Spatial and Temporal Variations of Satellite-Derived Multi-Year Particulate Data of Saudi Arabia: An Exploratory Analysis

International Journal of Environmental Research and Public Health

Yusuf A. Aina³  Johannes H. van der Merwe¹  Habib M. Alshuwaikhat² 

[1] Department of Geography and Environmental Studies, University of Stellenbosch, Stellenbosch 7600, South Africa; E-Mail:;Department of City and Regional Planning, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia; E-Mail:;Department of Geomatics Engineering Technology, Yanbu Industrial College, Yanbu 41912, Saudi Arabia

关键词: satellite data;    fine particulate matter;    air pollution;    geographic information system;    health risks;    spatial analysis;    Saudi Arabia;   

DOI  :  10.3390/ijerph111111152

来源: mdpi

【 摘 要 】

The effects of concentrations of fine particulate matter on urban populations have been gaining attention because fine particulate matter exposes the urban populace to health risks such as respiratory and cardiovascular diseases. Satellite-derived data, using aerosol optical depth (AOD), have been adopted to improve the monitoring of fine particulate matter. One of such data sources is the global multi-year PM_2.5 data (2001–2010) released by the Center for International Earth Science Information Network (CIESIN). This paper explores the satellite-derived PM_2.5 data of Saudi Arabia to highlight the trend of PM_2.5 concentrations. It also examines the changes in PM_2.5 concentrations in some urbanized areas of Saudi Arabia. Concentrations in major cities like Riyadh, Dammam, Jeddah, Makkah, Madinah and the industrial cities of Yanbu and Jubail are analyzed using cluster analysis. The health risks due to exposure of the populace are highlighted by using the World Health Organization (WHO) standard and targets. The results show a trend of increasing concentrations of PM_2.5 in urban areas. Significant clusters of high values are found in the eastern and south-western part of the country. There is a need to explore this topic using images with higher spatial resolution and validate the data with ground observations to improve the analysis.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.