Exploring spatiotemporal dynamics of air quality in Jordan from 2019 to 2022, using cloud computing

• Published in: Spatial information research (Online) Vol. 33; no. 3; p. 18
• Main Authors: Al-Jarrah, Murad, Hazaymeh, Khaled, Al-Shboul, Deema Ali, Mosleh, Mostafa K., Kasawneh, Awni M.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.06.2025; 대한공간정보학회
• Subjects: Database Management; Earth and Environmental Science; Geographical Information Systems/Cartography; Geography; Original Article; Remote Sensing/Photogrammetry; 공학일반; Sulfur dioxide (SO₂); Elevation; GEE; Air pollution; Nitrogen dioxide (NO₂); Land surface temperature; Carbon monoxide (CO)
• DOI: 10.1007/s41324-025-00619-y
• ISSN: 2366-3286

This study examines the spatiotemporal dynamics of air quality in Jordan from 2019 to 2022, utilizing remote sensing and ground-based data processed through Google Earth Engine (GEE). Sentinel-5P measurements of NO₂, SO₂, and CO, alongside MODIS-derived land surface temperature (LST) and ALOS PALSAR elevation data, were analyzed. Linear regression models, applied to 4,627 random sample points, assessed relationships between pollutant concentrations, LST, and elevation. Results indicate that densely populated areas, such as Amman, Zarqa, and Irbid, consistently exhibited higher NO₂ levels. Elevated SO₂ concentrations were observed in northern governorates, while CO levels were concentrated along the Jordan Rift Valley. Correlations between pollutants and LST were weak (R² = 0.01–0.19), with wind speed showing negligible influence. Elevation demonstrated a strong correlation with CO (R² = 0.86–0.90) across all seasons, contrasting with weaker relationships for NO₂ and SO₂. The central region experienced the highest air pollution levels, followed by the north and south. Seasonally, NO₂ and SO₂ peaked in winter, whereas CO remained stable year-round. These findings underscore the necessity for region-specific and seasonally adaptive air quality management strategies in Jordan.