Dissertations and Theses

Date of Award


Document Type




First Advisor

Fred Moshary


Lidar, Laser, Remote Sensing, Atmosphere


Over the years, a major component of the research carried out at the Optical Remote Sensing Laboratory of the City College of New York has been on active sensing technologies and their different applications in atmospheric studies. This thesis builds upon and looks to further advance this field by demonstrating innovative applications of laser remote sensing technologies for studies involving trace gases, aerosol particles and wind; which are key components of the Earth’s atmosphere. First, we present the demonstration of gas concentration measurements using a quantum cascade laser open path system with characteristics that make it promising for mobile and/or multidirectional remote detection of gas leaks. This work looks to address an important environmental concern as fugitive methane emissions from industrial plants and pipelines can contribute to the global increase of greenhouse gas concentration and are a security and safety issue because of the risk of fire, explosion or toxicity. Second, we present horizontal measurements of the spatial distribution of aerosols over New York City using a scanning eye-safe elastic micro-pulse lidar system. Two case studies are presented in which different methodologies are applied in order to estimate the backscatter and extinction coefficients. These observations demonstrate capabilities to monitor local emission sources and rapid transport of aerosols, which are of great importance for air quality monitoring in urban areas due to the harmful effects of particulate pollution on human health. Lastly, we present the analysis of airborne wind measurements using a micro pulse Doppler lidar and comparison against ground measurements. Moreover, in order to evaluate the performance of the airborne system, we investigate some of the factors that may influence wind measurement uncertainty and provide insights on how to improve measurement precision while minimizing errors.



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