Date of Award:

12-2010

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Electrical and Computer Engineering

Committee Chair(s)

Todd K. Moon

Committee

Todd K. Moon

Committee

Jacob H. Gunther

Committee

Scott E. Budge

Committee

Vladimir V. Zavyalov

Committee

Gail E. Bingham

Abstract

Agricultural aerosol sources can contribute significantly to air pollution in many regions of the country. Characterization of the aerosol emissions of agricultural operations is required to establish a scientific basis for crafting regulations concerning agricultural aerosols. A new lidar instrument for measuring aerosol emissions is described, as well as two new algorithms for converting lidar measurements into aerosol concentration data. The average daily aerosol emission rate is estimated from a dairy using lidar.

The Aglite Lidar is a portable scanning lidar for mapping the concentration of particulate matter from agricultural and other sources. The instrument is described and performance and lidar sensitivity data are presented. Its ability to map aerosol plumes is demonstrated, as well as the ability to extract wind-speed information from the lidar data.

An iterative least-squares method is presented for estimating the solution to the lidar equation. The method requires a priori knowledge of aerosol relationships from point sensors. The lidar equation is formulated and solved in vector form. The solution is stable for signals with extremely low signal-to-noise ratios and for signals at ranges far beyond the boundary point.

Another lidar algorithm is also presented as part of a technique for estimating aerosol concentration and particle-size distribution. This technique uses a form of the extended Kalman Filter, wherein the target aerosol is represented as a linear combination of basis aerosols. For both algorithms, the algorithm is demonstrated using both synthetic test data and field measurements of biological aerosol simulants. The estimated particle size distribution allows straightforward calculation of parameters such as volume-fraction concentration and effective radius.

Particulate matter emission rates from a dairy in the San Joaquin Valley of California were investigated during June 2008. Vertical particulate matter concentration profiles were measured both upwind and downwind of the facility using lidar, and a mass balance technique was used to estimate the average emission rate. Emission rates were also estimated using an inverse modeling technique coupled with the filter-based measurements. The concentrations measured by lidar and inverse modeling are of similar magnitude to each other, as well as to those from studies with similar conditions.

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This work made publicly available electronically on December 23, 2010.

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