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Int. J. Environ. Res. Public Health 2017, 14(10), 1244;

The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition

Department of Environmental Health, Emory University, Atlanta, GA 30322, USA
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Author to whom correspondence should be addressed.
Received: 27 July 2017 / Revised: 30 September 2017 / Accepted: 10 October 2017 / Published: 18 October 2017
(This article belongs to the Special Issue Spatial Modelling for Public Health Research)
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Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, ~70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations. View Full-Text
Keywords: PM2.5; MAIAC AOD; non-random missingness; cloud properties; RUC/RAP PM2.5; MAIAC AOD; non-random missingness; cloud properties; RUC/RAP

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Belle, J.H.; Chang, H.H.; Wang, Y.; Hu, X.; Lyapustin, A.; Liu, Y. The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition. Int. J. Environ. Res. Public Health 2017, 14, 1244.

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