Applications of Remote Sensing to Air Quality Monitoring
Air pollution has always been considered a major problem worldwide. The formation of air pollutants depends upon the sources of their precursors whether natural or anthropogenic. The level of air pollution, and therefore a measure of air quality, is derived from measurement of concentrations of pollutants such as O3, NO2 and particulate matter (PM). These measurements are usually collected at a limited number of ground-based monitoring stations and may not convey a full characterization of their spatial distribution. This presentation discusses the potential of various remote sensing systems to estimate such measurements and consider their limitations. As an example, an approach to estimate PM10 from MODIS aerosol optical depth over Al Ain region will be detailed and its results discussed.
Speaker
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Dr. Nazmi SaleousAssociate Professor of Remote Sensing and GIS Department of Geography and Urban Sustainability, UAEUDr. Nazmi Saleous holds a computer engineering degree (1987) and a Ph.D. in Electronics (1990) from the University of Science and Technology of Lille (France). He joined the United Arab Emirates University (UAEU) in August 2006 where he currently holds a position of Associate Professor of Remote Sensing and GIS in the Department of Geography and Urban Sustainability. He serves as the coordinator of UAEU’s Master of Science in Remote Sensing and GIS program. Dr. Saleous is involved in multiple research projects including the use of Geospatial technology in estimating carbon sequestration, and monitoring and modeling urban growth. Prior to joining UAEU, Dr. Saleous spent 14 years at NASA Goddard Space Flight Center where he conducted research on satellite data processing and the development of long-term remote sensing data sets for use in land and environmental applications. Dr. Saleous’ research interests are in the application of remote sensing and GIS for solving environmental and urban problems. They include remote sensing data processing, creation of data time series, land use and land cover change, carbon sequestration in plantations, desertification, and modeling of environmental processes using geospatial technology.