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Christopher, SA, Gupta, P (2010). Satellite Remote Sensing of Particulate Matter Air Quality: The Cloud-Cover Problem. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 60(5), 596-602.

Satellite assessments of particulate matter (PM) air quality that use solar reflectance methods are dependent on availability of clear sky; in other words, mass concentrations of PM less than 2.5 mu m in aerodynamic diameter (PM2.5) cannot be estimated from satellite observations under cloudy conditions or bright surfaces such as snow/ice. Whereas most ground monitors measure PM2.5 concentrations on an hourly basis regardless of cloud conditions, space-borne sensors can only estimate daytime PM2.5 in cloud-free conditions, therefore introducing a bias. In this study, an estimate of this clear-sky bias is provided from monthly to yearly time scales over the continental United States. One year of the Moderate Resolution Imaging Spectroradiometer (MODIS) 550-nm aerosol optical depth (AOD) retrievals from Terra and Aqua satellites, collocated with 371 U.S. Environmental Protection Agency (EPA) ground monitors, have been analyzed. The results indicate that the mean differences between PM2.5 reported by ground monitors and PM2.5 calculated from ground monitors during the satellite overpass times during cloud-free conditions are less than +/- 2.5 mu g m(-3), although this value varies by season and location. The mean differences are not significant as calculated by t tests (alpha = 0.05). On the basis of this analysis, it is concluded that for the continental United States, cloud cover is not a major problem for inferring monthly to yearly PM2.5 from space-borne sensors.



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