value of 265 and a maximum of 489, the AAQI ranged from very unhealthy to dangerous in the current study. According to the data, the area being studied has poor air quality, which impacts the environment and public health. The results of this study have significant implications for environmental sustainability and human health and could be used in other areas.

Keywords: GEE; AQI; AAQI; air pollutants; remote sensing; sustainability of environment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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