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Dust storm episodes are among the most important weather phenomena in arid countries around the world. Fine particulate matter in the air is a major health hazard; besides, dust storms can alter air temperatures as a result of scattering and absorption of solar radiation. The size and type of aerosol have a major influence on radiation, in addition to cloud cover and surface albedo. Heavy dust storms have been reported to result in the lowering of ocean temperatures which may affect primary productivity of seas and oceans, and impact carbon dioxide sequestration. The rates of dust fallout in Kuwait had been reported to be among the highest in the world with mean monthly concentrations as high as 1400 μg m -3 . Remote sensing measurements by virtue of their synoptic monitoring capabilities were used to quantify dust on a large spatiotemporal scale. The satellite data set from the Multiangle Imaging Spectro Radio Meter (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) were used for determining the aerosol optical depth (AOD) over land and ocean surfaces through observation at visible and infrared wavelengths. The AODs were used for PM2.5 quantification and generation of PM2.5 spatio-temporal trends. These data sets were downloaded from the Atmospheric Sciences Data Center of the National Aeronautical and Space Administration (NASA) Langley Research Center (LARC) from 2008 to 2014. The satellite-derived particulate matter (PM) counts were verified by field measurements using an infrared particle counter and high-volume air samplers. One high-volume air sampler (HVAS) equipped with a six-stage cascade impactor was deployed at Abdalli (Iraq-Kuwait Border) and at Kuwait City (KISR). Particulate counts and particulate mass were recorded using Aerocet 531 particle counters at both sites to further calibrate the measurements. The accuracy of the MISR determination was 68% and 62% for MODIS. This satellite data derived PM2.5, using AOD can be used as surrogate for PM measurement.

Use of Satellite Images to Map Spatio-temporal Variability of PM2.5 in Air