Aerosol tracking using lidar-based atmospheric profiling and Bayesian estimation

Abstract

Soot aerosol in atmosphere is one of deadliest forms of air pollution and is a major cause for health problems according to EPA. Compact light detection and ranging (lidar) systems can be used to obtain aerosol profile measurements by identifying aerosol scattering ratios as function of altitude. Aerosol ratio parameters are known to vary with aerosol type, size, and shape. This work employs a ground-based lidar system to detect the source of soot emissions in atmosphere in a wide area around the campus of Old Dominion University (ODU), Norfolk, VA. Different aerosol scattering ratio parameters including lidar and color ratios are obtained from collected lidar data around the campus and these ratios are analyzed for detection and quantification of soot aerosol. A Bayesian estimation algorithm was chosen, after extensive study, to determine the source of the soot in the measurements by tracking the pollution concentration. Results of the analysis using lidar data show that the source of soot pollution is a nearby major Hampton Blvd. Hampton Blvd. is a major arterial with traffic signals through the ODU campus where diesel trucks frequently travel to serve Port of Virginia, which ranks as the third largest container port on the East Coast.